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He holds Master degree in Computer Science from University of Science, Vietnam in 2005. He holds PhD degree in Computer Science and Education at Ho Chi Minh University of Science in 2009. His PhD dissertation was honored by World Engineering Education Forum (WEEF) and awarded by Standard Scientific Research and Essays as excellent PhD dissertation in 2014. He holds Postdoctoral degree in Computer Science from 2013, certified by Institute for Systems and Technologies of Information, Control and Communication (INSTICC) by 2015. Now he is interested in poetry, computer science, statistics, mathematics, education, and medicine. He serves as reviewer, editor, speaker, and lecturer in a wide range of international journals and conferences from 2014. He is volunteer of Statistics Without Borders from 2015. He was granted as Mathematician by London Mathematical Society for Postdoctoral research in Mathematics from 2016. He is awarded as Professor by Scientific Advances and Science Publishing Group from 2016. He was awarded Doctorate of Statistical Medicine by Ho Chi Minh City Society for Reproductive Medicine (HOSREM) from 2016. He was awarded and glorified as contributive scientist by International Cross-cultural Exchange and Professional Development-Thailand (ICEPD-Thailand) from 2021 and by Eudoxia Research University USA (ERU) and Eudoxia Research Centre India (ERC) from 2022. He has published 101 papers and preprints in journals, books, conference proceedings, and preprint services. He is author of 5 scientific books. He is author and creator of 10 scientific and technological products. Moreover, he is a Vietnamese-language poet who has created 1 verse narrative and 9 collections of 409 poems from 1993. He also has 8 recitation albums in which many poems are chanted by famous artists. Especially, he is very attractive, enthusiastic, and creative. His favorite statement is "Creative man is The Creator". Thus, why don't you contact him for sharing inspiration and knowledge? His online homepage is http://www.locnguyen.net<br /><span class="u-fw700">Phone: </span>+84975250362<br /><b>Address: </b>1/4B Ton Duc Thang street, My Binh ward, Long Xuyen city, An Giang province 881092, Vietnam<br /><div class="js-profile-less-about u-linkUnstyled u-tcGrayDarker u-textDecorationUnderline u-displayNone">less</div></div></div><div class="suggested-academics-container"><div class="suggested-academics--header"><p class="ds2-5-body-md-bold">Related Authors</p></div><ul class="suggested-user-card-list"><div class="suggested-user-card"><div class="suggested-user-card__avatar social-profile-avatar-container"><a href="https://independent.academia.edu/TuyenNguyen200"><img class="profile-avatar u-positionAbsolute" alt="Tuyen Nguyen" border="0" onerror="if (this.src != '//a.academia-assets.com/images/s200_no_pic.png') this.src = '//a.academia-assets.com/images/s200_no_pic.png';" width="200" height="200" 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data-dom-id="Pill-react-component-21c9b80d-2511-47c8-a041-b20616cf044c"></div> <div id="Pill-react-component-21c9b80d-2511-47c8-a041-b20616cf044c"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="88862579" href="https://www.academia.edu/Documents/in/Mathematics"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Mathematics"]}" data-trace="false" data-dom-id="Pill-react-component-86d09583-4649-43b7-80d6-2216f13e574b"></div> <div id="Pill-react-component-86d09583-4649-43b7-80d6-2216f13e574b"></div> </a><a data-click-track="profile-user-info-expand-research-interests" data-has-card-for-ri-list="88862579" href="https://www.academia.edu/Documents/in/Statistics"><div class="js-react-on-rails-component" style="display:none" data-component-name="Pill" data-props="{"color":"gray","children":["Statistics"]}" data-trace="false" 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data-dom-id="Pill-react-component-956cd4eb-6a90-43c6-9178-288bff08e57b"></div> <div id="Pill-react-component-956cd4eb-6a90-43c6-9178-288bff08e57b"></div> </a></div></div><div class="external-links-container"><ul class="profile-links new-profile js-UserInfo-social"><li class="left-most js-UserInfo-social-cv" data-broccoli-component="user-info.cv-button" data-click-track="profile-user-info-cv" data-cv-filename="cv.pdf" data-placement="top" data-toggle="tooltip" href="/LocNguyen2000/CurriculumVitae"><button class="ds2-5-text-link ds2-5-text-link--small" style="font-size: 20px; letter-spacing: 0.8px"><span class="ds2-5-text-link__content">CV</span></button></li><li class="profile-profiles js-social-profiles-container"><i class="fa fa-spin fa-spinner"></i></li></ul></div></div></div><div class="right-panel-container"><div class="user-content-wrapper"><div class="uploads-container" id="social-redesign-work-container"><div class="upload-header"><h2 class="ds2-5-heading-sans-serif-xs">Uploads</h2></div><div class="nav-container backbone-profile-documents-nav hidden-xs"><ul class="nav-tablist" role="tablist"><li class="nav-chip active" role="presentation"><a data-section-name="" data-toggle="tab" href="#all" role="tab">all</a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Videos" data-toggle="tab" href="#videos" role="tab" title="Videos"><span>7</span> <span class="ds2-5-body-sm-bold">Videos</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Papers" data-toggle="tab" href="#papers" role="tab" title="Papers"><span>69</span> <span class="ds2-5-body-sm-bold">Papers</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Books" data-toggle="tab" href="#books" role="tab" title="Books"><span>16</span> <span class="ds2-5-body-sm-bold">Books</span></a></li><li class="nav-chip" role="presentation"><a class="js-profile-docs-nav-section u-textTruncate" data-click-track="profile-works-tab" data-section-name="Conference-Presentations" data-toggle="tab" href="#conferencepresentations" role="tab" title="Conference Presentations"><span>3</span> <span class="ds2-5-body-sm-bold">Conference Presentations</span></a></li><li class="nav-chip more-tab" role="presentation"><a class="js-profile-documents-more-tab link-unstyled u-textTruncate" data-toggle="dropdown" role="tab">More  <i class="fa fa-chevron-down"></i></a><ul class="js-profile-documents-more-dropdown dropdown-menu dropdown-menu-right profile-documents-more-dropdown" role="menu"><li role="presentation"><a data-click-track="profile-works-tab" data-section-name="Thesis-Chapters" data-toggle="tab" href="#thesischapters" role="tab" style="border: none;"><span>2</span> Thesis Chapters</a></li><li role="presentation"><a data-click-track="profile-works-tab" data-section-name="Collections" data-toggle="tab" href="#collections" role="tab" style="border: none;"><span>8</span> Collections</a></li><li role="presentation"><a data-click-track="profile-works-tab" data-section-name="Drafts" data-toggle="tab" href="#drafts" role="tab" style="border: none;"><span>36</span> Drafts</a></li></ul></li></ul></div><div class="divider ds-divider-16" style="margin: 0px;"></div><div class="documents-container backbone-social-profile-documents" style="width: 100%;"><div class="u-taCenter"></div><div class="profile--tab_content_container js-tab-pane tab-pane active" id="all"><div class="profile--tab_heading_container js-section-heading" data-section="Videos" id="Videos"><h3 class="profile--tab_heading_container">Videos by Loc Nguyen</h3></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25281"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/1dKNZj"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/1dKNZj/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=06b8cd2ac8ab7daa6b5558558b9a918fc5688b1a982f85293443f80e1ebe85d4" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">01:05:52</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/1dKNZj">Recitation album “Tặng”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Tặng” also available at https://youtu.be/7bXmY8PhKtc Nguyễn Ph...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Tặng” also available at <a href="https://youtu.be/7bXmY8PhKtc" rel="nofollow">https://youtu.be/7bXmY8PhKtc</a> <br />Nguyễn Phước Lộc - Hồng Vân - Bích Ngọc - Lê Hương - Ngô Đình Long <br />2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="1dKNZj"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/1dKNZj" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25286"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/lD3VK1"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/lD3VK1/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=a2bc8142627159448f7ec8adb7d2f5f269d53cbc98d523242a1fb1989c9b80fb" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">47:09</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/lD3VK1">Recitation album “Cổ tích trái tim”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Cổ tích trái tim” also available at https://youtu.be/0TCS9Rbvt6...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Cổ tích trái tim” also available at <a href="https://youtu.be/0TCS9Rbvt6U" rel="nofollow">https://youtu.be/0TCS9Rbvt6U</a> <br />Nguyễn Phước Lộc - Ngọc Sang <br />2020/01/11</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="lD3VK1"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/lD3VK1" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25282"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/jJ6O8j"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/jJ6O8j/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=b6b902ce04d55dee0b02104233342568142da74ec69334858287e6fffafaa47d" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">51:10</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/jJ6O8j">Recitation album “Lục bát truyền nhân”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Lục bát truyền nhân” also available at https://youtu.be/waf0OMT...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Lục bát truyền nhân” also available at <a href="https://youtu.be/waf0OMTyFRU" rel="nofollow">https://youtu.be/waf0OMTyFRU</a> <br />Nguyễn Phước Lộc - Ngô Đình Long <br />2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="jJ6O8j"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/jJ6O8j" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25284"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/1qJv4l"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/1qJv4l/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=3b841ae5b3d4dd9290baad8ef229979a1b6c45dd8bf955cc535115ede20fc8f5" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">35:35</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/1qJv4l">Recitation album “Chiếc lá hồng”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Chiếc lá hồng” also available at https://youtu.be/aXpqIrYG3Zs ...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Chiếc lá hồng” also available at <a href="https://youtu.be/aXpqIrYG3Zs" rel="nofollow">https://youtu.be/aXpqIrYG3Zs</a> <br />Nguyễn Phước Lộc - Mộng Thu <br />2017/05</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="1qJv4l"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/1qJv4l" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25285"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/kVYKql"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/kVYKql/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=a15c18afdc6b475adf37af760434aa3058f2f743b76a0491c26ca0bf50207a6d" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">51:06</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/kVYKql">Recitation album “Lục Bát Mấy Lần Thương”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Lục Bát Mấy Lần Thương” also available at https://youtu.be/_ckS...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Lục Bát Mấy Lần Thương” also available at <a href="https://youtu.be/_ckSmDJ6__c" rel="nofollow">https://youtu.be/_ckSmDJ6__c</a> <br />Nguyễn Phước Lộc - Ngọc Sang <br />2019/11/25</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="kVYKql"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/kVYKql" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="26528"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/jXbD8l"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/jXbD8l/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=f9fcfe5ce8cb5be6a4b7413d20d86c1da6a5daf8c23ddff26c70686185d1f762" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">36:48</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/jXbD8l">Recitation album “Dị”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Dị” also available at https://youtu.be/XVdn_CyAXHU Nguyễn Phướ...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Dị” also available at <a href="https://youtu.be/XVdn_CyAXHU" rel="nofollow">https://youtu.be/XVdn_CyAXHU</a> <br />Nguyễn Phước Lộc - Hoàng Đức Tâm - Nhật Quỳnh - Thu Thủy <br />2022.04.14</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="jXbD8l"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/jXbD8l" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25287"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/jQY6Rj"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/jQY6Rj/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=819adf1dfb83c169da5afed5bb933f9585a324fd736f969aea381dee6e7b1fca" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">55:12</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/jQY6Rj">Recitation album “Đại hiệp”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Đại hiệp” also available at https://youtu.be/b3LgcJuvnjI Nguyễ...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Đại hiệp” also available at <a href="https://youtu.be/b3LgcJuvnjI" rel="nofollow">https://youtu.be/b3LgcJuvnjI</a> <br />Nguyễn Phước Lộc - Ngọc Sang <br />2021/03/20</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="jQY6Rj"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/jQY6Rj" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><div class="profile--tab_heading_container js-section-heading" data-section="Papers" id="Papers"><h3 class="profile--tab_heading_container">Papers by Loc Nguyen</h3></div><div class="js-work-strip profile--work_container" data-work-id="115743270"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115743270/User_Model_Clustering"><img alt="Research paper thumbnail of User Model Clustering" class="work-thumbnail" src="https://attachments.academia-assets.com/112063089/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115743270/User_Model_Clustering">User Model Clustering</a></div><div class="wp-workCard_item"><span>Journal of Data Analysis and Information Processing</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">User model which is the representation of information about user is the heart of adaptive systems...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user; 2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3ff7835209639fc55d4743f54339a0e7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112063089,"asset_id":115743270,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112063089/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115743270"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115743270"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115743270; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=115743270]").text(description); $(".js-view-count[data-work-id=115743270]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 115743270; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='115743270']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="108807871"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/108807871/Numerical_Similarity_Measures_Versus_Jaccard_for_Collaborative_Filtering"><img alt="Research paper thumbnail of Numerical Similarity Measures Versus Jaccard for Collaborative Filtering" class="work-thumbnail" src="https://attachments.academia-assets.com/107100250/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/108807871/Numerical_Similarity_Measures_Versus_Jaccard_for_Collaborative_Filtering">Numerical Similarity Measures Versus Jaccard for Collaborative Filtering</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI2023), part of the book series: Lecture Notes on Data Engineering and Communications Technologies (LNDECT), volume 184, pages 221-229</span><span>, Sep 19, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Collaborative filtering (CF) is an important method for recommendation systems, which are employe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Collaborative filtering (CF) is an important method for recommendation systems, which are employed in many facets of our lives and are particularly prevalent in online-based commercial systems. The K-nearest neighbors (KNN) technique is a well-liked CF algorithm that uses similarity measurements to identify a user's closest neighbors in order to quantify the degree of dependency between the respective user and item pair. As a result, the CF approach is not only dependent on the choice of the similarity measure but also sensitive to it. However, some traditional "numerical" similarity measures, like cosine and Pearson, concentrate on the size of ratings, whereas Jaccard, one of the most frequently employed similarity measures for CF tasks, concerns the existence of ratings. Jaccard, in particular, is not a dominant measure, but it has long been demonstrated to be a key element in enhancing any measure. Therefore, this research focuses on presenting novel similarity measures by combining Jaccard with a multitude of numerical measures in our ongoing search for the most effective similarity measures for CF. Both existence and magnitude would benefit the combined measurements. Experimental results demonstrated that the combined measures are superior, surpassing all single measures across the considered assessment metrics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="744a9ac9f4d462c933b15cccd454abf6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107100250,"asset_id":108807871,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107100250/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="108807871"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="108807871"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 108807871; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=108807871]").text(description); $(".js-view-count[data-work-id=108807871]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 108807871; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='108807871']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "744a9ac9f4d462c933b15cccd454abf6" } } $('.js-work-strip[data-work-id=108807871]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":108807871,"title":"Numerical Similarity Measures Versus Jaccard for Collaborative Filtering","translated_title":"","metadata":{"doi":"10.1007/978-3-031-43247-7_20","volume":"184","abstract":"Collaborative filtering (CF) is an important method for recommendation systems, which are employed in many facets of our lives and are particularly prevalent in online-based commercial systems. 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The K-nearest neighbors (KNN) technique is a well-liked CF algorithm that uses similarity measurements to identify a user's closest neighbors in order to quantify the degree of dependency between the respective user and item pair. As a result, the CF approach is not only dependent on the choice of the similarity measure but also sensitive to it. However, some traditional \"numerical\" similarity measures, like cosine and Pearson, concentrate on the size of ratings, whereas Jaccard, one of the most frequently employed similarity measures for CF tasks, concerns the existence of ratings. Jaccard, in particular, is not a dominant measure, but it has long been demonstrated to be a key element in enhancing any measure. Therefore, this research focuses on presenting novel similarity measures by combining Jaccard with a multitude of numerical measures in our ongoing search for the most effective similarity measures for CF. Both existence and magnitude would benefit the combined measurements. Experimental results demonstrated that the combined measures are superior, surpassing all single measures across the considered assessment metrics.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":107100250,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/107100250/thumbnails/1.jpg","file_name":"81.NumericalSimilarityMeasures_JaccardCF_AbdallaAmerNguyenAmerAlMaqaleh_AISI23_2023.09.18.pdf","download_url":"https://www.academia.edu/attachments/107100250/download_file","bulk_download_file_name":"Numerical_Similarity_Measures_Versus_Jac.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/107100250/81.NumericalSimilarityMeasures_JaccardCF_AbdallaAmerNguyenAmerAlMaqaleh_AISI23_2023.09.18-libre.pdf?1698916441=\u0026response-content-disposition=attachment%3B+filename%3DNumerical_Similarity_Measures_Versus_Jac.pdf\u0026Expires=1738797504\u0026Signature=OjuHEspa-Md~pA0WiX-z3wrI-XEd2L4CyjIPvjDIvl4CsoZ408qJQVcTc97--mf6q4olTJyCFQlGihNQQpHBUKhi20C-jKxxb5CX4UIGso3mtYn5WRSGW0ik5tw5VHzIqz0id4dWHAVQyHHmu299HxazbWIFI7jGAzZaA6qaJ85XqmZzOvB3qj5fOCJbZYhqFl5MQabBi-8-kR8-hCePFK2GPNmH4a5zsvDy2Sb3Cx2in3iV5CU~6aNVmPUMaC6fRjYavPQJsLpnMdm0WxK65m~xplj02hfjVSGafjtQRk-gHYVmB2d3cvftvUNvdmN75gA5K9WFtdH7wsxx1JIcOg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":177103,"name":"Similarity Measures","url":"https://www.academia.edu/Documents/in/Similarity_Measures"},{"id":298644,"name":"Recommendation Systems","url":"https://www.academia.edu/Documents/in/Recommendation_Systems"},{"id":995498,"name":"Jaccard Index","url":"https://www.academia.edu/Documents/in/Jaccard_Index"}],"urls":[{"id":35158973,"url":"https://link.springer.com/chapter/10.1007/978-3-031-43247-7_20"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105637584"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105637584/Boosting_the_Item_Based_Collaborative_Filtering_Model_with_Novel_Similarity_Measures"><img alt="Research paper thumbnail of Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures" class="work-thumbnail" src="https://attachments.academia-assets.com/105038095/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105637584/Boosting_the_Item_Based_Collaborative_Filtering_Model_with_Novel_Similarity_Measures">Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>International Journal of Computational Intelligence Systems (IJCIS), Volume 16, Issue 1</span><span>, Jul 29, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Collaborative filtering (CF), one of the most widely employed methodologies for recommender syste...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system's performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top “state-of-the-art” performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4ed75d7f5f12acb9d7e6e757397692fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105038095,"asset_id":105637584,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105038095/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105637584"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105637584"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105637584; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105637584]").text(description); $(".js-view-count[data-work-id=105637584]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105637584; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105637584']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4ed75d7f5f12acb9d7e6e757397692fe" } } $('.js-work-strip[data-work-id=105637584]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105637584,"title":"Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures","translated_title":"","metadata":{"doi":"10.1007/s44196-023-00299-2","issue":"1","volume":"16","abstract":"Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system's performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top “state-of-the-art” performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model.","event_date":{"day":29,"month":7,"year":2023,"errors":{}},"journal_name":"International Journal of Computational Intelligence Systems (IJCIS)","organization":"Springer","publication_date":{"day":29,"month":7,"year":2023,"errors":{}},"publication_name":"International Journal of Computational Intelligence Systems (IJCIS), Volume 16, Issue 1","conference_end_date":{"day":29,"month":7,"year":2023,"errors":{}},"conference_start_date":{"day":29,"month":7,"year":2023,"errors":{}}},"translated_abstract":"Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system's performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top “state-of-the-art” performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model.","internal_url":"https://www.academia.edu/105637584/Boosting_the_Item_Based_Collaborative_Filtering_Model_with_Novel_Similarity_Measures","translated_internal_url":"","created_at":"2023-08-15T23:18:55.529-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":40228780,"work_id":105637584,"tagging_user_id":12043864,"tagged_user_id":26698005,"co_author_invite_id":null,"email":"a***6@yahoo.co.uk","display_order":1,"name":"Ali Amer","title":"Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures"},{"id":40228781,"work_id":105637584,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":2,"name":"Loc Nguyen","title":"Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures"},{"id":40228779,"work_id":105637584,"tagging_user_id":12043864,"tagged_user_id":null,"co_author_invite_id":7897417,"email":"a***4@yahoo.com","display_order":3,"name":"Hassan I. 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Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system's performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top “state-of-the-art” performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":105038095,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105038095/thumbnails/1.jpg","file_name":"77.BoostingItemBasedCFModelNovelSimilarityMeasures_AbdallaAmerAmerNguyenAlMaqaleh_Springer_2023.07.29.pdf","download_url":"https://www.academia.edu/attachments/105038095/download_file","bulk_download_file_name":"Boosting_the_Item_Based_Collaborative_Fi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105038095/77.BoostingItemBasedCFModelNovelSimilarityMeasures_AbdallaAmerAmerNguyenAlMaqaleh_Springer_2023.07.29-libre.pdf?1692169045=\u0026response-content-disposition=attachment%3B+filename%3DBoosting_the_Item_Based_Collaborative_Fi.pdf\u0026Expires=1738797504\u0026Signature=OU0KVFxlGSNe8XkU0-gxz-gyOb5LnR-pTtjenM0roLSTRziJy~Ofak3JajPpCk~n9lUvKqPFmeo7jRkumSWq5DS~QuUmz9hL~S~BbAVQGyMwuDXQWu5al8xRDVqCFqU7pZRVrmqUfNbo7PEWY-VuHkZDMeaKejBB9Ow-DkItskLkeZr-kM4toaigk8vJuga3ndjn7f66B9knq48bbiqT1W8pzwz6-XMkAavXE0mxb5O~9krUADkiKvDuUVPPdHZ~B2SiW~mcx~QAiIZLldRmshVrG-HPBbduwoeM20Ng9e-GwIuiiEWgz-0GLRT5KODjKl5IXw9MAGooRi7XdNbx~w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval"},{"id":2900,"name":"Recommender Systems","url":"https://www.academia.edu/Documents/in/Recommender_Systems"},{"id":77193,"name":"Collaborative Filtering","url":"https://www.academia.edu/Documents/in/Collaborative_Filtering"},{"id":177103,"name":"Similarity Measures","url":"https://www.academia.edu/Documents/in/Similarity_Measures"}],"urls":[{"id":33447905,"url":"https://link.springer.com/article/10.1007/s44196-023-00299-2"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105294401"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/105294401/Learning_concept_recommendation_based_on_sequential_pattern_mining"><img alt="Research paper thumbnail of Learning concept recommendation based on sequential pattern mining" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/105294401/Learning_concept_recommendation_based_on_sequential_pattern_mining">Learning concept recommendation based on sequential pattern mining</a></div><div class="wp-workCard_item"><span>2009 3rd IEEE International Conference on Digital Ecosystems and Technologies</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105294401"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105294401"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105294401; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105294401]").text(description); $(".js-view-count[data-work-id=105294401]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105294401; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105294401']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=105294401]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105294401,"title":"Learning concept recommendation based on sequential pattern mining","internal_url":"https://www.academia.edu/105294401/Learning_concept_recommendation_based_on_sequential_pattern_mining","owner_id":88862579,"coauthors_can_edit":true,"owner":{"id":88862579,"first_name":"Loc","middle_initials":null,"last_name":"Nguyen","page_name":"LocNguyen2000","domain_name":"independent","created_at":"2018-08-19T05:35:13.668-07:00","display_name":"Loc Nguyen","url":"https://independent.academia.edu/LocNguyen2000"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105294400"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/105294400/Evolution_of_Parameters_in_Bayesian_Overlay_Model"><img alt="Research paper thumbnail of Evolution of Parameters in Bayesian Overlay Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/105294400/Evolution_of_Parameters_in_Bayesian_Overlay_Model">Evolution of Parameters in Bayesian Overlay Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Adaptive learning systems require well-organized user model along with solid inference mechanism....</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Adaptive learning systems require well-organized user model along with solid inference mechanism. Overlay modeling is the method in which the domain is decomposed into a set of elements and the user model is simply a set of masteries over those elements. The combination between overlay model and Bayesian network (BN) will make use of the flexibility and simplification of overlay modeling and the power inference of BN. Thus it is compulsory to pre-define parameters, namely, Conditional Probability Tables (CPT (s)) in BN but no one ensured absolutely the correctness of these CPT (s). This research focuses on how to enhance parameters’ quality in Bayesian overlay model, in other words, this is the evolution of CPT(s).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105294400"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105294400"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105294400; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105294400]").text(description); $(".js-view-count[data-work-id=105294400]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105294400; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105294400']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=105294400]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105294400,"title":"Evolution of Parameters in Bayesian Overlay Model","internal_url":"https://www.academia.edu/105294400/Evolution_of_Parameters_in_Bayesian_Overlay_Model","owner_id":88862579,"coauthors_can_edit":true,"owner":{"id":88862579,"first_name":"Loc","middle_initials":null,"last_name":"Nguyen","page_name":"LocNguyen2000","domain_name":"independent","created_at":"2018-08-19T05:35:13.668-07:00","display_name":"Loc Nguyen","url":"https://independent.academia.edu/LocNguyen2000"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105294383"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105294383/Combination_of_Bayesian_Network_and_Overlay_Model_in_User_Modeling"><img alt="Research paper thumbnail of Combination of Bayesian Network and Overlay Model in User Modeling" class="work-thumbnail" src="https://attachments.academia-assets.com/104787399/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105294383/Combination_of_Bayesian_Network_and_Overlay_Model_in_User_Modeling">Combination of Bayesian Network and Overlay Model in User Modeling</a></div><div class="wp-workCard_item"><span>International Journal of Emerging Technologies in Learning (iJET)</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The core of adaptive system is user model containing personal information such as knowledge, lear...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The core of adaptive system is user model containing personal information such as knowledge, learning styles, goalsâ?¦ which is requisite for learning personalized process. There are many modeling approaches, for example: stereotype, overlay, plan recognitionâ?¦ but they donâ??t bring out the solid method for reasoning from user model. This paper introduces the statistical method that combines Bayesian network and overlay modeling so that it is able to infer userâ??s knowledge from evidences collected during userâ??s learning process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="30ffac134d40eb4646b9b4e3d27f656a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104787399,"asset_id":105294383,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104787399/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105294383"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105294383"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105294383; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105294383]").text(description); $(".js-view-count[data-work-id=105294383]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105294383; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105294383']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "30ffac134d40eb4646b9b4e3d27f656a" } } $('.js-work-strip[data-work-id=105294383]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105294383,"title":"Combination of Bayesian Network and Overlay Model in User Modeling","internal_url":"https://www.academia.edu/105294383/Combination_of_Bayesian_Network_and_Overlay_Model_in_User_Modeling","owner_id":88862579,"coauthors_can_edit":true,"owner":{"id":88862579,"first_name":"Loc","middle_initials":null,"last_name":"Nguyen","page_name":"LocNguyen2000","domain_name":"independent","created_at":"2018-08-19T05:35:13.668-07:00","display_name":"Loc Nguyen","url":"https://independent.academia.edu/LocNguyen2000"},"attachments":[{"id":104787399,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104787399/thumbnails/1.jpg","file_name":"978-3-642-01973-9_2.pdf","download_url":"https://www.academia.edu/attachments/104787399/download_file","bulk_download_file_name":"Combination_of_Bayesian_Network_and_Over.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104787399/978-3-642-01973-9_2-libre.pdf?1691289860=\u0026response-content-disposition=attachment%3B+filename%3DCombination_of_Bayesian_Network_and_Over.pdf\u0026Expires=1740509749\u0026Signature=Z2SjX6VIwGInkvx7jHyHQnOnqfcFl0a7N-~4Ycq28jndCVp2USCv~Dr~d7JlxIMUva-Knoccp0owuiK4sU-t3MKZcRrT5B5Li0IVQD7Z4Ftp-dG-WZfxrbO1NAGUht~Mf7SPwh8cnSRSWCwHHax2bc8RYHDOxeW2L-IJzC-K8mW2Wu6pB9deytA-XVPvjwAM82H4q0yVjWFILAKlTJH0gpPBZQxKKOS4QRQsoru4MTLhxlpowcqBl03kC2lQ2w7uJQgG7mp6YBuOxF-K1-V-rJoOL8vs8N-xVCzaZxsEFTYwRblyx74fKx~rZU0qm2R5su3~a~bOhMtaCoBgV5scig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76520501"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76520501/A_Framework_of_Fetal_Age_and_Weight_Estimation"><img alt="Research paper thumbnail of A Framework of Fetal Age and Weight Estimation" class="work-thumbnail" src="https://attachments.academia-assets.com/84203883/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76520501/A_Framework_of_Fetal_Age_and_Weight_Estimation">A Framework of Fetal Age and Weight Estimation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Journal of Gynecology and Obstetrics (JGO)</span><span>, Mar 30, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fetal age and weight estimation plays the important role in pregnant treatments. There are many e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. Moreover, the framework gives facilities to scientists for exploiting useful information under pregnant statistical data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4cb4282d5be4d713b4ca47e47cf83030" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84203883,"asset_id":76520501,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84203883/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76520501"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76520501"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76520501; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76520501]").text(description); $(".js-view-count[data-work-id=76520501]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76520501; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76520501']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4cb4282d5be4d713b4ca47e47cf83030" } } $('.js-work-strip[data-work-id=76520501]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76520501,"title":"A Framework of Fetal Age and Weight Estimation","translated_title":"","metadata":{"doi":"10.4172/2157-7420.C1.012","issue":"2","volume":"2","abstract":"Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. Moreover, the framework gives facilities to scientists for exploiting useful information under pregnant statistical data.","publisher":"Science Publishing Group","event_date":{"day":30,"month":3,"year":2014,"errors":{}},"ai_title_tag":"A Framework for Estimating Fetal Age and Weight","journal_name":"Journal of Gynecology and Obstetrics (JGO)","organization":"Science Publishing Group","page_numbers":"20-25","publication_date":{"day":30,"month":3,"year":2014,"errors":{}},"publication_name":"Journal of Gynecology and Obstetrics (JGO)","conference_end_date":{"day":30,"month":3,"year":2014,"errors":{}},"conference_start_date":{"day":30,"month":3,"year":2014,"errors":{}}},"translated_abstract":"Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. Moreover, the framework gives facilities to scientists for exploiting useful information under pregnant statistical data.","internal_url":"https://www.academia.edu/76520501/A_Framework_of_Fetal_Age_and_Weight_Estimation","translated_internal_url":"","created_at":"2022-04-15T05:48:43.833-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"paper","co_author_tags":[{"id":38043157,"work_id":76520501,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"A Framework of Fetal Age and Weight Estimation"}],"downloadable_attachments":[{"id":84203883,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84203883/thumbnails/1.jpg","file_name":"10.11648.j.jgo.20140202.13.pdf","download_url":"https://www.academia.edu/attachments/84203883/download_file","bulk_download_file_name":"A_Framework_of_Fetal_Age_and_Weight_Esti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84203883/10.11648.j.jgo.20140202.13-libre.pdf?1650027021=\u0026response-content-disposition=attachment%3B+filename%3DA_Framework_of_Fetal_Age_and_Weight_Esti.pdf\u0026Expires=1738797504\u0026Signature=RelTfdAVhv3VZD28Xn0bEsdDNI5fr0KrCvPIjHVQB-TmIPYUOyB0lPgh02uhmvIc6aEoOOUP6Z8dse04iw1RDPo-bO5e8PL5UG~d6gyfJ~w8ZvSdukCe5l5Gb6hv9t2U0iP2K8uTeoyyyDTYElh3rRWilN1SCcJqPrfnjDbjaEgbOBqkkfi42fxNQ~gH33ER-qFRErYCaJygjfjsAYAjbvfQowvI~ltEHYKKSe~bgaj8UcEbxZ7rhY-nKSgUXQTV16FScCzCgF~Hm5HAYE3gRGpE9a4jC-Wtl0oYChc8rJ3pTNYSMmUPV5FVtgiB3talzLRBSmyXG4RZ7k2FApNKwQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":84204317,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/84204317/thumbnails/1.jpg","file_name":"14.PhoebeFramework_10.11648.j.jgo.20140202.13.pdf","download_url":"https://www.academia.edu/attachments/84204317/download_file","bulk_download_file_name":"A_Framework_of_Fetal_Age_and_Weight_Esti.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/84204317/14.PhoebeFramework_10.11648.j.jgo.20140202.13-libre.pdf?1650027799=\u0026response-content-disposition=attachment%3B+filename%3DA_Framework_of_Fetal_Age_and_Weight_Esti.pdf\u0026Expires=1738797504\u0026Signature=JU0vmGsA3fAaBR8dv35W-0YLI8tEOKgwoBGj1pAnyj9CJWjX9sm3Icxo1bpo6jI9Zcfxn3-xTQMF5U60YOyZ9V8sjW-QvzOsBji83QmDPWAoLPtfjdRS3ABT3R0jRsoXRHc~8STQrOWiRjwn~GVs6assUhxaZ1qoo5HAPAA762KgDkTEr3YL-rfsfbnCQjh5fWU0CegaZgfszhjftG77knWoPywcwIWyS1ru5iKMV6PPu2ocr4a2LkY2cDokwHhUqOebJb8XQGbXU2Y5ZeNe-JuhSbf~kKiBjBjzlA2NeD0USpRZlyosCDPrD~PSYW6K~OulRXC8O3kaQPlpJZO4Vg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"A_Framework_of_Fetal_Age_and_Weight_Estimation","translated_slug":"","page_count":6,"language":"en","content_type":"Work","summary":"Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25548296"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25548296/Convergence_of_Confucianism_Buddhism_and_Taoism"><img alt="Research paper thumbnail of Convergence of Confucianism, Buddhism and Taoism" class="work-thumbnail" src="https://attachments.academia-assets.com/45880321/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25548296/Convergence_of_Confucianism_Buddhism_and_Taoism">Convergence of Confucianism, Buddhism and Taoism</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Leu Chong Zen Club‘s Conference</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The article discusses the convergence of three largest Oriental religions such as Confucianism, B...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The article discusses the convergence of three largest Oriental religions such as Confucianism, Buddhism, and Taoism.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="45419ca694ca82e655361e74a8aa0471" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45880321,"asset_id":25548296,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45880321/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25548296"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25548296"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25548296; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25548078"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25548078/Beta_Likelihood_Estimation_and_Its_Application_to_Specify_Prior_Probabilities_in_Bayesian_Network"><img alt="Research paper thumbnail of Beta Likelihood Estimation and Its Application to Specify Prior Probabilities in Bayesian Network" class="work-thumbnail" src="https://attachments.academia-assets.com/56308214/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25548078/Beta_Likelihood_Estimation_and_Its_Application_to_Specify_Prior_Probabilities_in_Bayesian_Network">Beta Likelihood Estimation and Its Application to Specify Prior Probabilities in Bayesian Network</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>British Journal of Mathematics & Computer Science, Volume 16, Issue 3, page 1-21</span><span>, Apr 27, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Maximum likelihood estimation (MLE) is a popular technique of statistical parameter estimation. W...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Maximum likelihood estimation (MLE) is a popular technique of statistical parameter estimation. When random variable conforms beta distribution, the research focuses on applying MLE into beta density function. This method is called beta likelihood estimation, which results out useful estimation equations. It is easy to calculate statistical estimates based on these equations in case that parameters of beta distribution are positive integer numbers. Essentially, the method takes advantages of interesting features of functions gamma, digamma, and trigamma. An application of beta likelihood estimation is to specify prior probabilities in Bayesian network.<br /><br />Keywords: maximum likelihood estimation, beta distribution, beta likelihood estimation, gamma function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0f1a70a1110dc919b945c25af56d8038" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":56308214,"asset_id":25548078,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/56308214/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25548078"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25548078"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25548078; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25548012"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25548012/Theorem_of_SIGMA_gate_Inference_in_Bayesian_Network"><img alt="Research paper thumbnail of Theorem of SIGMA-gate Inference in Bayesian Network" class="work-thumbnail" src="https://attachments.academia-assets.com/56308112/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25548012/Theorem_of_SIGMA_gate_Inference_in_Bayesian_Network">Theorem of SIGMA-gate Inference in Bayesian Network</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Wulfenia Journal, Volume 23, Issue 3, pages 280-289</span><span>, Mar 28, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Bayesian network is a powerful mathematical tool for doing diagnosis and assessment tasks. Parame...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Bayesian network is a powerful mathematical tool for doing diagnosis and assessment tasks. Parameter learning in Bayesian network is complicated study but I discover that parameter learning becomes easy in some situations. Especially, when Bayesian network is weighted graph and its child node is aggregation of mutually independent parent nodes, there is a simple way to specify conditional probability tables which are parameters of Bayesian network. In this research, I propose and prove the theorem of SIGMA-gate inference which is the fundamental of such simple way helping us to transform weighted graph into Bayesian network.<br /><br />Keywords: Bayesian network, parameter learning, SIGMA-gate inference.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="75d645b43ab058b25a430abbf2819b67" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":56308112,"asset_id":25548012,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/56308112/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25548012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25548012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25548012; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=25548012]").text(description); $(".js-view-count[data-work-id=25548012]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 25548012; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='25548012']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546932"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546932/Specifying_Prior_Probabilities_in_Bayesian_Network_by_Maximum_Likelihood_Estimation_method"><img alt="Research paper thumbnail of Specifying Prior Probabilities in Bayesian Network by Maximum Likelihood Estimation method" class="work-thumbnail" src="https://attachments.academia-assets.com/56308106/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546932/Specifying_Prior_Probabilities_in_Bayesian_Network_by_Maximum_Likelihood_Estimation_method">Specifying Prior Probabilities in Bayesian Network by Maximum Likelihood Estimation method</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Sylwan Journal, Volume 160, Issue 2, February 2016, pages 281-298</span><span>, Feb 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Bayesian network provides the solid inference mechanism when convincing the hypothesis by collect...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Bayesian network provides the solid inference mechanism when convincing the hypothesis by collecting evidences. Bayesian network is instituted of two models such as qualitative model quantitative model. The qualitative model is its structure and the quantitative model is its parameters, namely conditional probability tables (CPT) whose entries are probabilities quantifying the dependences among variables in network. The quality of CPT depends on the initialized values of its entries. Such initial values are prior probabilities. Because the beta function provides some conveniences when specifying CPT (s), this function is used as the basic distribution in my method. The main problem of defining prior probabilities is how to estimate parameters in beta distribution. It is slightly unfortunate when the equations whose solutions are parameter estimators are differential equations and it is too difficult to solve them. By applying the maximum likelihood estimation (MLE) technique, I invent the simple equations so that differential equations are eliminated and it is much easier to estimate parameters in case that such parameters are positive integer numbers. Thus, I also propose the algorithm to find out the approximate solutions of these simple equations.<br /><br />Keywords: prior probabilities, Bayesian network, maximum likelihood estimation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e2ec5517a80705c08aad0e6943a2ad58" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":56308106,"asset_id":25546932,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/56308106/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25546932"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25546932"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25546932; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546518"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546518/Introduction_to_a_Framework_of_E_commercial_Recommendation_Algorithms"><img alt="Research paper thumbnail of Introduction to a Framework of E-commercial Recommendation Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/45878369/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546518/Introduction_to_a_Framework_of_E_commercial_Recommendation_Algorithms">Introduction to a Framework of E-commercial Recommendation Algorithms</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>American Journal of Computer Science and Information Engineering (AJCSIE), Vol. 2, No. 4, September 2015, pages 33-44</span><span>, Sep 28, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recommendation algorithm is very important for e-commercial websites when it can recommend online...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Recommendation algorithm is very important for e-commercial websites when it can recommend online customers favorite products, which results out an increase in sale revenue. I propose the framework of e-commercial recommendation algorithms. This is a middleware framework or “operating system” for e-commercial recommendation software, which support scientists and software developers build up their own recommendation algorithms based on this framework with low cost, high achievement and fast speed.<br /><br />Keywords: Recommendation Algorithm, Recommendation Server, Middleware Framework.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c346602b22a166a69ba86e7a227a79a9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45878369,"asset_id":25546518,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45878369/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25546518"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25546518"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25546518; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546208"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546208/Feasible_length_of_Taylor_polynomial_on_given_interval_and_application_to_find_the_number_of_roots_of_equation"><img alt="Research paper thumbnail of Feasible length of Taylor polynomial on given interval and application to find the number of roots of equation" class="work-thumbnail" src="https://attachments.academia-assets.com/45878249/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546208/Feasible_length_of_Taylor_polynomial_on_given_interval_and_application_to_find_the_number_of_roots_of_equation">Feasible length of Taylor polynomial on given interval and application to find the number of roots of equation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>International Journal of Mathematical Analysis and Applications, Vol. 1, No. 5, December 2014, pages 80-83</span><span>, Jan 10, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is very necessary to represent arbitrary function as a polynomial in many situations because p...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is very necessary to represent arbitrary function as a polynomial in many situations because polynomial has many valuable properties. Fortunately, any analytic function can be approximated by Taylor polynomial. The higher the degree of Taylor polynomial is, the better the approximation is gained. There is problem that how to achieve optimal approximation with restriction that the degree is not so high because of computation cost. This research proposes a method to estimate feasible degree of Taylor polynomial so that it is likely that Taylor polynomial with degree being equal to or larger than such feasible degree is good approximation of a function in given interval. The feasible degree is called the feasible length of Taylor polynomial. The research also introduces an application that combines Sturm theorem and the method to approximate a function by Taylor polynomial with feasible length in order to count the number of roots of equation in given interval.<br /><br />Keywords: Taylor polynomial, roots of equation, analytic function approximation, feasible length.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="59ae96825a2c2d4f5060e3ade9501d32" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45878249,"asset_id":25546208,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45878249/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper 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src="https://academia-edu-videos.s3.amazonaws.com/transcoded/lD3VK1/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=a2bc8142627159448f7ec8adb7d2f5f269d53cbc98d523242a1fb1989c9b80fb" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">47:09</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/lD3VK1">Recitation album “Cổ tích trái tim”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Cổ tích trái tim” also available at https://youtu.be/0TCS9Rbvt6...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Cổ tích trái tim” also available at <a href="https://youtu.be/0TCS9Rbvt6U" rel="nofollow">https://youtu.be/0TCS9Rbvt6U</a> <br />Nguyễn Phước Lộc - Ngọc Sang <br />2020/01/11</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="lD3VK1"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/lD3VK1" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25282"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/jJ6O8j"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/jJ6O8j/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=b6b902ce04d55dee0b02104233342568142da74ec69334858287e6fffafaa47d" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">51:10</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/jJ6O8j">Recitation album “Lục bát truyền nhân”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Lục bát truyền nhân” also available at https://youtu.be/waf0OMT...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Lục bát truyền nhân” also available at <a href="https://youtu.be/waf0OMTyFRU" rel="nofollow">https://youtu.be/waf0OMTyFRU</a> <br />Nguyễn Phước Lộc - Ngô Đình Long <br />2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="jJ6O8j"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/jJ6O8j" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25284"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/1qJv4l"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/1qJv4l/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=3b841ae5b3d4dd9290baad8ef229979a1b6c45dd8bf955cc535115ede20fc8f5" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">35:35</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/1qJv4l">Recitation album “Chiếc lá hồng”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Chiếc lá hồng” also available at https://youtu.be/aXpqIrYG3Zs ...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Chiếc lá hồng” also available at <a href="https://youtu.be/aXpqIrYG3Zs" rel="nofollow">https://youtu.be/aXpqIrYG3Zs</a> <br />Nguyễn Phước Lộc - Mộng Thu <br />2017/05</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="1qJv4l"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/1qJv4l" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div><style type="text/css">/*thumbnail*/ .video-thumbnail-container { position: relative; height: 88px !important; box-sizing: content-box; } .thumbnail-image { height: 100%; width: 100%; object-fit: cover; } .play-icon { position: absolute; width: 40px; height: 40px; top: calc(50% - 20px); left: calc(50% - 20px); } .video-duration { position: absolute; bottom: 2px; right: 2px; color: #ffffff; background-color: #000000; font-size: 12px; font-weight: 500; line-height: 12px; padding: 2px; }</style><div class="js-work-strip profile--work_container" data-video-id="25285"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" href="https://www.academia.edu/video/kVYKql"><div class="work-thumbnail video-thumbnail-container"><img class="thumbnail-image" onerror="this.src='//a.academia-assets.com/images/videoicon.svg'" src="https://academia-edu-videos.s3.amazonaws.com/transcoded/kVYKql/thumbnail.jpg?response-content-disposition=inline%3B%20filename%3D%22thumbnail.jpg%22%3B%20filename%2A%3DUTF-8%27%27thumbnail.jpg&response-content-type=image%2Fjpeg&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=ASIATUSBJ6BANZA4T2OH%2F20250225%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20250225T175549Z&X-Amz-Expires=20178&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEBIaCXVzLWVhc3QtMSJGMEQCIDFGIR2VZEzy8aiNZwQCoTILYXWkAlWluabkTvcGj3ABAiBwGqL7llQxmR9iWrWa0cd4O5sa0tmnJXFcP2W5tlJseCqNBAhLEAAaDDI1MDMxODgxMTIwMCIM3fb2b1XqgaRm%2BefUKuoDWqQzeq45TEiwzSavlWoAE6Kkw9FH%2FnUv%2BPOBnEYGQ604xkIzXYbcdP6QHf%2FATTxVgKdlR2ibBtKrCGpQEjqUS5V4Dmv8YrNBNZx95c1ibcQVdEtBy%2BbbUXl4zpLZIXeUoVszQlBU%2FwPGlBH1QcOk7iCI9mGEwtynWjmw04BzoV3KTYDNjSa5GSwyI7FHgcYhVukh0BA4t4P%2FrvR8xWfKx5y2lC2OU2cdM9h4CVxofcscowBlTql%2BS7lcojqsesrp2IZLnFaX3u0x4thpBeX931zk8xklp4FwHGavijXjSwLBfQHJXcBszZf8%2FQVZAnaMlT83NgntNtWzebIo3AWVxb2Eq3YdaUiWwXo65caFeCqpc%2FbNPRNBBaif%2BXC5x7nMIpinPot2CStSsbFiB4ATxBulGx1x7Lw%2F9UMrlxSL%2FrW1MdVFzEo4e4sdNFNAuS%2F%2FJJoYn5SrXIAaEakP9c%2FchEn9gUVq%2F6ebu2tEqB6zO%2B3V6qcNFBG8f6xbW42OjM7%2Ba3ynLrSdslP5qyHa9pyUrwQqYdCLgTjIEDo8huweH9hFbiC7Cmk3W68Z8xfXW2vQNerMGhq4xu4FuWta0DwVIrzDQ6D2Poz4SWXIJNTvG6hEArM1Hx4u0JFfXZ%2BZmbl6fmPBQ3HAKhnkzzCX%2Ffe9BjqmATm2hrrlebwfr7Us0xie13q%2FwNCHgqANVT8Q5D10d8GbRg9s63ZAC4KvrRkwlQV9wfoqBuWxJDYtIkIDbH5Os%2BiRzQFFlRgpAEdwldhlfIgbJNXv%2BYYzKr5aGrWiKq7%2FcHfCvQ0oUlbrZaYI%2FrnrghQSsBV4WXoPTli%2BF0vlmwC12xWhz3S59%2BXqQp25WWVuu29IlMZNZI%2FVeOiwdFrwLCuDfHxPdU4%3D&X-Amz-SignedHeaders=host&X-Amz-Signature=a15c18afdc6b475adf37af760434aa3058f2f743b76a0491c26ca0bf50207a6d" /><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">51:06</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/kVYKql">Recitation album “Lục Bát Mấy Lần Thương”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Lục Bát Mấy Lần Thương” also available at https://youtu.be/_ckS...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Lục Bát Mấy Lần Thương” also available at <a href="https://youtu.be/_ckSmDJ6__c" rel="nofollow">https://youtu.be/_ckSmDJ6__c</a> <br />Nguyễn Phước Lộc - Ngọc Sang <br />2019/11/25</span></div><div 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/><img alt="Play" class="play-icon" src="//a.academia-assets.com/images/video-play-icon.svg" /><div class="video-duration">36:48</div></div></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" href="https://www.academia.edu/video/jXbD8l">Recitation album “Dị”</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thank to listen recitation album “Dị” also available at https://youtu.be/XVdn_CyAXHU Nguyễn Phướ...</span><a class="js-work-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thank to listen recitation album “Dị” also available at <a href="https://youtu.be/XVdn_CyAXHU" rel="nofollow">https://youtu.be/XVdn_CyAXHU</a> <br />Nguyễn Phước Lộc - Hoàng Đức Tâm - Nhật Quỳnh - Thu Thủy <br />2022.04.14</span></div><div class="wp-workCard_item 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wp-workCard--actions"><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-video-id="jQY6Rj"><a class="js-profile-work-strip-edit-button" href="https://independent.academia.edu/video/edit/jQY6Rj" rel="nofollow" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div></div></div></div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="8541865" id="papers"><div class="js-work-strip profile--work_container" data-work-id="115743270"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/115743270/User_Model_Clustering"><img alt="Research paper thumbnail of User Model Clustering" class="work-thumbnail" src="https://attachments.academia-assets.com/112063089/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/115743270/User_Model_Clustering">User Model Clustering</a></div><div class="wp-workCard_item"><span>Journal of Data Analysis and Information Processing</span><span>, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">User model which is the representation of information about user is the heart of adaptive systems...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">User model which is the representation of information about user is the heart of adaptive systems. It helps adaptive systems to perform adaptation tasks. There are two kinds of adaptations: 1) Individual adaptation regarding to each user; 2) Group adaptation focusing on group of users. To support group adaptation, the basic problem which needs to be solved is how to create user groups. This relates to clustering techniques so as to cluster user models because a group is considered as a cluster of similar user models. In this paper we discuss two clustering algorithms: k-means and k-medoids and also propose dissimilarity measures and similarity measures which are applied into different structures (forms) of user models like vector, overlay, and Bayesian network.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3ff7835209639fc55d4743f54339a0e7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112063089,"asset_id":115743270,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112063089/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="115743270"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="115743270"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 115743270; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="108807871"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/108807871/Numerical_Similarity_Measures_Versus_Jaccard_for_Collaborative_Filtering"><img alt="Research paper thumbnail of Numerical Similarity Measures Versus Jaccard for Collaborative Filtering" class="work-thumbnail" src="https://attachments.academia-assets.com/107100250/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/108807871/Numerical_Similarity_Measures_Versus_Jaccard_for_Collaborative_Filtering">Numerical Similarity Measures Versus Jaccard for Collaborative Filtering</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI2023), part of the book series: Lecture Notes on Data Engineering and Communications Technologies (LNDECT), volume 184, pages 221-229</span><span>, Sep 19, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Collaborative filtering (CF) is an important method for recommendation systems, which are employe...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Collaborative filtering (CF) is an important method for recommendation systems, which are employed in many facets of our lives and are particularly prevalent in online-based commercial systems. The K-nearest neighbors (KNN) technique is a well-liked CF algorithm that uses similarity measurements to identify a user's closest neighbors in order to quantify the degree of dependency between the respective user and item pair. As a result, the CF approach is not only dependent on the choice of the similarity measure but also sensitive to it. However, some traditional "numerical" similarity measures, like cosine and Pearson, concentrate on the size of ratings, whereas Jaccard, one of the most frequently employed similarity measures for CF tasks, concerns the existence of ratings. Jaccard, in particular, is not a dominant measure, but it has long been demonstrated to be a key element in enhancing any measure. Therefore, this research focuses on presenting novel similarity measures by combining Jaccard with a multitude of numerical measures in our ongoing search for the most effective similarity measures for CF. Both existence and magnitude would benefit the combined measurements. Experimental results demonstrated that the combined measures are superior, surpassing all single measures across the considered assessment metrics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="744a9ac9f4d462c933b15cccd454abf6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":107100250,"asset_id":108807871,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/107100250/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="108807871"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="108807871"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 108807871; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=108807871]").text(description); $(".js-view-count[data-work-id=108807871]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 108807871; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='108807871']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "744a9ac9f4d462c933b15cccd454abf6" } } $('.js-work-strip[data-work-id=108807871]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":108807871,"title":"Numerical Similarity Measures Versus Jaccard for Collaborative Filtering","translated_title":"","metadata":{"doi":"10.1007/978-3-031-43247-7_20","volume":"184","abstract":"Collaborative filtering (CF) is an important method for recommendation systems, which are employed in many facets of our lives and are particularly prevalent in online-based commercial systems. 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Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system's performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top “state-of-the-art” performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4ed75d7f5f12acb9d7e6e757397692fe" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105038095,"asset_id":105637584,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105038095/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105637584"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105637584"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105637584; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105637584]").text(description); $(".js-view-count[data-work-id=105637584]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105637584; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105637584']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4ed75d7f5f12acb9d7e6e757397692fe" } } $('.js-work-strip[data-work-id=105637584]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105637584,"title":"Boosting the Item-Based Collaborative Filtering Model with Novel Similarity Measures","translated_title":"","metadata":{"doi":"10.1007/s44196-023-00299-2","issue":"1","volume":"16","abstract":"Collaborative filtering (CF), one of the most widely employed methodologies for recommender systems, has drawn undeniable attention due to its effectiveness and simplicity. Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system's performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. 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Nevertheless, a few papers have been published on the CF-based item-based model using similarity measures than the user-based model due to the model's complexity and the time required to build it. Additionally, the substantial shortcomings in the user-based measurements when the item-based model is taken into account motivated us to create stronger models in this work. Not to mention that the common trickiest challenge is dealing with the cold-start problem, in which users' history of item-buying behavior is missing (i.e., new users) or items for which activity is not provided (i.e., new items). Therefore, our novel five similarity measures, which have the potential to solve sparse data, are developed to alleviate the impact of this important problem. Most importantly, a thorough empirical analysis of how the item-based model affects the CF-based recommendation system's performance has also been a critical part of this work, which presents a benchmarking study for thirty similarity metrics. The MAE, MSE, and accuracy metrics, together with fivefold cross-validation, are used to properly assess and examine the influence of all considered similarity measures using the Movie-lens 100 K and Film Trust datasets. The findings demonstrate how competitive the proposed similarity measures are in comparison to their alternatives. Surprisingly, some of the top “state-of-the-art” performers (such as SMD and NHSM) have been unable to fiercely compete with our proposed rivals when utilizing the item-based model.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":105038095,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105038095/thumbnails/1.jpg","file_name":"77.BoostingItemBasedCFModelNovelSimilarityMeasures_AbdallaAmerAmerNguyenAlMaqaleh_Springer_2023.07.29.pdf","download_url":"https://www.academia.edu/attachments/105038095/download_file","bulk_download_file_name":"Boosting_the_Item_Based_Collaborative_Fi.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105038095/77.BoostingItemBasedCFModelNovelSimilarityMeasures_AbdallaAmerAmerNguyenAlMaqaleh_Springer_2023.07.29-libre.pdf?1692169045=\u0026response-content-disposition=attachment%3B+filename%3DBoosting_the_Item_Based_Collaborative_Fi.pdf\u0026Expires=1738797504\u0026Signature=OU0KVFxlGSNe8XkU0-gxz-gyOb5LnR-pTtjenM0roLSTRziJy~Ofak3JajPpCk~n9lUvKqPFmeo7jRkumSWq5DS~QuUmz9hL~S~BbAVQGyMwuDXQWu5al8xRDVqCFqU7pZRVrmqUfNbo7PEWY-VuHkZDMeaKejBB9Ow-DkItskLkeZr-kM4toaigk8vJuga3ndjn7f66B9knq48bbiqT1W8pzwz6-XMkAavXE0mxb5O~9krUADkiKvDuUVPPdHZ~B2SiW~mcx~QAiIZLldRmshVrG-HPBbduwoeM20Ng9e-GwIuiiEWgz-0GLRT5KODjKl5IXw9MAGooRi7XdNbx~w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval"},{"id":2900,"name":"Recommender Systems","url":"https://www.academia.edu/Documents/in/Recommender_Systems"},{"id":77193,"name":"Collaborative Filtering","url":"https://www.academia.edu/Documents/in/Collaborative_Filtering"},{"id":177103,"name":"Similarity Measures","url":"https://www.academia.edu/Documents/in/Similarity_Measures"}],"urls":[{"id":33447905,"url":"https://link.springer.com/article/10.1007/s44196-023-00299-2"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105294401"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/105294401/Learning_concept_recommendation_based_on_sequential_pattern_mining"><img alt="Research paper thumbnail of Learning concept recommendation based on sequential pattern mining" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/105294401/Learning_concept_recommendation_based_on_sequential_pattern_mining">Learning concept recommendation based on sequential pattern mining</a></div><div class="wp-workCard_item"><span>2009 3rd IEEE International Conference on Digital Ecosystems and Technologies</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">ABSTRACT</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105294401"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105294401"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105294401; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105294401]").text(description); $(".js-view-count[data-work-id=105294401]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105294401; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105294401']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=105294401]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105294401,"title":"Learning concept recommendation based on sequential pattern mining","internal_url":"https://www.academia.edu/105294401/Learning_concept_recommendation_based_on_sequential_pattern_mining","owner_id":88862579,"coauthors_can_edit":true,"owner":{"id":88862579,"first_name":"Loc","middle_initials":null,"last_name":"Nguyen","page_name":"LocNguyen2000","domain_name":"independent","created_at":"2018-08-19T05:35:13.668-07:00","display_name":"Loc Nguyen","url":"https://independent.academia.edu/LocNguyen2000"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105294400"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" rel="nofollow" href="https://www.academia.edu/105294400/Evolution_of_Parameters_in_Bayesian_Overlay_Model"><img alt="Research paper thumbnail of Evolution of Parameters in Bayesian Overlay Model" class="work-thumbnail" src="https://a.academia-assets.com/images/blank-paper.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" rel="nofollow" href="https://www.academia.edu/105294400/Evolution_of_Parameters_in_Bayesian_Overlay_Model">Evolution of Parameters in Bayesian Overlay Model</a></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Adaptive learning systems require well-organized user model along with solid inference mechanism....</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Adaptive learning systems require well-organized user model along with solid inference mechanism. Overlay modeling is the method in which the domain is decomposed into a set of elements and the user model is simply a set of masteries over those elements. The combination between overlay model and Bayesian network (BN) will make use of the flexibility and simplification of overlay modeling and the power inference of BN. Thus it is compulsory to pre-define parameters, namely, Conditional Probability Tables (CPT (s)) in BN but no one ensured absolutely the correctness of these CPT (s). This research focuses on how to enhance parameters’ quality in Bayesian overlay model, in other words, this is the evolution of CPT(s).</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105294400"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105294400"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105294400; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105294400]").text(description); $(".js-view-count[data-work-id=105294400]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105294400; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105294400']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (false){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "-1" } } $('.js-work-strip[data-work-id=105294400]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105294400,"title":"Evolution of Parameters in Bayesian Overlay Model","internal_url":"https://www.academia.edu/105294400/Evolution_of_Parameters_in_Bayesian_Overlay_Model","owner_id":88862579,"coauthors_can_edit":true,"owner":{"id":88862579,"first_name":"Loc","middle_initials":null,"last_name":"Nguyen","page_name":"LocNguyen2000","domain_name":"independent","created_at":"2018-08-19T05:35:13.668-07:00","display_name":"Loc Nguyen","url":"https://independent.academia.edu/LocNguyen2000"},"attachments":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105294383"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105294383/Combination_of_Bayesian_Network_and_Overlay_Model_in_User_Modeling"><img alt="Research paper thumbnail of Combination of Bayesian Network and Overlay Model in User Modeling" class="work-thumbnail" src="https://attachments.academia-assets.com/104787399/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105294383/Combination_of_Bayesian_Network_and_Overlay_Model_in_User_Modeling">Combination of Bayesian Network and Overlay Model in User Modeling</a></div><div class="wp-workCard_item"><span>International Journal of Emerging Technologies in Learning (iJET)</span><span>, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The core of adaptive system is user model containing personal information such as knowledge, lear...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The core of adaptive system is user model containing personal information such as knowledge, learning styles, goalsâ?¦ which is requisite for learning personalized process. There are many modeling approaches, for example: stereotype, overlay, plan recognitionâ?¦ but they donâ??t bring out the solid method for reasoning from user model. This paper introduces the statistical method that combines Bayesian network and overlay modeling so that it is able to infer userâ??s knowledge from evidences collected during userâ??s learning process.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="30ffac134d40eb4646b9b4e3d27f656a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":104787399,"asset_id":105294383,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/104787399/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105294383"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105294383"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105294383; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105294383]").text(description); $(".js-view-count[data-work-id=105294383]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105294383; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105294383']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "30ffac134d40eb4646b9b4e3d27f656a" } } $('.js-work-strip[data-work-id=105294383]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105294383,"title":"Combination of Bayesian Network and Overlay Model in User Modeling","internal_url":"https://www.academia.edu/105294383/Combination_of_Bayesian_Network_and_Overlay_Model_in_User_Modeling","owner_id":88862579,"coauthors_can_edit":true,"owner":{"id":88862579,"first_name":"Loc","middle_initials":null,"last_name":"Nguyen","page_name":"LocNguyen2000","domain_name":"independent","created_at":"2018-08-19T05:35:13.668-07:00","display_name":"Loc Nguyen","url":"https://independent.academia.edu/LocNguyen2000"},"attachments":[{"id":104787399,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/104787399/thumbnails/1.jpg","file_name":"978-3-642-01973-9_2.pdf","download_url":"https://www.academia.edu/attachments/104787399/download_file","bulk_download_file_name":"Combination_of_Bayesian_Network_and_Over.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/104787399/978-3-642-01973-9_2-libre.pdf?1691289860=\u0026response-content-disposition=attachment%3B+filename%3DCombination_of_Bayesian_Network_and_Over.pdf\u0026Expires=1740509749\u0026Signature=Z2SjX6VIwGInkvx7jHyHQnOnqfcFl0a7N-~4Ycq28jndCVp2USCv~Dr~d7JlxIMUva-Knoccp0owuiK4sU-t3MKZcRrT5B5Li0IVQD7Z4Ftp-dG-WZfxrbO1NAGUht~Mf7SPwh8cnSRSWCwHHax2bc8RYHDOxeW2L-IJzC-K8mW2Wu6pB9deytA-XVPvjwAM82H4q0yVjWFILAKlTJH0gpPBZQxKKOS4QRQsoru4MTLhxlpowcqBl03kC2lQ2w7uJQgG7mp6YBuOxF-K1-V-rJoOL8vs8N-xVCzaZxsEFTYwRblyx74fKx~rZU0qm2R5su3~a~bOhMtaCoBgV5scig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="76520501"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/76520501/A_Framework_of_Fetal_Age_and_Weight_Estimation"><img alt="Research paper thumbnail of A Framework of Fetal Age and Weight Estimation" class="work-thumbnail" src="https://attachments.academia-assets.com/84203883/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/76520501/A_Framework_of_Fetal_Age_and_Weight_Estimation">A Framework of Fetal Age and Weight Estimation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Journal of Gynecology and Obstetrics (JGO)</span><span>, Mar 30, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fetal age and weight estimation plays the important role in pregnant treatments. There are many e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. Moreover, the framework gives facilities to scientists for exploiting useful information under pregnant statistical data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4cb4282d5be4d713b4ca47e47cf83030" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84203883,"asset_id":76520501,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84203883/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76520501"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76520501"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76520501; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=76520501]").text(description); $(".js-view-count[data-work-id=76520501]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 76520501; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='76520501']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4cb4282d5be4d713b4ca47e47cf83030" } } $('.js-work-strip[data-work-id=76520501]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":76520501,"title":"A Framework of Fetal Age and Weight Estimation","translated_title":"","metadata":{"doi":"10.4172/2157-7420.C1.012","issue":"2","volume":"2","abstract":"Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. Moreover, the framework gives facilities to scientists for exploiting useful information under pregnant statistical data.","publisher":"Science Publishing Group","event_date":{"day":30,"month":3,"year":2014,"errors":{}},"ai_title_tag":"A Framework for Estimating Fetal Age and Weight","journal_name":"Journal of Gynecology and Obstetrics (JGO)","organization":"Science Publishing Group","page_numbers":"20-25","publication_date":{"day":30,"month":3,"year":2014,"errors":{}},"publication_name":"Journal of Gynecology and Obstetrics (JGO)","conference_end_date":{"day":30,"month":3,"year":2014,"errors":{}},"conference_start_date":{"day":30,"month":3,"year":2014,"errors":{}}},"translated_abstract":"Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25548296"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25548296/Convergence_of_Confucianism_Buddhism_and_Taoism"><img alt="Research paper thumbnail of Convergence of Confucianism, Buddhism and Taoism" class="work-thumbnail" src="https://attachments.academia-assets.com/45880321/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25548296/Convergence_of_Confucianism_Buddhism_and_Taoism">Convergence of Confucianism, Buddhism and Taoism</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Leu Chong Zen Club‘s Conference</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The article discusses the convergence of three largest Oriental religions such as Confucianism, B...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The article discusses the convergence of three largest Oriental religions such as Confucianism, Buddhism, and Taoism.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="45419ca694ca82e655361e74a8aa0471" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45880321,"asset_id":25548296,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45880321/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25548296"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25548296"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25548296; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25548078"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25548078/Beta_Likelihood_Estimation_and_Its_Application_to_Specify_Prior_Probabilities_in_Bayesian_Network"><img alt="Research paper thumbnail of Beta Likelihood Estimation and Its Application to Specify Prior Probabilities in Bayesian Network" class="work-thumbnail" src="https://attachments.academia-assets.com/56308214/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25548078/Beta_Likelihood_Estimation_and_Its_Application_to_Specify_Prior_Probabilities_in_Bayesian_Network">Beta Likelihood Estimation and Its Application to Specify Prior Probabilities in Bayesian Network</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>British Journal of Mathematics & Computer Science, Volume 16, Issue 3, page 1-21</span><span>, Apr 27, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Maximum likelihood estimation (MLE) is a popular technique of statistical parameter estimation. W...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Maximum likelihood estimation (MLE) is a popular technique of statistical parameter estimation. When random variable conforms beta distribution, the research focuses on applying MLE into beta density function. This method is called beta likelihood estimation, which results out useful estimation equations. It is easy to calculate statistical estimates based on these equations in case that parameters of beta distribution are positive integer numbers. Essentially, the method takes advantages of interesting features of functions gamma, digamma, and trigamma. An application of beta likelihood estimation is to specify prior probabilities in Bayesian network.<br /><br />Keywords: maximum likelihood estimation, beta distribution, beta likelihood estimation, gamma function.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0f1a70a1110dc919b945c25af56d8038" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":56308214,"asset_id":25548078,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/56308214/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25548078"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25548078"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25548078; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25548012"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25548012/Theorem_of_SIGMA_gate_Inference_in_Bayesian_Network"><img alt="Research paper thumbnail of Theorem of SIGMA-gate Inference in Bayesian Network" class="work-thumbnail" src="https://attachments.academia-assets.com/56308112/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25548012/Theorem_of_SIGMA_gate_Inference_in_Bayesian_Network">Theorem of SIGMA-gate Inference in Bayesian Network</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Wulfenia Journal, Volume 23, Issue 3, pages 280-289</span><span>, Mar 28, 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Bayesian network is a powerful mathematical tool for doing diagnosis and assessment tasks. Parame...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Bayesian network is a powerful mathematical tool for doing diagnosis and assessment tasks. Parameter learning in Bayesian network is complicated study but I discover that parameter learning becomes easy in some situations. Especially, when Bayesian network is weighted graph and its child node is aggregation of mutually independent parent nodes, there is a simple way to specify conditional probability tables which are parameters of Bayesian network. In this research, I propose and prove the theorem of SIGMA-gate inference which is the fundamental of such simple way helping us to transform weighted graph into Bayesian network.<br /><br />Keywords: Bayesian network, parameter learning, SIGMA-gate inference.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="75d645b43ab058b25a430abbf2819b67" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":56308112,"asset_id":25548012,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/56308112/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25548012"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25548012"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25548012; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546932"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546932/Specifying_Prior_Probabilities_in_Bayesian_Network_by_Maximum_Likelihood_Estimation_method"><img alt="Research paper thumbnail of Specifying Prior Probabilities in Bayesian Network by Maximum Likelihood Estimation method" class="work-thumbnail" src="https://attachments.academia-assets.com/56308106/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546932/Specifying_Prior_Probabilities_in_Bayesian_Network_by_Maximum_Likelihood_Estimation_method">Specifying Prior Probabilities in Bayesian Network by Maximum Likelihood Estimation method</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Sylwan Journal, Volume 160, Issue 2, February 2016, pages 281-298</span><span>, Feb 2016</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Bayesian network provides the solid inference mechanism when convincing the hypothesis by collect...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Bayesian network provides the solid inference mechanism when convincing the hypothesis by collecting evidences. Bayesian network is instituted of two models such as qualitative model quantitative model. The qualitative model is its structure and the quantitative model is its parameters, namely conditional probability tables (CPT) whose entries are probabilities quantifying the dependences among variables in network. The quality of CPT depends on the initialized values of its entries. Such initial values are prior probabilities. Because the beta function provides some conveniences when specifying CPT (s), this function is used as the basic distribution in my method. The main problem of defining prior probabilities is how to estimate parameters in beta distribution. It is slightly unfortunate when the equations whose solutions are parameter estimators are differential equations and it is too difficult to solve them. By applying the maximum likelihood estimation (MLE) technique, I invent the simple equations so that differential equations are eliminated and it is much easier to estimate parameters in case that such parameters are positive integer numbers. Thus, I also propose the algorithm to find out the approximate solutions of these simple equations.<br /><br />Keywords: prior probabilities, Bayesian network, maximum likelihood estimation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e2ec5517a80705c08aad0e6943a2ad58" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":56308106,"asset_id":25546932,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/56308106/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25546932"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25546932"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25546932; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546518"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546518/Introduction_to_a_Framework_of_E_commercial_Recommendation_Algorithms"><img alt="Research paper thumbnail of Introduction to a Framework of E-commercial Recommendation Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/45878369/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546518/Introduction_to_a_Framework_of_E_commercial_Recommendation_Algorithms">Introduction to a Framework of E-commercial Recommendation Algorithms</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>American Journal of Computer Science and Information Engineering (AJCSIE), Vol. 2, No. 4, September 2015, pages 33-44</span><span>, Sep 28, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recommendation algorithm is very important for e-commercial websites when it can recommend online...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Recommendation algorithm is very important for e-commercial websites when it can recommend online customers favorite products, which results out an increase in sale revenue. I propose the framework of e-commercial recommendation algorithms. This is a middleware framework or “operating system” for e-commercial recommendation software, which support scientists and software developers build up their own recommendation algorithms based on this framework with low cost, high achievement and fast speed.<br /><br />Keywords: Recommendation Algorithm, Recommendation Server, Middleware Framework.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c346602b22a166a69ba86e7a227a79a9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45878369,"asset_id":25546518,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45878369/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25546518"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25546518"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25546518; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546208"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546208/Feasible_length_of_Taylor_polynomial_on_given_interval_and_application_to_find_the_number_of_roots_of_equation"><img alt="Research paper thumbnail of Feasible length of Taylor polynomial on given interval and application to find the number of roots of equation" class="work-thumbnail" src="https://attachments.academia-assets.com/45878249/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546208/Feasible_length_of_Taylor_polynomial_on_given_interval_and_application_to_find_the_number_of_roots_of_equation">Feasible length of Taylor polynomial on given interval and application to find the number of roots of equation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>International Journal of Mathematical Analysis and Applications, Vol. 1, No. 5, December 2014, pages 80-83</span><span>, Jan 10, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is very necessary to represent arbitrary function as a polynomial in many situations because p...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is very necessary to represent arbitrary function as a polynomial in many situations because polynomial has many valuable properties. Fortunately, any analytic function can be approximated by Taylor polynomial. The higher the degree of Taylor polynomial is, the better the approximation is gained. There is problem that how to achieve optimal approximation with restriction that the degree is not so high because of computation cost. This research proposes a method to estimate feasible degree of Taylor polynomial so that it is likely that Taylor polynomial with degree being equal to or larger than such feasible degree is good approximation of a function in given interval. The feasible degree is called the feasible length of Taylor polynomial. The research also introduces an application that combines Sturm theorem and the method to approximate a function by Taylor polynomial with feasible length in order to count the number of roots of equation in given interval.<br /><br />Keywords: Taylor polynomial, roots of equation, analytic function approximation, feasible length.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="59ae96825a2c2d4f5060e3ade9501d32" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45878249,"asset_id":25546208,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45878249/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25546208"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25546208"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25546208; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546099"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546099/Evaluating_Adaptive_Learning_Model"><img alt="Research paper thumbnail of Evaluating Adaptive Learning Model" class="work-thumbnail" src="https://attachments.academia-assets.com/45878200/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546099/Evaluating_Adaptive_Learning_Model">Evaluating Adaptive Learning Model</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Interactive Collaborative Learning (ICL), 2014 International Conference on, pages 818-822</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Distance learning or e-learning is a trend of modern education, which brings new chance of study ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Distance learning or e-learning is a trend of modern education, which brings new chance of study to everyone. Thus, everyone can study at anywhere and anytime so that they can improve and update their knowledge in lifelong time. Adaptive learning is a research branch of e-learning, which give adaptation and personalization to users in learning context. Different people receive different learning materials / teaching methods in accordance with their individual information / characteristics such as knowledge, goal, experience, interest, background, etc. Such individual information is structured in a format so-called user model. User model is the heart of adaptive learning system. User model is managed by user modeling system. There are many theories and practical methods to build up user model and adaptive learning system and each method has particular aspects but it is very difficult to determine which method or system is good because there is no evaluation standard and each method has particular strong point and drawbacks. Therefore, the goal of this research is to propose criterions to evaluate adaptive learning system and user modeling system. Moreover, research gives an evaluation scenario considered as an example for applying proposed criterion into evaluating adaptive learning system and user modeling in learning context.<br /><br />Keywords: adaptive learning, user modeling, evaluation.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="56f5e0c51bdf97ca4de55855328270f0" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45878200,"asset_id":25546099,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45878200/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25546099"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25546099"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25546099; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25546063"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25546063/Improving_analytic_function_approximation_by_minimizing_square_error_of_Taylor_polynomial"><img alt="Research paper thumbnail of Improving analytic function approximation by minimizing square error of Taylor polynomial" class="work-thumbnail" src="https://attachments.academia-assets.com/45878150/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25546063/Improving_analytic_function_approximation_by_minimizing_square_error_of_Taylor_polynomial">Improving analytic function approximation by minimizing square error of Taylor polynomial</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>International Journal of Mathematical Analysis and Applications, Vol. 1, No. 4, October 2014, pages 63-67</span><span>, Oct 21, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is very necessary to represent arbitrary function as a polynomial in many situations because p...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is very necessary to represent arbitrary function as a polynomial in many situations because polynomial has many valuable properties. Fortunately, any analytic function can be approximated by Taylor polynomial. The quality of Taylor approximation within given interval is dependent on degree of Taylor polynomial and the width of such interval. Taylor polynomial gains highly precise approximation at the point where the polynomial is expanded and so, the farer from such point it is, the worse the approximation is. Given two successive Taylor polynomials which are approximations of the same analytic function in given interval, this research proposes a method to improve the later one by minimizing their deviation so-called square error. Based on such method, the research also propose a so-called shifting algorithm which results out optimal approximated Taylor polynomial in given interval by dividing such interval into sub-intervals and shifting along with sequence of these sub-intervals in order to improve Taylor polynomials in successive process, based on minimizing square error.<br /><br />Keywords: Taylor polynomial, analytic function approximation, square error.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d4c75c84c2996517f51f68d14eafc06f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45878150,"asset_id":25546063,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45878150/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25546063"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25546063"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25546063; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25545896"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25545896/Theorem_of_Logarithm_Expectation_and_Its_Application_to_Prove_Sample_Correlation_Coefficient_as_Unbiased_Estimate"><img alt="Research paper thumbnail of Theorem of Logarithm Expectation and Its Application to Prove Sample Correlation Coefficient as Unbiased Estimate" class="work-thumbnail" src="https://attachments.academia-assets.com/45878085/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25545896/Theorem_of_Logarithm_Expectation_and_Its_Application_to_Prove_Sample_Correlation_Coefficient_as_Unbiased_Estimate">Theorem of Logarithm Expectation and Its Application to Prove Sample Correlation Coefficient as Unbiased Estimate</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Journal of Mathematics and System Science, Volume 4, Number 9, September 2014, pages 605-608</span><span>, Sep 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">In statistical theory, a statistic that is function of sample observations is used to estimate di...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">In statistical theory, a statistic that is function of sample observations is used to estimate distribution parameter. This statistic is called unbiased estimate if its expectation is equal to theoretical parameter. Proving whether or not a statistic is unbiased estimate is very important but this proof may require a lot of efforts when statistic is complicated function. Therefore, this research facilitates this proof by proposing a theorem which states that the expectation of variable x > 0 is μ if and only if the limit of logarithm expectation of x approaches logarithm of μ. In order to make clear of this theorem, the research gives an example of proving correlation coefficient as unbiased estimate by taking advantages of this theorem.<br /><br />Keywords: logarithm expectation, correlation coefficient, unbiased estimate.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d742e273cf0678d44c79dbe4fe523e52" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45878085,"asset_id":25545896,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45878085/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25545896"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25545896"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25545896; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25545788"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25545788/A_framework_of_fetal_age_and_weight_estimation"><img alt="Research paper thumbnail of A framework of fetal age and weight estimation" class="work-thumbnail" src="https://attachments.academia-assets.com/45877730/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25545788/A_framework_of_fetal_age_and_weight_estimation">A framework of fetal age and weight estimation</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Journal of Gynecology and Obstetrics (JGO), Vol. 2, No. 2, 2014, pages 20-25</span><span>, Mar 30, 2014</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Fetal age and weight estimation plays the important role in pregnant treatments. There are many e...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Fetal age and weight estimation plays the important role in pregnant treatments. There are many estimate formulas created by the combination of statistics and obstetrics. However, such formulas give optimal estimation if and only if they are applied into specified community or ethnic group with characteristics of such ethnic group. This paper proposes a framework that supports scientists to discover and create new formulas more appropriate to community or region where scientists do their research. The discovery algorithm used inside the framework is the core of the architecture of framework. This algorithm is based on heuristic assumptions, which aims to produce good estimate formula as fast as possible. Moreover, the framework gives facilities to scientists for exploiting useful information under pregnant statistical data.<br /><br />Keywords: fetal age estimation, fetal weight estimation, regression model, estimate formula, estimate framework.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e2f4bea11f224d440a6d6f2a6def5bae" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45877730,"asset_id":25545788,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45877730/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25545788"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25545788"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25545788; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25545628"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25545628/A_New_Approach_for_Modeling_and_Discovering_Learning_Styles_by_Using_Hidden_Markov_Model"><img alt="Research paper thumbnail of A New Approach for Modeling and Discovering Learning Styles by Using Hidden Markov Model" class="work-thumbnail" src="https://attachments.academia-assets.com/45877704/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25545628/A_New_Approach_for_Modeling_and_Discovering_Learning_Styles_by_Using_Hidden_Markov_Model">A New Approach for Modeling and Discovering Learning Styles by Using Hidden Markov Model</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Global Journal of Human Social Science: G - Linguistics & Education, Volume 13, Issue 4 Version 1.0 Year 2013, pages 1-10</span><span>, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Adaptive learning systems are developed rapidly in recent years and the “heart” of such systems i...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Adaptive learning systems are developed rapidly in recent years and the “heart” of such systems is user model. User model is the representation of information about an individual that is essential for an adaptive system to provide the adaptation effect, i.e., to behave differently for different users. There are some main features in user model such as: knowledge, goals, learning styles, interests, background... but knowledge, learning styles and goals are features attracting researchers’ attention in adaptive e-learning domain. Learning styles were surveyed in psychological theories but it is slightly difficult to model them in the domain of computer science because learning styles are too unobvious to represent them and there is no solid inference mechanism for discovering users’ learning styles now. Moreover, researchers in domain of computer science will get confused by so many psychological theories about learning style when choosing which theory is appropriate to adaptive system.<br />In this paper I give the overview of learning styles for answering the question “what are learning styles?” and then propose the new approach to model and discover students’ learning styles by using Hidden Markov model (HMM). HMM is such a powerful statistical tool that it allows us to predict users’ learning styles from observed evidences about them.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="03b6654ddcf581668271cddd2ea7a45f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":45877704,"asset_id":25545628,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/45877704/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25545628"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25545628"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25545628; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25545546"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25545546/The_Bayesian_approach_and_suggested_stopping_criterion_in_Computerized_Adaptive_Testing"><img alt="Research paper thumbnail of The Bayesian approach and suggested stopping criterion in Computerized Adaptive Testing" class="work-thumbnail" src="https://attachments.academia-assets.com/55289735/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25545546/The_Bayesian_approach_and_suggested_stopping_criterion_in_Computerized_Adaptive_Testing">The Bayesian approach and suggested stopping criterion in Computerized Adaptive Testing</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>International Journal of Research in Engineering and Technology (IJRET)</span><span>, Jun 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Computer-based tests have more advantages than the traditional paper-based tests when there is a ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Computer-based tests have more advantages than the traditional paper-based tests when there is a boom of internet and computer. Computer-based testing allows examinees to perform tests at any time and any place and testing environment becomes more realistic. Moreover, it is very easy to assess examinees’ ability by using computerized adaptive testing (CAT). CAT is considered as a branch of computer-based testing but it improves the accuracy of test core when CAT systems try to choose items (tests, exams, questions, etc.) which are suitable to examinees’ abilities; such items are called adaptive items. The important problem in CAT is how to estimate examinees’ abilities so as to select the best items for examinees. There are some methods to solve this problem such as maximization likelihood estimation but I apply the Bayesian method into computing ability estimates. In this paper, I suggest a stopping criterion for CAT algorithm: the process of testing ends only when examinee’s knowledge becomes saturated (she/he can’t do test better or worse) and such knowledge is her/his actual knowledge.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f618243345c733a1b447256057044750" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":55289735,"asset_id":25545546,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/55289735/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25545546"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25545546"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25545546; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25542979"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25542979/Zebra_A_New_User_Modeling_System_for_Triangular_Model_of_Learners_Characteristics"><img alt="Research paper thumbnail of Zebra: A New User Modeling System for Triangular Model of Learners' Characteristics" class="work-thumbnail" src="https://attachments.academia-assets.com/55632728/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25542979/Zebra_A_New_User_Modeling_System_for_Triangular_Model_of_Learners_Characteristics">Zebra: A New User Modeling System for Triangular Model of Learners' Characteristics</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>AIED 2009: 14th conference on Artificial Intelligence in Education, Proceedings of the Workshop on “Enabling creative learning design: how HCI, User Modeling and Human Factors Help”</span><span>, Jul 10, 2009</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">The core of adaptive system is the user model that is representation of information about an indi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">The core of adaptive system is the user model that is representation of information about an individual. User model is necessary for an adaptive system to provide the adaptation effect, i.e., to behave differently for different users. The system that collects user information to build up user model and reasons out new assumptions about user is called user modeling system (UMS). There are two main tendencies towards implementing UMS: domain-independent UMS and domain-dependent UMS. The latter is called generic UMS known widely but our approach focuses on the domain-dependent UMS applied into adaptive e-learning especially. The reason is that domain-independent UMS is too generic to “cover” all learners’ characteristics in e-learning, which may cause unpredictable bad consequences in adaptation process. Note that user is considered as learner in e-learning context. Many users’ characteristics can be modeled but each characteristic is in accordance with respective modeling method. It is impossible to model all learners’ characteristics because of such reason “there is no modeling method fit all characteristics”. To overcome these obstacles and difficulties, we propose the new model of learner “Triangular Learner Model (TLM)” composed by three main learners’ characteristics: knowledge, learning style and learning history. TLM with such three underlying characteristics will cover the whole of learner’s information required by learning adaptation process. The UMS which builds up and manipulates TLM is also described in detail and named Zebra. We also propose the new architecture of an adaptive application and the interaction between such application and Zebra.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fcb87275e19453beb94de607e1bfec9f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":55632728,"asset_id":25542979,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/55632728/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25542979"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25542979"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25542979; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="9832718" id="books"><div class="js-work-strip profile--work_container" data-work-id="121221422"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/121221422/Locs_Poems"><img alt="Research paper thumbnail of Loc's Poems" class="work-thumbnail" src="https://attachments.academia-assets.com/116158132/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121221422/Locs_Poems">Loc's Poems</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Iterative International Publishers (IIP)</span><span>, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Selected collection LOC’S POEMS (THƠ LỘC) includes native poems in Vietnamese and Chinese, create...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Selected collection LOC’S POEMS (THƠ LỘC) includes native poems in Vietnamese and Chinese, created by poet Loc Nguyen (Nguyễn Phước Lộc) from 1993 to 2022. These poems are classified into 8 collections and 1 verse narrative such as “Tặng”, “Ca dao blog”, “Chưa đặt tên”, “Lại chưa đặt tên”, “华语”, “Viết tiếp thơ ơi”, “Vẽ”, “Tình” và “Lục Kiều thời @”. Thank for concerning my poems. My poems, which would rather lean forward melody than lean forward prosody, are half popular half academic, half elegant half vulgar, half deep half humorous, half queer half naive, half modern half ancient. There are mundane men with many careers, as well as many fairies, ghosts, heroes, nymphs in my poems. There are also tears, smiles, unreal stories, hot news and many things. I see myself in my poems and I will be very glad if catching you in my poems.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="577a43e2ca31fbbbe294fb3863b866c4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116158132,"asset_id":121221422,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116158132/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121221422"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121221422"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121221422; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121221422]").text(description); $(".js-view-count[data-work-id=121221422]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121221422; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121221422']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "577a43e2ca31fbbbe294fb3863b866c4" } } $('.js-work-strip[data-work-id=121221422]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121221422,"title":"Loc's Poems","translated_title":"","metadata":{"abstract":"Selected collection LOC’S POEMS (THƠ LỘC) includes native poems in Vietnamese and Chinese, created by poet Loc Nguyen (Nguyễn Phước Lộc) from 1993 to 2022. 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(2022, March 25). Some Applications of Expectation Maximization Algorithm (1st ed.). (O. Sabazova, Ed.) Eliva Press</span><span>, Feb 25, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Expectation maximization (EM) algorithm is a popular and powerful mathematical method for statist...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Expectation maximization (EM) algorithm is a popular and powerful mathematical method for statistical parameter estimation in case that there exist both observed data and hidden data. This book focuses on applications of EM in which the implicit relationship is essential to connect observed data and hidden data. In other words, such applications reinforce EM which in turn extends estimation methods like maximum likelihood estimation (MLE) or moment method.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e5e31e520301447af4f47bde3c6f6a35" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":83044408,"asset_id":75165407,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/83044408/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="75165407"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="75165407"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 75165407; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=75165407]").text(description); $(".js-view-count[data-work-id=75165407]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 75165407; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='75165407']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e5e31e520301447af4f47bde3c6f6a35" } } $('.js-work-strip[data-work-id=75165407]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":75165407,"title":"Some Applications of Expectation Maximization Algorithm","translated_title":"","metadata":{"abstract":"Expectation maximization (EM) algorithm is a popular and powerful mathematical method for statistical parameter estimation in case that there exist both observed data and hidden data. 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This book focuses on applications of EM in which the implicit relationship is essential to connect observed data and hidden data. 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(O. Sabazova, Ed.) Eliva Press</span><span>, Feb 16, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nowadays modern society requires that every citizen always updates and improves her/his knowledge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Nowadays modern society requires that every citizen always updates and improves her/his knowledge and skills necessary to working and researching. E-learning or distance learning gives everyone a chance to study at anytime and anywhere with full support of computer technology and network. Adaptive learning, a variant of e-learning, aims to satisfy the demand of personalization in learning. Learners’ information and characteristics such as knowledge, goal, experience, interest, and background are the most important to adaptive system. These characteristics are organized in a structure called learner model (or user model) and the system or computer software that builds up and manipulates learner model is called user modeling system or learner modeling system. In this book, I propose a learner model that consists of three essential kinds of information about learners such as knowledge, learning style and learning history. Such three characteristics form a triangle and so this learner model is called Triangular Learner Model (TLM). The book contains seven chapters, which covers mathematical features of TLM. Chapter I is a survey of user model, user modeling, and adaptive learning. Chapter II introduces the general architecture of the proposed TLM and a user modeling system named Zebra. Chapter III, IV, V describes three sub-models of TLM such as knowledge sub-model, learning style sub-model, and learning history sub-model in full of mathematical formulas and fundamental methods. Chapter VI gives some approaches to evaluate TLM and Zebra. Chapter VII summarizes the research and discusses future trend of Zebra.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="3f2e4645cc5d605ba1bc8bbd90b42b84" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":82388602,"asset_id":74123049,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/82388602/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="74123049"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="74123049"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 74123049; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="74121827"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/74121827/Overview_of_Bayesian_Network"><img alt="Research paper thumbnail of Overview of Bayesian Network" class="work-thumbnail" src="https://attachments.academia-assets.com/82387961/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/74121827/Overview_of_Bayesian_Network">Overview of Bayesian Network</a></div><div class="wp-workCard_item"><span> Overview Of Bayesian Network (1st ed.). (R. Rauda, Ed.) Lambert Academic Publishing</span><span>, Feb 12, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Bayesian network is a combination of probabilistic model and graph model. It is applied widely in...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Bayesian network is a combination of probabilistic model and graph model. It is applied widely in machine learning, data mining, diagnosis, etc. because it has a solid evidence-based inference which is familiar to human intuition. However, Bayesian network may cause confusions because there are many complicated concepts, formulas and diagrams relating to it. Such concepts should be organized and presented in such a clear manner that understanding it is easy. This is the goal of this report. The report includes 5 main sections that cover principles of Bayesian network. The section 1 is an introduction to Bayesian network giving some basic concepts. Advanced concepts are mentioned in section 2. Inference mechanism of Bayesian network is described in section 3. Parameter learning which tells us how to update parameters of Bayesian network is described in section 4. Section 5 focuses on structure learning which mentions how to build up Bayesian network. In general, three main subjects of Bayesian network are inference, parameter learning, and structure learning which are mentioned in successive sections 3, 4, and 5. Section 6 is the conclusion.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="5d0b6d46c41d7d04b33187c185d60c15" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":82387961,"asset_id":74121827,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/82387961/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="74121827"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="74121827"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 74121827; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="36724270"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/36724270/Tutorial_on_EM_Algorithm"><img alt="Research paper thumbnail of Tutorial on EM Algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/81752987/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/36724270/Tutorial_on_EM_Algorithm">Tutorial on EM Algorithm</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span> Tutorial on EM Algorithm (2nd ed.). (R. Rauda, Ed.) Lambert Academic Publishing</span><span>, Feb 18, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Maximum likelihood estimation (MLE) is a popular method for parameter estimation in both applied ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Maximum likelihood estimation (MLE) is a popular method for parameter estimation in both applied probability and statistics but MLE cannot solve the problem of incomplete data or hidden data because it is impossible to maximize likelihood function from hidden data. Expectation maximum (EM) algorithm is a powerful mathematical tool for solving this problem if there is a relationship between hidden data and observed data. Such hinting relationship is specified by a mapping from hidden data to observed data or by a joint probability between hidden data and observed data. The essential ideology of EM is to maximize the expectation of likelihood function over observed data based on the hinting relationship instead of maximizing directly the likelihood function of hidden data. Pioneers in EM algorithm proved its convergence. As a result, EM algorithm produces parameter estimators as well as MLE does. This tutorial aims to provide explanations of EM algorithm in order to help researchers comprehend it. Moreover, in the 2nd edition, some EM applications such as mixture model, handling missing data and learning hidden Markov model are introduced.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b3ced420734c9b1439ffe4793b63947c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81752987,"asset_id":36724270,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81752987/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="36724270"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="36724270"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36724270; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36724270]").text(description); $(".js-view-count[data-work-id=36724270]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36724270; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='36724270']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "b3ced420734c9b1439ffe4793b63947c" } } $('.js-work-strip[data-work-id=36724270]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":36724270,"title":"Tutorial on EM Algorithm","translated_title":"","metadata":{"abstract":"Maximum likelihood estimation (MLE) is a popular method for parameter estimation in both applied probability and statistics but MLE cannot solve the problem of incomplete data or hidden data because it is impossible to maximize likelihood function from hidden data. 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Expectation maximum (EM) algorithm is a powerful mathematical tool for solving this problem if there is a relationship between hidden data and observed data. Such hinting relationship is specified by a mapping from hidden data to observed data or by a joint probability between hidden data and observed data. The essential ideology of EM is to maximize the expectation of likelihood function over observed data based on the hinting relationship instead of maximizing directly the likelihood function of hidden data. Pioneers in EM algorithm proved its convergence. As a result, EM algorithm produces parameter estimators as well as MLE does. This tutorial aims to provide explanations of EM algorithm in order to help researchers comprehend it. Moreover, in the 2nd edition, some EM applications such as mixture model, handling missing data and learning hidden Markov model are introduced.","internal_url":"https://www.academia.edu/36724270/Tutorial_on_EM_Algorithm","translated_internal_url":"","created_at":"2018-05-27T09:46:42.467-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"book","co_author_tags":[{"id":37804536,"work_id":36724270,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Tutorial on EM Algorithm"}],"downloadable_attachments":[{"id":81752987,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81752987/thumbnails/1.jpg","file_name":"03.TutorialOnEM_Lambert_2022.02.18_978_620_4_74542_8.pdf","download_url":"https://www.academia.edu/attachments/81752987/download_file","bulk_download_file_name":"Tutorial_on_EM_Algorithm.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81752987/03.TutorialOnEM_Lambert_2022.02.18_978_620_4_74542_8-libre.pdf?1646483755=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_EM_Algorithm.pdf\u0026Expires=1738797506\u0026Signature=KzzS13uHvtYamBZJQ5crkmFIXbWrxXgNSV-TH6mrfqQFKvd3~TMe2eWVZB8K1vxo4rE6-iyzseiiXERHyVmPZS4wiWha4TirPDieQ4hzQH4OMqWQnNfw-BLkQI2952qlGtkrf5hHR5c6NgmbFpsMe-PqQJMjKhpc7YiRG3dARtaHdzVbsUHnqMJo5J3jwx5xUQCx3lOMPQJxqq8b7mPuXmawXo8uAP6xIIgd7FCiFOKKA7MQpHDInz-JmJ0bkl7IrIcTNteMQtB5a5QNd0WRwhfYZP7s~cxQRwvwWbpWEYNAyWUDEGhr9wsWD1hPt5Ql2P38zf5sCPbpEtnYag6Pig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tutorial_on_EM_Algorithm","translated_slug":"","page_count":185,"language":"en","content_type":"Work","summary":"Maximum likelihood estimation (MLE) is a popular method for parameter estimation in both applied probability and statistics but MLE cannot solve the problem of incomplete data or hidden data because it is impossible to maximize likelihood function from hidden data. Expectation maximum (EM) algorithm is a powerful mathematical tool for solving this problem if there is a relationship between hidden data and observed data. Such hinting relationship is specified by a mapping from hidden data to observed data or by a joint probability between hidden data and observed data. The essential ideology of EM is to maximize the expectation of likelihood function over observed data based on the hinting relationship instead of maximizing directly the likelihood function of hidden data. Pioneers in EM algorithm proved its convergence. As a result, EM algorithm produces parameter estimators as well as MLE does. This tutorial aims to provide explanations of EM algorithm in order to help researchers comprehend it. Moreover, in the 2nd edition, some EM applications such as mixture model, handling missing data and learning hidden Markov model are introduced.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":81752987,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81752987/thumbnails/1.jpg","file_name":"03.TutorialOnEM_Lambert_2022.02.18_978_620_4_74542_8.pdf","download_url":"https://www.academia.edu/attachments/81752987/download_file","bulk_download_file_name":"Tutorial_on_EM_Algorithm.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81752987/03.TutorialOnEM_Lambert_2022.02.18_978_620_4_74542_8-libre.pdf?1646483755=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_EM_Algorithm.pdf\u0026Expires=1738797506\u0026Signature=KzzS13uHvtYamBZJQ5crkmFIXbWrxXgNSV-TH6mrfqQFKvd3~TMe2eWVZB8K1vxo4rE6-iyzseiiXERHyVmPZS4wiWha4TirPDieQ4hzQH4OMqWQnNfw-BLkQI2952qlGtkrf5hHR5c6NgmbFpsMe-PqQJMjKhpc7YiRG3dARtaHdzVbsUHnqMJo5J3jwx5xUQCx3lOMPQJxqq8b7mPuXmawXo8uAP6xIIgd7FCiFOKKA7MQpHDInz-JmJ0bkl7IrIcTNteMQtB5a5QNd0WRwhfYZP7s~cxQRwvwWbpWEYNAyWUDEGhr9wsWD1hPt5Ql2P38zf5sCPbpEtnYag6Pig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1001151,"name":"Expectation Maximization (EM) Algorithm","url":"https://www.academia.edu/Documents/in/Expectation_Maximization_EM_Algorithm"},{"id":2961664,"name":"generalized expectation maximization","url":"https://www.academia.edu/Documents/in/generalized_expectation_maximization"}],"urls":[{"id":18264482,"url":"https://www.morebooks.shop/store/gb/book/tutorial-on-em-algorithm-second-edition/isbn/978-620-4-74542-8"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="25548817"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25548817/Matrix_Analysis_and_Calculus"><img alt="Research paper thumbnail of Matrix Analysis and Calculus" class="work-thumbnail" src="https://attachments.academia-assets.com/81922093/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25548817/Matrix_Analysis_and_Calculus">Matrix Analysis and Calculus</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Lambert Academic Publishing</span><span>, Dec 15, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Statistics, multivariate data analysis and convex optimization are applied widely in many scienti...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Statistics, multivariate data analysis and convex optimization are applied widely in many scientific domains and most analytical techniques are developed based on matrix analysis and matrix calculus because matrix is abstract representation of multivariate data. Although it is slightly confused for us to comprehend their concepts and theories, matrix analysis and calculus give us exciting results which enhance data analysis techniques to be more plentiful and accurate. So the report is survey of matrix analysis and calculus, which includes five main sections such as basic concepts, matrix analysis, matrix derivative, composite derivative, and applications of matrix. Matrix derivative and composite derivative are subjects of matrix calculus.<br /><br />Keywords: Matrix Algebra, Matrix Analysis, Matrix Calculus.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4d7c61313a47466780b3474f495844dd" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81922093,"asset_id":25548817,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81922093/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25548817"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25548817"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25548817; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=25548817]").text(description); $(".js-view-count[data-work-id=25548817]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 25548817; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='25548817']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4d7c61313a47466780b3474f495844dd" } } $('.js-work-strip[data-work-id=25548817]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":25548817,"title":"Matrix Analysis and Calculus","translated_title":"","metadata":{"abstract":"Statistics, multivariate data analysis and convex optimization are applied widely in many scientific domains and most analytical techniques are developed based on matrix analysis and matrix calculus because matrix is abstract representation of multivariate data. Although it is slightly confused for us to comprehend their concepts and theories, matrix analysis and calculus give us exciting results which enhance data analysis techniques to be more plentiful and accurate. So the report is survey of matrix analysis and calculus, which includes five main sections such as basic concepts, matrix analysis, matrix derivative, composite derivative, and applications of matrix. Matrix derivative and composite derivative are subjects of matrix calculus.\n\nKeywords: Matrix Algebra, Matrix Analysis, Matrix Calculus.","location":"Germany and Singapore","publisher":"Lambert Academic Publishing","ai_title_tag":"Survey of Matrix Analysis and Calculus Techniques","organization":"Lambert Academic Publishing","publication_date":{"day":15,"month":12,"year":2015,"errors":{}},"publication_name":"Lambert Academic Publishing"},"translated_abstract":"Statistics, multivariate data analysis and convex optimization are applied widely in many scientific domains and most analytical techniques are developed based on matrix analysis and matrix calculus because matrix is abstract representation of multivariate data. Although it is slightly confused for us to comprehend their concepts and theories, matrix analysis and calculus give us exciting results which enhance data analysis techniques to be more plentiful and accurate. So the report is survey of matrix analysis and calculus, which includes five main sections such as basic concepts, matrix analysis, matrix derivative, composite derivative, and applications of matrix. Matrix derivative and composite derivative are subjects of matrix calculus.\n\nKeywords: Matrix Algebra, Matrix Analysis, Matrix Calculus.","internal_url":"https://www.academia.edu/25548817/Matrix_Analysis_and_Calculus","translated_internal_url":"","created_at":"2016-05-23T06:21:04.823-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"book","co_author_tags":[{"id":37804537,"work_id":25548817,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Matrix Analysis and Calculus"}],"downloadable_attachments":[{"id":81922093,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81922093/thumbnails/1.jpg","file_name":"02.MatrixAnalysisCalculus.Lambert.2015.12.15_978_3_659_69400_4.pdf","download_url":"https://www.academia.edu/attachments/81922093/download_file","bulk_download_file_name":"Matrix_Analysis_and_Calculus.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81922093/02.MatrixAnalysisCalculus.Lambert.2015.12.15_978_3_659_69400_4.pdf?1738481658=\u0026response-content-disposition=attachment%3B+filename%3DMatrix_Analysis_and_Calculus.pdf\u0026Expires=1738797506\u0026Signature=EVrWAfZwroavAn-FuVpX6NCMjnAfaUK0t1GNoEYON6XfM~-G1R8r2xqrPQ45BC-K8Uqs4RLAvR5d4oEhPLFTP47uNNR3RolQUQwFg7ljjDheLTqlIOO8gPSeduuR2hP~17l3zZaiI-jTOl5R6H8OzAEmff6Vx0-FkUHdMN9aYJeNZ5I94ZTiq6IXQazAOZk1z1kb~k-Yzc8jq-YOtSovG9T6ysOBde5V~wPyN332KLpSTQkPw2Si8G7B-CW9QsGn6dIONkXER5H9zcyxKhSJhN8KOhyD1l4Sj3TjxM66h6k~D13o3NRYSd5z68wF-9dzSv0TBxeU1kAM3X54YWExgQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Matrix_Analysis_and_Calculus","translated_slug":"","page_count":73,"language":"en","content_type":"Work","summary":"Statistics, multivariate data analysis and convex optimization are applied widely in many scientific domains and most analytical techniques are developed based on matrix analysis and matrix calculus because matrix is abstract representation of multivariate data. Although it is slightly confused for us to comprehend their concepts and theories, matrix analysis and calculus give us exciting results which enhance data analysis techniques to be more plentiful and accurate. So the report is survey of matrix analysis and calculus, which includes five main sections such as basic concepts, matrix analysis, matrix derivative, composite derivative, and applications of matrix. 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40835793"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40835793/Some_Novel_Algorithms_for_Global_Optimization_and_Relevant_Subjects"><img alt="Research paper thumbnail of Some Novel Algorithms for Global Optimization and Relevant Subjects" class="work-thumbnail" src="https://attachments.academia-assets.com/61120948/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40835793/Some_Novel_Algorithms_for_Global_Optimization_and_Relevant_Subjects">Some Novel Algorithms for Global Optimization and Relevant Subjects</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Applied and Computational Mathematics, Special Issue “Some Novel Algorithms for Global Optimization and Relevant Subjects”</span><span>, Jul 1, 2017</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">We always try our best to create best results but how we can do so? Mathematical optimization is ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">We always try our best to create best results but how we can do so? Mathematical optimization is a good answer for above question if our problems can be modeled by mathematical model. The common model is analytic function and it is very easy for us to know that optimization becomes finding out extreme points of such function. The issue focuses on global optimization which means that how to find out the global peak over the whole function. It is very interesting problem because there are two realistic cases as follows: 1. We want to get the best solution and there is no one better than this solution. 2. Given a good solution, we want to get another better solution. However, global optimization is also complicated because it is relevant to other mathematical subject such as solution existence and approximation. The issue also mentions these subjects. Your attention please, the issue focuses on algorithms and applied methods to solve problem of global optimization. Thus, theoretical aspects relevant to functional analysis are mentioned very little.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6ddb7f678c771afe1fb2c6bf821f5dc1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":61120948,"asset_id":40835793,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/61120948/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40835793"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40835793"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40835793; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40835793]").text(description); $(".js-view-count[data-work-id=40835793]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40835793; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40835793']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6ddb7f678c771afe1fb2c6bf821f5dc1" } } $('.js-work-strip[data-work-id=40835793]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40835793,"title":"Some Novel Algorithms for Global Optimization and Relevant Subjects","translated_title":"","metadata":{"issue":"4","volume":"6","abstract":"We always try our best to create best results but how we can do so? 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Schmidt for the Apollo program. Extended Kalman filters or particle filters are just some examples of these algorithms that have been extensively applied to logistics, medical services, search and rescue operations, or automotive safety, among others. This book takes a look at both theoretical foundations of Bayesian inference and practical implementations in different fields. 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Viết tiếp thơ ơi và biển cả thơ ca vẫn mãi mãi còn một dòng thơ mênh mang bất tận đổ về. Tập thơ này cảm tác từ một người phụ nữ tuyệt vời.<br /><br />Cảm ơn đã đọc tập thơ.<br /><a href="http://viettiep.locnguyen.net" rel="nofollow">http://viettiep.locnguyen.net</a><br /><br />Nguyễn Phước Lộc<br />2011-2016</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="f271efcf8312f6cc9d7ac4c2c188db96" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098230,"asset_id":40762610,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098230/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40762610"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40762610"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40762610; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40762610]").text(description); $(".js-view-count[data-work-id=40762610]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40762610; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40762610']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "f271efcf8312f6cc9d7ac4c2c188db96" } } $('.js-work-strip[data-work-id=40762610]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40762610,"title":"Viết tiếp thơ ơi","translated_title":"","metadata":{"abstract":"Làm thơ còn hơn cả thiên chức nghệ sỹ, đó chính là sứ mệnh, chàng Danko thắp ngọn đuốc bằng trái tim rực lửa. Viết tiếp thơ ơi và biển cả thơ ca vẫn mãi mãi còn một dòng thơ mênh mang bất tận đổ về. Tập thơ này cảm tác từ một người phụ nữ tuyệt vời.\n\nCảm ơn đã đọc tập thơ.\nhttp://viettiep.locnguyen.net\n\nNguyễn Phước Lộc\n2011-2016","event_date":{"day":null,"month":null,"year":2016,"errors":{}},"journal_name":"Poetic collection \"Viết tiếp thơ ơi\"","organization":"Loc Nguyen's Academic Network","publication_date":{"day":null,"month":null,"year":2016,"errors":{}},"publication_name":"Poetic collection \"Viết tiếp thơ ơi\", Loc Nguyen's Academic Network","conference_end_date":{"day":null,"month":null,"year":2016,"errors":{}},"conference_start_date":{"day":null,"month":null,"year":2016,"errors":{}}},"translated_abstract":"Làm thơ còn hơn cả thiên chức nghệ sỹ, đó chính là sứ mệnh, chàng Danko thắp ngọn đuốc bằng trái tim rực lửa. Viết tiếp thơ ơi và biển cả thơ ca vẫn mãi mãi còn một dòng thơ mênh mang bất tận đổ về. 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Tập thơ này cảm tác từ một người phụ nữ tuyệt vời.\n\nCảm ơn đã đọc tập thơ.\nhttp://viettiep.locnguyen.net\n\nNguyễn Phước Lộc\n2011-2016","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":66098230,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098230/thumbnails/1.jpg","file_name":"06.VietTiepThoOi.pdf","download_url":"https://www.academia.edu/attachments/66098230/download_file","bulk_download_file_name":"Vit_tip_th_i.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098230/06.VietTiepThoOi-libre.pdf?1616666903=\u0026response-content-disposition=attachment%3B+filename%3DVit_tip_th_i.pdf\u0026Expires=1738797506\u0026Signature=ZqXjkop0IfUTe6Lf2yI7AFDv22wqgY-CwyzoOamtwxHxu1TKgUlaFm10AiR7nKgSK-tyn3W9Cv-dQ3HyjDefB2JUb-W5FvPtZnq9nTyF-lzyU8kB0JiA8yGJSjXTMjV2Xyaw~12DIUkQ2DgtCT96c-m~c07NcticKUMycwigSTmcfvVcxaP1fRsmEoMtEUHSjnFQZkzFFP16ORy9SI1TyHmzyJyjmpbcLr~9qvuqkvWCtNTa0Wu7lxTcohFQ6tqseuq7SXemcTY2MMo6U5EkPhX8NCmL~3iR0LjeN8latcyOYjVeKLuPCoBQ5Ia0SL~jZXtzuu33lW8Wa3OD-wIKnw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":3581,"name":"Poetry","url":"https://www.academia.edu/Documents/in/Poetry"},{"id":10187,"name":"Love","url":"https://www.academia.edu/Documents/in/Love"}],"urls":[{"id":8879053,"url":"http://viettiep.locnguyen.net/"},{"id":8879054,"url":"https://allpoetry.com/list/586263-Vi%E1%BA%BFt-ti%E1%BA%BFp-th%C6%A1-%C6%A1i"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40762689"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40762689/V%E1%BA%BD"><img alt="Research paper thumbnail of Vẽ" class="work-thumbnail" src="https://attachments.academia-assets.com/66098209/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40762689/V%E1%BA%BD">Vẽ</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Poetic collection "Vẽ", Loc Nguyen's Academic Network</span><span>, 2019</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Trong những tập thơ trước, tôi đã vẽ ra cho các bạn một bức tranh đầy cảm xúc với những con người...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Trong những tập thơ trước, tôi đã vẽ ra cho các bạn một bức tranh đầy cảm xúc với những con người rất thật, những chuyện tình rất thật nhưng cũng đầy tưởng tượng vượt ra không thời gian hiện tại. Thơ tôi, xem trọng nhạc điệu hơn niêm luật, nửa bình dân nửa bác học, nửa thanh nửa tục, nửa thâm trầm nửa hài hước, nửa quái dị nửa ngây thơ, nửa hiện đại nửa cổ kính, có những con người trần tục đủ ngành nghề, có thần tiên, có yêu ma, có anh hùng, có mỹ nhân, có nước mắt, có nụ cười, có chuyện hoang đường, có tin sốt dẻo và đủ thứ. Tôi bắt gặp tôi trong đấy và rất vui nếu biết bạn bắt gặp bạn trong đấy.<br /><br />Với tập thơ mới với tên là “Vẽ”, tôi sẽ vẽ một bức tranh mới với mong muốn bước vào địa hạt siêu huyền, những con người/sự việc có thể là thực nhưng sẽ trở thành những hình siêu thực trong bức tranh ấy. Tất cả không còn cụ thể nữa. Tên tất cả bài thơ đều bắt đầu bằng chữ cái “H” viết tắt từ “Hình” cùng với số thứ tự. Ví dụ, H1 là bài thơ thứ nhất tượng trưng cho hình siêu thực 1. Tất cả mảnh ghép siêu thực tạo thành bức tranh siêu huyền mang tên “Vẽ”.<br /><br />Rất mong các bạn đón nhận tập thơ “Vẽ”.<br /><a href="http://ve.locnguyen.net" rel="nofollow">http://ve.locnguyen.net</a><br /><br />Nguyễn Phước Lộc<br />2016-2019</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="93fd0b0203d28159fb6f2da23aa2287f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098209,"asset_id":40762689,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098209/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40762689"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40762689"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40762689; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40762689]").text(description); $(".js-view-count[data-work-id=40762689]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40762689; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40762689']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "93fd0b0203d28159fb6f2da23aa2287f" } } $('.js-work-strip[data-work-id=40762689]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40762689,"title":"Vẽ","translated_title":"","metadata":{"abstract":"Trong những tập thơ trước, tôi đã vẽ ra cho các bạn một bức tranh đầy cảm xúc với những con người rất thật, những chuyện tình rất thật nhưng cũng đầy tưởng tượng vượt ra không thời gian hiện tại. Thơ tôi, xem trọng nhạc điệu hơn niêm luật, nửa bình dân nửa bác học, nửa thanh nửa tục, nửa thâm trầm nửa hài hước, nửa quái dị nửa ngây thơ, nửa hiện đại nửa cổ kính, có những con người trần tục đủ ngành nghề, có thần tiên, có yêu ma, có anh hùng, có mỹ nhân, có nước mắt, có nụ cười, có chuyện hoang đường, có tin sốt dẻo và đủ thứ. Tôi bắt gặp tôi trong đấy và rất vui nếu biết bạn bắt gặp bạn trong đấy.\n\nVới tập thơ mới với tên là “Vẽ”, tôi sẽ vẽ một bức tranh mới với mong muốn bước vào địa hạt siêu huyền, những con người/sự việc có thể là thực nhưng sẽ trở thành những hình siêu thực trong bức tranh ấy. Tất cả không còn cụ thể nữa. Tên tất cả bài thơ đều bắt đầu bằng chữ cái “H” viết tắt từ “Hình” cùng với số thứ tự. Ví dụ, H1 là bài thơ thứ nhất tượng trưng cho hình siêu thực 1. 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Thơ tôi, xem trọng nhạc điệu hơn niêm luật, nửa bình dân nửa bác học, nửa thanh nửa tục, nửa thâm trầm nửa hài hước, nửa quái dị nửa ngây thơ, nửa hiện đại nửa cổ kính, có những con người trần tục đủ ngành nghề, có thần tiên, có yêu ma, có anh hùng, có mỹ nhân, có nước mắt, có nụ cười, có chuyện hoang đường, có tin sốt dẻo và đủ thứ. Tôi bắt gặp tôi trong đấy và rất vui nếu biết bạn bắt gặp bạn trong đấy.\n\nVới tập thơ mới với tên là “Vẽ”, tôi sẽ vẽ một bức tranh mới với mong muốn bước vào địa hạt siêu huyền, những con người/sự việc có thể là thực nhưng sẽ trở thành những hình siêu thực trong bức tranh ấy. Tất cả không còn cụ thể nữa. Tên tất cả bài thơ đều bắt đầu bằng chữ cái “H” viết tắt từ “Hình” cùng với số thứ tự. Ví dụ, H1 là bài thơ thứ nhất tượng trưng cho hình siêu thực 1. Tất cả mảnh ghép siêu thực tạo thành bức tranh siêu huyền mang tên “Vẽ”.\n\nRất mong các bạn đón nhận tập thơ “Vẽ”.\nhttp://ve.locnguyen.net\n\nNguyễn Phước Lộc\n2016-2019","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":66098209,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098209/thumbnails/1.jpg","file_name":"07.Ve.pdf","download_url":"https://www.academia.edu/attachments/66098209/download_file","bulk_download_file_name":"V.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098209/07.Ve-libre.pdf?1616666900=\u0026response-content-disposition=attachment%3B+filename%3DV.pdf\u0026Expires=1738797506\u0026Signature=eDXNC5VUhQ5Asrzmp21NBdQqgX18NmoTWAujOVI6zbJg4~FxYxJasQjKeK2apfZdRQsrYFFeQRaDpfT6-8Qv~rIdAy5BUh4l2QQwPeA4kTUdJNtAEBak2Q7ErplBm~MHqGiPs48D8-ZOECiBlFHcCk0NkqXBpqMTHDHkV7ls1l-lAFzixAevcamFPI6VaZ3TDWZnYgI-ebHHNcaXljsRAh0M83O8YcVNc10eZECYCfvyvHSsvO0Hwdk9rffPuMeYF5Xj41hWFgNtLkEZS6XjPZmAYK9XbvuzifIaVJK~DK3H7LloTzkQC5b-4TW3DxkB1aLh1iy8b9ntGnjHCxxtAg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":3581,"name":"Poetry","url":"https://www.academia.edu/Documents/in/Poetry"},{"id":10187,"name":"Love","url":"https://www.academia.edu/Documents/in/Love"}],"urls":[{"id":8879057,"url":"http://ve.locnguyen.net/"},{"id":8879058,"url":"https://allpoetry.com/list/591354-V%E1%BA%BD"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40762552"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40762552/L%E1%BA%A1i_ch%C6%B0a_%C4%91%E1%BA%B7t_t%C3%AAn"><img alt="Research paper thumbnail of Lại chưa đặt tên" class="work-thumbnail" src="https://attachments.academia-assets.com/66098245/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40762552/L%E1%BA%A1i_ch%C6%B0a_%C4%91%E1%BA%B7t_t%C3%AAn">Lại chưa đặt tên</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Poetic collection "Lại chưa đặt tên", Loc Nguyen's Academic Network</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Sáng tác là thiên chức nghệ sỹ, như tằm phải nhả tơ nhưng tôi chưa chú ý đến, thiên chức này tưởn...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Sáng tác là thiên chức nghệ sỹ, như tằm phải nhả tơ nhưng tôi chưa chú ý đến, thiên chức này tưởng chừng rất mờ nhạt nên tập thơ vẫn chưa được đặt tên. Tôi làm thơ vì cảm hứng, vì tình yêu thơ ca, vì “mắt nhìn sáu tám bơ vơ, không sao cầm được người ơi nỗi lòng”, dù sao, qua muôn ngả nhưng với tình yêu dẫn lối, rốt cuộc cũng trở về với “muối mặn gừng cay”.<br /><br />Cảm ơn đã đọc tập thơ<br /><a href="http://laichuadatten.locnguyen.net" rel="nofollow">http://laichuadatten.locnguyen.net</a><br /><br />Nguyễn Phước Lộc<br />2010 - 2011</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="303c1e6c2b35bbd499b7c7d06df4dec6" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098245,"asset_id":40762552,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098245/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40762552"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40762552"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40762552; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40762552]").text(description); $(".js-view-count[data-work-id=40762552]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40762552; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40762552']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "303c1e6c2b35bbd499b7c7d06df4dec6" } } $('.js-work-strip[data-work-id=40762552]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40762552,"title":"Lại chưa đặt tên","translated_title":"","metadata":{"abstract":"Sáng tác là thiên chức nghệ sỹ, như tằm phải nhả tơ nhưng tôi chưa chú ý đến, thiên chức này tưởng chừng rất mờ nhạt nên tập thơ vẫn chưa được đặt tên. Tôi làm thơ vì cảm hứng, vì tình yêu thơ ca, vì “mắt nhìn sáu tám bơ vơ, không sao cầm được người ơi nỗi lòng”, dù sao, qua muôn ngả nhưng với tình yêu dẫn lối, rốt cuộc cũng trở về với “muối mặn gừng cay”.\n\nCảm ơn đã đọc tập thơ\nhttp://laichuadatten.locnguyen.net\n\nNguyễn Phước Lộc\n2010 - 2011","event_date":{"day":null,"month":null,"year":2011,"errors":{}},"journal_name":"Poetic collection \"Lại chưa đặt tên\"","organization":"Loc Nguyen's Academic Network","publication_date":{"day":null,"month":null,"year":2011,"errors":{}},"publication_name":"Poetic collection \"Lại chưa đặt tên\", Loc Nguyen's Academic Network","conference_end_date":{"day":null,"month":null,"year":2011,"errors":{}},"conference_start_date":{"day":null,"month":null,"year":2011,"errors":{}}},"translated_abstract":"Sáng tác là thiên chức nghệ sỹ, như tằm phải nhả tơ nhưng tôi chưa chú ý đến, thiên chức này tưởng chừng rất mờ nhạt nên tập thơ vẫn chưa được đặt tên. Tôi làm thơ vì cảm hứng, vì tình yêu thơ ca, vì “mắt nhìn sáu tám bơ vơ, không sao cầm được người ơi nỗi lòng”, dù sao, qua muôn ngả nhưng với tình yêu dẫn lối, rốt cuộc cũng trở về với “muối mặn gừng cay”.\n\nCảm ơn đã đọc tập thơ\nhttp://laichuadatten.locnguyen.net\n\nNguyễn Phước Lộc\n2010 - 2011","internal_url":"https://www.academia.edu/40762552/L%E1%BA%A1i_ch%C6%B0a_%C4%91%E1%BA%B7t_t%C3%AAn","translated_internal_url":"","created_at":"2019-10-28T03:53:43.835-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"book","co_author_tags":[{"id":33212244,"work_id":40762552,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Lại chưa đặt tên"}],"downloadable_attachments":[{"id":66098245,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098245/thumbnails/1.jpg","file_name":"05.LaiChuaDatTen.pdf","download_url":"https://www.academia.edu/attachments/66098245/download_file","bulk_download_file_name":"Li_cha_dt_ten.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098245/05.LaiChuaDatTen-libre.pdf?1616666902=\u0026response-content-disposition=attachment%3B+filename%3DLi_cha_dt_ten.pdf\u0026Expires=1738797506\u0026Signature=RyF1U6Rg4weGi2xsdthAfBozkXHEjYpBjND4YwUQZWdHLSLCv-imvfUVBtq09OYjkEoyPYj~aDypC396c321rGktF-5bxq5MrRQ7hCh9Idnoyayy2hW1FrBaicqLTm1-q5C7Gvoh8Rp2QdfDqGdmtkwstXrVg2CN4JcgUzwEMyP8MYkzDyDbCu4DF7pyXnPOukJADK3GlzU6TcH5qFUQmlHAhzz8svBzsw8LqP7vIJvnb~HbODAA8uBbdty-MxrWTZTROUcyzwNTqHO3KJFpQLoHnz9LbAlOSNxRQNYkyMtBatcr9flLlpj7ZzZlhluqcc7~BQOZ4PjilOvpNxC1Ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Lại_chưa_đặt_tên","translated_slug":"","page_count":24,"language":"vi","content_type":"Work","summary":"Sáng tác là thiên chức nghệ sỹ, như tằm phải nhả tơ nhưng tôi chưa chú ý đến, thiên chức này tưởng chừng rất mờ nhạt nên tập thơ vẫn chưa được đặt tên. Tôi làm thơ vì cảm hứng, vì tình yêu thơ ca, vì “mắt nhìn sáu tám bơ vơ, không sao cầm được người ơi nỗi lòng”, dù sao, qua muôn ngả nhưng với tình yêu dẫn lối, rốt cuộc cũng trở về với “muối mặn gừng cay”.\n\nCảm ơn đã đọc tập thơ\nhttp://laichuadatten.locnguyen.net\n\nNguyễn Phước Lộc\n2010 - 2011","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":66098245,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098245/thumbnails/1.jpg","file_name":"05.LaiChuaDatTen.pdf","download_url":"https://www.academia.edu/attachments/66098245/download_file","bulk_download_file_name":"Li_cha_dt_ten.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098245/05.LaiChuaDatTen-libre.pdf?1616666902=\u0026response-content-disposition=attachment%3B+filename%3DLi_cha_dt_ten.pdf\u0026Expires=1738797506\u0026Signature=RyF1U6Rg4weGi2xsdthAfBozkXHEjYpBjND4YwUQZWdHLSLCv-imvfUVBtq09OYjkEoyPYj~aDypC396c321rGktF-5bxq5MrRQ7hCh9Idnoyayy2hW1FrBaicqLTm1-q5C7Gvoh8Rp2QdfDqGdmtkwstXrVg2CN4JcgUzwEMyP8MYkzDyDbCu4DF7pyXnPOukJADK3GlzU6TcH5qFUQmlHAhzz8svBzsw8LqP7vIJvnb~HbODAA8uBbdty-MxrWTZTROUcyzwNTqHO3KJFpQLoHnz9LbAlOSNxRQNYkyMtBatcr9flLlpj7ZzZlhluqcc7~BQOZ4PjilOvpNxC1Ig__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":3581,"name":"Poetry","url":"https://www.academia.edu/Documents/in/Poetry"},{"id":10187,"name":"Love","url":"https://www.academia.edu/Documents/in/Love"}],"urls":[{"id":8879047,"url":"http://laichuadatten.locnguyen.net/"},{"id":8879048,"url":"https://allpoetry.com/list/586259-L%E1%BA%A1i-ch%C6%B0a-%C4%91%E1%BA%B7t-t%C3%AAn"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40762378"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40762378/L%E1%BB%A5c_Ki%E1%BB%81u_Th%E1%BB%9Di_at_"><img alt="Research paper thumbnail of Lục Kiều Thời @" class="work-thumbnail" src="https://attachments.academia-assets.com/66098266/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40762378/L%E1%BB%A5c_Ki%E1%BB%81u_Th%E1%BB%9Di_at_">Lục Kiều Thời @</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Verse story "Lục Kiều Thời @", Loc Nguyen's Academic Network</span><span>, 2008</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nguyễn Du viết Kiều, “máu nhỏ qua đầu bút, nước mắt thắm qua trang giấy”. Nguyễn Đình Chiểu viết ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Nguyễn Du viết Kiều, “máu nhỏ qua đầu bút, nước mắt thắm qua trang giấy”.<br />Nguyễn Đình Chiểu viết Lục Vân Tiên, “chở bao nhiêu đạo thuyền không khẳm”.<br /><br />“Kiều” tài hoa mà hình dung ủy lụy.<br />“Lục Vân Tiên” anh hùng hào sảng mà hơi gàn.<br />Hai cụ ơi, hãy nắm tay nhau, Kiều Lục thành đôi, tượng đài viên mãn.<br /><br />Yêu hai cụ, những mong một buổi chiều bảng lảng hoàng hôn nào đó, hai cụ cùng ngồi đối ẩm trao tâm tình cho nhau, cợt cười danh lợi, gởi lời âu yếm cho con cháu ngày sau.<br /><br />Ơ kìa, sông Lam hòa nước Cửu Long ồn ào mà đằm thắm hòa vào trùng dương thời đại.<br /><br />Truyện thơ này cũng vì yêu hai cụ và “mua vui cũng được một vài trống canh” hoàn toàn với tinh thần nghĩa hiệp và hài hước. Tác phẩm chứa nhiều ẩn ngữ và ngụ ngôn nhưng vì tục ca nên có những yếu tố dung tục, cúi mong lượng thứ.<br /><br />Dù thế nào đi nữa: yếu tố hài hước lúc nào cũng phải có. Vậy mới “dzui”!<br /><br />Tác phẩm được cảm hứng từ một bài thơ vui rất dài của một tác giả mà tôi không thể nhớ tên. Xin chân thành cảm ơn tác giả này.<br /><br />Cảm ơn đã đọc truyện thơ này.<br /><a href="http://luckieu.locnguyen.net" rel="nofollow">http://luckieu.locnguyen.net</a><br /><br />Nguyễn Phước Lộc<br />2007 - 2008</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="31d23a5c613eb6e32a049676c0604575" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098266,"asset_id":40762378,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098266/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40762378"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40762378"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40762378; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40762378]").text(description); $(".js-view-count[data-work-id=40762378]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40762378; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40762378']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "31d23a5c613eb6e32a049676c0604575" } } $('.js-work-strip[data-work-id=40762378]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40762378,"title":"Lục Kiều Thời @","translated_title":"","metadata":{"abstract":"Nguyễn Du viết Kiều, “máu nhỏ qua đầu bút, nước mắt thắm qua trang giấy”.\nNguyễn Đình Chiểu viết Lục Vân Tiên, “chở bao nhiêu đạo thuyền không khẳm”.\n\n“Kiều” tài hoa mà hình dung ủy lụy.\n“Lục Vân Tiên” anh hùng hào sảng mà hơi gàn.\nHai cụ ơi, hãy nắm tay nhau, Kiều Lục thành đôi, tượng đài viên mãn.\n\nYêu hai cụ, những mong một buổi chiều bảng lảng hoàng hôn nào đó, hai cụ cùng ngồi đối ẩm trao tâm tình cho nhau, cợt cười danh lợi, gởi lời âu yếm cho con cháu ngày sau.\n\nƠ kìa, sông Lam hòa nước Cửu Long ồn ào mà đằm thắm hòa vào trùng dương thời đại.\n\nTruyện thơ này cũng vì yêu hai cụ và “mua vui cũng được một vài trống canh” hoàn toàn với tinh thần nghĩa hiệp và hài hước. Tác phẩm chứa nhiều ẩn ngữ và ngụ ngôn nhưng vì tục ca nên có những yếu tố dung tục, cúi mong lượng thứ.\n\nDù thế nào đi nữa: yếu tố hài hước lúc nào cũng phải có. Vậy mới “dzui”!\n\nTác phẩm được cảm hứng từ một bài thơ vui rất dài của một tác giả mà tôi không thể nhớ tên. Xin chân thành cảm ơn tác giả này.\n\nCảm ơn đã đọc truyện thơ này.\nhttp://luckieu.locnguyen.net\n\nNguyễn Phước Lộc\n2007 - 2008","event_date":{"day":null,"month":null,"year":2008,"errors":{}},"journal_name":"Verse story \"Lục Kiều Thời @\"","organization":"Loc Nguyen's Academic Network","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Verse story \"Lục Kiều Thời @\", Loc Nguyen's Academic Network","conference_end_date":{"day":null,"month":null,"year":2008,"errors":{}},"conference_start_date":{"day":null,"month":null,"year":2008,"errors":{}}},"translated_abstract":"Nguyễn Du viết Kiều, “máu nhỏ qua đầu bút, nước mắt thắm qua trang giấy”.\nNguyễn Đình Chiểu viết Lục Vân Tiên, “chở bao nhiêu đạo thuyền không khẳm”.\n\n“Kiều” tài hoa mà hình dung ủy lụy.\n“Lục Vân Tiên” anh hùng hào sảng mà hơi gàn.\nHai cụ ơi, hãy nắm tay nhau, Kiều Lục thành đôi, tượng đài viên mãn.\n\nYêu hai cụ, những mong một buổi chiều bảng lảng hoàng hôn nào đó, hai cụ cùng ngồi đối ẩm trao tâm tình cho nhau, cợt cười danh lợi, gởi lời âu yếm cho con cháu ngày sau.\n\nƠ kìa, sông Lam hòa nước Cửu Long ồn ào mà đằm thắm hòa vào trùng dương thời đại.\n\nTruyện thơ này cũng vì yêu hai cụ và “mua vui cũng được một vài trống canh” hoàn toàn với tinh thần nghĩa hiệp và hài hước. 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Xin chân thành cảm ơn tác giả này.\n\nCảm ơn đã đọc truyện thơ này.\nhttp://luckieu.locnguyen.net\n\nNguyễn Phước Lộc\n2007 - 2008","internal_url":"https://www.academia.edu/40762378/L%E1%BB%A5c_Ki%E1%BB%81u_Th%E1%BB%9Di_at_","translated_internal_url":"","created_at":"2019-10-28T03:29:59.777-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"book","co_author_tags":[{"id":33212131,"work_id":40762378,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Lục Kiều Thời @"}],"downloadable_attachments":[{"id":66098266,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098266/thumbnails/1.jpg","file_name":"02.LucKieu.pdf","download_url":"https://www.academia.edu/attachments/66098266/download_file","bulk_download_file_name":"Lc_Kiu_Thi_at.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098266/02.LucKieu-libre.pdf?1616666902=\u0026response-content-disposition=attachment%3B+filename%3DLc_Kiu_Thi_at.pdf\u0026Expires=1738797506\u0026Signature=DhnsNSlqlxjV9jDJPWMhLP1BLc4g04ry3sd2zSbtXTIxm4GWg5QiesJ09qo9BwCADnj5iGX1YVMK2gsWbgewbl1OMjwSrVf1mHu52i60ZAH1dbXl4CFs5Dti6rAJuywvGrIimTU4ZS5hx53gdiHNLhTSkkO4ddx3poHXjVCgU-D~jNzZbuXuUxdSJch5NV1~k3ZBFIIZRPzKPXQR01OknZvgnv0Z33w5FaQkKKl7oFJvp5cUqZjZBYUrB8EXn6jWt-B~KFnCU8hWLJ0aLU6pQShlHsKH-aweuy6mOzpIYnQFt~GpOfK5swsx4m4DVV4YBCGEMEUojX6pQTkEI9LraQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Lục_Kiều_Thời_at_","translated_slug":"","page_count":24,"language":"vi","content_type":"Work","summary":"Nguyễn Du viết Kiều, “máu nhỏ qua đầu bút, nước mắt thắm qua trang giấy”.\nNguyễn Đình Chiểu viết Lục Vân Tiên, “chở bao nhiêu đạo thuyền không khẳm”.\n\n“Kiều” tài hoa mà hình dung ủy lụy.\n“Lục Vân Tiên” anh hùng hào sảng mà hơi gàn.\nHai cụ ơi, hãy nắm tay nhau, Kiều Lục thành đôi, tượng đài viên mãn.\n\nYêu hai cụ, những mong một buổi chiều bảng lảng hoàng hôn nào đó, hai cụ cùng ngồi đối ẩm trao tâm tình cho nhau, cợt cười danh lợi, gởi lời âu yếm cho con cháu ngày sau.\n\nƠ kìa, sông Lam hòa nước Cửu Long ồn ào mà đằm thắm hòa vào trùng dương thời đại.\n\nTruyện thơ này cũng vì yêu hai cụ và “mua vui cũng được một vài trống canh” hoàn toàn với tinh thần nghĩa hiệp và hài hước. Tác phẩm chứa nhiều ẩn ngữ và ngụ ngôn nhưng vì tục ca nên có những yếu tố dung tục, cúi mong lượng thứ.\n\nDù thế nào đi nữa: yếu tố hài hước lúc nào cũng phải có. Vậy mới “dzui”!\n\nTác phẩm được cảm hứng từ một bài thơ vui rất dài của một tác giả mà tôi không thể nhớ tên. 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Cảm ơn đã đọc tập...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tập thơ kỷ niệm những ngày viết blog hòa cùng cung bậc buồn vui với mọi người.<br /><br />Cảm ơn đã đọc tập thơ.<br /><a href="http://cadaoblog.locnguyen.net" rel="nofollow">http://cadaoblog.locnguyen.net</a><br /><br />Nguyễn Phước Lộc<br />2008 - 2009</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0e02d305e86d4bab0b47c115b5ebe946" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098282,"asset_id":40762335,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098282/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40762335"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40762335"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40762335; 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="40762529"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/40762529/Ch%C6%B0a_%C4%91%E1%BA%B7t_t%C3%AAn"><img alt="Research paper thumbnail of Chưa đặt tên" class="work-thumbnail" src="https://attachments.academia-assets.com/66098254/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/40762529/Ch%C6%B0a_%C4%91%E1%BA%B7t_t%C3%AAn">Chưa đặt tên</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Poetic collection "Chưa đặt tên", Loc Nguyen's Academic Network</span><span>, 2010</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Tình yêu làm nên thi sỹ nhưng không có định nghĩa tình yêu – nào những ánh mắt, những nụ cười, nh...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tình yêu làm nên thi sỹ nhưng không có định nghĩa tình yêu – nào những ánh mắt, những nụ cười, những xao xuyến, những say mê, những tình tứ, và còn nhiều nữa. Tập thơ đồng nghĩa với tình yêu và thế nên chưa được đặt tên.<br /><br />Cảm ơn đã đọc tập thơ<br /><a href="http://chuadatten.locnguyen.net" rel="nofollow">http://chuadatten.locnguyen.net</a><br /><br />Nguyễn Phước Lộc<br />2009 - 2010</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="9600d28702e1554e0146bf69d6538dc9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098254,"asset_id":40762529,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098254/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40762529"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40762529"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40762529; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40762529]").text(description); $(".js-view-count[data-work-id=40762529]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40762529; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40762529']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "9600d28702e1554e0146bf69d6538dc9" } } $('.js-work-strip[data-work-id=40762529]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40762529,"title":"Chưa đặt tên","translated_title":"","metadata":{"abstract":"Tình yêu làm nên thi sỹ nhưng không có định nghĩa tình yêu – nào những ánh mắt, những nụ cười, những xao xuyến, những say mê, những tình tứ, và còn nhiều nữa. 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Thơ Lộc cũng có đủ những khát vọng tuổi thơ, những tình yêu vụng dại nồng nàn, những ước mơ cháy bỏng và một điều gì đó không hiện thực... Không sao, cuộc đời vốn buồn nhiều hơn vui mà Lộc!<br /><br />Thơ Lộc cũng có tình mẹ con, thầy trò, bè bạn và tình yêu đôi lứa, tình yêu quê hương với những trang sử ít nhiều Lộc cũng mang trong mình khí phách hào hùng của những tráng sĩ thời xưa.<br /><br />“Tình sâu không nói bằng lời<br />Mà trong ánh mắt nụ cười cho nhau”<br /><br />Mong rằng thơ Nguyễn Phước Lộc cũng như cuộc đời của mình, luôn may mắn, may mắn như cái tên mà Lộc đang mang đi.<br /><br />“Của tặng không bằng cách tặng”. Đây cũng là một cách tặng tế nhị của Lộc đến với độc giả và tùy cách “nhận” của mỗi người. Có lẽ chỉ với đôi điều đó thôi cũng đủ đánh thức bạn tri âm với những chia sẻ tự đáy lòng mình.<br /><br />Saigon, tháng 3/2008<br />Nhà báo Nguyễn Công Thụ - bút hiệu Thụ Nhân<br /><br />◦◦◊◦◦<br /><br />LỜI TÂM SỰ<br />Yêu thơ từ bé, đến nay mười bốn năm mới có được tập thơ xem như là người yêu đầu tiên để thương để nhớ, tôi cũng không hi vọng có được người yêu kế, một lần kỷ niệm – giữ mãi mối tình – không mong cải giá. Không phải là nhà thơ hay nghệ sĩ, tôi như một người đang đi chợt trông thấy vườn hoa bên đường, dừng chân đứng ngắm rồi đắm say hương sắc, mê trong chốc lát, tất nhiên rồi phải tỉnh để đi tiếp trọn con đường của mình.<br /><br />Đây là tập hợp các bài thơ rải rác trong mười bốn năm viết thơ khi không khi có nên không có chủ đề, đáng tiếc còn những bài thất lạc do tôi quên hay để vương vãi đâu đó, sau này nếu ai tìm được xin đừng để muối mặn lời yêu trong thơ tôi tan vào biển cả cuộc đời. Vì không chủ đề nên người đọc có thể thấy tạp nham, cúi mong lượng thứ, mở rộng tấm lòng để thơ thốt được nên lời như cậu học trò lần đầu lắp bắp nói tiếng yêu.<br /><br />Tôi không sắp xếp các bài thơ theo thứ tự thời gian do không muốn có sự chỉnh chu, nếu quá chỉnh chu thì còn gì là thơ. Và mỗi giai đoạn các bài thơ có thể có vị riêng, tôi muốn xóa ranh giới thời gian để trộn chúng vào nhau nhằm dậy men của các vị xúc cảm: nồng, cay, mặn, đắng, ngọt, chua...<br /><br />Tôi không quan tâm đến giá trị nghệ thuật, tập thơ này chỉ là những kỷ niệm, kỷ niệm một lần yêu thơ, một lần yêu người, một lần vẩn vơ, một lần tâm sự, một lần rung động, một lần cả nghĩ, một lần trăn trở hay một lần… Hầu như mỗi bài thơ đều cảm tác từ một người và tất nhiên tôi dành tặng cho người ấy – họ chính là nguồn cảm xúc hay đúng hơn họ làm ra bài thơ đó, tôi chỉ là người thể hiện lại tình cảm thiêng liêng bằng ngôn từ của riêng mình. Nhưng thơ tồn tại trong lòng người đâu bằng ngôn từ, tình ở ngoài lời – lời chưa tận ý, ngôn từ chỉ là giả tướng. Vì thế tôi lấy tên tập thơ là “Tặng”, tặng mỗi bài cho từng người mà tôi đã và đang yêu quý, và cả tập thơ này, xin dành tặng cho cuộc đời đã mang thơ lại cho tôi.<br /><br />Cảm ơn đã đọc và cảm tạ những người đã giúp tôi hoàn thành tập thơ này.<br /><a href="http://tang.locnguyen.net" rel="nofollow">http://tang.locnguyen.net</a><br /><br />Nguyễn Phước Lộc<br />1993 - 2007</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="635b86c61c8c54342be4dc2b8c3b1013" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098293,"asset_id":40762105,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098293/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40762105"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40762105"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40762105; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40762105]").text(description); $(".js-view-count[data-work-id=40762105]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40762105; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40762105']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "635b86c61c8c54342be4dc2b8c3b1013" } } $('.js-work-strip[data-work-id=40762105]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40762105,"title":"Tặng","translated_title":"","metadata":{"abstract":"TỰA\nĐọc xong “Tặng” của nhà thơ Nguyễn Phước Lộc tôi liên tưởng đến một dòng sông nhỏ giữa trưa hè nắng gắt.\n\nDòng sông xanh nhỏ lững lờ trôi, mang trên mình những hương hoa cỏ thơm, thơ của đồng nội, có những hoa cỏ theo cơn gió mạnh lìa cành và bay theo dòng nước trong xanh.\nTrong bài “Tình Quê”, Phước Lộc đã để xen lẫn trong ước mơ của mình về một quê hương yêu dấu, đẹp đẽ là “những điều u uất nặng chìm sông sâu”, có lẽ trong đời Lộc có một điều gì đó chưa nói được thành lời, chưa trọn vẹn như mơ ước.\n\nCàng đọc thơ Phước Lộc, tôi thấy Lộc một con người đa dạng vừa mơ mộng vừa thực tế như một nhà toán học chứ không như một tiến sĩ công nghệ thông tin, vừa đa tình vừa không phải... Thơ Lộc cũng có đủ những khát vọng tuổi thơ, những tình yêu vụng dại nồng nàn, những ước mơ cháy bỏng và một điều gì đó không hiện thực... Không sao, cuộc đời vốn buồn nhiều hơn vui mà Lộc!\n\nThơ Lộc cũng có tình mẹ con, thầy trò, bè bạn và tình yêu đôi lứa, tình yêu quê hương với những trang sử ít nhiều Lộc cũng mang trong mình khí phách hào hùng của những tráng sĩ thời xưa.\n\n“Tình sâu không nói bằng lời\nMà trong ánh mắt nụ cười cho nhau”\n\nMong rằng thơ Nguyễn Phước Lộc cũng như cuộc đời của mình, luôn may mắn, may mắn như cái tên mà Lộc đang mang đi.\n\n“Của tặng không bằng cách tặng”. Đây cũng là một cách tặng tế nhị của Lộc đến với độc giả và tùy cách “nhận” của mỗi người. Có lẽ chỉ với đôi điều đó thôi cũng đủ đánh thức bạn tri âm với những chia sẻ tự đáy lòng mình.\n\nSaigon, tháng 3/2008\nNhà báo Nguyễn Công Thụ - bút hiệu Thụ Nhân\n\n◦◦◊◦◦\n\nLỜI TÂM SỰ\nYêu thơ từ bé, đến nay mười bốn năm mới có được tập thơ xem như là người yêu đầu tiên để thương để nhớ, tôi cũng không hi vọng có được người yêu kế, một lần kỷ niệm – giữ mãi mối tình – không mong cải giá. Không phải là nhà thơ hay nghệ sĩ, tôi như một người đang đi chợt trông thấy vườn hoa bên đường, dừng chân đứng ngắm rồi đắm say hương sắc, mê trong chốc lát, tất nhiên rồi phải tỉnh để đi tiếp trọn con đường của mình.\n\nĐây là tập hợp các bài thơ rải rác trong mười bốn năm viết thơ khi không khi có nên không có chủ đề, đáng tiếc còn những bài thất lạc do tôi quên hay để vương vãi đâu đó, sau này nếu ai tìm được xin đừng để muối mặn lời yêu trong thơ tôi tan vào biển cả cuộc đời. Vì không chủ đề nên người đọc có thể thấy tạp nham, cúi mong lượng thứ, mở rộng tấm lòng để thơ thốt được nên lời như cậu học trò lần đầu lắp bắp nói tiếng yêu.\n\nTôi không sắp xếp các bài thơ theo thứ tự thời gian do không muốn có sự chỉnh chu, nếu quá chỉnh chu thì còn gì là thơ. Và mỗi giai đoạn các bài thơ có thể có vị riêng, tôi muốn xóa ranh giới thời gian để trộn chúng vào nhau nhằm dậy men của các vị xúc cảm: nồng, cay, mặn, đắng, ngọt, chua...\n\nTôi không quan tâm đến giá trị nghệ thuật, tập thơ này chỉ là những kỷ niệm, kỷ niệm một lần yêu thơ, một lần yêu người, một lần vẩn vơ, một lần tâm sự, một lần rung động, một lần cả nghĩ, một lần trăn trở hay một lần… Hầu như mỗi bài thơ đều cảm tác từ một người và tất nhiên tôi dành tặng cho người ấy – họ chính là nguồn cảm xúc hay đúng hơn họ làm ra bài thơ đó, tôi chỉ là người thể hiện lại tình cảm thiêng liêng bằng ngôn từ của riêng mình. Nhưng thơ tồn tại trong lòng người đâu bằng ngôn từ, tình ở ngoài lời – lời chưa tận ý, ngôn từ chỉ là giả tướng. Vì thế tôi lấy tên tập thơ là “Tặng”, tặng mỗi bài cho từng người mà tôi đã và đang yêu quý, và cả tập thơ này, xin dành tặng cho cuộc đời đã mang thơ lại cho tôi.\n\nCảm ơn đã đọc và cảm tạ những người đã giúp tôi hoàn thành tập thơ này.\nhttp://tang.locnguyen.net\n\nNguyễn Phước Lộc\n1993 - 2007","event_date":{"day":null,"month":null,"year":2008,"errors":{}},"journal_name":"Poetic collection \"Tặng\"","organization":"Tre Publisher","publication_date":{"day":null,"month":null,"year":2008,"errors":{}},"publication_name":"Poetic collection \"Tặng\", Tre Publisher","conference_end_date":{"day":null,"month":null,"year":2008,"errors":{}},"conference_start_date":{"day":null,"month":null,"year":2008,"errors":{}}},"translated_abstract":"TỰA\nĐọc xong “Tặng” của nhà thơ Nguyễn Phước Lộc tôi liên tưởng đến một dòng sông nhỏ giữa trưa hè nắng gắt.\n\nDòng sông xanh nhỏ lững lờ trôi, mang trên mình những hương hoa cỏ thơm, thơ của đồng nội, có những hoa cỏ theo cơn gió mạnh lìa cành và bay theo dòng nước trong xanh.\nTrong bài “Tình Quê”, Phước Lộc đã để xen lẫn trong ước mơ của mình về một quê hương yêu dấu, đẹp đẽ là “những điều u uất nặng chìm sông sâu”, có lẽ trong đời Lộc có một điều gì đó chưa nói được thành lời, chưa trọn vẹn như mơ ước.\n\nCàng đọc thơ Phước Lộc, tôi thấy Lộc một con người đa dạng vừa mơ mộng vừa thực tế như một nhà toán học chứ không như một tiến sĩ công nghệ thông tin, vừa đa tình vừa không phải... Thơ Lộc cũng có đủ những khát vọng tuổi thơ, những tình yêu vụng dại nồng nàn, những ước mơ cháy bỏng và một điều gì đó không hiện thực... Không sao, cuộc đời vốn buồn nhiều hơn vui mà Lộc!\n\nThơ Lộc cũng có tình mẹ con, thầy trò, bè bạn và tình yêu đôi lứa, tình yêu quê hương với những trang sử ít nhiều Lộc cũng mang trong mình khí phách hào hùng của những tráng sĩ thời xưa.\n\n“Tình sâu không nói bằng lời\nMà trong ánh mắt nụ cười cho nhau”\n\nMong rằng thơ Nguyễn Phước Lộc cũng như cuộc đời của mình, luôn may mắn, may mắn như cái tên mà Lộc đang mang đi.\n\n“Của tặng không bằng cách tặng”. Đây cũng là một cách tặng tế nhị của Lộc đến với độc giả và tùy cách “nhận” của mỗi người. Có lẽ chỉ với đôi điều đó thôi cũng đủ đánh thức bạn tri âm với những chia sẻ tự đáy lòng mình.\n\nSaigon, tháng 3/2008\nNhà báo Nguyễn Công Thụ - bút hiệu Thụ Nhân\n\n◦◦◊◦◦\n\nLỜI TÂM SỰ\nYêu thơ từ bé, đến nay mười bốn năm mới có được tập thơ xem như là người yêu đầu tiên để thương để nhớ, tôi cũng không hi vọng có được người yêu kế, một lần kỷ niệm – giữ mãi mối tình – không mong cải giá. Không phải là nhà thơ hay nghệ sĩ, tôi như một người đang đi chợt trông thấy vườn hoa bên đường, dừng chân đứng ngắm rồi đắm say hương sắc, mê trong chốc lát, tất nhiên rồi phải tỉnh để đi tiếp trọn con đường của mình.\n\nĐây là tập hợp các bài thơ rải rác trong mười bốn năm viết thơ khi không khi có nên không có chủ đề, đáng tiếc còn những bài thất lạc do tôi quên hay để vương vãi đâu đó, sau này nếu ai tìm được xin đừng để muối mặn lời yêu trong thơ tôi tan vào biển cả cuộc đời. Vì không chủ đề nên người đọc có thể thấy tạp nham, cúi mong lượng thứ, mở rộng tấm lòng để thơ thốt được nên lời như cậu học trò lần đầu lắp bắp nói tiếng yêu.\n\nTôi không sắp xếp các bài thơ theo thứ tự thời gian do không muốn có sự chỉnh chu, nếu quá chỉnh chu thì còn gì là thơ. Và mỗi giai đoạn các bài thơ có thể có vị riêng, tôi muốn xóa ranh giới thời gian để trộn chúng vào nhau nhằm dậy men của các vị xúc cảm: nồng, cay, mặn, đắng, ngọt, chua...\n\nTôi không quan tâm đến giá trị nghệ thuật, tập thơ này chỉ là những kỷ niệm, kỷ niệm một lần yêu thơ, một lần yêu người, một lần vẩn vơ, một lần tâm sự, một lần rung động, một lần cả nghĩ, một lần trăn trở hay một lần… Hầu như mỗi bài thơ đều cảm tác từ một người và tất nhiên tôi dành tặng cho người ấy – họ chính là nguồn cảm xúc hay đúng hơn họ làm ra bài thơ đó, tôi chỉ là người thể hiện lại tình cảm thiêng liêng bằng ngôn từ của riêng mình. Nhưng thơ tồn tại trong lòng người đâu bằng ngôn từ, tình ở ngoài lời – lời chưa tận ý, ngôn từ chỉ là giả tướng. Vì thế tôi lấy tên tập thơ là “Tặng”, tặng mỗi bài cho từng người mà tôi đã và đang yêu quý, và cả tập thơ này, xin dành tặng cho cuộc đời đã mang thơ lại cho tôi.\n\nCảm ơn đã đọc và cảm tạ những người đã giúp tôi hoàn thành tập thơ này.\nhttp://tang.locnguyen.net\n\nNguyễn Phước Lộc\n1993 - 2007","internal_url":"https://www.academia.edu/40762105/T%E1%BA%B7ng","translated_internal_url":"","created_at":"2019-10-28T03:11:07.380-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"book","co_author_tags":[{"id":33212021,"work_id":40762105,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Tặng"}],"downloadable_attachments":[{"id":66098293,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098293/thumbnails/1.jpg","file_name":"01.Tang.pdf","download_url":"https://www.academia.edu/attachments/66098293/download_file","bulk_download_file_name":"Tng.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098293/01.Tang-libre.pdf?1616668668=\u0026response-content-disposition=attachment%3B+filename%3DTng.pdf\u0026Expires=1738619098\u0026Signature=U9ZRoE1sPybnJcs8PUgE-DlPFvzT8fUuphx9jB9vj97Fwui~QR5667OPfT8Resg9DzSS8zsCVdT75ngroiiP1CPRq5FLEiHhQZ~t0Rqnho5-jJFQQqMjUyCoTFkDcECQX9Y15xE94DfRM3C-dLY5ANNUxFyIyl0gJkZoQ7LCsBkBT0JtG7KpLOLeOQDiAjwEis2XWQiEBiKcNMO1J95PUvcoQOsk8jsprCNRPZRYyt8G1kLX~HWBkjjv8vS6BY4mP0zk5sxMAssZbrMPEQ9bVkwjLcWll2WY0NUHiajInYNpLe94Zrvg38eKRdrM~j7hFs4wz32BGLjrKYPUmMw3Dg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tặng","translated_slug":"","page_count":33,"language":"vi","content_type":"Work","summary":"TỰA\nĐọc xong “Tặng” của nhà thơ Nguyễn Phước Lộc tôi liên tưởng đến một dòng sông nhỏ giữa trưa hè nắng gắt.\n\nDòng sông xanh nhỏ lững lờ trôi, mang trên mình những hương hoa cỏ thơm, thơ của đồng nội, có những hoa cỏ theo cơn gió mạnh lìa cành và bay theo dòng nước trong xanh.\nTrong bài “Tình Quê”, Phước Lộc đã để xen lẫn trong ước mơ của mình về một quê hương yêu dấu, đẹp đẽ là “những điều u uất nặng chìm sông sâu”, có lẽ trong đời Lộc có một điều gì đó chưa nói được thành lời, chưa trọn vẹn như mơ ước.\n\nCàng đọc thơ Phước Lộc, tôi thấy Lộc một con người đa dạng vừa mơ mộng vừa thực tế như một nhà toán học chứ không như một tiến sĩ công nghệ thông tin, vừa đa tình vừa không phải... Thơ Lộc cũng có đủ những khát vọng tuổi thơ, những tình yêu vụng dại nồng nàn, những ước mơ cháy bỏng và một điều gì đó không hiện thực... Không sao, cuộc đời vốn buồn nhiều hơn vui mà Lộc!\n\nThơ Lộc cũng có tình mẹ con, thầy trò, bè bạn và tình yêu đôi lứa, tình yêu quê hương với những trang sử ít nhiều Lộc cũng mang trong mình khí phách hào hùng của những tráng sĩ thời xưa.\n\n“Tình sâu không nói bằng lời\nMà trong ánh mắt nụ cười cho nhau”\n\nMong rằng thơ Nguyễn Phước Lộc cũng như cuộc đời của mình, luôn may mắn, may mắn như cái tên mà Lộc đang mang đi.\n\n“Của tặng không bằng cách tặng”. Đây cũng là một cách tặng tế nhị của Lộc đến với độc giả và tùy cách “nhận” của mỗi người. Có lẽ chỉ với đôi điều đó thôi cũng đủ đánh thức bạn tri âm với những chia sẻ tự đáy lòng mình.\n\nSaigon, tháng 3/2008\nNhà báo Nguyễn Công Thụ - bút hiệu Thụ Nhân\n\n◦◦◊◦◦\n\nLỜI TÂM SỰ\nYêu thơ từ bé, đến nay mười bốn năm mới có được tập thơ xem như là người yêu đầu tiên để thương để nhớ, tôi cũng không hi vọng có được người yêu kế, một lần kỷ niệm – giữ mãi mối tình – không mong cải giá. Không phải là nhà thơ hay nghệ sĩ, tôi như một người đang đi chợt trông thấy vườn hoa bên đường, dừng chân đứng ngắm rồi đắm say hương sắc, mê trong chốc lát, tất nhiên rồi phải tỉnh để đi tiếp trọn con đường của mình.\n\nĐây là tập hợp các bài thơ rải rác trong mười bốn năm viết thơ khi không khi có nên không có chủ đề, đáng tiếc còn những bài thất lạc do tôi quên hay để vương vãi đâu đó, sau này nếu ai tìm được xin đừng để muối mặn lời yêu trong thơ tôi tan vào biển cả cuộc đời. Vì không chủ đề nên người đọc có thể thấy tạp nham, cúi mong lượng thứ, mở rộng tấm lòng để thơ thốt được nên lời như cậu học trò lần đầu lắp bắp nói tiếng yêu.\n\nTôi không sắp xếp các bài thơ theo thứ tự thời gian do không muốn có sự chỉnh chu, nếu quá chỉnh chu thì còn gì là thơ. Và mỗi giai đoạn các bài thơ có thể có vị riêng, tôi muốn xóa ranh giới thời gian để trộn chúng vào nhau nhằm dậy men của các vị xúc cảm: nồng, cay, mặn, đắng, ngọt, chua...\n\nTôi không quan tâm đến giá trị nghệ thuật, tập thơ này chỉ là những kỷ niệm, kỷ niệm một lần yêu thơ, một lần yêu người, một lần vẩn vơ, một lần tâm sự, một lần rung động, một lần cả nghĩ, một lần trăn trở hay một lần… Hầu như mỗi bài thơ đều cảm tác từ một người và tất nhiên tôi dành tặng cho người ấy – họ chính là nguồn cảm xúc hay đúng hơn họ làm ra bài thơ đó, tôi chỉ là người thể hiện lại tình cảm thiêng liêng bằng ngôn từ của riêng mình. Nhưng thơ tồn tại trong lòng người đâu bằng ngôn từ, tình ở ngoài lời – lời chưa tận ý, ngôn từ chỉ là giả tướng. Vì thế tôi lấy tên tập thơ là “Tặng”, tặng mỗi bài cho từng người mà tôi đã và đang yêu quý, và cả tập thơ này, xin dành tặng cho cuộc đời đã mang thơ lại cho tôi.\n\nCảm ơn đã đọc và cảm tạ những người đã giúp tôi hoàn thành tập thơ này.\nhttp://tang.locnguyen.net\n\nNguyễn Phước Lộc\n1993 - 2007","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":66098293,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098293/thumbnails/1.jpg","file_name":"01.Tang.pdf","download_url":"https://www.academia.edu/attachments/66098293/download_file","bulk_download_file_name":"Tng.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098293/01.Tang-libre.pdf?1616668668=\u0026response-content-disposition=attachment%3B+filename%3DTng.pdf\u0026Expires=1738619098\u0026Signature=U9ZRoE1sPybnJcs8PUgE-DlPFvzT8fUuphx9jB9vj97Fwui~QR5667OPfT8Resg9DzSS8zsCVdT75ngroiiP1CPRq5FLEiHhQZ~t0Rqnho5-jJFQQqMjUyCoTFkDcECQX9Y15xE94DfRM3C-dLY5ANNUxFyIyl0gJkZoQ7LCsBkBT0JtG7KpLOLeOQDiAjwEis2XWQiEBiKcNMO1J95PUvcoQOsk8jsprCNRPZRYyt8G1kLX~HWBkjjv8vS6BY4mP0zk5sxMAssZbrMPEQ9bVkwjLcWll2WY0NUHiajInYNpLe94Zrvg38eKRdrM~j7hFs4wz32BGLjrKYPUmMw3Dg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":3581,"name":"Poetry","url":"https://www.academia.edu/Documents/in/Poetry"},{"id":10187,"name":"Love","url":"https://www.academia.edu/Documents/in/Love"}],"urls":[{"id":8879016,"url":"http://tang.locnguyen.net/"},{"id":8879017,"url":"https://allpoetry.com/list/586139-T%E1%BA%B7ng"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="14206000" id="conferencepresentations"><div class="js-work-strip profile--work_container" data-work-id="25549139"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/25549139/Hudup_A_Framework_of_E_commercial_Recommendation_Algorithms"><img alt="Research paper thumbnail of Hudup: A Framework of E-commercial Recommendation Algorithms" class="work-thumbnail" src="https://attachments.academia-assets.com/55563043/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/25549139/Hudup_A_Framework_of_E_commercial_Recommendation_Algorithms">Hudup: A Framework of E-commercial Recommendation Algorithms</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Final Program and Book of Abstracts of The 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015)</span><span>, Nov 13, 2015</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Recommendation algorithm is very important to e-commercial websites when it can provide favorite ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Recommendation algorithm is very important to e-commercial websites when it can provide favorite products to online customers, which results out an increase in sale revenue. I propose an infrastructure for e-commercial recommendation solutions. It is a middleware framework of e-commercial recommendation software, which supports scientists and software developers to build up their own recommendation algorithms with low cost, high achievement and fast speed. This report is a full description of the proposed framework, which begins with general architectures and then concentrates on programming classes. Finally, a tutorial will help readers to comprehend the framework.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="37394cac6d256026e0bc2326a7cd99ef" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":55563043,"asset_id":25549139,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/55563043/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="25549139"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="25549139"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 25549139; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=25549139]").text(description); $(".js-view-count[data-work-id=25549139]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 25549139; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='25549139']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "37394cac6d256026e0bc2326a7cd99ef" } } $('.js-work-strip[data-work-id=25549139]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":25549139,"title":"Hudup: A Framework of E-commercial Recommendation Algorithms","translated_title":"","metadata":{"abstract":"Recommendation algorithm is very important to e-commercial websites when it can provide favorite products to online customers, which results out an increase in sale revenue. I propose an infrastructure for e-commercial recommendation solutions. It is a middleware framework of e-commercial recommendation software, which supports scientists and software developers to build up their own recommendation algorithms with low cost, high achievement and fast speed. This report is a full description of the proposed framework, which begins with general architectures and then concentrates on programming classes. Finally, a tutorial will help readers to comprehend the framework.","location":"Lisbon, Portuga","publisher":"The European Project Space","event_date":{"day":13,"month":11,"year":2015,"errors":{}},"journal_name":"Final Program and Book of Abstracts of The 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015)","organization":"Institute for Systems and Technologies of Information, Control and Communication (INSTICC)","page_numbers":"56","publication_date":{"day":13,"month":11,"year":2015,"errors":{}},"publication_name":"Final Program and Book of Abstracts of The 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2015)","conference_end_date":{"day":14,"month":11,"year":2015,"errors":{}},"conference_start_date":{"day":12,"month":11,"year":2015,"errors":{}}},"translated_abstract":"Recommendation algorithm is very important to e-commercial websites when it can provide favorite products to online customers, which results out an increase in sale revenue. I propose an infrastructure for e-commercial recommendation solutions. It is a middleware framework of e-commercial recommendation software, which supports scientists and software developers to build up their own recommendation algorithms with low cost, high achievement and fast speed. This report is a full description of the proposed framework, which begins with general architectures and then concentrates on programming classes. 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Viscous dissipation and Joule heating effects are taken into account. By using the appropriate transformations for the velocity and temperature, the basic equations are reduced to a set of ordinary differential equations. The resulting nonlinear differential equations are solved by homotopy analysis method (HAM). The results are presented graphically. Variation of skin friction coefficient and Nusselt number are tabulated. The horizontal component of velocity is a decreasing function of couple stress parameter however, in the vicinity of stretching sheet the velocity component decreases but it increases away from the stretching sheet. Skin friction coefficient increases when unsteadiness and couple stress parameters are increased.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0b9c36de08d9f5fe3d0d7a10e7257f1a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":76834836,"asset_id":65097624,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/76834836/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="65097624"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="65097624"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 65097624; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=65097624]").text(description); $(".js-view-count[data-work-id=65097624]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 65097624; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='65097624']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0b9c36de08d9f5fe3d0d7a10e7257f1a" } } $('.js-work-strip[data-work-id=65097624]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":65097624,"title":"Series solution of heat transfer in thin film flow of couple stress liquid","translated_title":"","metadata":{"abstract":"Present research aims to analyze the effects of heat transfer in a thin film flow of couple stress fluid. Viscous dissipation and Joule heating effects are taken into account. By using the appropriate transformations for the velocity and temperature, the basic equations are reduced to a set of ordinary differential equations. The resulting nonlinear differential equations are solved by homotopy analysis method (HAM). The results are presented graphically. Variation of skin friction coefficient and Nusselt number are tabulated. The horizontal component of velocity is a decreasing function of couple stress parameter however, in the vicinity of stretching sheet the velocity component decreases but it increases away from the stretching sheet. Skin friction coefficient increases when unsteadiness and couple stress parameters are increased.","location":"Hanoi, Vietnam","event_date":{"day":16,"month":11,"year":2021,"errors":{}},"organization":"Institute of Physics, Vietnam Academy of Science and Technology (IOP - VAST)","publication_date":{"day":16,"month":11,"year":2021,"errors":{}},"publication_name":"The 7th International Conference on Applied \u0026 Engineering Physics (CAEP7)","conference_end_date":{"day":17,"month":11,"year":2021,"errors":{}},"conference_start_date":{"day":14,"month":11,"year":2021,"errors":{}}},"translated_abstract":"Present research aims to analyze the effects of heat transfer in a thin film flow of couple stress fluid. Viscous dissipation and Joule heating effects are taken into account. By using the appropriate transformations for the velocity and temperature, the basic equations are reduced to a set of ordinary differential equations. The resulting nonlinear differential equations are solved by homotopy analysis method (HAM). The results are presented graphically. Variation of skin friction coefficient and Nusselt number are tabulated. The horizontal component of velocity is a decreasing function of couple stress parameter however, in the vicinity of stretching sheet the velocity component decreases but it increases away from the stretching sheet. 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Viscous dissipation and Joule heating effects are taken into account. By using the appropriate transformations for the velocity and temperature, the basic equations are reduced to a set of ordinary differential equations. The resulting nonlinear differential equations are solved by homotopy analysis method (HAM). The results are presented graphically. Variation of skin friction coefficient and Nusselt number are tabulated. The horizontal component of velocity is a decreasing function of couple stress parameter however, in the vicinity of stretching sheet the velocity component decreases but it increases away from the stretching sheet. Skin friction coefficient increases when unsteadiness and couple stress parameters are increased.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":76834836,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/76834836/thumbnails/1.jpg","file_name":"69.ThinFilmFlowInCoupleStressFluid_Poster_CAEP7_ShafiqNguyen_2021.11.16.pdf","download_url":"https://www.academia.edu/attachments/76834836/download_file","bulk_download_file_name":"Series_solution_of_heat_transfer_in_thin.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/76834836/69.ThinFilmFlowInCoupleStressFluid_Poster_CAEP7_ShafiqNguyen_2021.11.16-libre.pdf?1639925905=\u0026response-content-disposition=attachment%3B+filename%3DSeries_solution_of_heat_transfer_in_thin.pdf\u0026Expires=1738797506\u0026Signature=cUZw83v7E38X1P4cpT4S25MjWKrrvuvGVDjuxqcbqih8HV~LQmj~G7GTN1AiOiZPkRfjn-XhOm-1OeIVF6liNjaz1YmixrZWPdGYDyFreLnjVSW35UV-aT0eYAcNIgmzqMfL8ad8KCAqKpzQllB2pgElY7tq2aP8FhWics2LRoUT4sUdH2Yx6Ot6oq5sAmSGWhfFcR-SXUD3wSVb6jLtzole~lrkheGSviAn2TLsafo-W1SmLJc2-9hXTYr3c71P5GT-WL1EuLeZS1TKPNFIZYS9a8AXmwvw4RQPXuhV253C8sa6n5oLA5PsWR1lmlYrNctHcYupGpQzSTfiF9LJGQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":8067,"name":"Heat Transfer","url":"https://www.academia.edu/Documents/in/Heat_Transfer"},{"id":871158,"name":"Couple Stress Fluid","url":"https://www.academia.edu/Documents/in/Couple_Stress_Fluid"},{"id":1424580,"name":"Thin Film Flow","url":"https://www.academia.edu/Documents/in/Thin_Film_Flow"},{"id":1598470,"name":"Viscous Dissipation","url":"https://www.academia.edu/Documents/in/Viscous_Dissipation"}],"urls":[{"id":15402564,"url":"https://padlet.com/CAEP7/poster/wish/1866958830"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="35428076"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/35428076/A_new_method_to_determine_separated_hyper_plane_for_non_parametric_sign_test_in_multivariate_data"><img alt="Research paper thumbnail of A new method to determine separated hyper-plane for non-parametric sign test in multivariate data" class="work-thumbnail" src="https://attachments.academia-assets.com/55289040/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/35428076/A_new_method_to_determine_separated_hyper_plane_for_non_parametric_sign_test_in_multivariate_data">A new method to determine separated hyper-plane for non-parametric sign test in multivariate data</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>STATISTICS and its INTERACTIONS with OTHER DISCIPLINES (SIOD 2013)</span><span>, Jun 5, 2013</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Non-parametric testing is very necessary in case that the statistical sample does not conform nor...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Non-parametric testing is very necessary in case that the statistical sample does not conform normal distribution or we have no knowledge about sample distribution. Sign test is a popular and effective test for non-parametric model but it cannot be applied into multivariate data in which observations are vectors because the ordering and comparative operators are not defined in n-dimension vector space. So, this research proposes a new approach to perform sign test on multivariate sample by using a hyper-plane to separate multi-dimensional observations into two sides. Therefore, it is possible for the sign test to assign plus signs and minus signs to observations in each side. Moreover, this research introduces a new method to determine the separated hyper-plane. This method is a variant of support vector machine (SVM), thus, the optimized hyper-plane is the one that contains null hypothesis and splits observations as discriminatively as possible.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="fa45c6f40c81531fed3cbbc618c355ff" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":55289040,"asset_id":35428076,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/55289040/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="35428076"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="35428076"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 35428076; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=35428076]").text(description); $(".js-view-count[data-work-id=35428076]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 35428076; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='35428076']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "fa45c6f40c81531fed3cbbc618c355ff" } } $('.js-work-strip[data-work-id=35428076]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":35428076,"title":"A new method to determine separated hyper-plane for non-parametric sign test in multivariate data","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.20886.86080","abstract":"Non-parametric testing is very necessary in case that the statistical sample does not conform normal distribution or we have no knowledge about sample distribution. 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Sign test is a popular and effective test for non-parametric model but it cannot be applied into multivariate data in which observations are vectors because the ordering and comparative operators are not defined in n-dimension vector space. So, this research proposes a new approach to perform sign test on multivariate sample by using a hyper-plane to separate multi-dimensional observations into two sides. Therefore, it is possible for the sign test to assign plus signs and minus signs to observations in each side. Moreover, this research introduces a new method to determine the separated hyper-plane. 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T. (2011). Research on Fetal Age and Weight Estimation by Two-Dimensional and Three-Dimensional Ultrasound Measures. Hanoi: Hanoi Medical Univerisy</span><span>, 2011</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Mong muốn của nghiên cứu này nhằm: - Chọn lọc được phương pháp ước lượng cân nặng thai, tuổi tha...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Mong muốn của nghiên cứu này nhằm: <br />- Chọn lọc được phương pháp ước lượng cân nặng thai, tuổi thai qua các số đo bằng siêu âm sao cho đơn giản, dễ thực hiện, chính xác. <br />- Từ giá trị trung bình về cân nặng và tuổi thai sẽ xác lập được biểu đồ phát triển về cân nặng và tuổi thai bình thường liên quan đến số đo phần thai bằng siêu âm để ứng dụng trong lâm sàng. Khi sử dụng siêu âm đo các phần thai sẽ đối chiếu lên biểu đồ phát triển nói trên và ước lượng được cân nặng hoặc tuổi thai một cách nhanh chóng, đồng thời đánh giá được tình trạng phát triển của thai.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="946210302f1c9c4d4cd7b626a752d3ac" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":56310215,"asset_id":36399171,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/56310215/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="36399171"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="36399171"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 36399171; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=36399171]").text(description); $(".js-view-count[data-work-id=36399171]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 36399171; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='36399171']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "946210302f1c9c4d4cd7b626a752d3ac" } } $('.js-work-strip[data-work-id=36399171]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":36399171,"title":"Research on Fetal Age and Weight Estimation by Two-Dimensional and Three-Dimensional Ultrasound Measures (Nghiên Cứu Phương Pháp Ước Lượng Trọng Lượng Thai, Tuổi Thai Bằng Siêu Âm Hai và Ba Chiều)","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.33184.48645","abstract":"Mong muốn của nghiên cứu này nhằm: \n- Chọn lọc được phương pháp ước lượng cân nặng thai, tuổi thai qua các số đo bằng siêu âm sao cho đơn giản, dễ thực hiện, chính xác. \n- Từ giá trị trung bình về cân nặng và tuổi thai sẽ xác lập được biểu đồ phát triển về cân nặng và tuổi thai bình thường liên quan đến số đo phần thai bằng siêu âm để ứng dụng trong lâm sàng. 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(O. Sabazova, Ed.) Eliva Press</span><span>, Feb 16, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nowadays modern society requires that every citizen always updates and improves her/his knowledge...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Nowadays modern society requires that every citizen always updates and improves her/his knowledge and skills necessary to working and researching. E-learning or distance learning gives everyone a chance to study at anytime and anywhere with full support of computer technology and network. Adaptive learning, a variant of e-learning, aims to satisfy the demand of personalization in learning. Learners’ information and characteristics such as knowledge, goal, experience, interest, and background are the most important to adaptive system. These characteristics are organized in a structure called learner model (or user model) and the system or computer software that builds up and manipulates learner model is called user modeling system or learner modeling system. In this book, I propose a learner model that consists of three essential kinds of information about learners such as knowledge, learning style and learning history. Such three characteristics form a triangle and so this learner model is called Triangular Learner Model (TLM). The book contains seven chapters, which covers mathematical features of TLM. Chapter I is a survey of user model, user modeling, and adaptive learning. Chapter II introduces the general architecture of the proposed TLM and a user modeling system named Zebra. Chapter III, IV, V describes three sub-models of TLM such as knowledge sub-model, learning style sub-model, and learning history sub-model in full of mathematical formulas and fundamental methods. Chapter VI gives some approaches to evaluate TLM and Zebra. Chapter VII summarizes the research and discusses future trend of Zebra.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e93c0b9ba95254ea8a0d817fb944a88d" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":81753511,"asset_id":35692085,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/81753511/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="35692085"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="35692085"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 35692085; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=35692085]").text(description); $(".js-view-count[data-work-id=35692085]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 35692085; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='35692085']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e93c0b9ba95254ea8a0d817fb944a88d" } } $('.js-work-strip[data-work-id=35692085]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":35692085,"title":"Mathematical Approaches to User Modeling","translated_title":"","metadata":{"abstract":"Nowadays modern society requires that every citizen always updates and improves her/his knowledge and skills necessary to working and researching. 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E-learning or distance learning gives everyone a chance to study at anytime and anywhere with full support of computer technology and network. Adaptive learning, a variant of e-learning, aims to satisfy the demand of personalization in learning. Learners’ information and characteristics such as knowledge, goal, experience, interest, and background are the most important to adaptive system. These characteristics are organized in a structure called learner model (or user model) and the system or computer software that builds up and manipulates learner model is called user modeling system or learner modeling system. In this book, I propose a learner model that consists of three essential kinds of information about learners such as knowledge, learning style and learning history. Such three characteristics form a triangle and so this learner model is called Triangular Learner Model (TLM). The book contains seven chapters, which covers mathematical features of TLM. Chapter I is a survey of user model, user modeling, and adaptive learning. Chapter II introduces the general architecture of the proposed TLM and a user modeling system named Zebra. Chapter III, IV, V describes three sub-models of TLM such as knowledge sub-model, learning style sub-model, and learning history sub-model in full of mathematical formulas and fundamental methods. Chapter VI gives some approaches to evaluate TLM and Zebra. Chapter VII summarizes the research and discusses future trend of Zebra.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":81753511,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/81753511/thumbnails/1.jpg","file_name":"01.Mum_eliva_2022.02.16_978_163_6_48538_6.pdf","download_url":"https://www.academia.edu/attachments/81753511/download_file","bulk_download_file_name":"Mathematical_Approaches_to_User_Modeling.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/81753511/01.Mum_eliva_2022.02.16_978_163_6_48538_6-libre.pdf?1646485290=\u0026response-content-disposition=attachment%3B+filename%3DMathematical_Approaches_to_User_Modeling.pdf\u0026Expires=1738797506\u0026Signature=FBa9dLZQ4Q528CcHKHDt-cQg5DCu1xyY5QTF-zDn8ALXe5YHz9pDzXPNePcJEk5oLMumLKKmCNMEOhBzXct6wo-ek7IbSHIpLiMCOQzd9-GLH-0SLpOzhqNJ3jOnstXxF2ujTo9gPSpqj6JNgbUtI6y9J-Ez9x0h-Sz14pJ~jNDBkPsg1ZdNAA9SxHJnBUpMAQjFQUvZyLvUf2QZ7kL-78PQjl~UL~X5lhX9ebQIJdlbEIvYZRnohDma1Wuy33w5pbzzb9EdkGordj3kpykvQFEVUl8iIAQn7ZSkdy9kFOuyo-3rH-fAZfwpSRRv8qsRNeIIfkyVJf7K2dKNZN-zHA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science"},{"id":449,"name":"Software Engineering","url":"https://www.academia.edu/Documents/in/Software_Engineering"},{"id":922,"name":"Education","url":"https://www.academia.edu/Documents/in/Education"},{"id":1385,"name":"User Modeling","url":"https://www.academia.edu/Documents/in/User_Modeling"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":2009,"name":"Data Mining","url":"https://www.academia.edu/Documents/in/Data_Mining"},{"id":41815,"name":"Applied Probability","url":"https://www.academia.edu/Documents/in/Applied_Probability"}],"urls":[{"id":18264792,"url":"https://www.elivapress.com/en/book/book-6035512576"}]}, dispatcherData: dispatcherData }); 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Nguyễn Phước Lộc - Hoàng Đức Tâm - Nhật Quỳnh...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Cảm ơn đã nghe album <a href="https://youtu.be/XVdn_CyAXHU" rel="nofollow">https://youtu.be/XVdn_CyAXHU</a>. <br />Nguyễn Phước Lộc - Hoàng Đức Tâm - Nhật Quỳnh - Thu Thủy. <br />2022.04.14</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="cdd6c07913d21dbab6f37e7029555de7" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":84471703,"asset_id":76956178,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/84471703/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="76956178"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="76956178"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 76956178; 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data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Album ngâm thơ “Lục Bát Mấy Lần Thương” mở bằng tình yêu nồng nàn ngờ nghệch với thơ và kết bằng lời cảm tạ với thơ và người yêu thơ.<br />Cảm ơn đã nghe album <a href="https://youtu.be/_ckSmDJ6__c" rel="nofollow">https://youtu.be/_ckSmDJ6__c</a><br /><br />Nguyễn Phước Lộc - Ngọc Sang<br />2019/11/25</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="b2271bfb2e8b6c2f50acf371363cb1da" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098070,"asset_id":42124385,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098070/download_file?s=profile"><span><i class="fa 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class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Recitation album “Cổ tích trái tim”, Loc Nguyen's Academic Network</span><span>, Jan 11, 2020</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Khi nghe nghệ sĩ Ngọc Sang ngâm thơ, chúng ta có cảm nhận ông đang kể câu chuyện cổ tích về tình ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Khi nghe nghệ sĩ Ngọc Sang ngâm thơ, chúng ta có cảm nhận ông đang kể câu chuyện cổ tích về tình yêu bất diệt của những bài thơ cũng là những mảnh đời trôi trong nhân gian. Vậy album ngâm thơ này có tên “Cổ tích trái tim”.<br />Cảm ơn đã nghe album <a href="https://youtu.be/0TCS9Rbvt6U" rel="nofollow">https://youtu.be/0TCS9Rbvt6U</a><br /><br />Nguyễn Phước Lộc - Ngọc Sang<br />2020/01/11</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d357f17fae3ad8dad4de023d3a8ac2d4" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098047,"asset_id":42124474,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098047/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="42124474"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="42124474"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 42124474; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=42124474]").text(description); $(".js-view-count[data-work-id=42124474]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 42124474; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='42124474']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d357f17fae3ad8dad4de023d3a8ac2d4" } } $('.js-work-strip[data-work-id=42124474]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":42124474,"title":"Recitation album “Cổ tích trái tim”","translated_title":"","metadata":{"abstract":"Khi nghe nghệ sĩ Ngọc Sang ngâm thơ, chúng ta có cảm nhận ông đang kể câu chuyện cổ tích về tình yêu bất diệt của những bài thơ cũng là những mảnh đời trôi trong nhân gian. 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Thân ái kính chào quý thính giả cùng những người thân thương. “Mư...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tiếng thơ “Nguyễn Phước Lộc”.<br /><br />Thân ái kính chào quý thính giả cùng những người thân thương.<br /><br />“Mười năm khổ luyện làm người<br />Lòng thành có chạm đến trời mây không<br />Đời người một khúc quanh sông<br />Côn trùng rả rích chạnh lòng đêm khuya !”<br /><br />Thời gian vô tình trôi cứ trôi, lòng người vẫn rộng mở mến thương muôn lối, còn trao tặng cho nhau kỉ niệm luyến lưu đến suốt đời chưa phai hình bóng cuộc tình trong tâm não.<br /><br />“Yêu người từ dạo ấy<br />Rung động suốt mùa thơ<br />Tặng người giây phút nhớ<br />Tặng luôn hồn mộng mơ.”<br /><br />Một mai rồi cũng qua đi, cảm xúc còn ở lại và cảm xúc nào là thiêng liêng xin gởi tặng hết cho người cho tình muôn thuở xưa sau.<br /><br />Quý vị và các bạn vừa thưởng thức những bài thơ mang chủ đề “Tặng” của tác giả Nguyễn Phước Lộc qua các giọng ngâm quen thuộc trên thi đàn thành phố: Hồng Vân, Bích Ngọc, Lê Hương và Ngô Đình Long. Phần nhạc đệm: sáo trúc Thanh Bình, đàn tranh Minh Thành, đàn bầu Thúy Hạnh. Xin chân thành cảm ơn và hẹn gặp lại.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="ac9375ae08660532b8ccfbb28f63f300" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098112,"asset_id":40786164,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098112/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40786164"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40786164"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40786164; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40786164]").text(description); $(".js-view-count[data-work-id=40786164]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40786164; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40786164']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "ac9375ae08660532b8ccfbb28f63f300" } } $('.js-work-strip[data-work-id=40786164]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40786164,"title":"Recitation album \"Tặng\"","translated_title":"","metadata":{"abstract":"Tiếng thơ “Nguyễn Phước Lộc”.\n\nThân ái kính chào quý thính giả cùng những người thân thương.\n\n“Mười năm khổ luyện làm người\nLòng thành có chạm đến trời mây không\nĐời người một khúc quanh sông\nCôn trùng rả rích chạnh lòng đêm khuya !”\n\nThời gian vô tình trôi cứ trôi, lòng người vẫn rộng mở mến thương muôn lối, còn trao tặng cho nhau kỉ niệm luyến lưu đến suốt đời chưa phai hình bóng cuộc tình trong tâm não.\n\n“Yêu người từ dạo ấy\nRung động suốt mùa thơ\nTặng người giây phút nhớ\nTặng luôn hồn mộng mơ.”\n\nMột mai rồi cũng qua đi, cảm xúc còn ở lại và cảm xúc nào là thiêng liêng xin gởi tặng hết cho người cho tình muôn thuở xưa sau.\n\nQuý vị và các bạn vừa thưởng thức những bài thơ mang chủ đề “Tặng” của tác giả Nguyễn Phước Lộc qua các giọng ngâm quen thuộc trên thi đàn thành phố: Hồng Vân, Bích Ngọc, Lê Hương và Ngô Đình Long. 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Thân ái kính chào quý thính giả cùng ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Tiếng thơ “Lục Bát Truyền Nhân”, tác giả Nguyễn Phước Lộc.<br /><br />Thân ái kính chào quý thính giả cùng các bạn yêu thơ khắp bốn phương.<br />Yêu thơ từ bé, đến nay mười bốn năm mới có được tiếng thơ này, xem như là người yêu đầu tiên để thương để nhớ, Phước Lộc cũng không hi vọng có được người yêu kế, một lần kỷ niệm – giữ mãi mối tình – không mong cải giá. Không phải là nhà thơ hay nghệ sĩ, tác giả như một người đang đi chợt trông thấy vườn hoa bên đường, dừng chân đứng ngắm rồi đắm say hương sắc, mê trong chốc lát, tất nhiên rồi phải tỉnh để đi tiếp trọn con đường của mình.<br /><br />Tiếng thơ này chỉ là những kỷ niệm, kỷ niệm một lần yêu thơ, một lần yêu người, một lần vẩn vơ, một lần tâm sự, một lần rung động, một lần cả nghĩ, một lần trăn trở hay một lần… Thơ tồn tại trong lòng người đâu bằng ngôn từ, tình ở ngoài lời – lời chưa tận ý, ngôn từ chỉ là giả tướng. Tác giả Phước Lộc xin chân thành dành tặng cho cuộc đời đã mang lại thơ, rong chơi đi vào cõi nhớ mai sau.<br /><br />Nguyễn Phước Lộc - Ngô Đình Long.<br /><br />2015</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="23174f6bc51473706b570642fcaea0c3" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":66098093,"asset_id":40786472,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/66098093/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="40786472"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="40786472"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 40786472; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=40786472]").text(description); $(".js-view-count[data-work-id=40786472]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 40786472; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='40786472']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "23174f6bc51473706b570642fcaea0c3" } } $('.js-work-strip[data-work-id=40786472]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":40786472,"title":"Recitation album \"Lục bát truyền nhân\"","translated_title":"","metadata":{"abstract":"Tiếng thơ “Lục Bát Truyền Nhân”, tác giả Nguyễn Phước Lộc.\n\nThân ái kính chào quý thính giả cùng các bạn yêu thơ khắp bốn phương.\nYêu thơ từ bé, đến nay mười bốn năm mới có được tiếng thơ này, xem như là người yêu đầu tiên để thương để nhớ, Phước Lộc cũng không hi vọng có được người yêu kế, một lần kỷ niệm – giữ mãi mối tình – không mong cải giá. 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Tác giả Phước Lộc xin chân thành dành tặng cho cuộc đời đã mang lại thơ, rong chơi đi vào cõi nhớ mai sau.\n\nNguyễn Phước Lộc - Ngô Đình Long.\n\n2015","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":66098093,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/66098093/thumbnails/1.jpg","file_name":"TiengThoLucBatTruyenNhan.pdf","download_url":"https://www.academia.edu/attachments/66098093/download_file","bulk_download_file_name":"Recitation_album_Lc_bat_truyn_nhan.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/66098093/TiengThoLucBatTruyenNhan-libre.pdf?1616665369=\u0026response-content-disposition=attachment%3B+filename%3DRecitation_album_Lc_bat_truyn_nhan.pdf\u0026Expires=1738797507\u0026Signature=YoLOsfySMVYK~Tr8NzXv8rXdOH-qYt~Lw3~q~qEIyCqGzgqrJhKsTov48l3iaxTptsLd0ov0F~riqwM4flFVcl1RiMNV~k7FqoyWYRk3ag6v3ig1eSaFcybw1iqJPoU~aDFgqNif8JDY5kjiauWseZgepVlP5noSyVzLzfxhHbRvprk9Mk0wMfCHXRMTTZv2L7lv~ep-Pup-AF7j38kZudMy9p4GS3RjmKus2Xc427FnMFzs9pL0Wu5Qhunb~q8IX~8yvRrMQYA2KwI4IS-7Mk~uu3N9BKGqi5gTAzFsqpkPKNX0ijuJ64VvFm7icALPYyLJ8qGqS8zse-gRAxj-MA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":671,"name":"Music","url":"https://www.academia.edu/Documents/in/Music"},{"id":3581,"name":"Poetry","url":"https://www.academia.edu/Documents/in/Poetry"},{"id":10187,"name":"Love","url":"https://www.academia.edu/Documents/in/Love"}],"urls":[{"id":8880434,"url":"http://lucbat.locnguyen.net/"},{"id":8880435,"url":"http://www.locnguyen.net/art/music/lucbat"},{"id":8965733,"url":"https://youtu.be/waf0OMTyFRU"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> </div><div class="profile--tab_content_container js-tab-pane tab-pane" data-section-id="8555845" id="drafts"><div class="js-work-strip profile--work_container" data-work-id="124938498"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/124938498/H%E1%BB%8Dc_thuy%E1%BA%BFt_%C3%BD_ni%E1%BB%87m_v%C3%A0_tri%E1%BA%BFt_h%E1%BB%8Dc_ph%C3%A1p_quy%E1%BB%81n"><img alt="Research paper thumbnail of Học thuyết ý niệm và triết học pháp quyền" class="work-thumbnail" src="https://attachments.academia-assets.com/119700047/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/124938498/H%E1%BB%8Dc_thuy%E1%BA%BFt_%C3%BD_ni%E1%BB%87m_v%C3%A0_tri%E1%BA%BFt_h%E1%BB%8Dc_ph%C3%A1p_quy%E1%BB%81n">Học thuyết ý niệm và triết học pháp quyền</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>ResearchGate preprint</span><span>, Oct 22, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Thế giới là ý niệm, con người là ý niệm, cái cây là ý niệm, tiên đề là ý niệm, tất cả mọi thứ đều...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Thế giới là ý niệm, con người là ý niệm, cái cây là ý niệm, tiên đề là ý niệm, tất cả mọi thứ đều là ý niệm. Mỗi ý niệm đều “là”, “sống trong” hay “ảo tưởng trong” bản sao thế giới của riêng mình mà mỗi bản sao này không thật mà cũng thật, đó là những bong bóng. Chỉ có ta với tư cách là ý niệm bằng ý lực (free will) biện minh tồn tại cho chính ta biến chuyển thành biện minh tồn tại cho vô số ý niệm khác nhưng bong bóng ý niệm khác không thể biết (không tồn tại) đối với ta, đây chính là trọng tâm của học thuyết ý niệm mà trong đó: ý thức và vật chất là một hay nói chiết trung rằng ranh giới giữa vật chất và ý thức đang nhòa dần. Triết học pháp quyền nghiên cứu ý niệm pháp quyền đúng thật làm tham chiếu cho pháp quyền thực định và ý niệm pháp quyền có ý chí tự do (free will) làm biện minh tồn tại. Bài nghiên cứu này gồm hai mục tiêu: 1) phát biểu và cố gắng chứng minh học thuyết ý niệm bởi/và đối sánh với triết học pháp quyền Hegel, và 2) kết nối học thuyết ý niệm với triết học pháp quyền Hegel.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="db970f72d917566e2347b66e86de375e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":119700047,"asset_id":124938498,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/119700047/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="124938498"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="124938498"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 124938498; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=124938498]").text(description); $(".js-view-count[data-work-id=124938498]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 124938498; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='124938498']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "db970f72d917566e2347b66e86de375e" } } $('.js-work-strip[data-work-id=124938498]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":124938498,"title":"Học thuyết ý niệm và triết học pháp quyền","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.25462.20807","abstract":"Thế giới là ý niệm, con người là ý niệm, cái cây là ý niệm, tiên đề là ý niệm, tất cả mọi thứ đều là ý niệm. Mỗi ý niệm đều “là”, “sống trong” hay “ảo tưởng trong” bản sao thế giới của riêng mình mà mỗi bản sao này không thật mà cũng thật, đó là những bong bóng. Chỉ có ta với tư cách là ý niệm bằng ý lực (free will) biện minh tồn tại cho chính ta biến chuyển thành biện minh tồn tại cho vô số ý niệm khác nhưng bong bóng ý niệm khác không thể biết (không tồn tại) đối với ta, đây chính là trọng tâm của học thuyết ý niệm mà trong đó: ý thức và vật chất là một hay nói chiết trung rằng ranh giới giữa vật chất và ý thức đang nhòa dần. Triết học pháp quyền nghiên cứu ý niệm pháp quyền đúng thật làm tham chiếu cho pháp quyền thực định và ý niệm pháp quyền có ý chí tự do (free will) làm biện minh tồn tại. Bài nghiên cứu này gồm hai mục tiêu: 1) phát biểu và cố gắng chứng minh học thuyết ý niệm bởi/và đối sánh với triết học pháp quyền Hegel, và 2) kết nối học thuyết ý niệm với triết học pháp quyền Hegel.","publisher":"ResearchGate","event_date":{"day":22,"month":10,"year":2024,"errors":{}},"journal_name":"ResearchGate preprint","organization":"ResearchGate","publication_date":{"day":22,"month":10,"year":2024,"errors":{}},"publication_name":"ResearchGate preprint","conference_end_date":{"day":22,"month":10,"year":2024,"errors":{}},"conference_start_date":{"day":22,"month":10,"year":2024,"errors":{}}},"translated_abstract":"Thế giới là ý niệm, con người là ý niệm, cái cây là ý niệm, tiên đề là ý niệm, tất cả mọi thứ đều là ý niệm. Mỗi ý niệm đều “là”, “sống trong” hay “ảo tưởng trong” bản sao thế giới của riêng mình mà mỗi bản sao này không thật mà cũng thật, đó là những bong bóng. Chỉ có ta với tư cách là ý niệm bằng ý lực (free will) biện minh tồn tại cho chính ta biến chuyển thành biện minh tồn tại cho vô số ý niệm khác nhưng bong bóng ý niệm khác không thể biết (không tồn tại) đối với ta, đây chính là trọng tâm của học thuyết ý niệm mà trong đó: ý thức và vật chất là một hay nói chiết trung rằng ranh giới giữa vật chất và ý thức đang nhòa dần. Triết học pháp quyền nghiên cứu ý niệm pháp quyền đúng thật làm tham chiếu cho pháp quyền thực định và ý niệm pháp quyền có ý chí tự do (free will) làm biện minh tồn tại. 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Bài nghiên cứu này gồm hai mục tiêu: 1) phát biểu và cố gắng chứng minh học thuyết ý niệm bởi/và đối sánh với triết học pháp quyền Hegel, và 2) kết nối học thuyết ý niệm với triết học pháp quyền Hegel.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":119700047,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119700047/thumbnails/1.jpg","file_name":"17.HocThuyetYNiem_ResearchGate_2024.11.20.pdf","download_url":"https://www.academia.edu/attachments/119700047/download_file","bulk_download_file_name":"Hc_thuyt_y_nim_va_trit_hc_phap_quy.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119700047/17.HocThuyetYNiem_ResearchGate_2024.11.20-libre.pdf?1732091589=\u0026response-content-disposition=attachment%3B+filename%3DHc_thuyt_y_nim_va_trit_hc_phap_quy.pdf\u0026Expires=1738797507\u0026Signature=OJ1YUajTJEEoTqFYwSzki8z3zXZDYHUS5uYkUkKLp-e~~5uMll5sqtfDule2aCcHX2WCsY8uQPsrfOKC9gOSF~FxRdq7SeIz9WNqCnaEiKFaxZan2SGuska~OcEX-ymx0~LC8zZffX2GCI0YI6CCVPQnqeDzxg~QpW8LXzflLBc8De5yNmfjmDMbPZQfxCBzWRjRLSNC4GmcjK9IlKHtYipz3dQ1KJ2JElBbcM5T3QA~JhsDuNhLVKltCYneQ952tZyC-xEXbSKXDpXnSYd4AshAiBKQNjL3x71Kt-Xr3dRVS98vjMNzxMtLvH0zXwwhnIImGZudgCOCvGPZlztmbQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[],"urls":[]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="123613881"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/123613881/Tutorial_on_Deep_Transformer"><img alt="Research paper thumbnail of Tutorial on Deep Transformer" class="work-thumbnail" src="https://attachments.academia-assets.com/118005290/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/123613881/Tutorial_on_Deep_Transformer">Tutorial on Deep Transformer</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Generative AI eJournal</span><span>, Sep 3, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Development of transformer is a far progressive step in the long journeys of both generative arti...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Development of transformer is a far progressive step in the long journeys of both generative artificial intelligence (GenAI) and statistical translation machine (STM) with support of deep neural network (DNN), in which STM can be known as interesting result of GenAI because of encoder-decoder mechanism for sequence generation built in transformer. But why is transformer being preeminent in GenAI and STM? Firstly, transformer has a so-called self-attention mechanism that discovers contextual meaning of every token in sequence, which contributes to reduce ambiguousness. Secondly, transformer does not concern ordering of tokens in sequence, which allows to train transformer from many parts of sequences in parallel. Thirdly, the third reason which is result of the two previous reasons is that transformer can be trained from large corpus with high accuracy as well as highly computational performance. Moreover, transformer is implemented by DNN which is one of important and effective approaches in artificial intelligence (AI) in recent time. Although transformer is preeminent because of its good consistency, it is not easily understandable. Therefore, this technical report aims to describe transformer with explanations which are as easily understandable as possible.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="7307def5fcff1335dc7707f356b4273c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118005290,"asset_id":123613881,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118005290/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="123613881"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="123613881"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 123613881; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=123613881]").text(description); $(".js-view-count[data-work-id=123613881]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 123613881; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='123613881']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "7307def5fcff1335dc7707f356b4273c" } } $('.js-work-strip[data-work-id=123613881]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":123613881,"title":"Tutorial on Deep Transformer","translated_title":"","metadata":{"doi":"10.2139/ssrn.4908234","issue":"84","volume":"2","abstract":"Development of transformer is a far progressive step in the long journeys of both generative artificial intelligence (GenAI) and statistical translation machine (STM) with support of deep neural network (DNN), in which STM can be known as interesting result of GenAI because of encoder-decoder mechanism for sequence generation built in transformer. But why is transformer being preeminent in GenAI and STM? Firstly, transformer has a so-called self-attention mechanism that discovers contextual meaning of every token in sequence, which contributes to reduce ambiguousness. Secondly, transformer does not concern ordering of tokens in sequence, which allows to train transformer from many parts of sequences in parallel. Thirdly, the third reason which is result of the two previous reasons is that transformer can be trained from large corpus with high accuracy as well as highly computational performance. Moreover, transformer is implemented by DNN which is one of important and effective approaches in artificial intelligence (AI) in recent time. Although transformer is preeminent because of its good consistency, it is not easily understandable. Therefore, this technical report aims to describe transformer with explanations which are as easily understandable as possible.","publisher":"Social Science Research Network (SSRN)","event_date":{"day":3,"month":9,"year":2024,"errors":{}},"ai_title_tag":"Understanding the Deep Transformer Model","journal_name":"Generative AI eJournal","organization":"Social Science Research Network (SSRN)","page_numbers":"1-28","publication_date":{"day":3,"month":9,"year":2024,"errors":{}},"publication_name":"Generative AI eJournal","conference_end_date":{"day":3,"month":9,"year":2024,"errors":{}},"conference_start_date":{"day":3,"month":9,"year":2024,"errors":{}}},"translated_abstract":"Development of transformer is a far progressive step in the long journeys of both generative artificial intelligence (GenAI) and statistical translation machine (STM) with support of deep neural network (DNN), in which STM can be known as interesting result of GenAI because of encoder-decoder mechanism for sequence generation built in transformer. But why is transformer being preeminent in GenAI and STM? Firstly, transformer has a so-called self-attention mechanism that discovers contextual meaning of every token in sequence, which contributes to reduce ambiguousness. Secondly, transformer does not concern ordering of tokens in sequence, which allows to train transformer from many parts of sequences in parallel. Thirdly, the third reason which is result of the two previous reasons is that transformer can be trained from large corpus with high accuracy as well as highly computational performance. Moreover, transformer is implemented by DNN which is one of important and effective approaches in artificial intelligence (AI) in recent time. Although transformer is preeminent because of its good consistency, it is not easily understandable. Therefore, this technical report aims to describe transformer with explanations which are as easily understandable as possible.","internal_url":"https://www.academia.edu/123613881/Tutorial_on_Deep_Transformer","translated_internal_url":"","created_at":"2024-09-06T06:03:45.458-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":42337026,"work_id":123613881,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Tutorial on Deep Transformer"}],"downloadable_attachments":[{"id":118005290,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118005290/thumbnails/1.jpg","file_name":"87.TutorialTransformer_SSRN_id4908234_2024.09.03.pdf","download_url":"https://www.academia.edu/attachments/118005290/download_file","bulk_download_file_name":"Tutorial_on_Deep_Transformer.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118005290/87.TutorialTransformer_SSRN_id4908234_2024.09.03-libre.pdf?1725631484=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_Deep_Transformer.pdf\u0026Expires=1738797507\u0026Signature=FIZ1hhIWMtFK6-71ALoaKSMwDSlvqRAf0owT7Y-e8tSnxYNcHbmx8CGsBIo-kILqVC1TJXybG4RX6ry1b9u19j~oKoG8-utfAMEUep2~NIGp29oXYmwuLJHfrSBfHRuscP7UXIQK-SAN6~qcviMuIYfpMhBN7aSjHGoRIiXc7FWle2o4ocCfoNF9ve~F2aVi6cIifWhjE2HkRGywwrIDorRkXgeTzFYkez0-5Uvkvlmnqm1GD9V3D3eMUCdD1O9NdCx36MALfsuU~OPaLJOmq5iq2TLmuUn1ZdtRQ-Ca2bD1ip8YtcycLdquQ5r0jH8q7X-9MjGImrzulAHctc10gw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":119679062,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119679062/thumbnails/1.jpg","file_name":"87.TutorialTransformer_Aytin_CSE010104_2024.10.08.pdf","download_url":"https://www.academia.edu/attachments/119679062/download_file","bulk_download_file_name":"Tutorial_on_Deep_Transformer.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119679062/87.TutorialTransformer_Aytin_CSE010104_2024.10.08-libre.pdf?1732007464=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_Deep_Transformer.pdf\u0026Expires=1738797507\u0026Signature=EHxbtK7QIOF-r1V0XeS5cUcy~nqriBvyMgsPiDoZ0EVkAZMoKF9VXEd5dLg~ZDeq6RiCKWUz3l~Wn~M-0iHLY5fSwwvm8Dfpe30jRC5eMSJcxV~Cma6tJlcT3tTnzcNsdO4NlIjQP8kWP0R7nkhHeHPaIDj4b4a~pYHCZPr4zmqluz0I-fylQEU3qBUq93S8LV3vg4S2DqdDnBF-w80PFzBrYRIURak30j8Fm6IO7c8D32LfmvYZjRurLCFguX-oZSsl4DAxfv4-YRJAWFGJwEm-i1C8b7PrqL7ArJXZHIP~JfCnUg3qeAO7ZF4otjTsS8-EVKNPaIQgeXkZCqgOpg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tutorial_on_Deep_Transformer","translated_slug":"","page_count":28,"language":"en","content_type":"Work","summary":"Development of transformer is a far progressive step in the long journeys of both generative artificial intelligence (GenAI) and statistical translation machine (STM) with support of deep neural network (DNN), in which STM can be known as interesting result of GenAI because of encoder-decoder mechanism for sequence generation built in transformer. But why is transformer being preeminent in GenAI and STM? Firstly, transformer has a so-called self-attention mechanism that discovers contextual meaning of every token in sequence, which contributes to reduce ambiguousness. Secondly, transformer does not concern ordering of tokens in sequence, which allows to train transformer from many parts of sequences in parallel. Thirdly, the third reason which is result of the two previous reasons is that transformer can be trained from large corpus with high accuracy as well as highly computational performance. Moreover, transformer is implemented by DNN which is one of important and effective approaches in artificial intelligence (AI) in recent time. Although transformer is preeminent because of its good consistency, it is not easily understandable. Therefore, this technical report aims to describe transformer with explanations which are as easily understandable as possible.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":118005290,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118005290/thumbnails/1.jpg","file_name":"87.TutorialTransformer_SSRN_id4908234_2024.09.03.pdf","download_url":"https://www.academia.edu/attachments/118005290/download_file","bulk_download_file_name":"Tutorial_on_Deep_Transformer.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118005290/87.TutorialTransformer_SSRN_id4908234_2024.09.03-libre.pdf?1725631484=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_Deep_Transformer.pdf\u0026Expires=1738797508\u0026Signature=SV1qkuhwLQu9gX-7lpggLaGTXc3pzGwQpmbnzWWCbRTT3LJZtGfkFKExKOT-Lw8tZZdRc6vpGZPBCxwLHXs0LTMXxRZCwO57TlITOuTLr3DZ39GXt1vBeMKxFdaCq7zAU27eyqKgSxzNVG5aMcUJD0yxq1CTFo4CDX-yZdnr7ohvqfXD7ezYULgLPTCRJksVNAGwMf4tEIH4beg-rq48ANfDN3tIEkGMUJA23eDJ5P~5GO7sSwpuClxhxiw5hs0LKveiHAJDL~YFAjYYpWDxO7c~jWR8V7k3hnFQDsFwqyQiJfp2~fzOZ-5UBtBDfeWLhB7-bHNqE790AuJNy75bEQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":119679062,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/119679062/thumbnails/1.jpg","file_name":"87.TutorialTransformer_Aytin_CSE010104_2024.10.08.pdf","download_url":"https://www.academia.edu/attachments/119679062/download_file","bulk_download_file_name":"Tutorial_on_Deep_Transformer.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/119679062/87.TutorialTransformer_Aytin_CSE010104_2024.10.08-libre.pdf?1732007464=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_Deep_Transformer.pdf\u0026Expires=1738797508\u0026Signature=gtL9SGBLTTvVqzwjGLLGnSREKk783N1ogaXyVj-hqiLvGxhQfpVPoYiG5HglAmibPrDLtRDZEdnlAr8i0e5bWMqjZ9IqIOBUwAaJmeQ0X~lzJ9PyMsX~OBhE7P3e0apqo3s-4N7iFBLHx0F~NPrjEeE7jaJuq~oLPvPYD3a07v1WPr-kXQ4aqrZ3mCsM96n600UKx7ztQSpqSDnhlzqH0ZO~jafPZ20HJfC6X3RHDN-MsUbr66e8RyfpgaC9HHj6J7q1IUN5T1EGQp4zqCz9hoz5KhdclnTRxS2kCDssuFugvmqP1olA-d1jz3IqkQ-IbDGHNg1rQ56zXdISUBllJQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":17553,"name":"Statistical Machine Translation","url":"https://www.academia.edu/Documents/in/Statistical_Machine_Translation"},{"id":304709,"name":"Self-Attention","url":"https://www.academia.edu/Documents/in/Self-Attention"},{"id":890015,"name":"Deep Neural Networks","url":"https://www.academia.edu/Documents/in/Deep_Neural_Networks"},{"id":4240030,"name":"Large language models","url":"https://www.academia.edu/Documents/in/Large_language_models"},{"id":4671047,"name":"Generative artificial intelligence (AI)","url":"https://www.academia.edu/Documents/in/Generative_artificial_intelligence_AI_"}],"urls":[{"id":44502168,"url":"https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4908234"},{"id":45695719,"url":"https://aytinpublications.com/Open-Access-Journal-of-Computer-Science-and-Engineering/Files/CSE010104.html"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="121221958"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/121221958/Tutorial_on_deep_generative_model"><img alt="Research paper thumbnail of Tutorial on deep generative model" class="work-thumbnail" src="https://attachments.academia-assets.com/116158465/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121221958/Tutorial_on_deep_generative_model">Tutorial on deep generative model</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Preprints</span><span>, May 21, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Artificial intelligence (AI) is a current trend in computer science, which extends itself its ama...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Artificial intelligence (AI) is a current trend in computer science, which extends itself its amazing capacities to other technologies such as mechatronics and robotics. Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. This technical report focuses on deep generative model (DGM) for digital content generation, which is a short summary of approaches to implement DGMs. Researchers can read this work as an introduction to DGM with easily understandable explanations.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="72f1c98e5ef4e04720d439d1b6ac85d9" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116158465,"asset_id":121221958,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116158465/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121221958"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121221958"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121221958; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121221958]").text(description); $(".js-view-count[data-work-id=121221958]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121221958; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121221958']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "72f1c98e5ef4e04720d439d1b6ac85d9" } } $('.js-work-strip[data-work-id=121221958]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121221958,"title":"Tutorial on deep generative model","translated_title":"","metadata":{"doi":"10.20944/preprints202405.1348.v1","abstract":"Artificial intelligence (AI) is a current trend in computer science, which extends itself its amazing capacities to other technologies such as mechatronics and robotics. Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. 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Going beyond technological applications, the philosophy behind AI is that there is a vague and potential convergence of artificial manufacture and natural world although the limiting approach may be still very far away, but why? The implicit problem is that Darwin theory of evolution focuses on natural world where breeding conservation is the cornerstone of the existence of creature world but there is no similar concept of breeding conservation in artificial world whose things are created by human. However, after developing for a long time until now, AI issues an interesting concept of generation in which artifacts created by computer science can derive their new generations inheriting their aspects / characteristics. Such generated artifacts make us look back on offsprings by the process of breeding conservation in natural world. Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. 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Therefore, it is possible to think that AI generation, which is a recent subject of AI, is a significant development in computer science as well as high-tech domain. AI generation does not help us to reach near biological evolution even in the case that AI can combine with biological technology but, AI generation can help us to extend our viewpoint about Darwin theory of evolution as well as there may exist some uncertain relationship between man-made world and natural world. Anyhow AI generation is a current important subject in AI and there are two main generative models in computer science: 1) generative model that applies large language model into generating natural language texts understandable by human and 2) generative model that applies deep neural network into generating digital content such as sound, image, and video. 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Bài viết này chưa thể tìm hiểu lý do biến đổi khí hậu trở nên cấp thiết đến mức xuất hiện khắp nơi trên phương tiện truyền thông nhưng điểm sáng là năng lượng tái tạo được phát triển mạnh mẽ và nhanh chóng, theo đó hydrogen xanh phái sinh từ điện mặt trời và điện gió có thể thay đổi cuộc chơi trên thị trường năng lượng, cạnh tranh và dần có thể thay thế năng lượng hóa thạch. Nếu không có hydrogen hoặc giả sử chất tương tự hydrogen thì năng lượng tái tạo có thể chưa sớm thay thế năng lượng hóa thạch với ước số năm dự trữ còn lại của dầu mỏ, khí thiên nhiên và than lần lượt khoảng 40 năm, 60 năm và 150 năm tính từ thập niên 2000, vì vậy cam kết phát thải ròng bằng 0 (net zero) trước năm 2050 cũng gần như chạm đến giới hạn dự trữ của năng lượng hóa thạch ngoài vấn đề nghiêm trọng về mức tăng nhiệt độ trái đất có thể vượt mức 1.5 độ C vào năm 2100 cuối thế kỷ này so với thời kỳ tiền công nghiệp. Bài viết này tập trung tìm hiểu những nét cơ bản về hydrogen, hy vọng những nhà nghiên cứu chuyên sâu, doanh nghiệp và các nhà hoạch định chính sách quan tâm đến năng lượng tái tạo cũng như hydrogen.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="14e88996b5efe86d7b1545f9ecab1ff8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116158378,"asset_id":121221891,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116158378/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121221891"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121221891"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121221891; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121221891]").text(description); $(".js-view-count[data-work-id=121221891]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121221891; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121221891']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "14e88996b5efe86d7b1545f9ecab1ff8" } } $('.js-work-strip[data-work-id=121221891]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121221891,"title":"Tổng quan năng lượng hydrogen","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.16814.93761","abstract":"Khoa học công nghệ dường như đang bất ngờ hụt hơi trong cuộc chạy đua với biến đổi khí hậu tuy được cảnh báo nhiều thập niên trước nhưng mức độ khốc liệt lúc này trở nên rõ ràng và công nghệ vũ trụ chưa có dấu hiệu cho thấy khả năng của sự di cư hoặc khai thác tài nguyên ngoài không gian. Bài viết này chưa thể tìm hiểu lý do biến đổi khí hậu trở nên cấp thiết đến mức xuất hiện khắp nơi trên phương tiện truyền thông nhưng điểm sáng là năng lượng tái tạo được phát triển mạnh mẽ và nhanh chóng, theo đó hydrogen xanh phái sinh từ điện mặt trời và điện gió có thể thay đổi cuộc chơi trên thị trường năng lượng, cạnh tranh và dần có thể thay thế năng lượng hóa thạch. Nếu không có hydrogen hoặc giả sử chất tương tự hydrogen thì năng lượng tái tạo có thể chưa sớm thay thế năng lượng hóa thạch với ước số năm dự trữ còn lại của dầu mỏ, khí thiên nhiên và than lần lượt khoảng 40 năm, 60 năm và 150 năm tính từ thập niên 2000, vì vậy cam kết phát thải ròng bằng 0 (net zero) trước năm 2050 cũng gần như chạm đến giới hạn dự trữ của năng lượng hóa thạch ngoài vấn đề nghiêm trọng về mức tăng nhiệt độ trái đất có thể vượt mức 1.5 độ C vào năm 2100 cuối thế kỷ này so với thời kỳ tiền công nghiệp. Bài viết này tập trung tìm hiểu những nét cơ bản về hydrogen, hy vọng những nhà nghiên cứu chuyên sâu, doanh nghiệp và các nhà hoạch định chính sách quan tâm đến năng lượng tái tạo cũng như hydrogen.","event_date":{"day":20,"month":5,"year":2024,"errors":{}},"journal_name":"ResearchGate preprint","organization":"ResearchGate","publication_date":{"day":20,"month":5,"year":2024,"errors":{}},"publication_name":"ResearchGate preprint","conference_end_date":{"day":20,"month":5,"year":2024,"errors":{}},"conference_start_date":{"day":20,"month":5,"year":2024,"errors":{}}},"translated_abstract":"Khoa học công nghệ dường như đang bất ngờ hụt hơi trong cuộc chạy đua với biến đổi khí hậu tuy được cảnh báo nhiều thập niên trước nhưng mức độ khốc liệt lúc này trở nên rõ ràng và công nghệ vũ trụ chưa có dấu hiệu cho thấy khả năng của sự di cư hoặc khai thác tài nguyên ngoài không gian. Bài viết này chưa thể tìm hiểu lý do biến đổi khí hậu trở nên cấp thiết đến mức xuất hiện khắp nơi trên phương tiện truyền thông nhưng điểm sáng là năng lượng tái tạo được phát triển mạnh mẽ và nhanh chóng, theo đó hydrogen xanh phái sinh từ điện mặt trời và điện gió có thể thay đổi cuộc chơi trên thị trường năng lượng, cạnh tranh và dần có thể thay thế năng lượng hóa thạch. Nếu không có hydrogen hoặc giả sử chất tương tự hydrogen thì năng lượng tái tạo có thể chưa sớm thay thế năng lượng hóa thạch với ước số năm dự trữ còn lại của dầu mỏ, khí thiên nhiên và than lần lượt khoảng 40 năm, 60 năm và 150 năm tính từ thập niên 2000, vì vậy cam kết phát thải ròng bằng 0 (net zero) trước năm 2050 cũng gần như chạm đến giới hạn dự trữ của năng lượng hóa thạch ngoài vấn đề nghiêm trọng về mức tăng nhiệt độ trái đất có thể vượt mức 1.5 độ C vào năm 2100 cuối thế kỷ này so với thời kỳ tiền công nghiệp. Bài viết này tập trung tìm hiểu những nét cơ bản về hydrogen, hy vọng những nhà nghiên cứu chuyên sâu, doanh nghiệp và các nhà hoạch định chính sách quan tâm đến năng lượng tái tạo cũng như hydrogen.","internal_url":"https://www.academia.edu/121221891/T%E1%BB%95ng_quan_n%C4%83ng_l%C6%B0%E1%BB%A3ng_hydrogen","translated_internal_url":"","created_at":"2024-06-19T03:39:38.756-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":41909056,"work_id":121221891,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Tổng quan năng lượng hydrogen"}],"downloadable_attachments":[{"id":116158378,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158378/thumbnails/1.jpg","file_name":"83.Hydrogen_ResearchGate_2024.05.20.pdf","download_url":"https://www.academia.edu/attachments/116158378/download_file","bulk_download_file_name":"Tng_quan_nang_lng_hydrogen.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158378/83.Hydrogen_ResearchGate_2024.05.20-libre.pdf?1718794576=\u0026response-content-disposition=attachment%3B+filename%3DTng_quan_nang_lng_hydrogen.pdf\u0026Expires=1738797508\u0026Signature=VRz-UMrABBao3Ledy3qHaKS8NKX3IRSPk9eGirWin64Cnq7ggyCU8DYw6ih23-L2ZXevL0VKRiMORcfCzwXe4~znEEP549KH80fiuMOZK2puEJF-pekJgfJ7Hl4z~nAFAfNJ-roA6aHmQ1MBtba4UKqQeV0oq1epHxbHXNrN~csXQYVVoGBhkCLrXZNBud2ju1fVZV7WANm8QQ1al--UBc-2fnD9NBJxAmbcmQAtdC3S0eOWjEr-y0Nlnkj1QDCZOBF4MVdPnEx2GEp6y~SHDNLp~1IOcHRdpbANaiuMMX~ywEPw3zYXMVVqOF79k-pvpDqrWP6itIMcViUx0UCj9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tổng_quan_năng_lượng_hydrogen","translated_slug":"","page_count":5,"language":"vi","content_type":"Work","summary":"Khoa học công nghệ dường như đang bất ngờ hụt hơi trong cuộc chạy đua với biến đổi khí hậu tuy được cảnh báo nhiều thập niên trước nhưng mức độ khốc liệt lúc này trở nên rõ ràng và công nghệ vũ trụ chưa có dấu hiệu cho thấy khả năng của sự di cư hoặc khai thác tài nguyên ngoài không gian. Bài viết này chưa thể tìm hiểu lý do biến đổi khí hậu trở nên cấp thiết đến mức xuất hiện khắp nơi trên phương tiện truyền thông nhưng điểm sáng là năng lượng tái tạo được phát triển mạnh mẽ và nhanh chóng, theo đó hydrogen xanh phái sinh từ điện mặt trời và điện gió có thể thay đổi cuộc chơi trên thị trường năng lượng, cạnh tranh và dần có thể thay thế năng lượng hóa thạch. Nếu không có hydrogen hoặc giả sử chất tương tự hydrogen thì năng lượng tái tạo có thể chưa sớm thay thế năng lượng hóa thạch với ước số năm dự trữ còn lại của dầu mỏ, khí thiên nhiên và than lần lượt khoảng 40 năm, 60 năm và 150 năm tính từ thập niên 2000, vì vậy cam kết phát thải ròng bằng 0 (net zero) trước năm 2050 cũng gần như chạm đến giới hạn dự trữ của năng lượng hóa thạch ngoài vấn đề nghiêm trọng về mức tăng nhiệt độ trái đất có thể vượt mức 1.5 độ C vào năm 2100 cuối thế kỷ này so với thời kỳ tiền công nghiệp. Bài viết này tập trung tìm hiểu những nét cơ bản về hydrogen, hy vọng những nhà nghiên cứu chuyên sâu, doanh nghiệp và các nhà hoạch định chính sách quan tâm đến năng lượng tái tạo cũng như hydrogen.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":116158378,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158378/thumbnails/1.jpg","file_name":"83.Hydrogen_ResearchGate_2024.05.20.pdf","download_url":"https://www.academia.edu/attachments/116158378/download_file","bulk_download_file_name":"Tng_quan_nang_lng_hydrogen.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158378/83.Hydrogen_ResearchGate_2024.05.20-libre.pdf?1718794576=\u0026response-content-disposition=attachment%3B+filename%3DTng_quan_nang_lng_hydrogen.pdf\u0026Expires=1738797508\u0026Signature=VRz-UMrABBao3Ledy3qHaKS8NKX3IRSPk9eGirWin64Cnq7ggyCU8DYw6ih23-L2ZXevL0VKRiMORcfCzwXe4~znEEP549KH80fiuMOZK2puEJF-pekJgfJ7Hl4z~nAFAfNJ-roA6aHmQ1MBtba4UKqQeV0oq1epHxbHXNrN~csXQYVVoGBhkCLrXZNBud2ju1fVZV7WANm8QQ1al--UBc-2fnD9NBJxAmbcmQAtdC3S0eOWjEr-y0Nlnkj1QDCZOBF4MVdPnEx2GEp6y~SHDNLp~1IOcHRdpbANaiuMMX~ywEPw3zYXMVVqOF79k-pvpDqrWP6itIMcViUx0UCj9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":3771,"name":"Hydrogen","url":"https://www.academia.edu/Documents/in/Hydrogen"}],"urls":[{"id":43062308,"url":"https://www.researchgate.net/publication/380721984_Tong_quan_nang_luong_hydrogen"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="121221752"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/121221752/Drone_v%C3%A0_t%C3%A1c_chi%E1%BA%BFn_qu%C3%A2n_s%E1%BB%B1"><img alt="Research paper thumbnail of Drone và tác chiến quân sự" class="work-thumbnail" src="https://attachments.academia-assets.com/116158309/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121221752/Drone_v%C3%A0_t%C3%A1c_chi%E1%BA%BFn_qu%C3%A2n_s%E1%BB%B1">Drone và tác chiến quân sự</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>ResearchGate preprint</span><span>, Apr 9, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Giao thông vận tải (GTVT) là huyết mạch quốc gia, hạ tầng cơ sở của phát triển kinh tế, đầu mối c...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Giao thông vận tải (GTVT) là huyết mạch quốc gia, hạ tầng cơ sở của phát triển kinh tế, đầu mối của phúc lợi xã hội, điểm nhìn của an ninh, vận hành sức mạnh lực lượng vũ trang và GTVT đi lại giữa dân sự và quân sự, tương tác theo những chiều hướng mạn đàm rằng chưa thể hiểu hết vì năng lực khai mở cũng như khoanh vùng ngoại trừ tầm mức quan trọng của GTVT luôn phải được quan tâm. GTVT có ba hình thức gồm đường thủy/biển, đường bộ/sắt và hàng không mà không thể giảm nhẹ bất cứ hình thức nào vì đường thủy/biển ưu thế số lượng, đường bộ/sắt ưu thế thuận tiện và hàng không ưu thế tốc độ nhưng hàng không có tiềm năng lớn nhất còn nhiều dư địa do sự phát triển của công nghệ. Drone, UAV hay máy bay không người lái hiện đang phát triển mạnh mẽ phục vụ quân sự lẫn dân sự nhưng ứng dụng của drone trong dân sự còn nằm ở mức tiện ích nên rất có khả năng sẽ gia nhập vào mạng lưới giao thông hàng không, trước tiên là nâng cấp tiện ích giao hàng của drone. Hơn nữa những phương tiện giao thông tự động (không người lái) hiện đang phát triển và khả năng tự hành thuộc về bản chất phát triển của drone và drone hoạt động thuận tiện ở nhiều địa hình. Bài viết này giới thiệu một số nét cơ bản của drone cùng những ứng dụng của nó trong tác chiến quân sự – drone nằm trong điểm nhìn của an ninh như là phương tiện giao thông nhưng cũng là vũ khí chiến đấu ngày càng chứng tỏ tính hiệu quả.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="a2f0430a24d65ffb60da69640e2302d8" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116158309,"asset_id":121221752,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116158309/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121221752"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121221752"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121221752; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121221752]").text(description); $(".js-view-count[data-work-id=121221752]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121221752; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121221752']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "a2f0430a24d65ffb60da69640e2302d8" } } $('.js-work-strip[data-work-id=121221752]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121221752,"title":"Drone và tác chiến quân sự","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.36006.33609","abstract":"Giao thông vận tải (GTVT) là huyết mạch quốc gia, hạ tầng cơ sở của phát triển kinh tế, đầu mối của phúc lợi xã hội, điểm nhìn của an ninh, vận hành sức mạnh lực lượng vũ trang và GTVT đi lại giữa dân sự và quân sự, tương tác theo những chiều hướng mạn đàm rằng chưa thể hiểu hết vì năng lực khai mở cũng như khoanh vùng ngoại trừ tầm mức quan trọng của GTVT luôn phải được quan tâm. GTVT có ba hình thức gồm đường thủy/biển, đường bộ/sắt và hàng không mà không thể giảm nhẹ bất cứ hình thức nào vì đường thủy/biển ưu thế số lượng, đường bộ/sắt ưu thế thuận tiện và hàng không ưu thế tốc độ nhưng hàng không có tiềm năng lớn nhất còn nhiều dư địa do sự phát triển của công nghệ. Drone, UAV hay máy bay không người lái hiện đang phát triển mạnh mẽ phục vụ quân sự lẫn dân sự nhưng ứng dụng của drone trong dân sự còn nằm ở mức tiện ích nên rất có khả năng sẽ gia nhập vào mạng lưới giao thông hàng không, trước tiên là nâng cấp tiện ích giao hàng của drone. Hơn nữa những phương tiện giao thông tự động (không người lái) hiện đang phát triển và khả năng tự hành thuộc về bản chất phát triển của drone và drone hoạt động thuận tiện ở nhiều địa hình. Bài viết này giới thiệu một số nét cơ bản của drone cùng những ứng dụng của nó trong tác chiến quân sự – drone nằm trong điểm nhìn của an ninh như là phương tiện giao thông nhưng cũng là vũ khí chiến đấu ngày càng chứng tỏ tính hiệu quả.","event_date":{"day":9,"month":4,"year":2024,"errors":{}},"journal_name":"ResearchGate preprint","organization":"ResearchGate","publication_date":{"day":9,"month":4,"year":2024,"errors":{}},"publication_name":"ResearchGate preprint","conference_end_date":{"day":9,"month":4,"year":2024,"errors":{}},"conference_start_date":{"day":9,"month":4,"year":2024,"errors":{}}},"translated_abstract":"Giao thông vận tải (GTVT) là huyết mạch quốc gia, hạ tầng cơ sở của phát triển kinh tế, đầu mối của phúc lợi xã hội, điểm nhìn của an ninh, vận hành sức mạnh lực lượng vũ trang và GTVT đi lại giữa dân sự và quân sự, tương tác theo những chiều hướng mạn đàm rằng chưa thể hiểu hết vì năng lực khai mở cũng như khoanh vùng ngoại trừ tầm mức quan trọng của GTVT luôn phải được quan tâm. GTVT có ba hình thức gồm đường thủy/biển, đường bộ/sắt và hàng không mà không thể giảm nhẹ bất cứ hình thức nào vì đường thủy/biển ưu thế số lượng, đường bộ/sắt ưu thế thuận tiện và hàng không ưu thế tốc độ nhưng hàng không có tiềm năng lớn nhất còn nhiều dư địa do sự phát triển của công nghệ. Drone, UAV hay máy bay không người lái hiện đang phát triển mạnh mẽ phục vụ quân sự lẫn dân sự nhưng ứng dụng của drone trong dân sự còn nằm ở mức tiện ích nên rất có khả năng sẽ gia nhập vào mạng lưới giao thông hàng không, trước tiên là nâng cấp tiện ích giao hàng của drone. Hơn nữa những phương tiện giao thông tự động (không người lái) hiện đang phát triển và khả năng tự hành thuộc về bản chất phát triển của drone và drone hoạt động thuận tiện ở nhiều địa hình. Bài viết này giới thiệu một số nét cơ bản của drone cùng những ứng dụng của nó trong tác chiến quân sự – drone nằm trong điểm nhìn của an ninh như là phương tiện giao thông nhưng cũng là vũ khí chiến đấu ngày càng chứng tỏ tính hiệu quả.","internal_url":"https://www.academia.edu/121221752/Drone_v%C3%A0_t%C3%A1c_chi%E1%BA%BFn_qu%C3%A2n_s%E1%BB%B1","translated_internal_url":"","created_at":"2024-06-19T03:36:56.721-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":41909038,"work_id":121221752,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Drone và tác chiến quân sự"}],"downloadable_attachments":[{"id":116158309,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158309/thumbnails/1.jpg","file_name":"82.Drone_ResearchGate_2024.04.09.pdf","download_url":"https://www.academia.edu/attachments/116158309/download_file","bulk_download_file_name":"Drone_va_tac_chin_quan_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158309/82.Drone_ResearchGate_2024.04.09-libre.pdf?1718794636=\u0026response-content-disposition=attachment%3B+filename%3DDrone_va_tac_chin_quan_s.pdf\u0026Expires=1738797508\u0026Signature=Ft4dLBzOXZcnmsyUO8kUiwbe77tJEDFEoOJcac6T8gQ5eYKsf54f6c-GjoRrbbaP3JLbil9K6c0crQTT5vl1PiWYAHUT6gN1hgsTzLs1OoZyz0v09uVt0WkS4dwUzQM5Sn~JP3t~yNy0Oy33G6Sytt6vZ6Dk4~noeEH9bmbtgcbvD3stWwHeWKrTzHjGGnsaoYWY1IuAZjVPdxYUNfb2InADCE5J3cy~g~ukqBvN389wVjIloPEPDCHU0yfrrN~MkdgIWePPB1QGxgDGVvYHnuDTNTCs7iSJXgZfTcTGASZQaFBwf-zT4~c4wsSmnEbnccbYSNF5Q--pOfH9SdY72A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Drone_và_tác_chiến_quân_sự","translated_slug":"","page_count":23,"language":"vi","content_type":"Work","summary":"Giao thông vận tải (GTVT) là huyết mạch quốc gia, hạ tầng cơ sở của phát triển kinh tế, đầu mối của phúc lợi xã hội, điểm nhìn của an ninh, vận hành sức mạnh lực lượng vũ trang và GTVT đi lại giữa dân sự và quân sự, tương tác theo những chiều hướng mạn đàm rằng chưa thể hiểu hết vì năng lực khai mở cũng như khoanh vùng ngoại trừ tầm mức quan trọng của GTVT luôn phải được quan tâm. GTVT có ba hình thức gồm đường thủy/biển, đường bộ/sắt và hàng không mà không thể giảm nhẹ bất cứ hình thức nào vì đường thủy/biển ưu thế số lượng, đường bộ/sắt ưu thế thuận tiện và hàng không ưu thế tốc độ nhưng hàng không có tiềm năng lớn nhất còn nhiều dư địa do sự phát triển của công nghệ. Drone, UAV hay máy bay không người lái hiện đang phát triển mạnh mẽ phục vụ quân sự lẫn dân sự nhưng ứng dụng của drone trong dân sự còn nằm ở mức tiện ích nên rất có khả năng sẽ gia nhập vào mạng lưới giao thông hàng không, trước tiên là nâng cấp tiện ích giao hàng của drone. Hơn nữa những phương tiện giao thông tự động (không người lái) hiện đang phát triển và khả năng tự hành thuộc về bản chất phát triển của drone và drone hoạt động thuận tiện ở nhiều địa hình. Bài viết này giới thiệu một số nét cơ bản của drone cùng những ứng dụng của nó trong tác chiến quân sự – drone nằm trong điểm nhìn của an ninh như là phương tiện giao thông nhưng cũng là vũ khí chiến đấu ngày càng chứng tỏ tính hiệu quả.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":116158309,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158309/thumbnails/1.jpg","file_name":"82.Drone_ResearchGate_2024.04.09.pdf","download_url":"https://www.academia.edu/attachments/116158309/download_file","bulk_download_file_name":"Drone_va_tac_chin_quan_s.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158309/82.Drone_ResearchGate_2024.04.09-libre.pdf?1718794636=\u0026response-content-disposition=attachment%3B+filename%3DDrone_va_tac_chin_quan_s.pdf\u0026Expires=1738797508\u0026Signature=Ft4dLBzOXZcnmsyUO8kUiwbe77tJEDFEoOJcac6T8gQ5eYKsf54f6c-GjoRrbbaP3JLbil9K6c0crQTT5vl1PiWYAHUT6gN1hgsTzLs1OoZyz0v09uVt0WkS4dwUzQM5Sn~JP3t~yNy0Oy33G6Sytt6vZ6Dk4~noeEH9bmbtgcbvD3stWwHeWKrTzHjGGnsaoYWY1IuAZjVPdxYUNfb2InADCE5J3cy~g~ukqBvN389wVjIloPEPDCHU0yfrrN~MkdgIWePPB1QGxgDGVvYHnuDTNTCs7iSJXgZfTcTGASZQaFBwf-zT4~c4wsSmnEbnccbYSNF5Q--pOfH9SdY72A__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":329707,"name":"Drones","url":"https://www.academia.edu/Documents/in/Drones"}],"urls":[{"id":43062279,"url":"https://www.researchgate.net/publication/379270618_Drone_va_tac_chien_quan_su"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="121221517"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/121221517/Nam_To%C3%A0n_C%E1%BA%A7u_k%E1%BB%B3_v%E1%BB%8Dng_v%C3%A0_hi%E1%BB%87n_th%E1%BB%B1c"><img alt="Research paper thumbnail of Nam Toàn Cầu: kỳ vọng và hiện thực" class="work-thumbnail" src="https://attachments.academia-assets.com/118612431/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/121221517/Nam_To%C3%A0n_C%E1%BA%A7u_k%E1%BB%B3_v%E1%BB%8Dng_v%C3%A0_hi%E1%BB%87n_th%E1%BB%B1c">Nam Toàn Cầu: kỳ vọng và hiện thực</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>ResearchGate preprint</span><span>, Jun 18, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Nam Toàn Cầu (NTC) và Bắc Toàn Cầu (BTC), đó không phải phân chia địa lý và tất nhiên càng không ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Nam Toàn Cầu (NTC) và Bắc Toàn Cầu (BTC), đó không phải phân chia địa lý và tất nhiên càng không phải phân tách nam bắc theo đường xích đạo như giới tuyến chia đôi một thế giới trộn giữa vật chất và tinh thần, giữa tự nhiên và xã hội, giữa thương mại và sản xuất mà đó gần như ảo tưởng phân tách thế giới giữa Phương Tây và phi Phương Tây giữa giàu và nghèo, với kỳ vọng đạt thế cân bằng sức mạnh khi bắt đầu bứt lên tiếng nói với trọng lượng của dân số, tài nguyên và hơn hết là khát vọng. Một khi “ảo tưởng” này được thúc ép bởi khát vọng, hỗ trợ bởi tài nguyên trí tuệ đang lan tỏa cũng như được cổ vũ bởi sự suy giảm quyền lực kiểm soát của Phương Tây cùng diễn biến chính trị phức tạp đan xen xung đột sẽ dần trở thành hiện thực tiến đến điểm cân bằng mà tiến trình toàn cầu hóa với luận điểm tự do đã bị chặn lại trong những năm gần đây bởi chủ nghĩa bảo hộ khai sinh từ khủng hoảng. Bước lùi này tương tự quả bóng bị bóp để hình thành nên xu hướng NTC và BTC hay hiện thực hóa của ảo tưởng NTC và BTC. Có lẽ hoạt động của NTC bắt đầu bằng thương mại, tài chính và ngoại giao để hút sức mạnh công nghệ và chính trị tựu trung vẫn là lợi ích nhưng tạo nên một tưởng tượng giả lập của thượng viện BTC và hạ viện NTC. Tuy nhiên tôi không nghĩ rằng NTC tạo nên cực mà đúng hơn là một phong trào, một sân khấu nơi các cường quốc cố gắng tạo nên cực và những quốc gia khác chen chân mưu cầu lợi ích chính đáng.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="0f178542b2abf9f3a545da4fef650779" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118612431,"asset_id":121221517,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118612431/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="121221517"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="121221517"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 121221517; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=121221517]").text(description); $(".js-view-count[data-work-id=121221517]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 121221517; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='121221517']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "0f178542b2abf9f3a545da4fef650779" } } $('.js-work-strip[data-work-id=121221517]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":121221517,"title":"Nam Toàn Cầu: kỳ vọng và hiện thực","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.23066.66241","abstract":"Nam Toàn Cầu (NTC) và Bắc Toàn Cầu (BTC), đó không phải phân chia địa lý và tất nhiên càng không phải phân tách nam bắc theo đường xích đạo như giới tuyến chia đôi một thế giới trộn giữa vật chất và tinh thần, giữa tự nhiên và xã hội, giữa thương mại và sản xuất mà đó gần như ảo tưởng phân tách thế giới giữa Phương Tây và phi Phương Tây giữa giàu và nghèo, với kỳ vọng đạt thế cân bằng sức mạnh khi bắt đầu bứt lên tiếng nói với trọng lượng của dân số, tài nguyên và hơn hết là khát vọng. Một khi “ảo tưởng” này được thúc ép bởi khát vọng, hỗ trợ bởi tài nguyên trí tuệ đang lan tỏa cũng như được cổ vũ bởi sự suy giảm quyền lực kiểm soát của Phương Tây cùng diễn biến chính trị phức tạp đan xen xung đột sẽ dần trở thành hiện thực tiến đến điểm cân bằng mà tiến trình toàn cầu hóa với luận điểm tự do đã bị chặn lại trong những năm gần đây bởi chủ nghĩa bảo hộ khai sinh từ khủng hoảng. Bước lùi này tương tự quả bóng bị bóp để hình thành nên xu hướng NTC và BTC hay hiện thực hóa của ảo tưởng NTC và BTC. Có lẽ hoạt động của NTC bắt đầu bằng thương mại, tài chính và ngoại giao để hút sức mạnh công nghệ và chính trị tựu trung vẫn là lợi ích nhưng tạo nên một tưởng tượng giả lập của thượng viện BTC và hạ viện NTC. Tuy nhiên tôi không nghĩ rằng NTC tạo nên cực mà đúng hơn là một phong trào, một sân khấu nơi các cường quốc cố gắng tạo nên cực và những quốc gia khác chen chân mưu cầu lợi ích chính đáng.","event_date":{"day":18,"month":6,"year":2024,"errors":{}},"journal_name":"ResearchGate preprint","organization":"ResearchGate","publication_date":{"day":18,"month":6,"year":2024,"errors":{}},"publication_name":"ResearchGate preprint","conference_end_date":{"day":18,"month":6,"year":2024,"errors":{}},"conference_start_date":{"day":18,"month":6,"year":2024,"errors":{}}},"translated_abstract":"Nam Toàn Cầu (NTC) và Bắc Toàn Cầu (BTC), đó không phải phân chia địa lý và tất nhiên càng không phải phân tách nam bắc theo đường xích đạo như giới tuyến chia đôi một thế giới trộn giữa vật chất và tinh thần, giữa tự nhiên và xã hội, giữa thương mại và sản xuất mà đó gần như ảo tưởng phân tách thế giới giữa Phương Tây và phi Phương Tây giữa giàu và nghèo, với kỳ vọng đạt thế cân bằng sức mạnh khi bắt đầu bứt lên tiếng nói với trọng lượng của dân số, tài nguyên và hơn hết là khát vọng. Một khi “ảo tưởng” này được thúc ép bởi khát vọng, hỗ trợ bởi tài nguyên trí tuệ đang lan tỏa cũng như được cổ vũ bởi sự suy giảm quyền lực kiểm soát của Phương Tây cùng diễn biến chính trị phức tạp đan xen xung đột sẽ dần trở thành hiện thực tiến đến điểm cân bằng mà tiến trình toàn cầu hóa với luận điểm tự do đã bị chặn lại trong những năm gần đây bởi chủ nghĩa bảo hộ khai sinh từ khủng hoảng. Bước lùi này tương tự quả bóng bị bóp để hình thành nên xu hướng NTC và BTC hay hiện thực hóa của ảo tưởng NTC và BTC. Có lẽ hoạt động của NTC bắt đầu bằng thương mại, tài chính và ngoại giao để hút sức mạnh công nghệ và chính trị tựu trung vẫn là lợi ích nhưng tạo nên một tưởng tượng giả lập của thượng viện BTC và hạ viện NTC. Tuy nhiên tôi không nghĩ rằng NTC tạo nên cực mà đúng hơn là một phong trào, một sân khấu nơi các cường quốc cố gắng tạo nên cực và những quốc gia khác chen chân mưu cầu lợi ích chính đáng.","internal_url":"https://www.academia.edu/121221517/Nam_To%C3%A0n_C%E1%BA%A7u_k%E1%BB%B3_v%E1%BB%8Dng_v%C3%A0_hi%E1%BB%87n_th%E1%BB%B1c","translated_internal_url":"","created_at":"2024-06-19T03:32:11.675-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":41909011,"work_id":121221517,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Nam Toàn Cầu: kỳ vọng và hiện thực"}],"downloadable_attachments":[{"id":118612431,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118612431/thumbnails/1.jpg","file_name":"15.NTC_ResearchGate_2024.10.03.pdf","download_url":"https://www.academia.edu/attachments/118612431/download_file","bulk_download_file_name":"Nam_Toan_Cu_k_vng_va_hin_thc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118612431/15.NTC_ResearchGate_2024.10.03-libre.pdf?1727933948=\u0026response-content-disposition=attachment%3B+filename%3DNam_Toan_Cu_k_vng_va_hin_thc.pdf\u0026Expires=1738797508\u0026Signature=PG7Hw-fvDql8QwsMxeW5GQhDjEwZIHbhq~x~rke6pbTFTyNvlqQvSGMTNoiMEnUNZIptywtxd0cD78Hf4-0ikRubKUGV8hxZQeoNrp5ZONuPpLTPdd5w3Rv0kFbfe2Wy8RRJudLgUQAh0HKm1UoYgJ-EVJJmgOrDmrhlGTa8yopzgWU0B4Pq35SIrz9D3QiI2XF7wWut~KIY~AdGVmlmmmGLimKmJgixU8rmq~b4R8sQOs5MJlcU3cEgUuAKODtcOZ2ennMaHXvcNLEHCbu7jLC3HWJbRR0EfAhFlvQYT01w-1rPQegVOFVxdQEtHJR0wJkXz5gxSzI1bOr4xOv6tg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":116158194,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158194/thumbnails/1.jpg","file_name":"15.NTC_ResearchGate_2024.06.18.pdf","download_url":"https://www.academia.edu/attachments/116158194/download_file","bulk_download_file_name":"Nam_Toan_Cu_k_vng_va_hin_thc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158194/15.NTC_ResearchGate_2024.06.18-libre.pdf?1718794615=\u0026response-content-disposition=attachment%3B+filename%3DNam_Toan_Cu_k_vng_va_hin_thc.pdf\u0026Expires=1738797508\u0026Signature=PZUWApWztDPsRlXTDHMxLhY1mp2Uvhj-wrJ05mUafFuKEwZ4jAuY45TL03D~xkAF7tn4y4Y~TIOAaGC74TrLJr5FXn1l2jU9Uj5dcdUwd-2fvAVdUGnZxCvciBNhBAUj0znmbdjTyQt9MvfUjRO01C9T4CU0OKlsqJPMJI2TViRR1eYtyOYglwU-TzrJz0PCpcnKL~TkAewHXKf~47VtlbKamePzz2b8-YJSz81C-QR~srFK~DuOMjNAMApF26etNiWItvk3kd8tmtrPx46UaVt-GXtxdOnZQ3bSS6gOldOt3toJWIHs9Iu3TF4fwNJqFl4Z265z2Hrky4WqlR9Pzg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Nam_Toàn_Cầu_kỳ_vọng_và_hiện_thực","translated_slug":"","page_count":8,"language":"vi","content_type":"Work","summary":"Nam Toàn Cầu (NTC) và Bắc Toàn Cầu (BTC), đó không phải phân chia địa lý và tất nhiên càng không phải phân tách nam bắc theo đường xích đạo như giới tuyến chia đôi một thế giới trộn giữa vật chất và tinh thần, giữa tự nhiên và xã hội, giữa thương mại và sản xuất mà đó gần như ảo tưởng phân tách thế giới giữa Phương Tây và phi Phương Tây giữa giàu và nghèo, với kỳ vọng đạt thế cân bằng sức mạnh khi bắt đầu bứt lên tiếng nói với trọng lượng của dân số, tài nguyên và hơn hết là khát vọng. Một khi “ảo tưởng” này được thúc ép bởi khát vọng, hỗ trợ bởi tài nguyên trí tuệ đang lan tỏa cũng như được cổ vũ bởi sự suy giảm quyền lực kiểm soát của Phương Tây cùng diễn biến chính trị phức tạp đan xen xung đột sẽ dần trở thành hiện thực tiến đến điểm cân bằng mà tiến trình toàn cầu hóa với luận điểm tự do đã bị chặn lại trong những năm gần đây bởi chủ nghĩa bảo hộ khai sinh từ khủng hoảng. Bước lùi này tương tự quả bóng bị bóp để hình thành nên xu hướng NTC và BTC hay hiện thực hóa của ảo tưởng NTC và BTC. Có lẽ hoạt động của NTC bắt đầu bằng thương mại, tài chính và ngoại giao để hút sức mạnh công nghệ và chính trị tựu trung vẫn là lợi ích nhưng tạo nên một tưởng tượng giả lập của thượng viện BTC và hạ viện NTC. Tuy nhiên tôi không nghĩ rằng NTC tạo nên cực mà đúng hơn là một phong trào, một sân khấu nơi các cường quốc cố gắng tạo nên cực và những quốc gia khác chen chân mưu cầu lợi ích chính đáng.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":118612431,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118612431/thumbnails/1.jpg","file_name":"15.NTC_ResearchGate_2024.10.03.pdf","download_url":"https://www.academia.edu/attachments/118612431/download_file","bulk_download_file_name":"Nam_Toan_Cu_k_vng_va_hin_thc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118612431/15.NTC_ResearchGate_2024.10.03-libre.pdf?1727933948=\u0026response-content-disposition=attachment%3B+filename%3DNam_Toan_Cu_k_vng_va_hin_thc.pdf\u0026Expires=1738797508\u0026Signature=PG7Hw-fvDql8QwsMxeW5GQhDjEwZIHbhq~x~rke6pbTFTyNvlqQvSGMTNoiMEnUNZIptywtxd0cD78Hf4-0ikRubKUGV8hxZQeoNrp5ZONuPpLTPdd5w3Rv0kFbfe2Wy8RRJudLgUQAh0HKm1UoYgJ-EVJJmgOrDmrhlGTa8yopzgWU0B4Pq35SIrz9D3QiI2XF7wWut~KIY~AdGVmlmmmGLimKmJgixU8rmq~b4R8sQOs5MJlcU3cEgUuAKODtcOZ2ennMaHXvcNLEHCbu7jLC3HWJbRR0EfAhFlvQYT01w-1rPQegVOFVxdQEtHJR0wJkXz5gxSzI1bOr4xOv6tg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":116158194,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158194/thumbnails/1.jpg","file_name":"15.NTC_ResearchGate_2024.06.18.pdf","download_url":"https://www.academia.edu/attachments/116158194/download_file","bulk_download_file_name":"Nam_Toan_Cu_k_vng_va_hin_thc.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158194/15.NTC_ResearchGate_2024.06.18-libre.pdf?1718794615=\u0026response-content-disposition=attachment%3B+filename%3DNam_Toan_Cu_k_vng_va_hin_thc.pdf\u0026Expires=1738797508\u0026Signature=PZUWApWztDPsRlXTDHMxLhY1mp2Uvhj-wrJ05mUafFuKEwZ4jAuY45TL03D~xkAF7tn4y4Y~TIOAaGC74TrLJr5FXn1l2jU9Uj5dcdUwd-2fvAVdUGnZxCvciBNhBAUj0znmbdjTyQt9MvfUjRO01C9T4CU0OKlsqJPMJI2TViRR1eYtyOYglwU-TzrJz0PCpcnKL~TkAewHXKf~47VtlbKamePzz2b8-YJSz81C-QR~srFK~DuOMjNAMApF26etNiWItvk3kd8tmtrPx46UaVt-GXtxdOnZQ3bSS6gOldOt3toJWIHs9Iu3TF4fwNJqFl4Z265z2Hrky4WqlR9Pzg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":38429,"name":"Global South","url":"https://www.academia.edu/Documents/in/Global_South"}],"urls":[{"id":43062230,"url":"https://www.researchgate.net/publication/381509464_Nam_Toan_Cau_ky_vong_va_hien_thuc"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="116031639"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/116031639/Ngh%E1%BB%8Bch_d%C3%A2n_ch%E1%BB%A7_lu%E1%BA%ADn"><img alt="Research paper thumbnail of Nghịch dân chủ luận" class="work-thumbnail" src="https://attachments.academia-assets.com/112274752/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/116031639/Ngh%E1%BB%8Bch_d%C3%A2n_ch%E1%BB%A7_lu%E1%BA%ADn">Nghịch dân chủ luận</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>ResearchGate 2024 Preprint</span><span>, Mar 2, 2024</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Vũ trụ có vật chất và phản vật chất, xã hội có xung đột và hữu hảo để phát triển và suy tàn rồi s...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Vũ trụ có vật chất và phản vật chất, xã hội có xung đột và hữu hảo để phát triển và suy tàn rồi suy tàn và phát triển. Tôi dựa vào đó để biện minh cho một bài viết có tính chất phản động nghịch chuyển thời cuộc nhưng bạn đọc sẽ tự tìm ra ý nghĩa bất ly của các hình thái xã hội. Ngoài ra bài viết này không đi sâu vào nghiên cứu pháp luật, chỉ đưa ra một cách nhìn tổng quan về dân chủ và thể chế chính trị liên quan đến triết học và tôn giáo, mà theo đó đóng góp của bài viết là khái niệm “nương tạm” của tư pháp không thật sự từ bầu cử và cũng không thật sự từ bổ nhiệm.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="556c8990c9b69be7f4901d8f7da8978f" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":112274752,"asset_id":116031639,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/112274752/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="116031639"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="116031639"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 116031639; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=116031639]").text(description); $(".js-view-count[data-work-id=116031639]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 116031639; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='116031639']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "556c8990c9b69be7f4901d8f7da8978f" } } $('.js-work-strip[data-work-id=116031639]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":116031639,"title":"Nghịch dân chủ luận","translated_title":"","metadata":{"doi":"10.13140/RG.2.2.31751.37289/1","abstract":"Vũ trụ có vật chất và phản vật chất, xã hội có xung đột và hữu hảo để phát triển và suy tàn rồi suy tàn và phát triển. 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The K-nearest neighbors (KNN) technique is a well-liked CF algorithm that uses similarity measurements to identify a user's closest neighbors in order to quantify the degree of dependency between the respective user and item pair. As a result, the CF approach is not only dependent on the choice of the similarity measure but also sensitive to it. However, some numerical measures, like cosine and Pearson, concentrate on the size of ratings, whereas Jaccard, one of the most frequently employed similarity measures, concerns the existence of ratings. Jaccard, in particular, is not a dominant measure, but it has long been demonstrated to be a key element in enhancing any measure. Therefore, in our ongoing search for the most effective similarity measures for CF, this research focuses on presenting combined similarity measures by fusing Jaccard with a multitude of numerical measures. Both existence and magnitude would benefit the combined measurements. Experimental results, on movielens-100K and Film Trust datasets, demonstrated that the combined measures are superior, surpassing all single measures across the considered assessment metrics.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="405c1db38032539d58a1415117b37e78" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105621453,"asset_id":106418539,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105621453/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="106418539"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="106418539"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 106418539; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=106418539]").text(description); $(".js-view-count[data-work-id=106418539]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 106418539; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='106418539']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "405c1db38032539d58a1415117b37e78" } } $('.js-work-strip[data-work-id=106418539]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":106418539,"title":"On the Impact of Jaccard Fusion with Numerical Measures for Collaborative Filtering Enhancement","translated_title":"","metadata":{"doi":"10.21203/rs.3.rs-3304224/v1","abstract":"Collaborative filtering (CF) is an important method for recommendation systems, which are employed in many facets of our lives and are particularly prevalent in online-based commercial systems. 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The K-nearest neighbors (KNN) technique is a well-liked CF algorithm that uses similarity measurements to identify a user's closest neighbors in order to quantify the degree of dependency between the respective user and item pair. As a result, the CF approach is not only dependent on the choice of the similarity measure but also sensitive to it. However, some numerical measures, like cosine and Pearson, concentrate on the size of ratings, whereas Jaccard, one of the most frequently employed similarity measures, concerns the existence of ratings. Jaccard, in particular, is not a dominant measure, but it has long been demonstrated to be a key element in enhancing any measure. Therefore, in our ongoing search for the most effective similarity measures for CF, this research focuses on presenting combined similarity measures by fusing Jaccard with a multitude of numerical measures. Both existence and magnitude would benefit the combined measurements. Experimental results, on movielens-100K and Film Trust datasets, demonstrated that the combined measures are superior, surpassing all single measures across the considered assessment metrics.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":105621453,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105621453/thumbnails/1.jpg","file_name":"80.ImpactJaccardFusionNumericalMeasuresCollaborativeFiltering_AmerAbdallaNguyenAlMaqaleh_ResearchSquare_2023.08.29.pdf","download_url":"https://www.academia.edu/attachments/105621453/download_file","bulk_download_file_name":"On_the_Impact_of_Jaccard_Fusion_with_Num.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105621453/80.ImpactJaccardFusionNumericalMeasuresCollaborativeFiltering_AmerAbdallaNguyenAlMaqaleh_ResearchSquare_2023.08.29-libre.pdf?1694268440=\u0026response-content-disposition=attachment%3B+filename%3DOn_the_Impact_of_Jaccard_Fusion_with_Num.pdf\u0026Expires=1738797508\u0026Signature=Qgg4TO32CYM8lus0FZjG3VLihzJ9hw0dfs7zTz9GtqDgXwZHNow-Mw1wlBTbX7xryU4ESsZcF17AcBsYTlSPdyL82doc82MrU5BT~mcxJkop4MNYijGuUPmd5l2DMmkBUieRTJ5zzX6ZJkPnrsgzbSkyIz7foWWSpL~EePXrfxDYH9ESN51sAfKIun650YQ9Z3EMTdSiOqCmKaEuoHwy7dVLH-8wxX4chBnBUt9UVAlerRBNWw-~CsyOPKWdYiffnJMQjtpf6Ig5Z9457bwL85GmC~8yUPe8HSZ5yxDIeI8Rwx~9vq1tK~wTfYTOpcTMjCHXqjJ-ta5ABIbMTJrmpQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":177103,"name":"Similarity Measures","url":"https://www.academia.edu/Documents/in/Similarity_Measures"},{"id":298644,"name":"Recommendation Systems","url":"https://www.academia.edu/Documents/in/Recommendation_Systems"},{"id":862129,"name":"K Nearest Neighbors","url":"https://www.academia.edu/Documents/in/K_Nearest_Neighbors"},{"id":995498,"name":"Jaccard Index","url":"https://www.academia.edu/Documents/in/Jaccard_Index"}],"urls":[{"id":33852202,"url":"https://www.researchsquare.com/article/rs-3304224/v1"},{"id":33852203,"url":"http://dx.doi.org/10.21203/rs.3.rs-3304224/v1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105636781"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105636781/Tr%C3%AD_tu%E1%BB%87_lu%E1%BA%ADn"><img alt="Research paper thumbnail of Trí tuệ luận" class="work-thumbnail" src="https://attachments.academia-assets.com/105037328/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105636781/Tr%C3%AD_tu%E1%BB%87_lu%E1%BA%ADn">Trí tuệ luận</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>OSF Preprints</span><span>, Jul 29, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Trí tuệ phức tạp, tinh vi, đậm đặc, khả trắc hay phân tán đến rỗng không với những nghịch lý tồn ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Trí tuệ phức tạp, tinh vi, đậm đặc, khả trắc hay phân tán đến rỗng không với những nghịch lý tồn tại trong thế giới. Trong bài viết này tôi mượn ngành trí tuệ nhân tạo mồi lửa những luận bàn về trí tuệ với nương tựa vào thuyết tính không mà nếu không có tính không tôi sẽ bế tắc trong vòng lẩn quẩn của biện luận và lý giải.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="809df0091025daaa896ed292deb4a25a" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105037328,"asset_id":105636781,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105037328/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105636781"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105636781"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105636781; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105636781]").text(description); $(".js-view-count[data-work-id=105636781]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105636781; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105636781']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); 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$(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105636957"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105636957/Tutorial_on_artificial_neural_network"><img alt="Research paper thumbnail of Tutorial on artificial neural network" class="work-thumbnail" src="https://attachments.academia-assets.com/116158122/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105636957/Tutorial_on_artificial_neural_network">Tutorial on artificial neural network</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>OSF Preprints</span><span>, Jul 1, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">It is undoubtful that artificial intelligence (AI) is being the trend of computer science and thi...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e7f9f68ff42f1a84c99fb2b37982e60c" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":116158122,"asset_id":105636957,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/116158122/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105636957"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105636957"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105636957; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105636957]").text(description); $(".js-view-count[data-work-id=105636957]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105636957; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105636957']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e7f9f68ff42f1a84c99fb2b37982e60c" } } $('.js-work-strip[data-work-id=105636957]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105636957,"title":"Tutorial on artificial neural network","translated_title":"","metadata":{"doi":"10.31219/osf.io/k8syc","abstract":"It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.","event_date":{"day":1,"month":7,"year":2023,"errors":{}},"ai_title_tag":"Introduction to Artificial Neural Networks","journal_name":"OSF Preprints","organization":"Open Science Framework (OSF)","publication_date":{"day":1,"month":7,"year":2023,"errors":{}},"publication_name":"OSF Preprints","conference_end_date":{"day":1,"month":7,"year":2023,"errors":{}},"conference_start_date":{"day":1,"month":7,"year":2023,"errors":{}}},"translated_abstract":"It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.","internal_url":"https://www.academia.edu/105636957/Tutorial_on_artificial_neural_network","translated_internal_url":"","created_at":"2023-08-15T22:56:56.698-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":40228655,"work_id":105636957,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Tutorial on artificial neural network"}],"downloadable_attachments":[{"id":116158122,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158122/thumbnails/1.jpg","file_name":"76.TutorialANN_IIPV3EBS09_G1_2024.05.15.pdf","download_url":"https://www.academia.edu/attachments/116158122/download_file","bulk_download_file_name":"Tutorial_on_artificial_neural_network.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158122/76.TutorialANN_IIPV3EBS09_G1_2024.05.15-libre.pdf?1718794649=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_artificial_neural_network.pdf\u0026Expires=1738797508\u0026Signature=UrhcS-Dc6hxQ~~DVLXHe6Hc7OZdjUL6twsZ9SLFhidFRi6uJZrSfd9rnJdRY1nv4--fIV416UqI6hVdihSQdweFDLqMFerGbl3v5CadtkoneNWRnmzxoXEoHfNcBnId3uscH~GT8Op4yxoe1FL5uaz7-jobWvJC5-D6Qi3HDEGLPJlu3VH89A6tBUk7X-Vr0tbH-qRx6VNy8mU5cudZyoLPsCuvPAfbb9Oyf7EPmUP~BZtG9md1bVsHt2nhcTd162gewUpxUmLHYP8t96xyi2v04x6u7KG~zGYGNH5-5CmOPkg0b4Il2LmdqfbaTFz88UX6qrJz1Fz875w8AdIngDw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":105037389,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105037389/thumbnails/1.jpg","file_name":"76.TutorialANN_OSF_2023.07.01.pdf","download_url":"https://www.academia.edu/attachments/105037389/download_file","bulk_download_file_name":"Tutorial_on_artificial_neural_network.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105037389/76.TutorialANN_OSF_2023.07.01-libre.pdf?1692169190=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_artificial_neural_network.pdf\u0026Expires=1738797508\u0026Signature=JhAiJbg5rO-AGCYBM~P0IOyzwvF3yNNNwiXRDj2Ph6M~3aa6nkUmVrjlKE-~eQ3GzRZGWH0MTqcRqkQ-LXDpzyjNbU18gvY~P-KiD44NrjC2DLA-ZTPCgZixi8yvduB8Cr8OdpFNLRLJM~DYSVn42YiUlmJoyzIlYvMVi2t0wNI0a3f2KomARwt2qXKpgdcdQpORH9-0N5-uNHZdvsVIkc6vNTchV8HNAIp-KPA7TTgdGMLIuHLCSpqMVYGMwZ4LVdJaxEpnZjkFHCSQm~9Q7PUkNWh34ihw4v6BBBPXNR0zg8UtgSePzwLwP~jiZSTLcYV0-DDV1ThLdXGDgr5pqw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tutorial_on_artificial_neural_network","translated_slug":"","page_count":50,"language":"en","content_type":"Work","summary":"It is undoubtful that artificial intelligence (AI) is being the trend of computer science and this trend is still ongoing in the far future even though technologies are being developed suddenly fast because computer science does not reach the limitation of approaching biological world yet. Machine learning (ML), which is a branch of AI, is a spearhead but not a key of AI because it sets first bricks to build up an infinitely long bridge from computer to human intelligence, but it is also vulnerable to environmental changes or input errors. There are three typical types of ML such as supervised learning, unsupervised learning, and reinforcement learning (RL) where RL, which is adapt progressively to environmental changes, can alleviate vulnerability of machine learning but only RL is not enough because the resilience of RL is based on iterative adjustment technique, not based on naturally inherent aspects like data mining approaches and moreover, mathematical fundamentals of RL lean forwards swing of stochastic process. Fortunately, artificial neural network, or neural network (NN) in short, can support all three types of ML including supervised learning, unsupervised learning, and RL where the implicitly regressive mechanism with high order through many layers under NN can improve the resilience of ML. Moreover, applications of NN are plentiful and multiform because three ML types are supported by NN; besides, NN training by backpropagation algorithm is simple and effective, especially for sample of data stream. Therefore, this study research is an introduction to NN with easily understandable explanations about mathematical aspects under NN as a beginning of stepping into deep learning which is based on multilayer NN. Deep learning, which is producing amazing results in the world of AI, is undoubtfully being both spearhead and key of ML with expectation that ML improved itself by deep learning will become both spearhead and key of AI, but this expectation is only for ML researchers because there are many AI subdomains are being invented and developed in such a way that we cannot understand exhaustedly. It is more important to recall that NN, which essentially simulates human neuron system, is appropriate to the philosophy of ML that constructs an infinitely long bridge from computer to human intelligence.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":116158122,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/116158122/thumbnails/1.jpg","file_name":"76.TutorialANN_IIPV3EBS09_G1_2024.05.15.pdf","download_url":"https://www.academia.edu/attachments/116158122/download_file","bulk_download_file_name":"Tutorial_on_artificial_neural_network.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/116158122/76.TutorialANN_IIPV3EBS09_G1_2024.05.15-libre.pdf?1718794649=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_artificial_neural_network.pdf\u0026Expires=1738797508\u0026Signature=UrhcS-Dc6hxQ~~DVLXHe6Hc7OZdjUL6twsZ9SLFhidFRi6uJZrSfd9rnJdRY1nv4--fIV416UqI6hVdihSQdweFDLqMFerGbl3v5CadtkoneNWRnmzxoXEoHfNcBnId3uscH~GT8Op4yxoe1FL5uaz7-jobWvJC5-D6Qi3HDEGLPJlu3VH89A6tBUk7X-Vr0tbH-qRx6VNy8mU5cudZyoLPsCuvPAfbb9Oyf7EPmUP~BZtG9md1bVsHt2nhcTd162gewUpxUmLHYP8t96xyi2v04x6u7KG~zGYGNH5-5CmOPkg0b4Il2LmdqfbaTFz88UX6qrJz1Fz875w8AdIngDw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":105037389,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105037389/thumbnails/1.jpg","file_name":"76.TutorialANN_OSF_2023.07.01.pdf","download_url":"https://www.academia.edu/attachments/105037389/download_file","bulk_download_file_name":"Tutorial_on_artificial_neural_network.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105037389/76.TutorialANN_OSF_2023.07.01-libre.pdf?1692169190=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_artificial_neural_network.pdf\u0026Expires=1738797508\u0026Signature=JhAiJbg5rO-AGCYBM~P0IOyzwvF3yNNNwiXRDj2Ph6M~3aa6nkUmVrjlKE-~eQ3GzRZGWH0MTqcRqkQ-LXDpzyjNbU18gvY~P-KiD44NrjC2DLA-ZTPCgZixi8yvduB8Cr8OdpFNLRLJM~DYSVn42YiUlmJoyzIlYvMVi2t0wNI0a3f2KomARwt2qXKpgdcdQpORH9-0N5-uNHZdvsVIkc6vNTchV8HNAIp-KPA7TTgdGMLIuHLCSpqMVYGMwZ4LVdJaxEpnZjkFHCSQm~9Q7PUkNWh34ihw4v6BBBPXNR0zg8UtgSePzwLwP~jiZSTLcYV0-DDV1ThLdXGDgr5pqw__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks"}],"urls":[{"id":33447403,"url":"https://osf.io/k8syc"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105638425"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105638425/Adversarial_Variational_Autoencoders_to_extend_and_improve_generative_model"><img alt="Research paper thumbnail of Adversarial Variational Autoencoders to extend and improve generative model" class="work-thumbnail" src="https://attachments.academia-assets.com/118000201/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105638425/Adversarial_Variational_Autoencoders_to_extend_and_improve_generative_model">Adversarial Variational Autoencoders to extend and improve generative model</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a>, <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a>, and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/HassanIAbdalla">Hassan I. Abdalla</a></span></div><div class="wp-workCard_item"><span>Preprints</span><span>, Aug 2, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Generative artificial intelligence (GenAI) has been developing with many incredible achievements ...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="4b9e366dbf0880a371ddb6de6bd36ecb" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":118000201,"asset_id":105638425,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/118000201/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105638425"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105638425"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105638425; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105638425]").text(description); $(".js-view-count[data-work-id=105638425]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105638425; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105638425']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "4b9e366dbf0880a371ddb6de6bd36ecb" } } $('.js-work-strip[data-work-id=105638425]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105638425,"title":"Adversarial Variational Autoencoders to extend and improve generative model","translated_title":"","metadata":{"doi":"10.20944/preprints202308.0131.v1","abstract":"Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. 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In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.","event_date":{"day":2,"month":8,"year":2023,"errors":{}},"ai_title_tag":"Adversarial Variational Autoencoders for Enhanced Generative Models","journal_name":"MDPI Preprints","organization":"Multidisciplinary Digital Publishing Institute (MDPI)","publication_date":{"day":2,"month":8,"year":2023,"errors":{}},"publication_name":"Preprints","conference_end_date":{"day":2,"month":8,"year":2023,"errors":{}},"conference_start_date":{"day":2,"month":8,"year":2023,"errors":{}}},"translated_abstract":"Generative artificial intelligence (GenAI) has been developing with many incredible achievements like ChatGPT and Bard. 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Deep generative model (DGM) is a branch of GenAI, which is preeminent in generating raster data such as image and sound due to strong points of deep neural network (DNN) in inference and recognition. The built-in inference mechanism of DNN, which simulates and aims to synaptic plasticity of human neuron network, fosters generation ability of DGM which produces surprised results with support of statistical flexibility. Two popular approaches in DGM are Variational Autoencoders (VAE) and Generative Adversarial Network (GAN). Both VAE and GAN have their own strong points although they share and imply underline theory of statistics as well as incredible complex via hidden layers of DNN when DNN becomes effective encoding/decoding functions without concrete specifications. In this research, VAE and GAN is unified into a consistent and consolidated model called Adversarial Variational Autoencoders (AVA) in which VAE and GAN complement each other, for instance, VAE is good at generator by encoding data via excellent ideology of Kullback-Leibler divergence and GAN is a significantly important method to assess reliability of data which is realistic or fake. In other words, AVA aims to improve accuracy of generative models, besides AVA extends function of simple generative models. In methodology this research focuses on combination of applied mathematical concepts and skillful techniques of computer programming in order to implement and solve complicated problems as simply as possible.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":118000201,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/118000201/thumbnails/1.jpg","file_name":"78.AVA_ARCT_2024_0003_375_2024.08.28.pdf","download_url":"https://www.academia.edu/attachments/118000201/download_file","bulk_download_file_name":"Adversarial_Variational_Autoencoders_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/118000201/78.AVA_ARCT_2024_0003_375_2024.08.28-libre.pdf?1725615710=\u0026response-content-disposition=attachment%3B+filename%3DAdversarial_Variational_Autoencoders_to.pdf\u0026Expires=1738797508\u0026Signature=R~T2PglnL39zxgpTzbHR7h5xUmsMd6NndmhsXAqPtKpjESaX3d454n1Kj1j7xOAJ~up5ysu43XkMqrt4WTdgDMKwv0CETdIhYOM3G8Dr2NUvm-noAfEHTYpbx9udmNp-BhjCMn3O~j52KU6L09pZMKuDyslw~6t6StNMJZ8dXgh5qshDsUplWxfSE3p56H0rKb6G5dh~YYPqCXOiAr9wXUFSYijJZNDea78XBIGrWF1ibynXRuWGa6RQFvlfRznphm1n09MR5byGuv39q9xh19w9szHG3dxhTtelrgL5qQeFDliK3dblALDXsvPPbmrEKsDs5D3KSlsYOujo-F9LWQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":105039003,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105039003/thumbnails/1.jpg","file_name":"78.AVA_Preprints_preprints202308.0131.v1_2023.08.02.pdf","download_url":"https://www.academia.edu/attachments/105039003/download_file","bulk_download_file_name":"Adversarial_Variational_Autoencoders_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105039003/78.AVA_Preprints_preprints202308.0131.v1_2023.08.02-libre.pdf?1692168930=\u0026response-content-disposition=attachment%3B+filename%3DAdversarial_Variational_Autoencoders_to.pdf\u0026Expires=1738797508\u0026Signature=frzwkiWOZyGGIczVyT56MKo5NnWNeJSJUx~n1ISNVAAlj0sAQACYfSw50A1quX20RE0AwNUwkUZaQxW4b-rKdw35hPPf9aYUemC8JwM3zIr-YzuRbGatlkkssNqKBGnbNroEc1hqXtdsSBWDfqGlIZBLsadkTBwIykc0ULMc0v6XUQCDt2gJbBFea3xhxaW25FDB3G-QIVMNu8igFnR4VsYmZJAG1o9XBEI2NVrQ4vvrJ4VzW~KekvWf6DY6JweTy6YFD-vLiNcctrbIcg5zYvXVrN-2rD8AhlenBnu0FXftResGi-U9koT0TlI72kwKk6WwcaDTkzLrreGKCRyVAA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"},{"id":113420589,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/113420589/thumbnails/1.jpg","file_name":"78.AVA_Preprints_preprints202308.0131.v2_2024.04.16.pdf","download_url":"https://www.academia.edu/attachments/113420589/download_file","bulk_download_file_name":"Adversarial_Variational_Autoencoders_to.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/113420589/78.AVA_Preprints_preprints202308.0131.v2_2024.04.16-libre.pdf?1713324247=\u0026response-content-disposition=attachment%3B+filename%3DAdversarial_Variational_Autoencoders_to.pdf\u0026Expires=1738797508\u0026Signature=Xhor1X0qJSC1OEuyeZKPTicXj7xME1b~-LK2xrHoudEL8-a-Ef5WmDiYtMbGA7sERM1Dyau-IiNHq-ybFmZPYQ4dobH3z20fQAw-lSwWr4HAZky3Df4uA5PPn6DuZDfgW06nMwjFgnybBUZxFAQ5m0K7QGWZfsgKoxQzJEEzM0ukWGmGFgMchDDpmYAs1mshyu-WTv5yXlnQLWi4Hgzsv3IyVBs7WbZcPKLCFIF7OzbnR96g8sR-qaA8qrgYNmRNph9QNG~5Dz8acRziX~KNz-vYjBO-rJb29vjJ3vlkhPzANinD3qv4EYKJt1CKtN5t9ox5y4jeavMzbuGNxFCbLQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":2655288,"name":"Generative Adversarial Networks","url":"https://www.academia.edu/Documents/in/Generative_Adversarial_Networks"},{"id":3184567,"name":"Variational Autoencoders","url":"https://www.academia.edu/Documents/in/Variational_Autoencoders"},{"id":4024741,"name":"deep generative model","url":"https://www.academia.edu/Documents/in/deep_generative_model"}],"urls":[{"id":33448008,"url":"https://www.preprints.org/manuscript/202308.0131/v1"},{"id":44498118,"url":"https://www.intechopen.com/journals/1/articles/375"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="105638622"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/105638622/Simple_image_deconvolution_based_on_reverse_image_convolution_and_backpropagation_algorithm"><img alt="Research paper thumbnail of Simple image deconvolution based on reverse image convolution and backpropagation algorithm" class="work-thumbnail" src="https://attachments.academia-assets.com/105039174/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/105638622/Simple_image_deconvolution_based_on_reverse_image_convolution_and_backpropagation_algorithm">Simple image deconvolution based on reverse image convolution and backpropagation algorithm</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Research Square Preprint</span><span>, Aug 13, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Deconvolution task is not important in convolutional neural network (CNN) because it is not imper...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Deconvolution task is not important in convolutional neural network (CNN) because it is not imperative to recover convoluted image when convolutional layer is important to extract features. However, the deconvolution task is useful in some cases of inspecting and reflecting a convolutional filter as well as trying to improve a generated image when information loss is not serious with regard to trade-off of information loss and specific features such as edge detection and sharpening. This research proposes a duplicated and reverse process of recovering a filtered image. Firstly, source layer and target layer are reversed in accordance with traditional image convolution so as to train the convolutional filter. Secondly, the trained filter is reversed again to derive a deconvolutional operator for recovering the filtered image. The reverse process is associated with backpropagation algorithm which is most popular in learning neural network. Experimental results show that the proposed technique in this research is better to learn the filters that focus on discovering pixel differences. Therefore, the main contribution of this research is to inspect convolutional filters from data.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="d008ce529e1718187232bd461646fede" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":105039174,"asset_id":105638622,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/105039174/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="105638622"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="105638622"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 105638622; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=105638622]").text(description); $(".js-view-count[data-work-id=105638622]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 105638622; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='105638622']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "d008ce529e1718187232bd461646fede" } } $('.js-work-strip[data-work-id=105638622]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":105638622,"title":"Simple image deconvolution based on reverse image convolution and backpropagation algorithm","translated_title":"","metadata":{"doi":"10.21203/rs.3.rs-3247106/v1","abstract":"Deconvolution task is not important in convolutional neural network (CNN) because it is not imperative to recover convoluted image when convolutional layer is important to extract features. However, the deconvolution task is useful in some cases of inspecting and reflecting a convolutional filter as well as trying to improve a generated image when information loss is not serious with regard to trade-off of information loss and specific features such as edge detection and sharpening. This research proposes a duplicated and reverse process of recovering a filtered image. Firstly, source layer and target layer are reversed in accordance with traditional image convolution so as to train the convolutional filter. Secondly, the trained filter is reversed again to derive a deconvolutional operator for recovering the filtered image. The reverse process is associated with backpropagation algorithm which is most popular in learning neural network. Experimental results show that the proposed technique in this research is better to learn the filters that focus on discovering pixel differences. Therefore, the main contribution of this research is to inspect convolutional filters from data.","event_date":{"day":13,"month":8,"year":2023,"errors":{}},"journal_name":"Research Square Preprint","organization":"Research Square","publication_date":{"day":13,"month":8,"year":2023,"errors":{}},"publication_name":"Research Square Preprint","conference_end_date":{"day":13,"month":8,"year":2023,"errors":{}},"conference_start_date":{"day":13,"month":8,"year":2023,"errors":{}}},"translated_abstract":"Deconvolution task is not important in convolutional neural network (CNN) because it is not imperative to recover convoluted image when convolutional layer is important to extract features. However, the deconvolution task is useful in some cases of inspecting and reflecting a convolutional filter as well as trying to improve a generated image when information loss is not serious with regard to trade-off of information loss and specific features such as edge detection and sharpening. This research proposes a duplicated and reverse process of recovering a filtered image. Firstly, source layer and target layer are reversed in accordance with traditional image convolution so as to train the convolutional filter. Secondly, the trained filter is reversed again to derive a deconvolutional operator for recovering the filtered image. The reverse process is associated with backpropagation algorithm which is most popular in learning neural network. Experimental results show that the proposed technique in this research is better to learn the filters that focus on discovering pixel differences. Therefore, the main contribution of this research is to inspect convolutional filters from data.","internal_url":"https://www.academia.edu/105638622/Simple_image_deconvolution_based_on_reverse_image_convolution_and_backpropagation_algorithm","translated_internal_url":"","created_at":"2023-08-15T23:53:43.149-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":40228830,"work_id":105638622,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Simple image deconvolution based on reverse image convolution and backpropagation algorithm"}],"downloadable_attachments":[{"id":105039174,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105039174/thumbnails/1.jpg","file_name":"79.Deconv_ResearchSquare_2023.08.14.pdf","download_url":"https://www.academia.edu/attachments/105039174/download_file","bulk_download_file_name":"Simple_image_deconvolution_based_on_reve.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105039174/79.Deconv_ResearchSquare_2023.08.14-libre.pdf?1692168911=\u0026response-content-disposition=attachment%3B+filename%3DSimple_image_deconvolution_based_on_reve.pdf\u0026Expires=1738797508\u0026Signature=QDPCZ3MmwamIjgSPDcwi9QEUisfG52TCh9AFWIs1HDcHC2Nun-fVOfCsCHlcTJBq-hABm0yGImoyIa9RoSi2ihJxFxDwBjOcDI~3280-hS2FY8Ez8vd9lMnc-MgbzloLzEanlSyPkMB39lxGA~y9L7~VXmuRlkM5GwINCTM8~ELDUarNkJW1uN95jdrGra~XESxCe~hfNTAQ2UP~HQ5ZZKTJ6ujuv0LqiqibzZvgcDOxkBS8p28iCzrQtiJ67yeFsnUWUZ8Kh7H-Y1xBt3wxzw568gbfETOHZG2UyRFtdaU9bE9-ZR9WLjzNiLqJ2nl65DzPS7pHby3sZg0hhKpSkg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Simple_image_deconvolution_based_on_reverse_image_convolution_and_backpropagation_algorithm","translated_slug":"","page_count":8,"language":"en","content_type":"Work","summary":"Deconvolution task is not important in convolutional neural network (CNN) because it is not imperative to recover convoluted image when convolutional layer is important to extract features. However, the deconvolution task is useful in some cases of inspecting and reflecting a convolutional filter as well as trying to improve a generated image when information loss is not serious with regard to trade-off of information loss and specific features such as edge detection and sharpening. This research proposes a duplicated and reverse process of recovering a filtered image. Firstly, source layer and target layer are reversed in accordance with traditional image convolution so as to train the convolutional filter. Secondly, the trained filter is reversed again to derive a deconvolutional operator for recovering the filtered image. The reverse process is associated with backpropagation algorithm which is most popular in learning neural network. Experimental results show that the proposed technique in this research is better to learn the filters that focus on discovering pixel differences. Therefore, the main contribution of this research is to inspect convolutional filters from data.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":105039174,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/105039174/thumbnails/1.jpg","file_name":"79.Deconv_ResearchSquare_2023.08.14.pdf","download_url":"https://www.academia.edu/attachments/105039174/download_file","bulk_download_file_name":"Simple_image_deconvolution_based_on_reve.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/105039174/79.Deconv_ResearchSquare_2023.08.14-libre.pdf?1692168911=\u0026response-content-disposition=attachment%3B+filename%3DSimple_image_deconvolution_based_on_reve.pdf\u0026Expires=1738797508\u0026Signature=QDPCZ3MmwamIjgSPDcwi9QEUisfG52TCh9AFWIs1HDcHC2Nun-fVOfCsCHlcTJBq-hABm0yGImoyIa9RoSi2ihJxFxDwBjOcDI~3280-hS2FY8Ez8vd9lMnc-MgbzloLzEanlSyPkMB39lxGA~y9L7~VXmuRlkM5GwINCTM8~ELDUarNkJW1uN95jdrGra~XESxCe~hfNTAQ2UP~HQ5ZZKTJ6ujuv0LqiqibzZvgcDOxkBS8p28iCzrQtiJ67yeFsnUWUZ8Kh7H-Y1xBt3wxzw568gbfETOHZG2UyRFtdaU9bE9-ZR9WLjzNiLqJ2nl65DzPS7pHby3sZg0hhKpSkg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1185,"name":"Image Processing","url":"https://www.academia.edu/Documents/in/Image_Processing"},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning"},{"id":1433808,"name":"Convolutional Neural Networks","url":"https://www.academia.edu/Documents/in/Convolutional_Neural_Networks"},{"id":1957837,"name":"Image deconvolution","url":"https://www.academia.edu/Documents/in/Image_deconvolution"},{"id":2240993,"name":"Backpropagation Algorithm","url":"https://www.academia.edu/Documents/in/Backpropagation_Algorithm"}],"urls":[{"id":33448091,"url":"https://www.researchsquare.com/article/rs-3247106/v1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="99172042"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/99172042/Tutorial_on_Bayesian_Optimization"><img alt="Research paper thumbnail of Tutorial on Bayesian Optimization" class="work-thumbnail" src="https://attachments.academia-assets.com/100329972/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/99172042/Tutorial_on_Bayesian_Optimization">Tutorial on Bayesian Optimization</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Preprints 2023, 2023030292</span><span>, Mar 16, 2023</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Machine learning forks into three main branches such as supervised learning, unsupervised learnin...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Machine learning forks into three main branches such as supervised learning, unsupervised learning, and reinforcement learning where reinforcement learning is much potential to artificial intelligence (AI) applications because it solves real problems by progressive process in which possible solutions are improved and finetuned continuously. The progressive approach, which reflects ability of adaptation, is appropriate to the real world where most events occur and change continuously and unexpectedly. Moreover, data is getting too huge for supervised learning and unsupervised learning to draw valuable knowledge from such huge data at one time. Bayesian optimization (BO) models an optimization problem as a probabilistic form called surrogate model and then directly maximizes an acquisition function created from such surrogate model in order to maximize implicitly and indirectly the target function for finding out solution of the optimization problem. A popular surrogate model is Gaussian process regression model. The process of maximizing acquisition function is based on updating posterior probability of surrogate model repeatedly, which is improved after every iteration. Taking advantages of acquisition function or utility function is also common in decision theory but the semantic meaning behind BO is that BO solves problems by progressive and adaptive approach via updating surrogate model from a small piece of data at each time, according to ideology of reinforcement learning. Undoubtedly, BO is a reinforcement learning algorithm with many potential applications and thus it is surveyed in this research with attention to its mathematical ideas. Moreover, the solution of optimization problem is important to not only applied mathematics but also AI.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e3b982fd99b2e4b05f7cdcbafb1c6c10" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100329972,"asset_id":99172042,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100329972/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="99172042"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="99172042"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 99172042; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=99172042]").text(description); $(".js-view-count[data-work-id=99172042]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 99172042; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='99172042']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e3b982fd99b2e4b05f7cdcbafb1c6c10" } } $('.js-work-strip[data-work-id=99172042]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":99172042,"title":"Tutorial on Bayesian Optimization","translated_title":"","metadata":{"doi":"10.20944/preprints202303.0292.v1","abstract":"Machine learning forks into three main branches such as supervised learning, unsupervised learning, and reinforcement learning where reinforcement learning is much potential to artificial intelligence (AI) applications because it solves real problems by progressive process in which possible solutions are improved and finetuned continuously. The progressive approach, which reflects ability of adaptation, is appropriate to the real world where most events occur and change continuously and unexpectedly. Moreover, data is getting too huge for supervised learning and unsupervised learning to draw valuable knowledge from such huge data at one time. Bayesian optimization (BO) models an optimization problem as a probabilistic form called surrogate model and then directly maximizes an acquisition function created from such surrogate model in order to maximize implicitly and indirectly the target function for finding out solution of the optimization problem. A popular surrogate model is Gaussian process regression model. The process of maximizing acquisition function is based on updating posterior probability of surrogate model repeatedly, which is improved after every iteration. Taking advantages of acquisition function or utility function is also common in decision theory but the semantic meaning behind BO is that BO solves problems by progressive and adaptive approach via updating surrogate model from a small piece of data at each time, according to ideology of reinforcement learning. Undoubtedly, BO is a reinforcement learning algorithm with many potential applications and thus it is surveyed in this research with attention to its mathematical ideas. Moreover, the solution of optimization problem is important to not only applied mathematics but also AI.","event_date":{"day":16,"month":3,"year":2023,"errors":{}},"journal_name":"Preprints","organization":"Multidisciplinary Digital Publishing Institute (MDPI)","publication_date":{"day":16,"month":3,"year":2023,"errors":{}},"publication_name":"Preprints 2023, 2023030292","conference_end_date":{"day":16,"month":3,"year":2023,"errors":{}},"conference_start_date":{"day":16,"month":3,"year":2023,"errors":{}}},"translated_abstract":"Machine learning forks into three main branches such as supervised learning, unsupervised learning, and reinforcement learning where reinforcement learning is much potential to artificial intelligence (AI) applications because it solves real problems by progressive process in which possible solutions are improved and finetuned continuously. The progressive approach, which reflects ability of adaptation, is appropriate to the real world where most events occur and change continuously and unexpectedly. Moreover, data is getting too huge for supervised learning and unsupervised learning to draw valuable knowledge from such huge data at one time. Bayesian optimization (BO) models an optimization problem as a probabilistic form called surrogate model and then directly maximizes an acquisition function created from such surrogate model in order to maximize implicitly and indirectly the target function for finding out solution of the optimization problem. A popular surrogate model is Gaussian process regression model. The process of maximizing acquisition function is based on updating posterior probability of surrogate model repeatedly, which is improved after every iteration. Taking advantages of acquisition function or utility function is also common in decision theory but the semantic meaning behind BO is that BO solves problems by progressive and adaptive approach via updating surrogate model from a small piece of data at each time, according to ideology of reinforcement learning. Undoubtedly, BO is a reinforcement learning algorithm with many potential applications and thus it is surveyed in this research with attention to its mathematical ideas. Moreover, the solution of optimization problem is important to not only applied mathematics but also AI.","internal_url":"https://www.academia.edu/99172042/Tutorial_on_Bayesian_Optimization","translated_internal_url":"","created_at":"2023-03-27T06:09:28.579-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":39672902,"work_id":99172042,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Tutorial on Bayesian Optimization"}],"downloadable_attachments":[{"id":100329972,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100329972/thumbnails/1.jpg","file_name":"75.TutorialBO_preprints202303.0292.v1.pdf","download_url":"https://www.academia.edu/attachments/100329972/download_file","bulk_download_file_name":"Tutorial_on_Bayesian_Optimization.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100329972/75.TutorialBO_preprints202303.0292.v1-libre.pdf?1679922669=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_Bayesian_Optimization.pdf\u0026Expires=1738797509\u0026Signature=FLbD6HfpJ4SugAVNUNH2jPCpQuhZ5cNVFAPCIulq2FwowA1YqFAqH25~5p6E5S9NM3a0YGwySenWQIyWOCrwfx6m2OtPPzYTWSB35QijtjsuKm8466Kl-H4Wxf2nbIggiCkFx0g07KcOMR~0WsZ9lyhDk1kvMEkagCUj0-YD4a7XpLXsk-x5BsSITZIl40pP3YSu0i1jJk7Casyy5zvzKEfekeq-l~O~-j5TBLWDy0Qk8~P4ksMl3mP0V2cZOR4NuI~Qv0XzX2klpPbv2vkpDdmG8rKIOnOinhHJC5UpZzLKyhY60PIt~LEq~3DYZ3kHkSGJT~B7gafSEm6Fze9uAA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tutorial_on_Bayesian_Optimization","translated_slug":"","page_count":24,"language":"en","content_type":"Work","summary":"Machine learning forks into three main branches such as supervised learning, unsupervised learning, and reinforcement learning where reinforcement learning is much potential to artificial intelligence (AI) applications because it solves real problems by progressive process in which possible solutions are improved and finetuned continuously. The progressive approach, which reflects ability of adaptation, is appropriate to the real world where most events occur and change continuously and unexpectedly. Moreover, data is getting too huge for supervised learning and unsupervised learning to draw valuable knowledge from such huge data at one time. Bayesian optimization (BO) models an optimization problem as a probabilistic form called surrogate model and then directly maximizes an acquisition function created from such surrogate model in order to maximize implicitly and indirectly the target function for finding out solution of the optimization problem. A popular surrogate model is Gaussian process regression model. The process of maximizing acquisition function is based on updating posterior probability of surrogate model repeatedly, which is improved after every iteration. Taking advantages of acquisition function or utility function is also common in decision theory but the semantic meaning behind BO is that BO solves problems by progressive and adaptive approach via updating surrogate model from a small piece of data at each time, according to ideology of reinforcement learning. Undoubtedly, BO is a reinforcement learning algorithm with many potential applications and thus it is surveyed in this research with attention to its mathematical ideas. Moreover, the solution of optimization problem is important to not only applied mathematics but also AI.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":100329972,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100329972/thumbnails/1.jpg","file_name":"75.TutorialBO_preprints202303.0292.v1.pdf","download_url":"https://www.academia.edu/attachments/100329972/download_file","bulk_download_file_name":"Tutorial_on_Bayesian_Optimization.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100329972/75.TutorialBO_preprints202303.0292.v1-libre.pdf?1679922669=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_Bayesian_Optimization.pdf\u0026Expires=1738797509\u0026Signature=FLbD6HfpJ4SugAVNUNH2jPCpQuhZ5cNVFAPCIulq2FwowA1YqFAqH25~5p6E5S9NM3a0YGwySenWQIyWOCrwfx6m2OtPPzYTWSB35QijtjsuKm8466Kl-H4Wxf2nbIggiCkFx0g07KcOMR~0WsZ9lyhDk1kvMEkagCUj0-YD4a7XpLXsk-x5BsSITZIl40pP3YSu0i1jJk7Casyy5zvzKEfekeq-l~O~-j5TBLWDy0Qk8~P4ksMl3mP0V2cZOR4NuI~Qv0XzX2klpPbv2vkpDdmG8rKIOnOinhHJC5UpZzLKyhY60PIt~LEq~3DYZ3kHkSGJT~B7gafSEm6Fze9uAA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":535039,"name":"Bayesian Optimization","url":"https://www.academia.edu/Documents/in/Bayesian_Optimization"},{"id":958135,"name":"Gaussian Process Regression","url":"https://www.academia.edu/Documents/in/Gaussian_Process_Regression"}],"urls":[{"id":30107105,"url":"https://www.preprints.org/manuscript/202303.0292/v1"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="91374967"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/91374967/Extreme_bound_analysis_based_on_correlation_coefficient_for_optimal_regression_model"><img alt="Research paper thumbnail of Extreme bound analysis based on correlation coefficient for optimal regression model" class="work-thumbnail" src="https://attachments.academia-assets.com/95690502/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/91374967/Extreme_bound_analysis_based_on_correlation_coefficient_for_optimal_regression_model">Extreme bound analysis based on correlation coefficient for optimal regression model</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>OSF Preprints</span><span>, Nov 18, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Regression analysis is an important tool in statistical analysis, in which there is a demand of d...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Regression analysis is an important tool in statistical analysis, in which there is a demand of discovering essential independent variables among many other ones, especially in case that there is a huge number of random variables. Extreme bound analysis is a powerful approach to extract such important variables called robust regressors. In this research, a so-called Regressive Expectation Maximization with RObust regressors (REMRO) algorithm is proposed as an alternative method beside other probabilistic methods for analyzing robust variables.  By the different ideology from other probabilistic methods, REMRO searches for robust regressors forming optimal regression model and sorts them according to descending ordering given their fitness values determined by two proposed concepts of local correlation and global correlation. Local correlation represents sufficient explanatories to possible regressive models and global correlation reflects independence level and stand-alone capacity of regressors. Moreover, REMRO can resist incomplete data because it applies Regressive Expectation Maximization (REM) algorithm into filling missing values by estimated values based on ideology of expectation maximization (EM) algorithm. From experimental results, REMRO is more accurate for modeling numeric regressors than traditional probabilistic methods like Sala-I-Martin method but REMRO cannot be applied in case of nonnumeric regression model yet in this research.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="e943b18f59801aebc435659b5ae570a5" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":95690502,"asset_id":91374967,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/95690502/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="91374967"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="91374967"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 91374967; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=91374967]").text(description); $(".js-view-count[data-work-id=91374967]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 91374967; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='91374967']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "e943b18f59801aebc435659b5ae570a5" } } $('.js-work-strip[data-work-id=91374967]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":91374967,"title":"Extreme bound analysis based on correlation coefficient for optimal regression model","translated_title":"","metadata":{"doi":"10.18637/jss.v072.i09","abstract":"Regression analysis is an important tool in statistical analysis, in which there is a demand of discovering essential independent variables among many other ones, especially in case that there is a huge number of random variables. 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Recently, some evolutional algorithms which are inspired from biological activities are proposed to solve the global optimization by acceptable heuristic level. Among them is particle swarm optimization (PSO) algorithm which is proved as an effective and feasible solution for global optimization in real applications. Although the ideology of PSO is not complicated, it derives many variants, which can make new researchers confused. Therefore, this tutorial focuses on describing, systemizing, and classifying PSO by succinct and straightforward way. Moreover, combinations of PSO and other evolutional algorithms for improving PSO itself or solving other advanced problems are mentioned too.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="257ee6b30c4c6dd22685c921282eb109" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":100384852,"asset_id":89679184,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/100384852/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="89679184"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="89679184"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 89679184; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=89679184]").text(description); $(".js-view-count[data-work-id=89679184]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 89679184; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='89679184']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "257ee6b30c4c6dd22685c921282eb109" } } $('.js-work-strip[data-work-id=89679184]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":89679184,"title":"Tutorial on particle swarm optimization and its combinations to other evolutionary algorithms","translated_title":"","metadata":{"doi":"10.31219/osf.io/hs5bj","abstract":"Local optimization with convex function is solved perfectly by traditional mathematical methods such as Newton-Raphson and gradient descent but it is not easy to solve the global optimization with arbitrary function although there are some purely mathematical approaches such as approximation, cutting plane, branch and bound, and interval method which can be impractical because of their complexity and high computation cost. Recently, some evolutional algorithms which are inspired from biological activities are proposed to solve the global optimization by acceptable heuristic level. Among them is particle swarm optimization (PSO) algorithm which is proved as an effective and feasible solution for global optimization in real applications. Although the ideology of PSO is not complicated, it derives many variants, which can make new researchers confused. Therefore, this tutorial focuses on describing, systemizing, and classifying PSO by succinct and straightforward way. Moreover, combinations of PSO and other evolutional algorithms for improving PSO itself or solving other advanced problems are mentioned too.","event_date":{"day":1,"month":11,"year":2022,"errors":{}},"organization":"Open Science Framework (OSF)","publication_date":{"day":1,"month":11,"year":2022,"errors":{}},"publication_name":"Open Science Framework (OSF) Preprints","conference_end_date":{"day":1,"month":11,"year":2022,"errors":{}},"conference_start_date":{"day":1,"month":11,"year":2022,"errors":{}}},"translated_abstract":"Local optimization with convex function is solved perfectly by traditional mathematical methods such as Newton-Raphson and gradient descent but it is not easy to solve the global optimization with arbitrary function although there are some purely mathematical approaches such as approximation, cutting plane, branch and bound, and interval method which can be impractical because of their complexity and high computation cost. 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Abdalla","title":"Tutorial on particle swarm optimization and its combinations to other evolutionary algorithms"},{"id":39680387,"work_id":89679184,"tagging_user_id":12043864,"tagged_user_id":26698005,"co_author_invite_id":null,"email":"a***6@yahoo.co.uk","display_order":3,"name":"Ali Amer","title":"Tutorial on particle swarm optimization and its combinations to other evolutionary algorithms"}],"downloadable_attachments":[{"id":100384852,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100384852/thumbnails/1.jpg","file_name":"73.TutorialPSO_OSF_2023.03.28.pdf","download_url":"https://www.academia.edu/attachments/100384852/download_file","bulk_download_file_name":"Tutorial_on_particle_swarm_optimization.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100384852/73.TutorialPSO_OSF_2023.03.28-libre.pdf?1680055588=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_particle_swarm_optimization.pdf\u0026Expires=1738797509\u0026Signature=gY~424HXtDWW14oUn1rJphw5Vol3niJhpustXWidY1RPBUJe~tmS8hc6wEVcqPGXcYMw0xuMv8XdTjE8Q99uzcH8h7EL8uFGp2mBm5b9JCvLPcDz0llJVt2fZmQl90AcRwB-uW4cRanXAbVZP5st0H274baDt9uusf1iWBUby5aWpY9P4UyniUu7J43tz3mqT0tmyRw76dbR65tbbG~2tuAKoOtd1OAXha1rOkJi9vn5aq3-oXnQXpNGsySVelcNBMIBByJIUGBBm0vjZ56De~fGM0Jlc21J8LV0PQIa-vEvmapq0gQUHKhxpQiOKnifz-8cvcPOFgAwEuK0YokK9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Tutorial_on_particle_swarm_optimization_and_its_combinations_to_other_evolutionary_algorithms","translated_slug":"","page_count":26,"language":"en","content_type":"Work","summary":"Local optimization with convex function is solved perfectly by traditional mathematical methods such as Newton-Raphson and gradient descent but it is not easy to solve the global optimization with arbitrary function although there are some purely mathematical approaches such as approximation, cutting plane, branch and bound, and interval method which can be impractical because of their complexity and high computation cost. Recently, some evolutional algorithms which are inspired from biological activities are proposed to solve the global optimization by acceptable heuristic level. Among them is particle swarm optimization (PSO) algorithm which is proved as an effective and feasible solution for global optimization in real applications. Although the ideology of PSO is not complicated, it derives many variants, which can make new researchers confused. Therefore, this tutorial focuses on describing, systemizing, and classifying PSO by succinct and straightforward way. Moreover, combinations of PSO and other evolutional algorithms for improving PSO itself or solving other advanced problems are mentioned too.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":100384852,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/100384852/thumbnails/1.jpg","file_name":"73.TutorialPSO_OSF_2023.03.28.pdf","download_url":"https://www.academia.edu/attachments/100384852/download_file","bulk_download_file_name":"Tutorial_on_particle_swarm_optimization.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/100384852/73.TutorialPSO_OSF_2023.03.28-libre.pdf?1680055588=\u0026response-content-disposition=attachment%3B+filename%3DTutorial_on_particle_swarm_optimization.pdf\u0026Expires=1738797509\u0026Signature=gY~424HXtDWW14oUn1rJphw5Vol3niJhpustXWidY1RPBUJe~tmS8hc6wEVcqPGXcYMw0xuMv8XdTjE8Q99uzcH8h7EL8uFGp2mBm5b9JCvLPcDz0llJVt2fZmQl90AcRwB-uW4cRanXAbVZP5st0H274baDt9uusf1iWBUby5aWpY9P4UyniUu7J43tz3mqT0tmyRw76dbR65tbbG~2tuAKoOtd1OAXha1rOkJi9vn5aq3-oXnQXpNGsySVelcNBMIBByJIUGBBm0vjZ56De~fGM0Jlc21J8LV0PQIa-vEvmapq0gQUHKhxpQiOKnifz-8cvcPOFgAwEuK0YokK9g__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":25896,"name":"Particle Swarm Optimization","url":"https://www.academia.edu/Documents/in/Particle_Swarm_Optimization"},{"id":107131,"name":"Global Optimization","url":"https://www.academia.edu/Documents/in/Global_Optimization"}],"urls":[{"id":25388386,"url":"https://osf.io/hs5bj"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83634207"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83634207/Triangular_Learner_Model"><img alt="Research paper thumbnail of Triangular Learner Model" class="work-thumbnail" src="https://attachments.academia-assets.com/88914528/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83634207/Triangular_Learner_Model">Triangular Learner Model</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>OSF Preprints</span><span>, Jun 28, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">User model is description of users' information and characteristics in abstract level. User model...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">User model is description of users' information and characteristics in abstract level. User model is very important to adaptive software which aims to support user as much as possible. The process to construct user model is called user modeling. Within learning context where users are learners, the research proposes a so-called Triangular Learner Model (TLM) which is composed of three essential learners' properties such as knowledge, learning style, and learning history. TLM is the user model that supports built-in inference mechanism. So the strong point of TLM is to reason out new information from users, based on mathematical tools. This paper focuses on fundamental algorithms and mathematical tools to construct three basic components of TLM such as knowledge sub-model, learning style sub-model, and learning history submodel. In general, the paper is a summary of results from research on TLM. Algorithms and formulas are described by the succinct way.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="37c514193e8c7ac368d2b222d40e4f1e" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88914528,"asset_id":83634207,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88914528/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83634207"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83634207"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83634207; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83634207]").text(description); $(".js-view-count[data-work-id=83634207]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83634207; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83634207']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "37c514193e8c7ac368d2b222d40e4f1e" } } $('.js-work-strip[data-work-id=83634207]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83634207,"title":"Triangular Learner Model","translated_title":"","metadata":{"doi":"10.31219/osf.io/42cbn","abstract":"User model is description of users' information and characteristics in abstract level. User model is very important to adaptive software which aims to support user as much as possible. The process to construct user model is called user modeling. Within learning context where users are learners, the research proposes a so-called Triangular Learner Model (TLM) which is composed of three essential learners' properties such as knowledge, learning style, and learning history. TLM is the user model that supports built-in inference mechanism. So the strong point of TLM is to reason out new information from users, based on mathematical tools. This paper focuses on fundamental algorithms and mathematical tools to construct three basic components of TLM such as knowledge sub-model, learning style sub-model, and learning history submodel. In general, the paper is a summary of results from research on TLM. 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Within learning context where users are learners, the research proposes a so-called Triangular Learner Model (TLM) which is composed of three essential learners' properties such as knowledge, learning style, and learning history. TLM is the user model that supports built-in inference mechanism. So the strong point of TLM is to reason out new information from users, based on mathematical tools. This paper focuses on fundamental algorithms and mathematical tools to construct three basic components of TLM such as knowledge sub-model, learning style sub-model, and learning history submodel. In general, the paper is a summary of results from research on TLM. Algorithms and formulas are described by the succinct way.","internal_url":"https://www.academia.edu/83634207/Triangular_Learner_Model","translated_internal_url":"","created_at":"2022-07-24T01:48:34.540-07:00","preview_url":null,"current_user_can_edit":null,"current_user_is_owner":null,"owner_id":12043864,"coauthors_can_edit":true,"document_type":"draft","co_author_tags":[{"id":38575807,"work_id":83634207,"tagging_user_id":12043864,"tagged_user_id":88862579,"co_author_invite_id":null,"email":"n***c@gmail.com","display_order":1,"name":"Loc Nguyen","title":"Triangular Learner Model"}],"downloadable_attachments":[{"id":88914528,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88914528/thumbnails/1.jpg","file_name":"07.TLM_OSF_2022.06.28.pdf","download_url":"https://www.academia.edu/attachments/88914528/download_file","bulk_download_file_name":"Triangular_Learner_Model.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88914528/07.TLM_OSF_2022.06.28-libre.pdf?1658653256=\u0026response-content-disposition=attachment%3B+filename%3DTriangular_Learner_Model.pdf\u0026Expires=1738797509\u0026Signature=aBNIbiLF8b-xXDKpDuPgy9GlMyJ5f5vC2aq8gKne9ZWWVotvbj8No-CHCfTqcux-Edxd5S3wNjoNJmBTsYY2NVPJ~iltBPJRLHAr8TYfLIJzmj8LFmQs1hgz6hepFaJOfrtrSVna67Kj1zv-Y0L8oYoEaMXd8GthGq37rbzIcNW6TunhaeDSRTWiC06lGK7Ib3kL5uKcoUCzcAaQ1-ppDalTTb5MTA7~Hpf2IqZ3cLOnLvbokzOemIC5bgCY4QGmSDrJzP51kxtltplx4L2zm-oU5En7s1apY9k-JQX4apa5sClPSqogzw4UT9J5mqDM-ebX2D3-3ka~UB5NwzPJHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"slug":"Triangular_Learner_Model","translated_slug":"","page_count":24,"language":"en","content_type":"Work","summary":"User model is description of users' information and characteristics in abstract level. 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Algorithms and formulas are described by the succinct way.","owner":{"id":12043864,"first_name":"Loc Nguyen's","middle_initials":null,"last_name":"Academic Network","page_name":"LocNguyen","domain_name":"independentscholar","created_at":"2014-05-14T14:04:32.566-07:00","display_name":"Loc Nguyen's Academic Network","url":"https://independentscholar.academia.edu/LocNguyen"},"attachments":[{"id":88914528,"title":"","file_type":"pdf","scribd_thumbnail_url":"https://attachments.academia-assets.com/88914528/thumbnails/1.jpg","file_name":"07.TLM_OSF_2022.06.28.pdf","download_url":"https://www.academia.edu/attachments/88914528/download_file","bulk_download_file_name":"Triangular_Learner_Model.pdf","bulk_download_url":"https://d1wqtxts1xzle7.cloudfront.net/88914528/07.TLM_OSF_2022.06.28-libre.pdf?1658653256=\u0026response-content-disposition=attachment%3B+filename%3DTriangular_Learner_Model.pdf\u0026Expires=1738797509\u0026Signature=aBNIbiLF8b-xXDKpDuPgy9GlMyJ5f5vC2aq8gKne9ZWWVotvbj8No-CHCfTqcux-Edxd5S3wNjoNJmBTsYY2NVPJ~iltBPJRLHAr8TYfLIJzmj8LFmQs1hgz6hepFaJOfrtrSVna67Kj1zv-Y0L8oYoEaMXd8GthGq37rbzIcNW6TunhaeDSRTWiC06lGK7Ib3kL5uKcoUCzcAaQ1-ppDalTTb5MTA7~Hpf2IqZ3cLOnLvbokzOemIC5bgCY4QGmSDrJzP51kxtltplx4L2zm-oU5En7s1apY9k-JQX4apa5sClPSqogzw4UT9J5mqDM-ebX2D3-3ka~UB5NwzPJHQ__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA"}],"research_interests":[{"id":1385,"name":"User Modeling","url":"https://www.academia.edu/Documents/in/User_Modeling"},{"id":10514,"name":"Adaptive Learning Systems","url":"https://www.academia.edu/Documents/in/Adaptive_Learning_Systems"}],"urls":[{"id":22396959,"url":"https://osf.io/42cbn"}]}, dispatcherData: dispatcherData }); $(this).data('initialized', true); } }); $a.trackClickSource(".js-work-strip-work-link", "profile_work_strip") }); </script> <div class="js-work-strip profile--work_container" data-work-id="83633982"><div class="profile--work_thumbnail hidden-xs"><a class="js-work-strip-work-link" data-click-track="profile-work-strip-thumbnail" href="https://www.academia.edu/83633982/A_Short_Study_on_Minima_Distribution"><img alt="Research paper thumbnail of A Short Study on Minima Distribution" class="work-thumbnail" src="https://attachments.academia-assets.com/88914385/thumbnails/1.jpg" /></a></div><div class="wp-workCard wp-workCard_itemContainer"><div class="wp-workCard_item wp-workCard--title"><a class="js-work-strip-work-link text-gray-darker" data-click-track="profile-work-strip-title" href="https://www.academia.edu/83633982/A_Short_Study_on_Minima_Distribution">A Short Study on Minima Distribution</a></div><div class="wp-workCard_item wp-workCard--coauthors"><span>by </span><span><a class="" data-click-track="profile-work-strip-authors" href="https://independentscholar.academia.edu/LocNguyen">Loc Nguyen's Academic Network</a> and <a class="" data-click-track="profile-work-strip-authors" href="https://independent.academia.edu/LocNguyen2000">Loc Nguyen</a></span></div><div class="wp-workCard_item"><span>Preprints</span><span>, Jun 27, 2022</span></div><div class="wp-workCard_item"><span class="js-work-more-abstract-truncated">Global optimization is an imperative development of local optimization because there are many pro...</span><a class="js-work-more-abstract" data-broccoli-component="work_strip.more_abstract" data-click-track="profile-work-strip-more-abstract" href="javascript:;"><span> more </span><span><i class="fa fa-caret-down"></i></span></a><span class="js-work-more-abstract-untruncated hidden">Global optimization is an imperative development of local optimization because there are many problems in artificial intelligence and machine learning requires highly acute solutions over entire domain. There are many methods to resolve the global optimization, which can be classified into three groups such as analytic methods (purely mathematical methods), probabilistic methods, and heuristic methods. Especially, heuristic methods like particle swarm optimization and ant bee colony attract researchers because their effective and practical techniques which are easy to be implemented by computer programming languages. However, these heuristic methods are lacking in theoretical mathematical fundamental. Fortunately, minima distribution establishes a strict mathematical relationship between optimized target function and its global minima. In this research, I try to study minima distribution and apply it into explaining convergence and convergence speed of optimization algorithms. Especially, weak conditions of convergence and monotonicity within minima distribution are drawn so as to be appropriate to practical optimization methods.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="c959a221d7fe7e9b4714158f271c5cc1" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":88914385,"asset_id":83633982,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/88914385/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="83633982"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="83633982"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 83633982; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=83633982]").text(description); $(".js-view-count[data-work-id=83633982]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 83633982; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='83633982']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "c959a221d7fe7e9b4714158f271c5cc1" } } $('.js-work-strip[data-work-id=83633982]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":83633982,"title":"A Short Study on Minima Distribution","translated_title":"","metadata":{"doi":"10.20944/preprints202206.0361.v1","abstract":"Global optimization is an imperative development of local optimization because there are many problems in artificial intelligence and machine learning requires highly acute solutions over entire domain. There are many methods to resolve the global optimization, which can be classified into three groups such as analytic methods (purely mathematical methods), probabilistic methods, and heuristic methods. Especially, heuristic methods like particle swarm optimization and ant bee colony attract researchers because their effective and practical techniques which are easy to be implemented by computer programming languages. However, these heuristic methods are lacking in theoretical mathematical fundamental. Fortunately, minima distribution establishes a strict mathematical relationship between optimized target function and its global minima. In this research, I try to study minima distribution and apply it into explaining convergence and convergence speed of optimization algorithms. Especially, weak conditions of convergence and monotonicity within minima distribution are drawn so as to be appropriate to practical optimization methods.","event_date":{"day":27,"month":6,"year":2022,"errors":{}},"ai_title_tag":"Exploring Minima Distribution in Optimization","journal_name":"Preprints","organization":"Multidisciplinary Digital Publishing Institute (MDPI)","publication_date":{"day":27,"month":6,"year":2022,"errors":{}},"publication_name":"Preprints","conference_end_date":{"day":27,"month":6,"year":2022,"errors":{}},"conference_start_date":{"day":27,"month":6,"year":2022,"errors":{}}},"translated_abstract":"Global optimization is an imperative development of local optimization because there are many problems in artificial intelligence and machine learning requires highly acute solutions over entire domain. 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JSI là phần mềm nhỏ gọn hỗ trợ nhà đầu tư quản lý những tài sản này trong quá trình đầu tư nhiều rủi ro. Phần mềm được lập trình bằng ngôn ngữ Java chạy trên nhiều hệ điều hành, có dung lượng nhỏ và giao diện thân thiện nên thích hợp cho những cá nhân quản lý các tài sản được đầu tư với số lượng không nhiều.</span></div><div class="wp-workCard_item wp-workCard--actions"><span class="work-strip-bookmark-button-container"></span><a id="6666aa84ff8d23bf7b994b78634e19ef" class="wp-workCard--action" rel="nofollow" data-click-track="profile-work-strip-download" data-download="{"attachment_id":85511830,"asset_id":78477129,"asset_type":"Work","button_location":"profile"}" href="https://www.academia.edu/attachments/85511830/download_file?s=profile"><span><i class="fa fa-arrow-down"></i></span><span>Download</span></a><span class="wp-workCard--action visible-if-viewed-by-owner inline-block" style="display: none;"><span class="js-profile-work-strip-edit-button-wrapper profile-work-strip-edit-button-wrapper" data-work-id="78477129"><a class="js-profile-work-strip-edit-button" tabindex="0"><span><i class="fa fa-pencil"></i></span><span>Edit</span></a></span></span></div><div class="wp-workCard_item wp-workCard--stats"><span><span><span class="js-view-count view-count u-mr2x" data-work-id="78477129"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78477129; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78477129]").text(description); $(".js-view-count[data-work-id=78477129]").attr('title', description).tooltip(); }); });</script></span></span><span><span class="percentile-widget hidden"><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78477129; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-work-strip[data-work-id='78477129']"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></span></div><div id="work-strip-premium-row-container"></div></div></div><script> require.config({ waitSeconds: 90 })(["https://a.academia-assets.com/assets/wow_profile-a9bf3a2bc8c89fa2a77156577594264ee8a0f214d74241bc0fcd3f69f8d107ac.js","https://a.academia-assets.com/assets/work_edit-ad038b8c047c1a8d4fa01b402d530ff93c45fee2137a149a4a5398bc8ad67560.js"], function() { // from javascript_helper.rb var dispatcherData = {} if (true){ window.WowProfile.dispatcher = window.WowProfile.dispatcher || _.clone(Backbone.Events); dispatcherData = { dispatcher: window.WowProfile.dispatcher, downloadLinkId: "6666aa84ff8d23bf7b994b78634e19ef" } } $('.js-work-strip[data-work-id=78477129]').each(function() { if (!$(this).data('initialized')) { new WowProfile.WorkStripView({ el: this, workJSON: {"id":78477129,"title":"Phần mềm quản lý đầu tư JSI","translated_title":"","metadata":{"doi":"10.31219/osf.io/xf5va","abstract":"Việc đầu tư vào những tài sản như cổ phiếu, chứng khoáng phái sinh (CFD), kim loại quý, tiền điện tử cần những tác vụ đặc thù so với quản lý kế toán tài chính thông thường vì liên quan đến đòn bẫy tài chính và khả năng dự đoán xu hướng tăng/giảm giá tài sản để tránh cạn kiệt vốn. JSI là phần mềm nhỏ gọn hỗ trợ nhà đầu tư quản lý những tài sản này trong quá trình đầu tư nhiều rủi ro. Phần mềm được lập trình bằng ngôn ngữ Java chạy trên nhiều hệ điều hành, có dung lượng nhỏ và giao diện thân thiện nên thích hợp cho những cá nhân quản lý các tài sản được đầu tư với số lượng không nhiều.","event_date":{"day":20,"month":4,"year":2022,"errors":{}},"journal_name":"OSF Preprints","organization":"Open Science Framework (OSF)","publication_date":{"day":22,"month":4,"year":2022,"errors":{}},"publication_name":"OSF Preprints","conference_end_date":{"day":20,"month":4,"year":2022,"errors":{}},"conference_start_date":{"day":20,"month":4,"year":2022,"errors":{}}},"translated_abstract":"Việc đầu tư vào những tài sản như cổ phiếu, chứng khoáng phái sinh (CFD), kim loại quý, tiền điện tử cần những tác vụ đặc thù so với quản lý kế toán tài chính thông thường vì liên quan đến đòn bẫy tài chính và khả năng dự đoán xu hướng tăng/giảm giá tài sản để tránh cạn kiệt vốn. 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