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overflow: hidden; text-overflow: ellipsis; -webkit-line-clamp: 3; -webkit-box-orient: vertical; }</style><div class="col-xs-12 clearfix"><div class="u-floatLeft"><h1 class="PageHeader-title u-m0x u-fs30">Probabilistic Logic</h1><div class="u-tcGrayDark">346 Followers</div><div class="u-tcGrayDark u-mt2x">Recent papers in <b>Probabilistic Logic</b></div></div></div></div></div></div><div class="TabbedNavigation"><div class="container"><div class="row"><div class="col-xs-12 clearfix"><ul class="nav u-m0x u-p0x list-inline u-displayFlex"><li class="active"><a href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Top Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Probabilistic_Logic/MostCited">Most Cited Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Probabilistic_Logic/MostDownloaded">Most Downloaded Papers</a></li><li><a href="https://www.academia.edu/Documents/in/Probabilistic_Logic/MostRecent">Newest Papers</a></li><li><a class="" href="https://www.academia.edu/People/Probabilistic_Logic">People</a></li></ul></div><style type="text/css">ul.nav{flex-direction:row}@media(max-width: 567px){ul.nav{flex-direction:column}.TabbedNavigation li{max-width:100%}.TabbedNavigation li.active{background-color:var(--background-grey, #dddde2)}.TabbedNavigation li.active:before,.TabbedNavigation li.active:after{display:none}}</style></div></div></div><div class="container"><div class="row"><div class="col-xs-12"><div class="u-displayFlex"><div class="u-flexGrow1"><div class="works"><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_73749035" data-work_id="73749035" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/73749035/Quantitative_Model_Checking_of_an_RSA_based_Email_Protocol_on_Mobile_Devices">Quantitative Model Checking of an RSA-based Email Protocol on Mobile Devices</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Abstract—The current proliferation of mobile devices has resulted in a large diversity of hardware specifications, each designed for different services and applications (e.g. cell phones, smart phones, PDAs). At the same time, e-mail... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_73749035" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Abstract—The current proliferation of mobile devices has resulted in a large diversity of hardware specifications, each designed for different services and applications (e.g. cell phones, smart phones, PDAs). At the same time, e-mail message delivery has become a vital part of everyday communications. This article provides a cost-aware study of an RSA-based e-mail protocol executed upon the widely used Apple iPhone1,2 with ARM1176JZF-S, operating in an High Speed Downlink Packet Access (HSDPA) mobile environment. The proposed study employs formal analysis techniques, such as probabilistic model checking, and proceeds to a quantitative analysis of the e-mail protocol, taking into account computational parameters derived by the devices ’ specifications. The value of this study is to form a computer-aided framework which balances the tradeoff between gaining in security, using high-length RSA keys, and conserving CPU resources, due to hardware limitations of mobile devices. 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Analysis","url":"https://www.academia.edu/Documents/in/Data_Analysis?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5748" href="https://www.academia.edu/Documents/in/Power_System">Power System</a>, <script data-card-contents-for-ri="5748" type="text/json">{"id":5748,"name":"Power System","url":"https://www.academia.edu/Documents/in/Power_System?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="11598" href="https://www.academia.edu/Documents/in/Neural_Networks">Neural Networks</a>, <script data-card-contents-for-ri="11598" type="text/json">{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="11898" href="https://www.academia.edu/Documents/in/Fault_Detection">Fault Detection</a><script data-card-contents-for-ri="11898" type="text/json">{"id":11898,"name":"Fault 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u-tcGrayDarkest"><div class="summarized">The features are considered the cornerstone of text summarization. The most important issue is what feature to be considered in a text summarization process. Including all the features in the summarization process may not be considered as... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_71682326" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The features are considered the cornerstone of text summarization. The most important issue is what feature to be considered in a text summarization process. Including all the features in the summarization process may not be considered as an optimal solution. Therefore, other methods need to be deployed. In this paper, random five features used and investigated using a (pseudo) Genetic concept as an optimized trainable features selection mechanism. The Document Understanding Conference (DUC2002) used to train our proposed model; hence the objective of this paper is to learn the weight (importance) of each used feature. For each input document using the genetic concept, the size of the generation is defined and the chromosome dimension (genes) is equal to number of features used. Each gene is represents a feature and in binary format. A chromosome with high fitness value is selected to be enrolled in the final round. The average of each gene is computed for all best chromosomes and c...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/71682326" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="184035935" href="https://independent.academia.edu/AmeerHamza683">Ameer Hamza</a><script data-card-contents-for-user="184035935" type="text/json">{"id":184035935,"first_name":"Ameer","last_name":"Hamza","domain_name":"independent","page_name":"AmeerHamza683","display_name":"Ameer Hamza","profile_url":"https://independent.academia.edu/AmeerHamza683?f_ri=43610","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_71682326 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="71682326"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 71682326, container: ".js-paper-rank-work_71682326", }); 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The average of each gene is computed for all best chromosomes and c...","downloadable_attachments":[],"ordered_authors":[{"id":184035935,"first_name":"Ameer","last_name":"Hamza","domain_name":"independent","page_name":"AmeerHamza683","display_name":"Ameer Hamza","profile_url":"https://independent.academia.edu/AmeerHamza683?f_ri=43610","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":156,"name":"Genetics","url":"https://www.academia.edu/Documents/in/Genetics?f_ri=43610","nofollow":false},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false},{"id":464,"name":"Information Retrieval","url":"https://www.academia.edu/Documents/in/Information_Retrieval?f_ri=43610","nofollow":false},{"id":5187,"name":"Statistical Analysis","url":"https://www.academia.edu/Documents/in/Statistical_Analysis?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic 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Summarization","url":"https://www.academia.edu/Documents/in/Text_Summarization?f_ri=43610"},{"id":971413,"name":"Summarization","url":"https://www.academia.edu/Documents/in/Summarization?f_ri=43610"},{"id":1993320,"name":"Optimal Solution","url":"https://www.academia.edu/Documents/in/Optimal_Solution?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_47266702" data-work_id="47266702" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/47266702/Probabilistic_Analysis_of_Dynamic_Scenes_and_Collision_Risks_Assessment_to_Improve_Driving_Safety">Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList 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href="https://www.academia.edu/Documents/in/Intelligent_Transportation_Systems">Intelligent Transportation Systems</a>, <script data-card-contents-for-ri="9589" type="text/json">{"id":9589,"name":"Intelligent Transportation Systems","url":"https://www.academia.edu/Documents/in/Intelligent_Transportation_Systems?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="10583" href="https://www.academia.edu/Documents/in/Motion_control">Motion control</a>, <script data-card-contents-for-ri="10583" type="text/json">{"id":10583,"name":"Motion control","url":"https://www.academia.edu/Documents/in/Motion_control?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="12061" href="https://www.academia.edu/Documents/in/Risk_Management">Risk Management</a>, <script data-card-contents-for-ri="12061" type="text/json">{"id":12061,"name":"Risk 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}); });</script></span><script>$(function() { $(".js-view-count-work_22405256").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="22405256"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="311" href="https://www.academia.edu/Documents/in/Approximation_Theory">Approximation Theory</a>, <script data-card-contents-for-ri="311" type="text/json">{"id":311,"name":"Approximation Theory","url":"https://www.academia.edu/Documents/in/Approximation_Theory?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2345" href="https://www.academia.edu/Documents/in/Wireless_Communications">Wireless Communications</a>, <script data-card-contents-for-ri="2345" type="text/json">{"id":2345,"name":"Wireless Communications","url":"https://www.academia.edu/Documents/in/Wireless_Communications?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4252" href="https://www.academia.edu/Documents/in/Computer_Networks">Computer Networks</a>, <script data-card-contents-for-ri="4252" type="text/json">{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9136" href="https://www.academia.edu/Documents/in/Wireless_Sensor_Networks">Wireless Sensor Networks</a><script data-card-contents-for-ri="9136" type="text/json">{"id":9136,"name":"Wireless Sensor Networks","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Networks?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=22405256]'), work: {"id":22405256,"title":"A deterministic energy-efficient clustering protocol for wireless sensor networks","created_at":"2016-02-24T14:34:04.028-08:00","url":"https://www.academia.edu/22405256/A_deterministic_energy_efficient_clustering_protocol_for_wireless_sensor_networks?f_ri=43610","dom_id":"work_22405256","summary":null,"downloadable_attachments":[{"id":43027983,"asset_id":22405256,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38518082,"first_name":"Femi","last_name":"Aderohunmu","domain_name":"otago","page_name":"FAderohunmu","display_name":"Femi Aderohunmu","profile_url":"https://otago.academia.edu/FAderohunmu?f_ri=43610","photo":"/images/s65_no_pic.png"},{"id":49045,"first_name":"Jeremiah","last_name":"Deng","domain_name":"otago","page_name":"JeremiahDeng","display_name":"Jeremiah Deng","profile_url":"https://otago.academia.edu/JeremiahDeng?f_ri=43610","photo":"https://0.academia-photos.com/49045/850787/1058872/s65_jeremiah.deng.png"}],"research_interests":[{"id":311,"name":"Approximation Theory","url":"https://www.academia.edu/Documents/in/Approximation_Theory?f_ri=43610","nofollow":false},{"id":2345,"name":"Wireless Communications","url":"https://www.academia.edu/Documents/in/Wireless_Communications?f_ri=43610","nofollow":false},{"id":4252,"name":"Computer Networks","url":"https://www.academia.edu/Documents/in/Computer_Networks?f_ri=43610","nofollow":false},{"id":9136,"name":"Wireless Sensor Networks","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Networks?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610"},{"id":82999,"name":"Protocols","url":"https://www.academia.edu/Documents/in/Protocols?f_ri=43610"},{"id":459384,"name":"Energy efficient","url":"https://www.academia.edu/Documents/in/Energy_efficient?f_ri=43610"},{"id":1211847,"name":"Wireless Sensor Network","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Network?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_63302036" data-work_id="63302036" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/63302036/Efficient_Algorithms_for_Prolog_Based_Probabilistic_Logic_Programming_Effici%C3%ABnte_algoritmen_voor_prolog_gebaseerd_probabilistisch_logisch_programmeren_">Efficient Algorithms for Prolog Based Probabilistic Logic Programming (Efficiënte algoritmen voor prolog gebaseerd probabilistisch logisch programmeren)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The integration of probabilistic reasoning with logic programming has become one of the challenges in Artificial Intelligence. Lately, a lot of Probabilistic Logic Programming (PLP) formalisms have surfaced. Given that PLP is the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_63302036" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The integration of probabilistic reasoning with logic programming has become one of the challenges in Artificial Intelligence. Lately, a lot of Probabilistic Logic Programming (PLP) formalisms have surfaced. Given that PLP is the combination of logic programming and probabilities which are two very different fields, it is expected that researchers from several fields come up with different approaches to tackle the presented challenges. This has resulted in a new discipline called Probabilistic Logic Learning (PLL) or Statistical Relational Learning (SRL) and a very active research community. Within this community, ProbLog a probabilistic extension of Prolog, has appeared. ProbLog was motivated by the task of mining links in large probabilistic graphs. The simple but powerful ProbLog formulation was extended in order to support inference on several different models. Soon ProbLog evolved into a general purpose probabilistic programming language that provides infrastructure for many PL...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/63302036" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="031cb9c15fd6c7eb65a0b44678469de9" rel="nofollow" data-download="{"attachment_id":75857288,"asset_id":63302036,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/75857288/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="70921572" href="https://independent.academia.edu/TheofrastosMantadelis">Theofrastos Mantadelis</a><script data-card-contents-for-user="70921572" type="text/json">{"id":70921572,"first_name":"Theofrastos","last_name":"Mantadelis","domain_name":"independent","page_name":"TheofrastosMantadelis","display_name":"Theofrastos Mantadelis","profile_url":"https://independent.academia.edu/TheofrastosMantadelis?f_ri=43610","photo":"https://0.academia-photos.com/70921572/65469760/53806723/s65_theofrastos.mantadelis.png"}</script></span></span></li><li class="js-paper-rank-work_63302036 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="63302036"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 63302036, container: ".js-paper-rank-work_63302036", }); 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$(".js-view-count[data-work-id=63302036]").text(description); $(".js-view-count-work_63302036").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_63302036").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="63302036"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">5</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="465" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="39699" href="https://www.academia.edu/Documents/in/Probabilistic_Graphical_Models">Probabilistic Graphical Models</a>, <script data-card-contents-for-ri="39699" type="text/json">{"id":39699,"name":"Probabilistic Graphical Models","url":"https://www.academia.edu/Documents/in/Probabilistic_Graphical_Models?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a>, <script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="144497" href="https://www.academia.edu/Documents/in/Statistical_Relational_Learning">Statistical Relational Learning</a><script data-card-contents-for-ri="144497" type="text/json">{"id":144497,"name":"Statistical Relational Learning","url":"https://www.academia.edu/Documents/in/Statistical_Relational_Learning?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=63302036]'), work: {"id":63302036,"title":"Efficient Algorithms for Prolog Based Probabilistic Logic Programming (Efficiënte algoritmen voor prolog gebaseerd probabilistisch logisch programmeren)","created_at":"2021-12-05T23:04:18.794-08:00","url":"https://www.academia.edu/63302036/Efficient_Algorithms_for_Prolog_Based_Probabilistic_Logic_Programming_Effici%C3%ABnte_algoritmen_voor_prolog_gebaseerd_probabilistisch_logisch_programmeren_?f_ri=43610","dom_id":"work_63302036","summary":"The integration of probabilistic reasoning with logic programming has become one of the challenges in Artificial Intelligence. Lately, a lot of Probabilistic Logic Programming (PLP) formalisms have surfaced. Given that PLP is the combination of logic programming and probabilities which are two very different fields, it is expected that researchers from several fields come up with different approaches to tackle the presented challenges. This has resulted in a new discipline called Probabilistic Logic Learning (PLL) or Statistical Relational Learning (SRL) and a very active research community. Within this community, ProbLog a probabilistic extension of Prolog, has appeared. ProbLog was motivated by the task of mining links in large probabilistic graphs. The simple but powerful ProbLog formulation was extended in order to support inference on several different models. Soon ProbLog evolved into a general purpose probabilistic programming language that provides infrastructure for many PL...","downloadable_attachments":[{"id":75857288,"asset_id":63302036,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":70921572,"first_name":"Theofrastos","last_name":"Mantadelis","domain_name":"independent","page_name":"TheofrastosMantadelis","display_name":"Theofrastos Mantadelis","profile_url":"https://independent.academia.edu/TheofrastosMantadelis?f_ri=43610","photo":"https://0.academia-photos.com/70921572/65469760/53806723/s65_theofrastos.mantadelis.png"}],"research_interests":[{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=43610","nofollow":false},{"id":39699,"name":"Probabilistic Graphical Models","url":"https://www.academia.edu/Documents/in/Probabilistic_Graphical_Models?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":144497,"name":"Statistical Relational Learning","url":"https://www.academia.edu/Documents/in/Statistical_Relational_Learning?f_ri=43610","nofollow":false},{"id":3056116,"name":"Problog","url":"https://www.academia.edu/Documents/in/Problog?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67540131" data-work_id="67540131" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/67540131/Classification_of_Classic_Turkish_Music_Makams">Classification of Classic Turkish Music Makams</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this work, Classical Turkish Music songs are classified into six makams. Makam is a modal framework for melodic development in Classical Turkish Music. The effect of the sound clip length on the system performance was also evaluated.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_67540131" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this work, Classical Turkish Music songs are classified into six makams. Makam is a modal framework for melodic development in Classical Turkish Music. The effect of the sound clip length on the system performance was also evaluated. The Mel Frequency Cepstral Coefficients (MFCC) were used as features. Obtained data were classified by using Probabilistic Neural Network. The best correct recognition ratio was obtained as 89,4% by using a clip length of 6 s.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/67540131" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a0ae502672615437dba6d3fd69d51b31" rel="nofollow" data-download="{"attachment_id":78318014,"asset_id":67540131,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/78318014/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57156840" href="https://independent.academia.edu/BolatB%C3%BClent">Bülent Bolat</a><script data-card-contents-for-user="57156840" type="text/json">{"id":57156840,"first_name":"Bülent","last_name":"Bolat","domain_name":"independent","page_name":"BolatBülent","display_name":"Bülent Bolat","profile_url":"https://independent.academia.edu/BolatB%C3%BClent?f_ri=43610","photo":"https://0.academia-photos.com/57156840/15580899/16166150/s65_b_lent.bolat.jpg"}</script></span></span></li><li class="js-paper-rank-work_67540131 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="67540131"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 67540131, container: ".js-paper-rank-work_67540131", }); 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$(".js-view-count[data-work-id=67540131]").text(description); $(".js-view-count-work_67540131").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67540131").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67540131"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">19</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2560" href="https://www.academia.edu/Documents/in/Probabilistic_Networks">Probabilistic Networks</a>, <script data-card-contents-for-ri="2560" type="text/json">{"id":2560,"name":"Probabilistic Networks","url":"https://www.academia.edu/Documents/in/Probabilistic_Networks?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4095" href="https://www.academia.edu/Documents/in/Classification_Machine_Learning_">Classification (Machine Learning)</a>, <script data-card-contents-for-ri="4095" type="text/json">{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4148" href="https://www.academia.edu/Documents/in/Audio_Signal_Processing">Audio Signal Processing</a><script data-card-contents-for-ri="4148" type="text/json">{"id":4148,"name":"Audio Signal Processing","url":"https://www.academia.edu/Documents/in/Audio_Signal_Processing?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67540131]'), work: {"id":67540131,"title":"Classification of Classic Turkish Music Makams","created_at":"2022-01-07T09:00:36.346-08:00","url":"https://www.academia.edu/67540131/Classification_of_Classic_Turkish_Music_Makams?f_ri=43610","dom_id":"work_67540131","summary":"In this work, Classical Turkish Music songs are classified into six makams. Makam is a modal framework for melodic development in Classical Turkish Music. The effect of the sound clip length on the system performance was also evaluated. The Mel Frequency Cepstral Coefficients (MFCC) were used as features. Obtained data were classified by using Probabilistic Neural Network. The best correct recognition ratio was obtained as 89,4% by using a clip length of 6 s.","downloadable_attachments":[{"id":78318014,"asset_id":67540131,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":57156840,"first_name":"Bülent","last_name":"Bolat","domain_name":"independent","page_name":"BolatBülent","display_name":"Bülent Bolat","profile_url":"https://independent.academia.edu/BolatB%C3%BClent?f_ri=43610","photo":"https://0.academia-photos.com/57156840/15580899/16166150/s65_b_lent.bolat.jpg"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false},{"id":2560,"name":"Probabilistic Networks","url":"https://www.academia.edu/Documents/in/Probabilistic_Networks?f_ri=43610","nofollow":false},{"id":4095,"name":"Classification (Machine Learning)","url":"https://www.academia.edu/Documents/in/Classification_Machine_Learning_?f_ri=43610","nofollow":false},{"id":4148,"name":"Audio Signal Processing","url":"https://www.academia.edu/Documents/in/Audio_Signal_Processing?f_ri=43610","nofollow":false},{"id":9038,"name":"Digital Signal Processing","url":"https://www.academia.edu/Documents/in/Digital_Signal_Processing?f_ri=43610"},{"id":11598,"name":"Neural Networks","url":"https://www.academia.edu/Documents/in/Neural_Networks?f_ri=43610"},{"id":40754,"name":"Classical Music","url":"https://www.academia.edu/Documents/in/Classical_Music?f_ri=43610"},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610"},{"id":45658,"name":"MFCC","url":"https://www.academia.edu/Documents/in/MFCC?f_ri=43610"},{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks?f_ri=43610"},{"id":70154,"name":"Musical Information Retrieval","url":"https://www.academia.edu/Documents/in/Musical_Information_Retrieval?f_ri=43610"},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=43610"},{"id":220049,"name":"Accuracy","url":"https://www.academia.edu/Documents/in/Accuracy?f_ri=43610"},{"id":450311,"name":"Probabilistic Neural Network","url":"https://www.academia.edu/Documents/in/Probabilistic_Neural_Network?f_ri=43610"},{"id":545008,"name":"Mel Frequency Cepstral Coefficient","url":"https://www.academia.edu/Documents/in/Mel_Frequency_Cepstral_Coefficient?f_ri=43610"},{"id":552150,"name":"Filter Banks","url":"https://www.academia.edu/Documents/in/Filter_Banks?f_ri=43610"},{"id":592191,"name":"Turkish makam maqam classical music","url":"https://www.academia.edu/Documents/in/Turkish_makam_maqam_classical_music?f_ri=43610"},{"id":1374329,"name":"Mel-frequency Cepstral Coefficients","url":"https://www.academia.edu/Documents/in/Mel-frequency_Cepstral_Coefficients?f_ri=43610"},{"id":3142462,"name":"Neural nets","url":"https://www.academia.edu/Documents/in/Neural_nets?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_78690354" data-work_id="78690354" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/78690354/Probabilistic_Interpretation_of_Complex_Fuzzy_Set">Probabilistic Interpretation of Complex Fuzzy Set</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" 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window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78690354]").text(description); $(".js-view-count-work_78690354").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_78690354").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="78690354"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">17</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="344" href="https://www.academia.edu/Documents/in/Probability_Theory">Probability Theory</a>, <script data-card-contents-for-ri="344" type="text/json">{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="465" href="https://www.academia.edu/Documents/in/Artificial_Intelligence">Artificial Intelligence</a>, <script data-card-contents-for-ri="465" type="text/json">{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5394" href="https://www.academia.edu/Documents/in/Fuzzy_set_theory">Fuzzy set theory</a><script data-card-contents-for-ri="5394" type="text/json">{"id":5394,"name":"Fuzzy set theory","url":"https://www.academia.edu/Documents/in/Fuzzy_set_theory?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=78690354]'), work: {"id":78690354,"title":"Probabilistic Interpretation of Complex Fuzzy Set","created_at":"2022-05-07T10:10:55.973-07:00","url":"https://www.academia.edu/78690354/Probabilistic_Interpretation_of_Complex_Fuzzy_Set?f_ri=43610","dom_id":"work_78690354","summary":null,"downloadable_attachments":[{"id":85649442,"asset_id":78690354,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":124327316,"first_name":"Nilambar","last_name":"Sethi","domain_name":"independent","page_name":"NilambarSethi","display_name":"Nilambar Sethi","profile_url":"https://independent.academia.edu/NilambarSethi?f_ri=43610","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":344,"name":"Probability Theory","url":"https://www.academia.edu/Documents/in/Probability_Theory?f_ri=43610","nofollow":false},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false},{"id":465,"name":"Artificial Intelligence","url":"https://www.academia.edu/Documents/in/Artificial_Intelligence?f_ri=43610","nofollow":false},{"id":5394,"name":"Fuzzy set theory","url":"https://www.academia.edu/Documents/in/Fuzzy_set_theory?f_ri=43610","nofollow":false},{"id":12022,"name":"Numerical Analysis","url":"https://www.academia.edu/Documents/in/Numerical_Analysis?f_ri=43610"},{"id":22613,"name":"Probability and statistics","url":"https://www.academia.edu/Documents/in/Probability_and_statistics?f_ri=43610"},{"id":31412,"name":"Probability and Mathematical Statistics","url":"https://www.academia.edu/Documents/in/Probability_and_Mathematical_Statistics?f_ri=43610"},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=43610"},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610"},{"id":315908,"name":"Probability distributions to model air pollutant data","url":"https://www.academia.edu/Documents/in/Probability_distributions_to_model_air_pollutant_data?f_ri=43610"},{"id":343413,"name":"Applied Probability and Statistics","url":"https://www.academia.edu/Documents/in/Applied_Probability_and_Statistics?f_ri=43610"},{"id":571797,"name":"Introduction to Probability","url":"https://www.academia.edu/Documents/in/Introduction_to_Probability?f_ri=43610"},{"id":2008020,"name":"Complex Fuzzy Set","url":"https://www.academia.edu/Documents/in/Complex_Fuzzy_Set?f_ri=43610"},{"id":2008021,"name":"Type-2 Complex Fuzzy Set","url":"https://www.academia.edu/Documents/in/Type-2_Complex_Fuzzy_Set?f_ri=43610"},{"id":2976127,"name":"Complex fuzzy relation","url":"https://www.academia.edu/Documents/in/Complex_fuzzy_relation?f_ri=43610"},{"id":2976128,"name":"complement of complex fuzzy relation","url":"https://www.academia.edu/Documents/in/complement_of_complex_fuzzy_relation?f_ri=43610"},{"id":2976129,"name":"δ -equality","url":"https://www.academia.edu/Documents/in/%CE%B4_-equality?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_12326418" data-work_id="12326418" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/12326418/Active_Visual_Segmentation">Active Visual Segmentation</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/12326418" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="06ef1873a0349a4328fba7c698c831cc" rel="nofollow" data-download="{"attachment_id":37598563,"asset_id":12326418,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/37598563/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="30982945" href="https://independent.academia.edu/AjayMishra36">Ajay Mishra</a><script data-card-contents-for-user="30982945" type="text/json">{"id":30982945,"first_name":"Ajay","last_name":"Mishra","domain_name":"independent","page_name":"AjayMishra36","display_name":"Ajay Mishra","profile_url":"https://independent.academia.edu/AjayMishra36?f_ri=43610","photo":"https://0.academia-photos.com/30982945/9072983/10120555/s65_ajay.mishra.jpg"}</script></span></span></li><li class="js-paper-rank-work_12326418 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden 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data-has-card-for-ri="37" href="https://www.academia.edu/Documents/in/Information_Systems">Information Systems</a>, <script data-card-contents-for-ri="37" type="text/json">{"id":37,"name":"Information Systems","url":"https://www.academia.edu/Documents/in/Information_Systems?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="428" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a>, <script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="854" href="https://www.academia.edu/Documents/in/Computer_Vision">Computer Vision</a>, <script data-card-contents-for-ri="854" type="text/json">{"id":854,"name":"Computer Vision","url":"https://www.academia.edu/Documents/in/Computer_Vision?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" 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type="text/json">{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a>, <script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="103994" href="https://www.academia.edu/Documents/in/Intelligent">Intelligent</a><script data-card-contents-for-ri="103994" type="text/json">{"id":103994,"name":"Intelligent","url":"https://www.academia.edu/Documents/in/Intelligent?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=752070]'), work: {"id":752070,"title":"Joint and marginal 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Design","url":"https://www.academia.edu/Documents/in/Circuit_Design?f_ri=43610"},{"id":320537,"name":"Belief Propagation","url":"https://www.academia.edu/Documents/in/Belief_Propagation?f_ri=43610"},{"id":609191,"name":"Logic Design","url":"https://www.academia.edu/Documents/in/Logic_Design?f_ri=43610"},{"id":628286,"name":"Logic Gates","url":"https://www.academia.edu/Documents/in/Logic_Gates?f_ri=43610"},{"id":648356,"name":"Integrated Circuit","url":"https://www.academia.edu/Documents/in/Integrated_Circuit?f_ri=43610"},{"id":813981,"name":"Fault Tolerant","url":"https://www.academia.edu/Documents/in/Fault_Tolerant?f_ri=43610"},{"id":1216932,"name":"Rule Based","url":"https://www.academia.edu/Documents/in/Rule_Based?f_ri=43610"},{"id":2240546,"name":"Integrated Circuit Design","url":"https://www.academia.edu/Documents/in/Integrated_Circuit_Design?f_ri=43610"},{"id":2287400,"name":"Markov Random Field","url":"https://www.academia.edu/Documents/in/Markov_Random_Field?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_14726369 coauthored" data-work_id="14726369" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/14726369/Hyper_graph_matching_via_reweighted_random_walks">Hyper-graph matching via reweighted random walks</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/14726369" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" 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data-has-card-for-user="31049395" href="https://postech.academia.edu/MinsuCHO">Minsu Cho</a><script data-card-contents-for-user="31049395" type="text/json">{"id":31049395,"first_name":"Minsu","last_name":"Cho","domain_name":"postech","page_name":"MinsuCHO","display_name":"Minsu Cho","profile_url":"https://postech.academia.edu/MinsuCHO?f_ri=43610","photo":"https://0.academia-photos.com/31049395/9102810/10152865/s65_minsu.cho.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-14726369">+2</span><div class="hidden js-additional-users-14726369"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/KyoungMuLee">Kyoung Mu Lee</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/JungminL">Lee Jungmin</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-14726369'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-14726369').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_14726369 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="14726369"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 14726369, container: ".js-paper-rank-work_14726369", }); });</script></li><li class="js-percentile-work_14726369 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 14726369; 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Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2616" href="https://www.academia.edu/Documents/in/Graph_Theory">Graph Theory</a>, <script data-card-contents-for-ri="2616" type="text/json">{"id":2616,"name":"Graph Theory","url":"https://www.academia.edu/Documents/in/Graph_Theory?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5109" href="https://www.academia.edu/Documents/in/Pattern_Recognition">Pattern Recognition</a><script data-card-contents-for-ri="5109" type="text/json">{"id":5109,"name":"Pattern Recognition","url":"https://www.academia.edu/Documents/in/Pattern_Recognition?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=14726369]'), work: {"id":14726369,"title":"Hyper-graph matching via reweighted random 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Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610"},{"id":49247,"name":"Higher Order Thinking","url":"https://www.academia.edu/Documents/in/Higher_Order_Thinking?f_ri=43610"},{"id":68431,"name":"Noise","url":"https://www.academia.edu/Documents/in/Noise?f_ri=43610"},{"id":72304,"name":"Graph matching","url":"https://www.academia.edu/Documents/in/Graph_matching?f_ri=43610"},{"id":78086,"name":"Random Walk","url":"https://www.academia.edu/Documents/in/Random_Walk?f_ri=43610"},{"id":97644,"name":"Computer Vision and Pattern Recognition","url":"https://www.academia.edu/Documents/in/Computer_Vision_and_Pattern_Recognition?f_ri=43610"},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=43610"},{"id":1154530,"name":"Tensile Stress","url":"https://www.academia.edu/Documents/in/Tensile_Stress?f_ri=43610"},{"id":1167632,"name":"Synthetic Data Generation","url":"https://www.academia.edu/Documents/in/Synthetic_Data_Generation?f_ri=43610"},{"id":1340817,"name":"CVPR","url":"https://www.academia.edu/Documents/in/CVPR?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_20544986" data-work_id="20544986" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/20544986/Probabilistic_Logics">Probabilistic Logics</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The main goal of this work is connection between theory of probability and logic (expecially logical deduction). Probabilistic logic are combining the capacity of probability theory with the capacity of deductive logic to exploit the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_20544986" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The main goal of this work is connection between theory of probability and logic (expecially logical deduction). Probabilistic logic are combining the capacity of probability theory with the capacity of deductive logic to exploit the structure. The result is a richer and more expressive formalism with a broad range of possible application areas. In probabilistic logic statements have no value of truth and false, but a series of truths between 0 and 1, where 0 represents an impossible situation, and 1 complete security. Difficulties arising from the probabilistic logic is that they multiply computing complexity and allow non-intuitive results as supporting the theory of probability. Probabilistic logic is divided into two forms : 1. expressions that have the probability associated with tem, and 2. evidential probability that are based on the frequency interpretation of probability. In first chapter I describe some  historical connections between logic and theory of probability. The second part is devoted to the problems of probabilistic logic, semantic and syntax, probabilistic argumentation and Bayesian epistemology, as well. In the third part, I solve problem of probabilistic logical networks, expecially on Markov logic netwok (MLN). In coclusion, there will be presented some problems of other philosophical disciplines, such as epistemology, which problems can be solved by probabilistic calculus.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/20544986" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="3e015322f716928c194f653b4ec89e37" rel="nofollow" data-download="{"attachment_id":41428751,"asset_id":20544986,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/41428751/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="31584754" href="https://unizg.academia.edu/DDSSPD">Mario Jurčec</a><script data-card-contents-for-user="31584754" type="text/json">{"id":31584754,"first_name":"Mario","last_name":"Jurčec","domain_name":"unizg","page_name":"DDSSPD","display_name":"Mario Jurčec","profile_url":"https://unizg.academia.edu/DDSSPD?f_ri=43610","photo":"https://0.academia-photos.com/31584754/31191168/131549980/s65_mario.jur_ec.jpg"}</script></span></span></li><li class="js-paper-rank-work_20544986 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="20544986"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 20544986, container: ".js-paper-rank-work_20544986", }); });</script></li><li class="js-percentile-work_20544986 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20544986; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_20544986"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_20544986 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="20544986"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20544986; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20544986]").text(description); $(".js-view-count-work_20544986").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_20544986").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="20544986"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="28512" href="https://www.academia.edu/Documents/in/Bayesian_Networks">Bayesian Networks</a>, <script data-card-contents-for-ri="28512" type="text/json">{"id":28512,"name":"Bayesian Networks","url":"https://www.academia.edu/Documents/in/Bayesian_Networks?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="28846" href="https://www.academia.edu/Documents/in/Bayesian_Probabilistic_Analysis">Bayesian Probabilistic Analysis</a>, <script data-card-contents-for-ri="28846" type="text/json">{"id":28846,"name":"Bayesian Probabilistic Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Probabilistic_Analysis?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="31711" href="https://www.academia.edu/Documents/in/Non-Classical_Logic">Non-Classical Logic</a>, <script data-card-contents-for-ri="31711" type="text/json">{"id":31711,"name":"Non-Classical Logic","url":"https://www.academia.edu/Documents/in/Non-Classical_Logic?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a><script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=20544986]'), work: {"id":20544986,"title":"Probabilistic Logics","created_at":"2016-01-22T06:46:12.337-08:00","url":"https://www.academia.edu/20544986/Probabilistic_Logics?f_ri=43610","dom_id":"work_20544986","summary":"The main goal of this work is connection between theory of probability and logic (expecially logical deduction). Probabilistic logic are combining the capacity of probability theory with the capacity of deductive logic to exploit the structure. The result is a richer and more expressive formalism with a broad range of possible application areas. In probabilistic logic statements have no value of truth and false, but a series of truths between 0 and 1, where 0 represents an impossible situation, and 1 complete security. Difficulties arising from the probabilistic logic is that they multiply computing complexity and allow non-intuitive results as supporting the theory of probability. Probabilistic logic is divided into two forms : 1. expressions that have the probability associated with tem, and 2. evidential probability that are based on the frequency interpretation of probability. In first chapter I describe some historical connections between logic and theory of probability. The second part is devoted to the problems of probabilistic logic, semantic and syntax, probabilistic argumentation and Bayesian epistemology, as well. In the third part, I solve problem of probabilistic logical networks, expecially on Markov logic netwok (MLN). In coclusion, there will be presented some problems of other philosophical disciplines, such as epistemology, which problems can be solved by probabilistic calculus. ","downloadable_attachments":[{"id":41428751,"asset_id":20544986,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":31584754,"first_name":"Mario","last_name":"Jurčec","domain_name":"unizg","page_name":"DDSSPD","display_name":"Mario Jurčec","profile_url":"https://unizg.academia.edu/DDSSPD?f_ri=43610","photo":"https://0.academia-photos.com/31584754/31191168/131549980/s65_mario.jur_ec.jpg"}],"research_interests":[{"id":28512,"name":"Bayesian Networks","url":"https://www.academia.edu/Documents/in/Bayesian_Networks?f_ri=43610","nofollow":false},{"id":28846,"name":"Bayesian Probabilistic Analysis","url":"https://www.academia.edu/Documents/in/Bayesian_Probabilistic_Analysis?f_ri=43610","nofollow":false},{"id":31711,"name":"Non-Classical Logic","url":"https://www.academia.edu/Documents/in/Non-Classical_Logic?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":1394061,"name":"Markov Logic Networks","url":"https://www.academia.edu/Documents/in/Markov_Logic_Networks?f_ri=43610"},{"id":2005459,"name":"Theory of Total Probability","url":"https://www.academia.edu/Documents/in/Theory_of_Total_Probability?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_67419748" data-work_id="67419748" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/67419748/Outlier_aware_robust_clustering">Outlier-aware robust clustering</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div 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InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="67419748"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 67419748; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=67419748]").text(description); $(".js-view-count-work_67419748").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_67419748").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="67419748"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">18</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="300" href="https://www.academia.edu/Documents/in/Mathematics">Mathematics</a>, <script data-card-contents-for-ri="300" type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="363" href="https://www.academia.edu/Documents/in/Set_Theory">Set Theory</a>, <script data-card-contents-for-ri="363" type="text/json">{"id":363,"name":"Set Theory","url":"https://www.academia.edu/Documents/in/Set_Theory?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2008" href="https://www.academia.edu/Documents/in/Machine_Learning">Machine Learning</a><script data-card-contents-for-ri="2008" type="text/json">{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=67419748]'), work: {"id":67419748,"title":"Outlier-aware robust clustering","created_at":"2022-01-06T11:56:41.931-08:00","url":"https://www.academia.edu/67419748/Outlier_aware_robust_clustering?f_ri=43610","dom_id":"work_67419748","summary":null,"downloadable_attachments":[{"id":78243341,"asset_id":67419748,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":195720446,"first_name":"Pedro","last_name":"Forero","domain_name":"independent","page_name":"PedroForero10","display_name":"Pedro Forero","profile_url":"https://independent.academia.edu/PedroForero10?f_ri=43610","photo":"https://0.academia-photos.com/195720446/58065715/46291998/s65_pedro.forero.png"}],"research_interests":[{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=43610","nofollow":false},{"id":363,"name":"Set Theory","url":"https://www.academia.edu/Documents/in/Set_Theory?f_ri=43610","nofollow":false},{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false},{"id":2008,"name":"Machine Learning","url":"https://www.academia.edu/Documents/in/Machine_Learning?f_ri=43610","nofollow":false},{"id":2141,"name":"Signal Processing","url":"https://www.academia.edu/Documents/in/Signal_Processing?f_ri=43610"},{"id":7909,"name":"Compressed Sensing","url":"https://www.academia.edu/Documents/in/Compressed_Sensing?f_ri=43610"},{"id":8056,"name":"Speech Acoustics","url":"https://www.academia.edu/Documents/in/Speech_Acoustics?f_ri=43610"},{"id":13143,"name":"Clustering Algorithms","url":"https://www.academia.edu/Documents/in/Clustering_Algorithms?f_ri=43610"},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610"},{"id":43981,"name":"Optimization","url":"https://www.academia.edu/Documents/in/Optimization?f_ri=43610"},{"id":229267,"name":"Expectation Maximization","url":"https://www.academia.edu/Documents/in/Expectation_Maximization?f_ri=43610"},{"id":279495,"name":"Robustness","url":"https://www.academia.edu/Documents/in/Robustness?f_ri=43610"},{"id":349137,"name":"Data Models","url":"https://www.academia.edu/Documents/in/Data_Models?f_ri=43610"},{"id":364962,"name":"Data Model","url":"https://www.academia.edu/Documents/in/Data_Model?f_ri=43610"},{"id":423482,"name":"Mixture Model","url":"https://www.academia.edu/Documents/in/Mixture_Model?f_ri=43610"},{"id":568878,"name":"Covariance Matrix","url":"https://www.academia.edu/Documents/in/Covariance_Matrix?f_ri=43610"},{"id":627850,"name":"K Means","url":"https://www.academia.edu/Documents/in/K_Means?f_ri=43610"},{"id":1191609,"name":"Likelihood Function","url":"https://www.academia.edu/Documents/in/Likelihood_Function?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_47089310" data-work_id="47089310" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/47089310/Probabilistic_logic_based_characterization_of_knowledge_discovery_in_databases">Probabilistic logic-based characterization of knowledge discovery in databases</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/47089310" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8b56c97a20f5bb42c691b3f679200255" rel="nofollow" data-download="{"attachment_id":66368548,"asset_id":47089310,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" 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type="text/json">{"id":2979,"name":"Manufacturing","url":"https://www.academia.edu/Documents/in/Manufacturing?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="6160" href="https://www.academia.edu/Documents/in/Computer_Aided_Design">Computer Aided Design</a>, <script data-card-contents-for-ri="6160" type="text/json">{"id":6160,"name":"Computer Aided Design","url":"https://www.academia.edu/Documents/in/Computer_Aided_Design?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="17733" href="https://www.academia.edu/Documents/in/Nanotechnology">Nanotechnology</a><script data-card-contents-for-ri="17733" type="text/json">{"id":17733,"name":"Nanotechnology","url":"https://www.academia.edu/Documents/in/Nanotechnology?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1036418]'), work: 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data-has-card-for-ri="2534" href="https://www.academia.edu/Documents/in/Multiagent_Systems">Multiagent Systems</a>, <script data-card-contents-for-ri="2534" type="text/json">{"id":2534,"name":"Multiagent Systems","url":"https://www.academia.edu/Documents/in/Multiagent_Systems?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4165" href="https://www.academia.edu/Documents/in/Fuzzy_Logic">Fuzzy Logic</a><script data-card-contents-for-ri="4165" type="text/json">{"id":4165,"name":"Fuzzy Logic","url":"https://www.academia.edu/Documents/in/Fuzzy_Logic?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=1626144]'), work: {"id":1626144,"title":"Uncertainty and Randomness: A Holonic 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href="https://www.academia.edu/18267717/Pedestrian_detection_based_on_LIDAR_driven_sliding_window_and_relational_parts_based_detection">Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/18267717" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="e3b86f80dbf5e6b844ccca10c4d9b518" rel="nofollow" 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window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_18267717"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_18267717 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="18267717"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 18267717; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=18267717]").text(description); $(".js-view-count-work_18267717").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_18267717").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="18267717"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="1252" href="https://www.academia.edu/Documents/in/Remote_Sensing">Remote Sensing</a>, <script data-card-contents-for-ri="1252" type="text/json">{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="10408" href="https://www.academia.edu/Documents/in/Support_Vector_Machines">Support Vector Machines</a>, <script data-card-contents-for-ri="10408" type="text/json">{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="33069" href="https://www.academia.edu/Documents/in/Probability">Probability</a>, <script data-card-contents-for-ri="33069" type="text/json">{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a><script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=18267717]'), work: {"id":18267717,"title":"Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection","created_at":"2015-11-13T02:22:55.359-08:00","url":"https://www.academia.edu/18267717/Pedestrian_detection_based_on_LIDAR_driven_sliding_window_and_relational_parts_based_detection?f_ri=43610","dom_id":"work_18267717","summary":null,"downloadable_attachments":[{"id":42137975,"asset_id":18267717,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":38250234,"first_name":"Urbano","last_name":"Nunes","domain_name":"independent","page_name":"UrbanoNunes","display_name":"Urbano Nunes","profile_url":"https://independent.academia.edu/UrbanoNunes?f_ri=43610","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":1252,"name":"Remote Sensing","url":"https://www.academia.edu/Documents/in/Remote_Sensing?f_ri=43610","nofollow":false},{"id":10408,"name":"Support Vector Machines","url":"https://www.academia.edu/Documents/in/Support_Vector_Machines?f_ri=43610","nofollow":false},{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":56368,"name":"Image Classification","url":"https://www.academia.edu/Documents/in/Image_Classification?f_ri=43610"},{"id":160144,"name":"Feature Extraction","url":"https://www.academia.edu/Documents/in/Feature_Extraction?f_ri=43610"},{"id":169392,"name":"Detectors","url":"https://www.academia.edu/Documents/in/Detectors?f_ri=43610"},{"id":383728,"name":"Vectors","url":"https://www.academia.edu/Documents/in/Vectors?f_ri=43610"},{"id":985283,"name":"Laser Radar","url":"https://www.academia.edu/Documents/in/Laser_Radar?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_10155958" data-work_id="10155958" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/10155958/Uncertainty_Aware_Sensor_Network_Deployment">Uncertainty-Aware Sensor Network Deployment</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_10155958" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/10155958" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="74dd5dbbb53f915db2efb8acff26d2ae" rel="nofollow" data-download="{"attachment_id":47504559,"asset_id":10155958,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/47504559/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="24801214" href="https://ifsttar.academia.edu/latifaOukhellou">latifa Oukhellou</a><script data-card-contents-for-user="24801214" type="text/json">{"id":24801214,"first_name":"latifa","last_name":"Oukhellou","domain_name":"ifsttar","page_name":"latifaOukhellou","display_name":"latifa Oukhellou","profile_url":"https://ifsttar.academia.edu/latifaOukhellou?f_ri=43610","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_10155958 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="10155958"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 10155958, container: ".js-paper-rank-work_10155958", }); 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$(".js-view-count[data-work-id=10155958]").text(description); $(".js-view-count-work_10155958").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_10155958").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="10155958"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">9</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="9136" href="https://www.academia.edu/Documents/in/Wireless_Sensor_Networks">Wireless Sensor Networks</a>, <script data-card-contents-for-ri="9136" type="text/json">{"id":9136,"name":"Wireless Sensor Networks","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Networks?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a>, <script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="92088" href="https://www.academia.edu/Documents/in/Information_Fusion">Information Fusion</a>, <script data-card-contents-for-ri="92088" type="text/json">{"id":92088,"name":"Information Fusion","url":"https://www.academia.edu/Documents/in/Information_Fusion?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="111436" href="https://www.academia.edu/Documents/in/IEEE">IEEE</a><script data-card-contents-for-ri="111436" type="text/json">{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=10155958]'), work: {"id":10155958,"title":"Uncertainty-Aware Sensor Network Deployment","created_at":"2015-01-14T01:15:01.650-08:00","url":"https://www.academia.edu/10155958/Uncertainty_Aware_Sensor_Network_Deployment?f_ri=43610","dom_id":"work_10155958","summary":"In this paper, we address the issue of handling uncertainty and information fusion for an efficient WSN deployment. We present a flexible framework for collaborative target detection within the transferable belief model. Using the developed framework, we propose an uncertainty-aware deployment algorithm that is able to determine the minimum number of sensors and their locations such that full area coverage is achieved. The issues of connectivity, obstacles, preferential coverage, challenging environments and sensor reliability are also discussed. Experimental results are provided to demonstrate the ability of our approach to achieve an efficient sensor deployment by exploiting a collaborative target detection scheme.","downloadable_attachments":[{"id":47504559,"asset_id":10155958,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":24801214,"first_name":"latifa","last_name":"Oukhellou","domain_name":"ifsttar","page_name":"latifaOukhellou","display_name":"latifa Oukhellou","profile_url":"https://ifsttar.academia.edu/latifaOukhellou?f_ri=43610","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":9136,"name":"Wireless Sensor Networks","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Networks?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":92088,"name":"Information Fusion","url":"https://www.academia.edu/Documents/in/Information_Fusion?f_ri=43610","nofollow":false},{"id":111436,"name":"IEEE","url":"https://www.academia.edu/Documents/in/IEEE?f_ri=43610","nofollow":false},{"id":143115,"name":"Sensor Fusion","url":"https://www.academia.edu/Documents/in/Sensor_Fusion?f_ri=43610"},{"id":158569,"name":"Sensor Network","url":"https://www.academia.edu/Documents/in/Sensor_Network?f_ri=43610"},{"id":330048,"name":"Target Detection","url":"https://www.academia.edu/Documents/in/Target_Detection?f_ri=43610"},{"id":762830,"name":"Object Detection","url":"https://www.academia.edu/Documents/in/Object_Detection?f_ri=43610"},{"id":1211847,"name":"Wireless Sensor Network","url":"https://www.academia.edu/Documents/in/Wireless_Sensor_Network?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_4023103" data-work_id="4023103" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4023103/Case_study_on_increasing_the_transport_capacity_of_220_kV_DC_OHL_Iernut_Baia_Mare_by_reconductoring_using_LM_technologies">Case study on increasing the transport capacity of 220 kV DC OHL Iernut-Baia Mare by reconductoring using LM technologies</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In 2009 we have celebrated 30 years since the first live maintenance techniques (LMT) have been carried out in Romania. This paper, in acknowledgement of the Romanian Live Maintenance Association's valuable activity in this field, is... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4023103" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In 2009 we have celebrated 30 years since the first live maintenance techniques (LMT) have been carried out in Romania. This paper, in acknowledgement of the Romanian Live Maintenance Association's valuable activity in this field, is tackling a major issue for the Romanian Power Grid Company Transelectrica S.A.: the increase of the overhead line transmission capacity applying reconductoring with high temperature low sag (HTLS) conductors. The authors are proposing the use of live maintenance techniques, which considerably reduce the overhead line unavailability.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4023103" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="42ac8521812ebe57f8841e232acf0849" rel="nofollow" data-download="{"attachment_id":31565660,"asset_id":4023103,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/31565660/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4833828" href="https://independent.academia.edu/MariusOltean">Marius Oltean</a><script data-card-contents-for-user="4833828" type="text/json">{"id":4833828,"first_name":"Marius","last_name":"Oltean","domain_name":"independent","page_name":"MariusOltean","display_name":"Marius Oltean","profile_url":"https://independent.academia.edu/MariusOltean?f_ri=43610","photo":"https://0.academia-photos.com/4833828/2066627/2432655/s65_marius.oltean.jpg"}</script></span></span></li><li class="js-paper-rank-work_4023103 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="4023103"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 4023103, container: ".js-paper-rank-work_4023103", }); 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This paper, in acknowledgement of the Romanian Live Maintenance Association's valuable activity in this field, is tackling a major issue for the Romanian Power Grid Company Transelectrica S.A.: the increase of the overhead line transmission capacity applying reconductoring with high temperature low sag (HTLS) conductors. The authors are proposing the use of live maintenance techniques, which considerably reduce the overhead line unavailability.","downloadable_attachments":[{"id":31565660,"asset_id":4023103,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4833828,"first_name":"Marius","last_name":"Oltean","domain_name":"independent","page_name":"MariusOltean","display_name":"Marius Oltean","profile_url":"https://independent.academia.edu/MariusOltean?f_ri=43610","photo":"https://0.academia-photos.com/4833828/2066627/2432655/s65_marius.oltean.jpg"}],"research_interests":[{"id":5748,"name":"Power System","url":"https://www.academia.edu/Documents/in/Power_System?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":96047,"name":"Case Study","url":"https://www.academia.edu/Documents/in/Case_Study?f_ri=43610","nofollow":false},{"id":981256,"name":"Load 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class="u-tcGrayDark u-fw700" data-has-card-for-user="28859030" href="https://independent.academia.edu/vipinKumar114">vipin Kumar</a><script data-card-contents-for-user="28859030" type="text/json">{"id":28859030,"first_name":"vipin","last_name":"Kumar","domain_name":"independent","page_name":"vipinKumar114","display_name":"vipin Kumar","profile_url":"https://independent.academia.edu/vipinKumar114?f_ri=43610","photo":"https://0.academia-photos.com/28859030/8200794/9176007/s65_vipin.kumar.jpg"}</script></span></span></li><li class="js-paper-rank-work_11751691 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="11751691"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 11751691, container: ".js-paper-rank-work_11751691", }); });</script></li><li class="js-percentile-work_11751691 InlineList-item InlineList-item--bordered 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href="https://www.academia.edu/27035347/Landslide_hazard_mapping_of_Penang_island_using_probabilistic_methods_and_logistic_regression">Landslide hazard mapping of Penang island using probabilistic methods and logistic regression</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/27035347" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4f38e11d5df240d22c623519999b91b5" rel="nofollow" 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type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="224767" href="https://www.academia.edu/Documents/in/Prediction_Model">Prediction Model</a>, <script data-card-contents-for-ri="224767" type="text/json">{"id":224767,"name":"Prediction Model","url":"https://www.academia.edu/Documents/in/Prediction_Model?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="284270" href="https://www.academia.edu/Documents/in/Hazard_Mapping">Hazard Mapping</a><script data-card-contents-for-ri="284270" type="text/json">{"id":284270,"name":"Hazard Mapping","url":"https://www.academia.edu/Documents/in/Hazard_Mapping?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=27035347]'), work: {"id":27035347,"title":"Landslide hazard mapping of Penang island using probabilistic methods and logistic regression","created_at":"2016-07-17T01:09:29.751-07:00","url":"https://www.academia.edu/27035347/Landslide_hazard_mapping_of_Penang_island_using_probabilistic_methods_and_logistic_regression?f_ri=43610","dom_id":"work_27035347","summary":null,"downloadable_attachments":[{"id":47291147,"asset_id":27035347,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":28105625,"first_name":"Habibah","last_name":"Lateh","domain_name":"independent","page_name":"HabibahLateh","display_name":"Habibah Lateh","profile_url":"https://independent.academia.edu/HabibahLateh?f_ri=43610","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":32433,"name":"Logistic Regression","url":"https://www.academia.edu/Documents/in/Logistic_Regression?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic 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u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/3129703/Quantum_Physical_Symbol_Systems">Quantum Physical Symbol Systems</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/3129703" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="027c3dac60322a97a4b65d713c26d64c" rel="nofollow" data-download="{"attachment_id":31042951,"asset_id":3129703,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen 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window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_3129703"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_3129703 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="3129703"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 3129703; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=3129703]").text(description); $(".js-view-count-work_3129703").attr('title', description).tooltip(); }); });</script></span><script>$(function() { 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Process","url":"https://www.academia.edu/Documents/in/Stochastic_Process?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="433" href="https://www.academia.edu/Documents/in/Computer_Architecture">Computer Architecture</a>, <script data-card-contents-for-ri="433" type="text/json">{"id":433,"name":"Computer Architecture","url":"https://www.academia.edu/Documents/in/Computer_Architecture?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="518" href="https://www.academia.edu/Documents/in/Quantum_Physics">Quantum Physics</a><script data-card-contents-for-ri="518" type="text/json">{"id":518,"name":"Quantum Physics","url":"https://www.academia.edu/Documents/in/Quantum_Physics?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=3129703]'), work: {"id":3129703,"title":"Quantum Physical Symbol 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Century","url":"https://www.academia.edu/Documents/in/Nineteenth_Century?f_ri=43610"},{"id":25660,"name":"Decision Theory","url":"https://www.academia.edu/Documents/in/Decision_Theory?f_ri=43610"},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610"},{"id":55378,"name":"Bayesian Learning","url":"https://www.academia.edu/Documents/in/Bayesian_Learning?f_ri=43610"},{"id":171449,"name":"Computer Application","url":"https://www.academia.edu/Documents/in/Computer_Application?f_ri=43610"},{"id":202566,"name":"Quantum Computer","url":"https://www.academia.edu/Documents/in/Quantum_Computer?f_ri=43610"},{"id":206590,"name":"Task Performance","url":"https://www.academia.edu/Documents/in/Task_Performance?f_ri=43610"},{"id":242640,"name":"Learning from experience","url":"https://www.academia.edu/Documents/in/Learning_from_experience?f_ri=43610"},{"id":258979,"name":"Computer Program","url":"https://www.academia.edu/Documents/in/Computer_Program?f_ri=43610"},{"id":274599,"name":"Bayesian Network","url":"https://www.academia.edu/Documents/in/Bayesian_Network?f_ri=43610"},{"id":403174,"name":"Quantum Measurement","url":"https://www.academia.edu/Documents/in/Quantum_Measurement?f_ri=43610"},{"id":469749,"name":"Human Computer Interface","url":"https://www.academia.edu/Documents/in/Human_Computer_Interface?f_ri=43610"},{"id":821017,"name":"Theory of Computing","url":"https://www.academia.edu/Documents/in/Theory_of_Computing?f_ri=43610"},{"id":871906,"name":"Decision Models","url":"https://www.academia.edu/Documents/in/Decision_Models?f_ri=43610"},{"id":886652,"name":"Graphical Model","url":"https://www.academia.edu/Documents/in/Graphical_Model?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_22701682" data-work_id="22701682" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/22701682/Probabilistic_Logics_and_Probabilistic_Networks">Probabilistic Logics and Probabilistic Networks</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/22701682" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="fbe57a3d3e52d40a39ec9d5a69182cfd" rel="nofollow" data-download="{"attachment_id":43277876,"asset_id":22701682,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/43277876/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="268998" href="https://frankfurt-school.academia.edu/GregoryWheeler">Gregory Wheeler</a><script data-card-contents-for-user="268998" 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data-card-contents-for-ri="67968" type="text/json">{"id":67968,"name":"Statistical Inference","url":"https://www.academia.edu/Documents/in/Statistical_Inference?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="100094" href="https://www.academia.edu/Documents/in/Bayesian_statistics">Bayesian statistics</a>, <script data-card-contents-for-ri="100094" type="text/json">{"id":100094,"name":"Bayesian statistics","url":"https://www.academia.edu/Documents/in/Bayesian_statistics?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="274599" href="https://www.academia.edu/Documents/in/Bayesian_Network">Bayesian Network</a><script data-card-contents-for-ri="274599" type="text/json">{"id":274599,"name":"Bayesian Network","url":"https://www.academia.edu/Documents/in/Bayesian_Network?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=22701682]'), work: {"id":22701682,"title":"Probabilistic Logics and Probabilistic Networks","created_at":"2016-03-02T11:52:30.513-08:00","url":"https://www.academia.edu/22701682/Probabilistic_Logics_and_Probabilistic_Networks?f_ri=43610","dom_id":"work_22701682","summary":null,"downloadable_attachments":[{"id":43277876,"asset_id":22701682,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":268998,"first_name":"Gregory","last_name":"Wheeler","domain_name":"frankfurt-school","page_name":"GregoryWheeler","display_name":"Gregory Wheeler","profile_url":"https://frankfurt-school.academia.edu/GregoryWheeler?f_ri=43610","photo":"https://0.academia-photos.com/268998/11779358/13130146/s65_gregory.wheeler.jpg"}],"research_interests":[{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":67968,"name":"Statistical 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href="https://www.academia.edu/78655578/Probabilistic_Seismic_Hazard_Analysis_and_Site_Characterization_of_a_Powerplant_Site_in_Chittagong_Bangladesh">Probabilistic Seismic Hazard Analysis and Site Characterization of a Powerplant Site in Chittagong, Bangladesh</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In the present study, the Probabilistic Seismic Hazard Analysis (PSHA) has been carried out for a powerplant site in Chittagong using the latest available information on seismicity in the region. The latest updated earthquake catalog... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_78655578" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In the present study, the Probabilistic Seismic Hazard Analysis (PSHA) has been carried out for a powerplant site in Chittagong using the latest available information on seismicity in the region. The latest updated earthquake catalog homogenized in uniform moment magnitude scale was prepared. Seismicity analysis was carried out, and the seismicity parameters for the region were estimated from the frequency magnitude distribution plot. The peak ground acceleration (PGA) and the spectral acceleration at the bedrock for 10% and 2% probability of exceedance within 50 years were evaluated using different source models and attenuation relations in a logic tree framework. The site was characterized based on the soil investigation data from 31 boreholes and amplification factors considering the local geology (SPT-N values) were obtained through ground response analysis. Subsequently, the surface level peak ground acceleration was estimated and depicted in the form of hazard contour maps.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/78655578" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="5e523e9902d3c1d1281087c7cddabdea" rel="nofollow" data-download="{"attachment_id":85627883,"asset_id":78655578,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/85627883/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="47127175" href="https://independent.academia.edu/SoumyadeepSengupta1">Soumyadeep Sengupta</a><script data-card-contents-for-user="47127175" type="text/json">{"id":47127175,"first_name":"Soumyadeep","last_name":"Sengupta","domain_name":"independent","page_name":"SoumyadeepSengupta1","display_name":"Soumyadeep Sengupta","profile_url":"https://independent.academia.edu/SoumyadeepSengupta1?f_ri=43610","photo":"https://0.academia-photos.com/47127175/23054840/22175656/s65_soumyadeep.sengupta.jpg"}</script></span></span></li><li class="js-paper-rank-work_78655578 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="78655578"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 78655578, container: ".js-paper-rank-work_78655578", }); });</script></li><li class="js-percentile-work_78655578 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 78655578; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_78655578"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_78655578 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="78655578"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 78655578; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=78655578]").text(description); $(".js-view-count-work_78655578").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_78655578").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="78655578"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="3157" href="https://www.academia.edu/Documents/in/Seismic_Hazard">Seismic Hazard</a>, <script data-card-contents-for-ri="3157" type="text/json">{"id":3157,"name":"Seismic Hazard","url":"https://www.academia.edu/Documents/in/Seismic_Hazard?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a><script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=78655578]'), work: {"id":78655578,"title":"Probabilistic Seismic Hazard Analysis and Site Characterization of a Powerplant Site in Chittagong, Bangladesh","created_at":"2022-05-07T01:12:19.813-07:00","url":"https://www.academia.edu/78655578/Probabilistic_Seismic_Hazard_Analysis_and_Site_Characterization_of_a_Powerplant_Site_in_Chittagong_Bangladesh?f_ri=43610","dom_id":"work_78655578","summary":"In the present study, the Probabilistic Seismic Hazard Analysis (PSHA) has been carried out for a powerplant site in Chittagong using the latest available information on seismicity in the region. The latest updated earthquake catalog homogenized in uniform moment magnitude scale was prepared. Seismicity analysis was carried out, and the seismicity parameters for the region were estimated from the frequency magnitude distribution plot. The peak ground acceleration (PGA) and the spectral acceleration at the bedrock for 10% and 2% probability of exceedance within 50 years were evaluated using different source models and attenuation relations in a logic tree framework. The site was characterized based on the soil investigation data from 31 boreholes and amplification factors considering the local geology (SPT-N values) were obtained through ground response analysis. Subsequently, the surface level peak ground acceleration was estimated and depicted in the form of hazard contour maps.","downloadable_attachments":[{"id":85627883,"asset_id":78655578,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":47127175,"first_name":"Soumyadeep","last_name":"Sengupta","domain_name":"independent","page_name":"SoumyadeepSengupta1","display_name":"Soumyadeep Sengupta","profile_url":"https://independent.academia.edu/SoumyadeepSengupta1?f_ri=43610","photo":"https://0.academia-photos.com/47127175/23054840/22175656/s65_soumyadeep.sengupta.jpg"}],"research_interests":[{"id":3157,"name":"Seismic Hazard","url":"https://www.academia.edu/Documents/in/Seismic_Hazard?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_2991319" data-work_id="2991319" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/2991319/Cluster_Validity_For_Kernel_Fuzzy_Clustering">Cluster Validity For Kernel Fuzzy Clustering</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/2991319" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d6109e56ebe3542d425b5e4d9fc984b5" rel="nofollow" data-download="{"attachment_id":30943327,"asset_id":2991319,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/30943327/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="36931" href="https://michiganstate.academia.edu/TimHavens">Timothy Havens</a><script data-card-contents-for-user="36931" type="text/json">{"id":36931,"first_name":"Timothy","last_name":"Havens","domain_name":"michiganstate","page_name":"TimHavens","display_name":"Timothy Havens","profile_url":"https://michiganstate.academia.edu/TimHavens?f_ri=43610","photo":"https://0.academia-photos.com/36931/12249/11496/s65_timothy.havens.jpeg"}</script></span></span></li><li class="js-paper-rank-work_2991319 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="2991319"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 2991319, container: ".js-paper-rank-work_2991319", }); });</script></li><li class="js-percentile-work_2991319 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x 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});</script></span><script>$(function() { $(".js-view-count-work_2991319").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="2991319"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">8</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="5394" href="https://www.academia.edu/Documents/in/Fuzzy_set_theory">Fuzzy set theory</a>, <script data-card-contents-for-ri="5394" type="text/json">{"id":5394,"name":"Fuzzy set theory","url":"https://www.academia.edu/Documents/in/Fuzzy_set_theory?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="13143" href="https://www.academia.edu/Documents/in/Clustering_Algorithms">Clustering Algorithms</a>, <script data-card-contents-for-ri="13143" type="text/json">{"id":13143,"name":"Clustering Algorithms","url":"https://www.academia.edu/Documents/in/Clustering_Algorithms?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="38511" href="https://www.academia.edu/Documents/in/Fuzzy_Clustering">Fuzzy Clustering</a>, <script data-card-contents-for-ri="38511" type="text/json">{"id":38511,"name":"Fuzzy Clustering","url":"https://www.academia.edu/Documents/in/Fuzzy_Clustering?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a><script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=2991319]'), work: {"id":2991319,"title":"Cluster Validity For Kernel Fuzzy Clustering","created_at":"2013-03-14T07:51:58.797-07:00","url":"https://www.academia.edu/2991319/Cluster_Validity_For_Kernel_Fuzzy_Clustering?f_ri=43610","dom_id":"work_2991319","summary":null,"downloadable_attachments":[{"id":30943327,"asset_id":2991319,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":36931,"first_name":"Timothy","last_name":"Havens","domain_name":"michiganstate","page_name":"TimHavens","display_name":"Timothy Havens","profile_url":"https://michiganstate.academia.edu/TimHavens?f_ri=43610","photo":"https://0.academia-photos.com/36931/12249/11496/s65_timothy.havens.jpeg"}],"research_interests":[{"id":5394,"name":"Fuzzy set theory","url":"https://www.academia.edu/Documents/in/Fuzzy_set_theory?f_ri=43610","nofollow":false},{"id":13143,"name":"Clustering Algorithms","url":"https://www.academia.edu/Documents/in/Clustering_Algorithms?f_ri=43610","nofollow":false},{"id":38511,"name":"Fuzzy Clustering","url":"https://www.academia.edu/Documents/in/Fuzzy_Clustering?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":191487,"name":"Kernel","url":"https://www.academia.edu/Documents/in/Kernel?f_ri=43610"},{"id":383728,"name":"Vectors","url":"https://www.academia.edu/Documents/in/Vectors?f_ri=43610"},{"id":1006047,"name":"Indexes","url":"https://www.academia.edu/Documents/in/Indexes?f_ri=43610"},{"id":1167632,"name":"Synthetic Data Generation","url":"https://www.academia.edu/Documents/in/Synthetic_Data_Generation?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_26858210" data-work_id="26858210" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/26858210/Theory_and_Practice_of_Bloom_Filters_for_Distributed_Systems">Theory and Practice of Bloom Filters for Distributed Systems</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/26858210" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f72b3e140b646b2e65ac8a5b5f87c9e9" rel="nofollow" data-download="{"attachment_id":47128375,"asset_id":26858210,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button 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System","url":"https://www.academia.edu/Documents/in/Distributed_System?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="33069" href="https://www.academia.edu/Documents/in/Probability">Probability</a>, <script data-card-contents-for-ri="33069" type="text/json">{"id":33069,"name":"Probability","url":"https://www.academia.edu/Documents/in/Probability?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="36837" href="https://www.academia.edu/Documents/in/Information_Processing">Information Processing</a><script data-card-contents-for-ri="36837" type="text/json">{"id":36837,"name":"Information Processing","url":"https://www.academia.edu/Documents/in/Information_Processing?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=26858210]'), work: {"id":26858210,"title":"Theory and Practice of Bloom Filters for 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href="https://www.academia.edu/78553149/Vulnerability_likelihood_A_probabilistic_approach_to_software_assurance">Vulnerability likelihood: A probabilistic approach to software assurance</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The 2001 workshop on information security system rating and ranking 4] discussed different aspects of security metrics. What should we count and what do the numbers mean as pertaining to software security metrics was one of the challenge... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_78553149" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The 2001 workshop on information security system rating and ranking 4] discussed different aspects of security metrics. What should we count and what do the numbers mean as pertaining to software security metrics was one of the challenge problems discussed by security experts at the 2003 UW-MSR Summer Institute 7]. The development of meaningful security metrics was chosen as a grand challenge at the onference on rand Research hallenges consecutively in 2002 5] and 2003 8]. 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work_247124" data-work_id="247124" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/247124/Image_Segmentation_by_Probabilistic_Bottom_Up_Aggregation_and_Cue_Integration">Image Segmentation by Probabilistic Bottom-Up Aggregation and Cue Integration</a></div></div><div class="u-pb4x u-mt3x"></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/247124" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7f7c22625b4b9586615a102b5035d729" 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type="text/json">{"id":300,"name":"Mathematics","url":"https://www.academia.edu/Documents/in/Mathematics?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="428" href="https://www.academia.edu/Documents/in/Algorithms">Algorithms</a><script data-card-contents-for-ri="428" type="text/json">{"id":428,"name":"Algorithms","url":"https://www.academia.edu/Documents/in/Algorithms?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=247124]'), work: {"id":247124,"title":"Image Segmentation by Probabilistic Bottom-Up 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integration","url":"https://www.academia.edu/Documents/in/Cue_integration?f_ri=43610"},{"id":904692,"name":"Bottom Up","url":"https://www.academia.edu/Documents/in/Bottom_Up?f_ri=43610"},{"id":912908,"name":"Noise Measurement","url":"https://www.academia.edu/Documents/in/Noise_Measurement?f_ri=43610"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=43610"},{"id":1707689,"name":"Pixel","url":"https://www.academia.edu/Documents/in/Pixel?f_ri=43610"},{"id":2542920,"name":"Probabilistic Approach","url":"https://www.academia.edu/Documents/in/Probabilistic_Approach?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_6991951" data-work_id="6991951" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/6991951/Fuzzy_Arithmetic_for_the_DC_Load_Flow">Fuzzy Arithmetic for the DC Load Flow</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The consideration of uncertainties in the future system operation is a key aspect in current planning methodologies. In this context, load flow studies based on probabilistic theory, fuzzy numbers, and Monte Carlo simulations have been... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_6991951" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The consideration of uncertainties in the future <br />system operation is a key aspect in current planning methodologies. <br />In this context, load flow studies based on probabilistic theory, <br />fuzzy numbers, and Monte Carlo simulations have been proposed <br />in the literature. This work analyzes in a novel way the application <br />of fuzzy number arithmetic for the DC load flow problem. In this <br />sense, fuzzy sets theory is reviewed and is showed that there are <br />two alternative and valid procedures to perform a subtraction. <br />A key aspect for the right selection of one of these procedures is <br />the independency or dependency among the involved variables. <br />Assuming input data as independent variables, this work is focused <br />on analyzing the fuzzy subtraction between dependent state <br />variables of the system, such as voltage phase angle. Accordingly, <br />two new valid alternative methodologies are proposed and applied <br />to case studies. Results are compared to previous related works <br />showing the consistency of the proposed methodologies. Future <br />research will be focused on the consideration of ohmic losses and <br />AC network modeling in the field of expansion planning studies.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/6991951" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ce5a2cd0afd20d5611e94bb848e1bf23" rel="nofollow" data-download="{"attachment_id":34503382,"asset_id":6991951,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/34503382/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="11873566" href="https://uantof.academia.edu/MarceloCortesCarmona">Marcelo Cortes-Carmona</a><script data-card-contents-for-user="11873566" type="text/json">{"id":11873566,"first_name":"Marcelo","last_name":"Cortes-Carmona","domain_name":"uantof","page_name":"MarceloCortesCarmona","display_name":"Marcelo Cortes-Carmona","profile_url":"https://uantof.academia.edu/MarceloCortesCarmona?f_ri=43610","photo":"https://0.academia-photos.com/11873566/4179561/4867196/s65_marcelo.cortes-carmona.jpg"}</script></span></span></li><li class="js-paper-rank-work_6991951 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="6991951"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 6991951, container: ".js-paper-rank-work_6991951", }); 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$(".js-view-count[data-work-id=6991951]").text(description); $(".js-view-count-work_6991951").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_6991951").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="6991951"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">12</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="1424" href="https://www.academia.edu/Documents/in/Power_Systems">Power Systems</a>, <script data-card-contents-for-ri="1424" type="text/json">{"id":1424,"name":"Power Systems","url":"https://www.academia.edu/Documents/in/Power_Systems?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="4392" href="https://www.academia.edu/Documents/in/Monte_Carlo_Simulation">Monte Carlo Simulation</a>, <script data-card-contents-for-ri="4392" type="text/json">{"id":4392,"name":"Monte Carlo Simulation","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Simulation?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="5394" href="https://www.academia.edu/Documents/in/Fuzzy_set_theory">Fuzzy set theory</a>, <script data-card-contents-for-ri="5394" type="text/json">{"id":5394,"name":"Fuzzy set theory","url":"https://www.academia.edu/Documents/in/Fuzzy_set_theory?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="34019" href="https://www.academia.edu/Documents/in/Monte_Carlo_Methods">Monte Carlo Methods</a><script data-card-contents-for-ri="34019" type="text/json">{"id":34019,"name":"Monte Carlo Methods","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Methods?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=6991951]'), work: {"id":6991951,"title":"Fuzzy Arithmetic for the DC Load Flow","created_at":"2014-05-08T11:07:57.107-07:00","url":"https://www.academia.edu/6991951/Fuzzy_Arithmetic_for_the_DC_Load_Flow?f_ri=43610","dom_id":"work_6991951","summary":"The consideration of uncertainties in the future\r\nsystem operation is a key aspect in current planning methodologies.\r\nIn this context, load flow studies based on probabilistic theory,\r\nfuzzy numbers, and Monte Carlo simulations have been proposed\r\nin the literature. This work analyzes in a novel way the application\r\nof fuzzy number arithmetic for the DC load flow problem. In this\r\nsense, fuzzy sets theory is reviewed and is showed that there are\r\ntwo alternative and valid procedures to perform a subtraction.\r\nA key aspect for the right selection of one of these procedures is\r\nthe independency or dependency among the involved variables.\r\nAssuming input data as independent variables, this work is focused\r\non analyzing the fuzzy subtraction between dependent state\r\nvariables of the system, such as voltage phase angle. Accordingly,\r\ntwo new valid alternative methodologies are proposed and applied\r\nto case studies. Results are compared to previous related works\r\nshowing the consistency of the proposed methodologies. Future\r\nresearch will be focused on the consideration of ohmic losses and\r\nAC network modeling in the field of expansion planning studies.","downloadable_attachments":[{"id":34503382,"asset_id":6991951,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":11873566,"first_name":"Marcelo","last_name":"Cortes-Carmona","domain_name":"uantof","page_name":"MarceloCortesCarmona","display_name":"Marcelo Cortes-Carmona","profile_url":"https://uantof.academia.edu/MarceloCortesCarmona?f_ri=43610","photo":"https://0.academia-photos.com/11873566/4179561/4867196/s65_marcelo.cortes-carmona.jpg"}],"research_interests":[{"id":1424,"name":"Power Systems","url":"https://www.academia.edu/Documents/in/Power_Systems?f_ri=43610","nofollow":false},{"id":4392,"name":"Monte Carlo Simulation","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Simulation?f_ri=43610","nofollow":false},{"id":5394,"name":"Fuzzy set theory","url":"https://www.academia.edu/Documents/in/Fuzzy_set_theory?f_ri=43610","nofollow":false},{"id":34019,"name":"Monte Carlo Methods","url":"https://www.academia.edu/Documents/in/Monte_Carlo_Methods?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610"},{"id":96047,"name":"Case Study","url":"https://www.academia.edu/Documents/in/Case_Study?f_ri=43610"},{"id":570169,"name":"Power System Planning","url":"https://www.academia.edu/Documents/in/Power_System_Planning?f_ri=43610"},{"id":642996,"name":"Fuzzy Set","url":"https://www.academia.edu/Documents/in/Fuzzy_Set?f_ri=43610"},{"id":871148,"name":"Network Model","url":"https://www.academia.edu/Documents/in/Network_Model?f_ri=43610"},{"id":981256,"name":"Load Flow","url":"https://www.academia.edu/Documents/in/Load_Flow?f_ri=43610"},{"id":1237788,"name":"Electrical And Electronic Engineering","url":"https://www.academia.edu/Documents/in/Electrical_And_Electronic_Engineering?f_ri=43610"},{"id":2278311,"name":"Fuzzy Number","url":"https://www.academia.edu/Documents/in/Fuzzy_Number?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_20703407 coauthored" data-work_id="20703407" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/20703407/Probabilistic_dynamic_belief_revision_Journal_Paper_">Probabilistic dynamic belief revision [Journal Paper]</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_20703407" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plau-sibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of " knowledge ". We develop a probabilistic version of this concept (" degree of safety ") and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief over conditional probabilistic models. We develop a theory of probabilistic dynamic belief revision, introducing probabilistic " action models " and proposing a notion of probabilistic update product, that comes together with appropriate reduction laws.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/20703407" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="ad7360125c4563f1624b840c7330067d" rel="nofollow" data-download="{"attachment_id":41514748,"asset_id":20703407,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/41514748/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="4230237" href="https://uva.academia.edu/Sonja_Smets">Sonja Smets</a><script data-card-contents-for-user="4230237" type="text/json">{"id":4230237,"first_name":"Sonja","last_name":"Smets","domain_name":"uva","page_name":"Sonja_Smets","display_name":"Sonja Smets","profile_url":"https://uva.academia.edu/Sonja_Smets?f_ri=43610","photo":"https://0.academia-photos.com/4230237/1669981/12665956/s65_sonja.smets.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-20703407">+1</span><div class="hidden js-additional-users-20703407"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://uva.academia.edu/AlexandruBaltag">Alexandru Baltag</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-20703407'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-20703407').html(); } } new HoverPopover(popoverSettings); })();</script></li><li class="js-paper-rank-work_20703407 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="20703407"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 20703407, container: ".js-paper-rank-work_20703407", }); });</script></li><li class="js-percentile-work_20703407 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 20703407; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_20703407"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_20703407 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="20703407"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 20703407; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=20703407]").text(description); $(".js-view-count-work_20703407").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_20703407").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="20703407"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="2531" href="https://www.academia.edu/Documents/in/Belief_Revision_Computer_Science_">Belief Revision (Computer Science)</a>, <script data-card-contents-for-ri="2531" type="text/json">{"id":2531,"name":"Belief Revision (Computer Science)","url":"https://www.academia.edu/Documents/in/Belief_Revision_Computer_Science_?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="10984" href="https://www.academia.edu/Documents/in/Formal_Epistemology">Formal Epistemology</a>, <script data-card-contents-for-ri="10984" type="text/json">{"id":10984,"name":"Formal Epistemology","url":"https://www.academia.edu/Documents/in/Formal_Epistemology?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="43610" href="https://www.academia.edu/Documents/in/Probabilistic_Logic">Probabilistic Logic</a>, <script data-card-contents-for-ri="43610" type="text/json">{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="85797" href="https://www.academia.edu/Documents/in/Doxastic_Logic">Doxastic Logic</a><script data-card-contents-for-ri="85797" type="text/json">{"id":85797,"name":"Doxastic Logic","url":"https://www.academia.edu/Documents/in/Doxastic_Logic?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=20703407]'), work: {"id":20703407,"title":"Probabilistic dynamic belief revision [Journal Paper]","created_at":"2016-01-24T08:36:37.989-08:00","url":"https://www.academia.edu/20703407/Probabilistic_dynamic_belief_revision_Journal_Paper_?f_ri=43610","dom_id":"work_20703407","summary":"We investigate the discrete (finite) case of the Popper–Renyi theory of conditional probability, introducing discrete conditional probabilistic models for knowledge and conditional belief, and comparing them with the more standard plau-sibility models. We also consider a related notion, that of safe belief, which is a weak (non-negatively introspective) type of \" knowledge \". We develop a probabilistic version of this concept (\" degree of safety \") and we analyze its role in games. We completely axiomatize the logic of conditional belief, knowledge and safe belief over conditional probabilistic models. We develop a theory of probabilistic dynamic belief revision, introducing probabilistic \" action models \" and proposing a notion of probabilistic update product, that comes together with appropriate reduction laws.","downloadable_attachments":[{"id":41514748,"asset_id":20703407,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":4230237,"first_name":"Sonja","last_name":"Smets","domain_name":"uva","page_name":"Sonja_Smets","display_name":"Sonja Smets","profile_url":"https://uva.academia.edu/Sonja_Smets?f_ri=43610","photo":"https://0.academia-photos.com/4230237/1669981/12665956/s65_sonja.smets.jpg"},{"id":42184985,"first_name":"Alexandru","last_name":"Baltag","domain_name":"uva","page_name":"AlexandruBaltag","display_name":"Alexandru Baltag","profile_url":"https://uva.academia.edu/AlexandruBaltag?f_ri=43610","photo":"https://0.academia-photos.com/42184985/18514772/18478918/s65_alexandru.baltag.jpg"}],"research_interests":[{"id":2531,"name":"Belief Revision (Computer Science)","url":"https://www.academia.edu/Documents/in/Belief_Revision_Computer_Science_?f_ri=43610","nofollow":false},{"id":10984,"name":"Formal Epistemology","url":"https://www.academia.edu/Documents/in/Formal_Epistemology?f_ri=43610","nofollow":false},{"id":43610,"name":"Probabilistic Logic","url":"https://www.academia.edu/Documents/in/Probabilistic_Logic?f_ri=43610","nofollow":false},{"id":85797,"name":"Doxastic Logic","url":"https://www.academia.edu/Documents/in/Doxastic_Logic?f_ri=43610","nofollow":false},{"id":85798,"name":"Belief Revision","url":"https://www.academia.edu/Documents/in/Belief_Revision?f_ri=43610"},{"id":102444,"name":"Dynamic Epistemic Logic","url":"https://www.academia.edu/Documents/in/Dynamic_Epistemic_Logic?f_ri=43610"},{"id":1399762,"name":"Belief Dynamics","url":"https://www.academia.edu/Documents/in/Belief_Dynamics?f_ri=43610"}]}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_66734559" data-work_id="66734559" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/66734559/Modeling_a_probabilistic_ontology_for_Maritime_Domain_Awareness">Modeling a probabilistic ontology for Maritime Domain Awareness</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Situational awareness and prediction are essential elements of information fusion. Both involve various types of uncertainty and require a sound automated inferential process. Probabilistic ontologies support uncertainty management in... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_66734559" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Situational awareness and prediction are essential elements of information fusion. Both involve various types of uncertainty and require a sound automated inferential process. Probabilistic ontologies support uncertainty management in se-mantically aware systems, and facilitate modular, interoperable systems. This paper describes the process of developing a probabilistic ontology for a Maritime Domain Awareness application. The ontology was created to support identification of ships behaving suspiciously enough to be declared ships of interest. The original model was expanded in two ways: to provide reasons for declaring a ship as being of interest, and to include individual crew member associations. The latter is achieved by supporting inferences about a person&#39;s close relations, group associations, communications, and background influences to assess his likelihood of having terrorist links.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/66734559" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="beb871f0759b9aa0702de4e7c84b88ef" rel="nofollow" data-download="{"attachment_id":77811841,"asset_id":66734559,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/77811841/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="33043525" href="https://independent.academia.edu/RichardHaberlin">Richard Haberlin</a><script data-card-contents-for-user="33043525" type="text/json">{"id":33043525,"first_name":"Richard","last_name":"Haberlin","domain_name":"independent","page_name":"RichardHaberlin","display_name":"Richard Haberlin","profile_url":"https://independent.academia.edu/RichardHaberlin?f_ri=43610","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_66734559 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="66734559"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 66734559, container: ".js-paper-rank-work_66734559", }); });</script></li><li class="js-percentile-work_66734559 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 66734559; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_66734559"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_66734559 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="66734559"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 66734559; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=66734559]").text(description); $(".js-view-count-work_66734559").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_66734559").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="66734559"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">14</a>  </div><span class="InlineList-item-text u-textTruncate u-pl10x"><a class="InlineList-item-text" data-has-card-for-ri="422" href="https://www.academia.edu/Documents/in/Computer_Science">Computer Science</a>, <script data-card-contents-for-ri="422" type="text/json">{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="1660" href="https://www.academia.edu/Documents/in/Terrorism">Terrorism</a>, <script data-card-contents-for-ri="1660" type="text/json">{"id":1660,"name":"Terrorism","url":"https://www.academia.edu/Documents/in/Terrorism?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="2627" href="https://www.academia.edu/Documents/in/Situation_awareness">Situation awareness</a>, <script data-card-contents-for-ri="2627" type="text/json">{"id":2627,"name":"Situation awareness","url":"https://www.academia.edu/Documents/in/Situation_awareness?f_ri=43610","nofollow":false}</script><a class="InlineList-item-text" data-has-card-for-ri="9545" href="https://www.academia.edu/Documents/in/Web_Services">Web Services</a><script data-card-contents-for-ri="9545" type="text/json">{"id":9545,"name":"Web Services","url":"https://www.academia.edu/Documents/in/Web_Services?f_ri=43610","nofollow":false}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=66734559]'), work: {"id":66734559,"title":"Modeling a probabilistic ontology for Maritime Domain Awareness","created_at":"2021-12-31T11:30:57.735-08:00","url":"https://www.academia.edu/66734559/Modeling_a_probabilistic_ontology_for_Maritime_Domain_Awareness?f_ri=43610","dom_id":"work_66734559","summary":"Situational awareness and prediction are essential elements of information fusion. Both involve various types of uncertainty and require a sound automated inferential process. Probabilistic ontologies support uncertainty management in se-mantically aware systems, and facilitate modular, interoperable systems. This paper describes the process of developing a probabilistic ontology for a Maritime Domain Awareness application. The ontology was created to support identification of ships behaving suspiciously enough to be declared ships of interest. The original model was expanded in two ways: to provide reasons for declaring a ship as being of interest, and to include individual crew member associations. The latter is achieved by supporting inferences about a person\u0026#39;s close relations, group associations, communications, and background influences to assess his likelihood of having terrorist links.","downloadable_attachments":[{"id":77811841,"asset_id":66734559,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":33043525,"first_name":"Richard","last_name":"Haberlin","domain_name":"independent","page_name":"RichardHaberlin","display_name":"Richard Haberlin","profile_url":"https://independent.academia.edu/RichardHaberlin?f_ri=43610","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":422,"name":"Computer Science","url":"https://www.academia.edu/Documents/in/Computer_Science?f_ri=43610","nofollow":false},{"id":1660,"name":"Terrorism","url":"https://www.academia.edu/Documents/in/Terrorism?f_ri=43610","nofollow":false},{"id":2627,"name":"Situation 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u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/4396362/Distributed_heuristic_algorithms_for_RAT_selection_in_wireless_heterogeneous_networks">Distributed heuristic algorithms for RAT selection in wireless heterogeneous networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In wireless heterogeneous networks, one of the most challenging problems is Radio Access Technology (RAT) selection that must be designed to avoid resource wastage. In this paper we adopt a hybrid model for RAT selection where the system... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_4396362" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In wireless heterogeneous networks, one of the most challenging problems is Radio Access Technology (RAT) selection that must be designed to avoid resource wastage. In this paper we adopt a hybrid model for RAT selection where the system allocates the downlink traffic between two different technologies in order to enhance global performance. We study the case of an integrated hybrid Wireless Local Area Network environment where the challenge we face is the high computational complexity necessary to obtain the global optimal solution. Therefore, we propose four distributed heuristic algorithms for RAT selection, where two of them are based on the distance between the user and the access points (APs), namely, distance based and probabilistic distance based algorithms. While the two others schemes are based on the peak rate that each user receives from these APs (peak rate based and probabilistic peak rate based algorithms). Results show that the proposed algorithms give efficient results compared to the optimal one depending on the spatial users distribution. Moreover these algorithms have a low computational complexity which makes them more advantageous compared to the optimal scheme in presence of a large number of users.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/4396362" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2f0e20b3d56a3034a85470d56221d08a" rel="nofollow" data-download="{"attachment_id":49883247,"asset_id":4396362,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/49883247/download_file?st=MTczMjQyOTk2OCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="5389172" href="https://usj.academia.edu/MarcIbrahim">Marc Ibrahim</a><script data-card-contents-for-user="5389172" type="text/json">{"id":5389172,"first_name":"Marc","last_name":"Ibrahim","domain_name":"usj","page_name":"MarcIbrahim","display_name":"Marc Ibrahim","profile_url":"https://usj.academia.edu/MarcIbrahim?f_ri=43610","photo":"/images/s65_no_pic.png"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-4396362">+1</span><div class="hidden js-additional-users-4396362"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/KindaKhawam">Kinda Khawam</a></span></div></div></span><script>(function(){ var popoverSettings = { el: $('.js-work-more-authors-4396362'), placement: 'bottom', hide_delay: 200, html: true, content: function(){ return $('.js-additional-users-4396362').html(); 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In this paper we adopt a hybrid model for RAT selection where the system allocates the downlink traffic between two different technologies in order to enhance global performance. We study the case of an integrated hybrid Wireless Local Area Network environment where the challenge we face is the high computational complexity necessary to obtain the global optimal solution. Therefore, we propose four distributed heuristic algorithms for RAT selection, where two of them are based on the distance between the user and the access points (APs), namely, distance based and probabilistic distance based algorithms. While the two others schemes are based on the peak rate that each user receives from these APs (peak rate based and probabilistic peak rate based algorithms). Results show that the proposed algorithms give efficient results compared to the optimal one depending on the spatial users distribution. Moreover these algorithms have a low computational complexity which makes them more advantageous compared to the optimal scheme in presence of a large number of users.","downloadable_attachments":[{"id":49883247,"asset_id":4396362,"asset_type":"Work","always_allow_download":false}],"ordered_authors":[{"id":5389172,"first_name":"Marc","last_name":"Ibrahim","domain_name":"usj","page_name":"MarcIbrahim","display_name":"Marc Ibrahim","profile_url":"https://usj.academia.edu/MarcIbrahim?f_ri=43610","photo":"/images/s65_no_pic.png"},{"id":174399265,"first_name":"Kinda","last_name":"Khawam","domain_name":"independent","page_name":"KindaKhawam","display_name":"Kinda Khawam","profile_url":"https://independent.academia.edu/KindaKhawam?f_ri=43610","photo":"https://0.academia-photos.com/174399265/162290405/159510873/s65_kinda.khawam.png"}],"research_interests":[{"id":2189,"name":"Computational 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