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{"work":{"id":4689228,"created_at":"2013-10-05T21:56:17.397-07:00","from_world_paper_id":null,"updated_at":"2024-11-27T01:43:41.914-08:00","_data":{"ai_title_tag":"Bayesian Filtering: The Best Method for Spam Detection","grobid_abstract":"Achieving a 98%+ spam detection rate using a mathematical approach This white paper describes how Bayesian filtering works and explains why it is the best way to combat spam.","grobid_abstract_attachment_id":"32022126"},"document_type":"teaching_document","pre_hit_view_count_baseline":0,"quality":"spam","language":"en","title":"Why Bayesian filtering is the most effective anti-spam technology","broadcastable":false,"draft":null,"has_indexable_attachment":true,"indexable":true}}["work"]; window.loswp.workCoauthors = [2363014]; window.loswp.locale = "en"; window.loswp.countryCode = "SG"; window.loswp.cwvAbTestBucket = ""; window.loswp.designVariant = "ds_vanilla"; window.loswp.fullPageMobileSutdModalVariant = "control"; 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However, as email communication is very cheap, spammers are taking advantage of it for advertising their products, for committing cybercrimes. So, researchers are working hard to combat with the spammers. Many spam detections techniques and systems are built to fight spammers. But the spammers are continuously finding new ways to defeat the existing filters. This paper describes the existing spam filters techniques and proposes a multi-level architecture for spam email detection. We present the analysis of the architecture to prove the effectiveness of the architecture.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A MULTI-LAYER ARCHITECTURE FOR SPAM-DETECTION SYSTEM","attachmentId":38302177,"attachmentType":"pdf","work_url":"https://www.academia.edu/14421376/A_MULTI_LAYER_ARCHITECTURE_FOR_SPAM_DETECTION_SYSTEM","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/14421376/A_MULTI_LAYER_ARCHITECTURE_FOR_SPAM_DETECTION_SYSTEM"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="1" data-entity-id="20423929" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/20423929/Review_on_Effective_Email_Classification_for_Spam_and_Non_Spam_Detection_on_Various_Machine_Learning_Techniques">Review on Effective Email Classification for Spam and Non Spam Detection on Various Machine Learning Techniques</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="4270915" href="https://ijritcc.academia.edu/ijritcc">International Journal IJRITCC</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Citation/Export MLA Mr. Atul A. Jamnekar, Mr. Falesh M. Shelke, Prof. Praful B. Sambhare, “Review on Effective Email Classification for Spam and Non Spam Detection on Various Machine Learning Techniques”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1621 - 1624, DOI: 10.17762/ijritcc2321-8169.1503158 APA Mr. Atul A. Jamnekar, Mr. Falesh M. Shelke, Prof. Praful B. Sambhare, March 15 Volume 3 Issue 3, “Review on Effective Email Classification for Spam and Non Spam Detection on Various Machine Learning Techniques”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 1621 - 1624, DOI: 10.17762/ijritcc2321-8169.1503158</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Review on Effective Email Classification for Spam and Non Spam Detection on Various Machine Learning Techniques","attachmentId":41361192,"attachmentType":"pdf","work_url":"https://www.academia.edu/20423929/Review_on_Effective_Email_Classification_for_Spam_and_Non_Spam_Detection_on_Various_Machine_Learning_Techniques","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/20423929/Review_on_Effective_Email_Classification_for_Spam_and_Non_Spam_Detection_on_Various_Machine_Learning_Techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="2" data-entity-id="7428233" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/7428233/Email_classification_for_Spam_Detection_using_Word_Stemming">Email classification for Spam Detection using Word Stemming</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="13188814" href="https://psgtech.academia.edu/KARTHIKARENUKA">KARTHIKA RENUKA</a></div><p class="ds-related-work--metadata ds2-5-body-xs">International Journal of Computer Applications, 2010</p><p class="ds-related-work--abstract ds2-5-body-sm">Unsolicited emails, known as spam, are one of the fast growing and costly problems associated with the Internet today. Among the many proposed solutions, a technique using Bayesian filtering is considered as the most effective weapon against spam. Bayesian filtering works by evaluating the probability of different words appearing in legitimate and spam mails and then classifying them based on that probabilities.Most of the current spam email detection systems use keywords to detect spam emails.These keywords can be written as misspellings eg: baank or bannk instead of bank. Misspellings are changed from time to time and hence spam email detection system needs to constantly update the blacklist to detect spam emails containing misspellings. It"s impossible to predict all possible misspellings for a given keyword and add those to the blacklist. In this paper a better and more successful approach for improving E-mail content classification for spam control is proposed. It used the Word Stemming or Word Hashing Technique for improving the efficiency of the content based spam filter.The proposed system extract the base or stem of a misspelled or modified word, to detect spam emails. It considers every misspelled keyword applies a word stemming technique and passes the base word to the content based filter. Using a proposed if-then rule, we can decide whether or not this unknown mail is spam [1].This paper also provides an Email archiving solution which classifies the E-mail relating to a person,</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Email classification for Spam Detection using Word Stemming","attachmentId":34012668,"attachmentType":"pdf","work_url":"https://www.academia.edu/7428233/Email_classification_for_Spam_Detection_using_Word_Stemming","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/7428233/Email_classification_for_Spam_Detection_using_Word_Stemming"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="3" data-entity-id="31538519" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/31538519/IRJET_Overview_of_Anti_spam_filtering_Techniques">IRJET-Overview of Anti-spam filtering Techniques</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="31493941" href="https://irjet.academia.edu/IRJET">IRJET Journal</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Electronic mail (E-mail) is an essential communication tool that has been greatly abused by spammers to disseminate unwanted information (messages) and spread malicious contents to Internet users. Current Internet technologies further accelerated the distribution of spam. Spam-reduction techniques have developed rapidly over the last few years, as spam volumes have increased. We believe that no one anti-spam solution is the “right” answer, and that the best approach is a multifaceted one, combining various forms of filtering with infrastructure changes, financial changes, legal recourse, and more, to provide a stronger barrier to spam than can be achieved with one solution alone. Spam Guru addresses the part of this multi-faceted approach that can be handled by technology on the recipient’s side, using plug-in tokenizes and parsers, plug-in classification modules, and machine-learning techniques to achieve high hit rates and low false-positive rates. Effective controls need to be deployed to countermeasure the ever growing spam problem. Machine learning provides better protective mechanisms that are able to control spam. This paper summarizes most common techniques used for anti-spam filtering by analyzing the e-mail content and also looks into machine learning algorithms such as Naïve Bayesian, support vector machine and neural network that have been adopted to detect and control spam. Each machine learning has its own strengths and limitations as such appropriate preprocessing need to be carefully considered to increase the effectiveness of any given machine learning. Key Words: Anti-spam filters, text categorization, electronic mail (E-mail), Spam Bayes, training email filters, content filtering, false negatives, user level spam filtering machine learning,</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"IRJET-Overview of Anti-spam filtering Techniques","attachmentId":51879974,"attachmentType":"pdf","work_url":"https://www.academia.edu/31538519/IRJET_Overview_of_Anti_spam_filtering_Techniques","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/31538519/IRJET_Overview_of_Anti_spam_filtering_Techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="4" data-entity-id="6366283" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/6366283/Filtering_Spam_Current_Trends_and_Techniques">Filtering Spam: Current Trends and Techniques</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="9953702" href="https://uni-koeln.academia.edu/GeerthikS">Geerthik S</a></div><p class="ds-related-work--abstract ds2-5-body-sm">This article gives an overview about latest trend and techniques in spam filtering. We analyzed the problems which is introduced by spam ,what spam actually do and how to measure the spam .This article mainly focuses on automated, non-interactive filters, with a broad review ranging from commercial implementations to ideas confined to current research papers. The solutions using both machine and non -machine learning approaches are reviewed and taxonomy of different approaches is presented. While a range of different techniques have and continue to be evaluated in academic research, heuristic and Bayesian filtering, along with its variants provide the greatest potential for future spam prevention.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Filtering Spam: Current Trends and Techniques","attachmentId":33184971,"attachmentType":"pdf","work_url":"https://www.academia.edu/6366283/Filtering_Spam_Current_Trends_and_Techniques","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/6366283/Filtering_Spam_Current_Trends_and_Techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="5" data-entity-id="10118502" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/10118502/Factors_Involved_in_Estimating_Cost_of_Email_Spam">Factors Involved in Estimating Cost of Email Spam</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="24678692" href="https://usim.academia.edu/FaridaRidzuan">Farida Ridzuan</a></div><p class="ds-related-work--metadata ds2-5-body-xs">2010</p><p class="ds-related-work--abstract ds2-5-body-sm">This paper analyses existing research work to identify all possible factors involved in estimating cost of spam. Main motivation of this paper is to provide unbiased spam costs estimation. For that, we first study the email spam lifecycle and identify all possible stakeholders. We then categorise cost and study the impact on each stakeholder. This initial study will form the backbone of the real time spam cost calculating engine that we are developing for Australia.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Factors Involved in Estimating Cost of Email Spam","attachmentId":47521300,"attachmentType":"pdf","work_url":"https://www.academia.edu/10118502/Factors_Involved_in_Estimating_Cost_of_Email_Spam","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/10118502/Factors_Involved_in_Estimating_Cost_of_Email_Spam"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="6" data-entity-id="22911620" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/22911620/A_Study_on_Spam_Filtering_Techniques">A Study on Spam Filtering Techniques</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="44602215" href="https://universityofcalicut.academia.edu/ChippyThomas">Chippy Thomas</a></div><p class="ds-related-work--abstract ds2-5-body-sm">Spam is one of the major problems of the today's Internet, bringing financial damage to companies and annoying individual users. Spam messages are almost always commercial or fraudulent messages, and are quite often sent with false return address information. Among the approaches developed to stop spam, filtering is an important and popular one. A spam filter is a program that is used to detect unsolicited and unwanted email and prevent those messages from getting to a user's inbox. Like other types of filtering programs, a spam filter looks for certain criteria on which it bases judgments. Researchers have proposed different types of spam filters and this paper provides a review of them.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A Study on Spam Filtering Techniques","attachmentId":43450281,"attachmentType":"doc","work_url":"https://www.academia.edu/22911620/A_Study_on_Spam_Filtering_Techniques","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/22911620/A_Study_on_Spam_Filtering_Techniques"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="7" data-entity-id="8353798" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/8353798/Can_we_CAN_the_Email_Spam">Can we CAN the Email Spam</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="14834640" href="https://westernsydney.academia.edu/SimiBajaj">Simi Bajaj</a></div><p class="ds-related-work--abstract ds2-5-body-sm">The purpose of email spam is to advertise to sell, phishing attacks, DDOS attacks and many more. Many solutions of various kinds such as blacklisting, whitelisting, grey-listing, content filtering have been proposed at the sender and receiver levels. There has been some level of success but the email spam still hits the inbox and more so the problem is false positives and false negatives. The current filtering solutions used are mostly a combination of few of the available techniques, the most common being a combination of listing and content filtering techniques. Apart from any attacks, email spam causes a great loss in resources and productivity of the user. Apart from this, this problem of false negatives exists as there is non-existence of filters to address user’s preferences and behaviors, timing of the day and year. The filters at the mail servers are trained with spam and ham training data that is generic in nature. Hence, there is need to address this problem. This paper aims to address all of the above i.e. an email that hits a particular users inbox by escaping the mail server spam filtering solutions. To do this, this paper describes the problem of spam, followed by the filtering mechanisms, techniques, learning email filter model and then proposes a model to fine tune the filter to increase the efficiency.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"Can we CAN the Email Spam","attachmentId":34754974,"attachmentType":"pdf","work_url":"https://www.academia.edu/8353798/Can_we_CAN_the_Email_Spam","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/8353798/Can_we_CAN_the_Email_Spam"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="8" data-entity-id="33254001" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/33254001/A_REVIEW_PAPER_ON_IMAGE_SPAM_FILTERING">A REVIEW PAPER ON IMAGE SPAM FILTERING</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="50175141" href="https://independent.academia.edu/CheiefEditor">Cheief Editor</a></div><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"A REVIEW PAPER ON IMAGE SPAM FILTERING","attachmentId":53325119,"attachmentType":"pdf","work_url":"https://www.academia.edu/33254001/A_REVIEW_PAPER_ON_IMAGE_SPAM_FILTERING","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/33254001/A_REVIEW_PAPER_ON_IMAGE_SPAM_FILTERING"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div><div class="ds-related-work--container js-wsj-grid-card" data-collection-position="9" data-entity-id="36435374" data-sort-order="default"><a class="ds-related-work--title js-wsj-grid-card-title ds2-5-body-md ds2-5-body-link" href="https://www.academia.edu/36435374/SPAM_AND_EMAIL_DETECTION_IN_BIG_DATA_PLATFORM_USING_NAIVES_BAYESIAN_CLASSIFIER">SPAM AND EMAIL DETECTION IN BIG DATA PLATFORM USING NAIVES BAYESIAN CLASSIFIER</a><div class="ds-related-work--metadata"><a class="js-wsj-grid-card-author ds2-5-body-sm ds2-5-body-link" data-author-id="2902340" href="https://ijcsmc.academia.edu/IJCSMCJournal">IJCSMC Journal</a></div><p class="ds-related-work--metadata ds2-5-body-xs">IJCSMC, 2018</p><p class="ds-related-work--abstract ds2-5-body-sm">Email spam is operations which are sending the undesirable messages to different email client. E-mail spam is the very recent problem for every individual. The e-mail spam is nothing it's an advertisement of any company/product or any kind of virus which is receiving by the email client mailbox without any notification. To solve this problem the different spam filtering technique is used. The spam filtering techniques are used to protect our mailbox for spam mails. In this project, we are using the Naives Bayesian Classifier with three layer framework that includes obfuscator, classifier and anomaly detector for spam classification for bulk emails. The Naïve Bayesian Classifier is very simple and efficient method for spam classification. Here we are using the real time dataset for classification of spam and non-spam mails. The feature extraction technique is used to extract the feature in terms of digest based on bucket classification. The result is to increase the accuracy of the system. And implement Self Acknowledgeable Intranet Mail System has been designed and implemented to benefit the sender about the status of his mail. Once a mail is sent, the sender can know the receiver activity in the mail system until the mail is viewed. Finally provide the pop up window to identify the mail content at the time of open the spam mails.</p><div class="ds-related-work--ctas"><button class="ds2-5-text-link ds2-5-text-link--inline js-swp-download-button" data-signup-modal="{"location":"wsj-grid-card-download-pdf-modal","work_title":"SPAM AND EMAIL DETECTION IN BIG DATA PLATFORM USING NAIVES BAYESIAN CLASSIFIER","attachmentId":56349315,"attachmentType":"pdf","work_url":"https://www.academia.edu/36435374/SPAM_AND_EMAIL_DETECTION_IN_BIG_DATA_PLATFORM_USING_NAIVES_BAYESIAN_CLASSIFIER","alternativeTracking":true}"><span class="material-symbols-outlined" style="font-size: 18px" translate="no">download</span><span class="ds2-5-text-link__content">Download free PDF</span></button><a class="ds2-5-text-link ds2-5-text-link--inline js-wsj-grid-card-view-pdf" href="https://www.academia.edu/36435374/SPAM_AND_EMAIL_DETECTION_IN_BIG_DATA_PLATFORM_USING_NAIVES_BAYESIAN_CLASSIFIER"><span class="ds2-5-text-link__content">View PDF</span><span class="material-symbols-outlined" style="font-size: 18px" translate="no">chevron_right</span></a></div></div></div></div><div class="ds-sticky-ctas--wrapper js-loswp-sticky-ctas hidden"><div class="ds-sticky-ctas--grid-container"><div class="ds-sticky-ctas--container"><button class="ds2-5-button js-swp-download-button" data-signup-modal="{"location":"continue-reading-button--sticky-ctas","attachmentId":32022126,"attachmentType":"pdf","workUrl":null}">See full PDF</button><button class="ds2-5-button ds2-5-button--secondary js-swp-download-button" data-signup-modal="{"location":"download-pdf-button--sticky-ctas","attachmentId":32022126,"attachmentType":"pdf","workUrl":null}"><span class="material-symbols-outlined" style="font-size: 20px" translate="no">download</span>Download PDF</button></div></div></div><div class="ds-below-fold--grid-container"><div class="ds-work--container js-loswp-embedded-document"><div class="attachment_preview" data-attachment="Attachment_32022126" style="display: none"><div class="js-scribd-document-container"><div class="scribd--document-loading js-scribd-document-loader" style="display: block;"><img alt="Loading..." src="//a.academia-assets.com/images/loaders/paper-load.gif" /><p>Loading Preview</p></div></div><div style="text-align: center;"><div class="scribd--no-preview-alert js-preview-unavailable"><p>Sorry, preview is currently unavailable. 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