{"id":526,"date":"2019-10-31T07:35:57","date_gmt":"2019-10-31T07:35:57","guid":{"rendered":"http:\/\/www.aiai2020.eu\/?page_id=526"},"modified":"2020-01-07T14:56:04","modified_gmt":"2020-01-07T14:56:04","slug":"mhdw","status":"publish","type":"page","link":"https:\/\/easyconferences.eu\/aiai2020\/mhdw\/","title":{"rendered":"9th Mining Humanistic Data Workshop"},"content":{"rendered":"<p>[et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;3.26.6&#8243; background_image=&#8221;https:\/\/easyconferences.eu\/aiai2020\/wp-content\/uploads\/2019\/09\/1.jpg&#8221; global_module=&#8221;371&#8243; saved_tabs=&#8221;all&#8221;][et_pb_row _builder_version=&#8221;3.26.6&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.26.6&#8243;][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=&#8221;1&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_row _builder_version=&#8221;3.26.6&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<h3 style=\"text-align: center;\"><span style=\"color: #008080;\">9th\u00a0Mining Humanistic Data Workshop<\/span><\/h3>\n<p>[\/et_pb_text][et_pb_divider color=&#8221;#0da0c5&#8243; _builder_version=&#8221;3.26.6&#8243;][\/et_pb_divider][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8221;3.26.6&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<p><span><strong>Workshop website:<\/strong> <a href=\"https:\/\/conferences.cwa.gr\/mhdw2020\/\">https:\/\/conferences.cwa.gr\/mhdw2020\/<\/a><\/span><\/p>\n<p style=\"text-align: justify;\">The Mining Humanistic Data Workshop (MHDW) aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing artificial intelligence, data matching, fusion and mining and knowledge discovery and management techniques to data derived from all areas of Humanistic Sciences.<\/p>\n<p>MHDW 2020 will be supported by experts in numerous fields of the theme of the workshop:<\/p>\n<p><strong><span>Program chairs<\/span><\/strong><\/p>\n<ul>\n<li>Andreas Kanavos, Department of Computer Engineering and Informatics, University of Patras, Greece<\/li>\n<li>Christos Makris, Department of Computer Engineering and Informatics, University of Patras, Greece<\/li>\n<li>Phivos Mylonas, Department of Informatics, Ionian University, Greece<\/li>\n<\/ul>\n<p><strong>Steering Committee<\/strong><\/p>\n<ul>\n<li>Ioannis Karydis, Department of Informatics, Ionian University, Greece &amp; Creative Web Applications P.C., Greece<\/li>\n<li>Katia Lida Kermanidis, Department of Informatics, Ionian University, Greece<\/li>\n<li>Spyros Sioutas, Department of Computer Engineering and Informatics, University of Patras, Greece<\/li>\n<\/ul>\n<p><strong><br \/> Aim of MHDW <\/strong><\/p>\n<p style=\"text-align: justify;\">The abundance of available data that is retrieved from or is related to the areas of Humanities and the human condition challenges the research community in processing and analyzing it. The aim is two-fold: on the one hand, to extract knowledge that will help understand human behavior, creativity, way of thinking, reasoning, learning, decision making, socializing and even biological processes; on the other hand, to exploit the extracted knowledge by incorporating it into intelligent systems that will support humans in their everyday activities.<\/p>\n<p style=\"text-align: justify;\">The nature of humanistic data can be multimodal, semantically heterogeneous, dynamic, time and space-dependent, and highly complicated. Translating humanistic information, e.g. behavior, state of mind, artistic creation, linguistic utterance, learning and genomic information into numerical or categorical low-level data is a significant challenge on its own. New techniques, appropriate to deal with this type of data, need to be proposed and existing ones adapted to its special characteristics.<\/p>\n<p style=\"text-align: justify;\">The workshop aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing data matching, fusion and mining and knowledge discovery and management techniques (like decision rules, decision trees, association rules, ontologies and alignments, clustering, filtering, learning, classifier systems, neural networks, support vector machines, preprocessing, post processing, feature selection, visualization techniques) to data derived from all areas of Humanistic Sciences, e.g. linguistic, historical, behavioral, psychological, artistic, musical, educational, social etc., Ubiquitous Computing and Bioinformatics. In that sense, MHDW is an astute match for AIAI\u2019s theme since it covers domains such as artificial intelligence\u2019s theoretical advances, artificial intelligence applications, knowledge engineering, signal processing techniques &amp; knowledge extraction, multimedia, and graphics &amp; Artificial Intelligence that are of related to the topics of AIAI, with a focus on the humanities.<\/p>\n<p>[\/et_pb_text][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<p><strong>Topics of interest<\/strong> of MHDW include (but are not limited to):<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=&#8221;1_2,1_2&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<ul>\n<li>Humanistic data collection and interpretation<\/li>\n<li><span>Data pre-processing<\/span><\/li>\n<li><span>Feature selection methodologies<\/span><\/li>\n<li>Supervised or unsupervised learning of humanistic knowledge<\/li>\n<li><span>Clustering\/Classification techniques<\/span><\/li>\n<li><span>Fuzzy modeling<\/span><\/li>\n<li><span>Heterogeneous data fusion<\/span><\/li>\n<li><span>Knowledge representation and reasoning<\/span><\/li>\n<li><span>Linguistic data mining<\/span><\/li>\n<li><span>Educational data mining<\/span><\/li>\n<li><span>Music information retrieval<\/span><\/li>\n<li><span>Data-driven profiling\/ personalization<\/span><\/li>\n<li><span>User modeling<\/span><\/li>\n<li><span>Behavior prediction<\/span><\/li>\n<li><span>Recommender systems<\/span><\/li>\n<li><span>Web sentiment analysis<\/span><\/li>\n<li><span>Social data mining<\/span><\/li>\n<li><span>Data visualization techniques<\/span><\/li>\n<li>Integration of data mining results into real-world applications with humanistic context<\/li>\n<li><span>Ontologies, ontology matching and alignment<\/span><\/li>\n<\/ul>\n<p>[\/et_pb_text][\/et_pb_column][et_pb_column type=&#8221;1_2&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<ul>\n<li>Mining humanistic data in the cloud<\/li>\n<li><span>Game data mining<\/span><\/li>\n<li><span>Virtual-world data mining<\/span><\/li>\n<li><span>Speech and audio data processing<\/span><\/li>\n<li>Data mining techniques for knowledge discovery<\/li>\n<li><span>Biomedical data mining<\/span><\/li>\n<li><span>Bioinformatics<\/span><\/li>\n<li>Content creation, annotation and modeling for semantic and social web<\/li>\n<li>Computational intelligence for media adaptation and personalization<\/li>\n<li>Semantics-driven indexing and retrieval of multimedia contents<\/li>\n<li><span>Semantic context modeling and extraction<\/span><\/li>\n<li><span>Context-aware applications<\/span><\/li>\n<li><span>Social web economics and business<\/span><\/li>\n<li>Privacy\/security issues in social and personalized applications<\/li>\n<li>Privacy preserving data mining and social networks<\/li>\n<li><span>Social data analytic<\/span>s<\/li>\n<\/ul>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=&#8221;3.26.6&#8243;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<p style=\"text-align: justify;\"><strong>Reviewing process<\/strong><\/p>\n<p style=\"text-align: justify;\">MHDW has long utilized a dingle-blind review process with approx. 3 reviews per submission using the widely accepted EasyChair software. Each review is finally to be checked by a member of the PC, prior to dispatching to authors, in order to verify the high level of comments made to authors.<\/p>\n<p style=\"text-align: justify;\"><strong>Participation<\/strong><\/p>\n<p style=\"text-align: justify;\">MHDW\u2019s 2020 edition will be the 9th\u00a0in a series of high quality participations. The evolution of the submissions made in MHDW makes us confident that about 15-20 submissions will be made of which only the highest quality top 50% will be selected for publication.<\/p>\n<p style=\"text-align: justify;\"><strong>MHDW\u2019s continuation<\/strong><\/p>\n<p style=\"text-align: justify;\">MHDW aims to bridge the domains of data mining and artificial intelligence with the Humanistic studies, a domain that despite receiving less of a spot-light is as crucial in our lives as ever. Current developments in ICT have, similarly to Medicine and Science &amp; Technology, lead to increased data creation rates in the Humanities as well and MHDW provides a theoretical and applied platform for researches to identify issues with these data and develop methodologies to address them. If is for these reasons that we believe that\u00a0 MHDW is as timely as ever, and thus request AIAI to support its 2020 edition.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=&#8221;submission&#8221; _builder_version=&#8221;3.26.6&#8243; locked=&#8221;off&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<h3 style=\"text-align: justify;\">Submission<\/h3>\n<p style=\"text-align: justify;\">Initial submissions for the MHDW 2020 workshop will be received through <strong><a href=\"https:\/\/easychair.org\/conferences\/?conf=mhdw2020\" target=\"_blank\" rel=\"noopener noreferrer\">EasyChair<\/a><\/strong>.<\/p>\n<h4>How to submit a contribution<\/h4>\n<p>All papers should be submitted either in a doc\/docx or in a pdf form and will be peer reviewed by at least 2 academic referees. Contributing authors must follow the<span>\u00a0<\/span><a href=\"https:\/\/easyconferences.eu\/aiai2020\/call-for-papers\/\" target=\"_blank\" rel=\"noopener noreferrer\">AIAI2020&#8217;s paper format guidelines<\/a><span>\u00a0<\/span>as far as the IFIP AICT file format.<\/p>\n<p>Submission site:\u00a0<a href=\"https:\/\/easychair.org\/conferences\/?conf=mhdw2020\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/easychair.org\/conferences\/?conf=mhdw2020<\/a>\u00a0<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row module_id=&#8221;submission&#8221; _builder_version=&#8221;3.26.6&#8243; locked=&#8221;off&#8221; disabled_on=&#8221;on|on|on&#8221; disabled=&#8221;on&#8221;][et_pb_column type=&#8221;4_4&#8243; _builder_version=&#8221;3.26.6&#8243;][et_pb_text _builder_version=&#8221;3.26.6&#8243;]<\/p>\n<h3 style=\"text-align: justify;\">Submission<\/h3>\n<p style=\"text-align: justify;\">Submissions for the MHDW 2020 workshop will be received through the online system used for the main conference.<\/p>\n<h4>How to submit a contribution<\/h4>\n<p>Important: While visiting EasyAcademia.org, please use Firefox or Chrome. Internet Explorer or other browsers may have compatibility issues which can prevent you from submitting.<\/p>\n<ul>\n<li>Create an \u201cEasyAcademia\u201d account through<span>\u00a0<\/span><a href=\"http:\/\/www.easyacademia.org\/\">www.easyacademia.org<\/a><\/li>\n<li>Activate your account by clicking on the activation link sent to your email.<br \/> Note: Please check your Spam folder if you have not received the email within a few minutes.<\/li>\n<li>Log into<span>\u00a0<\/span><a href=\"http:\/\/www.easyacademia.org\/aiai2020\">www.easyacademia.org\/aiai2020<\/a>, using the login details you provided at the beginning.<\/li>\n<li>Click on Start a new submission on the top right to enter the submission process.<\/li>\n<li>Select the appropriate track e.g. (Workshop \u2013 MHDW 2020)<\/li>\n<li>Go through the guidelines<\/li>\n<li>Enter the appropriate information in the Title step.<\/li>\n<li>Under Authors please input details for each author of the paper. At least one author must be marked for each type of role available (presenter, corresponding).<br \/> Note: Only authors marked as correspondents will receive updates and information regarding the submission.<\/li>\n<li>In the Upload step, please click on Upload Paper and find the relevant completed document for your submission on your computer (PDF).<\/li>\n<li>In the Attachment step, click on the Upload Attachment button on the right and find the Source file for your submission on your computer. Word files or a ZIP containing LaTeX files are accepted.<\/li>\n<li>Under Summary you may check the details of the submission. If you wish to go back to a section in order to change details, just click on the appropriate step on the left.<\/li>\n<li>If you are happy with the summary information, please click on Submit Now to finalize the process. A notification e-mail will be sent to the correspondents.<\/li>\n<\/ul>\n<p>If at any point you are having trouble submitting, or require more information, please contact us at<span>\u00a0<\/span><a href=\"mailto:support@easyconferences.org\">support@easyconferences.org<\/a><span>\u00a0<\/span>and we will respond back as soon as possible.<\/p>\n<p>[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>9th\u00a0Mining Humanistic Data WorkshopWorkshop website: https:\/\/conferences.cwa.gr\/mhdw2020\/ The Mining Humanistic Data Workshop (MHDW) aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing artificial intelligence, data matching, fusion and mining and knowledge discovery and management techniques to data derived from all areas of Humanistic Sciences. MHDW 2020 will be [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":""},"_links":{"self":[{"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/pages\/526"}],"collection":[{"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/comments?post=526"}],"version-history":[{"count":7,"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/pages\/526\/revisions"}],"predecessor-version":[{"id":633,"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/pages\/526\/revisions\/633"}],"wp:attachment":[{"href":"https:\/\/easyconferences.eu\/aiai2020\/wp-json\/wp\/v2\/media?parent=526"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}