{"id":833,"date":"2022-12-22T05:26:55","date_gmt":"2022-12-22T05:26:55","guid":{"rendered":"https:\/\/ifipaiai.org\/2024\/?page_id=833"},"modified":"2023-12-15T16:11:11","modified_gmt":"2023-12-15T16:11:11","slug":"mhdw","status":"publish","type":"page","link":"https:\/\/ifipaiai.org\/2024\/workshops\/mhdw\/","title":{"rendered":"MHDW"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"833\" class=\"elementor elementor-833\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-27dcc46 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"27dcc46\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ca029e6\" data-id=\"ca029e6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-dc34962 elementor-widget elementor-widget-heading\" data-id=\"dc34962\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 13-03-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h4 class=\"elementor-heading-title elementor-size-default\">The 13th Workshop on<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f9840b3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f9840b3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2f93547\" data-id=\"2f93547\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-54a4b15 elementor-widget elementor-widget-heading\" data-id=\"54a4b15\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Mining Humanistic Data (MHDW)<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a3dcdac elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a3dcdac\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-546fc5e\" data-id=\"546fc5e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f29cea9 elementor-widget elementor-widget-text-editor\" data-id=\"f29cea9\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;none&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 13-03-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<div class=\"et_pb_column et_pb_column_4_4 et_pb_column_2 et_pb_css_mix_blend_mode_passthrough et-last-child\"><div class=\"et_pb_module et_pb_text et_pb_text_1 et_pb_text_align_left et_pb_bg_layout_light\"><div class=\"et_pb_text_inner\"><p>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><p>MHDW 2024 will be supported by experts in numerous fields of the theme of the workshop:<\/p><p><strong><span>Program chairs<\/span><\/strong><\/p><ul><li>Ioanna Giannoukou, Department of Management Science and Technology, University of Patras, Greece<\/li><li>Andreas Kanavos, Department of Informatics, Ionian University, Greece<\/li><li>Christos Makris, Department of Computer Engineering and Informatics, University of Patras, Greece<\/li><li>Phivos Mylonas, Department of Informatics and Computer Engineering, University of West Attica, Greece<\/li><li>Aristidis G. Vrahatis, Department of Informatics, Ionian University, Greece<\/li><\/ul><p><strong>Steering Committe<\/strong><\/p><ul><li>Ioannis Karydis, Department of Informatics, Ionian University, Greece<\/li><li>Katia Lida Kermanidis, Department of Informatics, Ionian University, Greece<\/li><li>Spyros Sioutas, Department of Computer Engineering and Informatics, University of Patras, Greece<\/li><\/ul><p><strong><\/strong><\/p><p><strong>Aim of MHDW <\/strong><\/p><p>The abundance of available data, which 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 to 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><p>The nature of humanistic data can be multimodal, semantically heterogeneous, dynamic, context-, time- and space-dependent, as well as highly complicated. Translating humanistic information, e.g. behavior, interaction, state of mind, artistic creation, linguistic utterance, learning and genomic information into numerical or categorical low-level data, is considered a significant challenge on its own. New techniques, appropriate to deal with this type of data, need to be proposed whereas existing ones must be adapted to its special characteristics.<\/p><p>The workshop aims to bring together interdisciplinary approaches that focus on the application of innovative as well as existing data matching, fusion and mining as well as 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, Pervasive and Mobile Computing, as well as Bioinformatics.<\/p><\/div><\/div><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-95610f0 elementor-widget elementor-widget-text-editor\" data-id=\"95610f0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Topics of interest<\/strong> of MHDW include (but are not limited to):<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b7b816a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b7b816a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-0f21ac0\" data-id=\"0f21ac0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9f2ddc4 elementor-widget elementor-widget-text-editor\" data-id=\"9f2ddc4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"et_pb_module et_pb_text et_pb_text_3 et_pb_text_align_left et_pb_bg_layout_light\"><div class=\"et_pb_text_inner\"><ul><li>Humanistic data collection and interpretation<\/li><li>Data pre-processing<\/li><li>Feature selection methodologies<\/li><li>Supervised or unsupervised learning of humanistic knowledge<\/li><li>Deep learning for humanistic data<\/li><li>Clustering\/Classification techniques<\/li><li>Fuzzy modeling<\/li><li>Heterogeneous data fusion<\/li><li>Knowledge representation and reasoning<\/li><li>Linguistic data and text mining<\/li><li>Educational data mining<\/li><li>Music information retrieval<\/li><li>Data-driven profiling\/ personalization<\/li><li>User modeling<\/li><li>Behavior prediction<\/li><li>Recommender systems<\/li><li>Web sentiment analysis<\/li><li>Social data mining<\/li><li>Data visualization techniques<\/li><li>Integration of data mining results into real-world applications with humanistic context<\/li><li>Ontology matching and alignment<\/li><\/ul><\/div><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-50 elementor-top-column elementor-element elementor-element-89d326b\" data-id=\"89d326b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d29490f elementor-widget elementor-widget-text-editor\" data-id=\"d29490f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"et_pb_module et_pb_text et_pb_text_4 et_pb_text_align_left et_pb_bg_layout_light\"><div class=\"et_pb_text_inner\"><ul><li>Mining humanistic data in the cloud<\/li><li>Game data mining<\/li><li>Data Mining for Virtual and Augmented Reality<\/li><li>Speech and audio data processing<\/li><li>Data mining techniques for knowledge discovery<\/li><li>Biomedical data mining<\/li><li>Bioinformatics<\/li><li>Content creation, annotation and modeling for semantic and social web<\/li><li>Computational intelligence for media adaptation and personalization<\/li><li>Semantics-driven indexing and retrieval of multimedia contents<\/li><li>Semantic context modeling and extraction<\/li><li>Context-aware applications<\/li><li>Social web economics and business<\/li><li>Privacy\/security issues in social and personalized applications<\/li><li>Privacy preserving data mining and social networks<\/li><li>Social data analytics<\/li><li>Ubiquitous, Pervasive and Mobile Computing for the Humanities<\/li><\/ul><\/div><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6334d5b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6334d5b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-72b8d76\" data-id=\"72b8d76\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-274f2d5 elementor-widget elementor-widget-text-editor\" data-id=\"274f2d5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Reviewing process<\/strong><\/p>\n<p>MHDW has long utilized a single-blind review process with approximately 3 reviews per submission using the widely accepted EasyConferences 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><strong>Participation<\/strong><\/p>\n<p>MHDW\u2019s 2024 edition will be the 13<sup>th<\/sup> in a series of high quality participations. The evolution of the submissions made in MHDW makes us confident that 15-20 submissions will be made of which only the highest quality top 50% will be selected for publication.<\/p>\n<p><strong>MHDW\u2019s continuation<\/strong><\/p>\n<p>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. It is for these reasons that we believe that MHDW is as timely as ever, and thus request AIAI to support its 2024 edition.<\/p>\n<p><b>Important dates<\/b><\/p>\n<p>Please visit <a href=\"https:\/\/ifipaiai.org\/2024\/important-dates\/\">AIAI 2024 important dates<\/a> to be informed about the submission deadlines.<\/p>\n<p><b>Submission instructions<\/b><span><\/span><\/p>\n<p>Submission details can be found at AIAI conference<span>&nbsp;<\/span><a href=\"https:\/\/ifipaiai.org\/2024\/paper-submission\/\">submission page<\/a>.<\/p>\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>&nbsp;<\/span><a href=\"https:\/\/ifipaiai.org\/2024\/calls-for-papers\/\">AIAI2024\u2019s paper format guidelines<\/a><span>&nbsp;<\/span>as far as the IFIP AICT file format.<\/p>\n<p><span>Papers will be peer reviewed by at least two (-2-) members of the workshop\u2019s program committee.<\/span><\/p>\n<p><span>Accepted papers will be published in the Proceedings of AIAI VOLUME, under the Springer IFIP AICT Series.<\/span><\/p>\n<p><span lang=\"EN-US\">You can submit you MHDW paper here<span>&nbsp;<\/span><a href=\"http:\/\/www.easyacademia.org\/aiai2024\" target=\"_blank\" data-saferedirecturl=\"https:\/\/www.google.com\/url?q=http:\/\/www.easyacademia.org\/aiai2022&amp;source=gmail&amp;ust=1640959020019000&amp;usg=AOvVaw0xTTG6k6DEqtrHqlo61bP0\" rel=\"noopener\">http:\/\/www.easyacademia.org\/aiai2024<\/a>.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-863fff2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"863fff2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6d591c3\" data-id=\"6d591c3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-678a18d elementor-widget elementor-widget-spacer\" data-id=\"678a18d\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.20.0 - 13-03-2024 *\/\n.elementor-column .elementor-spacer-inner{height:var(--spacer-size)}.e-con{--container-widget-width:100%}.e-con-inner>.elementor-widget-spacer,.e-con>.elementor-widget-spacer{width:var(--container-widget-width,var(--spacer-size));--align-self:var(--container-widget-align-self,initial);--flex-shrink:0}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container,.e-con>.elementor-widget-spacer>.elementor-widget-container{height:100%;width:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer{height:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner{height:var(--container-widget-height,var(--spacer-size))}.e-con-inner>.elementor-widget-spacer.elementor-widget-empty,.e-con>.elementor-widget-spacer.elementor-widget-empty{position:relative;min-height:22px;min-width:22px}.e-con-inner>.elementor-widget-spacer.elementor-widget-empty .elementor-widget-empty-icon,.e-con>.elementor-widget-spacer.elementor-widget-empty .elementor-widget-empty-icon{position:absolute;top:0;bottom:0;left:0;right:0;margin:auto;padding:0;width:22px;height:22px}<\/style>\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>The 13th Workshop on Mining Humanistic Data (MHDW) 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 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":74,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/pages\/833"}],"collection":[{"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/comments?post=833"}],"version-history":[{"count":29,"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/pages\/833\/revisions"}],"predecessor-version":[{"id":1883,"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/pages\/833\/revisions\/1883"}],"up":[{"embeddable":true,"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/pages\/74"}],"wp:attachment":[{"href":"https:\/\/ifipaiai.org\/2024\/wp-json\/wp\/v2\/media?parent=833"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}