Machine Learning and Knowledge Extraction: 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, Virtual ... (Lecture Notes in Computer Science, 12844) PDF
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Andreas Holzinger Peter Kieseberg A Min Tjoa Edgar Weippl (Eds.) 4 4 8 2 Machine Learning 1 S C and Knowledge Extraction N L 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021 Virtual Event, August 17–20, 2021 Proceedings Lecture Notes in Computer Science 12844 Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA More information about this subseries at http://www.springer.com/series/7409 Andreas Holzinger Peter Kieseberg (cid:129) (cid:129) A Min Tjoa Edgar Weippl (Eds.) (cid:129) Machine Learning and Knowledge Extraction 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021 – Virtual Event, August 17 20, 2021 Proceedings 123 Editors Andreas Holzinger PeterKieseberg Institute for Medical Informatics, Statistics St.PöltenUniversity ofApplied Sciences andDocumentation andInstitute for St.Pölten,Austria Information Systems andComputer Media Edgar Weippl Medical University Graz andGraz SBAResearch University of Technology Vienna,Austria Graz,Austria Research Group Security andPrivacy xAILab, AlbertaMachineIntelligence University of Vienna Institute Vienna,Austria University of Alberta Edmonton, AB,Canada AMin Tjoa Institute of Software Technology andInteractive Systems Technische UniversitätWien Vienna,Austria ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Computer Science ISBN 978-3-030-84059-4 ISBN978-3-030-84060-0 (eBook) https://doi.org/10.1007/978-3-030-84060-0 LNCSSublibrary:SL3–InformationSystemsandApplications,incl.Internet/Web,andHCI ©IFIPInternationalFederationforInformationProcessing2021 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartofthe material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilarmethodologynow knownorhereafterdeveloped. Theuseofgeneraldescriptivenames,registerednames,trademarks,servicemarks,etc.inthispublication doesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfromtherelevant protectivelawsandregulationsandthereforefreeforgeneraluse. Thepublisher,theauthorsandtheeditorsaresafetoassumethattheadviceandinformationinthisbookare believedtobetrueandaccurateatthedateofpublication.Neitherthepublishernortheauthorsortheeditors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregardtojurisdictionalclaimsin publishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSwitzerlandAG Theregisteredcompanyaddressis:Gewerbestrasse11,6330Cham,Switzerland Preface The International Cross Domain Conference for Machine Learning & Knowledge Extraction (CD-MAKE) is a joint effort of IFIP TC 5, IFIP TC 12, IFIP WG 8.4, IFIP WG 8.9, and IFIP WG 12.9 and is held in conjunction with the International Conference on Availability, Reliability, and Security (ARES). This fifth installment was organized virtually by SBA Research due to the ongoing COVID-19 pandemic, and took place during August 17–20, 2021. ThelettersCDinCD-MAKEstandfor“Cross-Domain”anddescribetheintegration and appraisal of different fields and application domains to provide an atmosphere to foster different perspectives and opinions. The conference fosters an integrative machine learning approach, considering the importance of data science and visual- izationforthealgorithmicpipelinewithastrongemphasisonprivacy,dataprotection, safety,andsecurity.Itisdedicatedtoofferinganinternationalplatformfornovelideas and a fresh look on methodologies to put crazy ideas into business for the benefit of humans. Serendipity is a desired effect, which may lead to the cross-fertilization of methodologies and the transfer of algorithmic developments. The acronym MAKE stands for “MAchine Learning & Knowledge Extraction”, a field of artificial intelligence (AI) that, while quite old in its fundamentals, has just recentlybeguntothrivebasedonbothnoveldevelopmentsinthealgorithmicareaand the availability of vast computing resources at a comparatively low cost. Machine learning (ML) studies algorithms that can learn from data to gain knowledgefromexperienceandtogeneratedecisionsandpredictions.Agrandgoalis in understanding intelligence for the design and development of algorithms that work autonomously (ideally without a human-in-the-loop) and can improve their learning behavior over time. The challenge is to discover relevant structural and/or temporal patterns (“knowledge”) in data, which is often hidden in arbitrarily high dimensional spaces, and thus simply not accessible to humans. Knowledge extraction is one of the oldest fields in AI and is seeing a renaissance, particularly in the combination of statistical methods with classical ontological approaches. AI is currently undergoing a kindofCambrianexplosionandisthefastest-growingfieldincomputersciencetoday thanks to the usable successes in machine learning. There are many application domains, e.g., in medicine, etc., with many use cases from our daily lives, e.g., rec- ommendersystems,speechrecognition,autonomousdriving,etc.Thegrandchallenges lie in sensemaking, in context understanding, and in decision-making under uncer- tainty, as well as solving the problem of explainability. Our real world is full of uncertainties and probabilistic inference enormously influences AI generally and ML specifically. The inverse probability allows us to infer unknowns, to learn from data, and to make predictions to support decision-making. Whether in social networks, recommender systems, health applications, or industrial applications, the increasingly complexdatasetsrequireajointinterdisciplinaryeffortinvolvingthehuman-in-control vi Preface to foster a better understanding of the ethical and social issues accountability, retractability, explainability, causability, and privacy, safety and security! A few words about IFIP: IFIP – the International Federation for Information Pro- cessing—is the leading multi-national, non-governmental, apolitical organization in informationand communications technologies and computer sciences; it isrecognized by the United Nations (UN) and was established in 1960 under the auspices of the UNESCO as an outcome of the first World Computer Congress held in Paris in 1959. IFIP is incorporated in Austria by decree of the Austrian Foreign Ministry (September 20, 1996, GZ 1055.170/120-I.2/96) granting IFIP the legal status of a non-governmental international organization under the Austrian Law on the Granting of Privileges to Non-Governmental International Organizations (Federal Law Gazette 1992/174). IFIP brings together more than 3500 scientists without boundaries from both academia and industry, organized in more than 100 Working Groups (WGs) and 13 Technical Committees (TCs). To acknowledge all those who also contributed to the organizational effort and stimulatingdiscussionsatCD-MAKE2021wouldbeimpossibleinaprefacelikethis. Many people also contributed to the development of this volume, either directly or indirectly, so, again, it would be impossible to list all of them. We herewith thank all local, national, and international colleagues and friends for their positive and sup- portive encouragement. Finally, yet importantly, we thank the Springer management team and the Springer production team for their professional support. Thank you to all! Let’s MAKE it! June 2021 Andreas Holzinger Peter Kieseberg Edgar Weippl A Min Tjoa Organization Conference Organizers Andreas Holzinger Medical University of Graz and Graz University of Technology, Austria, and University of Alberta, Canada Peter Kieseberg FH St.Pölten, Austria Edgar Weippl SBA Research and University of Vienna, Austria A Min Tjoa TU Vienna, Austria Program Committee Frantisek Babic Technical University of Košice, Slovakia Smaranda Belciug University of Craiova, Romania Elisa Bertino Purdue University, USA Chris Biemann Universität Hamburg, Germany Jiang Bian University of Florida, USA Malin Bradley Vanderbilt University, USA Ivan Bratko University of Ljubljana, Slovenia Guido Bologna Université de Genève, Switzerland Francesco Buccafurri Universita Mediterranea di Reggio Calabria, Italy Federico Cabitza University of Milano-Bicocca, Italy Andre Calero-Valdez RWTH Aachen University, Germany Andrea Campagner University of Milano-Bicocca, Italy Angelo Cangelosi University of Manchester, UK Mirko Cesarini University of Milano-Bicocca, Milan, Italy Krzysztof J. Cios Virginia Commonwealth University, USA Carlo Combi University of Verona, Italy Beatriz De La Iglesia University of East Anglia, UK Gloria Cerasela Crisan Vasile Alecsandri University of Bacau, Romania Alexiei Dingli University of Malta, Malta Josep Domingo-Ferrer Universitat Rovira i Virgili, Spain Isao Echizen National Institute of Informatics, Japan Massimo Ferri University of Bologna, Italy Ulrich Furbach University of Koblenz, Germany Hugo Gamboa PLUX Wireless Biosensors and Universidade Nova de Lisboa, Portugal Barbara Hammer University of Bielefeld, Germany Pitoyo Hartono Chukyo University, Japan Barna Laszlo Iantovics “George Emil Palade” University of Medicine, Pharmacy, Sciences and Technology of Targu Mures, Romania viii Organization Epaminodas Kapetanios University of Westminster, UK Andreas Kerren Linköping University and Linnaeus University, Sweden Robert Laramee University of Nottingham, UK Max Little University of Birmingham, UK Luca Longo Technological University Dublin, Ireland Ljiljana Majnaric-Trtica University of Osijek, Croatia Donato Malerba Università degli Studi di Bari Aldo Moro, Italy Fabio Mercorio University of Milano-Bicocca, Italy Yoan Miche Nokia Bell Labs, Finland Paolo Mignone Università degli Studi di Bari Aldo Moro, Italy Vasile Palade Coventry University, UK Jan Paralic Technical University of Kosice, Slovakia Camelia-M. Pintea Technical University of Cluj-Napoca, Romania Luca Romeo UniversitàPolitecnicadelleMarcheandIstitutoItaliano di Tecnologia, Italy Pierangela Samarati Università degli Sudi di Milano, Italy Andrzej Skowron Systems Research Institute, Polish Academy of Sciences, and University of Warsaw, Poland Dominik Slezak University of Warsaw, Poland Hung Son Nguyen University of Warsaw, Poland Irena Spasic Cardiff University, UK Ivan Štajduhar University of Rijeka, Croatia Catalin Stoean University of Craiova, Romania Bharath Sudharsan National University of Ireland Galway, Ireland A Min Tjoa TU Wien, Austria Dimitar Trajanov Ss. Cyril and Methodius University in Skopje, Macedonia Marco Tulio Ribeiro University of Washington, USA Jean Vanderdonckt Université catholique de Louvain, Belgium Markus Wagner University of Applied Sciences St. Pölten, Austria Edgar Weippl SBA Research and University of Vienna, Austria Kai Xu Middlesex University London, UK Pinar Yildirim Okan University, Turkey Jianlong Zhou University of Technology Sydney, Australia Contents Digital Transformation for Sustainable Development Goals (SDGs) - A Security, Safety and Privacy Perspective on AI. . . . . . . . . . . . . . . . . . . . 1 Andreas Holzinger, Edgar Weippl, A Min Tjoa, and Peter Kieseberg WheninDoubt,Ask:GeneratingAnswerableandUnanswerableQuestions, Unsupervised . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Liubov Nikolenko and Pouya Rezazadeh Kalehbasti Self-propagating Malware Containment via Reinforcement Learning . . . . . . . 35 Sebastian Eresheim and Daniel Pasterk Text2PyCode: Machine Translation of Natural Language Intent to Python Source Code. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Sridevi Bonthu, S. Rama Sree, and M. H. M. Krishna Prasad Automated Short Answer Grading Using Deep Learning: A Survey . . . . . . . 61 Sridevi Bonthu, S. Rama Sree, and M. H. M. Krishna Prasad Fair and Adequate Explanations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Nicholas Asher, Soumya Paul, and Chris Russell Mining Causal Hypotheses in Categorical Time Series by Iterating on Binary Correlations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 Bora I. Kumova and Dirk Saller Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 Zhe Xu, Bo Wu, Aditya Ojha, Daniel Neider, and Ufuk Topcu Rice Seed Image-to-Image Translation Using Generative Adversarial Networks to Improve Weedy Rice Image Classification. . . . . . . . . . . . . . . . 137 Atthakorn Petchsod and Tanasai Sucontphunt Reliable AI Through SVDD and Rule Extraction . . . . . . . . . . . . . . . . . . . . 153 Alberto Carlevaro and Maurizio Mongelli Airbnb Price Prediction Using Machine Learning and Sentiment Analysis . . . 173 Pouya Rezazadeh Kalehbasti, Liubov Nikolenko, and Hoormazd Rezaei Towards Financial Sentiment Analysis in a South African Landscape . . . . . . 185 Michelle Terblanche and Vukosi Marivate
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