Lecture Notes in Artificial Intelligence 10352

Subseries of Lecture Notes in Computer Science

LNAI Series Editors

Randy GoebelUniversity of Alberta, Edmonton, Canada

Yuzuru TanakaHokkaido University, Sapporo, Japan

Wolfgang WahlsterDFKI and Saarland University, Saarbrücken, Germany

LNAI Founding Series Editor

Joerg SiekmannDFKI and Saarland University, Saarbrücken, Germany

More information about this series at http://www.springer.com/series/1244

Marzena Kryszkiewicz • Annalisa AppiceDominik Ślęzak • Henryk RybinskiAndrzej Skowron • Zbigniew W. Raś (Eds.)

Foundationsof Intelligent Systems23rd International Symposium, ISMIS 2017Warsaw, Poland, June 26–29, 2017Proceedings

123

EditorsMarzena KryszkiewiczInstitute of Computer ScienceWarsaw University of TechnologyWarsawPoland

Annalisa AppiceDepartment of Computer ScienceUniversity of Bari Aldo MoroBariItaly

Dominik ŚlęzakInstitute of InformaticsUniversity of WarsawWarsawPoland

Henryk RybinskiInstitute of Computer ScienceWarsaw University of TechnologyWarsawPoland

Andrzej SkowronInstitute of MathematicsWarsaw UniversityWarsawPoland

and

Systems Research InstitutePolish Academy of SciencesWarsaw UniversityWarsawPoland

Zbigniew W. RaśDepartment of Computer ScienceUniversity of North Carolina at CharlotteCharlotte, NCUSA

ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Artificial IntelligenceISBN 978-3-319-60437-4 ISBN 978-3-319-60438-1 (eBook)DOI 10.1007/978-3-319-60438-1

Library of Congress Control Number: 2017943070

LNCS Sublibrary: SL7 – Artificial Intelligence

© Springer International Publishing AG 2017This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of thematerial 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 informationstorage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology nowknown or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in this book arebelieved to be true and accurate at the date of publication. Neither the publisher nor the authors or the editorsgive a warranty, express or implied, with respect to the material contained herein or for any errors oromissions that may have been made. The publisher remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

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Preface

This volume contains the papers selected for presentation at the 23rd InternationalSymposium on Methodologies for Intelligent Systems (ISMIS 2017), which was heldat Warsaw University of Technology, Warsaw, Poland, June 26–29, 2017. The sym-posium was organized by the Institute of Computer Science at Warsaw University ofTechnology, in cooperation with University of Warsaw, Poland, and University of BariAldo Moro, Italy.

ISMIS is a conference series that started in 1986. Held twice every three years,ISMIS provides an international forum for exchanging scientific, research, and tech-nological achievements in building intelligent systems. In particular, major areasselected for ISMIS 2017 include both theoretical and practical aspects of machinelearning, data mining methods, deep learning, bioinformatics and health informatics,intelligent information systems, knowledge-based systems, mining temporal, spatialand spatio-temporal data, text and Web mining. In addition, four special sessions wereorganized: namely, Special Session on Big Data Analytics and Stream Data Mining,Special Session on Granular and Soft Clustering for Data Science, Special Session onKnowledge Discovery with Formal Concept Analysis and Related Formalisms, andSpecial Session devoted to ISMIS 2017 Data Mining Competition on Trading Based onRecommendations, which was launched as a part of the conference. The ISMIS con-ference was accompanied by the Industrial Session on Data Science in Industry:Algorithms, Tools, and Applications.

ISMIS 2017 received 118 submissions that were carefully reviewed by three ormore Program Committee members or external reviewers. Papers submitted to specialsessions were subject to the same reviewing procedure as those submitted to regularsessions. After a rigorous reviewing process, 56 regular papers and 15 short paperswere accepted for presentation at the conference and publication in the ISMIS 2017proceedings volume. This volume also contains one invited paper and three abstractsby the plenary keynote speakers.

It is truly a pleasure to thank all people who helped this volume come into being andmade ISMIS 2017 a successful and exciting event. In particular, we would like toexpress our appreciation for the work of the ISMIS 2017 Program Committee membersand external reviewers who helped assure the high standard of accepted papers. Wewould like to thank all authors of ISMIS 2017, without whose high-quality contribu-tions it would not have been possible to organize the conference. We are grateful to theorganizers of special sessions of ISMIS 2017: Martin Atzmueller and Jerzy Ste-fanowski (Special Session on Big Data Analytics and Stream Data Mining), PawanLingras, Georg Peters, and Richard Weber (Special Session on Granular and SoftClustering for Data Science), Davide Ciucci, Sergei Kuznetsov, and Amedeo Napoli(Special Session on Knowledge Discovery with Formal Concept Analysis and RelatedFormalisms), Andrzej Janusz and Kamil Żbikowski (Special Session devoted to ISMIS2017 Data Mining Competition on Trading Based on Recommendations). We would

also like to express our appreciation to the organizers of the accompanying events:Andrzej Janusz and Kamil Żbikowski, who successfully launched the competition;Robert Bembenik, Grzegorz Protaziuk, and Łukasz Skonieczny for organizing theIndustrial Session and for their involvement in all organizational matters related toISMIS 2017 as well as the creation and maintenance of the conference website. We aregrateful to Bożenna Skalska, Joanna Konczak, and Anna Filipowska-Chlebek for theiradministrative work.

We also wish to express our thanks to Perfecto Herrera Boyer, Paola Mello, MarekRusinkiewicz, and Osmar R. Zaїane for accepting our invitation to give plenary talks atISMIS 2017. We would like to thank mBank S.A. for sponsoring the ISMIS 2017 DataMining Competition, and Tipranks for providing data for this competition. Also, we arethankful to Alfred Hofmann of Springer for supporting the ISMIS 2017 Best PaperAward.

Moreover, our thanks are due to Alfred Hofmann for his continuous support and toAnna Kramer for her work on the proceedings.

We believe that the proceedings of ISMIS 2017 will become a valuable source ofreference for your ongoing and future research activities.

June 2017 Marzena KryszkiewiczAnnalisa AppiceDominik ŚlęzakHenryk RybinskiAndrzej SkowronZbigniew W. Raś

VI Preface

Organization

ISMIS 2017 was organized by the Institute of Computer Science, Warsaw Universityof Technology, Warsaw, Poland, in cooperation with University of Warsaw, Poland,and University of Bari Aldo Moro, Italy.

ISMIS 2017 Symposium Organizers

Symposium Chairs

Henryk Rybinski Warsaw University of Technology, PolandAndrzej Skowron University of Warsaw, Poland and Polish Academy

of Sciences, Poland

Program Chairs

Annalisa Appice University of Bari Aldo Moro, ItalyMarzena Kryszkiewicz Warsaw University of Technology, PolandDominik Ślęzak University of Warsaw, Poland

Steering Committee Chair

Zbigniew W. Raś University of North Carolina, USA and WarsawUniversity of Technology, Poland

Organizing Committee

Robert Bembenik Warsaw University of Technology, PolandGrzegorz Protaziuk Warsaw University of Technology, PolandŁukasz Skonieczny Warsaw University of Technology, Poland

Steering Committee

Troels Andreasen Roskilde University, DenmarkJaime Carbonell CMU, USALi Chen Hong Kong Baptist University, Hong Kong,

SAR ChinaHenning Christiansen Roskilde University, DenmarkJuan Carlos Cubero University of Granada, SpainFloriana Esposito University of Bari Aldo Moro, ItalyAlexander Felfernig Graz University of Technology, AustriaMohand-Saїd Hacid Université Claude Bernard Lyon 1, FranceMarzena Kryszkiewicz Warsaw University of Technology, PolandJiming Liu Hong Kong Baptist University, Hong Kong,

SAR China

Olivier Pivert IRISA-ENSSAT, University of Rennes 1, FranceHenryk Rybinski Warsaw University of Technology, PolandLorenza Saitta Università del Piemonte Orientale, ItalyAndrzej Skowron University of Warsaw, Poland and Polish Academy of

Sciences, PolandDominik Ślęzak University of Warsaw, PolandMaria Zemankova NSF, USA

Program Committee

ISMIS Regular Papers

Piotr Andruszkiewicz Warsaw University of Technology, PolandAnnalisa Appice University of Bari Aldo Moro, ItalyJarek Arabas Warsaw University of Technology, PolandMartin Atzmueller Tilburg University, The NetherlandsRobert Bembenik Warsaw University of Technology, PolandSalima Benbernou Université Paris Descartes, FrancePetr Berka University of Economics, Prague, Czech RepublicMarenglen Biba University of New York in Tirana, AlbaniaMaria Bielikova Slovak University of Technology in Bratislava,

SlovakiaKonstantinos Blekas University of Ioannina, GreeceJerzy Błaszczyński Poznań University of Technology, PolandGloria Bordogna National Research Council of Italy - CNR, ItalyJose Borges University of Porto, PortugalIvan Bratko University of Ljubljana, SloveniaHenrik Bulskov Roskilde University, DenmarkMichelangelo Ceci University of Bari Aldo Moro, ItalyEdward Chang HTC Research, TaiwanJianhua Chen Louisiana State University, USASilvia Chiusano Polytechnic University of Turin, ItalyDavide Ciucci University of Milano-Bicocca, ItalyEmmanuel Coquery Université Lyon 1 - LIRIS, FranceGermán Creamer Stevens Institute of Technology, USABruno Cremilleux Université de Caen, FranceAlfredo Cuzzocrea University of Trieste, ItalyClaudia D’Amato University of Bari Aldo Moro, ItalyJeroen De Knijf AVAST, The NetherlandsMarcilio De Souto LIFO/University of Orleans, USALuigi Di Caro University of Turin, ItalyNicola Di Mauro University of Bari Aldo Moro, ItalyStephan Doerfel University of Kassel, GermanyBrett Drury National University of Ireland, Galway, IrelandWouter Duivesteijn TU Eindhoven, The NetherlandsInês Dutra Universidade do Porto, Portugal

VIII Organization

Christoph F. Eick University of Houston, USATapio Elomaa Tampere University of Technology, FinlandFloriana Esposito University of Bari Aldo Moro, ItalyNicola Fanizzi University of Bari Aldo Moro, ItalyStefano Ferilli University of Bari Aldo Moro, ItalySebastien Ferre Université de Rennes 1, FranceCarlos Ferreira LIAAD INESC Porto LA, PortugalNaoki Fukuta Shizuoka University, JapanFabio Fumarola Math & IT Research and Development, ItalyMohamed Gaber Birmingham City University, UKTomasz Gambin Warsaw University of Technology, PolandPaolo Garza Polytechnic University of Turin, ItalyPiotr Gawrysiak Warsaw University of Technology, PolandLaura Giordano University of Eastern Piedmont, ItalyMichael Granitzer University of Passau, GermanyJacek Grekow Bialystok Technical University, PolandJerzy Grzymala-Busse University of Kansas, USAHakim Hacid Zayed University, United Arab EmiratesMohand-Saїd Hacid Université Claude Bernard Lyon 1, FranceAllel Hadjali LIAS/ENSMA, FranceMirsad Hadzikadic UNC Charlotte, USAMaria Halkidi University of Piraeus, GreeceShoji Hirano Shimane University, JapanLothar Hotz University of Hamburg, GermanyDino Ienco IRSTEA, FranceAndrzej Janusz University of Warsaw, PolandMatthias Jarke RWTH Aachen University, GermanySzymon Jaroszewicz Polish Academy of Sciences, PolandAdam Jatowt Kyoto University, JapanMehdi Kaytoue LIRIS - INSA de Lyon, FranceMatthias Klusch DFKI, GermanyMieczysław Kłopotek Polish Academy of Sciences, PolandDragi Kocev Jozef Stefan Institute, SloveniaJacek Koronacki Polish Academy of Sciences, PolandBozena Kostek Gdansk University of Technology, PolandLars Kotthoff University of British Columbia, CanadaMarzena Kryszkiewicz Warsaw University of Technology, PolandSergei O. Kuznetsov National Research University Higher School of

Economics, RussiaMark Last Ben-Gurion University of the Negev, IsraelAnne Laurent LIRMM - UM, FranceDominique Laurent Université Cergy-Pontoise, FranceMarie-Jeanne Lesot LIP6 - UPMC, FranceAntoni Ligęza AGH University of Science and Technology, PolandPawan Lingras Saint Mary’s University, Halifax, CanadaCorrado Loglisci University of Bari, Italy

Organization IX

Henrique Lopes Cardoso FEUP/LIACC, PortugalDonato Malerba University of Bari Aldo Moro, ItalyGiuseppe Manco ICAR-CNR, ItalyYannis Manolopoulos Aristotle University of Thessaloniki, GreeceKrzysztof Marasek Polish-Japanese Academy of Information Technology,

PolandMaria J. Martin-Bautista University of Granada, SpainElio Masciari ICAR-CNR, ItalyFlorent Masseglia Inria, FrancePaola Mello University of Bologna, ItalyErnestina Menasalvas Universidad Politecnica de Madrid, SpainCorrado Mencar University of Bari Aldo Moro, ItalyJoão Mendes-Moreira FEUP/DEI, PortugalEvangelos Milios Dalhousie University, CanadaAnna Monreale University of Pisa, ItalyLuis Moreira-Matias NEC Laboratories Europe, GermanyMikolaj Morzy Poznan University of Technology, PolandTadeusz Morzy Poznan University of Technology, PolandHiroshi Motoda Osaka University and AFOSR/AOARD, JapanNeil Murray ILS Institute, University at Albany - SUNY, USAAgnieszka Mykowiecka Polish Academy of Sciences, PolandMirco Nanni KDD-Lab ISTI-CNR Pisa, ItalyAmedeo Napoli LORIA/CNRS-Inria Nancy Grand Est-Université

de Lorraine, FranceJørgen Fischer Nilsson Technical University of Denmark (DTU), DenmarkXia Ning IUPUI, USADavid Olaleye SAS, USAPance Panov Jozef Stefan Institute, SloveniaGeorge A. Papadopoulos University of Cyprus, CyprusIoannis Partalas Viseo R&D, FranceRuggero G. Pensa University of Turin, ItalyGeorg Peters Munich University of Applied Sciences, Germany

and Australian Catholic University, AustraliaJean-Marc Petit Université de Lyon, INSA Lyon, FranceOlivier Pivert IRISA-ENSSAT, University of Rennes 1, FranceMarc Plantevit LIRIS - Université Claude Bernard Lyon 1, FrancePascal Poncelet LIRMM Montpellier, FranceLubos Popelinsky Masaryk University, Czech RepublicHenri Prade IRIT - CNRS, FranceRonaldo Prati Universidade Federal do ABC - UFABC, BrazilGrzegorz Protaziuk Warsaw University of Technology, PolandDavood Rafiei University of Alberta, CanadaJan Ramon Inria, FranceJan Rauch University of Economics, Prague, Czech RepublicChedy Raïssi Inria, FranceMarek Reformat University of Alberta, Canada

X Organization

Christophe Rey LIMOS - Université Clermont Auvergne, FranceRita P. Ribeiro University of Porto, PortugalJadwiga Rogowska University of Utah, USAPrzemysław Rokita Warsaw University of Technology, PolandAndré L.D. Rossi Universidade Estadual Paulista Júlio de Mesquita

Filho, BrazilSalvatore Ruggieri University of Pisa, ItalyHenryk Rybinski Warsaw University of Technology, PolandDominik Ryzko Warsaw University of Technology, PolandLorenza Saitta University of Turin, ItalyHiroshi Sakai Kyushu Institute of Technology, JapanYacine Sam Université François Rabelais Tours, FranceDaniel Sanchez University of Granada, SpainJose Salvador Sanchez Universitat Jaume I, SpainYucel Saygin Sabanci University, TurkeyChristoph Schommer University of Luxembourg, LuxembourgMatthias Schubert Ludwig Maximilians University of Munich, GermanyMarian Scuturici LIRIS-INSA de Lyon, FranceHamida Seba University of Lyon 1, FranceNazha Selmaoui-Folcher University of New Caledonia, CanadaGiovanni Semeraro University of Bari Aldo Moro, ItalyJunming Shao UESTC, ChinaŁukasz Skonieczny Warsaw University of Technology, PolandAndrzej Skowron University of Warsaw, Poland and Polish Academy of

Sciences, PolandDominik Ślęzak University of Warsaw, PolandGregory Smits IRISA-IUT de Lannion, FranceArnaud Soulet Université François Rabelais Tours, FranceUrszula Stanczyk Silesian University of Technology, PolandJerzy Stefanowski Poznan University of Technology, PolandLuis Enrique Sucar INAOE, MexicoMarcin Sydow Polish-Japanese Academy of Information Technology,

PolandXiaohui Tao University of Southern Queensland, AustraliaHadi Tork University of Oslo, NorwayVicenc Torra University of Skövde, SwedenHakim Touati Optym, USABrigitte Trousse Inria, FranceShusaku Tsumoto Shimane University, JapanTheodoros Tzouramanis University of the Aegean, GreeceGiorgio Valentini University of Milan, ItalyJulien Velcin Université de Lyon 2, FranceHerna Viktor University of Ottawa, CanadaChristel Vrain LIFO - University of Orléans, FranceYang Wang University of New South Wales, UKRichard Weber University of Chile Santiago, Chile

Organization XI

Alicja Wieczorkowska Polish-Japanese Academy of Information Technology,Poland

Yiyu Yao University of Regina, CanadaJure Zabkar University of Ljubljana, SloveniaSlawomir Zadrozny Polish Academy of Sciences, PolandWlodek Zadrozny UNCC, USABernard Zenko Jozef Stefan Institute, SloveniaYing Zhao Tsinghua University, ChiaXiaofeng Zhu Guangxi Normal University, ChinaCezary Zielinski Warsaw University of Technology, PolandKamil Żbikowski Warsaw University of Technology & mBank S.A.,

Poland

Special Session on Big Data Analytics and Stream Data Mining

Martin Atzmueller (Chair) Tilburg University, The NetherlandsJerzy Stefanowski (Chair) Poznan University of Technology, PolandPetr Berka University of Economics, Prague, Czech RepublicDariusz Brzezinski Poznan University of Technology, PolandBruno Cremilleux Université de Caen, FranceNicola Di Mauro University of Bari Aldo Moro, ItalyWouter Duivesteijn TU Eindhoven, The NetherlandsMohamed Gaber Birmingham City University, UKMatthias Jarke RWTH Aachen University, GermanySzymon Jaroszewicz Polish Academy of Sciences, PolandGeorg Krempl Otto von Guericke University of Magdeburg, GermanyMark Last Ben-Gurion University of the Negev, IsraelElio Masciari ICAR-CNR, ItalyErnestina Menasalvas Universidad Politechnica de Madrid, SpainJose Salvador Sanchez Universitat Jaume I, SpainHerna Viktor University of Ottawa, CanadaMichał Woźniak Wroclaw University of Technology, Poland

Special Session on Granular and Soft Clustering for Data Science

Pawan Lingras (Chair) Saint Mary’s University, Halifax, CanadaGeorg Peters (Chair) Munich University of Applied Sciences, Germany,

and Australian Catholic University, AustraliaRichard Weber (Chair) University of Chile Santiago, Chile

Special Session on Knowledge Discovery with Formal Concept Analysisand Related Formalisms

Davide Ciucci (Chair) University of Milano-Bicocca, ItalySergei O. Kuznetsov (Chair) National Research University Higher School of

Economics, RussiaAmedeo Napoli (Chair) LORIA/CNRS-Inria Nancy Grand Est-Université

de Lorraine, France

XII Organization

Mehwish Alam Université de Paris-Nord, FranceGabriela Arevalo Universidad Nacional de Quilmes, ArgentinaJaume Baixeries UPC Barcelona, SpainVictor Codocedo Inria Chile, ChileMiguel Couceiro LORIA Nancy/Université de Lorraine, FranceBruno Cremilleux GREYC Caen/Université de Basse Normandie, FranceFlorent Domenach University of Nicosia, CyprusMarianne Huchard LIRMM/Université de Montpellier, FranceDmitry I. Ignatov National Research University Higher School of

Economics, RussiaMehdi Kaytoue INSA-LIRIS Lyon, FranceJan Konecny Palacky University, Czech RepublicNizar Messai Université François Rabelais Tours, FranceJean-Marc Petit INSA-LIRIS Lyon, FranceMarc Plantevit INSA-LIRIS Lyon, FrancePascal Poncelet LIRMM/Université de Montpellier, FranceHenri Prade IRIT CNRS/Université de Toulouse, FranceArtem Revenko Technische Universität Dresden, GermanyChristian Sacarea Babes-Bolyai University, RomaniaHenry Soldano Université de Paris-Nord, FranceArnaud Soulet Université François Rabelais Tours, FranceDiana Troanca Babes-Bolyai University, RomaniaRenato Vimiero UFPE Recife, BrazilChristel Vrain LIFO/Université d’Orléans, France

Special Session on ISMIS 2017 Data Mining Competition on TradingBased on Recommendations

Andrzej Janusz (Chair) University of Warsaw, PolandKamil Żbikowski (Chair) Warsaw University of Technology and mBank S.A.,

PolandAnnalisa Appice University of Bari Aldo Moro, ItalyRobert Bembenik Warsaw University of Technology, PolandPiotr Gawrysiak Warsaw University of Technology, PolandMarzena Kryszkiewicz Warsaw University of Technology, PolandHenryk Rybinski Warsaw University of Technology, PolandŁukasz Skonieczny Warsaw University of Technology, PolandDominik Ślęzak University of Warsaw, Poland

Organization XIII

Additional Reviewers

Bioglio, LivioBradshaw, StephenBranco, PaulaCalefato, FabioCarbonnel, JessieCastelltort, ArnaudCerqueira, VitorCheniki, NasreddineChou, JasonCiecierski, KonradFawagreh, Khaled

Garcia, KemillyGašpar, PeterGuarascio, MassimoHaidar, DianaIaquinta, LeoImpedovo, AngeloKaššák, OndrejKhiari, JihedLippi, MarcoMettouris, ChristosPinto, Fábio

Polignano, MarcoRitacco, EttoreRocha, GilSaidani, Fayçal RédhaSaltos, RamiroSantos Costa, VítorWu, JakeYao, JingtaoYao, YiyuYeratziotis, AlexandrosYu, Hong

XIV Organization

Invited Talks

Elements of Musical Intelligence for the NextGeneration of Digital Musical Tools

Perfecto Herrera-Boyer

Music Technology Group, Universitat Pompeu Fabra, Roc Boronat 138, 08018,Barcelona, Spain

Departament de Sonologia, Escola Superior de Música de Catalunya, Padilla 155,08013, Barcelona, Spain

perfecto.herrera@upf.edu

Extended Abstract

Musical intelligence can be observed in many diverse situations: while tapping to thebeat, when recommending interesting tracks to a friend that we know well, when weplay the right notes even the score has a hole in their place… Intelligence has fre-quently been presented linked to observable behavior. The infamous Turing testrequires of overt (verbal) behavior. Deep blue played chess, again an observablebehavior, as it was the case of IBM’s Watson, the winner of the TV question-answeringcontest “Jeopardy”. Maybe less known are Aaron (a painting system), Libratus (a pokerplayer), or the Google AI system that writes sad poems. When considering intelligentmusical systems, those showing observable behavior abound. But maybe there is morethan that to deal with intelligence. Maybe the behavioral requirement is a residue of the“behavioral” perspective that ruled in the academia during some decades of the XXthcentury. In an influential, though speculative, work titled “On Intelligence” [2], we canread: “Ignoring what goes on in your head and focusing instead on behavior has been alarge impediment to understanding intelligence and building intelligent machines”. Theidea of intelligence as a compact, monolithic construct, as long as the possibility tomeasure or detect it with a single type of test has also been challenged, among manyother perspectives, by Gardner's theory of multiple intelligences [1].

In accordance with such critical views, musical intelligence starts up from basicbuilding blocks like the capacity to entrain a beat, or to categorize a reduced list ofpitches as related, or to detect recurring patterns (in time, in pitch, etc.). This is whatsome authors identify as “musicality” [3]. Although we do not then need to buildfull-fledged systems to observe musical intelligence, it can nevertheless be differentlyobserved under different circumstances and in different scenarios. In the end, it will bein the mind of the observer, and no “bot versus human” test will be convincing enoughor appropriate to conclude about that.

Which are the typical scenarios where we observe or induce intelligent musicbehavior? Among the earliest and more frequent “intelligent music systems” we findmostly music composition systems, which are developed with the goal to generatemusic scores with a minimal input from the users. It is usually the case that these

systems are aimed at the “imitation” of a well-known composer (e.g., Bach), or at thegeneration of pieces in a given style. Other systems have been shown to compose in ahighly idiomatic way, somehow inventing her own style (e.g., “Emily Howell”).A different flavor of musical intelligence can be found in music performance systems,capable of performing music in real time, accompanying one or more human players. Inthis category we also find systems that address “expressive playing” (i.e., playing ashumans do, not robotically following just what is notated in a score, but makingreasonable errors, adding grace notes, etc.). Music mixing systems are able to createmusical programs by means of the concatenation of pre-existing musical pieces or itsfragments, like disc-jockeys usually do and, also connected with the idea of “directing”music, there are intelligent systems that assist audio engineers in music mixing byproposing balances, equalization parameters, fades, etc. Finally, most of the intelligentmusic analysis systems do not target an overt behavior. They, instead, take the score oran audio file as input and transform the data into human-interpretable descriptions (see,for example, [4]). Content-based recommender systems (like Pandora radio) could beconsidered a special case of intelligent analysis systems capable of, again, generatingobservable behavior or even interaction with a user.

In this talk, after my overview of some remarkable intelligent music systems, I willargue about some necessary (but not sufficient) conditions for music systems to beconsidered intelligent (though, at the same time, I will question the utility of suchnotion for scientific or engineering purposes), such as:

– Multiple-faceted representation: the system manages different musical dimensions:rhythm, pitch, timbre, harmony, structure, expressivity...

– Multiple time-scale representation: the system manages music data using differenttemporal scopes (i.e., there are representations from onsets to sections, from notes totonality).

– Predictive/Anticipating representation: the system manages representations that arenot just current or past data but also representations of what is likely to happen next.

– Symbolic representation: the system manages abstract representations that can beeasily mapped to human-understandable words or concepts.

– Pattern completion: the system is capable to work with incomplete information (likewhen a beat is missed because of noise, or when there is a printing defect in themusic sheet).

– Interaction with the environment, where this environment consists of humans orother intelligent music systems.

– Experience-based learning: some of the internal representations can be modified bymeans of different learning processes (e.g. reinforcement, association, imitation...).

In connection with this perspective, the final minutes of my talk will be devoted topresent some of the outcomes of the EU-funded project GiantSteps, which has beenendeavoring on the development of intelligent musical agents and music interfaces that,in music creation contexts, functionally work as humble assistants to suggest or guidehuman users when they lack inspiration, or technical or musical knowledge.

XVIII P. Herrera-Boyer

References

1. Gardner, H.: Frames of Mind: The Theory of Multiple Intelligences. Basic Books, New York,(1993)

2. Hawkins, J.: On Intelligence. Times Books (2004)3. Honing, H., ten Cate, C., Peretz, I., Trehub, S.E.: Without it no music: cognition, biology and

evolution of musicality. Philos. Trans. R. Soc. B Biol. Sci. 370(1664) (2015). doi:10.1098/rstb.2014.0088

4. Schedl, M., Yang, Y-H., and Herrera-Boyer, P.: Introduction to intelligent music systems andapplications. ACM Trans. Intell. Syst. Technol. 8(2), Article 17, 8 (2016). doi:10.1145/2991468

Elements of Musical Intelligence for the Next Generation of Digital Musical Tools XIX

Security of Cyber-physical Systems

Marek Rusinkiewicz

New Jersey Institute of Technology, Newark, USAmarek.rusinkiewicz@njit.edu

Abstract. Cyberspace, the ubiquitous collection of interconnected IP networksand hosts, has become the nervous system of the country. Healthy functioning ofCyberspace is essential for the proper operation of numerous critical infras-tructures, including telecommunication, energy distribution, and transportation.It is also necessary to support the ever expanding business infrastructure forcommerce and banking. The increasing reliance on Cyberspace has been par-alleled by a corresponding increase in the variety, frequency and impact ofattacks from a range of assailants. Both commercial companies and governmentagencies face continuous and increasingly more sophisticated cyber-attacksranging from data exfiltration and spear phishing to sophisticated worms andlogic bombs. The targets include not only computer information systems, butalso the network communication infrastructure and power grids.

In this talk, I will discuss protecting cyber-physical systems from attacksand argue that cyber security can significantly benefit from multidisciplinaryresearch including Web Intelligence, Data Analytics and Network Science.

Classification by Association

Osmar R. Zaïane

University of Alberta, Edmonton, Canadazaiane@ualberta.ca

Abstract. In machine learning the centre of attention these days is on DeepLearning. The spotlight has turned towards this re-branding of Neural Networksbecause of the tremendous success it recently demonstrated for image classifi-cation, speech recognition, text categorization and other applications wherelabeled data is plentiful. However, this is not necessarily true when data is notsufficient enough to optimize the hyperparameters. Alternatives are still neededin most applications and we advocate for rule-based classifiers such as asso-ciative classifiers. Associative classifiers piggyback on association rule miningto discover classification rules. The learned model is a set of rules that are easyto understand and can be edited. Established associative classification algo-rithms have shown to be very effective in handling categorical data such as textdata. There were many improvements this last decade for rule representation,rule pruning, rule selection strategies, etc. Yet, most proposed approaches stillsuffer from the limitations of the support-confidence framework inherited fromassociation rule mining. We will talk about the challenges in mining theseclassification rules, the progress reached using this paradigm and the lastincarnation of associative classifiers that finds statistical dependency in the datato avoid the detection of meaningless rules and the omission of interesting oneswhile doing away with the restraining parameters support and confidence.

Contents

Invited Paper

Abductive Reasoning on Compliance Monitoring: Balancing Flexibilityand Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Federico Chesani, Paola Mello, and Marco Montali

Bioinformatics and Health Informatics

Multi-levels 3D Chromatin Interactions PredictionUsing Epigenomic Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Ziad Al Bkhetan and Dariusz Plewczynski

A Supervised Model for Predicting the Risk of Mortalityand Hospital Readmissions for Newly Admitted Patients . . . . . . . . . . . . . . . 29

Mamoun Almardini and Zbigniew W. Raś

An Expert System Approach to Eating Disorder Diagnosis . . . . . . . . . . . . . . 37Stefano Ferilli, Anna Maria Ferilli, Floriana Esposito,Domenico Redavid, and Sergio Angelastro

Multimodal System for Diagnosis and Polysensory Stimulationof Subjects with Communication Disorders . . . . . . . . . . . . . . . . . . . . . . . . 47

Adam Kurowski, Piotr Odya, Piotr Szczuko, Michał Lech,Paweł Spaleniak, Bożena Kostek, and Andrzej Czyżewski

Acute Kidney Injury Detection: An Alarm System to ImproveEarly Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Ana Rita Nogueira, Carlos Abreu Ferreira, and João Gama

New Method of Calculating SRCM Chirality Measure . . . . . . . . . . . . . . . . . 64Przemyslaw Szurmak and Jan Mulawka

Data Mining Methods

Selection of Initial Modes for Rough Possibilistic K-Modes Methods . . . . . . 77Asma Ammar and Zied Elouedi

Accelerating Greedy K-Medoids Clustering Algorithm with L1 Distanceby Pivot Generation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

Takayasu Fushimi, Kazumi Saito, Tetsuo Ikeda, and Kazuhiro Kazama

On the Existence of Kernel Function for Kernel-Trick of k-Means . . . . . . . . 97Mieczysław A. Kłopotek

Using Network Analysis to Improve Nearest Neighbor Classificationof Non-network Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Maciej Piernik, Dariusz Brzezinski, Tadeusz Morzy, and Mikolaj Morzy

An Accurate and Efficient Method to Detect Critical Links to MaintainInformation Flow in Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Kazumi Saito, Kouzou Ohara, Masahiro Kimura, and Hiroshi Motoda

Deep Learning

Continuous Embedding Spaces for Bank Transaction Data . . . . . . . . . . . . . . 129Ali Batuhan Dayioglugil and Yusuf Sinan Akgul

Shallow Reading with Deep Learning: Predicting Popularityof Online Content Using only Its Title . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

Wojciech Stokowiec, Tomasz Trzciński, Krzysztof Wołk,Krzysztof Marasek, and Przemysław Rokita

Recurrent Neural Networks for Online Video Popularity Prediction . . . . . . . . 146Tomasz Trzciński, Paweł Andruszkiewicz, Tomasz Bocheński,and Przemysław Rokita

Intelligent Information Systems

User-Based Context Modeling for Music Recommender Systems . . . . . . . . . 157Imen Ben Sassi, Sadok Ben Yahia, and Sehl Mellouli

Uncalibrated Visual Servo for the Remotely Operated Vehicle . . . . . . . . . . . 168Chi-Cheng Cheng and Tsan-Chu Lu

Comparative Analysis of Musical Performances by UsingEmotion Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

Jacek Grekow

Automated Web Services Composition with Iterated Services . . . . . . . . . . . . 185Alfredo Milani and Rajdeep Niyogi

On the Gradual Acceptability of Arguments in Bipolar WeightedArgumentation Frameworks with Degrees of Trust . . . . . . . . . . . . . . . . . . . 195

Andrea Pazienza, Stefano Ferilli, and Floriana Esposito

Automatic Defect Detection by Classifying Aggregated Vehicular Behavior . . . 205Felix Richter, Oliver Hartkopp, and Dirk C. Mattfeld

XXIV Contents

Automatic Speech Recognition Adaptation to the IoT DomainDialogue System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215

Maciej Zembrzuski, Heesik Jeon, Joanna Marhula, Katarzyna Beksa,Szymon Sikorski, Tomasz Latkowski, and Paweł Bujnowski

Knowledge-Based Systems

Rule-Based Reasoning with Belief Structures . . . . . . . . . . . . . . . . . . . . . . . 229Łukasz Białek, Barbara Dunin-Kęplicz, and Andrzej Szałas

Combining Machine Learning and Knowledge-Based Systemsfor Summarizing Interviews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240

Angel Luis Garrido, Oscar Cardiel, Andrea Aleyxendri,and Ruben Quilez

Validity of Automated Inferences in Mapping of Anatomical Ontologies . . . . 251Milko Krachunov, Peter Petrov, Maria Nisheva, and Dimitar Vassilev

An Experiment in Causal Structure Discovery. A ConstraintProgramming Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

Antoni Ligęza

Machine Learning

Actively Balanced Bagging for Imbalanced Data. . . . . . . . . . . . . . . . . . . . . 271Jerzy Błaszczyński and Jerzy Stefanowski

A Comparison of Four Classification Systems Using Rule Sets Inducedfrom Incomplete Data Sets by Local Probabilistic Approximations . . . . . . . . 282

Patrick G. Clark, Cheng Gao, and Jerzy W. Grzymala-Busse

Robust Learning in Expert Networks: A Comparative Analysis. . . . . . . . . . . 292Ashiqur R. KhudaBukhsh, Jaime G. Carbonell, and Peter J. Jansen

Efficient All Relevant Feature Selection with Random Ferns . . . . . . . . . . . . 302Miron Bartosz Kursa

Evaluating Difficulty of Multi-class Imbalanced Data . . . . . . . . . . . . . . . . . 312Mateusz Lango, Krystyna Napierala, and Jerzy Stefanowski

Extending Logistic Regression Models with Factorization Machines . . . . . . . 323Mark Pijnenburg and Wojtek Kowalczyk

Filtering Decision Rules with Continuous Attributes Governedby Discretisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

Urszula Stańczyk

Contents XXV

Mining Temporal, Spatial and Spatio-Temporal Data

OptiLocator: Discovering Optimum Location for a Business Using SpatialCo-location Mining and Spatio-Temporal Data . . . . . . . . . . . . . . . . . . . . . . 347

Robert Bembenik, Jacek Szwaj, and Grzegorz Protaziuk

Activity Recognition Model Based on GPS Data, Points of Interestand User Profile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358

Igor da Penha Natal, Rogerio de Avellar Campos Cordeiro,and Ana Cristina Bicharra Garcia

Extended Process Models for Activity Prediction . . . . . . . . . . . . . . . . . . . . 368Stefano Ferilli, Floriana Esposito, Domenico Redavid,and Sergio Angelastro

Automatic Defect Detection by One-Class Classificationon Raw Vehicle Sensor Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378

Julia Hofmockel, Felix Richter, and Eric Sax

Visualizing Switching Regimes Based on Multinomial Distributionin Buzz Marketing Sites. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 385

Yuki Yamagishi and Kazumi Saito

“Serial” versus “Parallel”: A Comparison of Spatio-TemporalClustering Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396

Yongli Zhang, Sujing Wang, Amar Mani Aryal, and Christoph F. Eick

Time-Frequency Representations for Speed Change Classification:A Pilot Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

Alicja Wieczorkowska, Elżbieta Kubera, Danijel Koržinek,Tomasz Słowik, and Andrzej Kuranc

Text and Web Mining

Opinion Mining on Non-English Short Text . . . . . . . . . . . . . . . . . . . . . . . . 417Esra Akbas

Pathway Computation in Models Derived from Bio-Science Text Sources . . . 424Troels Andreasen, Henrik Bulskov, Per Anker Jensen,and Jørgen Fischer Nilsson

Semantic Enriched Short Text Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . 435Marek Kozlowski and Henryk Rybinski

Exploiting Web Sites Structural and Content Features for WebPages Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446

Pasqua Fabiana Lanotte, Fabio Fumarola, Donato Malerba,and Michelangelo Ceci

XXVI Contents

Concept-Enhanced Multi-view Co-clustering of Document Data . . . . . . . . . . 457Valentina Rho and Ruggero G. Pensa

Big Data Analytics and Stream Data Mining

Scalable Framework for the Analysis of Population StructureUsing the Next Generation Sequencing Data . . . . . . . . . . . . . . . . . . . . . . . 471

Anastasiia Hryhorzhevska, Marek Wiewiórka, Michał Okoniewski,and Tomasz Gambin

Modification to K-Medoids and CLARA for EffectiveDocument Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481

Phuong T. Nguyen, Kai Eckert, Azzurra Ragone, and Tommaso Di Noia

Supporting the Page-Hinkley Test with Empirical Mode Decompositionfor Change Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492

Raquel Sebastião and José Maria Fernandes

Co-training Semi-supervised Learning for Single-Target Regressionin Data Streams Using AMRules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499

Ricardo Sousa and João Gama

Time-Series Data Analytics Using Spark and Machine Learning . . . . . . . . . . 509Patcharee Thongtra and Alla Sapronova

Granular and Soft Clustering for Data Science

Scalable Machine Learning with Granulated Data Summaries:A Case of Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519

Agnieszka Chądzyńska-Krasowska, Paweł Betliński, and Dominik Ślęzak

Clustering Ensemble for Prioritized Sampling Based on Averageand Rough Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530

Matt Triff, Ilya Pavlovski, Zhixing Liu, Lori-Anne Morgan,and Pawan Lingras

C&E Re-clustering: Reconstruction of Clustering Resultsby Three-Way Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 540

Pingxin Wang, Xibei Yang, and Yiyu Yao

Multi-criteria Based Three-Way Classifications with Game-TheoreticRough Sets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 550

Yan Zhang and JingTao Yao

Theoretical Aspects of Formal Concept Analysis

A Formal Context for Acyclic Join Dependencies . . . . . . . . . . . . . . . . . . . . 563Jaume Baixeries

Contents XXVII

On Containment of Triclusters Collections Generated by QuantifiedBox Operators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573

Dmitrii Egurnov, Dmitry I. Ignatov, and Engelbert Mephu Nguifo

The Inescapable Relativity of Explicitly Represented Knowledge:An FCA Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580

David Flater

Blocks of the Direct Product of Tolerance Relations . . . . . . . . . . . . . . . . . . 587Christian Jäkel and Stefan E. Schmidt

Viewing Morphisms Between Pattern Structures via Their Concept Latticesand via Their Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597

Lars Lumpe and Stefan E. Schmidt

Formal Concept Analysis for Knowledge Discovery

On-Demand Generation of AOC-Posets: Reducing the Complexityof Conceptual Navigation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611

Alexandre Bazin, Jessie Carbonnel, and Giacomo Kahn

From Meaningful Orderings in the Web of Data to Multi-levelPattern Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622

Quentin Brabant, Miguel Couceiro, Amedeo Napoli,and Justine Reynaud

On Locality Sensitive Hashing for Sampling Extent Generators . . . . . . . . . . 632Victor Codocedo and My Thao Tang

An Application of AOC-Posets: Indexing Large Corpusesfor Text Generation Under Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . 642

Alain Gutierrez, Michel Chein, Marianne Huchard,and Pierre Pompidor

On Neural Network Architecture Based on Concept Lattices . . . . . . . . . . . . 653Sergei O. Kuznetsov, Nurtas Makhazhanov, and Maxim Ushakov

Query-Based Versus Tree-Based Classification: Applicationto Banking Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 664

Alexey Masyutin and Yury Kashnitsky

Using Formal Concept Analysis for Checking the Structure of an Ontologyin LOD: The Example of DBpedia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674

Pierre Monnin, Mario Lezoche, Amedeo Napoli, and Adrien Coulet

XXVIII Contents

A Proposal for Classifying the Content of the Web of Data Basedon FCA and Pattern Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 684

Justine Reynaud, Mehwish Alam, Yannick Toussaint,and Amedeo Napoli

ISMIS 2017 Data Mining Competition on Trading Basedon Recommendations

ISMIS 2017 Data Mining Competition: Trading Basedon Recommendations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 697

Mathurin Aché, Andrzej Janusz, Kamil Żbikowski, Dominik Ślęzak,Marzena Kryszkiewicz, Henryk Rybinski, and Piotr Gawrysiak

Predicting Stock Trends Based on Expert RecommendationsUsing GRU/LSTM Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 708

Przemyslaw Buczkowski

Using Recommendations for Trade Returns Predictionwith Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 718

Ling Cen, Dymitr Ruta, and Andrzej Ruta

Heterogeneous Ensemble of Specialised Models - A Case Studyin Stock Market Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 728

Michał Kozielski, Katarzyna Dusza, Józef Flakus, Krzysztof Kozłowski,Sebastian Musiał, and Bartłomiej Szwej

Algorithmic Daily Trading Based on Experts’ Recommendations . . . . . . . . . 735Andrzej Ruta, Dymitr Ruta, and Ling Cen

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 745

Contents XXIX