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Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2019 Workshops, BDM, DLKT, LDRC, PAISI, WeL, Macau, China, April 14–17, 2019, Revised Selected Papers PDF

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Leong Hou U. Hady W. Lauw (Eds.) Trends and Applications 7 0 6 in Knowledge Discovery 1 1 I A and Data Mining N L PAKDD 2019 Workshops BDM, DLKT, LDRC, PAISI, WeL Macau, China, April 14–17, 2019 Revised Selected Papers 123 fi Lecture Notes in Arti cial Intelligence 11607 Subseries of Lecture Notes in Computer Science Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany More information about this series at http://www.springer.com/series/1244 Leong Hou U. Hady W. Lauw (Eds.) (cid:129) Trends and Applications in Knowledge Discovery and Data Mining PAKDD 2019 Workshops BDM, DLKT, LDRC, PAISI, WeL – Macau, China, April 14 17, 2019 Revised Selected Papers 123 Editors Leong HouU. Hady W.Lauw University of Macau Singapore ManagementUniversity Macao, China Singapore, Singapore ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Artificial Intelligence ISBN 978-3-030-26141-2 ISBN978-3-030-26142-9 (eBook) https://doi.org/10.1007/978-3-030-26142-9 LNCSSublibrary:SL7–ArtificialIntelligence ©SpringerNatureSwitzerlandAG2019 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 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is oneofthelongestestablishedandleadinginternationalconferencesintheareasofdata miningandknowledgediscovery.Itprovidesaninternationalforumforresearchersand industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD-related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering,visualization,decision-makingsystems,andtheemergingapplications.As thefirstjointevent,PAKDD2019washeldinMacauSAR,China,duringApril14–17, 2019. Along with the main conference, PAKDD workshops intend to provide an international forum for researchers to discuss and share research results. After reviewingtheworkshopproposals,wewereabletoacceptfiveworkshopsthatcovered topics in intelligence and security informatics, weakly supervised learning, data representation for clustering, biologically inspired techniques, and deep learning for knowledgetransfer.Thediversityoftopicsintheseworkshopscontributedtothemain themesoftheAPWeb-WAIMconference.Theseworkshopswereabletoaccept30full papersthatwerecarefullyreviewedfrom52submissions.Thefiveworkshopswereas follows: – The 14th Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2019) – PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future (WeL 2019), – Learning Data Representation for Clustering (LDRC 2019), – The 8th Workshop on Biologically Inspired Techniques for Knowledge Discovery and Data Mining (BDM 2019) – TheFirstPacificAsiaWorkshoponDeepLearningforKnowledgeTransfer(DLKT 2019) July 2019 Hady W. Lauw Leong Hou U. Organization Workshop Co-chairs Hady W. Lauw Singapore Management University, Singapore Leong Hou U. University of Macau, SAR China The 14th Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2019) Workshop Co-chairs Michael Chau The University of Hong Kong, SAR China G. Alan Wang Virginia Tech, USA Hsinchun Chen The University of Arizona, USA Program Committee Ahmed Abbasi Virginia Tech, USA Indranil Bose Indian Institute of Management, Calcutta Robert Chang Central Police University, Taiwan, China Uwe Glasser Simon Fraser University, Canada Eul Gyu Im Hanyang University, South Korea Da-Yu Kao Central Police University, Taiwan, China Siddharth Kaza Towson University, USA Paul W. H. Kwan The University of New England, Australia Mark Last Ben-Gurion University of the Negev, Israel Ickjai Lee James Cook University, Australia You-Lu Liao Central Police University, Taiwan, China Hongyan Liu Tsinghua University, China Hsin-min Lu National Taiwan University, Taiwan, China Xin Robert Luo University of Minnesota, USA Anirban Majumdar SAP Research, Germany Byron Marshall Oregon State University, USA Dorbin Ng The Chinese University of Hong Kong, SAR China Shaojie Qiao Southwest Jiaotong University, China Jialun Qin The University of Massachusetts Lowell, USA Shrisha Rao IIIT-Bangalore, India Srinath Srinivasa IIIT-Bangalore, India Aixin Sun Nanyang Technological University, Singapore Paul Thompson Dartmouth College, USA Jau-Hwang Wang Central Police University, Taiwan, China Shiuh-Jeng Wang Central Police University, Taiwan, China Jennifer Xu Bentley University, USA viii Organization Yilu Zhou Fordham University, USA William Zhu University of Electronic Science and Technology of China, China PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future (WeL 2019) Organizing Committee Yu-Feng Li Nanjing University, China Sheng-Jun Huang Nanjing University of Aeronautics and Astronautics, China Learning Data Representation for Clustering (LDRC 2019) General Chair Mohamed Nadif Paris University Descartes, France Program Co-chair Lazhar Labiod Paris University Descartes, France Workshop Organizers Lazhar Labiod Paris University Descartes, France Mohamed Nadif Paris University Descartes, France Allou Same l’Institut français des sciences et technologies des transports,del’aménagementetdesreseaux,France Program Committee Severine Affeldt Paris University Descartes, France Salah Aghiles Singapore Management University, Singapore Melissa Ailem USC Michelson Center, USA Daniel Berrar Tokyo Institute of Technology, Japan Patrice Bertrand CREMADE, Paris Dauphine University, France Marc Boullé Orange Labs, France Frabcusci de Assis Tenorio Universidade Federal de Pernambuco, Brazil de Carvalho Eruc Gaussier University Grenoble Alps, France Salvatore Ingrassia University of Catania, Italy Lazhar Labiod Paris University Descartes, France Vincent Lemaire Orange Labs, France Ahmed Moussa ENSAT, Abdelmalek Essaadi University, Morocco Mohamed Nadif LIPADE, Paris University Descartes, France François Role Paris University Descartes, France Fabrice Rossi Université Paris 1 Panthéon-Sorbonne, France Allou Same IFSTTAR, France Organization ix The 8th Workshop on Biologically-Inspired Techniques for Knowledge Discovery and Data Mining (BDM 2019) Program Chairs Shafiq Alam University of Auckland, New Zealand Gillian Dobbie University of Auckland, New Zealand Program Committee Patricia Riddle University of Auckland, New Zealand Kamran Shafi University of New South Wales, Australia Stephen Chen York University, Canada Mengjie Zhang Victoria University, New Zealand Kouroush Neshatian Victoria University, New Zealand Ganesh Kumar Missouri University of Science Venayagamoorthy and Technology, USA Yanjun Yan Western Carolina University, USA Mark Johnston Victoria University, New Zealand Asifullah Khan Pakistan Institute of Engineering and Applied Sciences, Pakistan Bo Yuan Tsinghua University, China Richi Nayak Queensland University of Technology, Australia Faiz Rasol University of Auckland, New Zealand The First Pacific Asia Workshop on Deep Learning for Knowledge Transfer (DLKT 2019) Organizers Fuzhen Zhuang Institute of Computing Technology, Chinese Academy of Sciences, China Deqing Wang Beihang University, China Pengpeng Zhao Soochow University, China Contents The 14th Pacific Asia Workshop on Intelligence and Security Informatics (PAISI 2019) A Supporting Tool for IT System Security Specification Evaluation Based on ISO/IEC 15408 and ISO/IEC 18045 . . . . . . . . . . . . . . . . . . . . . . 3 Da Bao, Yuichi Goto, and Jingde Cheng An Investigation on Multi View Based User Behavior Towards Spam Detection in Social Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Darshika Koggalahewa and Yue Xu A Cluster Ensemble Strategy for Asian Handicap Betting. . . . . . . . . . . . . . . 28 Yue Chen and Jian Shi Designing an Integrated Intelligence Center: New Taipei City Police Department as an Example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Chun-Young Chang, Lin-Chien Chien, and Yuh-Shyan Hwang Early Churn User Classification in Social Networking Service Using Attention-Based Long Short-Term Memory . . . . . . . . . . . . . . . . . . . . . . . . 45 Koya Sato, Mizuki Oka, and Kazuhiko Kato PAKDD 2019 Workshop on Weakly Supervised Learning: Progress and Future (WeL 2019) Weakly Supervised Learning by a Confusion Matrix of Contexts . . . . . . . . . 59 William Wu Learning a Semantic Space for Modeling Images, Tags and Feelings in Cross-Media Search. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 Sadaqat ur Rehman, Yongfeng Huang, Shanshan Tu, and Basharat Ahmad Adversarial Active Learning in the Presence of Weak and Malicious Oracles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Yan Zhou, Murat Kantarcioglu, and Bowei Xi The Most Related Knowledge First: A Progressive Domain Adaptation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 Yunyun Wang, Dan Zhao, Yun Li, Kejia Chen, and Hui Xue

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