Knowledge Management and Acquisition for Intelligent Systems: 17th Pacific Rim Knowledge Acquisition Workshop, PKAW 2020, Yokohama, Japan, January ... (Lecture Notes in Computer Science, 12280) PDF
Preview Knowledge Management and Acquisition for Intelligent Systems: 17th Pacific Rim Knowledge Acquisition Workshop, PKAW 2020, Yokohama, Japan, January ... (Lecture Notes in Computer Science, 12280)
Hiroshi Uehara Takayasu Yamaguchi Quan Bai (Eds.) Knowledge Management 0 8 2 and Acquisition 2 1 I A for Intelligent Systems N L 17th Pacific Rim Knowledge Acquisition Workshop, PKAW 2020 Yokohama, Japan, January 7–8, 2021 Proceedings 123 fi Lecture Notes in Arti cial Intelligence 12280 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 subseries at http://www.springer.com/series/1244 Hiroshi Uehara Takayasu Yamaguchi (cid:129) (cid:129) Quan Bai (Eds.) Knowledge Management and Acquisition for Intelligent Systems 17th Pacific Rim Knowledge Acquisition Workshop, PKAW 2020 Yokohama, Japan, January 7–8, 2021 Proceedings 123 Editors Hiroshi Uehara Takayasu Yamaguchi AkitaPrefectural University AkitaPrefectural University Akita, Japan Akita, Japan Quan Bai University of Tasmania SandyBay, TAS, Australia ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notesin Artificial Intelligence ISBN 978-3-030-69885-0 ISBN978-3-030-69886-7 (eBook) https://doi.org/10.1007/978-3-030-69886-7 LNCSSublibrary:SL7–ArtificialIntelligence ©SpringerNatureSwitzerlandAG2021 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 This volume contains the papers presented at the Principle and Practice of Data and Knowledge Acquisition Workshop 2020 (PKAW 2020), held in conjunction with the International Joint Conference on Artificial Intelligence - Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI), from January 7–8, 2021, in Yokohama, Japan. Over the past two decades, PKAW has provided researchers with opportunities to present ideas and have intensive discussions on their work related to knowledge acquisition,whichisoneofthecorefieldsofartificialintelligence(AI).Overtheyears, PKAW has dealt with a wide variety of topics, in accordance with the progress in AI research. In recent years, AI has been unprecedently in the spotlight owing to its remarkable successinseveralindustries,andpresently,researchonAIisenteringintoanimportant stage in terms of how it will contribute to the forthcoming AI society. Among the numerous AI-related workshops that are conducted throughout the world, PKAW primarily focuses on the multidisciplinary approach of human-driven and data-driven knowledgeacquisition,whichisthekeyconceptthathasremainedunchangedsincethe workshop was first established. In general, the recent approach of AI sheds light on a data-driven approach that requires an enormous volume of data. Even in the ongoing era of “big data”, quite a few cases of data analysis come across scarcity of data because of the cost, privacy issues, and the sporadic nature of its occurrence. We believe that human-driven approaches, such as modeling the implicit knowledge of human experts,might be effective insuchcases. Thus,amultidisciplinary approachis the much-needed breakthrough for efficient integration of AI-based advanced research for the upcoming AI society. This is the direction that PKAW takes. TheyearofPKAW2020hasgonethroughexceptionallydifficultcircumstancesdue totheglobalcoronaviruspandemic.Despitethissituation,wereceived28submissions, and finally accepted 10 regular papers and 5 short papers. All papers were peer reviewedbythreeindependentreviewers.Thesepapersdemonstrateadvancedresearch on AI and allied fields. These successes would not have been attained without the support of the people involved in this workshop. The workshop co-chairs would like to thank all the people who supported PKAW2020, including the PKAW Program Committee members and sub-reviewers who contributed their precious time towards reviewing the submitted papers, the IJCAI-PRICAI Organizing Committee, who dealt appropriately with our requests and all of the administrative and local matters. We thank Springer for pub- lishing the proceedings in the Lecture Notes in Artificial Intelligence (LNAI) series. vi Preface Further,wewouldliketoextendaspecialthankstoalltheauthorswhosubmittedtheir papers, presenters, and attendees. January 2021 Hiroshi Uehara Quan Bai Takayasu Yamaguchi PKAW 2020 Program Chairs Organization Program Chairs Quan Bai University of Tasmania, Australia Hiroshi Uehara Akita Prefectural University, Japan Takayasu Yamaguchi Akita Prefectural University, Japan Program Committee Takahira Yamaguchi Akita Prefectural University, Japan Matthew Kuo Auckland University of Technology, New Zealand Shuxiang Xu University of Tasmania, Australia Yi Yang Deakin University, Australia Xiongcai Cai University of New South Wales, Australia Weihua Li Auckland University of Technology, New Zealand Zi-Ke Zhang Zhejiang University, China Fenghui Ren University of Wollongong, Australia Xiang Zhao National University of Defense Technology, China Jihang Zhang University of Wollongong, Australia Toshiro Minami Kyushu Institute of Information Sciences and Kyushu University Library, Japan Dayong Ye University of Technology Sydney, Australia Kazumi Saito University of Shizuoka, Japan Toshihiro Kamishima National Institute of Advanced Industrial Science and Technology (AIST), Japan Akihiro Inokuchi Kwansei Gakuin University, Japan Hayato Ohwada Tokyo University of Science, Japan Lei Niu Central China Normal University, China Ulrich Reimer Eastern Switzerland University of Applied Sciences, Switzerland Tetsuya Yoshida Nara Women’s University, Japan Tomonobu Ozaki Nihon University, Japan Hye-Young Paik The University of New South Wales, Australia Qing Liu Data61, CSIRO, Australia Honorary Chairs Paul Compton University of New South Wales, Australia Hiroshi Motoda Osaka University, Japan viii Organization Advisory Committee Maria Lee Shih Chien University, Taiwan Kenichi Yoshida University of Tsukuba, Japan Byeong-Ho Kang University of Tasmania, Australia Deborah Richards Macquarie University, Australia Publicity Chairs Soyeon Han University of Sydney, Australia Son Tran University of Tasmania, Australia Weiwei Yuan Nanjing University of Aeronautics and Astronautics, China Webmaster Shiqing Wu University of Tasmania, Australia Contents Accelerating the Backpropagation Algorithm by Using NMF-Based Method on Deep Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Suhyeon Baek, Akira Imakura, Tetsuya Sakurai, and Ichiro Kataoka Collaborative Data Analysis: Non-model Sharing-Type Machine Learning for Distributed Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Akira Imakura, Xiucai Ye, and Tetsuya Sakurai ERA: Extracting Planning Macro-Operators from Adjacent and Non- adjacent Sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Sandra Castellanos-Paez, Romain Rombourg, and Philippe Lalanda Deep Neural Network Incorporating CNN and MF for Item-Based Fashion Recommendation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 TakuIto,IsseiNakamura,ShigekiTanaka,ToshikiSakai,TakeshiKato, Yusuke Fukazawa, and Takeshi Yoshimura C-LIME: A Consistency-Oriented LIME for Time-Series Health-Risk Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 Taku Ito, Keiichi Ochiai, and Yusuke Fukazawa Discriminant Knowledge Extraction from Electrocardiograms for Automated Diagnosis of Myocardial Infarction . . . . . . . . . . . . . . . . . . . 70 Girmaw Abebe Tadesse, Komminist Weldemariam, Hamza Javed, Yong Liu, Jin Liu, Jiyan Chen, and Tingting Zhu Stabilizing the Predictive Performance for Ear Emergence in Rice Crops Across Cropping Regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 Yasuhiro Iuchi, Hiroshi Uehara, Yusuke Fukazawa, and Yoshihiro Kaneta Description Framework for Stakeholder-Centric Value Chain of Data to Understand Data Exchange Ecosystem. . . . . . . . . . . . . . . . . . . . . . . . . . 98 Teruaki Hayashi, Gensei Ishimura, and Yukio Ohsawa Attributed Heterogeneous Network Embedding for Link Prediction. . . . . . . . 106 Tingting Wang, Weiwei Yuan, and Donghai Guan Automatic Generation and Classification of Malicious FQDN. . . . . . . . . . . . 120 Kenichi Yoshida, Kazunori Fujiwara, Akira Sato, and Shuji Sannomiya
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