Lecture Notes in Artificial Intelligence 12274

Subseries of Lecture Notes in Computer Science

Series Editors

Randy GoebelUniversity of Alberta, Edmonton, Canada

Yuzuru TanakaHokkaido University, Sapporo, Japan

Wolfgang WahlsterDFKI and Saarland University, Saarbrücken, Germany

Founding Editor

Jörg SiekmannDFKI and Saarland University, Saarbrücken, Germany

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

Gang Li • Heng Tao Shen •

Ye Yuan • Xiaoyang Wang •

Huawen Liu • Xiang Zhao (Eds.)

Knowledge Science,Engineeringand Management13th International Conference, KSEM 2020Hangzhou, China, August 28–30, 2020Proceedings, Part I

123

EditorsGang LiDeakin UniversityGeelong, VIC, Australia

Heng Tao ShenUniversity of Electronic Scienceand Technology of ChinaChengdu, China

Ye YuanBeijing Institute of TechnologyBeijing, China

Xiaoyang WangZhejiang Gongshang UniversityHangzhou, China

Huawen LiuZhejiang Normal UniversityJinhua, China

Xiang ZhaoNational University of Defense TechnologyChangsha, China

ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Artificial IntelligenceISBN 978-3-030-55129-2 ISBN 978-3-030-55130-8 (eBook)https://doi.org/10.1007/978-3-030-55130-8

LNCS Sublibrary: SL7 – Artificial Intelligence

© Springer Nature Switzerland AG 2020This 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, expressed 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

The International Conference on Knowledge Science, Engineering and Management(KSEM) provides a forum for researchers in the broad areas of knowledge science,knowledge engineering, and knowledge management to exchange ideas and to reportstate-of-the-art research results. KSEM 2020 is the 13th in this series, which builds onthe success of 12 previous events in Guilin, China (KSEM 2006); Melbourne, Australia(KSEM 2007); Vienna, Austria (KSEM 2009); Belfast, UK (KSEM 2010); Irvine,USA (KSEM 2011); Dalian, China (KSEM 2013); Sibiu, Romania (KSEM 2014);Chongqing, China (KSEM 2015); Passau, Germany (KSEM 2016); Melbourne,Australia (KSEM 2017); Changchun, China (KSEM 2018); and Athens, Greece(KSEM 2019).

The selection process this year was, as always, competitive. We received received291 submissions, and each submitted paper was reviewed by at least three membersof the Program Committee (PC) (including thorough evaluations by the PC co-chairs).Following this independent review, there were discussions between reviewers and PCchairs. A total of 58 papers were selected as full papers (19.9%), and 27 papers as shortpapers (9.3%), yielding a combined acceptance rate of 29.2%.

We were honoured to have three prestigious scholars giving keynote speeches at theconference: Prof. Zhi Jin (Peking University, China), Prof. Fei Wu (ZhejiangUniversity, China), and Prof. Feifei Li (Alibaba Group, China). The abstracts of Prof.Jin's and Prof Wu's talks are included in this volume.

We would like to thank everyone who participated in the development of the KSEM2020 program. In particular, we would give special thanks to the PC for their diligenceand concern for the quality of the program, and also for their detailed feedback to theauthors. The general organization of the conference also relies on the efforts of KSEM2020 Organizing Committee.

Moreover, we would like to express our gratitude to the KSEM Steering Committeehonorary chair, Prof. Ruqian Lu (Chinese Academy of Sciences, China), the KSEMSteering Committee chair, Prof. Dimitris Karagiannis (University of Vienna, Austria),Prof. Chengqi Zhang (University of Technology Sydney, Australia), who providedinsight and support during all the stages of this effort, and the members of the SteeringCommittee, who followed the progress of the conference very closely with sharpcomments and helpful suggestions. We also really appreciate the KSEM 2020 generalco-chairs, Prof. Hai Jin (Huazhong University of Science and Technology, China),Prof. Xuemin Lin (University of New South Wales, Australia), and Prof. Xun Wang(Zhejiang Gongshang University, China), who were extremely supportive in our effortsand in the general success of the conference.

We would like to thank the members of all the other committees and, in particular,those of the Local Organizing Committee, who worked diligently for more than a yearto provide a wonderful experience to the KSEM participants. We are also grateful toSpringer for the publication of this volume, who worked very efficiently andeffectively.

Finally and most importantly, we thank all the authors, who are the primary reasonwhy KSEM 2020 is so exciting, and why it will be the premier forum for presentationand discussion of innovative ideas, research results, and experience from around theworld as well as highlight activities in the related areas.

June 2020 Gang LiHeng Tao Shen

Ye Yuan

vi Preface

Organization

Steering Committee

Ruqian Lu (Honorary Chair) Chinese Academy of Sciences, ChinaDimitris Karagiannis

(Chair)University of Vienna, Austria

Yaxin Bi Ulster University, UKChristos Douligeris University of Piraeus, GreeceZhi Jin Peking University, ChinaClaudiu Kifor University of Sibiu, RomaniaGang Li Deakin University, AustraliaYoshiteru Nakamori Japan Advanced Institute of Science and Technology,

JapanJorg Siekmann German Research Centre of Artificial Intelligence,

GermanyMartin Wirsing Ludwig-Maximilians-Universität München, GermanyHui Xiong Rutgers University, USABo Yang Jilin University, ChinaChengqi Zhang University of Technology Sydney, AustraliaZili Zhang Southwest University, China

Organizing Committee

Honorary Co-chairs

Ruqian Lu Chinese Academy of Sciences, ChinaChengqi Zhang University of Technology Sydney, Australia

General Co-chairs

Hai Jin Huazhong University of Science and Technology,China

Xuemin Lin University of New South Wales, AustraliaXun Wang Zhejiang Gongshang University, China

Program Committee Co-chairs

Gang Li Deakin University, AustraliaHengtao Shen University of Electronic Science and Technology

of China, ChinaYe Yuan Beijing Institute of Technology, China

Keynote, Special Sessions, and Tutorial Chair

Zili Zhang Southwest University, China

Publication Committee Co-chairs

Huawen Liu Zhejiang Normal University, ChinaXiang Zhao National University of Defense Technology, China

Publicity Chair

Xiaoqin Zhang Wenzhou University, China

Local Organizing Committee Co-chairs

Xiaoyang Wang Zhejiang Gongshang University, ChinaZhenguang Liu Zhejiang Gongshang University, ChinaZhihai Wang Zhejiang Gongshang University, ChinaXijuan Liu Zhejiang Gongshang University, China

Program Committee

Klaus-Dieter Althoff DFKI and University of Hildesheim, GermanySerge Autexier DFKI, GermanyMassimo Benerecetti Università di Napoli Federico II, ItalySalem Benferhat Université d'Artois, FranceXin Bi Northeastern University, ChinaRobert Andrei Buchmann Babes-Bolyai University of Cluj Napoca, RomaniaChen Chen Zhejiang Gongshang University, ChinaHechang Chen Jilin University, ChinaLifei Chen Fujian Normal Univeristy, ChinaDawei Cheng Shanghai Jiao Tong University, ChinaYurong Cheng Beijing Institute of Technology, ChinaYong Deng Southwest University, ChinaLinlin Ding Liaoning University, ChinaShuai Ding Hefei University of Technology, ChinaChristos Douligeris University of Piraeus, GreeceXiaoliang Fan Xiamen University, ChinaKnut Hinkelmann FHNW University of Applied Sciences and Arts

Northwestern Switzerland, SwitzerlandGuangyan Huang Deakin University, AustraliaHong Huang UGOE, GermanyZhisheng Huang Vrije Universiteit Amsterdam, The NetherlandsFrank Jiang Deakin University, AustraliaJiaojiao Jiang RMIT University, AustraliaWang Jinlong Qingdao University of Technology, ChinaMouna Kamel IRIT, Université Toulouse III - Paul Sabatier, FranceKrzysztof Kluza AGH University of Science and Technology, Poland

viii Organization

Longbin Lai Alibaba Group, ChinaYong Lai Jilin University, ChinaQiujun Lan Hunan University, ChinaCheng Li National University of Singapore, SingaporeGe Li Peking University, ChinaJianxin Li Deakin University, AustraliaLi Li Southwest University, ChinaQian Li Chinese Academy of Sciences, ChinaShu Li Chinese Academy of Sciences, ChinaXiming Li Jilin University, ChinaXinyi Li National University of Defense Technology, ChinaYanhui Li Northeastern University, ChinaYuan Li North China University of Technology, ChinaShizhong Liao Tianjin University, ChinaHuawen Liu Zhejiang Normal University, ChinaShaowu Liu University of Technology Sydney, AustraliaZhenguang Liu Zhejiang Gongshang University, ChinaWei Luo Deakin University, AustraliaXudong Luo Guangxi Normal University, ChinaBo Ma Chinese Academy of Sciences, ChinaYuliang Ma Northeastern University, ChinaStewart Massie Robert Gordon University, UKMaheswari N VIT University, IndiaMyunghwan Na Chonnam National University, South KoreaBo Ning Dalian Maritime University, ChinaOleg Okun Cognizant Technology Solutions GmbH, ChinaJun-Jie Peng Shanghai University, ChinaGuilin Qi Southeast University, ChinaUlrich Reimer University of Applied Sciences St. Gallen, SwitzerlandWei Ren Southwest University, ChinaZhitao Shen Ant Financial Services Group, ChinaLeilei Sun Beihang University, ChinaJianlong Tan Chinese Academy of Sciences, ChinaZhen Tan National University of Defense Technology, ChinaYongxin Tong Beihang University, ChinaDaniel Volovici ULB Sibiu, RomaniaQuan Vu Deakin University, AustraliaHongtao Wang North China Electric Power University, ChinaJing Wang The University of Tokyo, JapanKewen Wang Griffith University, AustraliaXiaoyang Wang Zhejiang Gongshang University, ChinaZhichao Wang Tsinghua University, ChinaLe Wu Hefei University of Technology, ChinaJia Xu Guangxi University, ChinaTong Xu University of Science and Technology of China, ChinaZiqi Yan Beijing Jiaotong University, China

Organization ix

Bo Yang Jilin University, ChinaJianye Yang Hunan University, ChinaShiyu Yang East China Normal University, ChinaShuiqiao Yang University of Technology Sydney, AustraliaYating Yang Chinese Academy of Sciences, ChinaFeng Yi UESTC: Zhongshan College, ChinaMin Yu Chinese Academy of Sciences, ChinaLong Yuan Nanjing University of Science and Technology, ChinaQingtian Zeng Shandong University of Science and Technology,

ChinaChengyuan Zhang Central South University, ChinaChris Zhang Chinese Science Academy, ChinaChunxia Zhang Beijing Institute of Technology, ChinaFan Zhang Guangzhou University, ChinaSongmao Zhang Chinese Academy of Sciences, ChinaZili Zhang Deakin University, AustraliaXiang Zhao National University of Defense Technology, ChinaYe Zhu Monash University, AustraliaYi Zhuang Zhejiang Gongshang University, ChinaJiali Zuo Jiangxi Normal University, China

Additional Reviewers

Weronika T. AdrianTaotao CaiXiaojuan ChengViktor EisenstadtGlenn ForbesNur HaldarKongzhang HaoSili HuangFrancesco IsgroGongjin LanEnhui LiShuxia LinPatryk OrzechowskiRoberto PreveteNajmeh SamadianiBi ShengBeat TödtliBeibei WangYixuan Wang

Piotr WiśniewskiYanping WuZhiwei YangXuan ZangYunke ZhangQianru ZhouBorui CaiHui ChenShangfu DuanUno FangHuan GaoXin HanXin HeXuqian HuangKrzysztof KuttBoyang LiJiwen LinYuxin LiuNing Pang

x Organization

Pascal ReussJakob Michael SchoenbornAtsushi SuzukiAshish UpadhyayShuai WangYuhan Wang

Haiyan WuZhengyi YangGuoxian YuRoozbeh ZareiQing ZhaoXianglin Zuo

Organization xi

Abstracts of Invited Talks

Learning from Source Code

Zhi Jin

Key Laboratory of High-Confidence of Software Technologies (MoE),Peking University, Chinazhijin@pku.edu.cn

Abstract. Human beings communicate and exchange knowledge with eachother. The system of communication and knowledge exchanging among humanbeings is natural language, which is an ordinary, instinctive part of everyday life.Although natural languages have complex forms of expressive, it is most oftensimple, expedient and repetitive with everyday human communication evolved.This naturalness together with rich resources and advanced techniques has led toa revolution in natural language processing that help to automatically extractknowledge from natural language documents, i.e. learning from text documents.Although program languages are clearly artificial and highly restricted lan-

guages, programming is of course for telling computers what to do but is also asmuch an act of communication, for explaining to human beings what we want acomputer to do1. In this sense, we may think of applying machine learningtechniques to source code, despite its strange syntax and awash with punctua-tion, etc., to extract knowledge from it. The good thing is the very large publiclyavailable corpora of open-source code is enabling a new, rigorous, statisticalapproach to wide range of applications, in program analysis, software miningand program summarization.This talk will demonstrate the long, ongoing and fruitful journey on

exploiting the potential power of deep learning techniques in the area of soft-ware engineering. It will show how to model the code2,3. It will also show howsuch models can be leveraged to support software engineers to perform differenttasks that require proficient programming knowledge, such as code prediction

1 A. Hindle, E. T. Barr, M. Gabel, Z. Su and P. Devanbu, On the Naturalness of Software,Communication of the ACM, 59(5): 122–131, 2016.

2 L. Mou, G. Li, L. Zhang, T. Wang and Z. Jin, Convolutional Neural Networks over Tree Structuresfor Programming Language Processing, AAAI 2016: 1287–1293.

3 F. Liu, L. Zhang and Z. Jin, Modeling Programs Hierarchically with Stack-Augmented LSTM, TheJournal of Systems and Software, https://doi.org/10.1016/j.jss.2020.110547.

and completion4, code clone detection5, code comments6,7 and summarization8,etc. The exploratory work show that code implies the learnable knowledge,more precisely the learnable tacit knowledge. Although such knowledge isdifficult to transfer among human beings, it is able to transfer among theautomatically programming tasks. A vision for future research in this area willbe laid out as the conclusion.

Keywords: Software • Source code • Program languages • Programmingknowledge

4 B. Wei, G. Li, X. Xia, Z. Fu and Z. Jin, Code Generation as a Dual Task of Code Summarization,NeurIPS 2019.

5 W. Wang, G. Li, B. Ma, X. Xia and Z. Jin, Detecting Code Clones with Graph Neural Network andFlow-Augmented Abstract Syntax Tree, SANER 2020: 261–271.

6 X. Hu, G. Li, X. Xia, D. Lo, S. Lu and Z. Jin, Deep Code Comment Generation, ICPC 2018: 200–210.

7 X. Hu, G. Li, X. Xia, D. Lo, S. Lu and Z. Jin, Deep Code Comment Generation with Hybrid Lexicaland Syntactical Information, Empirical Software Engineering (2020) 25: 2179–2217.

8 X. Hu, G. Li, X. Xia, D. Lo, S. Lu and Z. Jin, Summarizing Source Code with Transferred APIKnowledge, IJCAI 2018: 2269–2275.

xvi Z. Jin

Memory-Augmented Sequence2equenceLearning

Fei Wu

Zhejiang University, Chinawufei@cs.zju.edu.cn

Abstract. Neural networks with a memory capacity provide a promisingapproach to media understanding (e.g., Q-A and visual classification). In thistalk, I will present how to utilize the information in external memory to boostmedia understanding. In general, the relevant information (e.g., knowledgeinstance and exemplar data) w.r.t the input data is sparked from externalmemory in the manner of memory-augmented learning. Memory-augmentedlearning is an appropriate method to integrate data-driven learning, knowledge-guided inference and experience exploration.

Keywords: Media understanding • Memory-augmented learning

Contents – Part I

Knowledge Graph

Event-centric Tourism Knowledge Graph—A Case Study of Hainan . . . . . . . 3Jie Wu, Xinning Zhu, Chunhong Zhang, and Zheng Hu

Extracting Short Entity Descriptions for Open-World Extensionto Knowledge Graph Completion Models. . . . . . . . . . . . . . . . . . . . . . . . . . 16

Wangpeng Zhu, Xiaoli Zhi, and Weiqin Tong

Graph Embedding Based on Characteristic of Rooted Subgraph Structure . . . 28Yan Liu, Xiaokun Zhang, Lian Liu, and Gaojian Li

Knowledge Graphs Meet Geometry for Semi-supervised MonocularDepth Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Yu Zhao, Fusheng Jin, Mengyuan Wang, and Shuliang Wang

Topological Graph Representation Learning on Property Graph . . . . . . . . . . 53Yishuo Zhang, Daniel Gao, Aswani Kumar Cherukuri, Lei Wang,Shaowei Pan, and Shu Li

Measuring Triplet Trustworthiness in Knowledge Graphs via ExpandedRelation Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

Aibo Guo, Zhen Tan, and Xiang Zhao

A Contextualized Entity Representation for Knowledge Graph Completion. . . 77Fei Pu, Bailin Yang, Jianchao Ying, Lizhou You, and Chenou Xu

A Dual Fusion Model for Attributed Network Embedding . . . . . . . . . . . . . . 86Kunjie Dong, Lihua Zhou, Bing Kong, and Junhua Zhou

Attention-Based Knowledge Tracing with Heterogeneous InformationNetwork Embedding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Nan Zhang, Ye Du, Ke Deng, Li Li, Jun Shen, and Geng Sun

Knowledge Representation

Detecting Statistically Significant Events in Large Heterogeneous AttributeGraphs via Densest Subgraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Yuan Li, Xiaolin Fan, Jing Sun, Yuhai Zhao, and Guoren Wang

Edge Features Enhanced Graph Attention Network for Relation Extraction. . . 121Xuefeng Bai, Chong Feng, Huanhuan Zhang, and Xiaomei Wang

MMEA: Entity Alignment for Multi-modal Knowledge Graph . . . . . . . . . . . 134Liyi Chen, Zhi Li, Yijun Wang, Tong Xu, Zhefeng Wang,and Enhong Chen

A Hybrid Model with Pre-trained Entity-Aware Transformerfor Relation Extraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Jinxin Yao, Min Zhang, Biyang Wang, and Xianda Xu

NovEA: A Novel Model of Entity Alignment Using Attribute Triplesand Relation Triples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

Tao Sun, Jiaojiao Zhai, and Qi Wang

A Robust Representation with Pre-trained Start and End Characters Vectorsfor Noisy Word Recognition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174

Chao Liu, Xiangmei Ma, Min Yu, Xinghua Wu, Mingqi Liu,Jianguo Jiang, and Weiqing Huang

Intention Multiple-Representation Model for Logistics IntelligentCustomer Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186

Jingxiang Hu, Junjie Peng, Wenqiang Zhang, Lizhe Qi, Miao Hu,and Huanxiang Zhang

Identifying Loners from Their Project Collaboration Records -A Graph-Based Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

Qing Zhou, Jiang Li, Yinchun Tang, and Liang Ge

Node Embedding over Attributed Bipartite Graphs . . . . . . . . . . . . . . . . . . . 202Hasnat Ahmed, Yangyang Zhang, Muhammad Shoaib Zafar,Nasrullah Sheikh, and Zhenying Tai

FastLogSim: A Quick Log Pattern Parser Scheme Basedon Text Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211

Weiyou Liu, Xu Liu, Xiaoqiang Di, and Binbin Cai

Knowledge Management for Education

Robotic Pushing and Grasping Knowledge Learning via AttentionDeep Q-learning Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223

Zipeng Yang and Huiliang Shang

A Dynamic Answering Path Based Fusion Model for KGQA . . . . . . . . . . . . 235Mingrong Tang, Haobo Xiong, Liping Wang, and Xuemin Lin

Improving Deep Item-Based Collaborative Filtering with BayesianPersonalized Ranking for MOOC Course Recommendation . . . . . . . . . . . . . 247

Xiao Li, Xiang Li, Jintao Tang, Ting Wang, Yang Zhang,and Hongyi Chen

xx Contents – Part I

Online Programming Education Modeling and Knowledge Tracing . . . . . . . . 259Yuting Sun, Liping Wang, Qize Xie, Youbin Dong, and Xuemin Lin

Enhancing Pre-trained Language Models by Self-supervised Learningfor Story Cloze Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271

Yuqiang Xie, Yue Hu, Luxi Xing, Chunhui Wang, Yong Hu,Xiangpeng Wei, and Yajing Sun

MOOCRec: An Attention Meta-path Based Model for Top-KRecommendation in MOOC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280

Deming Sheng, Jingling Yuan, Qing Xie, and Pei Luo

Knowledge-Based Systems

PVFNet: Point-View Fusion Network for 3D Shape Recognition . . . . . . . . . 291Jun Yang and Jisheng Dang

HEAM: Heterogeneous Network Embedding with AutomaticMeta-path Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304

Ruicong Shi, Tao Liang, Huailiang Peng, Lei Jiang, and Qiong Dai

A Graph Attentive Network Model for P2P Lending Fraud Detection . . . . . . 316Qiyi Wang, Hongyan Liu, Jun He, and Xiaoyong Du

An Empirical Study on Recent Graph Database Systems . . . . . . . . . . . . . . . 328Ran Wang, Zhengyi Yang, Wenjie Zhang, and Xuemin Lin

Bibliometric Analysis of Twitter Knowledge Management PublicationsRelated to Health Promotion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341

Saleha Noor, Yi Guo, Syed Hamad Hassan Shah, and Habiba Halepoto

Automatic Cerebral Artery System Labeling Using Registrationand Key Points Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 355

Mengjun Shen, Jianyong Wei, Jitao Fan, Jianlong Tan,Zhenchang Wang, Zhenghan Yang, Penggang Qiao, and Fangzhou Liao

Page-Level Handwritten Word Spotting via DiscriminativeFeature Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368

Jie Gao, Xiaopeng Guo, Mingyu Shang, and Jun Sun

NADSR: A Network Anomaly Detection Scheme Basedon Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380

Xu Liu, Xiaoqiang Di, Weiyou Liu, Xingxu Zhang, Hui Qi,Jinqing Li, Jianping Zhao, and Huamin Yang

A Knowledge-Based Scheduling Method for Multi-satellite Range System . . . 388Yingguo Chen, Yanjie Song, Yonghao Du, Mengyuan Wang, Ran Zong,and Cheng Gong

Contents – Part I xxi

IM-Net: Semantic Segmentation Algorithm for Medical Images Basedon Mutual Information Maximization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397

Yi Sun and Peisen Yuan

Data Processing and Mining

Fast Backward Iterative Laplacian Score for UnsupervisedFeature Selection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 409

Qing-Qing Pang and Li Zhang

Improving Low-Resource Chinese Event Detectionwith Multi-task Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421

Meihan Tong, Bin Xu, Shuai Wang, Lei Hou, and Juaizi Li

Feature Selection Using Sparse Twin Support Vector Machinewith Correntropy-Induced Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434

Xiaohan Zheng, Li Zhang, and Leilei Yan

Customized Decision Tree for Fast Multi-resolution ChartPatterns Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446

Qizhou Sun and Yain-Whar Si

Predicting User Influence in the Propagation of Toxic Information . . . . . . . . 459Shu Li, Yishuo Zhang, Penghui Jiang, Zhao Li, Chengwei Zhang,and Qingyun Liu

Extracting Distinctive Shapelets with Random Selectionfor Early Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 471

Guiling Li and Wenhe Yan

Butterfly-Based Higher-Order Clustering on Bipartite Networks . . . . . . . . . . 485Yi Zheng, Hongchao Qin, Jun Zheng, Fusheng Jin, and Rong-Hua Li

Learning Dynamic Pricing Rules for Flight Tickets . . . . . . . . . . . . . . . . . . . 498Jian Cao, Zeling Liu, and Yao Wu

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507

xxii Contents – Part I

Contents – Part II

Machine Learning

MA-TREX: Mutli-agent Trajectory-Ranked Reward Extrapolationvia Inverse Reinforcement Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Sili Huang, Bo Yang, Hechang Chen, Haiyin Piao, Zhixiao Sun,and Yi Chang

An Incremental Learning Network Model Based on Random SampleDistribution Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Wencong Wang, Lan Huang, Hao Liu, Jia Zeng, Shiqi Sun, Kainuo Li,and Kangping Wang

Parameter Optimization and Weights Assessment for Evidential ArtificialImmune Recognition System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Rihab Abdelkhalek and Zied Elouedi

Improving Policy Generalization for Teacher-StudentReinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Gong Xudong, Jia Hongda, Zhou Xing, Feng Dawei, Ding Bo,and Xu Jie

Recommendation Algorithms and Systems

Towards Effective Top-k Location Recommendation for BusinessFacility Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Pu Wang, Wei Chen, and Lei Zhao

Pairwise-Based Hierarchical Gating Networksfor Sequential Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Kexin Huang, Ye Du, Li Li, Jun Shen, and Geng Sun

Time-Aware Attentive Neural Network for News Recommendationwith Long- and Short-Term User Representation . . . . . . . . . . . . . . . . . . . . . 76

Yitong Pang, Yiming Zhang, Jianing Tong, and Zhihua Wei

A Time Interval Aware Approach for Session-BasedSocial Recommendation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Youjie Zhang, Ting Bai, Bin Wu, and Bai Wang

AutoIDL: Automated Imbalanced Data Learningvia Collaborative Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Jingqi Zhang, Zhongbin Sun, and Yong Qi

Fusion of Domain Knowledge and Text Features for Query Expansionin Citation Recommendation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Yanli Hu, Chunhui He, Zhen Tan, Chong Zhang, and Bin Ge

Robust Sequence Embedding for Recommendation . . . . . . . . . . . . . . . . . . . 114Rongzhi Zhang, Shuzi Niu, and Yucheng Li

Deep Generative Recommendation with Maximizing Reciprocal Rank . . . . . . 123Xiaoyi Sun, Huafeng Liu, Liping Jing, and Jian Yu

Spatio-Temporal Attentive Network for Session-Based Recommendation . . . . 131Chunkai Zhang and Junli Nie

Social Knowledge Analysis and Management

Category-Level Adversarial Network for Cross-DomainSentiment Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Shaokang Zhang, Huailiang Peng, Yanan Cao, Lei Jiang, Qiong Dai,and Jianlong Tan

Seeds Selection for Influence Maximization Based on Device-to-DeviceSocial Knowledge by Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . 155

Xu Tong, Hao Fan, Xiaofei Wang, Jianxin Li, and Xin Wang

CIFEF: Combining Implicit and Explicit Features for Friendship Inferencein Location-Based Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

Cheng He, Chao Peng, Na Li, Xiang Chen, Zhengfeng Yang,and Zhenhao Hu

A Knowledge Enhanced Ensemble Learning Model for Mental DisorderDetection on Social Media . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181

Guozheng Rao, Chengxia Peng, Li Zhang, Xin Wang, and Zhiyong Feng

Constrained Viral Marketing in Social Networks . . . . . . . . . . . . . . . . . . . . . 193Lei Yu, Guohui Li, and Ling Yuan

A Multi-source Self-adaptive Transfer Learning Model for MiningSocial Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

Kai Zhang, Longtao He, Chenglong Li, Shupeng Wang,and Xiao-yu Zhang

Text Mining and Document Analysis

Multi-hop Syntactic Graph Convolutional Networks for Aspect-BasedSentiment Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

Chang Yin, Qing Zhou, Liang Ge, and Jiaojiao Ou

xxiv Contents – Part II

A Matching-Integration-Verification Model for Multiple-ChoiceReading Comprehension . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225

Luxi Xing, Yue Hu, Yuqiang Xie, Chunhui Wang, and Yong Hu

How to Interact and Change? Abstractive Dialogue Summarizationwith Dialogue Act Weight and Topic Change Info . . . . . . . . . . . . . . . . . . . 238

Jiasheng Di, Xiao Wei, and Zhenyu Zhang

Chinese Text Classification via Bidirectional Lattice LSTM . . . . . . . . . . . . . 250Ning Pang, Weidong Xiao, and Xiang Zhao

MG-BERT: A Multi-glosses BERT Model for WordSense Disambiguation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263

Ping Guo, Yue Hu, and Yunpeng Li

Top Personalized Reviews Set Selection Based on SubjectAspect Modeling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276

Muhmmad Al-Khiza’ay, Noora Alallaq, Firas Qays Kamal,Tamather Naji Alshimmari, and Tianshi Liu

SCX-SD: Semi-supervised Method for Contextual Sarcasm Detection . . . . . . 288Meimei Li, Chen Lang, Min Yu, Yue Lu, Chao Liu, Jianguo Jiang,and Weiqing Huang

End-to-End Multi-task Learning for Allusion Detection in AncientChinese Poems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300

Lei Liu, Xiaoyang Chen, and Ben He

Defense of Word-Level Adversarial Attacks via RandomSubstitution Encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312

Zhaoyang Wang and Hongtao Wang

Document-Improved Hierarchical Modular Attention for Event Detection. . . . 325Yiwei Ni, Qingfeng Du, and Jincheng Xu

Fine-Tuned Transformer Model for Sentiment Analysis . . . . . . . . . . . . . . . . 336Sishun Liu, Pengju Shuai, Xiaowu Zhang, Shuang Chen, Li Li,and Ming Liu

An Algorithm for Emotion Evaluation and Analysis Based on CBOW. . . . . . 344JiaXin Guo and ZhengYou Xia

Predicting Crowdsourcing Worker Performance with Knowledge Tracing. . . . 352Zizhe Wang, Hailong Sun, and Tao Han

Contents – Part II xxv

Deep Learning

Watermarking Neural Network with Compensation Mechanism . . . . . . . . . . 363Le Feng and Xinpeng Zhang

Information Diffusion Prediction with Personalized GraphNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376

Yao Wu, Hong Huang, and Hai Jin

Relationship-Aware Hard Negative Generation in Deep Metric Learning . . . . 388Jiaqi Huang, Yong Feng, Mingliang Zhou, and Baohua Qiang

Striking a Balance in Unsupervised Fine-Grained Domain AdaptationUsing Adversarial Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401

Han Yu, Rong Jiang, and Aiping Li

Improved Performance of GANs via Integrating Gradient Penaltywith Spectral Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414

Hongwei Tan, Linyong Zhou, Guodong Wang, and Zili Zhang

Evidential Deep Neural Networks for Uncertain Data Classification. . . . . . . . 427Bin Yuan, Xiaodong Yue, Ying Lv, and Thierry Denoeux

GDCRN: Global Diffusion Convolutional Residual Network for TrafficFlow Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 438

Liujuan Chen, Kai Han, Qiao Yin, and Zongmai Cao

Depthwise Separable Convolutional Neural Network for ConfidentialInformation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 450

Yue Lu, Jianguo Jiang, Min Yu, Chao Liu, Chaochao Liu,Weiqing Huang, and Zhiqiang Lv

The Short-Term Exit Traffic Prediction of a Toll Station Based on LSTM . . . 462Ying Lin, Runfang Wang, Rui Zhu, Tong Li, Zhan Wang,and Maoyu Chen

Long and Short Term Risk Control for Online Portfolio Selection. . . . . . . . . 472Yizhe Bai, Jianfei Yin, Shunda Ju, Zhao Chen,and Joshua Zhexue Huang

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481

xxvi Contents – Part II