Prof. Changsheng Xu (徐常胜教授)
IEEE Fellow, IAPR Fellow
Chinese Academy of Sciences, China
Changsheng Xu is a professor of Institute of Automation,
Chinese Academy of Sciences. His research interests include
multimedia content analysis/indexing/retrieval, pattern
recognition and computer vision. He has hold 50+
granted/pending patents and published over 400 refereed
research papers including 100+ IEEE/ACM Trans. papers in
Prof. Xu serves as Editor-in-Chief of Multimedia Systems Journal and Associate Editor of ACM Trans. on Multimedia Computing, Communications and Applications. He received the Best Paper Awards of ACM Multimedia 2016, 2016 ACM Trans. on Multimedia Computing, Communications and Applications and 2017 IEEE Multimedia. He served as Associate Editor of IEEE Transactions on Multimedia and Program Chair of ACM Multimedia 2009. He has served as associate editor, guest editor, general chair, program chair, area/track chair and TPC member for over 20 IEEE and ACM prestigious multimedia journals, conferences and workshops. He is an ACM Distinguished Scientist, IEEE Fellow, and IAPR Fellow.
Title: Connecting Isolated Social Multimedia Big Data
Abstract: The explosion of social media has led to various Online Social Networking (OSN) services. Today's typical netizens are using a multitude of OSN services. Exploring the user-contributed cross-OSN heterogeneous data is critical to connect between the separated data islands and facilitate value mining from big social multimedia. From the perspective of data fusion, understanding the association among cross-OSN data is fundamental to advanced social media analysis and applications. From the perspective of user modeling, exploiting the available user data on different OSNs contributes to an integrated online user profile and thus improved customized social media services. This talk will introduce a user-centric research paradigm for cross-OSN mining and applications and some pilot works along two basic tasks: (1) From users: cross-OSN association mining and (2) For users: cross-OSN user modeling.
Prof. Jin Li (李进教授)
Guangzhou University, China
Jin Li is currently a professor and vice dean of School of Computer Science, Guangzhou University. He received his B.S. (2002) and M.S. (2004) from Southwest University and Sun Yat-sen University, both in Mathematics. He got his Ph.D degree in information security from Sun Yat-sen University at 2007. His research interests include design of secure protocols in Artificial Intelligence, Cloud Computing (secure cloud storage and outsourcing computation) and cryptographic protocols. He served as a senior research associate at Korea Advanced Institute of Technology (Korea) and Illinois Institute of Technology (U.S.A.) from 2008 to 2010, respectively. He has published more than 100 papers in international conferences and journals, including IEEE INFOCOM, IEEE TIFS, IEEE TPDS, IEEE TOC and ESORICS etc. His work has been cited more than 11000 times at Google Scholar and the H-Index is 40. He is Editor-in-Chief of International Journal of Intelligent Systems. He served as Associate editor for several international journals, including IEEE Transactions on Dependable and Secure Computing, Information Sciences.
Prof. Yiyu Yao (姚一豫教授)
Fellow of the International Rough Set Society (IRSS)
University of Regina, Canada
Yiyu Yao is a professor of computer science with the Department of Computer Science, University of Regina, Canada. His research interests include three-way decision, granular computing, Web intelligence, rough sets, formal concept analysis, information retrieval, and data mining. He proposed a theory of three-way decision, a decision-theoretic rough set model, and a triarchic theory of granular computing. He has published over 400 papers. He was selected as a highly cited researcher by Clarivate from 2015 to 2019.
Title: Three-way decision for machine learning
Abstract: Three-way decision concerns thinking, problem solving, and computing in threes. As a specific triadic structure, the concept of Symbols-Meaning-Value (SMV) spaces combines three powerful ideas, namely, a tripartite categorization of communications problems in terms of the symbols- meaning-effectiveness of a message, the trilevel data-knowledge-wisdom (DKW) hierarchy in information science, and the trilogy of perception-cognition-action in cognitive science and psychology. By following the principles of thinking in threes, this talk is organized into three parts: (1) a review the basics and main results of three-way decision, (2) an introduction to the concept of SMV spaces, and (3) a discussion of three-way decision and SMV spaces for machine learning.
Wenbing Zhao (赵文兵教授)
Department of Electrical Engineering and Computer Science,
Cleveland State University, USA
Wenbing Zhao received his Ph.D. in Electrical and Computer
Engineering at University of California, Santa Barbara, in
2002. Dr. Zhao has a Bachelor of Science degree in Physics
in 1990, and a Master of Science degree in Physics in 1993,
both at Peking University, Beijing, China. Dr. Zhao also
received a Master of Science degree in Electrical and
Computer Engineering in 1998 at University of California,
Santa Barbara. Dr. Zhao joined Cleveland State University
(CSU) faculty in 2004 and is currently a Professor in the
Department of Electrical Engineering and Computer Science
(EECS) at CSU. Dr. Zhao published over 200 peer-reviewed
papers in the area of distributed systems (three of them won
the best paper award), smart and connected health, computer
vision, machine learning, physics, and education. Dr. Zhao’s
research is supported in part by the US National Science
Foundation, the US Department of Energy, the US Department
of Transportation, Ohio State Bureau of Workers’
Compensation, Ohio Department of Higher Education, and by
Cleveland State University. Dr. Zhao is currently serving on
the organizing committee and the technical program committee
for numerous international conferences. He is an Associate
Editor for IEEE Access and for MDPI Computers. Dr. Zhao is a
senior member of IEEE.
Title: Sabermetrics and Beyond: Data Science in
Abstract: Sabermetrics refers to a form of sports analytics in baseball. In a baseball game, huge amount of data are collected regarding the ball pitched and batted, and regarding the player movements. The data can then be used to determine the player performance, make adjustment during a game, plan for future games, and ultimately influence the value of the players. The term “sabermetrics” was coined by Bill James, who started publishing Baseball Abstracts in 1977. The successful use of sabermetrics in the US Major League Baseball (MLB) began in 1990s by the Oakland Athletics. The release of the movie Moneyball gave broad exposure of the power of sabermetrics. Since then, virtually all MLB teams have embraced sabermetrics in team management and game planning. The availability of tremendously amount of data for MLB games have also powered research beyond the original scope of sabermetrics. Various statistical and machine-learning based models have been proposed for different aspects of game play, ranging from pitch type classification, pitching mechanism, pitching quality changes during a game, how to catch a fly ball, to quantifying a player’s performance, to predict the availability and performance in future seasons, to determining the value of a player for a new contract.