2024 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024)




Professor Wenbing Zhao

Cleveland State University

Dr. Zhao is a Professor at the Department of Electrical and Computer Engineering, Cleveland State University. He got his BS and MS degrees from the Physics Department in Peking University. He earned his Ph.D. at University of California, Santa Barbara in 2002. He has over 200 peer-reviewed publications and the author of the research monograph titled “From Traditional Fault Tolerance to Blockchain.” Dr. Zhao’s research spans from dependable distributed systems, human centered smart systems, and engineering education. His research has been funded by the US NSF, US Department of Energy, US Department of Education, US Department of Transportation, Ohio Bureau of Workers’ Compensation, Ohio Department of Higher Education, the Ohio Development Services Agency, and Woodruff Foundation. He has delivered more than 10 keynotes, tutorials, public talks and demonstrations in various conferences, industry and academic venues. Dr. Zhao is an associate editor for IEEE Access, MDPI Computers, and PeerJ Computer Science, and a member of the editorial board of several international journals, including Applied System Innovation, Internal Journal of Parallel, Emergent and Distributed Systems, and International Journal of Distributed Systems and Technologies. He is currently an IEEE Senior Member and serves as the Treasurer of the IEEE Cleveland Section.

Speech Title:Sabermetrics and Beyond: Data Science in Professional Baseball

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. 

Distinguished Professor Philippe Fournier-Viger

Shenzhen University

Philippe Fournier-Viger (Ph.D) is distinguished professor at Shenzhen University (China). Five years after completing his Ph.D., he came to China in 2015 and became full professor after receiving an important talent title from the National Science Foundation of China. He has published more than 380 research papers related to data mining algorithms for complex data (sequences, graphs), intelligent systems and applications, which have received more than 14,600 citations (H-Index 62 - Google Scholar). He is the founder of the popular SPMF data mining library, offering more than 250 algorithms to find patterns in data, cited in more than 1,000 research papers. He is former associate editor-in-chief of the Applied Intelligence journal and has been keynote speaker for over 30 international conferences and co-edited four books for Springer. He appears in the top 2% of researchers for scientific influence in the Stanford list, and is a Elsevier «Highly Cited Chinese Researcher» (2022). Website:http://www.philippe-fournier-viger.com.

Speech Title:Advances and challenges for the automatic discovery of interesting patterns in data

Abstract: Intelligent systems and tools can play an important role in various domains such as for factory automation, e-business, and manufacturing. To build intelligent systems and tools, high-quality data is generally required. Moreover, these systems need to process complex data and can yield large amounts of data such usage logs, images, videos, and data collected from industrial sensors. Managing data to gain insights and improve these systems is thus a key challenge. It is also desirable to be able to extract information or models from data that are easily understandable by humans. Based on these objectives, this talk will discuss the use of data mining algorithms for discovering interesting and useful patterns in data generated from intelligent systems and other applications.



Professor Yulin Wang

Wuhan University

Yulin Wang is a full professor in the School of Computer Science at Wuhan University in China. His research interests span a range of areas including image and video processing, digital rights management, information security, intelligent systems, e-commerce, the Internet of Things (IoT), and code clone analysis. He obtained his PhD degree from the University of London in the UK. Prior to that, he received his master's and bachelor's degrees from Huazhong University of Science and Technology (HUST) and Xi-Dian University, respectively, both located in China.

Before joining Wuhan University, Professor Wang had a successful career in the high-tech IT industry, including working at HUAWEI© and a national research institute, for over ten years. He has been involved in numerous national and international research projects, totaling more than 15.

In the past 10 years, Professor Wang has authored one book and published over 50 journal and conference papers, several of which have appeared in IEEE TIP. He holds 10 authorized patents. He has served as the Editor-in-Chief of two international journals and as a reviewer for top-tier IEEE and ACM journals. Additionally, he has served as a reviewer for innovative talent projects and national research funds, including the National High Technology Research and Development Program of China.

During 2008-2010, Professor Wang served as an external PhD advisor at Dublin City University in Ireland. In the past 10 years, he has chaired over 10 international conferences and delivered keynote speeches at more than 20 international conferences. He has also visited numerous countries, including the US, France, Italy, Portugal, Croatia, Australia, Germany, Korea, Ireland, Singapore, Malaysia, Japan, and Hong Kong.

Furthermore, since 2014, Professor Wang has been appointed as the deputy director of the Hubei Provincial Science and Technology Commission (CAPD).

Speech Title:Intelligent Multimedia Data Hiding: Techniques and Applications

Abstract: Digital music, podcasts, live and recorded webinars, video calls, and streaming video have changed the way in which we communicate, and have become ubiquitous in virtually every organization. We employ these methods to convey ideas, train our employees, engage our customers, and of course entertain. 


The question is, does digital multimedia pose a threat? Could these channels be used to communicate information covertly, ex-filtrate intellectual property, share insider information, be used to convey command and control information, or provide the needed enabling technology for advanced persistent threats? Additionally, since the size of multimedia files are typically much larger than a single digital photo, does this mean that larger payloads of hidden information could be exchanged or leaked by exploiting weaknesses inherent in multimedia carriers? Or, on the contrary, is the human auditory system sensitive to even small changes in multimedia information such that we could detect anomalies caused by embedding hidden information in such streams?


In this talk, we present the intelligent multimedia data hiding techniques and their possible application. We will cover some of the earliest and simplest forms of data hiding in digital multimedia and then move to some of the lasted innovations in order to provide insight into these questions. Some of the research branches, called reversible data hiding, is also depicted.

Professor Mouquan Shen

Nanjing Tech University

Mouquan Shen is a Professor and the "Six Talent Peaks" of Jiangsu Province. He completed his postdoctoral studies at Southeast University and has served as a visiting scholar at prestigious overseas universities such as the University of Hong Kong, Yeungnam University in South Korea, and the University of Adelaide in Australia.

He has led more than 10 provincial-level projects, including those funded by the National Natural Science Foundation of China, the National Bureau of Foreign Experts Affairs, and the Jiangsu Provincial Natural Science Foundation. Over the years, he has published over 100 papers with an H-index of 24 in respected journals such as IEEE · Transactions on Automatic Control, IEEE Transactions on Cybernetics, and IEEE Transactions on Systems, Man, and Cybernetics: Systems.

Furthermore, Mouquan Shen has held positions as Editor-in-Chief, Associate Editor or Editorial Board Member for 12 international journals. He is also an active reviewer for over 80 domestic and international journals like IEEE · TAC and Automatica. Additionally, he serves as a reviewer for grants from organizations such as the National Natural Science Foundation of China, and various provincial and municipal science and research projects.

Speech Title:Research on the key techniques of data driven control via event-triggered strategy

Abstract: This talk focuses on data-driven event-triggered control with some important techniques and the corresponding solutuions. Based on the modified index, two optimal data-driven control laws are provided by employing adaptive dynamic programming method and Q-learning algorithm, respectively. Two novel event-triggered mechanisms are constructed by utilizing instantaneous and averaged data as well as performance cost, respectively. Sufficient conditions are developed to ensure the ultimately uniform boundedness of the resultant systems. Finally, some examples are presented to verify the effectiveness of the proposed schemes.