Speakers of CTMCD 2023
Prof. Wenbing Zhao, IEEE Senior Member
Department of Electrical Engineering and Computer Science, Cleveland State University, USA
Biography: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. He is currently serving as the director of the Master of Science in Electrical Engineering, and the Chair of the Graduate Program Committee in the Department of EECS, and a member of the faculty senate at CSU.
Dr. Zhao has authored a research monograph titled: “Building Dependable Distributed Systems” published by Scrivener Publishing, an imprint of John Wiley and Sons. Furthermore, Dr. Zhao published over 120 peer-reviewed papers in the area of fault tolerant and dependable systems (three of them won the best paper award), computer vision and motion analysis, physics, and education. Dr. Zhao’s research is supported in part by the US National Science Foundation, the US Department of Transportation, Ohio State Bureau of Workers’ Compensation, and by Cleveland State University. Dr. Zhao is currently serving on the organizing committee and the technical program committee for numerous international conferences, and is a member of editorial board for PeerJ Computer Science, International Journal of Parallel Emergent and Distributed Systems, International Journal of Distributed Systems and Technologies, International Journal of Performability Engineering, International Journal of Web Science, and several international journals of the International Academy, Research, and Industry Association. Dr. Zhao is a senior member of IEEE. Dr. Zhao is also a senior member of International Economics Development and Research Center (IEDRC).
Title：Video-Based Baseball Pitch Type Recognition
Unlike many other sectors in the society, the professional sports industry is driving technical innovations. Team owners, managers, and players recognize the huge value of technology for improving athlete performance, game officiating, as well as helping fans to better enjoy the games. A good example is the US major league baseball (MLB). In this talk, I report a preliminary study on video-based pitch type recognition using deep learning. To facilitate the study, we first developed a semi-automated way of building datasets based on publicly available video and pitch information for MLB games. For pitch type recognition, we used the two-stream inflated 3D convolutional neural network (I3D). To improve the state-of-the-art of research, we trained and tuned the I3D model extensively, primarily combating the problem of overfitting while still trying to improve final validation accuracy. We are able to achieve an accuracy of 53.43% +/- 3.04% when oversampling and 57.10% +/- 2.99% when not oversampling, which is a significant improvement over the published best result of an accuracy of 36.4% on the same six pitch type classes.
IEEE Life Fellow
Prof. Witold Pedrycz
Department of Electrical & Computer Engineering University of Alberta, Edmonton, Canada
Dr. Witold Pedrycz (IEEE Life Fellow) is Professor and Canada Research Chair (CRC) in Computational Intelligence in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.,His main research directions involve Computational Intelligence, fuzzy modeling and Granular Computing, knowledge discovery, pattern recognition, data science, knowledge-based neural networks among others.,Dr. Pedrycz is involved in editorial activities. He is an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of Int. J. of Granular Computing (Springer) and J. of Data Information and Management (Springer).
Data Privacy, Energy Awareness and Credibility: Challenges in Machine Learning
Over the recent years, we have been witnessing spectacular achievements of Machine Learning with highly visible accomplishments encountered, in particular, in natural language processing and computer vision impacting numerous areas of human endeavours. Driven inherently by the technologically advanced learning and architectural developments, Machine Learning constructs are highly impactful comingwith far reaching consequences; just to mention autonomous vehicles, control, health care imaging, decision-making in critical areas, among others.
We advocate that the design and analysis of ML constructs have to be carried out in a holistic manner by identifying and addressing a series of central and unavoidable quests coming from industrial environments and implied by a plethora of requirements of interpretability, energy awareness (being also lucidly identified on the agenda of green AI), efficient quantification of quality of ML constructs, their brittleness and conceptual stability coming hand in hand with the varying levels of abstraction. They are highly intertwined and exhibit relationships with the technological end of ML. As such, they deserve prudent attention, in particular when a multicriterial facet of the problem is considered.
The talk elaborates on the above challenges, offers definitions and identifies the linkages among them. In the pursuit of coping with such quests, we advocate that Granular Computing can play a pivotal role offering a conceptual environment and realizing algorithmic development. As a detailed study, we discuss the ideas of knowledge transfer showing how a thoughtful and prudently arranged knowledge reuse to support energy-aware ML computing. We discuss passive and active modes of knowledge transfer. In both modes, the essential role of information granularity is identified. In the passive approach, information granularity serves as a vehicle to quantify the credibility of the transferred knowledge. In the active approach, a new model is constructed in the target domain whereas the design is guided by the loss function, which involves granular regularization produced by the granular model transferred from the source domain. A generalized scenario of multi-source domains is discussed. Knowledge distillation leading to model compression is also studied in the context of transfer learning.
Prof. Chun-Yi Su
Concordia University, Canada
Dr. Chun-Yi Su received his Ph.D. degrees in control engineering from South China University of Technology in 1990. After a seven-year stint at the University of Victoria, he joined the Concordia University in 1998, where he is currently a Professor of Mechanical and Industrial Engineering and Honorary Concordia University Research Chair. His research covers control theory and its applications to various mechanical systems, with a focus on control of systems involving hysteresis nonlinearities. He is the author or co-author of over 500 publications, which have appeared in journals, as book chapters and in conference proceedings. He has been identified as Highly Cited Researchers from Clarivate since 2019.
Dr. Su has served as Associate Editor for several journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Cybernetics, and several other journals. He is a Distinguished Lecturer of IEEE RA Society. He served for many conferences as an Organizing Committee Member, including the General Chairs and Program Chairs.
Prof. Jian Yao
Wuhan University, China
Jian Yao, born in December 1975, is from Lianyuan City, Hunan Province, China. Yao is a professor, Doctoral Supervisor, Distinguished Professor of "Chutian Scholar" Program of Hubei Province, Discipline Development Leader of the School of Remote Sensing Information Engineering of Wuhan University, a candidate of the National Major Talent Project A-type Youth Project, a member of the Strategic Talent Training Program of Changsha, a high-end talent of the 3551 Entrepreneurship and Innovation Program of Wuhan, a student of the Class 2019 of Baidu Alpha College, a leader of the Artificial Intelligence College of Guangdong Open University, director of the Artificial Intelligence Application Innovation Center of Guangdong Open University, dean of Research Institute of Desauto Technology (Shenzhen) Co., Ltd., Professor of Xiamen University of Aeronautics and Astronautics, distinguished researcher of Songhua River Thousand People Industry Research Institute, director of Wuhan University Computer Vision and Remote Sensing Lab (WHU-CVRS Lab), director of 3D Big Data Artificial Intelligence Innovation Research Center of Wuhan University, incumbent Member of the Chinese Society of Artificial Intelligence, member of the Computer Vision Professional Committee of the Chinese Computer Society (CCF), member of the Imaging Detection and Perception Committee of the Chinese Society of Image and Graphics, member of the Machine Vision Specialty Committee of the Chinese Society of Image and Graphics, member of 3D Vision Specialist Committee of the Chinese Society of Image and Graphics, member of the Big Data and Artificial Intelligence Working Committee of the Chinese Society of Surveying and Mapping, and director of the New Overseas Chinese Professionals Association of Hubei Province and Wuhan City. In April 2012, he was introduced to the School of Remote Sensing and Information Engineering of Wuhan University as a discipline development leader and has been a faculty member since then. He was invited as a specially-appointed professor of “Hubei Scholar” Program of Hubei in 2013. He has participated in many large-scale projects such as the EU's sixth and seventh framework plans, as well as cooperation projects with the International Atomic Energy Agency. In recent years, he has published over 150 papers on international journals including Pattern Recognition, Computer Vision & Image Understanding, International Journal of Robotics Research, IEEE Transactions on Image Processing, ISPRS Journal of Photogrammetry and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing (TGRS) and CVPR, applied for over 70 IPs and patents, with 30 authorized by the Chinese government. He has long been a reviewer of top journals and conference proceedings. After joining Wuhan University, Prof. Yao has established the WHU-CVRS Lab, which now consists of four advisors, one postdoctoral fellows, 30 PhDs and master students. Prof. Yao has chaired a series of research projects at national and provincial levels, including national key research programs, programs of the 973 Project, and the National Natural Science Fund. Meanwhile, in collaboration with well-known enterprises like Tencent, Huawei and Alibaba, he has initiated a range of joint research programs and talent training projects. Currently, the main research directions include: computer vision, artificial intelligence, robot technology, high-precision map, SLAM, navigation and positioning, unmanned driving, 3D technology, VR / AR, etc.
Prof. NAGARAJA G.S.
Dept of CSE, RV College of Engineering, Bengaluru-560059, India
Dr. Nagaraja G.S obtained his Graduate degree in Computer Science and Engineering, Post Graduate degree in Master of Engineering and Doctoral degree in Computer Science and Engineering. Dr Nagaraja G.S, is presently working as Professor and Associate Dean in the department of Computer Science and Engineering, R.V. College of Engineering, Bengaluru-59. He has 27+ years of Teaching and 18+Years of Research experience in computer network and its related domains as-well. His research interests include Computer Organization, Computer Architecture and its applications, Computer Networks, Networks Management, Storage Optimization, Wireless Networks, Cloud computing, Parallel processing, High Performance Computing, Routing and Switching, Protocol Design and Multimedia Communications. Currently Dr Nagaraja is teaching PG/UG students, guided 08 PhD students and supervising 05 Research scholars under VTU. Completed a major research project sanctioned by the University Grant Commission Titled "Effective Multimedia Information retrieval using Indexing Technique" during the tenure of 2012-2015. Completed a Research project “Solar Ironing Cart “sanctioned by National Institute of advanced studies IIsc, Bangalore collaboratively with EEE Department for the academic year-2020. Completed a Collaborative development project on Silkworm Seed production sanctioned by Central Silk Board-2021. He has published 07 papers in book chapter, 67 papers in an International Journals, presented 37 papers in an International conferences and 22 papers in National conferences. He has delivered many technical talks in different engineering colleges with the theme of CCNA modules, Research Methodology, Computer Communications, Mobile App Design and Development, Routing and Switching, Cloud Computing, Cyber Security, 5G Security issues and Technologies, IoT Protocols, Research challenges in Cloud Security and trust management, Cyber security and Writing Quality papers.
Prof. Ning Sun
Nankai University, China
Ning Sun received the B.S. degree in measurement & control technology and instruments (with honors) from Wuhan University, Wuhan, China, in 2009, and the Ph.D. degree in control theory and control engineering (with honors) from Nankai University, Tianjin, China, in 2014. He is currently a Full Professor with the Institute of Robotics and Automatic Information Systems, Nankai University, Tianjin, China. His research interests include intelligent control for mechatronic/robotic systems with an emphasis on (industrial) applications. He is an IEEE Senior Member.
Dr. Sun received the Machines 2021 Young Investigator Award, the prestigious Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowship for Research in Japan (Standard) in 2018, the Wu Wenjun Artificial Intelligence Excellent Youth Award in 2019, the China 10 Scientific and Technological Developments in Intelligent Manufacturing (2nd achiever) in 2019, several journal/conference best/outstanding paper awards, etc. He serves as an Associate Editor for several journals, including IEEE Transactions on Industrial Electronics, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and IEEE Systems Journal. In addition, he has been an Associate Editor of the IEEE Control Systems Society (CSS) Conference Editorial Board since July 2019 and is/was an Associate Editor for IEEE ICRA and IEEE/RSJ IROS.
Prof. Xinwei Yao
Zhejiang University of Technology, China