Prof. Hesheng Wang
Shanghai Jiao Tong University, China

Hesheng Wang received the Ph.D. degree in Automation & Computer-Aided Engineering from the Chinese University of Hong Kong. Currently, he is a Professor of Department of Automation, Shanghai Jiao Tong University, China. He has published more than 200 papers in refereed journals and conferences. He is an associate editor of IEEE Transactions on Automation Science Engineering, IEEE Robotics and Automation Letters, Assembly Automation and International Journal of Humanoid Robotics, a Technical Editor of IEEE/ASME Transactions on Mechatronics. He served as an associate editor for IEEE Transactions on Robotics from 2015 to 2019. He was the general chair of IEEE RCAR2016 and IEEE ROBIO2022, and program chair of IEEE AIM2019 and IEEE ROBIO2014. He was a recipient of Shanghai Rising Star Award in 2014, The National Science Fund for Outstanding Young Scholars in 2017 and Shanghai Shuguang Scholar in 2019. He is a Senior Member of IEEE. He will be the General Chair of IEEE/RSJ IROS2025.

Speech Title: Visual Servoing of Robots
Abstract: Visual servoing is an important technique that uses visual information for the feedback control of robots. By directly incorporating visual feedback in the dynamic control loop, it is possible to enhance the system stability and the control performance. Many challenges appear when robots come to our daily life. Compare to industrial applications, the robot need deal with many unexpected situations in unstructured environments. The system should estimate the depth information, the target information and many other information online. In this talk, various visual servoing approaches will be presented to work in unstructured environments. These methods are also implemented in many robot systems such as manipulator, mobile robot, soft robot, quadrotor and so on.

Prof. Yu Xinguo
Central China Normal University, China

Professor Xinguo Yu is the dean of CCNU Wollongong Joint Institute and a deputy director of National Engineering Research Center for E-Learning at Central China Normal University, Wuhan, China, an adjunct professor of University of Wollongong, Australia, chair of Hubei Society of Artificial Intelligence in Research and Education, and a member of steering board of Smart Educational Technology Branch Society under Automation Society, China. He received B.Sc. degree in Mathematics from Wuhan University of Technology, M. Eng degree from Huazhong University of Science and Technology, another M. Eng. degree from Nanyang Technological University, Singapore and Ph.D. degree in Computer Science from National University of Singapore. His current research mainly focuses on intelligent educational technology, artificial intelligence in research, educational robotics, multimedia analysis, computer vision, machine learning, and virtual reality. He has published over 160 research papers, where more 70 are first author papers and 20 are SCI papers.

Speech Title: Problem Solving for Tutorial Service for Basic Education
Abstract: Advance personalized learning is one of 14 engineering grand challenges in 21 century. Tutorial service is one of main functions of personalized learning and problem solving is its core technology. Problem solving is long standing challenge problem since 1960s. Many well-known research teams and big companies work on the problem in the recent years. They develop solving algorithms taking various approaches. Our team takes a relation-centric approach different from these approaches and it shows good properties. Then we design tutorial service model built on the relation-centric algorithms.

Prof. Anand Nayyar
Duy Tan University, Vietnam

Dr. Anand Nayyar received Ph.D (Computer Science) from Desh Bhagat University in 2017 in the area of Wireless Sensor Networks and Swarm Intelligence. He is currently working in School of Computer Science-Duy Tan University, Da Nang, Vietnam as Professor, Scientist, Vice-Chairman (Research) and Director- IoT and Intelligent Systems Lab. A Certified Professional with 75+ Professional certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam and many more. Published more than 125+ Research Papers in various High-Quality ISI-SCI/SCIE/SSCI Impact Factor Journals cum Scopus Journals, 50+ Papers in International Conferences indexed with Springer, IEEE Xplore and ACM Digital Library, 40+ Book Chapters in various SCOPUS, WEB OF SCIENCE Indexed Books with Springer, CRC Press, Elsevier and many more with Citations: 4200+, H-Index: 36 and I-Index: 120. He is currently researching in the area of Wireless Sensor Networks, IoT, Swarm Intelligence, Cloud Computing, Artificial Intelligence, Drones, Blockchain, Cyber Security, Network Simulation and Wireless Communications.

Speech Title: Autonomous Vehicles: Future Smart Transportation System
Abstract: An autonomous vehicle, or a driverless vehicle, is one that is able to operate itself and perform necessary functions without any human intervention, through ability to sense its surroundings. An autonomous vehicle utilises a fully automated driving system in order to allow the vehicle to respond to external conditions that a human driver would manage.The development and mass production of self-driving cars, also known as autonomous vehicles, has the potential to revolutionize transportation mobility and safety. In this lecture, a comprehensive overview of Autonomous Vehicles regarding the Techniques, Technologies and above all real time examples, issues and case studies will be discussed.

Assoc. Prof. Qiang Li
North Minzu University, China

Qiang Li received his Ph.D. degree in Heudiasyc (HEUristique et DIAgnostic des SYstèmes Complexes) laboratory that is managed jointly by Université de Technologie de Compiègne and CNRS (INS2I section), France, in 2013. Currently he is an associated professor at School of Computer Science and Engineering, North Minzu University (NMU). He is also a member of IEEE and CCF. His main research interests include collaboration engineering, multiple sensors data fusion, automatic speech recognition, multimodal user interface and collaborative working environment design. With more than thirty software copyrights and seven patents, he has published three textbooks and several research articles in reputed international journals and conferences.

Speech Title: Collaboration and Traces of Interactions: Concepts, Frameworks, and Applications
Abstract: Currently, the increasingly powerful browsers and smart devices provide considerable convenience for people’s daily life. Enormous empirical results show that remote collaborative work is one of the cost-effective ways to teamwork. In this context, team members utilize a variety of tools on the browser to quickly share information and naturally coordinate tasks. As a result, any activity may produce impressions and experiences as a set of traces in a computer-supported cooperative working environment. As collaborative activities among members become more frequent, such traces may be very voluminous and heterogeneous in a collaborative working context. Through this way, both the interactive actions among actors and the interactive actions between actors and the system could be reflected comprehensively and effectively. This talk will introduce the concepts of collaborative trace and collective trace, the related exploiting and visualization frameworks with several practical applications that are validated in the web-based collaborative working environment MCWE2.0.

Chief Researcher Yulai Xie
Hitachi China Research Laboratory, China

Yulai Xie received the Bachelor and Master degrees in engineering from the Tianjin University, Tianjin, China, in 2008 and 2010, respectively. Then he received the Ph.D. degree in engineering from the Hokkaido University, Sapporo, Japan, in 2014. He is currently a senior researcher with Hitachi China Research Laboratory. His research interests include machine learning, computer vision, crowd simulation and traffic analysis.

Speech Title: Multisize Patched Spatial-Temporal Transformer Network for Short-and Long-Term Crowd Flow Prediction
Abstract: The prediction of urban crowds is crucial not only to traffic management but also to studies on the city-level social phenomena, such as energy consumption, urban growth, city planning, and epidemic prevention. The challenges of accurately predicting crowd flow come from the non-linear spatial-temporal dependence of crowd flow data, periodic laws, such as daily and weekly periodicity, and external factors, such as weather and holidays. It is even more challenging for most existing short-term prediction models to make an accurate long-term prediction. In this paper, we propose a novel patched Transformer-based sequence-to-sequence model, called MultiSize Patched Spatial-Temporal Transformer Network (MSP-STTN), to incorporate rich and unified context modeling via a self-attention mechanism and global memory learning via a cross-attention mechanism for short-and long-term grid-based crowd flow prediction. In particular, a multisize patched spatial-temporal self-attention Transformer is designed to capture cross-space-time and cross-size contextual dependence of crowd data. The same structured cross-attention Transformer is developed to adaptively learn a global memory for long-term prediction in a responding-to-a-query style without error accumulation. In addition, a categorized space-time expectation is proposed as a unified regional encoding with temporal and external factors and is used as a base prediction for stable training. Furthermore, auxiliary tasks are introduced for promoting feature encoding and leveraging feature consistency to assist in the main prediction task. The experimental results reveal that MSP-STTN is competitive with the state of the art for one-step and multi-step short-term prediction within several hours and achieves practical long-term crowd flow prediction within one day on real-world grid-based crowd data sets TaxiBJ, BikeNYC, and CrowdDensityBJ.