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. |
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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. |
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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. |
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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. |
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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. |
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