Prof. Jiebo Luo
(SPIE Fellow, IEEE Fellow, IAPR Fellow, AAAI Fellow, ACM Fellow, IAPR Fellow, High Index 99)
University of Rochester, USA
Special Title: COVID-19: What Social Media and Machine Learning Can Inform Us
Abstract: The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic losses worldwide. However, its influence on public opinions and people's mental health conditions has not received as much attention. In addition, the related literature in these fields has primarily relied on interviews or surveys, largely limited to small-scale observations. In contrast, the rise of social media provides an opportunity to study many aspects of a pandemic at scale and in real-time. Meanwhile, the recent advances in machine learning and data mining allow us to perform automated data processing and analysis. We will introduce several recent studies ranging from 1) characterizing Twitter users and topics regarding the use of controversial terms for COVID-19, 2) understanding how college students respond differently than the general public to the pandemic, 3) monitoring depression trends throughout COVID-19, to 4) studying consumer hoarding behaviors during the pandemic.
Jiebo Luo is a Professor of Computer Science at the University of Rochester which he joined in 2011 after a prolific career of fifteen years at Kodak Research Laboratories. He has authored over 450 technical papers and holds over 90 U.S. patents. His research interests include computer vision, NLP, machine learning, data mining, computational social science, and digital health. He has been involved in numerous technical conferences, including serving as a program co-chair of ACM Multimedia 2010, IEEE CVPR 2012, ACM ICMR 2016, and IEEE ICIP 2017, as well as a general co-chair of ACM Multimedia 2018. He has served on the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), IEEE Transactions on Multimedia (TMM), IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), IEEE Transactions on Big Data (TBD), ACM Transactions on Intelligent Systems and Technology (TIST), Pattern Recognition, Knowledge and Information Systems (KAIS), Machine Vision and Applications, and Journal of Electronic Imaging. He is the current Editor-in-Chief of the IEEE Transactions on Multimedia. Professor Luo is also a Fellow of ACM, AAAI, SPIE, and IAPR. (Getting more)
Prof. Fuchun Sun
(IEEE Fellow, CAAI Fellow)
Tsinghua University, China
Special Title: Robot Skill Learning: Imitation, Transfer and Enhancement
Abstract: Humans can realize their intelligent behavior in a complex environment and are able to fulfill multiple tasks in different fields by cognitive learning. Since task is usually composed of a spatiotemporal combination of different skills, hopefully skill learning constitutes the bridge between human behavior and different tasks. However, this ability is exactly what the current robots lack and it has become a bottleneck of the improvement of robotic intelligence. In this talk, we discuss robot skill learning in three respects: skill imitation, transfer learning and skill enhancement. Firstly, imitation learning methods for observation and long-term tasks are developed, where the disagreement between perfect demonstration and partial observation one is revealed, and the simplified one-step model is proposed to improve the performance of hierarchical imitation learning. Furthermore, the generation and interaction of perceptual information such as vision, tactile and acoustic is still a challenging problem in digital twins, the elastic interaction of particles approach is proposed to robotic tactile simulation, and the sim-to-real transfer learning is discussed, enhancing the skills of real-world robots. Next, enhanced learning approaches from expert preference and inaccurate demonstration are developed for improving robotic manipulation performance. Finally, the applications of skill imitation, transfer learning and skill enhancement technology in UAVs and robot dexterous manipulations are introduced, and the development trend of robot manipulation skill learning is discussed.
Fuchun Sun is professor of Department of Computer Science and Technology and President of Academic Committee of the Department, Tsinghua University, deputy director of State Key Lab. of Intelligent Technology & Systems, Beijing, China. His research interests include robotic perception and intelligent control. He has won the Champion of Autonomous Grasp Challenges in IROS2016. He is Fellow of IEEE. Dr. Sun is the recipient of the excellent Doctoral Dissertation Prize of China in 2000 by MOE of China and the Choon-Gang Academic Award by Korea in 2003, and was recognized as a Distinguished Young Scholar in 2006 by the Natural Science Foundation of China. He served as Editor-in-Chief of International Journal on Cognitive Computation and Systems, and Associated Editors of IEEE Trans. on Neural Networks during 2006-2010, IEEE Trans. on Fuzzy Systems since 2011, IEEE Trans. on Systems, Man and Cybernetics: Systems since 2015 and IEEE Trans. on Cognitive and Development Systems since 2019.
Prof. Matthias Rätsch
(Head of the ‘Vision Systems for intelligent Robots’ (ViSiR))
Reutlingen University, Germany
Special Title: “Humanoid Robots and Artificial Super Intelligence - The Terminating End or the Last Hope for Humans?”
Abstract: Recent research in humanoid robotics and artificial intelligence, termed as fourth industrial or robot revolution, show that artificial intelligence and robots will play a major role in our future lives. When will the Technological Singularity take place and what happens when Transhumanism starts? The humanoid robots are gaining super intelligence based on machine learning and interaction with humans. Another field to use artificial intelligence is autonomous driving. Driving cars is the biggest group of workers today with 70 Mill employees. All big automotive companies work on autonomous driving cars. In this talk we will define what artificial super intelligence means, what is possible current and in future in the field of autonomous driving, robotics and human machine interaction. Why is reinforcement and transfer learning a new generation of deep learning and why mid-level fusion of RGB and depth-information is improving scene labeling for autonomous driving? The use of AI for Human-Robot-Interaction is illustrated on robots of the RT-Lions team taking part on World Championships in RoboCup. Practical examples are shown from collaborations with strong industrial partners, like BMW, Mercedes Benz Daimler, BOSCH or Kuka.
Matthias Rätsch is a professor at the Reutlingen University for Image Understanding, Artificial Intelligence and Interactive Mobile Robotics. In 2008, he received his Ph.D. degree in the Graphics and Vision Research Group (GraVis) at the University of Basel, Switzerland in 3DMM Face Analysis. His research interests are in the fields of Artificial Intelligence, Deep Learning, Image Understanding, Autonomous Driving, Human-Robot-Interaction, Humanoid Robots and Bionic Grasping. He is the head of the RoboCup team RT-Lions. The team could win several international competitions (World Champion in Graz 2009, Iran Master 2011, German Master 2009, Vice World Champion in Singapore 2010). After changing to the RoboCup@Home League the team gained the 4th place at the German Open, won the Portuguese Robotics Open and SICK Robot Day. The team was qualified with 35 teams at the World Championship in Nagoya, Japan, 2017, obtaining the 8th place and 2019 in Sydney, Australia, obtaining the 5h place. The team is qualified for 2021 at World Championship in Bordeaux, France. Prof. Rätsch has been a member of the program committee and a session chair for several international conferences and was invited for several speeches including keynote, seminal and training in Artificial Intelligence, Face Analysis and Robot Vision for academic and industrial sectors. Prof. Rätsch and his group has published more than 50 international academic research papers and journals, like at the top-rank IEEE Transactions on Image Processing journal or at the SIGGRAPH conference. His publications were recently honored with an award at the IEEE International Conference on Image Processing (ICIP), at the International Conference on Systems, Control and Communications (ICSCC), the Informatics Inside Conference for Human-Centered Computing, and at the IEEE Intelligent Data Acquisition and Advanced Computing Systems Journal. His working group ViSiR could win the Otto-Johansen-Price. Prof. Rätsch leaded the with 1.1 Mill EUR founded interdisciplinary project “KollRo 4.0” (BMBF, BOSCH) and current two ZIM-projects with 0,4 Mill EUR in the field of Human-Robot-Collaboration and was a member of other funded industrial projects like RTMO (BMBF), GES 3D (BMBF), Face-HMI (SAB, COG), and I-Search (BMBF).
Prof. Tok Wang LING
(ER Fellow, IEEE Senior Life Member)
National University of Singapore, Singapore
Special Title: Conceptual Modeling Views of Relational Databases vs Big Data and Machine Learning
Abstract: We first present a brief introduction to big data and describe the basic data models of the 4 major categories of NoSQL data stores for big data applications. We discuss some limitations and performance issues of RDBMS for big data applications. We revisit some basic concepts in relational data model which have big impact on the performance, such as normal forms, join of relations, ACID for handling concurrent transactions, etc. Next, we compare the relational data model and big data model using a set of application requirements and characteristics to help users to decide when to use SQL or NoSQL for big data applications. We describe some existing database techniques which can be used to improve the performances of some categories of database applications in RDBMS, such as materialized view, unnormalized relation, horizontal and vertical partitioning of data in physical database schema design, etc. We present some seldom mentioned but very important concepts related to data and schema integration, such as entity resolution vs relationship resolution, primary key vs object identifier (OID), and local OID vs global OID, etc. These concepts are related to Object-Relationship-Attribute Semantics (ORA-semantics) and they have significant impact on the quality and correctness of the integrated databases. In the second half of the talk, we briefly mention some traditional machine learning topics and some current deep learning systems. We notice that the types of data sets used by the machine learning systems and the systems have some limitations: such as only use one single data type of data from a single data source, can only handle single specific task applications, completeness and correctness issues of the training data and test data, use only data but not existing known knowledge, do not provide explanations on the knowledge learnt with different levels of details for different technical levels of users, cannot transfer the new knowledge learnt to other systems/applications or for future use, etc. Existing systems such as IBM Deep Blue, Google AlphaGo, Google Maps, image and speech recognitions, Goggle Search, chatbot, etc., are for some specific task applications; they are termed narrow AI (or weak AI). We list some possible research topics in machine learning for general AI (or called strong AI), i.e. with some human learning and thinking capability.
Tok Wang LING is a professor of the Department of Computer Science, School of Computing at the National University of Singapore. He was the Head of IT Division, Deputy Head of the Department of Information Systems and Computer Science, and Vice Dean of the School of Computing of the University. Before joining the University as a lecturer in 1979, he was a scientific staff at Bell Northern Research, Ottawa, Canada. He received his Ph.D. and M.Math., both in Computer Science, from University of Waterloo (Canada) and B.Sc.(1st class Hons) in Mathematics from Nanyang University (Singapore). His research interests include Data Modeling, Entity-Relationship Approach, Object-Oriented Data Model, Normalization Theory, Logic and Database, Integrity Constraint Checking, Semi-Structured Data Model, XML Twig Pattern Query Processing, ORA-semantics based XML and Relational Database Keyword Query Processing. He has published more than 230 international journal/conference papers and chapters in books, all in database research areas. He also co-edited 13 conference and workshop proceedings, co-authored one book, and edited one book. He is an ER Fellow, an ACM Distinguished Scientist, IEEE Senior Life Member, and Senior Member of Singapore Computer Society. He received the ACM Recognition of Service Award in 2007, the DASFAA Outstanding Contributions Award in 2010, and the Peter P. Chen Award in 2011.
Prof. Xiaochun Cheng
Middlesex University, UK
Title: Artificial Intelligence
Computing Solutions and Applications
Abstract: Artificial Intelligence (AI) has been applied to more and more applications. Xiaochun Cheng researched both symbolic and numeric AI computing solutions and applied different AI computing solutions into several projects, including security, e-learning, system engineering, management, communication network, et al. This speech will review relevant AI computing solutions and AI applications, rational the potential and limitations of relevant AI computing solutions, hence support future more and better AI applications by integrating diverse AI computing solutions.
Xiaochun Cheng received the BEng Degree in Computer Engineering in 1992, PhD in Computer Science in 1996. He visited Queen’s University of Belfast between 1997 and 1998. He was a Postdoc Research Associate at Sheffield University between 1998 and 2000. He was a Lecturer in Reading University between 2000 and 2005. He has been a Senior Lecturer since 2006 and since 2012 the Computer Science Project Coordinator in Middlesex University. One project was funded with 16 Million Euro budget. He is a member of the IEEE SMC Technical Committee on Computational Intelligence, IEEE SMC Technical Committee on Intelligent Internet Systems, IEEE Communications Society Communications and Information Security Technical Committee, IEEE Technical Committee on Cloud Computing, BCS AI Specialist Group, BCS Information Security Specialist Group. He has been Outstanding Ph.D. Thesis Award Chair of IEEE Technical Committee on Cloud Computing. He contributed for five times best conference paper awards so far. 3 his papers are in the 2020 top 1% of the academic field by Data from Essential Science Indicators. He won 3 times national competitions. He won National Science and Technology Advance Award.
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