Keynote Speaker 1:

Prof. Yuhui Shi 史玉回教授

IEEE Fellow
Chair Professor in the Department of Computer Science and Engineering

Southern University of Science and Technology, China

Speech Title: Unified Developmental Brain Storm Optimization Algorithms

Abstract: Swarm intelligence algorithms have been around for decades. They are a collection of population-based optimization algorithms, and have been designed and researched to solve problems which are very difficult, if not impossible, for traditional optimization approaches such as hill-climbing approaches to solve. In general, swarm intelligence algorithms are nature-inspired and/or bio-inspired, especially are inspired by objects with low level intelligence. Through local/global interactions, they can achieve higher level intelligence than any individual has. To have good search capability, swarm intelligence needs to have learning, emulating (imitating), and exploring (LEE) search capability. In this talk, the LEE search capability will be first discussed, followed by the discussion of developmental perspective learning capability, which, together with LEE capability, constitutes a unified developmental framework for swarm intelligence algorithms. The brain storm optimization (BSO) algorithm will be then introduced. BSO is a new population-based swarm intelligence algorithm and was developed with the inspiration from the brainstorming process with which human being together can solve difficult problems which any single person can not solve by himself/herself. Finally, the BSO algorithms will be looked at from the unified developmental learning perspective to confirm that BSO fits the unified developmental learning framework naturally.

Biography: Yuhui Shi received the Ph.D. degree in electronic engineering from Southeast University, Nanjing, China, in 1992. He is a chair professor in the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China. He is a Fellow of the IEEE. His main research interests include the areas of computational intelligence techniques (including swarm intelligence) and their applications. Dr. Shi is the Editor-in-Chief of the International Journal of Swarm Intelligence Research.

Keynote Speaker 2:

Prof. Chengzhong Xu 须成忠教授

IEEE Fellow

Chair Professor of Computer and Information Science

Dean of Faculty of Science and Technology

University of Macau, Macau, China

Speech Title: Connected and Autonomous Driving: Challenges and Opportunities

Abstract: Autonomous driving is breaking the dawn of a new era, mainly due to breakthroughs of AI technologies. This talk will provide a comprehensive review of state-of-the-art technologies in environment perception, scenario understanding, mapping and location, intelligent path planning. It will also introduce a MoCAD project for Macau Connected and Autonomous Driving, which is under development at University of Macau in collaboration with Chinese Academy of Sciences and Baidu Co. It aims to develop key enabling technologies in open environments with assistance of vehicle-infrastructure networking and cloud/edge computing technologies, and to construct a first-class test and evaluation platform for autonomous driving in the greater bay area. It will present recent research results on robustness deep machine learning algorithms in open environments and transfer learning approaches for model adaptivity in corner driving scenarios. Model compression and acceleration techniques for the inference and cloud/edge systems support for autonomous driving will also be discussed.

Biography: Dr. Cheng-Zhong Xu, IEEE Fellow, is the Dean of the Faculty of Science and Technology, University of Macau, Macao SAR, China and a Chair Professor of Computer Science of UM. He is also a Chief Scientist of a key project on “Internet of Things for Smart City” of Ministry of Science and Technology of China and a key project on “Intelligent Driving” of Macau SAR. He was a Chief Scientist of Shenzhen Institutes of Advanced Technology (SIAT) of Chinese Academy of Sciences and the Director of Institute of Advanced Computing and Digital Engineering. Prior to these, he was in the faculty of Wayne State University, USA for 18 years. Dr. Xu's research interest is mainly in the areas of parallel and distributed systems, cloud and edge computing, and data-driven intelligent applications. He has published over 400 peer-reviewed papers on these topics and awarded more than 120 patents. Dr. Xu was the Chair of IEEE Technical Committee of Distributed Processing. He received his B.S. and M.S. degrees in Computer Science from Nanjing University and his Ph.D. from the University of Hong Kong in 1993.

Keynote Speaker 3:

Prof. Hui Xiong 熊辉教授

Fellow of AAAS and IEEE

ACM Distinguished Scientist

Chair Professor

Hong Kong University of Science and Technology (Guangzhou), China

Speech Title: Talent Analytics

Abstract: The big data trend has made its way to human resource management. Indeed, the availability of large-scale human resource (HR) data provide unparalleled opportunities for business leaders to understand talent behaviors and generate useful talent knowledge, which in turn deliver intelligence for real-time decision making and effective people management at work. In this talk, we introduce the powerful set of innovative Artificial Intelligence (AI) techniques developed for intelligent human resource management, such as recruiting, performance evaluation, talent retention, talent development, job matching, team management, leadership development, and organization culture analysis. In addition, we will also demonstrate how the results of talent analytics can be used for other business applications, such as market trend analysis and financial investment.

Biography: Dr. Hui Xiong is currently a Chair Professor at the Hong Kong University of Science and Technology (Guangzhou). Dr. Xiong’s research interests include data mining, mobile computing, and their applications in business. Dr. Xiong received his PhD in Computer Science from University of Minnesota, USA. He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM), a General Co-Chair for the 2015 IEEE International Conference on Data Mining (ICDM), and a Program Co-Chair of the Research Track for the 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. He received the 2021 AAAI Best Paper Award and the 2011 IEEE ICDM Best Research Paper award. For his outstanding contributions to data mining and mobile computing, he was elected an AAAS Fellow and an IEEE Fellow in 2020.

Keynote Speaker 4:

Prof. Shuanghua Yang 杨双华教授

IET Fellow, SMIEEE, the Vice Dean for Academic Affairs of Graduate School,

Chair Professor in the Department of Computer Science and Engineering

Southern University of Science and Technology, China

Speech Title: A Data-driven Unsupervised Method for Detecting Anomalous Gas Consumption

Abstract: Using Shenzhen as a case study, this talk introduces how machine learning method, particularly an unsupervised method, can be developed and deployed for anomaly detection of gas usage behavior. The infrastructures of gas supplier have been highly improved in the last decade, smart meters have been widely installed and enormous gas consumption data have been collecting day after day, not only used for billing but also serve as a basis for anomaly detection. The objective of this research is to mine the big data collected and identify for the gas supplier possible problems such as malfunctioning gas meters, gas leakage, and gas theft etc. One challenge of using such data for anomaly detection is that it is difficult to obtain enough labeled data for model training. To overcome this challenge and provide a generic solution for detecting anomalous gas consumption, this talk first proposes a data-driven unsupervised method which combines a rule-based model with a deep learning-based model. With a real-world gas consumption dataset, the talk shows that the proposed method outperforms the existing anomaly detection methods in the literature.

Biography: Shuanghua Yang, graduated from the Department of Chemical Engineering, Zhejiang University in 1991, used to be the Head of Department of Computer Science and a chair professor at Loughborough University in the United Kingdom and is currently a professor at Southern University of Science and Technology, China. He is mainly engaged in the research of cyber-physical system information security, functional safety and emergency response, and has achieved a series of pioneering results in remote monitoring, accident monitoring, massive information processing, and control system safety based on the intelligent Internet of Things. In 2010, he won the InstMC Honeywell Award. In 2014, he was elected as an IET Fellow. In the same year, he was awarded the Doctor of Science (DSc) symbolizing the "Lifetime Achievement Award".