Invited Session

Session 1: Curriculum Reform for Introduction to Computer Science combine Artificial Intelligence

Session Chair:

Prof LUO Juan,Hunan University(


Prof. ZHAO Huan, Hunan University (
Prof. CAI YuHui,Hunan University (


Information technology and artificial intelligence technology have been more and more integrated with various disciplines including literature, social science and engineering. How to provide corresponding course content and tools for the Introduction to Computer Science and Introduction to Artificial Intelligence to adapt to this change is an urgent problem in the field of computer education question. The purpose of this session is to provide a forum to exchange experience and achievements in curriculum construction and reform.

Topics include but are not limited to:

● Exploration and practice of the teaching system construction of Introductory Computer Science oriented to the integration of disciplines;
● Exploration and practice of the teaching system construction of the Introduction to Artificial Intelligence oriented towards the integration of disciplines;
● Reform of teaching methods, tools and models;
● Evaluation of student learning outcomes.

Short Bio of Chairs

Juan Luo

Juan Luo, Ph.D., professor, doctor supervisor, associate dean, College of Computer science and Electronic Engineering. She graduated from National University of Defense Technology with a bachelor’s degree and achieve master degree and Ph.D. from Wuhan University. She used to work in the FiberHome Networks company of Wuhan Academy of Posts and Technology, and is a visiting scholar at the University of California, Irvine. She was selected as a New Century Outstanding Talent by the Ministry of Education, and won the Hunan Province Outstanding Youth Fund, the Hunan Province's Young Backbone Teachers.

Her current research interests include IoT, cloud computing and artificial intelligence.


Huan Zhao

Huan Zhao, Ph.D., Professor, doctor supervisor, associate dean, College of Computer science and Electronic Engineering. She is visiting scholar at the University of California, San Diego, the member of the Computer Basic Teaching Steering Committee of the Ministry of Education, and the member of the Steering Committee of the Education and Training of Industrial and Information Talents. She won the second prize of National Teaching Achievement Award, the Outstanding prize of BAOGANG distinction teacher and Huo Yingdong Education Foundation Education and Teaching Award. Her research interests include embedded computer systems and speech information processing.



YuHui CAI , Associate professor, College of Computer science and Electronic Engineering, Hunan University. His research direction is computer network, image processing, and artificial intelligence. He won a second prize of China University Science and Technology Award. In 2023, he will be hired as an expert in the course construction group of the Ministry of Education's undergraduate education and teaching reform pilot work plan in the field of computer science. He has completed the planning textbook "Computer Science Introduction" construction, translation and publication of a "Foreign Classic Textbook Series" textbook.

Session 2: Mathematical Model for Biosignals and Biomedical Imaging

Session Chair:

Hiroki TAKADA, University of Fukui, Japan.


In today’s world, academically examining the safety of viewing them is necessary where digital images and videos are flooding our homes. In this section, the new development of the biosignals and the biomedical Imaging are introduced and utilized in this field. Mathematical models including the artificial intelligence have been regarded as fundamental technique for the bio-signal. In connection with 5G/beyond 5G technology and networks, the biosignals and their utilization have been attracting attention. The application of the AI, which has made remarkable progress in recent years, to this field will also be discussed.

This invited session will collect papers of the following subjects, but not limited to:

● Machine Learning/AI
● Biomedical Imaging
● Computer–Human Interact
● Control and Communication
● Deep Learning
● Mechatronics and Robotics
● Visualization of Big Data
● Techniques, Models, and Algorithms

Short Bio of Chairs


Prof. Hiroki TAKADA, is a tenured Professor in the Department of Human and Artificial Intelligent Systems, the Graduate School of Engineering, University of Fukui, Japan. He is also the Co-Director of the Nonlinear Science Lab. His research is centered on the nonlinear analysis of time sequences. In his research, mathematical models have been obtained from the data sequences in Economics, Meteorology, and Erectrophysiology based on the stochastic process theory. He also received the Organization Contribution Award from the International Conference of Computer Science and Education (ICCSE) in 2020. Prof. Takada also serves as an editor in Environmental Health and Preventive Medicine and an editor-in-chief of Forma. He is a member of IEEE, Physical Society of Japan, and other organizations.

Session3: Machine learning and its applications in social science

Session Chair:

Yang Weng, Sichuan University,


Social scientists find themselves in an era of abundant data, increasingly turning to machine learning tools to extract valuable insights from datasets of varying sizes. This session aims to elucidate how the integration of machine learning into the realm of social sciences necessitates a reevaluation of not only the application of machine learning methods but also the adoption of best practices within the field. Diverging from the conventional applications of machine learning in computer science and statistics, its utilization in social scientific endeavors involves the exploration of new concepts, quantification of their prevalence, evaluation of causal relationships, and the formulation of predictive models. The wealth of data and available resources facilitates a departure from the deductive approach traditionally employed in social sciences, ushering in a more sequential, interactive, and ultimately inductive approach to the process of inference.

Short Bio of Chairs

Yang Weng

Yang Weng received the B.S. and Ph.D. degrees from the Department of Mathematics, Sichuan University. Since 2006, he has been with the College of Mathematics, Sichuan University, where he is currently an Associate Professor. He was a Postdoctoral Fellow with the Nanyang Technological University, Singapore, from August 2008 to July 2010. His current research interests include statistic machine learning and nonparametric Bayesian inference.