Prof. LUO Juan, Hunan University, China. (email@example.com)
Prof. ZHAO Huan, Hunan University, China. (firstname.lastname@example.org)
Prof. CAI Yuhui, Hunan University, China. (email@example.com)
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.
Prof. LUO Juan is a Professor and Ph.D. supervisor, currently serving as the Vice Dean of the College of Computer Science and Electronic Engineering, Hunan University, China. She holds a bachelor's degree from the National University of Defense Technology, China and obtained her master's and Ph.D. degrees from Wuhan University, China. Previously, she worked at Fiberhome Networks, Wuhan Academy of Posts and Technology, and has also served as a visiting scholar at the University of California, Irvine, USA. Prof. LUO has been recognized as a New Century Outstanding Talent by the Ministry of Education (China) and has received accolades such as the Hunan Province Outstanding Youth Fund and recognition as a young backbone teacher in Hunan Province.
Her current research interests include IoT, cloud computing and artificial intelligence.
Details of Prof. LUO's experiences can be found at: http://csee.hnu.edu.cn/people/luojuan
Prof. ZHAO Huan is a Professor and Ph.D. supervisor in the College of Computer Science and Electronic Engineering at Hunan University, China. Additionally, she holds the position of Deputy Division Director of the Academic Affairs Office at Hunan University. Prof. ZHAO has enriched her academic experience as a visiting scholar at the University of California, San Diego, USA. She is an esteemed member of the Computer Basic Teaching Steering Committee of the Ministry of Education (China) and the Steering Committee of the Education and Training of Industrial and Information Talents.
Her research interests include speech information processing, machine learning.
Details of Prof. ZHAO's experiences can be found at: http://csee.hnu.edu.cn/people/zhaohuan
Prof. CAI Yuhui is a faculty member at the School of Information Science and Engineering at Hunan University, holding the position of Associate Professor. He teaches courses such as Introduction to Computing and Artificial Intelligence and 3D Graphics Programming. He has led one key project funded by the Ministry of Education, one provincial educational reform project, and two collaborative education projects between academia and industry funded by the Ministry of Education. Additionally, he has overseen multiple research and development projects with various enterprises. He has been awarded a second-class prize in the Chinese University Scientific and Technological Awards and has translated and published one textbook in the "Foreign Classic Textbook Series." He has also guided students in participating in national-level programming competitions, where they have won gold medals. He has received the title of Excellent Guiding Teacher for Innovation and Entrepreneurship at Hunan University on multiple occasions."
His current research interests include computer networks, image processing, artificial intelligence, software engineering.
Details of Prof. CAI's experiences can be found at: http://csee.hnu.edu.cn/people/caiyuhui
Carsten Lecon, Aalen University, Germany. (firstname.lastname@example.org)
Virtual 3D Learning Environments are not only used for the visualization of complex learning matters, but get increasing importance in learning environments (currently, accelerated by the Corona pandemic). Students for example act as avatars in artificially generated worlds, in which they learn, develop, and present simultaneously. Full immersion is possible by so called head mounted displays. Nowadays, these are less expensive, so that many users can use this technique. Furthermore, Augmented Reality (AR) und Mixed Reality (MR) applications become more and more important in industrial application – and also in learning environments.
Topics include but are not limited to:
● Virtual 3D Environments for collaborative learning;
● Conversational Agents in virtual environments;
● AR and VR Learning Settings for Higher Education and School Education;
● Teaching VR/ AR/ MR techniques in Higher Education;
● Didactic and pedagogical aspects when designing VR/ AR/ MR applications;
● Evaluation of AR/ VR / MR applications;
● Kinetosis in VR environments.
Prof. Dr. Carsten Lecon
● Study of computer science (Technical University Braunschweig, Germany)
● Software Quality Assurance (Siemens AG, Braunschweig)
● Database systems, Media archives (University Luebeck, Germany)
● Virtual University of Applied Sciences (FH Luebeck, Germany)
● Since 04/2004 Professor for media computer science (Aalen University for Applied Sciences, Germany)
o Teaching: Foundations of digital media, VR/AR technologies, audiovisual media, game programming
o Research: E-/VR-Learning, kinetosis in VR environment, live motion capture
WENG Yang, Sichuan University, China. (email@example.com)
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.
Prof. WENG Yang received his 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.
Hiroki TAKADA, University of Fukui, Japan. (firstname.lastname@example.org)
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.
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.