Speaker

SPEAKERS

Speakers

Prof. Grigoris Antoniou, 

IEEE FELLOW

Institute of Computer Science

Foundation for Research and Technology 

Hellas Heraklion, Greece

Research Area:

inference mechanisms, knowledge representation, mobile computing, nonmonotonic reasoning, ontologies (artificial intelligence), ubiquitous computing, cooperative systems, decision making, distributed processing, educational computing, knowledge based systems, knowledge representation languages, mobility management (mobile radio), semantic Web, theorem proving, formal logic, Internet, electronic commerce, logic programming, software agents, Horn clauses, administrative data processing, backward chaining, computational complexity, decision support systems

Brief: 

Grigoris Antoniou is a Professor of computer science at the University of Crete, Greece, and head of the Information Systems Laboratory at FORTH, a Greek research institute involved in many European projects. Before joining FORTH, he held professorial positions at Griffith University, Australia, and the University of Bremen, Germany. His research interests lie in knowledge representation and reasoning, and its applications to the semantic web, e-commerce, digital preservation and ambient intelligence. He has published more than 150 technical papers in scientific journals and conferences. He is author of three books with prestigious international publishers (Addison-Wesley and MIT Press). His book A Semantic Web Primer is the standard textbook in this area. He participates in a number of research projects, among them the European projects REWERSE (reasoning on the Web) and CASPAR (digital preservation). In 2006, he was elected ECCAI Fellow, joining the prestigious list of the top European researchers in artificial intelligence.

Title:

AI for mental health

Abstract:

This talk will cover work on using AI as a clinical decision support tool for mental health conditions, with a focus on adult ADHD and suicide risk assessment. This work was done collaboratively with a clinical unit of the UK's National Health Service (NHS), and has resulted in developing new technological solutions. From an AI perspective, the research is interesting because it had to address various requirements (in terms of accuracy and explainability) and because we are using a hybrid AI approach: a combination of a machine learning and a knowledge-based model. We will also touch upon recent AI developments around knowledge graphs, large language models and their combinations.

Prof. Peng Lv, Central South University, China

Research Area:

Agent Based Modeling, system dynamics , Computational Social Sciences, Big data forecast,Contingency management, Public safety

Brief: 

Young Changjiang Scholar, Ministry of Education, Professor, School of Public Administration, Central South University, Professor, School of Automation, Central South University, Director, Social Computing Research Center, Central South University, and Vice President, Wuhan Institute of Artificial Intelligence, Peking University. Ph.D., Department of Sociology, Tsinghua University (2011-2014), Postdoctoral Fellow, Department of Automation, Tsinghua University (2014-2016). D. joint training at University of Chicago, USA, visiting scholar at Seoul National University, Korea, and visiting scholar at ISEF, Korea Foundation for Higher Education. Chief expert of the National Social Science Major Project [Standing Grant] and Chief expert of the National Social Science Major Project [Rolling Grant]. He has been selected as a member of the 4th Youth Federation of the Central Committee of the Communist Party of China, a "special researcher" of the Central Internet Information Office, and an "excellent young and middle-aged expert" of the National People's Committee. His main research interests include big data prediction, social physics, social public security, social system simulation, population intelligence, artificial intelligence, emergency management, big data analysis and prediction, computational social science, ABM intelligence modeling, multi-subject simulation, meta-automata simulation, big data mining, big data GIS visualization, spatial game theory simulation, etc. He has published many SCI/SSCI/CSSCI papers.

A. Prof. Bo Liu, South China Normal University, China

Research Area: 

Educational data analysis; computer-assisted teaching; CSCL; education big data mining

Research Experience:

Dr. Bo Liu is associate Professor of Teaching & Learning Technologies program in Department of Information Technology in Education at South China Normal University (SCNU) and earned his Ph.D. in Computer Application Technology from South China University of Technology (SCUT). He is now the director of the research laboratory of Learning Intelligence Program Science(LIPS) at SCNU and serving as a guest editor for Journal of Computational Information Systems. He was a visiting scholar for the academic year 2014-2015. He has shared several reports in International Conference such as ICMLC2005, ITTA2008, SKG2009, ICBL2017, ISET2017, GCCCE 2018, IFERD2019, ICBDIE2021, ICISE2021. He served as chair for International Conference on Technology in Education (ICTE2019) and as a technical committee member and reviewer in The 2022 5th International Conference on Big Data and Education(ICBDE 2022). His current researching field including how to design, develop and integrate innovative technology capable of promoting students’ motivation and engagement in digital learning to achieve a positive impact on student success. By integrating advanced computing technologies (e.g., artificial intelligence, web2.0 techniques, and etc.), he believes digital learning can be more pertinent and manageable in authentic teaching and learning practice. The advanced computing technologies can be used as not only communication or productive tools, but also cognitive tools to promote motivation for learning, to facilitate hands-on, and to provide scaffoldings for complex cognitive activities. He also believes that the informational environments also have the capacity to provide analytical insights and afford new analytical methodologies (such as, learning analytics and social network analysis) to better inform educational research and instructional design. Dr. Bo Liu is involved in several projects in collaboration with experts from various fields, such as, computer science and engineering, social science, psychology and cognitive science.

Title of Keynote Speech:

Research on the Evaluation of Interaction Behavior between Teacher and Student in Classroom Teaching Supported by Intelligent Technology

Abstract:

In the context of Digital transformation of education, education and teaching evaluation should be digital, intelligent and accurate. The integration of intelligent technology will help to achieve this goal. In classroom teaching, teacher-student interaction is a major element in the evaluation of the teaching process, and the effectiveness of teacher-student interaction will to some extent affect the quality of teaching. At present, most teachers and students carry out teaching activities in an information-based environment, and the interaction trajectory and learning data based on platforms and devices can be recorded as part of teaching evaluation. However, the interactive behavior of real classroom activities is difficult to capture and record in real time, and subjects such as teachers and students lack consideration for the dimension of interactive behavior when conducting teaching evaluations in the review classroom.

Therefore, this study focused on the recognition and analysis of teacher-student behavior and interaction behavior in information-based classrooms. By constructing algorithms for teacher-student behavior recognition, speech emotion recognition, and language text classification, integrating learning analysis technology, an automated teaching evaluation system based on intelligent technology will be constructed, and a teaching evaluation model supported by intelligent technology will be established based on this system. The research group will propose strategies and suggestions for teacher-student interaction in information-based classroom teaching through practical testing, in order to improve classroom quality and efficiency, and assist in the efficient and scientific development of education and teaching evaluation.

A. Prof. Muhammad Helmi Norman, Universiti Kebangsaan Malaysia (UKM), Malaysia

Research Area: 

Digital and Futuristic Education

Research Experience:

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