Speakers

Yiguang Liu

Sichuan University

Prof. Yiguang Liu, Changjiang Distinguished Professor at Sichuan University, serves as Chief Scientist for Aerospace Information Detection & Intelligent Perception Platform, and sole expert at “National High-end Aviation Equipment Innovation Center”. He holds academic committee positions in multiple provincial defense key laboratories and national engineering centers. Currently serving as Executive Council Member of China Society of Image & Graphics, he co-chaired CCIG2022 and has delivered 20+ keynote speeches at major forums including CAE's Tri-Service Aviation Summit.

With academic credentials spanning Nanjing University of Science & Technology (B.S. 1995), Peking University (M.S. 1998), Sichuan University (Ph.D. 2004), and postdoctoral training at National University of Singapore (2008), he conducted visiting research at Imperial College London (2011) and Michigan State University (2013).

His research integrates modern mathematics with next-gen detection technologies for aerospace applications, particularly in dynamic complex environments. Developed systems have been implemented in long-endurance UAV platforms. He has led over 10 national projects including NSFC key programs and major special projects, publishing 100+ SCI papers in IEEE Trans./CVPR/ICCV, etc. Holder of 26 patents and 18 software copyrights. Awards include the First Prize of Sichuan Science and Technology Progress Award (1/10) and Second Prize of Sichuan Natural Science Award (1/5), plus two IEEE ICME paper awards.

Title: Detection Perception and Adversarial Intelligence----Reflections on Airborne Unmanned Systems

Abstract: Enabling "stable-accurate-rapid" signal detection and intelligent sensing via optoelectronic/infrared/SAR sensors is fundamental for adversarial unmanned systems. This presentation reviews the state-of-the-art of global adversarial airborne unmanned systems, identifies limitations of big-data deep learning, and analyzes the essence of adversarial intelligence. Based on these insights, proposes requirements for future intelligent systems tailored to adversarial scenarios. Finally, preliminary research progress from our team is presented.

Kay Chen Tan

Hong Kong Polytechnic University

Kay Chen Tan is a Chair Professor (Computational Intelligence) of the Department of Data Science and Artificial Intelligence, The Hong Kong Polytechnic University. He has co-authored eight books and published over 300 peer-reviewed journal articles. Prof. Tan was the Vice-President (Publications) of the IEEE Computational Intelligence Society from 2021-2024. He was the Editor-in-Chief of IEEE Transactions on Evolutionary Computation from 2015-2020 and IEEE Computational Intelligence Magazine from 2010-2013. Prof. Tan is an IEEE Fellow and an Honorary Professor at the University of Nottingham in the UK. He is also the Chief Co-Editor of the Springer Book Series on Machine Learning: Foundations, Methodologies, and Applications.

Hamido FUJITA

Malaysia-Japan International Institute of Technology (MJIIT)

Universiti Teknologi Malaysia (UTM)

Hamido FUJITA is Professor at Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia (UTM), and head of i-kouza (SEiR-Software Engineering and industrial Revolution) Kuala Lumpur Malaysia. He is also Distinguished Professor of Iwate Prefectural University, Iwate Japan. He is also Research Professor at the Faculty of Science, University of Hradec Kralove, Hradec Kralove 50003, Czech Republic, and Research Professor at University of Granada (Spain), and HUTECH University Vietnam; Expert Excellence Professor at Shanghai University of Medicine & Health Sciences, and Foreign Expert at Hunan University, China. He is currently the Executive Chairman of i-SOMET Incorporated Association, Japan. He is Highly Cited Researcher in Cross-Field for the year 2019 and 2020, 2021, 2022, 2023, 2024 in Computer Science field, respectively from Clarivate Analytics. He is Editor-in-Chief of Applied Intelligence (Springer), Editor-in-Chief of Healthcare Management (Tayler&Francis), and Editor-in-Chief of Knowledge-Based Systems (Elsevier) (2010-2020) and Emeritus Editor of Knowledge-Based Systems, also he is Area-Editor of Array journal (Elsevier).

Title: Views on Artificial Intelligence and Machine Learning perspectives

Abstract: The hot topics in training in Machine Learning is a crucial aspect that affects the credibility of the system in terms of performance and is employed for robust applications such as in healthcare systems. Machines or algorithms, in wide challengeable applications in security or vision or health care early predictions, learn from data. Nevertheless, in most cases, the extensive and unbalanced data and noise make it unreliable in prediction accuracy. Supervised machine learning is and was one of the aspects of providing artificial intelligence-based solutions. However, this is and was limited due to the difficulty of labeling big data and many crucial problems in weak relations and noise in data. Semi-supervised learning, for example, Multiview learning, could assist in solving these problems. In many published research, there are still problems in providing machine learning models that are unbiased and efficient in terms of robustness and resilience in data-driven systems. Multiclass classification still has problems in terms of clear definition in class classification, bias, imbalance and weak relations, making machine learning for multiclass classification insecure for classification or regression analytics. This causes limitations in applying such technology in medical applications and diagnosis prediction. In this lecture, I will outline these problems in our one-class classification project. These are related to providing more robust accuracy prediction with some uncertainty that can help us have more accurate classification and prediction. We have applied such findings in health care for heart sickness and seizure early prediction.

We also have deep learning models, which also have challenges related to evidential deep learning and fairness relative to data. There are important issues in expanding research in evidential deep learning, in which uncertainty prediction of variational Auto encoders can provide decisions on evidential distribution, which in turn helps to provide a measure of uncertainty in decision.

We currently have a research project titled “Healthcare Risk Prediction on Data Streams Employing Signal Transformation Network (OCSTN)”, which is supported by grant from Japan Science Promotion Society (JSPS). In this project, we have employed one-class classification deep neural network for health care prediction. In this lecture I will outline of these perspectives and discuss challenging trends.

Guangming Shi

Xidian University

Guangming Shi, a chair professor at Xidian University, served as Vice President of the University from January 2018 to January 2022 and is currently Deputy Director of Peng Cheng Laboratory in Shenzhe of Chian. In 2012, he was selected as a Distinguished Professor of the Yangtze River Scholars. He is currently a Fellow of IEEE, AIAA, and IET, a Fellow of the Chinese Institute of Electronics, Director of the Human-Computer Interaction and Virtual Reality Special Committee of the Chinese Institute of Command and Control, Deputy Director of the Intelligent Human-Computer Interaction Committee of the Chinese Institute of Electronics, and Deputy Director of the Brain Science and Artificial Intelligence Special Committee of the Chinese Association for Artificial Intelligence. He is also a Teaching Master in Shaanxi Province. His research fields include AI theory and technology, computational imaging instruments, and semantic information communication and processing. He has presided over national basic strengthening projects and key projects of the National Natural Science Foundation of China, led and won 1 Second Prize of the National Natural Science Award, 3 First Prizes of Provincial and Ministerial Scientific and Technological Awards, 3 Second Prizes of National Teaching Achievement Awards, and was named one of the Top Ten Outstanding Scholars of ACM China in 2024.

Title: Reflections on the Evolution of AI Technology and the Essence of Intelligence

Abstract: Human society has entered the AI era, and AI technology is empowering all industries. The development of AI technology has undergone three ups and downs. From the three technical routes of symbolism, behaviorism, and connectionism, to deep learning, and then to large model technology, AI has reached a new height. Will large model AI technology dominate the world? However, there is still no theoretical framework to explain the essence of intelligence and the connotation of its processing objects. Regarding the essence of intelligence and the development direction of AI, this report will present the speaker's thoughts.

Speakers

Email： xb@cqut.edu.cn Tel： 023-62561406

Add: 69 Hongguang Dadao, Banan District, Chongqing 400050

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