Professor Jun Wang
FIEEE, FIAPR, MAE
Chair Professor of Computational Intelligence, Department of Computer Science and School of Data Science, City University of Hong Kong, China

Areas of Expertise: Computational intelligence, Neural computation, Optimization methods, Data processing, Intelligent control

Brief Introduction: Jun Wang is a Chair Professor of Computational Intelligence in the Department of Computer Science and School of Data Science at City University of Hong Kong. Prior to this position, he held various academic positions at Dalian University of Technology, Case Western Reserve University, University of North Dakota, and Chinese University of Hong Kong. He also held various part-time visiting positions at US Air Force Armstrong Laboratory, RIKEN Brain Science Institute, Huazhong University of Science and Technology, Dalian University of Technology, and Shanghai Jiao Tong University as a Changjiang Chair Professor. He received a B.S. degree in electrical engineering and an M.S. degree in systems engineering from Dalian University of Technology, Dalian, China. He received his Ph.D. degree in systems engineering from Case Western Reserve University, Cleveland, Ohio, USA. His current research interests include neural networks and their applications. He published about 280 journal papers, 15 book chapters, 11 edited books, and numerous conference papers in these areas. He is the Editor-in-Chief of the IEEE Transactions on Cybernetics. He also served as an Associate Editor of the IEEE Transactions on Neural Networks (1999-2009), IEEE Transactions on Cybernetics and its predecessor (2003-2013), and IEEE Transactions on Systems, Man, and Cybernetics – Part C (2002–2005), as a member of the editorial advisory board of International Journal of Neural Systems (2006-2013), and a member of the editorial board of Neural Networks (2012-2014) as a guest editor of special issues of European Journal of Operational Research (1996), International Journal of Neural Systems (2007), Neurocomputing (2008, 2014, 2016), and International Journal of Fuzzy Systems (2010, 2011). He was an organizer of several international conferences such as the General Chair of the 13th International Conference on Neural Information Processing (2006) and the 2008 IEEE World Congress on Computational Intelligence, and a Program Chair of the IEEE International Conference on Systems, Man, and Cybernetics (2012). He has been an IEEE Computational Intelligence Society Distinguished Lecturer (2010-2012, 2014-2016). In addition, he served as President of Asia Pacific Neural Network Assembly (APNNA) in 2006 and many organizations such as IEEE Fellow Committee (2011-2012); IEEE Computational Intelligence Society Awards Committee (2008, 2012, 2014), IEEE Systems, Man, and Cybernetics Society Board of Directors (2013-2015), He is an IEEE Life Fellow, IAPR Fellow, foreign member of Academia Europaea, and a recipient of an IEEE Transactions on Neural Networks Outstanding Paper Award and APNNA Outstanding Achievement Award in 2011, Natural Science Awards from Shanghai Municipal Government (2009) and Ministry of Education of China (2011), and Neural Networks Pioneer Award from IEEE Computational Intelligence Society (2014), among others.

Professor Yong Yue
BEng, PhD, CEng, FIET, FIMechE, FHEA
Professor at the Department of Computing, Director of the Virtual Engineering Centre (VEC), Xi'an Jiaotong-Liverpool University (XJTLU), China

Areas of Expertise: Virtual Reality, Computer Vision, Robot Applications, Operations Research

Speech Title: Intelligent Real-Time Path Planning for Unmanned Surface Vehicle

Abstract: Unmanned Surface Vehicles (USVs) play a key role in water environment monitoring. The backbone of USVs is intelligent path planning which is crucial for ensuring the safety, reliability and success of USVs amid challenges such as fluctuating currents and tides while detecting and avoiding obstacles. This talk will briefly introduce the USV, reviews contemporary techniques for path planning and present ongoing work on intelligent real-time path planning for the USV for water environment monitoring. The work covers a novel path-keeping algorithm based on artificial potential field (PK-APF), enhancing the USV ability to maintain its pre-set path under variable wind conditions; a novel riverbank following planner (RBFP) with point cloud data to realise autonomous navigation along riverbanks for surveying water environments; a self-supervised framework for autonomous USV docking without the need for traditional human labelling and camera calibration, leading to highly precise USV docking manoeuvres.

Brief Introduction: Yong Yue (BEng Northeastern China, PhD Heriot-Watt UK, CEng, FIET, FIMechE, FHEA) is currently a Professor and Director of the Virtual Engineering Centre (VEC). He was Head of Department of Computer Science and Software Engineering (2013-2019). Prior to joining XJTLU, he had held various positions in industry and academia in China and the UK, including Engineer, Project Manager, Professor, Director of Research and Head of Department.
Professor Yue has experience in learning and teaching, research and enterprise as well as management. He has led a variety of research and professional projects supported by major funding bodies and industry. He has also lead curriculum development at both undergraduate and postgraduate levels. His Research interests include computer vision, robotics, virtual reality and operations research. He has over 250 peer-reviewed publications and supervised 27 PhD students to successful completion.

Professor Rajanikanth Aluvalu
SMIEEE | Member-ACM | Member -Mirlabs | Life member ISTE | Member EAI
Department of IT, Chaitanya Bharathi Institute of Technology, India

Areas of Expertise: High Performance Computing, Bigdata and AI

Speech Title: Private AI opportunities and Challenges

Abstract: Private AI helps organizations realize value from their generative AI/AI initiatives faster while maintaining the privacy and control organizations expect for their sensitive data, whether it resides in a data center, public cloud, or at the edge. Data privacy is a strong consideration in all technology decisions—we can’t lose sight of that fact in the rush to gain productivity benefits from AI. Organizations still need to follow compliance regulations. Artificial Intelligence (AI), the privacy risks are many. AI can improve our lives, but may leak or misuse our private data. private AI can help to build trust and confidence in AI systems, users can have greater transparency and accountability in how their data is used and processed.

Prof. Yousef FARHAOUI
Moulay Ismail University, Faculty of sciences and Techniques, Morocco

Areas of Expertise: Artificial intelligence, Design and analysis of algorithms, High-Performance Computing, Human-Computer Interaction…

Brief Introduction: Chair of IDMS Team, Director of STI laboratory. Local Publishing and Research Coordinator, Cambridge International Academics in United Kingdom. He obtained his Ph.D. degree in Computer Security from Ibn Zohr University of Science. His research interests include learning, e-learning, computer security, big data analytics, and business intelligence. Farhaoui has three books in computer science. He is a coordinator and member of the organizing committee and also a member of the scientific committee of several international congresses, and is a member of various international associations. He has authored 6 Book and many Book Chapters with Reputed Publishers such as Springer and IGI. He is served as a Reviewer for IEEE, IET, Springer, Inderscience and Elsevier Journals. He is also the Guest Editor of many Journals with Wiley, Springer, Inderscience, etc. He has been the General Chair, Session Chair, and Panelist in Several Conferences. He is Senior Member of IEEE, IET, ACM and EAI Research Group.

Assoc. Prof Selvakumar Manickam
National Advanced IPv6 Centre
Universiti Sains Malaysia, Malaysia

Areas of Expertise: Cybersecurity, Internet of Things, Industry 4.0, Cloud Computing, Big Data, and Machine Learning

Brief Introduction: Prof Selvakumar Manickam has authored and co-authored more than210 articles in journals, conference proceedings, and book reviews and graduated 18PhDs in addition to masters and undergraduate students. He has given several keynote speeches and dozens of invited lectures and workshops at conferences, international universities, and industry. He has given talks and training on Internet Security, Internet of Things, Industry 4.0, IPv6, Machine Learning, Software Development, Embedded & OS Kernel technologies at various organizations and seminars. He also lectures in various Computer Science and IT courses, including developing new courseware in tandem with current technology trends. Dr. Selva is involved in various organizations and forums locally and globally. Previously, he was with Intel Corporation and fa ew start-ups working in related areas before moving to academia. While building his profile academically, he is still very involved in industrial projects involving SECS/GEM communication protocol, robotic process automation, machine learning, and data analytics using open-source platforms. He also has experience building IoT, embedded, server, mobile, and web-based applications.

Dr. Binqiang Wang
Inspur (Beijing) Electronic Information Industry Co. Ltd

Areas of Expertise: Spiking neural network; Emotion Recognition/Analysis

Speech Title: Innovative Pathways in Affective Computing: Multimodal Sentiment Analysis and Beyond

Abstract: Leveraging the advancements in artificial intelligence, the integration of emotion recognition paves the way for intelligent systems capable of empathy and nuanced interaction. Affective computing aims to equip computers with the skills to not only perceive and comprehend emotions but also to express them in a meaningful manner. In this presentation, we delve into the realm of multimodal sentiment analysis, examining the synergistic relationship between various modalities to amplify the efficacy and reliability of emotion recognition systems. We introduce a novel non-uniform attention mechanism that harnesses the collective power of multimodal features, ensuring a more refined and context-aware fusion of information. This mechanism is designed to recognize and weigh the contributions of different modalities, thereby enhancing the overall performance of emotion recognition tasks. Expanding beyond the spatiotemporal analysis, we present an innovative approach to emotion recognition through the frequency domain. By incorporating Fourier transform, our method fortifies traditional neural networks, facilitating a more nuanced understanding of emotional cues and consequently, improved recognition accuracy. Acknowledging the diversity and complexity of multimodal data, we introduce a new dataset specifically tailored for multimodal emotion recognition. This dataset, which captures emotional expressions through gestures, provides a more homogeneous perspective on the emotional tendencies exhibited in everyday life. Drawing inspiration from the fundamental human experience of emotion, we advocate for an emulation of brain structures to infer emotional states. With the high degree of biological fidelity offered by brain-inspired computing, we assert that a deeper simulation of brain processes is essential for unraveling the complexities of human emotions. Our research points towards a future where the intersection of artificial intelligence and affective computing leads to the development of systems with a deeper understanding of human emotions, fostering more empathetic and sophisticated interactions.

Brief Introduction: Dr. Binqiang Wang is a distinguished researcher with a Ph.D. in Signal and Information Processing from the University of Chinese Academy of Sciences. His academic journey began at Northwestern Polytechnical University, where he earned a Bachelor of Science in Computer Science and Technology. He has honed his expertise in deep learning, multi-modal information processing, and affective computing through extensive academic and industrial experiences. His research interests are at the forefront of technological innovation, with a particular emphasis on affective computing, emotion recognition and analysis, brain-inspired computing, and spiking neural networks. He currently holds the position of researcher with the title of Senior Engineer at Inspur (Beijing) Electronic Information Industry Co., Ltd. His academic prowess is reflected in his numerous accolades, including the Excellent Paper of Inspur Group in 2022, funding from the Shandong Provincial Natural Science in 2021, and the LiYuehua Zhu Scholarship in 2020. He leads a research group focused on information fusion algorithms for multi-modal emotion recognition in affective computing and brain-inspired computing. He has published 12 academic papers in renowned domestic and international journals, with 6 of them indexed by SCI and another 6 by EI. His work has garnered over 700 citations on Google Scholar, attesting to its influence and relevance. Additionally, He has applied for 9 patents, out of which 5 have been authorized as invention patents, highlighting his innovative and impactful research contributions.