Assoc. Prof. Liang Wang
Sun Yat-Sen University, China
Speech Title: Student-motivation analysis according to raising-hand videos
Short Bio: Associate professor and doctor tutor of School of Electronic and Communication Engineering, Sun Yat-Sen University. Graduated from Beijing Jiaotong University with a Ph.D., and a postdoctoral fellow at MIT, with profound theoretical foundation and rich engineering practice experience. As the person in charge and the main participant, he has successively undertaken a real-time warning system for dangerous vehicles at night (funded by the US Department of Justice), the bilateral cruise control theory sub-project of the Toyota-MIT Unmanned Vehicle Project (funded by the Toyota Research Institute), XX and other projects2 the total cost is more than 10 million yuan. Many research results are at the domestic and international advanced level. He has published more than 20 academic papers, of which 8 are indexed by SCI, 16 are indexed by EI, 1 monograph and 1 textbook. He teaches the course "Microcomputer Principles and Embedded Systems", teaching the basic principles, architecture and functions of computer systems. On the basis of theoretical learning, students are guided to implement corresponding machine vision and intelligent perception algorithms on the MCU system, so as to realize the demonstration of complete physical models (such as unmanned vehicles or drone models).
Prof. Syed Abdul Rahman Abu-Bakar
Universiti Teknologi Malaysia, Johor, Malaysia
Speech Title: Multi Attention-Based Approach to Detect and Localize DeepFake Face and Expression Swap
Abstract: Rapid advancement of deep learning techniques, most notably the Generative Adversarial Networks, have contributed to the proliferation of incredibly realistic fake media content known as DeepFake. DeepFake technology can generate high-quality videos and images of facial modifications that are indistinguishable from real ones. This form of media can be used to scam, discredit or blackmail individuals. Face (identity) swap and Expression swap are two most common types of DeepFake facial manipulations. While many DeepFake detection approaches address these issues, they focus only on binary classification for determining the likelihood of an image being altered. However, a handful of them attempts to identify the false region of the fake image where manipulation of face features typically occurs. In multimedia forensics, localization of fake region is considered more important and valuable with two-fold advantages: (1) It suppresses irrelevant information and directs the network's attention to potentially affected areas, thereby avoiding disruptions, and (2) Localization of modified regions improves feature understanding for the network. In this presentation, an attention-based multitask learning strategy to simultaneously classify and segment modified facial images is presented. In contrast to single-objective approaches, the proposed method provides the likelihood that the input is fake as well as provides localized maps of the manipulated regions in the input video frames. The efficiency of the approach is evaluated on widely employed DeepFake datasets such as FaceForensics++, CelebDF, DFDC and DFD. The results have demonstrated the efficacy of the approach.
Short Bio: Syed Abd Rahman Abu-Bakar received his PhD degree from the University of Bradford, England. He joined the Faculty of Electrical Engineering in Universiti Teknologi Malaysia since 1992. He is currently a full professor in the Electronics and Computer Engineering division. In 2004, he formed the Computer Vision, Video and Image Processing research lab and has become the head since then. His main research interest is in computer vision and image processing with applications in video-based security and surveillance, medical image processing, and biometrics. He has published more than 150 scientific papers both at national and international levels.
Assoc. Prof. Ruiheng Zhang
Beijing Institute of Technology, China
Short Bio: Ruiheng Zhang received the B.E. degree in 2014 from Beijing Institute of Technology, China, and the Dual-Ph.D. degree from University of Technology Sydney, Australia and Beijing Institute of Technology, China. He is currently an Associate Professor in Beijing Institute of Technology. He is the author of more than 30 research papers and one book, including Remote Sensing of Environment, IEEE TMM, ISPRS, Pattern Recognition, ICLR, IJCAI and so on. He is involved as a member of the Editorial Board of Frontiers in Robotics and AI, Artificial Intelligence and Applications. He has served as Co-Chair of IMASBD 2022, TPC of ICDIP 2022, VSIP 2022 and ICIVIS 2022. He is also a Reviewer for the ICLR, ACM MM, IJCAI, DICTA, IEEE TMM, IEEE TGRS, IEEE JSTARS, Pattern Recongition, Neurocomputing, Remote Sensing, Applied Science, Sensors, Electronics. His current research interests include deep learning, object understanding, and multi-modal remote sensing.
Assoc. Prof. Danilo Vasconcellos Vargas
Kyushu University, Japan
Speech Title: Unleashing Intelligence: Exploring New Horizons for Natural and Synthetic Minds
Abstract: Intelligence, whether biological or artificial, serves as a foundational pillar of society, distinguishing us as human beings. Despite its significance, comprehending and reasoning about intelligence can often challenge common sense. In this presentation, we embark on a journey to unravel the complexities of intelligence. The first part delves into the surprising lack of robustness and adaptivity in even the most accurate AI models, prompting the need for a novel paradigm. We outline a robust and adaptive AI approach as a solution to this issue. In the second part, we shift our focus to enhancing education and fostering reasoning abilities. By addressing conflicts and fostering collaborative augmented reasoning, we pave the way for improved education. Moreover, we explore the alignment between our efforts and the Sustainable Development Goals (SDGs) and provide a glimpse into future undertakings. Join us as we uncover new horizons for intelligence and explore their impact on education, reasoning, and the broader pursuit of societal well-being.
Short Bio: Danilo Vasconcellos Vargas is currently an Associate Professor at Kyushu University, Visiting Researcher at the University of Tokyo and CEO & Founder of MiraiX. His research interests span Artificial Intelligence (AI), evolutionary computation, complex adaptive systems, interdisciplinary studies involving or using an AI’s perspective and AI applications. Many of his works were published in prestigious journals such as Evolutionary Computation (MIT Press), IEEE Transactions on Evolutionary Computation and and IEEE Transactions of Neural Networks and Learning Systems with press coverage in news magazines such as BBC news. He received awards such as the IEEE Transactions on Evolutionary Computation Outstanding 2022 Paper award, the IEEE Excellent Student Award and scholarships to study in Brazil, Germany and Japan for many years. Regarding his community activities, he presented tutorials at GECCO2018, WCCI2020 and at the renowned top AI conference IJCAI2020. He was also co-organizer and advisor committee of various workshops both about AI and about multidisciplinary perspectives for AI with more than 10 invited talks, one of which was given in a workshop in CVPR 2019. Currently, he leads the Laboratory of Intelligent Systems aimed at building a new age of robust and adaptive artificial intelligence funded/supported by the two biggest Japan's funding agencies: JST and JSPS (including JST ACT-I, JST ACT-I Accelaration Phase, JSPS Kakenhi Wakate). More info can be found both in his Website and his Lab Page. More info can be found both in his Company and his Lab Page.
Assoc. Prof. Ran Cheng
Southern University of Science and Technology, China
IEEE Senior Member
Speech Title: Automated Hardware-Aware Deployment of Deep Learning Models
Abstract: In recent years, deep learning has achieved remarkable success in various fields. However, as the complexity of models increases, their deployment and execution on hardware-constrained devices become more challenging. This talk aims to explore how to incorporate hardware metrics (such as power consumption, latency, and resource usage) into the consideration of deep learning models to achieve efficient automated model design, ensuring a balance between model performance and hardware resource consumption. Using real-time semantic segmentation tasks in autonomous driving scenarios as an application case, this talk demonstrates the automated deployment performance on edge computing device.
Short Bio: Dr. Cheng Ran is currently a tenured Associate Professor at Southern University of Science and Technology. He focuses on research in Computational Intelligence and is dedicated to harnessing the potential of computational power through algorithmic intelligence, providing efficient solutions for complex computational problems. He has published over 100 peer reviewed papers with over 9,000 citations. He is the recipient of IEEE CIS Outstanding Ph.D. Dissertation Award (2019), IEEE TEVC Outstanding Paper Award (2018, 2021), and IEEE CIM Outstanding Paper Award (2020). He currently serves as the Chair of the IEEE CIS Shenzhen Chapter and is the Associate Editor of prestigious journals such as IEEE TEVC, IEEE TAI, IEEE TCDS, and IEEE TETCI.
Dr. Ali Kashif Bashir
Manchester Metropolitan University, United Kingdoms
IEEE Senior Member
Short Bio: ALI KASHIF BASHIR is a Reader of Networks and Security at the Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom. Also serving as Program Leader for BSc Cyber Forensics and Security and BSc Cyber Security. He also enjoys adjunct and honorary affiliations of the University of Electronics Science and Technology of China (UESTC), University of Science and Technology, Islamabad (NUST), and the University of Guelph, Canada. In the past, he has worked for the University of the Faroe Islands, Denmark; Osaka University, Japan; Nara National College of Technology, Japan; the National Fusion Research Institute, South Korea; Southern Power Company Ltd., South Korea, and the Seoul Metropolitan Government, South Korea.
He received his Ph.D. in computer science and engineering from Korea University, South Korea. He has authored over 200 research articles; and received over 3 Million USD funding as PI and Co-PI from research bodies of South Korea, Japan, EU, UK and the Middle East. His research interests include internet of things, wireless networks, distributed systems, network/cyber security, network function virtualization, machine learning, etc. He is serving as the Editor-in-chief of the IEEE FUTURE DIRECTIONS NEWSLETTER. He is leading many conferences as a chair (program, publicity, and track) and had organized workshops in flagship conferences like IEEE Infocom, IEEE Globecom, IEEE Mobicom, etc. He has given more than 35 invited and keynote talks at International conferences across the globe.
Assoc. Prof. Lili N Abdullah
Universiti Putra Malaysia, Malaysia
Speech Title: DRIVING AI RESEARCH TO COMMUNITIES
Abstract: Artificial Intelligence (AI) research has witnessed significant breakthroughs in recent years, leading to advancements in various domains. However, the true impact is when AI research has the potential to drive transformative change and innovation in communities. As AI continues to advance, it is crucial to bridge the gap between cutting-edge research and its practical implementation within the broader community. This talk explores the journey of AI, from research to community, highlighting the importance of translating AI breakthroughs into tangible benefits for society. We delve into the challenges and opportunities associated with bringing AI advancements into real-world applications, and the critical role of community engagement in driving AI adoption, emphasizing its role in driving innovation and fostering positive change.
Short Bio: Associate Prof. Lili N Abdullah currently working at Faculty of Computer Science and Information Technology, University Putra Malaysia. She is experienced and highly skilled academician to mentor students to achieve their academic goals, oversee computer science in a complex research and development environment, make professional and community contributions, be recognized as excellence for teaching and learning with technology, manage and collaborate with multiple partnership between national and international agencies and community and administer resources efficiently. She has (co)authored over 100 peer-reviewed articles, served as (Co-)Chair for international conferences, member of International/National Associations as Senior and Life Member, and associated with International Conferences as Programme Committee/Chair/Advisory Board/Review Board member. She is on the editorial boards of few journals. She has conducted many successful industrial projects and academic grants and awarded awards for Teaching and Research. She is now actively involving in image processing, forensics, cybersecurity and HCI research areas.