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Bin Hu Beijing Institute of Technology, China Title: |
ABSTRACT: In recent years, mental health issues have become increasingly prominent all the world. According to the report from the World Health Organization, approximately 970 million people suffer from mental disorders, accounting for 13% of the global population. Currently, the diagnosis of mental illnesses primarily relies on physician interviews and Brief Psychiatric Rating Scale (BPRS), lacking objective and quantifiable diagnostic indicators. Besides, the common treatment of mental disorders is pharmacotherapy, which is often associated with significant side effects. The rapid advancement of cutting-edge artificial intelligence and big data technologies offers new opportunities for the diagnosis and treatment of mental disorders. These technologies are shifting the approach to data driven screening and treatment, offering more precise, personalized, and effective solutions. This report will introduce the opportunities and challenges in the field of medical electronics and computational methodologies for the diagnosis and treatment of mental disorders.
BIO: Professor Hu is a (Full) Professor and the Dean of the School of Medical Technology at Beijing Institute of Technology, China. He is a National Distinguished Expert, Chief Scientist of 973 as well as National Advanced Worker in 2020. He is a Fellow of IEEE/IET/AAIA and IET Fellow Assessor & Fellowship Advisor. He serves as the Editor-in-Chief for the IEEE Transactions on Computational Social Systems and an Associate Editor for IEEE Transactions on Affective Computing. He is one of Clarivate Highly Cited Researchers and World's Top 2% Scientists. He is a Member of the Steering Council of the ACM China Council and the Vice-Chair of the China Committee of the International Society for Social Neuroscience. He is also the TC Co-Chair of computational psychophysiology in the IEEE Systems, Man, and Cybernetics Society (SMC). He is a Member of the Steering Committee of Computer Science at the Chinese Ministry of Education, Science and Technology Commission at the Chinese Ministry of Education.
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Henry Leung University of Calgary, Canada Title: |
ABSTRACT: In this talk we present our works on 3D computer vision based on RGBD sensing. A visual SLAM system on static and dynamic platforms is described that uses motion prior to obtain accurate motion estimation in metric scale to make dynamic features usable for SLAM on dynamic platforms. When depth info is not available, deep learning is used to perform depth prediction, and the predicted depth can be used for RGBD SLAM. In this talk, we will also discuss 3D object detection and tracking that can be used for obstacles avoidance, including approaches to enhance object detection in different environments. The proposed RGBD image processing techniques for SLAM, depth prediction, object detection and object tracking are applied to autonomous driving and the performance are evaluated using publicly available datasets and experimental datasets we collected for practical driving scenarios in real environments including highways, residential, semi-urban and urban roads.
BIO: Professor Leung is a Schulich Industry Research Chair Professor of the Department of Electrical and Software Engineering at the University of Calgary, Canada. His current research interests include data analysis, information fusion, machine learning, signal and image processing, robotics, and internet of things. He has published over 350 journal papers and 250 refereed conference papers. Dr. Leung has been the associate editor of various journals such as the IEEE Circuits and Systems Magazine, International Journal on Information Fusion, IEEE Trans. Aerospace and Electronic Systems, IEEE Signal Processing Letters, IEEE Trans. Circuits and Systems, Scientific Reports He has also served as guest editors for the special issue “Intelligent Transportation Systems” for the International Journal on Information Fusion and “Cognitive Sensor Networks” for the IEEE Sensor Journal. He is the editor of the Springer book series on “Information Fusion and Data Science”. He is a Fellow of IEEE, SPIE, Engineering Institute of Canada (EIC) and Canadian Academy of Engineering (CAE).
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Hui Yu University of Glasgow, UK Title: |
ABSTRACT: Human face is one of the key means for social communication and social signal conveying. It represents one of the principal features of natural interaction. Computational and psychophysical research has identified a wide range of social signals conveyed by the face. However, it still faces challenges to fully uncover and understand social signals from human faces. It is thus essential to develop computational models allowing us to perceive these social signals from images and video streams for various applications such as affective computing, social robotics, social interaction, social cognition, and cognitive neuroimaging. In addition, multimodal information including visual and biometric signals can record the facial muscle activity or brain activity closely related to facial movements and the internal emotional states. These multiple sensing channels would help provide an insight into the emotion and social signals of facial expressions. This talk will discuss both computational and psychophysical methods for understanding facial expression and the causative mechanism of emotion combining knowledge of visual computing with multiple disciplines, such as cognitive computing and machine learning.
BIO: Professor Yu is a Professor with the University of Glasgow, UK. He leads the Visual Computing and Social Robot Group at the university. His research interests lie in visual and cognitive computing as well as machine learning with applications to social signal analysis, social robot, human-machine interaction, intelligent vehicle, and video analysis. Professor Yu’s research work has led to several awards and successful collaboration with worldwide institutions and industries. He is the Associate Vice President of IEEE Systems, Man, and Cybernetics Society and a Scientific Advisor for a high-tech company in the UK. Prof. Yu is the PI on grants from a diverse range of funding sources including the EPSRC, EU FP7, RAEng, Royal Society, Innovate UK and Industry. He has been awarded Industrial Fellowship by the Royal Academy of Engineering. He serves as an Associate Editor for IEEE Transactions on Human-Machine Systems, IEEE Transactions on Computational Social Systems, IEEE Transactions on Intelligent Vehicles and IEEE/CAA, Journal of Automatica Sinica.
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Michael Milford Queensland University of Technology, Australia Title: |
ABSTRACT: Recently, for robots, autonomous vehicles and general technology platforms to ever be deployed ubiquitously in the world around us, they must meet certain requirements. Firstly, they must be performant - and this has been the focus of the vast majority of research attention, focusing on levels of performance as well as their generality and robustness. Secondly, most robot deployments will be in some manner collaborative or supervised - midway between the human-only traditional model and the speculative fully autonomous approach. Collaboration requires key capabilities from autonomous systems, most notably introspective capability, so that they can work and interact seamlessly with people. Thirdly, they must operate in a manner that is acceptable by end-users: a great example of this being complying with legal and social expectations around privacy in the case of perception systems. Finally, they must be safe and fit-for-purpose, and at least some of the metrics and the manner in which we measure this performance for research should ideally be directly predictive of these properties. In this talk I'll highlight challenges and limitations in all of these areas, and, using both applied industry and fundamental research projects as examples, showcase work we've done to address these challenges.
BIO: Professor Milford conducts interdisciplinary research at the boundary between robotics, neuroscience, computer vision and machine learning, and is a multi-award winning educational entrepreneur. His research models the neural mechanisms in the brain underlying tasks like navigation and perception to develop new technologies in challenging application domains such as all-weather, anytime positioning for autonomous vehicles. From 2022 – 2027 he is leading a large research team combining bio-inspired and computer science-based approaches to provide a ubiquitous alternative to GPS that does not rely on satellites. He is also one of Australia’s most in demand experts in technologies including self-driving cars, robotics and artificial intelligence, and is a passionate science communicator. He currently holds the position of Director of the QUT Centre for Robotics, Australian Research Council Laureate Fellow, Professor at the Queensland University of Technology, and is a Microsoft Research Faculty Fellow and Fellow of the Australian Academy of Technology and Engineering.
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Yoshihide Sekimoto The University of Tokyo, Japan Title: |
ABSTRACT: Recently, the term "smart city" has gained widespread popularity as a concept that represents futuristic cities utilizing cutting-edge information technology. Examples of such cities include autonomous driving cities with zero accidents or cashless cities equipped with numerous surveillance cameras. However, it is important to acknowledge that the majority of cities worldwide are not large or extraordinary, and their focus should be on sustainability for the benefit of their citizens. In this regard, fostering collaboration between citizens and local governments, utilizing self-controlled data that is not exclusively governed by a single stakeholder, such as a large corporation, becomes crucial. To address this need, the introduction of the "People Flow Project" and the "Geospatial Information Center" is proposed as research initiatives based on data governance. Furthermore, the "My City X" project is aimed at providing citizens with collaborative urban planning tools, including the "My City Forecast" for predicting city developments, and the "My City Report" for monitoring civil infrastructure. These tools will leverage a city dashboard, various types of open data, and machine learning techniques to facilitate effective urban management.
BIO: Professor Sekimoto directs the Human-Centered Urban Informatics Laboratory, established in April 2013, which is part of the Institute of Industrial Science (IIS) at the University of Tokyo. He is currently the Director and Professor of the Center for Spatial Information Science (CSIS) at the University of Tokyo. He received his Ph.D. in civil engineering from The University of Tokyo in 2002. He had previously worked at the National Institute for Land, Infrastructure and Management from 2002-2007 and the Center for Spatial Information Science at the University of Tokyo from 2007-2013. Lab HP: http://sekilab.iis.u-tokyo.ac.jp/
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Wenjing Lou Virginia Polytechnic Institute and State University, USA Title: |
ABSTRACT:
While AI is revolutionizing various industries, it raises considerable privacy concerns due to its reliance on the collection and analysis of extensive amounts of personal data. Federated learning, a distributed learning paradigm allowing institutions to collaboratively train models without moving data across institutional boundaries, is thus highly advantageous due to its ability to maintain data locality and address legal and ethical barriers to data sharing. However, recent research has shown that federated learning is susceptible to privacy attacks, such as data reconstruction and membership inference, where sensitive information can be inferred from model updates.
This talk will provide an overview of privacy attacks in federated learning, focusing on the underlying causes and examining the latest attack methodologies. We will introduce a STOA model inversion attack called scale-MIA. This attack efficiently reconstructs clients’ training samples from aggregated model updates in federated learning and undermines the effectiveness of secure aggregation protocols. We will also discuss the impact of such attacks and explore emerging solutions to enhance privacy in federated learning systems.
BIO: Wenjing Lou is the W. C. English Endowed Professor of Computer Science at Virginia Tech and a Fellow of the IEEE and ACM. Her research interests cover many topics in the cybersecurity field, with her current research interest focusing on security and privacy problems in wireless networks, blockchain, trustworthy machine learning, and Internet of Things (IoT) systems. Prof. Lou is a highly cited researcher by the Web of Science Group. She received the Virginia Tech Alumni Award for Research Excellence in 2018, the highest university-level faculty research award. She received the INFOCOM Test-of-Time paper award in 2020. She is the TPC chair for IEEE INFOCOM 2019 and ACM WiSec 2020. She was the Steering Committee Chair for IEEE CNS conference from 2013 to 2020. She is currently the vice chair of IEEE INFOCOM and a steering committee member of IEEE CNS. She served as a program director at the US National Science Foundation (NSF) from 2014 to 2017.
Organizers:
IEEE Ethics Reporting
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