Prof. Ping Wang, York University, Canada（IEEE Fellow, York Research Chair）Ping Wang is a Professor at the Department of Electrical Engineering and Computer Science, York University, and a Tier 2 York Research Chair. Prior to that, she was with Nanyang Technological University, Singapore, from 2008 to 2018. Her research interests are mainly in radio resource allocation, network design, performance analysis and optimization for wireless communication networks, mobile cloud computing and the Internet of Things. Her recent works focus on integrating Artificial Intelligence (AI) techniques into communications networks. Her scholarly works have been widely disseminated through top-ranked IEEE journals/conferences, received more than 28,000 citations, and received the Best Paper Awards from IEEE prestigious conference WCNC in 2012, 2020 and 2022, from IEEE Communication Society: Green Communications & Computing Technical Committee in 2018, from IEEE flagship conference ICC in 2007. She has been serving as an associate editor-in-chief for IEEE Communications Surveys & Tutorials and an editor for several reputed journals including IEEE Transactions on Wireless Communications. She is a Fellow of the IEEE and a Distinguished Lecturer of the IEEE Vehicular Technology Society. Speech Title: Towards Fast-Convergent Federated Learning Abstract: Federated Learning (FL) is a promising approach that allows for collaborative machine learning across distributed nodes while preserving privacy-sensitive data. It enables local nodes to train a task model under the guidance of a central server, without the need to access end-user data. However, achieving fast convergence in FL is not without its challenges. One of the main obstacles is the presence of non-independent and identically distributed (non-IID) data samples across participating nodes. This discrepancy can slow down model training and necessitate additional communication rounds for FL to converge. Furthermore, each round of information exchange between the server and the local nodes incurs significant communication overhead, requiring orthogonal channel resources for each node. In a network environment with limited radio resources, this can significantly slow down the FL process. Moreover, local nodes often have varying hardware capacities, such as CPU cycle, memory, and power. These resource-constrained nodes can become stragglers, complicating the model learning process. In this talk, I will discuss our recent efforts to overcome these challenges. Our goal is to speed up model convergence in FL within wireless networks, taking into account limited wireless resources, data, and device heterogeneities. |
|
| Prof. Hongzhi Wang, Harbin Institute of Technology, China（Head of massive data computing center,IEEE Senior member）Hongzhi Wang, Professor, PHD supervisor, the head of massive data computing center, the secretary general of ACM SIGMOD China, outstanding CCF member, IEEE Senior member, a standing committee member CCF databases and a member of CCF big data committee. Research Fields include big data management and analysis, database systems, knowledge engineering and data quality. He was “starring track” visiting professor at MSRA and postdoctoral fellow at University of California, Irvine. Prof. Wang has been PI for more than 10 national or international projects including NSFC key project, NSFC projects and National Technical support project, and co-PI for more than 10 national projects include 973 project, 863 project and NSFC key projects. He also serves as a member of ACM Data Science Task Force. He has won First natural science prize of Heilongjiang Province, MOE technological First award, Microsoft Fellowship, IBM PHD Fellowship and Chinese excellent database engineer. His publications include over 300 papers in the journals and conferences such as VLDB Journal, IEEE TKDE, VLDB, SIGMOD, ICDE and SIGIR, 6 books and 6 book chapters. His PHD thesis was elected to be outstanding PHD dissertation of CCF and Harbin Institute of Technology. He severs as the reviewer of more than 20 international journal including VLDB Journal, IEEE TKDE, and PC members of over 50 international conferences including SIGMOD, VLDB, KDD, ICML, NeurpIS, ICDE, etc. His papers were cited more than 4000 times. Speech Title: Data Management for Cloud-Edge-Device Collaboration Abstract: Cloud-Edge-Device (CED) collaboration is the foundation of applications such as intelligent manufacturing and smart cities, and collaborative computing is an important supporting technology. The new computing model of CED collaboration proposes effective integration of CED computing capabilities for data management, achieving comprehensive collaboration of data processing, communication, storage, and other capabilities, bringing challenges such as heterogeneity, high dimensionality, real-time performance, and availability. This talk focuses on these challenging issues and conducts in-depth research on the new data management theories and key technologies of cloud edge collaboration, focusing on the three key scientific issues of polymorphic collaboration, efficient balance, and dynamic availability in cloud edge collaborative data management. The speaker fully leverages the advantages of strong timeliness, high security, and low cost in CED collaborative data management. This talk will introduce the basic concepts, challenges, and exploration of the reporter team in cloud edge collaborative data management. |
Prof. Xin Xu, Wuhan University of Science and Technology, China（IEEE Senior Member）Xin Xu received the B.S. and Ph.D. degrees in computer science and engineering from Shanghai Jiao Tong University, Shanghai, China, in 2004 and 2012 respectively. He is currently a Full Professor with the School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, China. His research deals with image processing, computer vision, and deep learning. More specifically, his research areas focus on building a hierarchical person re-identification architecture including detection and recognition for nighttime surveillance scenarios. His publication was selected as the cover paper of the journal International Journal of Intelligent Systems in 2022. He was shortlist in the Best Paper Finalist of the IEEE International Conference on Multimedia and Expo (ICME) 2021. Speech Title: Rethinking interactive annotation of image data for re-identification task Abstract: AI's success is largely attributed to "data intelligence" and machine learning, which extracts knowledge, patterns, and models from data. One of the problems that big data and model have faced is that, while powerful and easy to demonstrate, it has been a challenge to make it widely available in a variety of scenarios. In many vertical domain, utilizing fully supervised/weakly supervised models usually perform better than using large models directly. To effectively address practical problems in vertical domain and promote AI's integration with the real economy, it is urgent to fully cooperation between big models and data annotation. |
|
| Prof. Yulin Wang, Wuhan University, China（Deputy Director of Hubei Provincial Science and Technology Commission）Yulin Wang is a full professor in the School of Computer Science, Wuhan University, China. His research interests include image and video processing, digital rights management, information security, intelligent system, e-commerce, IoT, code clone and so on. He got his PhD degree from University of London, UK. He got his master and bachelor degree from Huazhong University of Science and Technology（HUST）and Xi-Dian University respectively, both in China. Before joining the Wuhan University, he has worked in Hi-tech IT industry, including HUAWEI© and national research institute, for more than ten years. He has involved more than 15 national and international research projects. In recently 10 years, Prof. Wang has published 1 book, and 50+ journal and conference papers, including in IEEE TIP. He holds 10 authorized patents. Prof. Wang served as EiC of 2 international journals and reviewer of top IEEE and ACM journals. He also served as reviewer of Innovative talents projects and national research funds, including National High Technology Research and Development Program of China. Prof. Wang was the external PhD advisor of Dublin City University, Ireland during 2008-2010. In recently 10 years, Prof. Wang served as chairman of more than 10 international conferences, and keynote speakers in more than 20 international conferences. Besides UK, he visited US, France,Italy, Portugal,Croatia, Australia, Germany, korea, Ireland,Singapore, Malaysia, Japan, and Hong Kong. In addition, Prof. Wang has been appointed as the deputy director of Hubei provincial science and technology commission (CAPD) since 2014. Speech Title: Image Authentication and Tamper Localization Abstract: Image authentication can be used in many fields, including e-government, e-commerce, national security, news pictures, court evidence, medical image, engineering design, and so on. Since some content-preserving manipulations, such as JPEG compression, contrast enhancement, and brightness adjustment, are often acceptable—or even desired—in practical application, an authentication method needs to be able to distinguish them from malicious tampering, such as removal, addition, and modification of objects. Therefore, the traditional hash-based authentication is not suitable for the application. As for the semi-fragile watermarking technique, it meets the requirements of the above application at the expense of severely damaging image fidelity. In this talk, we propose a hybrid authentication technique based on what we call fragile hash value. The technique can blindly detect and localize malicious tampering, while maintaining reasonable tolerance to conventional content-preserving manipulations. The hash value is derived from the relative difference between each pair of the selected DCT coefficient in a central block and its counterpart which is estimated by the DC values of the center block and its adjacent blocks. In order to maintain the relative difference relationship when the image undergoes legitimate processing, we make a pre-compensation for the coefficients. Finally, we point out the direction using deep leaning technique for image authentication. |
Prof. Qiang Chen, Nanjing University of Science and Technology, China（CCF Senior Member）Qiang Chen received his doctoral degree from Nanjing University of Science and Technology and completed his postdoctoral research at Stanford University. He is a professor and doctoral supervisor at the School of Computer Science and Engineering/Artificial Intelligence Institute at Nanjing University of Science and Technology. He is a Senior Member of CCF and an editorial board member of "Frontiers in Medicine" and "Journal of Image and Graphics". He has served as an excellent reviewer for several journals such as Transactions on Medical Imaging and Acta Automatica. His research work mainly focuses on image processing, computer vision, and pattern recognition. He has published over a hundred papers in journals and conferences including MedIA, TMI, Ophthalmology, and MICCAI. and his publications have received over 3500 citations on Google Scholar. He has also been granted more than twenty national patents. Qiang Chen has been honored with the Second Prize of Jiangsu Science and Technology Award (ranked 2nd), the title of "333 Project" Talents in Jiangsu Province, the title of Excellent Young Backbone Teacher in Jiangsu Province Qinglan Project, and the Jiangsu Province Six Talent Peaks Award. Speech Title: Controllable image generation for medical image analysis Abstract: This presentation will first introduce the existed image generation method, and analyze the difference between the natural and medical image generation. Then, we will simply share our work to overcome several challenges of medical image analysis by using image generation, including low image quality, high cost of image annotation and acquisition of different modalities, difficulty in obtaining rare cases and temporal data, and data privacy protection. |
|
