KEYNOTE SPEAKER
Witold Pedrycz, University of Alberta, Canada
Speech Title:
Knowledge-Data Environment of Machine Learning
Abstract:
Over the recent years, we have been witnessing truly remarkable progress in Machine Learning (ML) with highly visible accomplishments encountered, in particular, in natural language processing and computer vision impacting numerous areas of human endeavours. Driven inherently by the technologically advanced learning and architectural developments, ML constructs are highly impactful coming with far reaching consequences; just to mention autonomous vehicles, control, health care imaging, decision-making in critical areas, among others.
Data are central and of paramount relevance to the design methodology and algorithms of ML. While they are behind successes of ML, there are also far-reaching challenges that require urgent attention especially with the growing importance of requirements of interpretability, transparency, credibility, stability, and explainability. As a new direction, data-knowledge ML concerns a prudent and orchestrated involvement of data and domain knowledge used holistically to realize learning mechanisms and support the formation of the models.
The objective of this talk is to identify the challenges and develop a unique and comprehensive setting of data-knowledge environment in the realization of the development of ML models. We review some existing directions including concepts arising under the name of physics informed ML.
We investigate the representative topologies of ML models identifying data and knowledge functional modules and interactions among them. We also elaborate on the central role of information granularity in this area.
Bio of the Keynote Speaker Witold Pedrycz has long been engaged in the research of intelligent computing, information processing, fuzzy systems, artificial intelligence, genetic algorithms and other related fields, and has made important contributions to the research of intelligent learning, knowledge mining and representation of hybrid intelligent systems, and his research work has received wide attention and recognition from peers worldwide. He is a Fellow of IEEE, a Fellow of the Royal Society of Canada, and has served as the chairman or member of prestigious conferences in the field of intelligent computing such as IFSA/NAFIPS World Congress, IEEE Int. Conference on Fuzzy Systems, and IEEE Congress on Computational Intelligence in the past years. Since 2000, he has been the editor of many international journals such as IEEE Trans.
Data are central and of paramount relevance to the design methodology and algorithms of ML. While they are behind successes of ML, there are also far-reaching challenges that require urgent attention especially with the growing importance of requirements of interpretability, transparency, credibility, stability, and explainability. As a new direction, data-knowledge ML concerns a prudent and orchestrated involvement of data and domain knowledge used holistically to realize learning mechanisms and support the formation of the models.
The objective of this talk is to identify the challenges and develop a unique and comprehensive setting of data-knowledge environment in the realization of the development of ML models. We review some existing directions including concepts arising under the name of physics informed ML.
We investigate the representative topologies of ML models identifying data and knowledge functional modules and interactions among them. We also elaborate on the central role of information granularity in this area.
Bio of the Keynote Speaker Witold Pedrycz has long been engaged in the research of intelligent computing, information processing, fuzzy systems, artificial intelligence, genetic algorithms and other related fields, and has made important contributions to the research of intelligent learning, knowledge mining and representation of hybrid intelligent systems, and his research work has received wide attention and recognition from peers worldwide. He is a Fellow of IEEE, a Fellow of the Royal Society of Canada, and has served as the chairman or member of prestigious conferences in the field of intelligent computing such as IFSA/NAFIPS World Congress, IEEE Int. Conference on Fuzzy Systems, and IEEE Congress on Computational Intelligence in the past years. Since 2000, he has been the editor of many international journals such as IEEE Trans.
Professor Guan Gui, Nanjing University of Posts and Telecommunications
Speech Title:
Intelligent Signal Sensing and Recognition Techniques Towards 6G Physical Layer Security
Abstract:
The dawn of 6G wireless communication introduces a transformative era characterized by pervasive sensing and advanced intelligent identification, essential for ensuring physical security. This keynote speech highlights the integration of Artificial Intelligence (AI) and Deep Learning (DL) as pivotal in addressing the dynamic and complex challenges of 6G networks. We emphasize the role of AI in revolutionizing signal sensing and recognition. Our discussion centers on the application of these neural networks in enhancing signal detection, classification, and Specific Emitter Identification (SEI). By leveraging gradient-based optimization techniques, we demonstrate how ANNs can improve model and algorithm parameterization, leading to a data-driven approach that surpasses traditional rule based systems. This advancement is crucial in the physical layer of wireless communications, where intelligent signal recognition plays a key role in maintaining security and efficiency. We also explore the challenges faced by conventional model-based methods in the evolving landscape of 6G communication systems, which are marked by complex interference and uncertain channel conditions. DL emerges as a solution, offering innovative strategies for redesigning baseband module functionalities, including coding/decoding and detection processes. In conclusion, this keynote underscores the significance of integrating intelligent signal sensing and recognition with DL technologies in 6G networks. This approach not only enhances physical security but also paves the way for a more robust, efficient, and intelligent wireless communication ecosystem, capable of meeting the security demands of the future.
Bio of the Keynote Speaker Guan Gui (Fellow, IEEE) obtained his Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China in 2012. He served as a Research Assistant and Post-Doctoral Research Fellow at Tohoku University from 2009 to 2014 before becoming an Assistant Professor at Akita Prefectural University in Japan from 2014 to 2015. Since then, he has been a Professor at Nanjing University of Posts and Telecommunications in China. Dr. Gui has published over 200 papers in IEEE journals/conferences with recent research interests including intelligence sensing and recognition, intelligent signal processing, and physical layer security. His contributions to intelligent signal analysis and wireless resource optimization have earned him fellowships with the IEEE, IET, and AAIA organizations along with several Best Paper Awards such as ICC 2017, ICC 2014, VTC 2014-Spring among others. In addition to these accolades for his work within academia thus far - which include being named one of the top two percent scientists worldwide by Stanford University between years spanning from '21-'23; receiving Clarivate Analytics' Highly Cited Researcher award for Cross-Field studies during that same time period; being recognized as one of Elsevier's Highly Cited Chinese Researchers between '20-'23; earning membership status alongside Global Activities Contributions Award honors back in '18 - Dr.Gui also received numerous awards related specifically towards editorial service: The Top Editor Award for IEEE Transactions on Vehicular Technology ('19), Outstanding Journal Service Award for KSII Transactions on Internet & Information System ('20), Exemplary Reviewer Award for IEEE Communications Letters ('17). Furthermore he was awarded both the Japan Society for Promotion of Science (JSPS) Postdoctoral Fellowship For Foreign Researchers back in '12 followed by their International Fellowship For Overseas Researchers six years later. He was appointed as the Jiangsu Specially-Appointed Professor in 2016, recognized as the Jiangsu High-Level Innovation and Entrepreneurial Talent in 2016, and honored as the Jiangsu Six Top Talent in 2018. Since 2022, he has held the esteemed position of Distinguished Lecturer at the IEEE Vehicular Technology Society.
Bio of the Keynote Speaker Guan Gui (Fellow, IEEE) obtained his Ph.D. degree from the University of Electronic Science and Technology of China, Chengdu, China in 2012. He served as a Research Assistant and Post-Doctoral Research Fellow at Tohoku University from 2009 to 2014 before becoming an Assistant Professor at Akita Prefectural University in Japan from 2014 to 2015. Since then, he has been a Professor at Nanjing University of Posts and Telecommunications in China. Dr. Gui has published over 200 papers in IEEE journals/conferences with recent research interests including intelligence sensing and recognition, intelligent signal processing, and physical layer security. His contributions to intelligent signal analysis and wireless resource optimization have earned him fellowships with the IEEE, IET, and AAIA organizations along with several Best Paper Awards such as ICC 2017, ICC 2014, VTC 2014-Spring among others. In addition to these accolades for his work within academia thus far - which include being named one of the top two percent scientists worldwide by Stanford University between years spanning from '21-'23; receiving Clarivate Analytics' Highly Cited Researcher award for Cross-Field studies during that same time period; being recognized as one of Elsevier's Highly Cited Chinese Researchers between '20-'23; earning membership status alongside Global Activities Contributions Award honors back in '18 - Dr.Gui also received numerous awards related specifically towards editorial service: The Top Editor Award for IEEE Transactions on Vehicular Technology ('19), Outstanding Journal Service Award for KSII Transactions on Internet & Information System ('20), Exemplary Reviewer Award for IEEE Communications Letters ('17). Furthermore he was awarded both the Japan Society for Promotion of Science (JSPS) Postdoctoral Fellowship For Foreign Researchers back in '12 followed by their International Fellowship For Overseas Researchers six years later. He was appointed as the Jiangsu Specially-Appointed Professor in 2016, recognized as the Jiangsu High-Level Innovation and Entrepreneurial Talent in 2016, and honored as the Jiangsu Six Top Talent in 2018. Since 2022, he has held the esteemed position of Distinguished Lecturer at the IEEE Vehicular Technology Society.
Professor Zuqing Zhu, University of Science and Technology of China
Speech Title:
Machine Learning in and for Optical Data-Center Networks
Abstract:
In the first part of this talk, we will first discuss the challenges on scalability, energy and manageability of data-center network (DCN) systems, and then explain why all-optical inter-connection can be a promising solution for future DCN systems. Next, we describe a novel all-optical inter-connection architecture based on arrayed waveguide grating router (AWGR) and wavelength-selective switches (WSS'), namely, Hyper-FleX-LION, explain its operation principle, and show experimental results of running distributed machine learning (DML) in a DCN in Hyper-FleX-LION. In the second part of this talk, we will explain how machine learning can be leveraged to realized knowledge-defined networking (KDN) and facilitate network automation in DCNs. Experimental results demonstrate that KDN can automatically reduce task completion time.
Bio of the Keynote Speaker Zuqing Zhu received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of California, Davis, in 2007. From 2007 to 2011, he worked in the Service Provider Technology Group of Cisco Systems, San Jose, California, as a Senior Engineer. In January 2011, he joined the University of Science and Technology of China, where he currently is a Full Professor in the School of Information Science and Technology. He has published 360+ papers in peer-reviewed journals and conferences. He is the Steering Committee Chair of the IEEE International Conference on High Performance Switching and Routing (HPSR), and was the Chair of the Technical Committee on Optical Networking (ONTC) in IEEE Communications Society. He has received the Best Paper Awards from ICC 2013, GLOBECOM 2013, ICNC 2014, ICC 2015, and ONDM 2018. He is a Fellow of IEEE.
Bio of the Keynote Speaker Zuqing Zhu received his Ph.D. degree from the Department of Electrical and Computer Engineering, University of California, Davis, in 2007. From 2007 to 2011, he worked in the Service Provider Technology Group of Cisco Systems, San Jose, California, as a Senior Engineer. In January 2011, he joined the University of Science and Technology of China, where he currently is a Full Professor in the School of Information Science and Technology. He has published 360+ papers in peer-reviewed journals and conferences. He is the Steering Committee Chair of the IEEE International Conference on High Performance Switching and Routing (HPSR), and was the Chair of the Technical Committee on Optical Networking (ONTC) in IEEE Communications Society. He has received the Best Paper Awards from ICC 2013, GLOBECOM 2013, ICNC 2014, ICC 2015, and ONDM 2018. He is a Fellow of IEEE.
Professor Rong Gu, Nanjing University
Speech Title:
Fluid: Elastic Data Abstraction and Acceleration for BigData/AI applications on Cloud
Abstract:
In today's cloud-native environments, it's increasingly common to run data-intensive applications, such as big data and AI workloads, utilizing containerization and orchestration technologies. These platforms offer high elasticity, flexible operational costs, and numerous other advantages. However, they also introduce new challenges, particularly concerning I/O throughput during training. These challenges include complex data access with intricate performance tuning requirements, insufficient cache capacity on specialized hardware to meet high and dynamic I/O demands, and inefficient I/O resource sharing across multiple training jobs. In this talk, I will introduce Fluid, a cloud-native platform that provides a unified data abstraction enabling big data and AI jobs to seamlessly access training data from heterogeneous sources. Fluid offers transparent and elastic data acceleration powered by auto-tuned cache runtimes. Additionally, it features an intelligent on-the-fly cache system autoscaler, which dynamically adjusts cache capacity to align with the online execution speed of individual jobs. To further enhance overall system performance, Fluid co-orchestrates data caching and job scheduling to optimize job execution order. Fluid is now an open-source project hosted by the Cloud Native Computing Foundation (CNCF), with notable adopters including Alibaba Cloud, Tencent Cloud, Baidu Cloud, Weibo.com, and China Telecom, among others.
Bio of the Keynote Speaker Rong Gu is a distinguished researcher and Ph.D. advisor at the School of Computer Science, Nanjing University. His research focuses on Cloud and Big Data computing systems, Distributed AI Training and Inference Systems, and Intelligent Data Management Systems. He has authored over 70 papers in prestigious journals and conferences, including USENIX ATC, VLDB, EuroSys, KDD, ICDE, WWW, INFOCOM, VLDBJ, IEEE TPDS, TKDE, ToN, TMC, IPDPS, ICPP, and IWQoS. Rong Gu has been recognized with numerous accolades, including the DAMO Academy Young Fellow Award, the IEEE TCSC Award for Excellence in Scalable Computing (Early Career), the IEEE HPCC 2022 Best Paper Award, the First Prize of the Jiangsu Science and Technology Prize (2018), the Outstanding Alibaba Innovative Research Program Award (2023), and the Tencent Cloud Valuable Professional (TVP) Award (2021). He also holds the record for first place in the 30th SortBenchmark Competition CloudSort Track. In addition to his academic achievements, Rong Gu is a community leader, serving as the community chair of the Fluid open-source project (a CNCF Sandbox project) and as a founding PMC member of the Alluxio (formerly Tachyon) open-source project. He has also played key roles as co-program chair for IEEE iThings’22, IEEE SocialCom’23, and co-chair for the 23rd ChinaSys.
Bio of the Keynote Speaker Rong Gu is a distinguished researcher and Ph.D. advisor at the School of Computer Science, Nanjing University. His research focuses on Cloud and Big Data computing systems, Distributed AI Training and Inference Systems, and Intelligent Data Management Systems. He has authored over 70 papers in prestigious journals and conferences, including USENIX ATC, VLDB, EuroSys, KDD, ICDE, WWW, INFOCOM, VLDBJ, IEEE TPDS, TKDE, ToN, TMC, IPDPS, ICPP, and IWQoS. Rong Gu has been recognized with numerous accolades, including the DAMO Academy Young Fellow Award, the IEEE TCSC Award for Excellence in Scalable Computing (Early Career), the IEEE HPCC 2022 Best Paper Award, the First Prize of the Jiangsu Science and Technology Prize (2018), the Outstanding Alibaba Innovative Research Program Award (2023), and the Tencent Cloud Valuable Professional (TVP) Award (2021). He also holds the record for first place in the 30th SortBenchmark Competition CloudSort Track. In addition to his academic achievements, Rong Gu is a community leader, serving as the community chair of the Fluid open-source project (a CNCF Sandbox project) and as a founding PMC member of the Alluxio (formerly Tachyon) open-source project. He has also played key roles as co-program chair for IEEE iThings’22, IEEE SocialCom’23, and co-chair for the 23rd ChinaSys.