Important Dates

Paper Submission Deadline

July 01, 2024

July 22, 2024


Author Notification

August 01, 2024

August 20, 2024


Camera-Ready Deadline

September 01, 2024

September 15, 2024

October 01, 2024

October 19, 2024

October 22, 2024


Registration Deadline

October 01, 2024


Conference Date

October 30 - November 02, 2024

Sponsored and supported by

Keynotes Speaker

Witold Pedrycz

Title:
Bringing Knowledge to Design and Analysis in Machine Learning

 

ABSTRACT: Over the recent years, we have been witnessing an unpreceded progress in Machine Learning (ML) that has resulted in highly visible and impactful accomplishments reported in numerous areas of applications.

Data are central and of paramount relevance to the design methodologies and algorithms of ML. While they are behind successes of ML, there are also far-reaching challenges that must be prudently addressed in light of the growing importance of quests for interpretability, transparency, credibility, stability, and explainability and a scope of applications and deployment requirements. Recently, knowledge associated with the problem for which ML models are constructed, has started to play a visible role and impacted the landscape of the ML methodologies by offering an original paradigm referred to as a knowledge-data ML. As a new discipline, knowledge-data ML focuses on a prudent and orchestrated engagement of data and knowledge in the design practices of the ML architectures.

Data and knowledge arise at very different levels of abstraction with knowledge being formalized and represented at symbolic level. We advocate that to develop a cohesive and unified framework of coping with data and knowledge in learning processes, one has to reconcile highly distinct levels of abstraction and with this regard information granules play a pivotal role.

We offer a taxonomy of knowledge by distinguishing between scientific and common-sense knowledge and elaborate on a spectrum of ensuing knowledge representation scheme. In the sequel, the main categories of knowledge-oriented ML design are discussed including physics-informed ML (with the reliance of scientific knowledge), an augmentation of data driven models through knowledge-oriented constraints, a development of granular expansion of the data-driven model and ways of building ML models in the presence of knowledge conveyed by rules. When analyzing the proposed categories, it is also clearly explained how the new ML environment helps avoid a growing effect of data blinding.

BIO: Witold Pedrycz (IEEE Life Fellow) is Professor in the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is also with the Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a foreign member of the Polish Academy of Sciences and a Fellow of the Royal Society of Canada. He is a recipient of several awards including Norbert Wiener award from the IEEE Systems, Man, and Cybernetics Society, IEEE Canada Computer Engineering Medal, a Cajastur Prize for Soft Computing from the European Centre for Soft Computing, a Killam Prize, a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society, and 2019 Meritorious Service Award from the IEEE Systems Man and Cybernetics Society.

His main research directions involve Computational Intelligence, Granular Computing, and Machine Learning, among others.

Professor Pedrycz serves as an Editor-in-Chief of Information Sciences, Editor-in-Chief of WIREs Data Mining and Knowledge Discovery (Wiley), and Co-editor-in-Chief of J. of Data Information and Management (Springer).


Vlado Stankovski

Title:
Advancing Sustainability through Software Innovation: Insights from Horizon Europe Projects

 

ABSTRACT: The accelerating pace of technological innovation brings with it both challenges and opportunities for sustainability. In this invited talk, I will present key software technologies and results from a range of Horizon Europe projects that are contributing to sustainable development across diverse domains. Key projects include DECENTER, which explores the convergence of AI and IoT for optimized edge computing in smart environments, providing energy-efficient solutions that reduce carbon footprints. ONTOCHAIN and TRUSTCHAIN focus on secure and transparent decentralized frameworks, leveraging blockchain to foster trust, sustainability, and efficiency in digital ecosystems, with particular attention to metadata management, trustworthiness, and ethical governance.

I will also discuss ExtremeXP and Swarmchestrate, which contribute to sustainable distributed computing by exploring extreme-scale processing capabilities and orchestrating swarms of devices and agents for optimized performance. Both projects are pivotal in scaling sustainable digital solutions, reducing energy consumption, and enhancing system efficiency.

The talk will showcase how these software technologies, developed in collaboration across Europe, offer practical and innovative solutions to some of the most pressing sustainability challenges. By emphasizing energy efficiency, transparency, and ethical use of technology, these Horizon Europe projects exemplify how cutting-edge research, innovation and development can align with the global goals of sustainability.

BIO: Professor Vlado Stankovski is a distinguished academic and researcher at the University of Ljubljana, Slovenia, where he also severs as the Vice-dean at Faculty of Computer and Information Science. He specializes in cloud and edge computing, artificial intelligence, blockchain, and distributed systems. As a leading figure in Horizon Europe research projects, including DECENTER, ONTOCHAIN, TRUSTCHAIN, BUILDCHAIN, Microcredentials for Microcompetences, EBSI, EBSI-VECTOR , ACES , ExtremeXP, and Swarmchestrate, Professor Stankovski has made significant contributions to the development of sustainable digital ecosystems. His research focuses on leveraging AI and distributed ledger technologies for secure, efficient, and transparent computing infrastructures.

In addition to his academic endeavors, Professor Stankovski plays a critical role in standardization activities within the European Commission and global initiatives, driving the evolution of technologies toward sustainable development. His interdisciplinary work bridges technical innovation and practical applications in areas such as smart cities, IoT, and energy-efficient computing systems. Professor Stankovski has published widely in leading journals and conferences and is frequently invited to speak at international venues on topics related to sustainability, AI, and future computing paradigms. His expertise is at the forefront of guiding the responsible use of advanced technologies in real-world applications.


Lei Ren

Title:
Industrial AI: Towards the Era of Industrial Foundation Models

 

ABSTRACT: Since 2023, the emergence of a series of AI large language models has had a huge impact on academia and industry. Industrial Internet has also accelerated from digitalization and networking to intellectualization, driven by the wave of new technologies of AI foundation models. This report will introduce the development context of the new generation of AI and the new generation of intelligent manufacturing, and discuss the emerging hot technologies of intelligent transformation of industrial Internet, including industrial edge intelligence, industrial big data intelligence, industrial cloud-edge collaborative intelligence, industrial digital twin intelligence, industrial metaverse and other technological frontiers. This report will also look forward to the future development direction of industrial foundation model, and introduce the work progress of the Beihang University team in building the base of the industrial foundation model.

BIO: Prof. Lei Ren, the first recipient of the National Outstanding Youth Fund in the field of Industrial Internet and the Chief Scientist of the National Key Research and Development Program (Industrial Software Special Project) of China. He is a professor at the School of Automation and the School of Software at Beihang University, as well as a doctoral advisor. He also serves as the Deputy Director of the Special Committee of the National Key Laboratory for Intelligent Manufacturing of Complex Products. His research areas include the Industrial Internet, industrial software, industrial AI, and large-scale industrial models.

Prof. Lei Ren has led over 20 national and provincial-level projects, including major national scientific and technological projects, the National Key Research and Development Program, and major research plans of the National Natural Science Foundation of China. He has published more than 100 papers in prestigious international journals, such as IEEE Transactions, with nearly 10,000 citations. He has been listed in Stanford's Top 2% Scientists for Lifetime Impact. He has led or participated in the development of 15 international/national standards, holds over 50 patents and software copyrights, and his core technologies have been applied in more than 300 companies, yielding significant economic and social benefits. He is a member of expert committees or professional committees of over 10 domestic and international academic organizations, including IEEE, CSF, CCF, CAAI, and CAA. He is the Vice Chair of the Special Committee on Intelligent IoT of the China Simulation Federation and the Vice Chair of the Cloud Control and Decision-Making Committee of the Chinese Society of Command and Control. He also serves on the editorial boards of prominent international journals such as IEEE TNNLS and TMECH. Prof. Lei Ren is the Vice Chair of the Talent Working Group of the China Industrial Internet Industry Alliance and pioneered the establishment of the "Industrial Internet" course and online digital course in universities nationwide. He has chaired dozens of IEEE series and other academic conferences both domestically and internationally, and has been invited to deliver over 100 keynote speeches at various conferences.


Dusit Niyato

Title:
Toward Scalable Generative AI via Mixture of Experts in Mobile Edge Networks

 

ABSTRACT: The evolution of generative artificial intelligence (GAI) has driven revolutionary applications like ChatGPT. The proliferation of these applications is underpinned by the mixture of experts (MoE), which contains multiple experts and selectively engages them for each task to lower operation costs while maintaining performance. Despite MoE's efficiencies, GAI still faces challenges in resource utilization when deployed on local user devices. Therefore, we first propose mobile edge networks supported MoE-based GAI. Rigorously, we review the MoE from traditional AI and GAI perspectives, scrutinizing its structure, principles, and applications. Next, we present a new framework for using MoE for GAI services in Metaverse. Moreover, we propose a framework that transfers subtasks to devices in mobile edge networks, aiding GAI model operation on user devices. Moreover, we introduce a novel approach utilizing MoE, augmented with Large Language Models (LLMs), to analyze user objectives and constraints of optimization problems based on deep reinforcement learning (DRL) effectively. This approach selects specialized DRL experts, and weights each decision from the participating experts. In this process, the LLM acts as the gate network to oversee the expert models, facilitating a collective of experts to tackle a wide range of new tasks. Furthermore, it can also leverage LLM's advanced reasoning capabilities to manage the output of experts for joint decisions. Lastly, we insightfully identify research opportunities of MoE and mobile edge networks.

BIO: Dusit Niyato is a President's Chair Professor in the College of Computing & Data Science (CCDS), Nanyang Technological University, Singapore. Dusit's research interests are in the areas of mobile generative AI, edge intelligence, quantum computing and networking, and incentive mechanism design. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Dusit won the IEEE Vehicular Technology Society Stuart Meyer Memorial Award. Currently, Dusit is serving as Editor-in-Chief of IEEE Communications Surveys and Tutorials (impact factor of 34.4 for 2023) and will serve as the Editor-in-Chief of IEEE Transactions on Network Science and Engineering (TNSE) from 2025. He is also an area editor of IEEE Transactions on Vehicular Technology (TVT), topical editor of IEEE Internet of Things Journal (IoTJ), lead series editor of IEEE Communications Magazine, and associate editor of IEEE Transactions on Wireless Communications (TWC), IEEE Transactions on Mobile Computing (TMC), IEEE Wireless Communications, IEEE Network, IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Cognitive Communications and Networking (TCCN), IEEE Transactions on Services Computing (TSC), and ACM Computing Surveys. Dusit is the Members-at-Large to the Board of Governors of IEEE Communications Society for 2024-2026. He was named the 2017-2023 highly cited researcher in computer science. He is a Fellow of IEEE and a Fellow of IET.


Jinjun Chen

Title:
Composite DP-unbias: Bounded and Unbiased Composite Differential Privacy

 

ABSTRACT: The most kind of traditional DP (Differential Privacy) mechanisms (e.g. Laplace, Gaussian, etc.) have unlimited output range. In real scenarios, most datasets have bounded output range. Users would then need to use post-processing or truncated mechanisms to forcibly bound output distribution. However, these mechanisms would incur bias problem which has been a long-known DP challenge, resulting in various unfairness issues in subsequent applications. A tremendous amount of research has been done on analyzing this bias problem and its consequences, but no solutions can solve it fully.

As the world first solution to solve this long-known DP bias problem, this talk will present a new innovative DP mechanism named Composite DP-unbias. It will first illustrate this long-known bias problem, and then detail the rational of the new mechanism and its example noise functions as well as their implementation algorithms. All source codes are publicly available on Github for any deployment or verification.

BIO: Dr Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include data privacy and security, cloud computing, scalable data processing, data systems and related various research topics. His research results have been published in more than 300 papers in international journals and conferences. He received various awards such as IEEE TCSC Award for Excellence in Scalable Computing and Australia’s Top Researchers. He has served as an Associate Editor for various journals such as ACM Computing Surveys, IEEE TC, TCC and TSUSC. He is a MAE (Academia Europea) and IEEE Fellow (IEEE Computer Society). He is Chair for IEEE TCSC (Technical Community for Scalable Computing).


Shuai Ma

Title:
Approximate Computation for Big Data Analytics

 

ABSTRACT: Over the past a few years, research and development has made significant progresses on big data analytics with the supports from both governments and industries all over the world, such as Spark, IBM Watson and Google AlphaGo. A fundamental issue for big data analytics is the efficiency, and various advances towards attacking this issues have been achieved recently, from theory to algorithms to systems. In this talk, we shall present the idea of approximate computation for efficient and effective big data analytics: query approximation and data approximation, based on our recent research experiences. Different from existing approximation techniques, the approximation computation that we are going to introduce does not necessarily ask for theoretically guaranteed approximation solutions, but asks for sufficiently efficient and effective solutions in practice.

BIO: Shuai Ma is a full professor in the School of Computer Science and Engineering, Beihang University, China. He obtained two PhD degrees: University of Edinburgh in 2010 and Peking University in 2004, respectively. His research interests include database theory and systems, and big data. He is a recipient of the best paper award of VLDB 2010, the best challenge paper award of WISE 2013, the National Science Fund of China for Outstanding Young Scholars in 2019, and the special award of Chinese Institute of Electronics for progress in science and technology in 2017 . He is/was an Associate Editor of VLDB Journal IEEE Transactions on Big Data and Knowledge and Information Systems.


Hongsheng Liu

Title:
AI scientific computing industry trends and MindSpore practice

 

ABSTRACT: AI is accelerating the development of natural sciences and improving scientists’ understanding of physical phenomena in various fields. This report reviews the latest academic and industrial progress of AI4Sci, and introduces the latest research of Huawei AI4Sci Lab based on Ascend AI basic software and hardware and MindSpore AI framework in AI empowering biology, fluids, meteorology and other directions.

BIO: He holds a bachelor's degree from the Special Class for the Gifted Young of the University of Science and Technology of China and a Ph.D. in statistics from the University of North Carolina at Chapel Hill. Currently, he is the MindSpore architect/AI fluid simulation leader of Huawei 2012 AI4Science Lab. He leads the team to cooperate with COMAC, Peking University and other teams to jointly incubate the large model "Eastern Wing Wind" for aerodynamic simulation of large aircraft wings, and won the WAIC 2023 SAIL Awards. Leading the design and implementation of the architecture and algorithms of MindSpore Science AI scientific computing platform, and publishing more than 10 patents and papers.


Mianxiong Dong

Title:
Empowering Music Research with AI: Advances in Automatic Music Transcription

 

ABSTRACT: The rapid development of artificial intelligence (AI) has transformed various fields, including music. AI-driven tools have already made significant strides in music composition, generation, and classification. However, a critical challenge remains: the transcription of vast amounts of musical data. Traditional manual transcription is resource-intensive, while the accuracy of existing automatic music transcription (AMT) systems continues to be a limitation. This presentation addresses these challenges by exploring emerging technologies to enhance the performance of AMT models. It highlights key advancements in leveraging unlabeled data for model pre-training and the use of synthetic data to overcome the scarcity of labeled datasets. Additionally, the potential of multimodal approaches in AMT is examined, providing a roadmap for future research in AI-powered music transcription.

BIO: Mianxiong Dong was born in Shanghai, China. He received B.S., M.S. and Ph.D. in Computer Science and Engineering from The University of Aizu, Japan. He is the Vice President and Professor of Muroran Institute of Technology, Japan. His research interests include Wireless Networks, Cloud Computing, and Cyber-physical Systems. He has published over 480 academic papers, of which more than half are published in top journals such as IEEE JSAC, IEEE TIFS, IEEE TPDS, as well as top conferences including IEEE INFOCOM. A total of 7 papers have been selected as ESI Hot Papers (top 0.1%), and 33 papers have been selected as ESI Highly Cited Papers (top 1%). He serves as the Principal Investigator of more than ten research projects, including Japan Society for the Promotion of Science (JSPS) KAKENHI and KDDI Foundation, Program Officer (PO) of JST Support for Pioneering Research Initiated by the Next Generation (SPRING). He is the recipient of NISTEP Researcher 2018 (one of only 11 people in Japan) in recognition of significant contributions in science and technology, The Young Scientists’ Award from MEXT in 2021, SUEMATSU-Yasuharu Award from IEICE in 2021, IEEE TCSC Middle Career Award in 2021. He is Clarivate Analytics 2019, 2021, 2022, 2023 Highly Cited Researcher (Web of Science), Fellow of AAIA, Fellow of AIIA, Foreign Fellow of EAJ.


Hui Li

Title:
The Thinking, Practice, Enlightenment on Cyber-security

 

ABSTRACT: IP network has three fatal defects, one is its Cyberspace is exclusive monopoly; Second, it does not have any security gene, so accidents emerge in endlessly, and cannot be eradicated; Third, the cost in evolution and upgrading of the architecture is huge and the time is very long. China has proposed the idea of a community of shared future in cyberspace for the first time in the world, which was widely recognized now. So where is the technical solution to implement this idea? Answer: CoG-MIN is the first and only comprehensive solution to the above three IP problems in the world. In this talk, we will introduce its principle, applications, challenges and open problems.

BIO: Dr Hui Li (李挥) is a Professor of Peking University, got his Bachelor degree from Tsinghua University, PhD from Chinese University of Hong Kong. He is the Director of Peking University Laboratory of National Major Science and Technology Infrastructure Future Network, Academician of Russian Academy of Natural Sciences, Chief Information Scientist of International Academician Science and Technology Innovation Center, member of the United Nations World Digital Technology Academy; Zhongguancun Military-civilian integration information equipment industry promotion Association GW constellation network chief engineer; Vice president of Network Security Mimic Technology and Industrial Innovation Alliance of Ministry of Industry and Information Technology; Director of IEEE Blockchain Shenzhen Expert Committee.

      


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