Li Qing
The Hong Kong Polytechnic University, China
Qing Li is a Chair Professor and Head of the Department of Computing, the
Hong Kong Polytechnic University. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and
Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His
research interests include multi-modal data management, conceptual data modeling, social media, Web
services, and e-learning systems. He has authored/co-authored over 500 publications in these areas, with
over 61,000 citations and H-index of 99 (source: Google Scholars). He is actively involved in the
research community and has served as an Editor-in-Chief of Computer & Education: X Reality (CEXR) by
Elsevier, an associate editor of IEEE Transactions on Artificial Intelligence (TAI), IEEE Transactions
on Cognitive and Developmental Systems (TCDS), IEEE Transactions on Knowledge and Data Engineering
(TKDE), ACM Transactions on Internet Technology (TOIT), Data Science and Engineering (DSE), and World
Wide Web (WWW) Journal, in addition to being a Conference and Program Chair/Co-Chair of numerous major
international conferences. He also sits/sat in the Steering Committees of DASFAA, ACM RecSys, IEEE
U-MEDIA, WISE and ICWL. Prof. Li is a Fellow of IEEE.
Speech Title: PolyRAG: a Multi-level Querying Method for an Indoor Robot Smart Space
Abstract
Smart Space denotes dynamic, adaptive environments enhanced with robotics
and AI technologies. Examples include smart homes/offices/cafes. By leveraging and integrating Computer
Vision, Natural Language Processing, AIoT, Data Mining, Recommender Systems, and Sympathetic Computing,
Smart Space can help improve efficiency, personalization, and user satisfactions with seamless
interactions. In this talk, we introduce PolyRAG, a multi-level knowledge QA framework supporting
multi-level querying for an indoor robot application system. Building on top of a naive RAG layer, we
build a knowledge pyramid by adding a knowledge graph layer and an ontology schema, so as to obtain a
good balance of recall and precision when applied to a specific domain such as coffee robot
interactions. We employ cross-layer augmentation techniques for comprehensive knowledge coverage and
dynamic updates of the Ontology scheme and instances. To ensure compactness, we utilize cross-layer
filtering methods for knowledge condensation in KGs. An experimental coffee robot prototype is
constructed, and preliminary empirical studies are conducted to show the effectiveness of our PolyRAG
supporting a waterfall model for querying from ontology to KG to chunk-based raw text.
Simon K.S. Cheung
Hong Kong Metropolitan University, China
Dr. Simon K.S. Cheung is currently the Chief Information Officer at the
Hong Kong Metropolitan University, responsible for overseeing IT services that support teaching,
learning, research, and administration. He holds a BSc and a PhD in Computer Science from the City
University of Hong Kong, and a Master of Public Administration with Distinction from the University of
Hong Kong. He has also completed executive education at the University of Oxford's Saïd Business
School and Harvard Kennedy School.
With over 35 years of experience in the higher education sector, Dr.
Cheung has held various administrative and academic roles at several institutions including the Hong
Kong Metropolitan University, the University of Hong Kong, Hong Kong Baptist University, and the
Chinese University of Hong Kong. He has led major initiatives in digital transformation, AI adoption,
enterprise systems, blended learning, and open educational resources. Among his significant
achievements is the establishment of Hong Kong’s first open-access textbook platform.
An active researcher, Dr. Cheung has authored one research monograph,
edited 35 books, and published around 200 refereed articles in educational technology and software
engineering. He has delivered 20 keynote speeches at international conferences. He serves on the
editorial boards of several prestigious journals, including the International Journal of Educational
Technology in Higher Education (SSCI Q1) and the Australasian Journal of Educational Technology (SSCI
Q1). A Fellow of the Institution of Engineering and Technology (FIET), his contributions have been
recognized with multiple awards including the Outstanding CIO Award and the Outstanding Research
Publication Award.
Speech Title: Detection and Avoidance for Conflicting Concurrent Processes with Augmented
Marked Graphs
Abstract
In robotics and system automation involving shared resources, one
difficult challenge is to deal with potential conflicting concurrent processes, which are easily prone
to erroneous situations such as deadlock and capacity overflow. This cannot be effectively tackled
without a theoretically sound methodology. Based on augmented marked graphs, this keynote presents a
formal method for detecting and avoiding potential conflicting concurrent processes. A subclass of
Petri nets, augmented marked graphs possess a special structure especially useful for modelling and
analyzing concurrent and competing processes. They possess some desirable properties pertaining to
liveness, boundedness, reversibility and conservativeness. Potential conflicting processes can be
detected via the conditioned property-preserving composition. Accordingly, control measures can be
applied to avoid occurrence of these conflicting processes. The Dining Philosopher Problem is used for
illustration.
Huiyu Zhou
University of Leicester, UK
Dr. Huiyu Zhou received a Bachelor of Engineering degree in Radio
Technology from Huazhong University of Science and Technology of China, and a Master of Science
degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was
awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh,
United Kingdom. Dr. Zhou currently is a full Professor at School of Computing and Mathematical
Sciences, University of Leicester, United Kingdom. He has published over 600 peer-reviewed papers in
the field. His research work has been or is being supported by UK EPSRC, ESRC, AHRC, MRC, EU,
Innovate UK, Royal Society, British Heart Foundation, Leverhulme Trust, Puffin Trust, Alzheimer’s
Research UK, Invest NI and industry.
Speech Title: Image completion: From missing pieces to
masterpieces.
Abstract
Image completion is a challenging task, particularly when ensuring that
generated content seamlessly integrates with existing parts of an image. While recent diffusion
models have shown promise, they often struggle with maintaining coherence between known and unknown
(missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during
the diffusion process, resulting in content that does not smoothly integrate with the original
image. Additionally, diffusion models typically rely on global learned distributions rather than
localized features, leading to inconsistencies between the generated and existing image parts. In
this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy
(CAD) model to ensure that intermediate distributions of known and unknown regions are closely
aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces
discrepancies between generated and original images at each diffusion step, leading to contextually
aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively
increases the sampling rate in regions with high reconstruction complexity. This approach enables
precise adjustments, enhancing detail and integration in restored areas. Extensive experiments
demonstrate that ConFill outperforms current methods, setting a new benchmark in image
completion.
Xiaodong (Aaron) Xu
Central South University, China
Xiaodong Xu (Member, IEEE) received the B.Eng. degree in process
control from the Beijing Institute of Technology, Beijing, China, in 2010, and the Ph.D. degree in
process control from the University of Alberta, Edmonton, AB, Canada, in 2017.,He is currently a
Full Professor with the School of Automation, Central South University, Changsha, China, and a
Visiting Scholar with the University of Alberta, Edmonton, AB, Canada. His research interests
include robust/optimal control and fault estimation of infinite-dimensional systems including
energy systems.
Speech Title:Robust BFMHE and Robust Data-driven MHE for
Lithium-ion Battery SOC Estimation
Abstract
Accurate state of charge (SOC) estimation is crucial for the safe
operation of lithium-ion batteries (LIBs), yet existing methods are limited by sensitivity to
initial SOC guess and high computational complexity. To address these issues, this study proposes
an onboard back-forth moving horizon estimation (BFMHE) framework based on a linear battery
state-space model. A simplified linear equivalent circuit model (ECM) is developed to capture the
battery’s dynamic characteristics by representing the OCV-SOC relationship with a linear function.
SOC estimation is achieved using a finite number of measure- ments by alternating between two MHE
estimators operating in opposite directions over a finite time horizon. This study also provides a
detailed proof of the stability of the BFMHE method for the battery system with current input.
Quanxin Zhu
Hunan Normal University, China
Quanxin Zhu (Senior Member, IEEE) received the M.S. degree in
probability and statistics from Hunan Normal University, Changsha, China, in 2002, and the Ph.D.
degree in probability and statistics from Sun Yat-sen (Zhongshan) University, Guangzhou, China,
in 2005.,He is currently a Professor with Hunan Normal University, a Distinguished Professor in
Hunan, and the Leading Talent of Scientific and Technological Innovation in Hunan, the Deputy
Director of the Key Laboratory of Computing and Stochastic Mathematics of the Ministry of
Education, and the Director of Control and Optimization of Complex Systems of Hunan Key
Laboratory of Colleges and Universities. He has authored or co-authored more than 300 journal
articles. His research interests include stochastic control, stochastic systems, stochastic
stability, stochastic delayed systems, Markovian jump systems, and stochastic complex
networks.,Dr. Zhu received the Alexander von Humboldt Foundation of Germany and the Highly Cited
Researcher Award by Clarivate Analytics from 2018 to 2022. He received the First Prize of Hunan
Natural Science Award and the list of 2% top Scientists in the World from 2020 to
2022.
Speech Title: Stabilization of stochastic nonlinear delay
systems driven by Levy processes
Abstract
In this talk, we mainly discuss the stabilization problem for a
class
of stochastic nonlinear delay systems drivenby Levy processes. Based on a novel event-triggered
strategy and stochastic analysis techniques, we solve the practically pth moment exponential
stability problem of the considered system. Comparing with those previous results, we do not
require the global Lipschitz condition and do not use the linear matrix inequality method. Also,
different from many results for stochastic systems in discrete time or stochastic systems in
continuous time driven by the usual Brownian motion, our results are mainly concentrated on the
event-triggered sampling problem of stochastic systems in continuous-time driven by Levy
processes, and delays are also involved.