Workshop 1
Summary:
This
workshop
focuses
on
data,
knowledge,
and
AI
driven
smart
design
and
manufacturing
systems,
aiming
to
explore
how
the
new
generation
of
intelligent
technologies
(such
as
big
data,
knowledge
graphs,
artificial
intelligence,
large
model,
AI
agent,
etc.)
can
reconstruct
the
design
and
manufacturing
systems
of
industrial
enterprises,
achieve
digital
and
intelligent
development
and
transformation
of
industrial
enterprises,
and
research
the
theory,
methods,
technologies,
systems,
platforms,
applications,
verification,
etc.
of
digital
and
intelligent
transformation.
Original researches are welcome in this workshop. Research areas may include (but not limited to) the following:
Original researches are welcome in this workshop. Research areas may include (but not limited to) the following:
- Enterprise digital transformation
- Smart manufacturing system
- Computer integrated manufacturing system
- Industrial big data analysis and decision-making in design and manufacturing systems
- Industrial knowledge automation technology in design and manufacturing systems
- Industrial data space technology in design and manufacturing systems
- Industrial artificial intelligence technology in design and manufacturing systems
- Industrial large model technology in design and manufacturing systems
- Industrial AI intelligent agent technology in design and manufacturing systems
- Industrial intelligent internet technology in design and manufacturing system
- Intelligent middle platform for business, data, technology and knowledge
Keywords: Smart manufacturing, Digitalization transformation, Industrial Artificial Intelligence, Industrial Large Model, Industrial intelligent agent
Dr.
Xianyu
Zhang,
Ph.D,
Assistant
Professor,
Master's
Supervisor,
graduated
with
a
Ph.D.
from
Shanghai
Jiao
Tong
University.
He
mainly
engages
in
research
in
the
fields
of
intelligent
manufacturing,
industrial
intelligence
(industrial
internet,
industrial
big
data,
industrial
artificial
intelligence),
mass
personalization,
and
enterprise
digital
transformation.
He
has
published
more
than
70
papers
in
international
SCI
journals
and
academic
conferences,
and
has
published
6
monographs.
He
has
undertaken
or
participated
in
more
than
30
national
natural
science
foundation
projects,
national
intelligent
manufacturing
projects,
national
green
manufacturing
projects,
Shanghai
industrial
internet
projects,
Shanghai
industrial
big
data
projects,
Shanghai
industrial
artificial
intelligence
projects
and
other
industry
university
research
cooperation
projects.
Xinguo
Ming
is
a
professor
of
School
of
Mechanical
Engineering,
Shanghai
Jiao
Tong
University.
His
research
interests
include
Industrial
Artificial
Intelligence,
Industrial
Internet,
Smart
Manufacturing
Systems,
Smart
Product
Innovation
Ecosystem,
Service-oriented
Manufacturing
(Smart
Product
Service
Ecosystem),
Green
Design
and
Supply
Chain,
Lean
Enterprise
and
Management,
etc.
Prof.
Ming
has
published
over
100
scientific
papers
and
10
books
(in
Chinese).
He
was
a
member
of
the
editorial
board
of
Concurrent
Engineering:
Research
and
Applications,
Business
Process
Management
Journal,
etc.
He
undertakes
and
participates
in
a
number
of
Industrial-Academic-Research
cooperative
projects
funded
by
national
and
Shanghai
government.
Dr.
Zhiwen
Liu,
Ph.D,
graduated
with
a
Ph.D.
from
Shanghai
Jiao
Tong
University,
Head
of
the
discipline
of
Intelligent
Manufacturing
Engineering,
He
mainly
engages
in
research
fields
that
focus
on
the
transformation
and
upgrading
of
modern
electronic
information
manufacturing
industry,
including
industrial
intelligent
equipment,
smart
product-service
systems,
and
digital
service
systems.
He
has
published
more
than
20
papers
in
international
SCI
journals
and
academic
conferences.
He
is
currently
serving
as
the
consultant
for
several
industrial
enterprise,
and
has
undertaken
a
number
of
research
projects
related
to
intelligent
manufacturing
in
electronic
information
industry.
Workshop 2
Summary:
Real-world data, such as images, networks, and text,
inherently possess multi-granularity structural
characteristics. Inspired by human multi-granularity
cognitive thinking, multi-granularity cognitive computing
has emerged as an advanced computational paradigm that
enables modeling and solving complex problems at different
granularities and scales. By integrating multi-granularity
analysis and cognitive computing, it is possible to
overcome the limitations of traditional data mining and
achieve more intelligent and efficient mining.
Additionally, current machine learning models often present as "black boxes," lacking interpretability and understandability, which limits their controllability and widespread deployment in real-world applications. Although multi-granularity cognitive computing models have demonstrated powerful modeling and problem-solving capabilities, their complexity may pose challenges in terms of interpretability. Moreover, the inherent fuzziness and uncertainty in multi-granularity cognitive computing models can further complicate their interpretability and reliability. Therefore, investigating ways to enhance the interpretability of models, address their fuzziness and uncertainty, and make their decision-making processes more transparent and understandable is crucial for developing trustworthy and reliable artificial intelligence.
This workshop aims to bring together researchers, practitioners, and industry experts to discuss the latest developments, challenges, and future directions of multi-granularity cognitive computing in data mining. We invite submissions of original research papers, and position papers that address the following topics, but are not limited to:
Additionally, current machine learning models often present as "black boxes," lacking interpretability and understandability, which limits their controllability and widespread deployment in real-world applications. Although multi-granularity cognitive computing models have demonstrated powerful modeling and problem-solving capabilities, their complexity may pose challenges in terms of interpretability. Moreover, the inherent fuzziness and uncertainty in multi-granularity cognitive computing models can further complicate their interpretability and reliability. Therefore, investigating ways to enhance the interpretability of models, address their fuzziness and uncertainty, and make their decision-making processes more transparent and understandable is crucial for developing trustworthy and reliable artificial intelligence.
This workshop aims to bring together researchers, practitioners, and industry experts to discuss the latest developments, challenges, and future directions of multi-granularity cognitive computing in data mining. We invite submissions of original research papers, and position papers that address the following topics, but are not limited to:
- Algorithms and models for multi-granularity cognitive computing in data mining.
- Interpretability, understandability, and robustness of multi-granularity cognitive computing models
- Multi-granularity representation learning and feature extraction methods.
- Integration of domain knowledge and cognitive mechanisms in data mining to handle fuzziness and uncertainty
- Applications of multi-granularity cognitive computing in graph data mining, natural language processing, computer vision, healthcare, social network analysis, and other domains.
- Evaluation metrics and benchmarks for assessing the interpretability, robustness, and reliability of models under fuzziness and uncertainty.
Keywords: Multi-granularity cognitive computing, Data mining, Model interpretability, Fuzziness and uncertainty, Representation learning, Domain knowledge integration, Evaluation metrics and benchmarks, , Natural language processing, Graph neural networks, Computer vision.
Research interests: Data Mining, Knowledge Graph, Graph
Neural Networks,
Model Explainability.
2019-present: Associate Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2021-2023: Post-Doc at Hong Kong Baptist University supported by the HK Scholar Programme.
2016-2019: Assistant Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2012-2016: Beijing Institute of Technology, PhD in Engineering
2019-present: Associate Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2021-2023: Post-Doc at Hong Kong Baptist University supported by the HK Scholar Programme.
2016-2019: Assistant Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2012-2016: Beijing Institute of Technology, PhD in Engineering
Research interests: Uncertainty Data Mining, Knowledge
Graph, Graph Neural Networks, Model Explainability.
2009-present: Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2003-2009: Associate Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2005-2008: Chongqing University, PhD in Computer Science
2009-present: Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2003-2009: Associate Professor, School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, China
2005-2008: Chongqing University, PhD in Computer Science
Workshop 3
Summary:
Mining operations constitute a complex production complex system characterized by human-natural environment coupling, where AI has demonstrated significant potential to enhance traditional operations, especially in the fields of image perception, time-series data monitoring, multi, source heterogeneous data joint prediction, and intelligent system optimization. As AI come into the deep learning era, mining is concurrently entering a deep-mining age, expanding into increasingly challenging environments including deep underground, deep sea and deep space domains.
This workshop focuses on applying AI technologies to pioneer intelligent mining innovations in extreme environments such as deep-sea, space, and mega scale mine. We will examine how AI can empower the complete mining value chain: from exploration and sensing to management and decision-making to achieve smarter and more advanced production systems.
Key Topics:
We invite contributions from experts across systems science, artificial intelligence, and mining engineering to collectively drive intelligent transformation of these complex systems and develop innovative solutions for extreme-environment resource development.
This workshop focuses on applying AI technologies to pioneer intelligent mining innovations in extreme environments such as deep-sea, space, and mega scale mine. We will examine how AI can empower the complete mining value chain: from exploration and sensing to management and decision-making to achieve smarter and more advanced production systems.
Key Topics:
- Multimodal Data Fusion for Oceanic Mineral Exploration and Reserve Estimation
- Intelligent Sensing and Monitoring for Deep-sea Environments
- Underwater Intelligent Perception, Communication Equipment and Technologies
- Smart Mining Models and System Optimization for Polymetallic Nodules
- Health Monitoring Systems for Deep-sea Resource Development Equipment
- Intelligent Mining Models and System Optimization for Space Mining
- Space-based Intelligent Perception and Communication Technologies
- Intelligent Mining Management and Optimization for Large-scale Terrestrial Mining Systems
We invite contributions from experts across systems science, artificial intelligence, and mining engineering to collectively drive intelligent transformation of these complex systems and develop innovative solutions for extreme-environment resource development.
Keywords: Deep-Tech Mining; Multimodal Data Fusion; Intelligent Sensing; AI-Driven Mining; Extreme-environment Resource Development
Jiang Song, Professor, Doctoral Supervisor, Vice Dean of the School of Resource Engineering at Xi'an University of Architecture and Technology, Deputy Secretary General of the Open pit Mining and Slope Engineering Committee of the Chinese Society of Rock Mechanics and Engineering, Outstanding Young Science and Technology Talent Award in the National University Mining, Petroleum and Safety Engineering Field, Shaanxi Province Youth Top notch Talent, Shaanxi Province Youth Science and Technology New Star Talent Plan, Jiangsu Province "Double Innovation Plan" Science and Technology Talent, Outstanding Young Talent in Xi'an City, mainly focuses on the field of mining intelligence and safety, with a focus on research in mining slope monitoring and early warning, mining computer vision and other directions. He proposed the "Geological Body Equipment Group" bidirectional coupling perception theory. I have successively presided over 2 projects funded by the National Natural Science Foundation of China, 1 project specially supported by the China Postdoctoral Fund (in station), 1 general project funded by the China Postdoctoral Fund, 7 other provincial and ministerial level funds, 2 departmental and bureau level projects, and edited 1 textbook on "Mining Computer Vision". Has 6 utility model patents, 3 invention patents, 10 software copyrights, participated in the compilation of the group standard "T/SXSAE 004-2022 Overall Technical Specification for Unmanned Transport Vehicles in Open pit Mines", and published more than 70 academic papers, including more than 30 SSCI/SCI search papers. The research results have won awards such as the first prize of China Metallurgical Mining Science and Technology, the first prize of China Nonferrous Metals Industry Association, and the first prize of Innovation Award of China Invention Association Invention and Entrepreneurship Award. Served as a guest editor and young editorial board member for over 10 Chinese and English journals, as a peer review expert for the National Natural Science Foundation of China, and as a thesis review expert for the Degree Center of the Ministry of Education.
Li Guo, Ph.D. in Management, Master's Supervisor. She has presided over multiple research projects, including the General Program of the National Natural Science Foundation of China, the General Program of the Shaanxi Natural Science Foundation, and the Special Fund of the Department of Education, among other horizontal and vertical projects. Her achievements include the Third Prize of the Shaanxi Science and Technology Award, the First Prize of the 2nd National Safety Science and Technology Progress Award, the Second Prize of the Outstanding Scientific Research Achievement Award for Shaanxi Higher Education Institutions, and six other research and teaching awards. She has published over 20 academic papers. Her research focuses on: Big Data Management and Applications in Resources, Intelligent Science and Engineering in Mining, Safety Monitoring and Control.
Workshop 4
Summary:
The explosive growth of applications with various requirements is pushing computation and decision-making to the edge network, which creates unprecedented demands on the underlying infrastructure. Novel research is urgently needed to develop intelligent, adaptive, and efficient techniques for edge networks. In this workshop, we invite researcher, engineers, and practitioners to submit original contributions on all aspects on edge network optimization. This workshop aims to bring together the latest research, innovative solutions, and practical experiences to address these complex challenges and advance the state-of-the-art in optimizing performance, efficiency, and reliability in edge environments.
Topics of interest include, but are not limited to:
Topics of interest include, but are not limited to:
- Architectures & Frameworks: novel edge network architectures, distributed edge-cloud frameworks, MEC integration, edge computing models;
- Resource Management: dynamic resource provisioning & scheduling, workload placement/offloading, energy-efficient operation;
- Network Optimization Techniques: traffic engineering, QoS/QoE aware routing, congestion control, latency reduction, bandwidth optimization;
- Content Delivery & Caching: proactive caching strategies, in-network computing, efficient content distribution, collaborative caching;
- AI for Optimization: predictive resource allocation, intelligent traffic management, decision-making via ML/DL/RL/LLM;
- Virtualization & Programmability: NFV at the edge, SDN for edge control, service chain, lightweight containerization;
- Mobility & Dynamics: optimization for mobile edge nodes, handling device mobility, dynamic topology management;
- Scalability & Reliability: scaling edge networks efficiently, fault tolerance, resilience strategies, traffic load balancing;
- Security-aware Optimization: privacy protection, confidential approach;
- Performance Measurement & Modeling: benchmarking, simulation platforms, analytical models, monitoring techniques;
- User Cases & Applications: Optimization for vehicular networks, IoT, AR/VR, real-time video analytics, industrial automation, smart cities, and other demanding edge applications.
Keywords: Multi-access Edge Computing, Edge Network, Network Optimization
Chuanfen Feng received the B.E. degree from Ludong University, Yantai, China, in 2002, and the Ph.D. degrees from Beijing University of Posts and Telecommunications, Beijing, China, in 2007. He is currently a professorate senior engineer with the School of Information Science and Engineering, Shandong Normal University. His current research interests include mobile network planning, mobile edge computing and internet of things.
Yantong Wang received the BE degree in Internet of Things from Shandong University, China, in 2014, the MEng degree in Electronics and Communication from Tsinghua University, China, in 2017, and the PhD degree in telecommunications from King's College London, UK, in 2022. He is currently a lecturer at the School of Information Science and Engineering, Shandong Normal University, China. His research interests include proactive caching, edge network optimization, mathematical programming, and next-generation network technologies.
Workshop 5
Summary:
Today, big data has become a kind of capital. The world’s largest technology companies are
all based on big data, which they continuously analyze to improve operational efficiency
and develop new products. Although it has been around for a long time, the utilization of big data
is just beginning. The value of big data lies in its application. It can help people understand data more
intuitively and conveniently, and can further mine other valuable data.
The aim of this workshop is to bring together the latest research results in the field of Big Data Application provided by researchers from academia and the industry. We encourage prospective authors to submit related distinguished research papers on the following topics. Please name the title of the submission email with “paper title_workshop title”.
The aim of this workshop is to bring together the latest research results in the field of Big Data Application provided by researchers from academia and the industry. We encourage prospective authors to submit related distinguished research papers on the following topics. Please name the title of the submission email with “paper title_workshop title”.
- Big Data Applications and Software in Science, Engineering, Healthcare,
- Finance, Business, Transportation, Telecommunications, etc.
- Finance, Business, Transportation, Telecommunications, etc.
- Big Data Industry Standards.
- Development and Deployment Experiences with Big Data Systems.
Keywords: big data application, big data analysis, big data industry standard, big data system
Jianjun Zhang, received a Ph.D. degree in Computer Science and Technology from Hunan University. He worked as an associate professor at the College of Engineering and Design, Hunan Normal University. His research interests include Big Data Application and Information Security.