Workshop 1
Summary:
In the context of the rapid
development of global informatization, big data has become
a strategic resource. The frequency, breadth, and
complexity of decision-making activities in various
industries are fundamentally different from before. The
uncertainty factors in the decision-making process
increase, and the difficulty of decision-making analysis
continues to increase. Traditional data analysis methods
and decision-making based on human experience are no
longer able to meet the decision-making needs of the big
data era, Big data-driven intelligent decision-making will
become the main theme of decision-making research. We need
to combine the characteristics of big data and analyze
some potential research directions from aspects such as
intelligent decision support systems, uncertainty
processing, information fusion, correlation analysis, and
incremental analysis. As a rapidly developing open
discipline, big data intelligent decision-making still
requires more research and practice in terms of
connotation and extension, model theory, technical
methods, and implementation strategies.
Keywords: big data, intelligent decision-making, information fusion, incremental learning
Xin NIE is a professor and master's supervisor at the
School of Computer Science and Engineering, Wuhan
Institute of Technology. His research interests include
intelligent optimization algorithms, machine vision, and
other fields. He is currently the director of the Software
Engineering Teaching and Research Office, a member of the
China Computer Society, a member of the China Artificial
Intelligence Society, and a member of the China Command
and Control Committee.
Workshop 2
Summary:
In many scenarios such as
target recognition, defect detection, image and text
generation, and intelligent Q&A, it is difficult to ensure
the accuracy of recognition, detection, or answering using
a single modal data modeling due to complex and variable
environments, strong background noise, and insufficient
information. To improve the modeling effect, it is
necessary to fully utilize the complementary advantages of
multi-modal data, integrate data from various modalities
such as RGB images, infrared images, ultrasound, text, and
electromagnetic signals, and construct a unified feature
representation model. This workshop will focus on several
academic issues in multi-modal fusion in artificial
intelligence, including but not limited to: object
recognition, defect detection, image and text generation,
intelligent Q&A, and other aspects.
Keywords: Multi-modal fusion, Artificial intelligence, Feature representation
As the project leader or main participant, Professor Chen
participated in many projects such as 863, National
Natural Science Foundation of China, and Science and
Technology Commission of the Military Commission. In
recent years, He has published more than 100 academic
papers in many important academic journals or
international conferences such as Physics Report,
Knowledge Based Systems, and Information Sciences. He
Received the second prize of the Natural Science Award of
the CCF in 2014 and the third prize of the Sichuan
Provincial Science and Technology Progress Award in 2022.
Workshop 3
Summary:
With the rapid development of
science and technology and the rapid rise of Internet
information technology, people get more and more effective
information from the Internet, and people's life has been
greatly facilitated. With the gradual innovation and
development of information technology, artificial
intelligence has been paid more attention and applied in
people's life. Artificial intelligence technology is
developed by analyzing the law of people's activities
through intelligent technology. It has a great degree of
application function in robots, control systems and
simulation. In artificial intelligence technology, the
application of big data technology can analyze the
potential law of data from a large amount of data, so as
to summarize a certain development law, and then promote
the further development of artificial intelligence.
This workshop aims to bring together the latest research progress of academic and industry researchers, such as big data science and foundations, big data infrastructure, big data management, big data search and mining, big data learning and analytics, data ecosystem, big data applications, artificial intelligence and technology, natural language processing, expert systems, multi-agent systems, knowledge engineering, neural network theory and architectures, artificial intelligence in modelling and simulation, pattern recognition, complex system, intelligent system, intelligent control, speech recognition and synthesis, machine translation, computer perception, machine learning, intelligent robot, image processing and computer vision and so on. We encourage prospective authors to submit related distinguished research papers on the subject of big data and artificial intelligence.
Keywords: Big Data, Artificial Intelligence, Data Mining, Data Science, Natural Language Processing, Expert Systems, Multi-Agent Systems, Knowledge Engineering, Neural Network, Pattern Recognition, Complex System, Intelligent System, Intelligent Control, Speech Recognition and Synthesis, Machine Translation, Computer Perception, Machine Learning, Intelligent Robot, Image Processing and Computer Vision
Shan Liu is Professor and Chair of the Department of
Intelligent Science at the Communication University of
China. She received her Ph.D. degree in Electrical
Engineering from Texas A&M University, College Station,
Texas, USA, in 2013. She is a committee member of the
Chinese Association for Artificial Intelligence, the
Chinese Institute of Electronics, the Beijing Society of
Image and Graphics, the Association of Fundamental
Computing Education in Chinese Universities, the China
Automation Society, and the Institute of Electrical and
Electronics Engineers. She has served as a peer reviewer
for a series of IEEE Transactions and other high-level SCI
journals. Her current research areas include complex
networks, intelligent tags, big data, and artificial
intelligence. Prof. Liu has been invited to the University
of California, the National Laboratory of the United
States, and other countries such as Brussels and Belgium
to give lectures and speeches and has gained broad
academic influence. She has presided over more than 30
research projects, such as the National Natural Science
Foundation of China, the National Social Science
Foundation of China, and the National Key Research and
Development Program of China, published more than 100
high-level academic papers, and received multiple best
paper awards.
Workshop 4
Summary:
Large language models, such as GPT-3.5, have gained
significant attention due to their remarkable ability to
generate human-like text and perform a wide range of
language-related tasks. These models are trained on vast
amounts of text data and can generate coherent,
contextually relevant responses to various prompts.
In terms of applications, large language models have found widespread use across multiple domains. They can assist in natural language understanding, translation, summarization, content generation, chatbots, and even code generation. These models have the potential to revolutionize industries such as customer service, content creation, and education by automating tasks that traditionally required human intervention.
However, along with their immense potential, large language models also raise security concerns. One major issue is the generation of misleading or malicious content. These models can inadvertently produce false information, spread misinformation, or be exploited to generate harmful content like hate speech or propaganda. The responsible use and deployment of large language models require careful monitoring, content filtering, and ethical considerations.
Another security concern is the potential for model inversion attacks. Attackers could use the model's responses to infer sensitive information about the training data or exploit vulnerabilities in the model's architecture to extract confidential information. Protecting the privacy and security of the underlying training data and preventing unauthorized access to the model itself are critical challenges that need to be addressed.
To mitigate these risks, researchers and developers are exploring techniques such as adversarial training, bias detection and mitigation, content filtering, and fine-tuning models on specific domains to improve their safety and security. Additionally, promoting transparency, accountability, and responsible use of large language models is crucial to ensure their positive impact while minimizing potential harm.
As the field of large language models continues to evolve, ongoing research and collaborations between industry, academia, and policymakers will be essential to address the security challenges and harness the benefits of these powerful AI systems responsibly.
Keywords: large language model; security; alignment
Ouyang Jianquan, Ph.D., Second-level professor of Hunan Province, doctoral supervisor, visiting scholar at the University of Georgia, U.S.A. He was selected as the third-level candidate of Hunan Province New Century 121 Talent Project in 2011. He was selected as the 2019-2020 Hunan Province "Three Districts" Talent Support Program. He was awarded the China Industry-University-Research Cooperation Promotion Award in 2019. He was awarded the Baosteel Education Award and the first outstanding science and technology worker in Xiangtan City in 2018. He was awarded the third prize of Hunan Province Natural Science in 2010. He was awarded the second prize of Hunan Province Teaching Achievement in 2013 and the second prize in 2016 and 2019. In 2014, he won the third prize of excellent achievements in educational scientific research in Hunan Province. In 2018, he won the national prize of teaching innovation in artificial intelligence and big data education in colleges and universities. In 2020, 2021 and 2022, he won the "Outstanding Contribution Award" for Artificial Intelligence Big Data Blockchain Education in National Universities. In 2007, he was selected as the training object of young backbone teachers in general colleges and universities in Hunan Province.
Workshop 5
Summary:
Intelligent medical image processing combines biomedicine
and bioinformatics and deploys data-driven machine
learning techniques for data analysis. The multi-modal
complex medical big data with the intertwined feature
relationships need to be tackled using novel statistics
methods instead of traditional statistical trials. Deep
learning was once considered a “black box”. However, it
works better than simple statistical methods and
traditional machine learning. Recently, big data-driven
deep learning techniques have developed rapidly and
achieved impressive performance in several fields,
including imaging, automatic speech recognition, and
bioinformatics. Precision medicine is becoming an
increasingly important application. In particular,
interpretable deep learning neural networks have been well
explored recently, showing great potential to provide more
insights into the disease mechanisms.
This workshop aims to describe the latest deep learning methods and biological and biomedicine applications. We hope that codes from methodology and data from real-world applications can all be presented in this workshop. The workshop also aims to guide deep learning researchers to examine more biomedical engineering applications and expand the vision of medical researchers in the field of deep learning.
Keywords: Deep learning; Medical image processing; Computer-Aided Diagnosis
Tao Zhou, received the Ph.D. degree from Northwestern
Polytechnical University, China, in 2010. he is a visiting
scholar in The University of Leicester, UK, in
2023，Chinese University of Hong Kong, HK, in 2012. he is
currently an Professor(Grade II) in School of Computer
Science & Engineering at North Minzu University. He is
director of CSIG, SSIG. Her research interests include
machine learning, (deep) representation learning and their
applications to Computer-aided System. He has published
over 200 academic papers, in which, as first and
corresponding authors, he has published 3 books, 3 ESI
Papers . Furthermore, he has obtained 6 authorized
patents.
Workshop 6
Summary:
Objective: This Special Issue aims to consolidate and present cutting-edge research, findings, and insights relating to the advancements in mathematical models utilized in data intelligence applications. It seeks to spotlight innovative methodologies, theoretical frameworks, and practical applications of mathematical models in unraveling complex data structures and patterns within diverse domains such as machine learning, artificial intelligence, data analysis, and predictive analytics.
Scope:
(1) Theoretical Advances:
a. Exploration and development of novel theories and methodologies in mathematical modeling enhancing data interpretation and analysis.
b. Evolutionary study of mathematical models adapting to the sophisticated needs of modern data intelligence.
(2) Application-Oriented Studies:
a. Examination of the real-world applicability of advanced mathematical models in fields like healthcare, finance, and IT.
b. Detailed case studies demonstrating the practical utility and adaptability of mathematical models in addressing and solving contemporary issues.
(3) Technological Integration:
a. Integration of mathematical models with state-of-the-art technologies for optimizing data-driven processes.
b. Study of the influence of mathematical modeling in refining algorithms and computational techniques in data science.
Anticipated Contributions:
We invite researchers, scholars, and practitioners to contribute original research articles, reviews, and studies that offer new insights, research findings, or theoretical frameworks in the field of mathematical modeling for data intelligence. The contributions should focus on, but are not limited to, the development, implementation, and application of advanced mathematical models, exploring their impact, challenges, and future prospects in data intelligence applications.
Outcome:
This Special Issue aspires to provide a comprehensive overview of the current state-of-the-art developments in mathematical models in data intelligence, offering a rich academic resource for enthusiasts, practitioners, and researchers. The diverse range of topics and methodologies covered will help in fostering new discussions, collaborations, and innovations in the application of mathematical models in data intelligence.
By focusing on the recent developments in mathematical models and their applications in data intelligence, this Special Issue will help in bridging the gap between theoretical advancements and practical implementations. It will play a crucial role in highlighting the unexplored potentials of mathematical models in advancing data intelligence applications, promoting a deeper understanding and exploration of this interdisciplinary field.
Keywords: Mathematical Models, Data Intelligence, Artificial Intelligence, Machine Learning, Data Analysis, Predictive Analytics, Application-Oriented Studies
Dr. Meng Lei earned her B.Sc. and Ph.D. degrees from China University of Mining and Technology in 2008 and 2013, respectively. Currently, she holds the position of Associate Professor and serves as a Master's supervisor at the School of Information and Control Engineering within the same university. She also worked with Dr. Z. Jane Wang (IEEE Fellow) as a visiting professor from December 2017 to December 2018 at The University of British Columbia (UBC), Canada. Her research interest includes machine learning and data mining. In recent years, as the project leader, she has undertaken the National Natural Science Foundation of China project, Jiangsu Province Natural Science Foundation project and many industry-sponsored grants. She has published one monograph and more than 40 academic papers. She has won one second prize for the advancement of coal industry science and technology in 2010, one second prize for Shandong Province coal industry science and technology in 2016, and one second prize for Jiangsu Province Coal Society coal science and technology advancement in 2018.
Yongwei Wang is a ZJU100 Young Professor working on Secure, Efficient, and Generalizable AI at Shanghai Institute for Advanced Study and College of Computer Science and Technology, Zhejiang University in China. He received his M.Sc and B.Eng degrees (Honors College) in 2017 and 2014 respectively from Northwestern Polytechnical University in Xi’an, China. He received his Ph.D. in Electrical and Computer Engineering from the University of British Columbia in 2021. From 2021 to 2023, he worked as a Research Fellow at Nanyang Technological University. His research interests include AI security, digital multimedia forensics, generative modeling and medical imaging. He has published more than 20 papers including IEEE TPAMI, Medical Image Analysis, ICLR, AAAI, ACM MM, WWW etc, and he was invited as reviewers for top-tier journals such as IEEE TIFS, IEEE TMI, IEEE TII, and top conference program committees such as ICLR, NeurIPS, CVPR, AAAI etc.
Workshop 7
Summary:
Graph computing is playing an important role in recent big data analysis as more and more emerging applications have to model irregular, complicated data as graphs. As one of the most widely adopted graph processing approaches, graph pattern mining (GPM) seeks for subgraphs (known as the “pattern”) identical with the query graph structure expected by the users from the graph data. Typical GPM use-cases include motif counting, frequent subgraph mining (FSM) and clique listing. Given the huge computational workload and requirements in such application scenarios, approximate GPM algorithms and frameworks are unfolding and growing in a rapid pace. Meanwhile, the emergence of parallel architectures and hardware, in the form of multi-core/many-core processors, GPU, NVM, and distributed platforms have provided new approaches for large-scale mining. This trend has dramatically broadened the scope of graph pattern mining research, and is enabling researchers to tackle new challenges.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry to present, discuss and exchange ideas, results, work in progress and experiences in the area of architecture-aware approximate graph pattern mining. In particular, while we retain our focus on novel approximate graph pattern mining approaches, we also welcome work that exploring accurate mining and interdisciplinary researchers to showcase innovative research that leverage the emerging trends in graph pattern mining.
Keywords: graph pattern mining, approximate pattern counting, distributed systems
Ruini Xue, received his master and Ph.D. degrees in computer science and technology from Tsinghua University. He worked as an associated professor at the School of Computer Science and Engineering, University of Electronic Science and Technology of China. His research interests include graph computing, blockchain, and distributed database. He participated in the National Natural Science Foundation and Chinese National Programs for High Technology Research and Development, etc. He published more than 30 papers and about 20 patents have been granted.
Workshop 8
Summary:
Federated learning represents a pivotal research avenue in harnessing the potential of distributed data and models. This paradigm offers a unique capability to safeguard the privacy of data from individual participants while concurrently capitalizing on collectively trained models. Simultaneously, blockchain technology emerges as a promising tool to enhance federated learning by facilitating the recording of aggregated models. It is of profound significance to delve into potential applications of blockchain in augmenting federated learning.
Keywords: Blockchain, federated learning, artificial intelligence
Liu Yizhong is a member of the China Electronics Society and has been selected as part of the China Association for Science and Technology's Youth Talent Support Program. He has taken charge of six research projects, including the National Natural Science Foundation of China's Youth Fund and the Beijing Natural Science Foundation's General Project. He has also participated in five other projects, such as the National Key Research and Development Program and the National Natural Science Foundation of China Joint Fund Key Project.
His research primarily focuses on cryptography, blockchain, and federated learning. As the first or corresponding author, he has published over 20 papers in esteemed journals and conferences, including IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, and Computer Science Review, DCN, COSE, CN, FGCS, IEEE TNSE, ACISP 2021、TrustCom 2020, ICA3PP 2020.
Additionally, he holds eight authorized patents and has 12 pending patent applications. He holds several prominent academic positions and responsibilities within the academic community. He serves as a Co-Chair for the ICBCTIS 2023. Additionally, he takes on the role of Guest Editor for the journal Frontiers in Blockchain. He also actively contributes to the academic review process, participating as a committee member for various conferences and journals. He serves as a Program Committee Member for IEEE ICCT 2023 and is involved with conferences such as TrustCom 2022, Inscrypt 2023, and SmartBlock 2020, demonstrating his commitment to advancing research and knowledge dissemination in the fields of blockchain, computer science, and artificial intelligence. Furthermore, he dedicates his expertise as a reviewer for several prestigious international journals, including IEEE TPDS, IEEE TDSC, IEEE TII, IEEE TBD, IEEE TSMC. He has received several awards, including the Best Paper Award from the Journal of Cryptologic Research in 2022, and he was honored with the First Prize in the 2023 China Internet Security Competition.
His research primarily focuses on cryptography, blockchain, and federated learning. As the first or corresponding author, he has published over 20 papers in esteemed journals and conferences, including IEEE Transactions on Dependable and Secure Computing, IEEE Transactions on Information Forensics and Security, and Computer Science Review, DCN, COSE, CN, FGCS, IEEE TNSE, ACISP 2021、TrustCom 2020, ICA3PP 2020.
Additionally, he holds eight authorized patents and has 12 pending patent applications. He holds several prominent academic positions and responsibilities within the academic community. He serves as a Co-Chair for the ICBCTIS 2023. Additionally, he takes on the role of Guest Editor for the journal Frontiers in Blockchain. He also actively contributes to the academic review process, participating as a committee member for various conferences and journals. He serves as a Program Committee Member for IEEE ICCT 2023 and is involved with conferences such as TrustCom 2022, Inscrypt 2023, and SmartBlock 2020, demonstrating his commitment to advancing research and knowledge dissemination in the fields of blockchain, computer science, and artificial intelligence. Furthermore, he dedicates his expertise as a reviewer for several prestigious international journals, including IEEE TPDS, IEEE TDSC, IEEE TII, IEEE TBD, IEEE TSMC. He has received several awards, including the Best Paper Award from the Journal of Cryptologic Research in 2022, and he was honored with the First Prize in the 2023 China Internet Security Competition.
Qianyun Zhang received her B.Sc. degree from Beijing University of Posts and Telecommunications, China, in 2014, and her Ph.D. degree from Queen Mary University of London, United Kingdom, in 2018. She is currently an Associate Professor with the School of Cyber Science and Technology, Beihang University, Beijing, China. Her research interests include wireless network security, semantic communication security, and blockchain-enabled reliable spectrum management.
She has published over 40 research papers, a chapter, and a monograph. She is the Associate Editor of IEEE OJAP, and the Cyber Security Committee member of Chinese Institute of Electronics. She has been selected into the Young Elite Scientists Sponsorship Program by China Association for Science and Technology. She is also the AINet 2022 co-chair and the symposium chair of IEEE ICCT 2023. She has been awarded best papers of KSEM 2022 and IEEE TrustCom MLSys Workshop 2021.
She has published over 40 research papers, a chapter, and a monograph. She is the Associate Editor of IEEE OJAP, and the Cyber Security Committee member of Chinese Institute of Electronics. She has been selected into the Young Elite Scientists Sponsorship Program by China Association for Science and Technology. She is also the AINet 2022 co-chair and the symposium chair of IEEE ICCT 2023. She has been awarded best papers of KSEM 2022 and IEEE TrustCom MLSys Workshop 2021.
Workshop 9
Summary:
Amid the transformative wave of urbanization, various methods and policies for urban operation management have been extensively discussed. Propelled by the advent of advanced technology concepts such as artificial intelligence, big data and the Internet of Things, various scientific tools such as intelligent technology, data analysis and decision support systems have been widely used in many fields of urban operation management, and many outstanding results have been achieved. To promote the digital and intelligent development of city operation management, this conference invites contributions from researchers and practitioners in the fields of artificial intelligence, data analysis, operation management, logistics management, disaster management, and related disciplines. We welcome original research articles, review papers, and case studies on the following related topics, but not limited to:
• Optimization models and algorithms for key resource allocation in urban operation management
• Application of the Internet of Things (IoT) in urban operation management
• Application of artificial intelligence and machine learning in urban operation management
• Application of real-time data analysis and big data management in urban operations
• Intelligent transportation systems applied within or between cities
• Ground, underground, and low-altitude transport logistics systems in urban operation management
• Big data analysis for risk assessment and disaster reduction in urban operations
• Optimization strategies and resilience analysis of the urban supply chain system
• Routing optimization and scheduling algorithms for urban freight or manned vehicles
• Intelligent decision support for disaster prediction and response in urban management
• Application of intelligent devices like Unmanned Aerial Vehicles (UAV) and autonomous vehicles in urban management
• Application of green technologies and strategies such as congestion mitigation and emission reduction for sustainable urban development
• Case applications of large-scale model empowerment and knowledge enhancement in urban operation management
• Lessons learned from various experiences and case studies in urban operation management.
Keywords: Big data, machine learning, artificial intelligent, urban operation management
Lili Yang is a professor in the Department of Statistics and Data Science at Southern University of Science and Technology. She was a tenure assistant professor, associate professor and then professor in the School of Business and Economics at Loughborough University, and a senate member of the Loughborough University in the UK. She has also served as Associate Director of the Service Management Research Center and the head of the Emergency Management Research Group. She was selected as a Fellow of the British Computer Society in 2008 (BCS Fellow). Prof. Yang has long been engaged in the research of theories and methods of public safety emergency management to preside over and complete more than 20 important projects funded by the European Commission (European Commission FP7), the UK National Engineering and Natural Sciences Fund (EPSRC), or the British Ministry of Defence (MoD), etc. Her many scientific research results have been successfully applied into real life practices. She has published three books and more than 140 papers.
Dr. Zhejun Huang is an assistant professor in the Department of Statistics and Data Science at Southern University of Science and Technology. He obtained his PhD in mathematics in 2017 from the University of New South Wales (UNSW), Australia. He was a postdoctoral fellow at UNSW and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. His research interests are public safety and emergency management, disaster risk analysis, disaster response for decision making, disaster risk reduction and management, decision making and control for autonomous vehicles, and reaction-diffusion models. Dr. Huang has published more than 30 papers in international high-level journals and conferences.
Workshop 10
Summary:
With the rapid development of artificial intelligence (AI) technology, its application in various industries is becoming increasingly widespread. This call for papers aims to explore the use of advanced AI in industrial and agricultural settings and its potential impact on these industries.
AI has already made significant progress in a variety of agricultural applications. From intelligent crop rotation systems that optimize soil fertility and crop yields to robotic milking systems that improve efficiency and reduce labor costs, AI is revolutionizing agriculture. Other areas of interest include the use of AI in precision agriculture, sustainable agriculture, and agricultural biotechnology.
We welcome submissions that explore these and other related topics.
Topics may include but are not limited to:
1、Advanced AI algorithms for agricultural and industrial processes.
2、AI-powered precision agriculture and sustainable agriculture.
3、AI in industrial process control, quality assurance, and predictive maintenance.
4、Use of AI in robotic agricultural processes.
5、Challenges and future prospects of AI in industrial and agricultural settings.
Topics may include but are not limited to:
1、Advanced AI algorithms for agricultural and industrial processes.
2、AI-powered precision agriculture and sustainable agriculture.
3、AI in industrial process control, quality assurance, and predictive maintenance.
4、Use of AI in robotic agricultural processes.
5、Challenges and future prospects of AI in industrial and agricultural settings.
Keywords: Artificial intelligence (AI), Industry, Agriculture, Application, Algorithms
He was born in Kaifeng, Henan, China, in 1977. He received the M.S. degree in circuit and system from Guangxi Normal University, in 2005, and the Ph.D. degree in communication and information system from Xidian University, in 2017.
From 2005 to 2008, he was a Lecturer with the School of Physics and Electronics, Henan University. Since 2023, he has been an Professor. He has presided over and participated in a number of National Natural Science and Technology Funds, and took part in the Shaanxi Province Key Science and Technology Innovation Team Project. He has applied for six national invention patents and published more than 30 articles in Multimedia Tools and Applications, Optical Engineering, Journal of Electronic Imaging, Journal of Zhengzhou University, and Journal of Henan University. His research interests include image processing and artificial intelligence.
From 2005 to 2008, he was a Lecturer with the School of Physics and Electronics, Henan University. Since 2023, he has been an Professor. He has presided over and participated in a number of National Natural Science and Technology Funds, and took part in the Shaanxi Province Key Science and Technology Innovation Team Project. He has applied for six national invention patents and published more than 30 articles in Multimedia Tools and Applications, Optical Engineering, Journal of Electronic Imaging, Journal of Zhengzhou University, and Journal of Henan University. His research interests include image processing and artificial intelligence.
He received the B.S. degree in mechanical design manufacture and automation from Central South Forestry University, China, in 2001, and the M.S. degree in precision instrument and machinery from Wuhan University, China, in 2005. From July 2005 to August 2008, he was an Assistant with Henan University, China, where he has been a Lecturer since September 2008.Published over 10 SCI and EI papers, authorized 4 invention patents, and held 5 software copyrights. Led or participated in 8 national and provincial projects. His current research interests include image processing, applied intelligence, object detection, and machine learning.
Workshop 11
Summary:
With the increasing use of technology and the internet, there are many threats to information security, including hacking, malware, phishing, and social engineering. To combat these threats, a variety of security measures have been developed, including firewalls, encryption, access controls, and intrusion detection systems. The chaotic system is known as a natural cryptographic system and is widely used in encryption technology.
The chaotic system is characterized by extreme sensitivity to initial conditions, unpredictability, and aperiodicity. It can be implemented not only through analog circuits, but also through digital circuits such as MCU, DSP, FPGA, etc. A system can generate chaotic signals, and its nonlinearity plays an important role. As the fourth basic circuit component, the nonlinearity of memristor is often used to design new chaotic systems and circuits. Then, the chaotic system is used to generate random numbers that can be used as encryption keys. The unpredictable nature of chaotic system makes it extremely difficult for an attacker to predict the output of a chaotic system. Therefore, chaotic secure communication has also become a hot research field in recent years.
The purposes are to investigate the latest progresses in the field of chaotic system and secure communication, including but not limited to the following topics: chaotic circuit design and modeling, memristive chaotic system, chaos cryptography, image encryption algorithm, chaotic secure communication, the application of artificial intelligence in information security, etc.
The chaotic system is characterized by extreme sensitivity to initial conditions, unpredictability, and aperiodicity. It can be implemented not only through analog circuits, but also through digital circuits such as MCU, DSP, FPGA, etc. A system can generate chaotic signals, and its nonlinearity plays an important role. As the fourth basic circuit component, the nonlinearity of memristor is often used to design new chaotic systems and circuits. Then, the chaotic system is used to generate random numbers that can be used as encryption keys. The unpredictable nature of chaotic system makes it extremely difficult for an attacker to predict the output of a chaotic system. Therefore, chaotic secure communication has also become a hot research field in recent years.
The purposes are to investigate the latest progresses in the field of chaotic system and secure communication, including but not limited to the following topics: chaotic circuit design and modeling, memristive chaotic system, chaos cryptography, image encryption algorithm, chaotic secure communication, the application of artificial intelligence in information security, etc.
Keywords: Chaotic system and circuit, Memristor, Information Security, Artificial Intelligence
Prof. Lidan Wang is a doctoral supervisor of the College of Artificial Intelligence, Southwest University, China, and director of Chongqing Key Laboratory of Brain-inspired Computing and Intelligent Control. In 2008, she received her Ph.D. degree in computer software and theory from Chongqing University. She visited Imperial College London in the UK from 2010 to 2011, University of Windsor in Canada in 2013, Nanyang Technological University in Singapore from 2016 to 2017, Texas A&M University in Qatar in 2017, and University of Tasmania in Australia in 2018, respectively. Her research interests include brain-inspired computing & memristor neuromorphic chips, nonlinear systems & chaotic circuits, artificial neural networks & FPGA technology, and various application fields including big data science, image processing, and biomedical engineering. She was awarded numerous grants from the National Key Research and Development Program of China and the National Natural Science Foundation of China. She has published over 100 academic papers indexed by SCI, and more than thirty students have received their master or doctoral degrees under her supervision. In addition, Prof. Wang serves as an Associate Editor for the Artificial Intelligence Review and as a Topic Editor for Electronics. She also serves as Secretary-general of Chongqing Artificial Intelligence Society.
Workshop 12
Summary:
With the continuous progress of big data technology and robotics technology, the integration and development of big data and robotics have penetrated into various industries. The purpose of this paper collection is to study the application of big data-based robot intelligence in various production and service fields, and to explore its potential impact on these fields. The progress of big data technology and deep learning has opened up various application scenarios for robot intelligence. In the field of education, there has been a breakthrough development in robot intelligence, thanks to the progress of big data technology and deep learning algorithms. Robots can provide personalized and intelligent teaching and learning solutions for teachers and students through recommendation systems. It is completely changing the way education to utilize big data technology and deep learning algorithms for achieving intelligent interaction between robot teachers and students.
Robot intelligence based on big data can also be applied in fields such as power grid maintenance, agricultural biotechnology, and industrial process control. By analyzing and mining big data, robots can more intelligently plan the optimal path for power maintenance, thereby improving the security of the power grid. In the field of agriculture, robot intelligence can be applied to agricultural biotechnology to help farmers improve the yield and quality of crops. In terms of industrial process control, robot intelligence based on big data can achieve automated and intelligent industrial production, thereby improving efficiency and reducing errors.
Welcome to submit relevant research for this paper collection, including but not limited to the following topics:
Big data analysis and mining methods in robot intelligence;
Robot path planning algorithm based on deep learning;
The application of intelligent robots based on big data in the field of education;
The application of big data-based robot intelligence in agricultural biotechnology;
The application of big data-based robot intelligence in industrial process control;
The challenges and future prospects of big data-based robot intelligence in industrial and agricultural environments.
Robot intelligence based on big data can also be applied in fields such as power grid maintenance, agricultural biotechnology, and industrial process control. By analyzing and mining big data, robots can more intelligently plan the optimal path for power maintenance, thereby improving the security of the power grid. In the field of agriculture, robot intelligence can be applied to agricultural biotechnology to help farmers improve the yield and quality of crops. In terms of industrial process control, robot intelligence based on big data can achieve automated and intelligent industrial production, thereby improving efficiency and reducing errors.
Welcome to submit relevant research for this paper collection, including but not limited to the following topics:
Big data analysis and mining methods in robot intelligence;
Robot path planning algorithm based on deep learning;
The application of intelligent robots based on big data in the field of education;
The application of big data-based robot intelligence in agricultural biotechnology;
The application of big data-based robot intelligence in industrial process control;
The challenges and future prospects of big data-based robot intelligence in industrial and agricultural environments.
Keywords: Robot intelligence, big data, deep learning
Mingshan Xie received his B.S. degree in Educational Technology from Southwest University, Chongqing, China, in 2004, the M.S. degree in Computer Application Technology from Hainan University, Haikou, China, in 2013, and Ph.D. degree in Information & Communication Engineering in 2019. Now, he is working with College of Big data & Information Engineering in Guizhou University. He used to be a senior software development engineer at IBM, and now he is the target of cultivation for academic discipline leaders at Guizhou University, as well as the head of provincial-level first-class courses. He is also an IEEE member. His current research interests include Smart Robot, Embodied AI, Data Mining, Knowledge Graph, Internet of Things (IoT). He has published 7 textbooks and monographs. He is the Program Chair of International Academic Conference ICCDA2023, an expert in the keynote report of the ICCDA, and the reviewer of international journals such as IEEE Internet of Things and IEEE Transactions on Systems, Man, and Cybernetics: Systems.
YANFANG DENG received the B.S. degree in information and computing science and the M.S. degree in applied mathematics from Hainan University, China, in 2007 and 2010, respectively, where she is currently pursuing the Ph.D. degree with the School of Information and Communication. Her current research interests include big data and robot charging.
Workshop 13
Summary:
Intelligent Perception and Computer Vision represents a dynamic and rapidly advancing field at the intersection of artificial intelligence, computer science, and image processing. Its primary aim is to empower machines with the ability not only to comprehend the visual environment but also to interpret and act upon it intelligently.
In this multidisciplinary domain, state-of-the-art technologies, such as deep learning, image recognition, object detection, and target tracking, are harnessed to simulate the complexities of the human visual system. These technologies are used in a wide range of fields, including autonomous vehicles, surveillance systems, facial recognition, medical image analysis, augmented reality, intelligent systems, 3D imaging and more, with the great potential to redefine human interactions with the surrounding world.
This workshop serves as a platform for in-depth exploration of the latest advancements and practical applications in the realm of computer vision, including areas such as image recognition, object detection, scene analysis, 3D display, intelligent robots and real-time decision-making. It highlights the convergence of deep learning, machine vision and intelligent systems, revealing their impact in areas such as autonomous vehicles, healthcare, surveillance, robots and military applications.
In this multidisciplinary domain, state-of-the-art technologies, such as deep learning, image recognition, object detection, and target tracking, are harnessed to simulate the complexities of the human visual system. These technologies are used in a wide range of fields, including autonomous vehicles, surveillance systems, facial recognition, medical image analysis, augmented reality, intelligent systems, 3D imaging and more, with the great potential to redefine human interactions with the surrounding world.
This workshop serves as a platform for in-depth exploration of the latest advancements and practical applications in the realm of computer vision, including areas such as image recognition, object detection, scene analysis, 3D display, intelligent robots and real-time decision-making. It highlights the convergence of deep learning, machine vision and intelligent systems, revealing their impact in areas such as autonomous vehicles, healthcare, surveillance, robots and military applications.
Keywords: Computer Vision; Intelligent Perception; Deep Learning; Machine Learning; Artificial Intelligence; Robot System
Fang Ming is a professor and doctoral supervisor in the School of Artificial Intelligence at Changchun University of Science and Technology. He also serves as the vice chairman of the Jilin Provincial Robotics Society, the executive director of the Jilin Provincial Artificial Intelligence Society, and the director of the Jilin Provincial Image and Graphics Society. In 2011, he graduated from Hokkaido University in Japan, where he received his Ph.D. degree in Engineering. His research interests including image processing and computer vision, especially in the fields of image fusion, target tracking, underwater image processing, video stabilization, and defect detection in the industrial and agricultural fields. Professor Fang has participated in projects such as Hitachi Medical Diagnosis Linkage System. Currently, he is leading one project funded by the Ministry of Education's Study Abroad and Communication Center, as well as three projects commissioned by enterprises and institutions. He has been recognized with several awards, including one First Prize and two Second Prizes for Technological Advancement in Jilin Province. Additionally, Professor Fang has authored three academic monographs and published over 50 research papers.
Workshop 14
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 with “paper title_workshop title”.
Big Data Applications and Software in Science, Engineering, Healthcare,Finance, Business, Transportation, Telecommunications, etc.
Big Data Analytics in Small Business Enterprises, Public Sector and Government.
Big Data Industry Standards.
Development and Deployment Experiences with Big Data Systems.
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 with “paper title_workshop title”.
Big Data Applications and Software in Science, Engineering, Healthcare,Finance, Business, Transportation, Telecommunications, etc.
Big Data Analytics in Small Business Enterprises, Public Sector and Government.
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.
Workshop 15
Summary:
Big data and artificial intelligence (AI) are envisaged to be applied as a key enabler for the next generation wireless network evolution. The conventional wireless networks are generally regulated by mathematical models, which may be insufficient to handle complex application scenarios with imperfect conditions. Thus, big data analytics and AI with machine learning techniques can be applied to further optimize the performance of wireless networks. The topics of interest include but are not limited to：big data driven wireless network personalization optimizations, wireless AI network architecture, AI-based wireless network management, intelligent network slicing, big data driven and AI-based wireless communications, etc.
Keywords: Big data, artificial intelligence, wireless networks
Hongming Zhang, IEEE Senior Member, China Institute of Communications Senior Member, received the Ph.D. degree in EEE from the University of Southampton. He is currently an Associate Professor with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China. He has published more than 50 SCI/EI indexed papers, and has served as the TPC member in IEEE Globecom, ICC, etc. He has received the Young Talent Program from China Association for Science and Technology. His research interests include wireless communications, heterogeneous network performance analysis and optimization, and machine learning.
Xiaoling Hu, IEEE Member, received the B.E. degree in electronics and information engineering from the Dalian University of Technology, Dalian, China, in 2016, and the Ph.D. degree in information and communication engineering from Zhejiang University, Hangzhou, China, in 2021. She is currently an Associate Professor with the Beijing University of Posts and Telecommunications, Beijing, China. Her research interests include reconfigurable intelligent surface, integrated sensing and communication, wireless sensing, massive MIMO and mmWave. She and her coauthors have been awarded a Best Paper Award at the IEEE GLOBECOM 2020, IEEE ICC 2019, IEEE ICC 2022, and IEEE WCSP 2023.
Workshop 16
Summary:
Statistical machine learning and deep
learning are two important branches of
technology in the field of computer
vision. Statistical machine learning is
a learning method based on statistical
theory and probabilistic models, which
achieves tasks such as classification,
detection, and segmentation by extracting features from images. Deep learning is a machine learning method based on neural networks, which learns and extracts data features through a multi-level neural network structure. The application of deep learning in computer vision not only improves the accuracy and efficiency of image analysis and understanding, but also opens up new research directions and application areas. With the further development and expansion of deep learning technology, we can expect to achieve more automated processing of complex tasks in computer vision and more breakthrough results in many fields.
Keywords: Statistical Learning, Deep Learning, Neural Network, Computer Vision
He Jiang, PhD, graduated from Shanghai Jiao Tong University successively in information engineering and automation. He is currently working in the School of Information and Control Engineering, China University of Mining and Technology. His research interests include image restoration, image retrieval, and depth estimation based on deep learning. He has host or participated in 4 projects of NSFC, 1 project of International Young Talent Fund of Zhejiang Lab, 1 project of MIIT, 1 project of MST. Up to now, he has published more than 20 SCI/EI papers in JCR Q1/Q2, CCF A/B conferences, and T1/T2 journals of communication societies. He has been invited as an invited editorial board member of the NeruoImage, and served as a reviewer for IEEE TMM, IEEE TCSVT, IEEE TCE, AI, JVC, MATP, CVPR, ECCV, ICASSP, ICME, ICIP, ICONIP.
Workshop 17
Summary:
With the rise of big data across domains, there are increasing opportunities to uncover causal relationships from observational data. However, causal discovery and inference on large-scale, high-dimensional data remains challenging. Additionally, many important data types like time series, text, and genomes are sequential in nature, requiring specialized techniques to model causal mechanisms.
This workshop focuses on advancing causality specifically for big data. We welcome submissions on:
By bringing together experts in causal inference and sequence modeling, the workshop aims to propel progress on causality in domains with abundant sequential signals. We encourage submissions from academics and industry practitioners engaged in this emerging field.
This workshop focuses on advancing causality specifically for big data. We welcome submissions on:
- Scalable causal discovery methods for high-dimensional data
- Causal representation learning techniques for big data
- Causal discovery on real-world time series, text, speech, and bioinformatics data;
- Evaluating causal discovery on real-world big data
- Theoretical analyses of causal discovery bounds and limitations;
- Evaluating causal discovery methods on big sequential data;
- Applications of causal modeling on healthcare records, finance, NLP, and more;
- Novel foundation model architectures for sequence data;
- Causal reasoning on big data
By bringing together experts in causal inference and sequence modeling, the workshop aims to propel progress on causality in domains with abundant sequential signals. We encourage submissions from academics and industry practitioners engaged in this emerging field.
Keywords: Causal Inference; Causal Discovery, Reasoning, Sequential data
Defu Cao is currently a visiting scholar at MBZUAI and also working at Thomas Lord Department of Computer Science at the University of Southern California. Prior, he earned his Master's Degree at school of EECS Peking University. Cao is primarily interested in developing machine learning and data mining algorithms that demonstrate a deep understanding of the world with special structures, including time series, spatio-temporal data, and relational data. He has published his research in top conference proceedings including NeurIPS, CVPR, ICLR, ICRA, ICDM and NAACL, etc. He has been invited as an invited editorial Section Editor for “Sustainable Resource”, Guest Editor for “Current Chinese Science” and served as a reviewer for IEEE TPAMI, IEEE TNNLS, ACM HEALTH, Neurocomputing, Pattern Recognition, ICML, NeurIPS, ICLR, AAAI, IJCAI, KDD, LoG, CVPR etc
Workshop 18
Summary:
AIoT integrates AI technology and IoT technology, generating and collecting massive amounts of data from different dimensions through the Internet of Things and storing them in the cloud and edge. Through big data analysis and higher forms of artificial intelligence, it achieves the digitization and intelligent connection of all things. This workshop includes but is not limited to models, algorithms, and related solutions.
Keywords: AI; IoT; Cloud Computing; Big Data
Yonghua Li, male, professor, PHD supervisor, now teaches at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. The main research directions are: Internet of Things technology, mobile communication technology, cloud computing and big data processing technology. He has many years of research and development experience in the key technical fields of intelligent hardware, Internet of Things and communication networks, undertaken more than 30 theoretical research and engineering projects, published more than 100 papers in academic journals and conferences, and applied for 50 patents. More than 30 monographs and textbooks have been published.
Workshop 19
Summary:
With the rapid development of computer and artificial intelligence technology, its application in industry, agriculture, medical health and other fields has been widely used. This call for papers aims to explore the theoretical research and practical applications of automation, data science, and artificial intelligence techniques in complex process industries, discrete industries, and modern smart farm environments. Intelligent and automated technologies have made significant progress in industry and agriculture in various scenarios. From intelligent detection, monitoring, environmental regulation to planning and scheduling and optimal control, intelligent and automated technology is completely changing the mode of modern industrial and agricultural production. Therefore, it is of great significance to study and explore the theory and application technology of automation and intelligent technology in complex production processes (such as complex industry and modern agriculture) to promote the upgrading of industrial and agricultural industries. Other areas of interest include automation and intelligent technology in complex environmental state monitoring, intelligent regulation of modern farm microclimate, theoretical and practical exploration of intelligent detection of key parameters in complex production processes, and environmental perception based on multi-source data. We welcome submissions that explore these and other related topics. Topics can include, but are not limited to, the following:
- 1.Advanced AI algorithms for complex process modeling, optimization.
- 2.Application research of big data technology in modern agricultural environment.
- 3.Artificial intelligence in abnormal condition detection of complex production processes.
- 4.Advanced perception technology based on artificial intelligence.
- 5. Challenges and future prospects of automation and intelligence in complex production processes.
Keywords: Automation, artificial intelligence, big data, modern agriculture, complex industrial process, fault diagnosis, soft sensor
Kaixun He received the Ph.D. degree in control science and engineering from the School of Information Science and Engineering from East China University of Science and Technology, Shanghai, China, in 2016. In September 2016, he joined the Shandong University of Science and Technology, Qingdao, China, where he is currently a Associate professor in automation with the College of Electrical Engineering and Automation. His current research interests include industrial process monitoring and fault diagnosis, artificial intelligent, and machine learning.
Zhi Li received the B.S. and Ph.D. degrees from the East China University of Science and Technology, Shanghai, China, in 2011 and 2017, respectively, all in control science and engineering. He is currently an associate professor at East China University of Science and Technology, Shanghai, China. His current research interests include process modeling and control, data minning, process monitoring, and their applications.
Dayu Tan received the Ph.D. degree in control science and engineering from the School of Information Science and Engineering from East China University of Science and Technology, Shanghai, China, in 2021. He was a visiting Ph.D. student of School of Engineering Practice and Technology, McMaster University, Hamilton, ON, Canada, for the period from Sep. 2019 to Oct. 2020. In September 2021, he joined the Anhui University, Hefei, China, where he is currently an Assistant Professor in Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education. His research interests include machine learning, computer vision, and data mining.
Workshop 20
Summary:
Recently, Artificial Intelligence has involved a wide range of scientific fields, the academia and industry have paid widely attention to evolve the core issue of ‘modeling thinking’ processing for the artificial intelligence 2D/3D computer vision and how to use these signals for reasoning. Under the environment of strong understanding of image, voice and text, the real industrial applications need the artificial intelligence processing methods with learning, reasoning and optimizing ability. Gradually, explanatory knowledge discovery has become the basis of the features of the next generation of convergent artificial intelligence, and it is more necessary to express the essence of multiple signals, such as diagrams, rules, functions, even neural networks as well as sub-model features contain multiple layers and distributions.
Artificial intelligence digital signal processing and analysis has played an important role for the intelligent development of the information society. Nowadays, intelligent 2D/3D computer vision based on a wide range of sensors have appeared, such as the intelligent signals of cameras, spectrometers, thermal imaging cameras, hyperspectral imagers, depth cameras, electronic transformers, electric fences, photometric systems, satellite laser ranging systems and radars, etc. Higher requirements of the technologies of the precise digital elevation model analysis, target detection, target recognition, signal diagnosis and prediction of the intelligent signals are needed.
Artificial intelligence digital signal processing and analysis has played an important role for the intelligent development of the information society. Nowadays, intelligent 2D/3D computer vision based on a wide range of sensors have appeared, such as the intelligent signals of cameras, spectrometers, thermal imaging cameras, hyperspectral imagers, depth cameras, electronic transformers, electric fences, photometric systems, satellite laser ranging systems and radars, etc. Higher requirements of the technologies of the precise digital elevation model analysis, target detection, target recognition, signal diagnosis and prediction of the intelligent signals are needed.
Keywords: Knowledge-driven, Deep Learning, Computer Vision, Signal Processing
Hu Zhu is now a professor in School of Communication and Information Engineering from Nanjing University of Posts and Telecommunications (Jiangsu Key Laboratory of Image Processing and Transmission). His research mainly focuses on artificial intelligence and computer vision. He have published a series of high-quality articles (all published in IEEE Transactions series journals) and developed a series of intelligent computational imaging devices for practical applications, making significant efforts to promote the development of industry, academia, and research in this field, and have achieved numerous research results. The related research achievements include the publication of 66 high-level academic papers and the filing of 56 invention patents. I have hosted more than 10 national, provincial, and other relevant projects. In addition, he served as a reviewer for several IEEE journals and technical committee chairs and members for a series of well-known international academic conferences and workshops.
Wei Qian is currently a Professor with The University of Texas at El Paso. His research interests include biomedical imaging and image processing. He is a fellow of the American Institute for Medical and Biological Engineering (AIMBE).