WS1-Title: Intelligent Perception and Interactive Cognition
Summary: Intelligent perception and interactive cognition technology ensures the behavior understanding and coordination of human-computer, machine-to-machine. Through technologies such as cross-modal perception, machine learning and cognitive computing, we can build intelligent expression and learning methods unified with the real world. Cross-modal perception can compensate for the shortcomings of single data, establish a harmonious human-machine environment, and make computers more intelligent. Intelligent perception and interactive cognition technology has been widely used in self-driving, medical, games, robotics and other fields in recent years, which focuses on how to make machines understand people's intentions better, realize the interaction between machines, human-computer cooperation, and form an intelligent and trusted system.
This special session aims to provide an opportunity for researchers, and discusses the latest trends of intelligent perception and interactive cognition areas, and to enhance the influence of intelligent perception and interactive cognition in national and international academia. To promote the techniques and concepts from different fields, the special session also encourages authors to submit relevant outstanding contributions on the topic of intelligent perception and interactive cognition research papers.

Keywords: Intelligent Perception, Intelligent Interaction, Visual Cognition, Cross-modal Perception, Scene Perception, Image Processing, Human-machine Hybrid Intelligence, Human Factors and Ergonomics, Safe Driving Behavior, Wearable Device Interaction, Multi-agent Collaboration

Chair: Prof. Nan Ma, Beijing University of Technology

Nan Ma, Professor at Beijing University of Technology. She is the Deputy Director of Engineering Research Center of Intelligence Perception and Autonomous Control, Ministry of Education, PhD Supervisor, the Deputy Secretary General of China Artificial Intelligence Society, the Deputy Director and Deputy Secretary General of the Education Working Committee of the China Artificial Intelligence Society, the Managing Director of Beijing Society of Image and Graphics. Her current research interests include interactive cognition, machine vision, intelligent driving, knowledge discovery and intelligent system. She won the first prize of Scientific and Technological Progress Award of China Society of Image and Graphics in 2022 and the second prize of Science and Technology Award [technological invention] of Chinese Institute of Electronics in 2020. She has successively presided over a number of projects of NSFC, projects of Beijing Natural Science Foundation and Beijing Intelligent Manufacturing and Robot Technology Innovation Projects. In recent years. She has presided over nine enterprise projects such as BAIC Group, Dongfeng Motor corporation. She led the team to win the championship in international and domestic intelligent driving competitions, and won the grand prize in the final of the second China "AI +" innovation and entrepreneurship competition, as well as the First Prize of Beijing Municipal Teaching Achievements.

Vice Chair: Dr. Tingting Su, Beijing University of Technology

Tingting Su, Lecturer at Beijing University of Technology. Her current research interests include human-computer interaction and robot control. She has presided over and participated in a number of projects of NSFC, projects of Beijing Natural Science Foundation and National Key R&D Program. She has published one monograph and nearly 30 papers, and also applied for/authorized more than 20 invention patents.

Workshop Secretary: Zhixuan Wu, Beijing Union University

Zhixuan Wu, she is currently working toward the Master degree from Beijing Union University. Her current research interests are human action recognition, machine vision and interactive cognition.She has published 4 academic papers indexed by SCI or EI. She won the grand prize in the final of the second China "AI +" innovation and entrepreneurship competition, and 2020 Beijing Excellent Undergraduate Graduation Design [Human Action Recognition Based on Feature Extraction] under the supervision of Prof. Nan Ma.
WS2-Title: Clustering, anomaly detection and their applications
Summary: Clustering and anomaly detection are fundamental tools for data analysis and processing, and have wide applications in image and video processing. In the context of big data and deep learning, clustering algorithms and anomaly detection research face new open issues. The main open issues include: how to efficiently cluster large-scale data, how to effectively cluster multi-view data, how to effectively cluster data with missing views or features, how to effectively integrate clustering algorithms with deep learning, and how to adaptively determine the number of clustering classes; Clustering algorithm is also one of the ways to complete data anomaly detection, and one open issue is how to use clustering algorithm for anomaly detection. A common issue with these issues is how to extract effective features. 
This workshop aims to bring researchers from both academia and industry together for academic and technical discussions, and to introduce the latest research findings. We encourage and hope scholars working in this field to actively participate in research and discussions on cutting-edge topics.

Keywords: Clustering, anomaly detection, large-scale, multi-view

Chair: Prof. En Zhu, National University of Defense Technology, China

En Zhu, Professor, Department of Computing Science, College of Computer Science, National University of Defense Technology. His main research interests are clustering analysis, anomaly detection, computer vision. His work has found applications in areas such as medical imaging, video recognition. He has published over 160 journal/conference papers. He was awarded the National Outstanding Ph.D. Thesis Award by the Ministry of Education and the Academic Degrees Committee of the State Council of China in 2007, the first prize of Hunan Province’s Natural Science Outstanding Paper Award in 2006 and 2008 respectively, the first prize of Hunan Province’s Natural Science Award in 2014, the Hunan Province’s Outstanding Supervisor Group Award in 2019.

WS3-Title: Big Data & Artificial Intelligence with Applications
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, 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 modeling 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

Chair: Assoc. Prof. Shan Liu, Communication University of China, China

Shan Liu is an associate professor and Chair of Intelligent Science Department at Communication University of China. She received her Ph.D. degree from Texas A&M University, in United States. Now she is a committee member of Chinese Association for Artificial Intelligence, a committee member of Chinese Institute of Electronics, a committee member of Beijing Society of Image and Graphics, a committee member of Association of Fundamental Computing Education in Chinese Universities, Member of China Automation Society, Member of 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 main research areas include complex networks, intelligent tags, big data and artificial intelligence. In recent years, she has been invited to the University of California, 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 National Science Foundation and National Science and Technology Support Program, published more than 100 high-level academic papers, received many best paper awards, and applied for more than 30 patents of invention.

WS4-Title: Equipment Safety Features Extraction, State Assessment, Degradation Prediction and Intelligent Maintenance
Summary: With the mutual integration of new-generation information technology and traditional industry, industrial Internet devices are progressing toward automation and intelligence, which also brings security and management challenges, especially with the development of 5G technology, discussions about network security and security standards and security threats have attracted the attention of more and more scholars. Related research can advance the establishment of device and network security standards, while facilitating the protection of user privacy and the safe and efficient operation of infrastructure. We will discuss about industrial equipment security parameter preference, security state assessment and security degradation prediction, as well as network attack and defence and other related equipment security operation and maintenance practices. 
This seminar aims to bring together the latest research results in the fields of security state characterization, security posture prediction, intelligent operation and maintenance. And we encourage the prospective authors to submit their distinguished research papers on the subjects. Please name the title of the submission email with “paper title_workshop title”.

Keywords: 5G Base Stations, Security Features Extraction, Security State Assessment, Security Degradation Prediction, Deep Learning

Chair: Assoc. Prof. Sheng Hong, School of Cyber Science and Technology, Beihang University, China

Sheng Hong is an associate professor and doctoral supervisor in the School of Cyber Science and Technology at the Beihang University (BUAA). He engaged in network and information security, complex system security research, antagonistic sample generation technology, deep learning, generative adversarial network, completed a number of related scientific research projects in the field of network security, Presided over the national key basic research and development plan projects, national key research and development project, industrial Internet innovation and development project, technical basic project, pre-research project, National Natural Science Foundation of China There are more than 10 national, provincial and ministerial-level topics, and more than 10 horizontal topics in key industries such as industrial Internet, intelligent manufacturing, State Grid, and avionics systems. He is the editorial board member of several international journals and conferences, including "Information Network Security", "Information Technology and Network Security" and "Aeronautical Engineering Progress". In the national key research and development plan, he established a multi-level industrial network security protection model, through dangerous targets, key targets, cloud detection and other protection strategies and protective measures to ensure the network from the aspects of environmental credibility, situational knowledge, and controllable operation. Security; in the industrial Internet innovation and development project, he proposed a system security enhancement protection technology for the industrial Internet; in the National Natural Science Foundation, he broke through the critical phase change mechanism of fault propagation by focusing on the coupling, chaos and spreading characteristics of dynamic network faults The bottleneck of the maintenance control strategy provides a new solution for the prevention of complex dynamic network fault propagation and fault-tolerant control. In the open topic of the Shanghai industrial control system safety innovation functional platform, he aimed at the industrial Internet network suffered from virus attacks and equipment paralysis. The actual problems of the research network virus transmission mechanism and protection strategies. He has published more than 70 papers, including 30+ SCI papers, one co-authored academic book, and 14 national invention patents.

WS5-Title: Object detection and classification, deep learning and application
Summary: Object detection and classification is an important task in the field of computer vision, which has a wide range of applications in artificial intelligence, automatic driving, security monitoring, image retrieval and other fields. The basic framework consists of three main parts: target location, target classification and target framework regression. Large scale objects are easier to detect because of their large area and rich features. Small objects have limited features to use because of their small size. Deep learning technology occupies an absolute position in the field of artificial intelligence at present. Compared with traditional machine learning algorithm, it shows the intelligence effect closest to human expectation in some fields. Current problems and challenges facing deep learning include: large amount of data is needed, insufficient concept expression ability, inability to transfer, no natural way to deal with hierarchical structure, inability to develop reasoning, lack of transparency, lack of combination with prior knowledge, inability to distinguish causality from correlation, approximation, not be completely trusted, and difficult to engineering. 
This workshop aims to bring researchers from both academia and industry together for academic and technical discussions, and to introduce the latest research findings. We encourage and hope scholars working in this field to actively participate in research and discussions on deep learning topics.

Keywords: Object detection, image classification, deep learning, machine learning

Chair: Prof. Jianjun Yuan, Southwest University, Chongqing, China

Jianjun Yuan, Professor, College of Artificial Intelligence, Southwest University, Chongqing, China. His research interests are machine learning, deep learning, image processing and analysis of big data. His work has found applications in areas such as medical imaging, image recognition, image detection and video prediction. He has published over 40 journal or conference papers. He has instructed his students to win many awards. He has won the Outstanding Coach award and the Outstanding Teacher title.

WS6-Title: Collaboration technology of multiple ICVs
Summary: Multi-vehicle collaboration technology is one of the core technologies of intelligent and connected vehicles, which can effectively improve road traffic efficiency and driving safety. We focus on the following open issues: how to coordinate conflict and cooperation between multiple vehicles; how to achieve the integrated optimization of multiple performances, including, driving safety, stability, energy saving, and traffic efficiency; How to implement cooperative control of multiple agents. This workshop aims to discuss and share the academic frontier results and engineering application problems in the field of multi-vehicle coordination, and we encourage and hope scholars in this field to actively participate in the discussion.

Keywords: Intelligent and connected vehicles; collaboration of multiple vehicles; vehicle platoon; cooperative control

Chair: Assoc. Prof. Weiwei Kong, China Agricultural University, China

Weiwei Kong, Associate Professor at the College of Engineering, China Agricultural University, specializes in the dynamics and control of intelligent connected vehicles, vehicle platoon control, and multi-vehicle cooperative control. She has published 29 SCI/EI indexed papers as the first/ corresponding author in this field. She has been awarded the Innovation Star of Beijing Science and Technology New Star Program and has led or participated in over 10 national and provincial-level research projects, including the National Natural Science Foundation of China, the National Key R&D Program, and the Beijing Municipal Science and Technology Project.

WS7-Title: Visual images-induced production performance monitoring and fault diagnosis in complex industrial processes
Summary: Visual imaging of industrial processes can provide rich and detailed information with respect to the corresponding process where the images are captured. Thus, leveraging visual images as a primary source of data plays an important role in monitoring the performance of industrial processes, as well as diagnosing faults or anomalies. With the advent of numerous well-behaved deep learning technologies, extracting and fusing essential features from big volume images (videos) and traditional process variables to address the process monitoring issues achieve significant advances. However, detecting anomalies and diagnosing faults from visual images and traditional process variables is challenging due to the complex nature of industrial processes because many open issues still exist, such as the low quality and variability of visual images due to the hostile imaging environment, the extreme difference in the sampling and data amount between visual images and different process variables, the real-time performance monitoring and early fault detection, and so on. This workshop is aimed at bringing researchers from both academia and industry together to actively participate in this topic and making comprehensive discussions on the latest findings.

Keywords: Machine vision, industrial process monitoring, visual images and process variables fusion, fault diagnosis.

Chair: Prof. Jinping Liu, Hunan Normal University, China

Jinping Liu, Professor, Ph.D. supervisor, coming from the College of Information Science and Engineering, Hunan Normal University. He is a young backbone teacher of Hunan Province, working as an assistant editorial board member of CAAI Transactions on Intelligent Systems, a Young editorial board member of Data science and Management, Journal of Wuhan University (Natural Science Edition), etc.. He has long been engaged in the research of intelligent monitoring, modeling, and optimal control of complex industrial processes and has developed several sets of related intelligent monitoring systems, which have been successfully applied to complex industrial processes, such as lead-zinc flotation process and steel smelting process, with remarkable economic benefits. He has presided over more than 10 projects. He has published more than 60 journal papers and applied for more than 20 national invention patents, 15 of which have been granted.

WS8-Title: Applictions of artificial intelligence of things (AIoT)
Summary: Artificial intelligence of things (AIoT) is the combination of artificial intelligence (AI) technologies and the internet of things (IoT) infrastructure. AIoT's goal is to create more efficient IoT operations, improve human-machine interactions and enhance data management and analytics. Potential topics includes intelligent future IoT architecture, intelligent security and privacy for the IoT, intelligent IoT solutions for smart eHealth and smart home, intelligent green computing in the IoT, intelligent industrial IoT, intelligent IoT communication and network virtualization, intelligent edge/cloud computing for the IoT, intelligent IoT applications, intelligent Internet-of-underwater things (IoUT), protocols and standards for intelligent IoT, intelligent Internet-of-vehicles, autonomous delivery robots, AIoT prototypes and case studies and Cloud-Edge-Terminal collaboration enabled AIoT computing.

Keywords: Artificial intelligence, Internet of Things, edge computing

Chair: Assoc. Prof. Zhiyang Jia, Department of Computer Science, China University of Petroleum (Beijing), China

Zhiyang Jia was born in Jilin City, Jilin Provice, P.R. China, in 1980. He is the senior member of China Computer Federation (CCF) and executive committee member of Computer Application Professional Committee He received the master‘s degree of Computer Software and Theory from Harbin University of Science and Technology, P.R. China. Now, he works in China University of Petroleum-Beijing at Karamay. His research interests include Artificial Intelligence & Internet of Things, machine learning and intelligent software.

Vice Chair: Engineer Minjie Zhang, Department of Computer Science, China University of Petroleum (Beijing), China

Minjie Zhang, An experimentalist at China University of Petroleum-Beijing at Karamay. His current research interests include Human pose Estimation and Graph and Image processing. He has participated in a number of projects of NSFC, projects of Beijing Natural Science Foundation and National Key R&D Program.

WS9-Title: Efficient AI in Vision , NLP and Cross-modal Learning
Summary: In recent years, the field of artificial intelligence has witnessed remarkable advancements, particularly in the domains of visual processing, natural language understanding, and cross-modal learning. However, as AI models grow in complexity and size, there is an urgent need to address the challenges posed by computational inefficiency, memory consumption, domain gaps, and energy requirements. The "Efficient AI in Visual, NLP, and Cross-modal Learning" workshop aims to bring together researchers, practitioners, and industry experts to explore novel approaches, methodologies, and cutting-edge techniques that optimize the efficiency of AI systems in these critical domains. 

Topics of interest for this workshop include, but are not limited to: 
1. Lightweight Models for Visual Recognition: Presentations focusing on compact neural architectures and parameter-efficient algorithms for tasks such as image classification, object detection, and semantic segmentation. 
2. Low-resource NLP Models: Discussions on building efficient natural language processing models that achieve competitive performance even with limited training data, with applications in machine translation, sentiment analysis, and text classification. 
3. Multimodal Learning: Exploring techniques to combine information from multiple modalities, such as images and text, to achieve improved AI performance, while also maintaining efficiency.

4. Model Compression and Quantization: Techniques for reducing the size of pre-trained models through quantization, pruning, and distillation, without compromising their accuracy.

5. Transfer Learning and Meta-learning: Exploring methods to leverage pre-trained models and adapt them efficiently to new tasks or domains.

6. Hardware Acceleration for AI: Discussions on specialized hardware and processing units designed to accelerate AI computations, improving both speed and energy efficiency.

7. AutoML for Efficiency: Presentations on automated machine learning techniques that efficiently search and optimize hyperparameters and architectures for AI models.

8. Green AI: Discussions on sustainable AI research that aims to minimize the environmental impact of AI systems, considering energy consumption and carbon footprints.

The workshop will consist of invited talks from prominent researchers, contributed paper presentations, and interactive panel discussions. We invite researchers from both academia and industry to submit their latest work and share insights into creating efficient AI systems that push the boundaries of performance while remaining mindful of resource constraints

Keywords: Efficient AI; Cross-modal Learning; Low-resource Learning; Domain Generalization.

Chair 1: Ming Yan , Center for Frontier AI Research , Agency of Science, Technology and Research, Singapore

Dr. Ming YAN is currently serving as a Senior Scientist at the Center for Frontier AI Research (CFAR) within the Agency for Science, Technology and Research (A*STAR) in Singapore. Previously, he held a position as a Visiting Scholar at Georgia State University. Dr. YAN's research contributions have been widely recognized through the publication of over 15 papers in esteemed international conferences and journals, including ACL, AAAI, IEEE TNNLS, IEEE TII, KBS, and Neural Computing.

He has been granted five national patents. His passion for advancing the field of artificial intelligence and data science is evident in his role as the Principal Investigator (PI) and Co-Investigator in more than 10 national science and technology research projects in both China and Singapore. He successful supervised several students, who have been recipients of prestigious awards such as SIPGA, CIARE, and CSC. 

Chair 2: Assoc. Prof. Xu Wang, College of Computer Science, Sichuan University, China

Xu Wang is currently an associate research professor at the College of Computer Science, Sichuan University. His research interests mainly focus on machine learning, cross-modal learning, and cross-domain learning. On these areas, he has authored more than 20 articles in the top-tier conferences and journals, including IEEE TPAMI, IEEE TIP, IEEE TCYB, IEEE TCSVT, IEEE TMM, AAAI, IEEE CVPR, ACM MM. He has been a Program Committee Member of AAAI, IEEE CVPR, ACM MM, IJCAI and other important international academic conferences for many times.

WS10-Title: Computational Biology and Biomedical Engineering in the Big Data Ara
Summary: Since the beginning of the Hodgkin-Huxley’s equation of the giant axon, computational biology has been an efficient and powerful tool in understanding the mysterious of life. Biomedical engineering benefited from these achievements and came out with many innovative diagnosis and treatment methods. In the era of big data, wearable equipment produces huge amount of life data that would evolutionarily changes the way people explore the life-process and innovate new tools. However, there many challenges in processing and effective use of biological big data, due to the complex, high-volume and noisy nature of the data. This workshop aims to bring together researchers in different field and to provide a common platform for holding discussion on topics as wearable device development, denoise techniques, data interpretation, organ or tissue level modeling, and other techniques that arrow the gap between biology and medical applications.

Keywords: Big Data, Computational Biology, Medical Engineering

Chair : Jinshan Xu, Zhejiang University of Technology, China

Jinshan Xu, Associated Professor, College of Computer Science and Technology, Zhejiang University of Technology. His main research interests are computational biology, medical signal processing techniques and intelligent medicine. He has published over 30 high quality journal papers, under the support of several research grants from both the National Nature Science Foundation of China and the Zhejiang Provincial Natural Science Foundation.