Workshop
 
Summary: Service robots are one of the most critical robot's applications that enter human life. This domain consists of elements from human health to industry. Somehow, this robot can save a human's life or help him secure from the load-carrying job and all repeated work that may interfere with job accuracy. Based on ISO 8373:2012, The service robots Split into personal service types of robots characteristically meant to use outside of manufacturing and the professional setting robots and professional service robots that can use as non-commercial individuals professional service robots can use as commercial professionals. A service robot is a semi-autonomous or fully autonomous robot. This type of robot was gradually accepted as a human assistant, used in various applications and jobs. The industry especially tried to hire this type of robot as an essential part of the production line. As history shows, so far three-stage of the industrial revolution have passed: the first industrial revolution is the change of mechanization, the second industrial revolution is the change of electric power, and in the era of industry 4.0, it combines digital technology and Internet technology, which has further innovation in technology.in this Talk, as the sample after review on AGV and MIR service robot, the modelling steps and simulation are described, which can help the researcher to learn and implement various control systems on  the modelled system 

Keywords：Service robot, Automated Guided vehicle, AGV, MIR

Chair: Dr. Ata Jahangir Moshayedi, School of information engineering Jiangxi university of science and technology, China
Dr Ata Jahangir Moshayedi, Associate professor at Jiangxi University of Science and Technology, China, received his Doctorate in Electronic Science in the field of mobile olfaction from the Department of Electronic Science Savitribai Phule Pune University, Pune, India. Member of IEEE, Instrument Society of India as a Life Member, Life Member of Speed Society of India, Member of the editorial team of various conferences and journals like; International Journal of Robotics and Control, JSME, Bulletin of Electrical Engineering and Informatics, International Journal of Physics and Robotics Applied Electronics, etc., published the various paper in national journals and conferences, three published books, owner of 2 patents and nine copyrights. His research interests include robotics and Automation/Sensor Modeling/Biology-Inspired Robots, Mobile Robot Olfaction/Column Tracking, Embedded Systems/Machine Vision-Based Systems/Virtual Reality, and Machine Vision/Artificial Intelligence.


Summary: Quantum computing is a revolutionary computing method, as a new computing method, it has the advantage of high efficiency compared with classical computing. At present, quantum computing is in the process of accelerating its evolution and development, among which quantum algorithms and quantum computing models are important research fields, having significant impact on current and future technological development. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and industry. Another goal is to show the latest research results in the field of quantum algorithms and quantum computing models. We encourage prospective authors to submit related distinguished research papers on the subjects: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title workshp title”.

Keywords：quantum computing, quantum algorithms, quantum computing models

Chair: Prof. Daowen Qiu, Computer Science, Sun Yat-Sen UniversityChina
Daowen Qiu received the M.S. degree from Jiangxi Normal University, Nachang, China, in 1993 and the Ph.D. degree from Sun Yat-Sen University, Guangzhou, China, in 2000, both in mathematics.,During 2000 and 2001, he was a Postdoctoral Researcher in computer science with Tsinghua University, Beijing, China. Since August 2002, he has been associated with Sun Yat-Sen University, and then a Full Professor of computer science in May 2004. His current research interests include quantum computing, discrete event systems, fuzzy and probabilistic computation, and he has focused on models of quantum and probabilistic computation, quantum information. He is the author or co-author of more than 160 peer-review papers published in various academic journals and conferences, including Information and Computation, Artificial Intelligence, Journal of Computer and System Sciences, Theoretical Computer Science, IEEE Transactions on Automatic Control, IEEE Transactions on Systems, Man, and Cybernetics Part B, IEEE Transactions on Fuzzy Systems, Physical Review A, Quantum Information and Computation, Journal of Physics A, and Science in China. He is an editor of the journal Theoretical Computer Science.


Summary: With the progress of big data, artificial intelligence, Internet of things, machine vision and other technologies, agricultural and forestry intelligent equipment has been rapidly developed. This workshop aim to discuss the key technologies of agricultural and forestry intelligent equipment such as intelligent perception, intelligent control, precise operation, autonomous navigation, end-terminal-cloud collaboration. Another goal is to look ahead to the development trend of agricultural and forestry intelligent equipment. Machine vision is an important way for intelligent agricultural and forestry equipment to perceive the environment. This workshop will discuss the application of machine vision technology in agricultural and forestry scenarios: vehicle and personnel management; prevent illegal logging and poaching; early forest fire monitoring; tree, crop growth monitoring; disease and insect pest monitoring; tree recognition and location of cutting robot; picking robot target recognition and positioning; estimation of forest wood storage and crop yield; estimation of felling area, afforestation area and crop area; population and quantity monitoring of rare animals. The topics in this workshop include but are not limited to the above.

Keywords：Agroforestry robot; Machine vision; Intelligent perception; Intelligent control; Accurate operation

Chair: Assoc. Prof. Hongqian Zhu, Central South University of Forestry and Technology, China.

Hongqian Zhu has long been engaged in scientific research and teaching of intelligent robot, machine vision, hydraulic servo and other aspects. He is the chief editor of the National 13th Five-Year Plan Textbook: Industrial Robot Technology (China Machine Press, July 2019). Team’s project "Pick orange robot target recognition" won first prize of national forestry robot design competition in 2019, team’s project "Forest security informatization construction based on intelligent vision" won first prize of national agriculture and forestry enterprise management optimization modeling competition in 2021.


Summary: GA is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. 
Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired by the collective behavior of social animals. In PSO the set of candidate solutions to the optimization problem is defined as a swarm of particles which may flow through the parameter space defining trajectories which are driven by their own and neighbors' best performances. 
GA, PSO and some other intelligent algorithms frequently used to solve optimization problems, in research for industrial and medical fields. The aim as well as objective of this workshop is to present the latest research and results of scientists working in the fields related to GA, PSO and other intelligent algorithms' application in above fields. This workshop will provide opportunities for the delegates to exchange new ideas and application through face-to-face discussions, to establish business or research relations and to find global partners for future collaborations.

Keywords：GA, PSO, Industrial and Medical Fields

Chair: Prof. Song Yu, Hitachi China Research Laboratory, China
Song Yu is a senior researcher from Hitachi China Research Laboratory. And he has many years of research experiences for artificial intelligence in the medical & industrial fields. His many papers have been published in international authoritative conferences and journals in medical system, disease risk prediction, simulation data generation, data mining algorithm improvement and so on. And he also has written more than ten patents in the aspects of job automation, recommendation algorithm, data simulation, disease risk prediction, medical data integration, industrial quality analysis, etc. In recent years, he was invited by HBDSS 2022 and other international academic conferences to give keynote speeches.


Summary: Biometric data is primarily collected to verify or establish a person’s identity.  Recently, employing the biometric data for extracting health cues and aiding in medical diagnosis has drawn much attention. Deducing health cues from different biometric modalities e.g. face, ocular, voice, gait etc by exploiting artificial intelligent technology, has witnessed an interest and promising application.

Keywords：health cue, biometric data, multi-modalities, data mining

Chair: Prof. Peirui Bai, Shandong University of Science and Technolog, China

Peirui Bai, received a Ph.D. degree in Biomedical Engineering from Xi'an Jiaotong University. He worked as a professor at the College of Electronic and Information Engineering, Shandong University of Science and Technology. His main research interests include biometrics, intelligent sports, pattern recognition and computer vision etc. He presided over the National Natural Science Foundation and the Natural Science Foundation of Shandong Province, etc. Based on these projects, he published more than 50 papers in high-level international and national journals/conferences.


Summary: 6G networks are expected to provide extremely reliable and low-latency communications and satisfy emerging services and applications. With the development of network communication, 6G networks present many challenges for machine vision. To satisfy typical scenarios and applications for the 2030-intelligent information society, machine vision, especially AI-based methods for high-level tasks, should be developed in parallel with 6G networks. To this end, this workshop will focus on AI-based machine vision. Topics include, but are not limited to, the following: (1) acceleration algorithm, (2) light-weight network architecture, and (3) knowledge-based learning. In fact, we welcome all the contributions to the success in closing the gap between human and machine vision. This workshop will provide opportunities to spark a new idea and build a bridge between global partners for future collaborations in the machine vision field.

Keywords：machine vision, machine learning, deep learning, 6G Wireless Networks

Chair: Dr. Hongjun Zhu, Chongqing University of Posts and Telecommunications, China
Hongjun Zhu received the M.S. and Ph.D. degree in instrument science and technology from Chongqing University. He is with the School of Software Engineering, Chongqing University of Posts and Telecommunications. His main research interests are the application of artificial intelligence to machine vision and social networks. He presided over the National Social Science Foundation and Chongqing Research Program of Basic Research and Frontier Technology, etc. He is the author or co-author of more than 20 papers published in various academic journals and conferences, including Applied Mathematical Modeling, Journal of the Franklin Institute, and Signal Processing. He won the Second Prize of the 2018 Chongqing Science and Technology Progress Award and the First Prize of the 2016 Science and Technology Award of China Highway and Transportation Society.


Summary: Reconfigurable Intelligent Surface (RIS) is an emerging technology for  reconfiguring wireless propagation environments through passive and tunable signal controls. It has potential applications to improve received signal strengths, reduce the transmit power consumption, and combat eavesdropping  in wireless communication scenarios.  However, due to the “multiplicative” fading effects, it is not trivial to acquire channel state information as the foundation of constructing and designing RIS-assisted wireless networks. As a key enabler for many intelligent solutions, Artificial Intelligent (AI) / Machine Learning (ML) could be integrated into RIS to alleviate the massive control and signaling overhead and simplify the design of RIS-assisted communications, leading to a more autonomous and self-adaptive configuration for future 6G systems.  
This special workshop is aimed to introduce the state‐of‐the‐art researches on performance analysis, algorithm design, systematic design and implementation, to accelerate the development of AI-empowered RISs for practical applications in wireless communications and networks. Suitable topics for this workshop include, but are not limited to, the following areas:
AI-enabled aerial RIS for wireless networks 
Learning based active and passive beamforming design in RIS transmissions 
AI-empowered RIS aided localization and sensing 
AI-assisted design for robust and secure communications in RIS-assisted wireless networks 
Joint design of RIS-based communication and channel estimation in the AI framework

Keywords：AI-enabled RIS, Learning based design, RIS aided localization and sensing, AI-assisted design for robust and secure communications, RIS-based communication and channel estimation

Chair 1: Prof. Gang Wang，Ningbo University, China
Gang Wang received the B.Eng. degree in electronic engineering from Shandong University, Jinan, China, in 2006, and the Ph.D. degree in electronic engineering from Xidian University, Xi’an, China, in 2011. He joined Ningbo University, Ningbo, China, in January 2012, where he is currently a full Professor. From June 2018 to June 2019, he was a Visiting Scholar with the University of Missouri, Columbia, MO, USA. His research interests include the area of target localization and tracking in wireless networks. 
Dr. Wang was the recipient of the Natural Science Funds for Outstanding Young Scholars from the National Natural Science Foundation of China and the Natural Science Funds for Distinguished Young Scholars from Zhejiang Provincial Natural Science Foundation. He is an elected member of the Sensor Array and Multichannel Technical Committee of the IEEE Signal Processing Society. He serves as the Associate Editor for IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, and the Handling Editor for Signal Processing (Elsevier).

Chair 2: Assoc. Juan Liu，Ningbo University, China
Juan Liu received the Ph.D. degree in electronic engineering from Tsinghua University, Beijing, China, in 2011. From March 2012 to June 2014, she was a Postdoc Research Scholar in Department of Electrical and Computer Engineering, NC State University (NCSU), Raleigh, NC, USA. From February 2015 to February 2016, she was a Research Associate in Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology (HKUST), Hong Kong. Since March 2016, she has been with Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China, where she is an Associate Professor. Now she is focusing on the research topics on UAV communications, wireless caching and edge computing, and deep learning for large-scale wireless networks.

Chair 3:  Assoc. Yangong Zheng，Ningbo University, China

Yangong Zheng is an Associate Professor in the Faculty of Electrical Engineering and Computer Science, Ningbo University, China. He received his PhD from the School of Electronic Science and Technology of Dalian University of Technology in 2014. During 2011-2013, he worked as a visiting scholar in Chemistry Department of The Ohio State University in USA. Currently, his research interests focus on electronic nose and neural networks.
Summary: Recently, a novel neural network called broad learning system (BLS) caves out a research wave in machine learning and pattern recognition. The BLS broadens the network by paralleling feature mapping nodes and enhancement nodes, and only the weights connected to the output layer need to be trained, resulting in a very fast and accurate learning capacity without deep structure. The designed neural networks expand the neural nodes broadly and update the weights of the neural network incrementally when additional nodes are needed or when the input data entering to the neural network continuously. Such a structure and incremental learning algorithm are perfectly suitable for modeling and learning big data environment. This workshop will discuss new broad learning models and their applications in regression and recognition tasks. The topics include but not limited to robust broad learning models, BLS for image processing, BLS for pattern recognition, BLS for data regression, deep broad learning networks, semi-supervised/un-supervised BLS, BLS for industry processing, and so on.

Keywords：Broad learning system, pattern recognition, data regression, image processing, machine learning, deep broad learning

Chair : Prof. Licheng Liu，Hunan university，China

Licheng Liu received the Ph.D. degree from the University of Macau, Macau, China, in 2016. He is currently an Associate Professor and a Yuelu Scholar in Hunan University, and a researcher in the National Engineering Research Center of Robot Visual Perception and Control Technology. His research interests include image processing, pattern recognition, deep learning and broad learning. Around these topics, he has published more than 35 papers in top journals/conferences, among which 12 papers are IEEE/ACM transactions papers (first author/corresponding author). Two papers are elected as ESI highly cited papers. He has severed as a reviewer for many top journals including the TIP, TNNLS, TCYB, TSMC: System, TCSVT, TMM, SPL, and so on. He has won the Macau Science and Technology Award for Postgraduates in 2016, and the Special Award for Teaching Achievements of Higher Education in Hunan Province.

Summary: Image processing and pattern recognition are the critical technologies in the field of computer vision. The traditional algorithms and techniques in the field of image processing and pattern recognition are growing at an unprecedented rate. In addition, with the rapid development of deep learning, numerous interesting networks are also developed to address the existing problems of image processing and pattern recognition. This special workshop aims to present new trends and advances in the field of image processing and pattern recognition, including traditional-based methods and learning-based methods. We encourage prospective authors to submit related research papers on the subjects: theoretical models, algorithms and practical applications in image processing and pattern recognition.

Keywords：Image Processing, Pattern Recognition, Computer Vision, Deep learning

Chair : Dr. Yun Liu，College of Artificial Intelligence, Southwest University, China

Yun Liu received the M.S. degree and the Ph.D. degree in computer science and technology from Sichuan University, Chengdu, China. He is currently with the College of Artificial Intelligence, Southwest University. His current research interests include image processing, pattern recognition, computer vision and deep learning. He has authored more than 20 technical articles in refereed journals and conferences, including IEEE TCSVT, Signal Processing, Engineering Applications of Artificial Intelligence, CVPR Workshop, ECCV, ACCV, etc. He presided over the National Sciences Foundation of Chongqing, the project of science and technology research program of Chongqing Education Commission, etc. He is a youth editor of the journal of Naval Aviation University.

Summary: Signal processing in communication is often a very complex problem. It is often very effective to deal with these problems with artificial intelligence (neural network, machine learning, etc.), which is named intelligent signal processing in communication. At present, it involves various parameters estimation and modulation recognition of communication signals, multiple access interference cancellation in communication, blind source separation of mixed communication signals, direction of arrival estimation in array signal processing, smart antenna system, perception and intelligent communication, etc. This topic also includes new theories, new technologies, new methods and new applications of intelligent communication signal processing.

Keywords：Neural network and machine learning, intelligent signal processing, parameter estimation and modulation recognition, blind source separation, perception and intelligent communication

Chair : Prof. Tianqi Zhang，Chongqing University of Posts and Telecommunications，China

Zhang Tianqi, male, born in 1971, received a bachelor's degree in applied physics from Southwest Normal University (now Southwest University) in 1994, and a master's degree in communications and electronic systems from the University of Electronic Science and Technology of China in 1997. He worked in Tianjin NEC Company and Guangdong TCL Company from 1997 to 1999, majors in switch testing, communication terminal development work, received a doctorate degree in circuit and systems from the University of Electronic Science and Technology of China in 2003, and a postdoctoral degree in information and communication engineering from Tsinghua University in 2005. Since 2005, he has been engaged in teaching and research work in the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications (CQUPT). 
Currently, he is a professor, doctoral tutor, signal and information processing discipline leader of Chongqing University of Posts and Telecommunications, and a doctoral supervisor of signal and information processing discipline at the University of Electronic Science and Technology of China. Director of the Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, was selected into the "Chongqing University Outstanding Talent Funding Program", the "New Century Outstanding Talent Program of the Ministry of Education" and the "Chongqing Outstanding Youth Fund" program. 
A total of more than 400 academic papers have been published, of which more than 250 papers have been searched by the SCI/EI database, 42 patents have been applied for (30 authorized), 1 textbook and 3 monographs have been published, and more than 30 public academic exchange reports have been held. Presided over and participated in the National Natural Science Foundation of China, the New Century Excellent Talents Program Fund of the Ministry of Education, the second batch of Chongqing Excellent Talents Funding Program for Higher Education, Chongqing Natural Science Foundation, Chongqing Key Laboratory Construction Program, Chongqing University of Posts and Telecommunications Research Fund He has waited for more than 20 scientific research topics and won 1 second prize of Chongqing Natural Science. He has long-term systematic research on signal and information blind processing, and has trained more than 100 graduate students.


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 learining; Medical image prcessing; Computer-Aided Diagnosis

Chair : Prof. Tao Zhou, School of Computer Science and Engineering, North Minzu University, China

Tao Zhou, Prof. Dr. Tao Zhou is an professor at School of Computer Science and Engineering, North Minzu University, Yinchuan, China. He is also a Ph.D. supervisor in Institute for Medical Informatics at University of Huaqiao, Fuzhou, China. He obtained his PhD degree from Computer Science and technology, Northwestern Poly-technology Univ. in China. he has been a visiting to Chinese University of Hong Kong, Shandong University, His research interests include pattern recognition, machine learning, machine vision, medical image analysis. He has published over 200 academic papers in top journal or conference, such as information fusion, applied soft computing, Acta Electronica Sinica,  etc. 2 ESI Papers. he has published 3 books. Furthermore, he has obtained 5 authorized patents. He served for the NCIG2020,ICIG2021,ICIGP 2022 & IFSP2022，ICDIP2022 as Technical Co-Chair or Publicity Co-Chair.

Co-Chair: Prof. Jing Bai , School of Computer Science and Engineering, North Minzu University, China

Jing Bai is a Professor of Computer Science at North Minzu University. She received her Ph.D. degree from Zhejiang University, China, in 2010. She has been a visiting scholar at Purdue University from Aug. 2015 to Aug. 2016. She was awarded the top young talents of Ningxia Province, the young and middle-aged talents of the National Ethnic Affairs Commission, the young scholar in the west, and the outstanding teacher of Ningxia Province. She is a senior member of the Chinese Computer Federation, and her main interests are machine learning, (deep) representation learning, and their application to computer vision. She has presided over 3 National Natural Science Foundation of China and many provincial scientific research projects. She has published more than 60 academic papers in academic journals and conferences, such as Computer-Aided Design and ECCV, including more than 40 SCI and EI retrieved papers, and applied for 13 invention patents, 11 of which have been authorized; won 1 first and 1 second prize of teaching achievements of Ningxia autonomous region, respectively.

Co-Chair: Assoc. Prof. Xiaofeng Wang, School of Computer Science and Engineering, North Minzu University, China

He is the master tutor of Northern Minzu University, a senior member of the China Computer Federation, and a member of the China Theoretical Computer Science Special Committee. He is mainly engaged in the research of intelligent computing, algorithm design and analysis, presided over two National Natural Science Foundation projects, presided over one Ningxia Natural Science Foundation project, and published more than 30 papers in top journals such as Journal of Software, Scientia Sinica, Acta Electronica Sinica, IEEE TRANSACTION, etc.

Summary: Numerous model parameters and the extreme dependence on training samples lead to the serious vulnerability of Deep Neural Networks (DNNs). Specifically, attackers add imperceptible small disturbances of the human visual system to the original data and generate confrontation samples, which can make DNNs produce wrong output results and achieve the purpose of attacking DNNs. This antagonism vulnerability of DNNs has induced a large influx of malicious attacks, which has brought severe security challenges to the application of deep learning in various fields. In order to deal with the threat of malicious attacks, it is particularly important to carry out adversarial defense to improve the robustness of the model. 
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of adversarial attack and defense, e.g. white-box attack, black-box attack, adversarial training. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshp title”.

Keywords：Adversarial Attack, Adversarial Defense, Adversarial Training

Chair : Assoc. Prof.  Chenhong Sui,  College of Physics and Electronic Information, Yantai University, China

Chenhong Sui, received a Ph.D. degree in Huazhong University of Science and Technology. She orked as a associate professor at the  College of Physics and Electronic Information, Yantai University. Her research interests include Multi-modality data fusion, remote sensing data analysis and understanding, adversarial attack and defense, etc. She presided over or participated in two projects of the National Natural Science Foundation of China. She has published more than 20 papers in IEEE Transactions on Geoscience and Remote Sensing, Information Sciences, Remote Sensing and and other journals/conferences.

Co-Chair : Assoc. Prof.  Yang Li School of Automation, Northwestern Polytechnical University, China
Yang Li is an associate professor with the school of automation at Northwestern Polytechnical University, Xi’an, China. After receiving his bachelor's and doctoral degrees from Northwestern Polytechnical University in 2014 and 2018 respectively, he worked as a research fellow in SenticTeam under Professor Erik Cambria at Nanyang Technological University in Singapore and also was an adjunct research fellow at the A *STAR High-Performance Computing Institute (IHPC). His research interests are in Adversarial Attack & Defense in AI, NLP, Recommender System, Explainable Artificial Intelligence, etc. He has published several papers on these topics at international conferences and peer-reviewed journals. He is an active reviewer of several journals, e.g., INFORM FUSION, IEEE TAFF, NEUCOM, KBS, KAIS, etc. He is also an advisory board member of Socio-Affective Computing and the guest editor of Future Generation Computer Systems.

Summary: Recently, artificial intelligence technology and its related algorithms have achieved rapid development in computer vision. Moreover, artificial intelligence has produced positive effects in medical field, among which the most common ones are clinical decision support and medical image analysis. Clinical decision support tools give healthcare providers quick access to information or research relevant to their patients to help them make decisions about treatment, medication, mental health and other needs. In medical imaging, AI tools can be used to analyze CT, X-rays, MRIs and other images, detecting and segmenting lesions, segmenting human organs, classifying lesions to assist the radiologists in making the diagnosis.
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of adversarial attack and defense, e.g. white-box attack, black-box attack, adversarial training. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshp title”.

Keywords：pattern recognition, artificial intelligent algorithms, deep learning, Medical Image Processing, medical image analysis

Chair : Assoc. Prof.  Jinlian Ma, School of Microelectronics, Shandong University, China

Jinlian Ma, received a Ph.D. degree in Applied Mathematics from Zhejiang University. She worked as an associate professor at the School of Microelectronics, Shandong University. Her research interests include Medical Image Analysis, Pattern Recognition, Machine learning and Deep learning. She participated in the National Natural Science Foundation, National Key Projects, National Key Research and Development Project, Provincial Youth Fund Project, etc. Based on these projects, she published multiple papers in international journals.



Summary: Diabetic retinopathy (DR) is considered to be one of the most common diseases that cause blindness currently. However, DR grading methods are still challenged by the presence of imbalanced class distributions, small lesions, low accuracy for less sample classes and poor explainability. To address the issues, a resampling-based cost loss attention network for explainable imbalanced diabetic retinopathy grading is proposed.     
Firstly, the progressively-balanced resampling strategy is put forward to create a balanced training data by mixing the two sets of samples obtained from instance-based sampling and class-based sampling.
Subsequently, a neuron and normalized channel- spatial attention module (Neu-NCSAM) is designed to learn the global features with 3-D weights and apply a weight sparsity penalty to the attention module to suppress irrelevant channels or pixels, thereby capturing detailed small lesion information. Thereafter, a weighted loss function of the Cost-Sensitive (CS) regularization and Gaussian label smoothing loss, is proposed to intelligently penalize the incorrect predictions and thus to improve the grading accuracy for less sample classes.    
Finally, the Gradient-weighted Class Activation Mapping (Grad-CAM) is performed to acquire the localization map of the questionable lesions in order to visually interpret and understand the effect of our model.   
Comprehensive experiments are carried out on two public datasets, and the subjective and objective results demonstrate that the proposed network outperforms the state-of-the-art methods, achieving the best DR grading results.

Keywords: Diabetic retinopathy grading, progressively-balanced resampling, neuron and normalized channel-spatial attention module, cost loss, Gradient-weighted Class Activation Mapping

Chair: Prof.  Haiyan Li，School of Information, Yunnan University, China

Haiyan Li , Professor, Doctoral supervisor, School of Information Science and Engineering,Yunnan University, China. Selected as one of the Yunnan Ten Thousand Talents Plan "Famous Yunling Teachers". Presided over 5 NSFC and provincial projects, and published more than 70 papers, in which more than 60 are SIC or EI indexed. Published 5 textbooks and monographs. Won more than 10 patents and software copyrights, and more than 120 international, national and provincial teaching awards.

Summary: Intelligent interaction technology ensures the behavior understanding and coordination of human-computer, machine and machine. Through technologies such as visual understanding, machine learning and cognitive computing, build intelligent expression and learning methods unified with the physical world, enhance the intelligent presentation of machines and promote the integration of human and machine. Visual understanding and interactive cognition have been widely used in self-driving, medical, games, robotics and other fields in recent years, which focuses on how to make machines understand people better, realize the interaction between machines and human-computer cooperation, and form an intelligent and trusted system.
This workshop aims to bring together the latest research progress of academic and industry researchers, such as intelligent interaction technology, human-computer hybrid intelligence, visual understanding, multi-intelligent system collaboration and so on. We encourage prospective authors to submit related distinguished research papers on the subject of intelligent interaction technology research. Please name the title of the submission email with “paper title_workshop title”.

Keywords：intelligent interaction technology, human-machine hybrid intelligent, visual understanding, interactive cognition

Chair : Prof.  Nan Ma, Beijing University of Technology, China
Nan Ma is a professor at Beijing University of Technology, the Deputy Director of Engineering Research Center of Intelligence Perception and Autonomous Control, Ministry of Education, the Deputy Secretary General of China Artificial Intelligence Society, IEEE Senior Member, Senior Member of China Computer Society, Member of Editorial Board of Journal of Intelligent Systems. She’s research directions are interactive cognition, machine vision, intelligent driving, knowledge discovery and intelligent system. She has successively presided over a number of projects of NSFC (National Natural Science Foundation of China) and projects of Beijing Natural Science Foundation. In recent years. She has presided over nine enterprise projects such as BAIC Group, Dongfeng Motor corporation. Projects focus on unmanned intelligent interaction technology, such as "intelligent vehicle and road network visual simulation interactive system". She served as the head of intelligent interaction group of unmanned vehicle team, which is conducted by Deyi Li (Academician of Chinese Academy of Engineering), and leading the team to win the championship in the virtual scene competition of 2018,2019 and 2020 World Intelligent Unmanned Driving Challenge. The team achievement "unmanned cloud intelligent interaction system" won the special prize in the final of the second China "AI +" innovation and entrepreneurship competition. She has edited three monographs and textbooks, in which the book is intelligent interactive technology and application. She set up a course named "intelligent interactive technology" in MOOC of China University, which makes more than 12000 people select courses in total.  She won the first and second prize of Beijing teaching achievement and the third prize of excellent engineering education achievement of China higher education society.

Vice Chair : Dr. Cheng Xu，Beijing Union University, China
Cheng Xu (Member, IEEE) received a Ph.D. degree from the Beijing University of Posts and Telecommunications (BUPT), China. He presided over and participated in more than ten national, provincial and ministerial projects. He has published 20 SCI journals, 7 invention patents and 6 software Copyrights. He completed the research and development of i10 series of autonomous driving platforms for autonomous driving research and industrial development. He won the second prize of the science and Technology Progress Award of Chinese Society for Artificial Intelligence in 2020. He won the first prize of excellent Entrepreneurial Team of College Students in Beijing in 2021. He won the first prize in the WACV 2021 AVVision Multi-Target Multi-Camera Tracking Challenge (MTMC) Tracking Challenge.



Summary: Visual analysis and machine learning are two important techniques in most academic, industrial, business, and medical applications. Visual analysis including image and video processing systems is closely related to various fields, such as internet of things, automatic navigation, intelligent robots and smart healthcare, etc. Machine learning has obtained great success in vision, graphics, natural language processing, gaming, and controlling. 

The workshop aims to bring together the leading researchers and developers from both academia and industry to discuss and present their latest research and innovations on the theory, algorithms, and system technologies that can substantially improve existing image processing and computer vision based on machine learning and artificial neural network. We encourage prospective authors to submit related distinguished research papers on this subject, including new theoretical methods, innovative applications and system prototypes. Please name the title of the submission email with “paper title_workshop title”.

Keywords：machine learning, deep learning, image processing, computer vision, pattern recognition, artificial neural network.

Chair : Assoc. Prof.  Lei Chen, Shandong University, China
Lei Chen received the B.Sc. and M.Sc. degrees in electrical engineering from Shandong University, Jinan, China, and the Ph.D. degree in electrical and computer engineering from University of Ottawa, Ontario, Canada. He is currently an Associate Professor with the School of Information Science and Engineering, Shandong University, China. His research interests include image processing and computer vision, visual quality assessment and pattern recognition, machine learning and artificial intelligence. He was the principal investigator of projects granted from the National Natural Science Foundation of China, National Natural Science Foundation of Shandong Province, China Postdoctoral Science Foundation, etc. He has published more than 30 papers on top international journals and conferences in recent years including IEEE TIP, Signal Process., ICME, etc. He was awarded the Future Plan for Young Scholars of Shandong University. He served for the ICIGP 2021, CSAI2022 and MLCCIM2022 as Technical Chair or Publicity Chair.

Summary: Rich computational cognitive models require the merge of symbolic reasoning and  robust machine learning models. While machine learning in general can extract patterns,  the pattern recognition is what may be understood as being fast and unconscious. The detected patterns are purely data driven i.e. we cannot satisfactorily ascertain the underlying reasoning, and there is also the question of whether such a reasoning exists too. The human mind  however, has the capability for systematic thinking involving deduction and deliberative thinking. The tool kit of symbolic manipulation or symbolic thinking comes closes to  this  form of systematic thinking. 
Neuro-symbolic AI  aims to combine the two learning approaches of neural and symbolic architectures to provide  AI systems capable of reasoning, cognitive modeling. The field is quite nascent and several directions of approach are proposed. Popular examples of AI algorithms using this approach include AlphaGO, the language models like BERT, GPT-3. The key challenges include the best way to integrate  neural and symbolic architectures,  how should common sense knowledge be learned and reasoned about, and how to code logic. In this workshop, we will highlight the latest trends in neuro-symbolic AI algorithms, the different neuro-symbolic architectures being proposed. We will highlight some of the currently popular neuro-symbolic models such as Logic Tensor Networks,  DeepProgLog and show their use cases.

Keywords：Neuro-symbolic AI,  intelligent algorithms,  cognitive learning, reasoning

Chair: Dr. Nikhil Nayanar, Purdue University – West Lafayette
Dr. Nikhil Nayanar is presently a research engineer in supply chain. He completed his Ph. D. in 2021 from Purdue University – West Lafayette, in the field of Operations Research. He has presented seminars on Multi-Agent Reinforcement Learning at atleast one global pharmaceutical company. He has consulted for implementation of advanced mathematical processes at companies in the US. More recently, in the later stages of his Ph. D., he founded a startup to research technologies to predict US equity price movements using deep learning.   He currently has 10 papers under various stages of publication, in diverse areas including  intelligent trading, non-linear dynamics and uncertainty quantification. His dominant research interests presently include neuro-symbolic AI and function approximations.
Summary: Smart connected car is an important development direction in the field of current automotive technology. With the development of in-vehicle Internet-connected technology, the number of external interfaces on cars is gradually increasing, and cyber attacks against cars have become a new problem. As the problem of information security of automobiles becomes more and more serious, many researchers have carried out remote control of target vehicles by different methods, so the research for information security of automobiles has been a research hotspot at home and abroad in recent years. 
The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. Another goal is to present the latest research results in the field of in-vehicle cyber data security.

Keywords：Smart Networked Vehicles; Network Security; Intrusion Detection; Fault Tolerance Control

Chair: Dr. Yujing Wu Yanbian University, China
Yujing Wu received the M.S. and Ph.D. degrees in electronic and information engineering from Chonbuk National University, South Korea, in 2013 and 2016, respectively. She is currently working with the Department of Electronics & Communication Engineering, Yanbian University, China. Her research interests include VLSI implementation for digital signal processing and communication systems, which include the design of CAN data reduction and DisplayPort, implementation of security protocol for in-vehicle networks. She has participated and chaired various projects and researches at the National Natural Science Foundation of China and provincial and ministerial levels. Based on these projects, she published more than 18 papers in IEEE Trans and other journals and conferences.
Summary: 
With the development of the deep learning or intelligent algorithms, many traditional problems in image processing has been redesigned from the deep learning view and achieved good performance, such as image restoration, single image super-resolution, image in-painting, and image coding. The purpose of this workshop is to bring together the researchers in image processing and intelligent algorithms to discuss recent improvements in these areas.   
In the image super-resolution area, the aim is to use a low-resolution image to produce a high-resolution image. Thus, it can increase the clarity and magnify an image. Since the SRCNN algorithm, which uses convolution neural network, many intelligent algorithms have been proposed for the image super-resolution and they have achieved better performance than traditional algorithms, especially for the human face image super-resolution. They also have many application areas in machine visions, which is a major topic of the conference.   
In the image in-painting area, the aim is to use an image with missing information or a hole in the image to estimate the missing information or the pixel values inside the image hole. Many intelligent algorithms have been proposed in this area which use an encoder and decoder by deep learning.     Any intelligent algorithm in the image or video processing area is welcome in the workshop.

Keywords：Image restoration; image super-resolution; image in-painting; image coding

Chair: Assoc. Prof. Chunjiang Duanmu, Zhejiang Normal University, China
Chunjiang Duanmu receives his bachelor and master degrees in the Southeast University in China, in 1996, and 1999, respectively. He received his doctor degree in Concordia University in Canada, in 2005. After that, he worked in the Zhejiang Normal University in China. He has published a paper in the IEEE Transactions on Circuits and Systems for Video Technology, where the block motion estimation process in video coding is accelerated greatly. He has published more than 30 papers indexed by SCI and EI databases. He has been in charge of several projects of natural science foundations in the Zhejiang Province. He has got 24 patents in the areas of image and signal processing. His research interests include video communications and image and video processing.
Summary: 
Facial expression analysis has attracted much attention in the application of human-computer interaction. Pose change and occlusion challenge are important factors affecting the quality of facial expression images. Deepfake also triggers the authenticity determination of facial expression images. It is a major academic challenge to fully reflect the connotation of facial expression image standardization and security, and to systematically study the core technologies of both. The aim of this workshop is to bring together the research accomplishments provided by researchers from academia and the industry. The other goal is to show the latest research results in the field of Pattern recognition image processing. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews. Please name the title of the submission email with “paper title_workshp title”.

Keywords：
Face liveness detection, Face attribute editing, Facial expression recognition

Chair: Prof. Lifang Zhou, Chongqing University of Posts and Telecommunications, China
Lifang Zhou, received a Ph.D. degree in Computer Science and Technology from Chongqing University. She worked as a professor at the school of Software Engineering, Chongqing University of Posts and Telecommunications. Her research interests include Face recognition, target detection, target tracking and medical image segmentation. She participated in the National Natural Science Foundation, National Key Projects, National Key Research and Development Project, etc. Based on these projects, she published more than 30 papers in IEEE Trans, PR , Journal of Computer-Aided Design & Computer Graphics and other journals/conferences. She has also been granted 13 patents and published 2 monographs.
Summary: 
The advent of smart cities is alleviating big-city issues, boost long-term economic growth, and enhance people’s quality of life. Thus, the emergence of intelligent communication systems and networks allowing to overcome the current impediments of the existing communication paradigms by proposing intelligent algorithms can guarantee a more efficient use of resources, sustainable developments, and green economy. Evolutionary Computation (EC), including a family of algorithms for global optimization inspired by biological evolution, has been extensively used to solve a variety of optimization problems from different research fields, which can help achieve intelligent networks in smart cities. This special workshop will focus on EC algorithms and their applications in communication networks for constructing smart cities.  This is a great opportunity for the delegates to exchange novel ideas, present latest cutting-edge researches, and establish global research collaborations in the areas of EC developments and applications for intelligent communication systems and networks. Please name the title of the submission email with “paper title_workshop title”.

Keywords：Smart cities, Evolutionary computation, Intelligent communication systems and networks

Chair: Dr. Khoa Nguyen, Carleton University, Canada

Dr. Khoa Nguyen worked as a Postdoctoral Fellow at Carleton University. He received M.Sc. degree in Telecommunications Engineering from the University of Sunderland, England, in 2013 and the Ph.D. degree in Electrical and Computer Engineering at the Department of Systems and Computer Engineering, Carleton University, Canada, in 2021, respectively. His main research interests include communication networks, cloud/edge computing, parked vehicle edge computing (PVEC), Internet of Vehicles (IoV), software-defined networks (SDN), network function virtualization (NFV), containerization technologies, evolutionary algorithms, and AI/ML applications.
Summary: 
To improve the diagnosis and treatment of diseases, a large amount of digital and automated techniques have been developed by scientists and eventually applied in medical practice. Computer plays an important role in the development and applications. This workshop is focused on computer-based approaches for the detection, characterization, discrimination, treatment selection and prognostic prediction of common diseases.
Computers have been used in almost all aspects of the health-care system nowadays. Real-time computation is indispensable in most modern imaging techniques including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), single photon emission computed tomography (SPECT), among others. Recent development of deep-learning (DL) approaches are based big-data and artificial intelligence (AI) techniques, which require heavy computational abilities and huge amount of storage.
To address relevant research issues, the workshop welcome papers and presentations that discuss any part of the above-mentioned techniques. More specifically, imaging and image-processing related topics, including CT and MRI image reconstruction, PET and SPECT image acquisition, medical image processing in a broad sense, disease detection in medical image, machine-learning or DL based image or disease classification, image-based disease assessment and prognostic prediction, computer-aided detection/diagnosis and medical informatics, are all appropriate for this workshop.

Keywords：computerized medical image analysis, quantitative image analysis, computer-aided diagnosis, computer-assisted diagnosis, machine learning, deep learning, pattern recognition, classification, prediction

Chair: Prof. Yahui Peng, Beijing Jiaotong University, China
Yahui Peng received the B.E. degree in Engineering Physics from Tsinghua University, Beijing, China, in 1998, the M.E. degree in Nuclear Technology and Applications from Tsinghua University, Beijing, China, in 2001, and the Ph.D. degree in Medical Physics from the University of Chicago, Chicago, IL, USA, in 2010. Currently, he is a full Professor affiliated with the School of Electronic and Information Engineering at Beijing Jiaotong University, Beijing, China. He has extensive research experience in computer-aided diagnosis of prostate cancer in MR images, supported by grants from National Institutes of Health, Department of Defense and Natural Science Foundation of China. His current research interest is focused on quantitative medical image analysis, pattern recognition, diagnostic accuracy assessment, computer vision, artificial intelligence in medical and industrial applications, etc. He has published more than 50 peer-reviewed scientific journal papers and given talks in international scientific conferences or for industrial audience.