I C I V I S
W O R K S H O P
Workshop 1:
IntelliSense and Cross-modal Recognition
Title: IntelliSense and Cross-modal Recognition
Summary:
Obtaining information with the help of cutting-edge technologies such as speech recognition and image recognition is widely studied and plays an important role in a variety of tasks. In the process of IntelliSense and cross-modal recognition, a large number of models from different sources are required to automate the process. Synchronization of multimodal streams (e.g., video/audio, RGB/depth, RGB/Lidar, vision/text, image/text) from multiple sensors have been a topic of interest in both academia and industry. Our goal is to enable interdisciplinary discussions on multi-agent relational reasoning from different research fields, such as autonomous driving, visual reasoning, object recognition, scene understanding, intelligent interaction, graph representation learning, and cognitive science.
This workshop aims to provide an opportunity to researchers to discuss the latest trends of IntelliSense and cross-modal recognition areas. To promote the techniques and concepts from different fields, the workshop also encourages authors to submit relevant outstanding contributions on the topic of IntelliSense and cross-modal recognition research papers.
Keywords:
Intellisense Technology, Cross-modal Recognition, Visual Cognition, Scene Perception, Image Processing, Multimodal Data Calculation
 Chair: Prof. Nan Ma | Beijing University of Technology, China
Nan Ma is a professor at Beijing University of Technology, the Deputy Secretary-General of China Artificial Intelligence Society, IEEE/CAAI/CCF Senior Member. Her research interests lie in interactive cognition, visual intelligence, knowledge discovery, and intelligent system. She has hosted 5 national 
and provincial projects, such as the National Natural Science Foundation of China and Beijing Natural Science Foundation. She serves as a reviewer and member of the procedure committee of CVPR and other international conferences. In recent years. She presided over six projects from enterprises, such as "intelligent vehicle and road network visual simulation interactive system". Her intelligent interaction team won many championships in some intelligent driving competitions, such as the virtual scene competition of 2018, 2019, and 2020 World Intelligent Unmanned Driving Challenge respectively.  
Also, her team of achievements "unmanned cloud intelligent interaction system" won the top prize in the final of the second China "AI +" innovation and entrepreneurship competition. She achieved the second prize of the science and technology award [technological invention] of China Electronics Society in 2020. She has edited 4 books, published more than 60 academic papers including over 40 papers indexed by SCI or EI, obtained more than 10 patents and 20 software copyrights. She has taught the online course "Intelligent Interactive Technology" in Chinese University MOOC five times, and more than 12000 people have studied the course online.
Co-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.
Co-chair: Dr. Jinli Zhang | Beijing University of Technology, China
Jinli Zhang(Member, IEEE) received the Ph.D. degree from Beijing University of Technology, China. She was as a researcher at Drexel University, USA.Shehas published 15SCI journals, 4invention patents. Her research interests are in Artificial Intelligence, Machine Learning, Data/Text/Web Mining. Her professional services are as follows
•Methods(April 21, 2020-present) - Guest editors (Impact factor:3.8)
•Machine Learning Research - Editorial Member( From June 18, 2019 to June 18, 2021;
From August 4, 2021 to August 5, 2022)
•IEEE International Conference Bioinformatics and Biomedicine(BIBM)-Program committee
•International Conference On Computational Intelligence and Security - Workshop Chair
Workshop 2:
Emerging Artificial Intelligence Technologies in Healthcare.
Title: Emerging Artificial Intelligence Technologies in Healthcare
Summary:
Artificial intelligence has significantly influenced the health sector for years by delivering novel assistive technologies from robotic surgery to versatile biosensors that enable remote diagnosis and efficient treatment. While the COVID-19 pandemic is devastating, the uses of AI in the health care sector are dramatically increasing and it is a critical time to look at its impact in different aspects. In this talk, I will introduce the application of new deep learning models in medical image understanding. Then, I will discuss Parkinson's disease(PD) whilst investigating the behaviour analysis of PD mice. I also present the use of machine learning technologies in sentiment analysis, followed by the discussion on several challenges.
Keywords:
Artificial intelligence; healthcare; image segmentation; behaviour analysis; challenges. 
Chair: Prof. Huiyu Zhou | University of Leicester, UK
Prof. Huiyu Zhou received a Bachelor of Engineering degree in Radio Technology from Huazhong University of Science and Technology of China, and a Master of Science degree in Biomedical Engineering from University of Dundee of United Kingdom, respectively. He was awarded a Doctor of Philosophy degree in Computer Vision from Heriot-Watt University, Edinburgh, United Kingdom. Dr. Zhou currently is a full Professor at School of Computing and Mathematical Sciences, University of Leicester, United Kingdom. He has published over 400 peer-reviewed papers in the field. He was the recipient of CVIU 2012 Most Cited Paper Award, MIUA 2020 Best Paper Award, ICPRAM 2016 Best Paper Award and was nominated for ICPRAM 2017 Best Student Paper Award and MBEC 2006 Nightingale Prize. His research work has been or is being supported by UK EPSRC, ESRC, AHRC, MRC, EU, Royal Society, Leverhulme Trust, Puffin Trust, Alzheimer’s Research UK, Invest NI and industry.
Workshop 3:
Emerging Artificial Intelligence Technologies in Healthcare.Innovative Modalities for High Performance Computational Imaging: Image Acquisition, Reconstruction, and Feature Extraction.
Title: Innovative Modalities for High Performance Computational Imaging: Image Acquisition, Reconstruction, and Feature Extraction
Summary:
In view of the increasingly sophisticated demand for image information acquisition, and the subsequent explosion of large-scale and high-dimensional (such as hyperspectral imaging, ultrafast imaging) data cubes, traditional one-to-one mapping imaging system is facing great pressure, gradually approaching the physical limit of the system or device, and there are also mutual constraints among various performance indexes. Therefore, it is urgent to subvert traditional imaging by innovative modalities for large-span performance improvement. The emerging signal processing theories represented by compressive sensing inject new vitality into computational imaging and will play a greater role, which benefits from the current computer technology.
We will conduct extensive discussions in the field of innovative modalities for high performance computational imaging guided by new theories. As an interdisciplinary technology field, this workshop aims to provide researchers with an academic exchange platform. We welcome researchers from universities, research institutions and industries to submit the latest research results, including new theoretical methods, hardware solutions or optimization algorithms. We also encourage researchers to provide constructive suggestions for the development of this field.
Keywords:
Computational Imaging, Computational Photography, Compressive Imaging, Compressive Sensing, Image Reconstruction, Coded Aperture, Super-resolution Imaging, Lensless Imaging, Hyperspectral Imaging.
Chair: Dr. Yun-Hui Li | Chinese Academy of Sciences, China

Yun-Hui Li, received a Ph.D. degree in Optical Engineering from University of Chinese Academy of Sciences. He worked as an associate researcher of Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. He is a member of Youth Innovation Promotion Association of Chinese Academy of Sciences. His research interests include Space Optical Remote Sensing Imaging System, Computational Imaging, Compressive Imaging and Image Processing.
He has successively participated in National Major Project "Mars high resolution camera for deep space exploration mission", Strategic Priority Research Program of Chinese Academy of Sciences "Planetary atmospheric spectrum telescope", etc., and won 1 provincial second prize. He presided over 1 National Natural Science Foundation and 1 Provincial Foundation. He has published over 10 papers indexed by SCI or EI, obtained more than 10 patents.
Workshop 4:
Image Processing and Computer Vision
Title: Image Processing in Remote Sensing Systems
Summary:
Remote sensing methods are widely used to collect data at a distance from the object under study using a recording device. Their use is rapidly expanding, finding new areas of application with the development of technologies for television, infrared, laser and radar surveillance systems with synthesized apertures. Such systems solve the problems of detecting, isolating and localizing objects of interest in images of various nature.
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 Remote Sensing Systems. 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:
Remote Sensing, Image Processing, Computer Vision, Object Detection and Localization
Chair: Prof. Vladimir Yurievich Volkov | Saint-Petersburg State Electrotechnical University (LETI) / Saint-Petersburg State University of Aerospace Instrumentation (SUAI), Russia

Vladimir Yurievich Volkov received a Dr. Sc. degree (Full doctor) in Computer Science and Technology from The Bonch-Bruevich Saint-Petersburg State University of Telecommunications. He works as a professor at the Dept. of Radioengineering, Saint-Petersburg State Electrotechnical University (LETI) and Saint-Petersburg State University of Aerospace Instrumentation.
His research interests include digital image processing for computer vision systems, adaptive, invariant and robust algorithms for signal discrimination and filtering, non-Gaussian models of random signals and fields. Professor V. Yu. Volkov received a certificate of participation in the presidential project "Golden names of higher education". He has more than 200 scientific publications, 22 copyright certificates for inventions and 8 registered computer programs.
Workshop 5:
Image Fusion Based on Multi-resolution Pyramid
Title: Image Fusion Based on Multi-resolution Pyramid
Summary:
Digital image processing now days used in various application domains. Because of the inherent drawbacks associated with the digital camera and its image quality, there has been a great scope in developing techniques to enhance the picture quality. Image fusion is a procedure where several images can be pooled together to form a single fused image. These several source images can be acquired by different imaging sensors at different wavelengths while concurrently viewing of the same sigh. The resultant combined image is made to improve image content. People can detect, recognize, and identify targets easily in the fused image. Moreover, the fused image contains more accurate information about the scene, which may be more suitable for computer processing. Images captured using the same camera may also vary in numerous ways and a single image out of them is not enough to analyze the exact situation
Keywords: 
Multi-focus images, Gaussian pyramid, Laplacian pyramid, Detail layer
Chair: Dr. Simrandeep Singh | Electronics Engineering, Chandigarh University, India

Simrandeep Singh received the B.Tech and M.Tech degree in Electronics & Communincation engineering from the Punjab Technical University in years 2008 and 2015, respectively. He has completed his Ph.D. from Chandigarh University, Gharuan, Punjab India.
Currently, he is working as Assistant Professor in the Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, India. He has 12 years of teaching experience. He has more than 35 research publications in various fields. His current research interests include diatom analysis based on, Wireless Sensor Networks, Digital image processing, multifocus image fusion, exposure fusion, edge-preserving filters, high dynamic range imaging and tone mapping, metaheuristic techniques, Image segmentation and Digital Signal Processing
Workshop 6:
Image Processing and Computer Vision
Title: Image Processing and Computer Vision
Summary:
My research interests spread across subareas in artificial intelligence (AI), including computer vision, data science, deep learning, image processing, machine learning, medical informatics, social media, video surveillance, data mining algorithms including multi-view learning, multi-label learning, visual search, face recognition and agriculture modeling and simulation.
Keywords: 
Machine Learning, Computer Vision, Artificial Intelligence, Image Processing, UAV’S, Cloud Computing, DevOps, Cross plate Form and Emerging Technologies.
Chair: Dr. Ahmed Mateen Buttar | University of Agriculture Faisalabad, Pakistan

Ahmed Mateen Buttar, 2021 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA 2021, Dalian, China)
2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP 2022) Xi'an Shiyou University and AEIC Academic Exchange Information Center
Technical Program Committee Member Springer Publication, NGCT 2017 2. Reviewer, International Multi-Conference on Complexity, Informatics and Cybernetics, USA. 2016-2019.
Technical Program Committee Member, Communications in Computer and Information Science, Smart and Innovative Trends in Next Generation Computing Technologies, Springer Publication Third International Conference, NGCT 2017 Dehradun, India, October 30–31, 2017
Workshop 7:
Adversarial Machine Learning and Trustworthy AI
Title: Adversarial Machine Learning and Trustworthy AI
Summary:
There is a growing trend of recognition that the current ML&AI technologies expose significant vulnerabilities. For example, advanced ML models suffer from various types of attacks including adversarial attacks, data poisoning, backdoor injection, reverse engineering attacks, etc. Under such circumstances, building the trustworthy AI system is critical and should be a long-term goal for both AI research communities and industrial AI application developers. 
The aim of this workshop is to provide opportunities for world-wide researchers to present new ideas, demonstrate valuable works, and propose new research directions in the domain of adversarial machine learning and trustworthy AI. We encourage prospective authors to submit related research papers on both theoretical approaches and practical applications, which include but are not limited to: new types of attacks, effective defenses, robust algorithms, interpretable and explainable techniques, etc.
Keywords: 
Adversarial Nachine Learning, Attacks and Defenses, Interpretability, Trustworthy AI.
Chair: Dr. Fuxun Yu | George Mason University, USA

Fuxun Yu received his Ph.D. degree in Electrical and Computer Engineering from George Mason University in 2022. Before that, he obtained the B.S. degree in Harbin Institute of Technology, China. He will join Microsoft (Redmond) as a Senior Researcher in summer 2022. 
His research interests include deep learning security and robustness, model compression and acceleration, full-stack model performance optimization on GPUs. He has published over 30 papers in top conferences such as SIGKDD, IJCAI, MLSYS, DAC, DATE, ICCAD and received one Best Paper Nomination in DATE’2020.
Workshop 8:
Computer Vison: Image Processing and Video Processing
Title: Computer Vision and Multimedia Information Processing and Application
Summary:
Computer vision has always been the cutting edge filed, which is also closely related to machine learning and artificial intelligence. Whit the development of information technology, digital multimedia is more and more widely emerging in people’s lives, Image and video undoubtedly occupy the main part of the digital multimedia information. The research of image processing and video processing could make the computers have the ability of perception, recognition and understanding of the real world. It has wide theoretical research value and wide application prospects, such as intelligent surveillance, human computer interaction, automatic pilot, etc. 

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 Computer vision and multimedia information 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: 
Image Processing, Image Segmentation, Visual Object Tracking, Object Detection, Image Coding and Steganography, Deep Learning
Chair: Assoc. ProfJun Wang Hebei University, China

Jun Wang, received a Ph.D. degree in Signal and Information Processing from Beijing Jiaotong University, China. He works as an associate professor at the College of Electronic Information Engineering, Hebei University. He is an AC of CCF YOCSEF Baoding, member of CCF. He participated in the National Natural Science Foundation, China, and published more than 6 SCI papers as the first author. His research interests include image processing, computer vision, visual object tracking and pattern recognition.
Workshop 9:
Security, Privacy, Attacks, and Forensics
Title: Deep Learning Based Solutions for Medical Applications in Image and video Processing
Summary:
The recent success of artificial intelligence (AI) and deep learning (DL) in areas of natural images has made adapting and further developing DL techniques to medical images an important and relevant research challenge. These developments have the potential to revolutionize medical imaging, medical data analysis, medical diagnostics and overall healthcare. X-rays, magnetic resonance, microwaves, ultrasound and optical methods have all been used extensively in the medical field to produce medical images. Clinical diagnosis is almost impossible without medical images. A clinician's ability to interpret these images depends on their training and experience. Advances in DL allow us to extract more information from images than ever before, and with greater confidence. AI and deep learning techniques such as medical image processing, computer-aided diagnosis, image interpretation, image fusion, image registration, and image segmentation. Convolutional Neural Networks, Deep Boltzmann Machines, Deep Belief Networks, and Stacked denoising Auto encoders are some of the most important deep learning algorithms can be utilized in solving the challenges of medical fields challenges. 
The goal of this Special Session is to compile the most recent methodologies and findings, as well as to explore the current issues of medical imaging, medical diagnostics, medical data analysis, healthcare etc.  We anticipate that this special session will address research topics in medical image/video processing using deep learning and artificial intelligence (DL and AI). Authors are expected to submit experimental, conceptual, and theoretical contributions based on medical image/video processing using deep learning and artificial intelligence environments.  The special session accepts original experimental results, review papers and case studies. This will provide platform for researchers to submit original manuscripts showcasing findings and exploring emerging trends and challenges in medical image processing. 

Potential topics of this special session include but are not limited to:
Medical image analysis to computer-aided diagnosis (CAD)
Deep learning and artificial intelligence for medical image/video;
Image segmentation, registration, and fusion;
Image processing and analysis;
Image formation/reconstruction and image quality assessment;
Medical image analysis;
Computer aided diagnosis;
Machine learning of big data in imaging;
Integration of imaging with non-imaging biomarkers;
Visualization in medical imaging.
Application of zero-shot learning to real-world challenges like medical imaging or autonomous systems.
Computer Vision in Dermatology and Primary Care

We invite high-quality articles in emerging pattern recognition subjects that include significant new theories, methods, applications, and systems.
 
Keywords: 
Location Privacy, Spatial Crowdsourcing, Blockchains, Decentralized Privacy Protection.
Chair: Assoc. ProfThippa Reddy Gadekallu | Vellore Institute of Technology, India

Thippa Reddy Gadekallu is currently working as Associate Professor in School of Information Technology and Engineering, VIT, Vellore, Tamil Nadu, India. He obtained his B.Tech. in CSE from Nagarjuna University, India, M.Tech. in CSE from Anna University, Chennai, Tamil Nadu, India and completed his Ph.D in VIT, Vellore, Tamil Nadu, India. He has more than 14 years of experience in teaching. He has than 100 international/national publications in reputed journals and conferences. Currently, his areas of research include Machine Learning, Internet of Things, Deep Neural Networks, Blockchain, Computer Vision. He is an editor in several publishers like Springer, Hindawi, Plosone, Scientific Reports (Nature), WIley. He also acted as a guest editor in several reputed publishers like IEEE, Springer, Hindawi, MDPI. He is recently recognized as one among the top 2% scientists in the world as per the survey conducted by Elsevier in the year 2021.
Workshop 10:
Multimedia Information Hiding and Content Security
Title: Multimedia Information Hiding and Content Security
Summary:
The development of computer technology and Internet poses new challenges to the secure storage, transmission and access of information. As a new technology to ensure information security, information hiding has attracted the attention of many scholars and has become one of the current research hotspots. This workshop aims to provide an academic platform for researchers in the field of multimedia information hiding and content security to exchange new ideas, methods and technologies. Experts and scholars in the fields of digital steganography and steganalysis technology, digital forensics technology, digital watermarking technology and other multimedia information hiding and content security technology are welcome to contribute.
 
Keywords: 
Multimedia Information Hiding, Multimedia Content Security, Steganography, Steganalysis
Chair: Prof. Songbin Li | Institute of Acoustics, Chinese Academy of Sciences, China

Songbin Li received the Ph.D. degree from the Institute of Acoustics, Chinese Academy of Sciences. He was a Postdoctoral Fellow and a Visiting Professor with Tsinghua University and the University of Southern California, respectively. He is currently a professor at the Institute of Acoustics, Chinese Academy of Sciences. He has been the Principle Investigator on several projects of the National Natural Science Foundation of China. His current research interests include machine learning, multimedia signal processing, and information forensics. In this areas, he has pulished more than 70 papers and holds more than 40 patents.
Workshop 11:
Deep Learning for Intelligent Scene Perception
Title: Deep Learning for Intelligent Scene Perception
Summary:
Visual understanding and multi-modality representation fusion are essential to intelligent scene perception. With the rapid progress in machine learning technologies, there are tons of remarkable advances in intelligent scene understanding, whose performance and application fields are extended greatly. However, the complexity of scene could be a challenge for efficient perception. For some application as automatic drive, pedestrian re-identification and robot tracking, the performance and efficiency are typically affected by disturbances in the natural scene. How to efficiently combine information from visual and other modalities to enhance the robustness of perception systems under accidental perturbation and complexity issues is crucial and meaningful.
This workshop aims 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 intelligent scene perception. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
 
Keywords: 
Computer Vision, Multi-modality Representation Learning, Intelligent Scene Perception and Application, Person re-identification, Deep Learning.
Chair: Prof. Zhigang Liu  |  Northeast Petroleum University, China

Zhigang Liu, received a Ph.D. degree in Computer Resources and Information Engineering from Northeast Petroleum University, and was a visiting scholar with the Department of Electrical & Computer Engineering at National University of Singapore from 2018 to 2019. As a senior CCFmember, he is currently the Department Director of Computer Science and Engineering, Northeast Petroleum University. His research interests include machine learning, computer vision, especially, data/label- and computation-efficient deep learning for visual recognition.
He participated in the National Natural Science Foundation, Natural Science Foundation of Heilongjiang Province, Scientific and Technological Projects of Petro-China, and Youth Science Foundation of Northeast Petroleum University. Based on these projects, he published many academic papers.
Workshop 12:
CG&CAM, Image Processing and Pattern Recognition, Artificial Intelligence
Title: CG&CAM, Image Processing and Pattern Recognition, Artificial Intelligence
Summary:
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 CG&CAM、Image processingand Pattern recognition、artificial intelligence. We encourage prospective authors to submit related distinguished research papers on the subject: theoretical approaches and practical application. Please name the title of the submission email with "paper title_workshp title".
 
Keywords: 
Image Processing and Pattern Recognition
Chair: Prof. Lianqiang Niu  |  Shenyang University of Technology, China

Lianqiang Niu, Professor of Shenyang University of technology,Dean of Software College of Shenyang University of technology.He has been a visiting scholar in Information Engineering at Toyama Prefecture University in Japan. His research interests include CG &CAM, image processing and pattern recognition, artificial intelligence, etc.
He has participated in many key scientific and technological projects oftheNational Natural Science Foundation of China, Liaoning Natural Science Foundation of China , and Liaoning Province, and published many academic papers.
Chair: Assoc. Prof. Mingliang Gao | Shandong University of Technology, China

Mingliang Gao, received his Ph.D. degree in Communication and Information system from Sichuan University. Now, He is the assistant to the dean and works as an associated professor at the School of Electrical and Electronic Engineering, Shandong University of Technology. His research interests include Computer Vision and Deep Learning.  
He directed the National Natural Science Foundation, China Postdoctoral Foundation, National Key Research and Development Project, National Key Research and Development Project, etc. Based on these projects, he published more than 100 journals/conferences papers in IEEE, Springer, Elsevier and Wiley.
Workshop 13:
Edge Intelligence Driven Cyber-Physical Systems: Algorithms and Applications
Title: Edge Intelligence Driven Cyber-Physical Systems: Algorithms and Applications
Summary:
A cyber-physical system (CPS) is a computer system in which a mechanism is controlled or monitored by computer-based algorithms. In cyber-physical systems, physical and software components are deeply intertwined, able to operate on different spatial and temporal scales, exhibit multiple and distinct behavioral modalities, and interact with each other in ways that change with context. CPS involves transdisciplinary approaches, merging theory of cybernetics, mechatronics, design and process science. Examples of CPS include smart grid, autonomous automobile systems, industrial control systems, and robotics systems. Edge intelligence aims to facilitate the deployment of artificial intelligence (AI) on cyber-physical systems. However, there are many challenges existing for a novel design of edge intelligence on cyber-physical applications, and their co-optimization. For instance, conventional AI techniques usually entail powerful computing facilities (e.g., cloud computing platforms), while cyber-physical systems may have only limited resources for computations and communications. This suggests that AI algorithms should be revisited for cyber-physical systems to provide the efficient processing. In this workshop, we are pleased to invite manuscripts on edge intelligence in cyber-physical systems. Research areas may include (but are not limited to) the following:

CPS system architecture
Control optimization of CPS
Machine learning for CPS
Design and verification of CPS
Security and privacy for CPS
Mobile and cloud computing for CPS
Practical application-oriented system design for CPS

Keywords: 
Cyber-Physical System; Edge Intelligence
Chair: Prof. Heng Li  |  Central South University, China

Heng Li received the bachelor’s and Ph.D. degrees from the School of Information Science and Engineering, Central South University, Changsha, China, in 2011 and 2017, respectively.
He is currently an Associate Professor with the School of Computer Science and Engineering, Central South University, Changsha, China.
From November 2015 to 2017, he worked as a Research Assistant with the Department of Computer Science, University of Victoria, Victoria, BC, Canada. His current research interests include cyber-physical systems, and digital twin.
Workshop 14:
Medical Image Processing
Title: Medical Image Processing
Summary:
My research interests include Bio Signal Processing, Medical Image Processing, wireless body sensor networks, and VLSI. He has published over 50 journal and 50 conference papers over the last several years. He has taught a wide variety of Electronics courses including Digital Image Processing, Multimedia Compression Techniques, VLSI Design, Medical Electronics, and Electronic Circuits.

Keywords: 
Bio Signal Processing, Medical Image Processing, Wireless Body Sensor Networks, VLSI
Chair: Prof. Sivakumar Rajagopal | Vellore Institute of Technology, India
Sivakumar Rajagopal received the BE in Electronics and Communication Engineering from Bharathiar University, and the ME and PhD degree in Information and Communication Engineering from the College of Engineering Guindy, Anna University,  Chennai. Dr Siva is a life member of the Indian Society of Technical Education, a senior member of IEEE. Dr Siva has been invited to deliver Keynote Speech and Chair at various International conferences.
Workshop 15:
Image Processing and Computer Vision
Title: Image Processing in Remote Sensing Systems
Summary:
Remote sensing methods are widely used to collect data at a distance from the object under study using a recording device. Their use is rapidly expanding, finding new areas of application with the development of technologies for television, infrared, laser and radar surveillance systems with synthesized apertures. Such systems solve the problems of detecting, isolating and localizing objects of interest in images of various nature.
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 Remote Sensing Systems. 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: 
Remote Sensing, Image Processing, Computer Vision, Object Detection and Localization
Chair: Prof. Andrea ReyUniversidad Tecnológica Nacional Facultad Regional Buenos Aires, Argentina
Andrea Rey, received a Ph.D. degree in Mathematics from the University of Buenos Aires, Argentina. She works as a professor at the Department of Mathematics and the Center of Image and Signal Processing, Universidad Tecnológica Nacional Facultad Regional Buenos Aires, Argentina. Her research interests include Image and Signal Analysis, Machine Learning and Statistics.
She has participated in the National Scientific and Technical Research Council (CONICET, Argentina), National Agency for Scientific and Technologic Promotion (ANPCyT, Argentina) Projects, University of Buenos Aires (UBA, Argentina) Projects, Universidad Tecnológica Nacional (UTN, Argentina) Projects and Victoria University of Wellington (New Zeland) Project. Based on these projects, she has published research papers and conferences in image and signal processing, and homological algebra.
Workshop 16:
Image Representation from Handcrafted to Deep Features
Title: Image Representation from Handcrafted to Deep Features
Summary:
Image representation plays an important role in a variety of visual tasks such as texture classification, face recognition, scene categorization, and object detection. Conventional handcrafted descriptors such as Gabor features, Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) were well studied which have high efficiency and good explainability. Now data-driven deep learning methods almost dominate the related fields and achieve promising results. However, these methods are usually data hungry and rely on costly computing resources, working with a black box model. Therefore, it remains several open questions about 1) which method would be better for some specific applications or task requirements, 2) how to achieve and balance the robustness (invariance) and distinctness of feature representations, and 3) how to use them in a hybrid way so that they can complement each other. Researchers are encouraged to submit original and novel research papers on the above related topics.

Keywords: 
Image Representation, Handcrafted Features, Deep Learning
Chair: Dr. Tiecheng Song | Chongqing University of Posts and Telecommunications, China
Tiecheng Song, received the Ph.D. degree in Signal and Information Processing from University of Electronic Science and Technology of China (UESTC) in 2015. He is currently an Associate Professor with the School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications (CQUPT), China. His research interests include image processing and computer vision. He has published more than 30 research papers in international journals/conferences, including IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Geoscience and Remote Sensing, Pattern Recognition, and IEEE Signal Processing Letters.
Workshop 17:
Image and Video Recognition: Algorithms and Applications
Title: Image and Video Recognition: Algorithms and Applications
Summary:
With the development of society, "electronic eye" gradually replaces human eye to observe and record things in the world, which resulting in massive multimedia data every day. In addition, the popularity of webcast, short video and other platforms has further accelerated the growth of data scale. Therefore, how to effectively analyze these multimedia data or mine useful information from them has become an urgent problem to be solved. Image and video are the main forms of multimedia, and it is of great practical significance to research the algorithms or application paradigms of image and video recognition.
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 image recognition or video recognition. 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: 
Image Processing, Image Recognition, Video Recognition, Image or Video based Face Recognition, Deep Learning
Chair: Dr. Xizhan Gao | University of Jinan, China
Xizhan Gao, received the Ph.D. degree in control science and engineering from Nanjing University of Science and Technology, Nanjing, China, in 2019. He is currently a lecturer of the School of Information Science and Engineering, University of Jinan. He published more than 10 SCI papers as the first author, and his research interests include pattern recognition, computer vision and video processing.
Workshop 18:
Intelligent Optimization and Knowledge Engineering for Manufacturing Industry
Title: Intelligent Optimization and Knowledge Engineering for Manufacturing Industry
Summary:
After implementing the "one belt, one road" and supply-side reform, the steel industry and the high-end equipment manufacturing industry began to boom in 2015, andin recent years business amountof steel enterprises and equipment manufacturing increased significantly. Due to the increasing structure diversity and scale of order, the original MES system is difficult to deal with the above enterprises’s optimal decision-makingproblems and or even fails, such as slab designfor steel plate productionand and component material nestingand componentprocess preparation for the high-end equipment manufacturing. At present, it has become the bottleneck of enterprise efficiency creation and product quality improvement, which seriously affects the national economic construction and the international competitiveness of products. At present, it is in the critical period of thegoal ofcarbon peaking and carbon neutrality in China. The scientific issues discussed in this meeting are the problems of product design, performance analysis and control decision-making to extract from the rigid demand of large steel enterprises and high-end equipment manufacturing enterprises, which have not been reported or completely solved.
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 intelligent optimization technology and understand how knowledge-based heuristic and multi-mechnism hybrid strategy can influence it. We encourage prospective authors to submit related distinguished research papers on the subject: theoretical approaches and practical application. Please name the title of the submission email with “paper title_workshp title”.

Keywords: 
Knowledge Engineering,Intelligent Optimization, Big Data, Artificial intelligence, Manufacturing Industry, high-end Equipment Manufacturing, Knowledge, Slab Design
Chair: Dr. Ziqiang Li | Xiangtan University, China
Ziqiang Li, received a Ph.D. degree in Computer Application Technology from Dalian Universityof Technology. He worked as a member at school of computer science of Xiangtan university. His research interests include Internet Security, Software Engineering, and Numerical Simulation.
He presided over the National Natural Science FoundationProject(NO:61272294), participated in National Science and Technology Supporting Program Project(NO: 2012BAF10B04), presided over Natural Science Foundation Projects(NO: 11JJ6050and 2017JJ2459) of Henan Province, Key scientific research projects of Department of Education in Hunan Province(NO:11A120),presided over seven Projects supported by enterprise innovation fund. Based on these projects, he published many academic papers.
He has publishedApplied Soft Computing, Computers and Mathematics with Applications,Computer, Computer search and development journal papers.He received a number of awards including Hunan Science Progress Award and Hunan excellent master thesis instructor, etc.
Workshop 19:
Computational Pathology: Advances and Applications
Title: Computational Pathology: Advances and Applications
Summary:
Cancer is among the leading causes of death worldwide, with about 10 million people died from cancer each year. The early and precise diagnosis can greatly reduce the mortality rates from cancer. Routine pathological examination is the gold standard for diagnosis and grading of various cancer types in the clinical setting. Pathologists were used to examine glass slides under the microscope, but nowadays they can also analyze digitized whole slide images directly with the help of digital scanning systems. Nevertheless, manual analysis performed by pathologists suffers from inter- and intra-observer variations, and it is a very tedious and labor-intensive process. Furthermore, whole slide images consist of a wide range of quantitative phenotypic information characterizing tumor microenvironments, which has not been well explored and utilized by qualitative analysis. To overcome these limitations, it is increasingly significant to develop and advance histological image analysis by using artificial intelligence, statistical analysis and other engineering computations. To this end, this workshop aims at providing an opportunity for leading researchers from academia and the industry to discuss research accomplishments in computational pathology. The topics include, but not limited to, color normalization, histological image analysis, registration, enhancement, segmentation, feature extraction, classification, computer-aided diagnosis, grading and prognosis, as well as other relevant histological tasks. To further promote the techniques and concepts from different fields, the workshop also encourages authors to submit outstanding contributions on relevant areas such as integration of multi-modal medical image analysis.Please name the title of the submission email with “paper title_workshop title”. 

Keywords: 
Computational pathology, Deep learning, Computer vision, Medical image analysis, Featureextraction, PatternRecognition
Chair: Dr. Hongming Xu | Dalian University of Technology, China
Hongming Xu is an associate professor at School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology. He obtained his PhD degree from Department of Electrical and Computer Engineering, University of Alberta in Canada, and completed his Postdoc Fellow training at Cleveland Clinic in USA. His research interests include artificial intelligence in biomedical imaging fields, especially computational pathology, medical image computing, imaging informatics and deep learning. He has published over 20 international peer-reviewed journal and conference papers and book chapters, which includes 10 first-authored journal papers in computational pathology domain. He has delivered several invited talks in international conferences or workshops such as BioKDD, AACR, etc. He is leading a couple of scientific research projects sponsored by the National Natural Science Foundation of China and Dalian University of Technology.
Chair: Chen Li | Northeastern UniversityChina
Chen Li is an associate professor at College of Medicine and Biological lnformation Engineering, Northeastern University, Shenyang, China. He is also a guest Ph.D. supervisor in Institute for Medical Informatics at University of Luebeck, Luebeck, Germany. He obtained his PhD degree from Computer Science, University of Siegen in Germany, and continuously completed his Postdoc Fellow training at University of Siegen and Johannes Gutenberg University Mainz in Germany. His research interests include pattern recognition, machine learning, machine vision, microscopic image analysis and medical image analysis. He has published over 100 academic works, in which, as first and corresponding authors, he has published 3 books, 18 book chapters, 45 journal articles (33 SCI index) and 20 conference papers (18 EI index). Furthermore, he has obtained 9 authorized patents and more than 62 software copyriths. In addition, he has released 5 open source medical image databases for non-commericial use in Github or Figureshare.
Workshop 20:
Efficient Image Processing and Computer Vision Algorithms
Title: Efficient Image Processing and Computer Vision Algorithms
Summary:
Deep Neural Networks (DNNs) have madea remarkable success in image processing and computer vision (e.g., classification, object detection, and super-resolution). Recent neural network designs with billions of parameters have demonstrated human-level capabilities but at the cost of significant computational and storage complexity.
There are many approaches to reduce the amount of computation and storage of image processing and computer vision algorithms, such as model compression including sparsification, binarization, quantization and pruning for efficient inference with deep networks. Network Architecture Search (NAS) is also a main stream method which searches for a highly efficient network structure from a large predefined search space. Besides, combining traditional methods with deep learning is also a solution to reduce the computation and storage of algorithms. In addition, mutual optimization of hardware and software systems is worth exploring to make algorithms more efficient in computation and storage. Finally, efficient algorithms can be applied to various image processing and computer vision tasks, such as detection, tracking, classification, segmentation, style transfer and super-resolution, etc. 
We will conduct extensive discussions in the field of efficient image processing and computer vision algorithms. Potential topics of this workshop include but are not limited to:
(1)Deep network compression and acceleration.
(2)Network binarization and low-bit quantization
(3)Network architecture search for efficient algorithms
(4)Mutual optimization of hardware and software systems 
(5)Combining traditional methods and deep learning algorithms to reduce model computation and storage
(6)Efficient algorithm for different vision tasks (detection, tracking, classification, segmentation, style transfer and super-resolution, etc.)

Keywords: 
Image Processing, Computer Vision, Efficient Neural Network, Compression, Architecture Search, Pruning, Quantization.
Chair: Assoc. Prof. Haoji Hu | University of Southampton, UK
Assoc. Prof. Haoji Hu has worked as a peer reviewer for Association for the Advancement of Artificial Intelligence (AAAI), Applied Intelligence, IEEE International Conference on Image Processing and several other conferences and journals. His research interests include image/video processing, machine learning and deep learning. He has published 40+ papers including PR, TIP, JSTSP, CVPR and AAAI.
Workshop 21:
Artificial Intelligence, Imaging, Omics
Title: Artificial Intelligence in Imaging Medicine
Summary:
Artificial intelligence in medicine (AIM) mainly uses computer techniques to perform clinical diagnoses and suggest treatments. It aimed to increase the efficiency of medical diagnosis and treatment with the aid of AI systems. AI has the capability of detecting meaningful relationships in multi-modalitydata-setsand has been widely used in many clinical situations to diagnose, treat, and predict the results. AIM has completely changed the traditional model of medicine, significantly improved the level of medical services, and guaranteed human health in various aspects. A broader development prospect for AIMis highly expected in the future.

Keywords: 
Artificial Intelligence, Medicine, Human Health
Chair: Prof. XufengYao | Shanghai University of Medicineand Health Sciences, China
XufengYao, Received a Ph.D. degree in Biomedical Engineering from Fudan University. He works as a professor, Doctoral supervisor, Vice Dean of the School of Medical Imaging, Shanghai University of Medicine and Health Sciences. His current research interests include Medical Artificial Intelligence and Digital Image Processing. He won the National Natural Science Foundation of China, Shanghai Natural Science Foundation, China Postdoctoral Foundation, Innovation Fund of Shanghai Education Commission, etc. He was also an reviewer for SCI journals and international conferences.
Workshop 22:
Lightweight Network for Real-time Semantic Segmentation
Title: Lightweight Network for Real-time Semantic Segmentation
Summary:
Semantic segmentation of the digital image is widely used in various fields, such as automatic driving, medical imaging, precision agriculture, etc. Data-driven deep learning efficiently solves difficult problems, replacing the conventional morphological segmentation algorithm. Up to now, Convolutional Neural Network (CNN), MLP-Mixer, and state-of-art Transformer are attracting the attention of academia and industry in computer vision.However, the hugenumber of parameters inlarger modelsmakesdeep learningdifficult to transfer to downstream real-time tasks. How to quickly apply the segmentation model to downstream real-time tasks while learning rich image information becomes a key challenge.
The workshop aims to bring together academic and industry research on the real-time segmentation tasks of lightweight networks. We encourage authors to submit relevant outstanding research papers, including theoretical approaches and practical cases.

Keywords: 
Lightweightnetwork, semantic segmentation, computer vision
Chair: Prof. Pan Gao | Shihezi University, China
Pan Gao is currently a professor at Shihezi University.He was a visiting student at University of California, Los Angeles.He has been the principal investigator on several projects of the National Natural Science Foundation of China, and guest editor in several well-known publishers like MDPI. His research interests include machine learning and image processing. He has published more than 30 papers in this field, holds more than 20 patents, and has won various provincial and ministerial awards.
Workshop 23:
Agricultural Image and Video Processing
Title: Image and Video Processing in Orchards: Crops, Pests and Growth
Summary:
Agricultural information monitoringtechnology, which refers to fields of agricultural artificialintelligenceand computer vision, is the fundamental technical basis and key management chain for achieving a high-yield and high-quality orchard. Recently, there has been rapid development in agricultural image and video processing for strengtheningorchard management activities, such as flower thinning, fruit protection, pest control,yield prediction, and other fields.
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 agricultural image and video processingin orchards. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.

Keywords: 
Precision agriculture, image processing, machine vision, orchard management
Chair: Prof. Zhen Li | South China Agricultural University, China
Zhen Li, Professor and doctoral supervisor, is currently the vice president of the College of Electronic Engineering (College of Artificial Intelligence) of South China Agricultural University, the expert of the National Agricultural Mechanization Science and technology innovation expert group, the director of the Mechanization Research Office of the National Citrus Industry Technology System, the post expert of orchard management mechanization, the member of the system implementation expert group, the deputy director of the mechanical innovation engineering technology research center of Mountain Orchard in Guangdong Province, also the special commissioner for rural science and technology of Guangdong Province. 
He is mainly engaged in the research of mountainous orchard management machinery and equipment, as well as orchard information technology. He is the project host of the National Natural Science Foundation of China, the special fund projects of the National Modern Agricultural Industrial Technology System, the development fund projects of local colleges and universities supported by the central financial government and the subsidiary projects of the National Key R & D program. He has won 11 scienceand technology awards which above provincial and ministerial level.
Workshop 24:
Advances in Agricultural Science and Technology
Title: Application of Computer Technology in Agriculture
Summary:
Agriculture was a major development in human history, leading to the rise and flourishing of civilization. Modern agriculture has undergone significant changes over several decades, and new technologies have expanded continuously. Significant scientific and technological advances over the years have led to great increases in agricultural productivity and reduced environmental impacts. Using artificial intelligence technology can promote the development of modern agriculture. For example, to predict climate change and analyze the impact of global climate change on agricultural production; to predict crop diseases and pests; to test and analyze the soil of agricultural land and formulate a reasonable planting scheme to promote the utilization of land resources.
Nevertheless, future agricultural systems face huge challenges in balancing and optimizing productivity and profitability against stewardship of ecosystems and natural resources. This workshop will allow scientists and engineers to discuss these challenges and share their latest research achievements in agricultural science and technology. Topics of the workshop will be organized around the usage of computer technology and artificial intelligence in agriculture. 

Keywords:
smartagriculture, precision agriculture, agriculture engineering, food security
Chair: Prof. Juan Wen | China Agricultural University, China
Juan Wen received her B.E.degree in information engineering and Ph.D.degree in signal and information processing from Beijing University of Posts and Telecommunications. She was a visiting scholar at the University of Florida in 2019 and 2020. She is now an associate professor in the College of Information and Electrical Engineering, China Agricultural University. Her research interests include artificial intelligence, machine learning, natural language processing, and information security.