I C I V I S
W O R K S H O P
Workshop 1:
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.
Workshop 2:
Advances in Agricultural Science and Technology: 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:
Smart agriculture, 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.
Workshop 3:
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.
Keywords:
Computational pathology, Deep learning, Computer vision, Medical image analysis, Feature extraction, Pattern Recognition
 
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.
 
Co-Chair: Chen Li | Northeastern University, China
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 4:
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. Xufeng Yao | Shanghai University of Medicineand Health Sciences, China
Xufeng Yao, 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 5:
The Impact of Brand Image Design and Digital Communication on Consumer Loyalty
Summary:
The constantly changing digital landscape and the rise and expansion of the Internet and new technologies have had a positive impact on the economy. Research has shown that almost all millennials are internet users, and about nine out of ten online millennials using social networking globalization has long been believed to blur cultural differences among consumers, especially through digital media. Enhance consumer experience, improve market positioning, and keep up with constantly changing customer needs. The purpose of this seminar is to gather academic and industry researchers to provide research results on digital brand experiences. We encourage authors to submit outstanding research papers on these two topics: theoretical methods and practical case reviews.
Keywords:
Digital brand, information visualization, digital communication, interaction design
 
Chair: Prof. Zhang Yaqin, Yunnan University, China
Mengyao Yu is currently pursuing a doctoral degree at the University of Malaysia and the Director of the Teaching and Research Department of Digital Art at the Yunnan School of Economics and Management. The research direction includes brand digital communication, visual information visualization, etc. Participated in National Natural Science Foundation of China, national key projects, etc. And published over 20 papers.
Workshop 6:
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.
Keywords:
Image Processing, Image Segmentation, Visual Object Tracking, Object Detection, Image Coding and Steganography, Deep Learning
 
Chair: Assoc. Prof. Jun 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 7:
Visual Analysis and Machine Learning
Summary:
Visual analysis and machine learning are two important techniques in most academic, industrial, business, and medical applications. Visual analysis including image/video processing and computer vision systems is closely related to various fields, such as 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/video 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.
Keywords:
machine learning, deep learning, image/video 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 40 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, ICIGP 2022, IoTCIT 2022, and MLCCIM 2022 as Technical Co-Chair or Publicity Co-Chair.
Workshop 8:
The AGV Service Robot from Design to Fusion Control
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 robots, mission and obstacle , modelling the AGV/MIR  ,fusion algorithm
 
Chair: Assoc. Prof. Ata Jahangir Moshayedi | Jiangxi University of Science and Technology, China
Dr. Ata Jahangir Moshayedi, Associate professor at Jiangxi University of Science and Technology, China, PhD. In Electronic Science from Savitribai Phule Pune University, India, IEEE member, Instrument Society of India as a Life Member, Lifetime Member of Speed Society of India, member of the editorial team of various conference 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., 80 papers published in national journals and conferences, 2 books published, Owns 1 patent, 5 copyright. His research interest includes: Robotics and Automation/ Sensor modelling /Bio-inspired robot, Mobile Robot Olfaction/Plume Tracking, Embedded Systems / Machin vision-based Systems/Virtual reality, Machine vision/Artificial Intelligence Dr Moshayedi ,Presently  working on his own AGV (Automated Guided Vehicles)  model and Food delivery service robot at Jiangxi University, china.
Workshop 9:
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 10:
Research on Multi-Agent and Complex Systems
Summary:
Multi-agent systems and complex systems are important research areas in computer science and control theory today. Multi-agent systems are composed of autonomous intelligent agents that interact with each other to achieve common goals, while the characteristic of complex networks is the complex interconnections and dynamic interactions between their components. Multi-agent systems and complex networks have broad applications in fields such as sociology, ecology, and economics. The research on these systems aims to understand their behavior and properties, and to design efficient control strategies. The study of multi-agent systems and complex networks has the potential to fundamentally change the way we approach complex problems and optimize system performance, making it an evolving research field in the coming years. In summary, we encourage authors to think and discuss the dynamic behavior of multi-agent systems, complex networks, and neural networks, and provide valuable insights and suggestions for the further development of this field through interdisciplinary fusion.
Keywords:
Multi-agent system, Complex system, Neural network, Synchronization control, Collaboration.
   
Chair: Dr. Hui Zhao | University of Jinan, China
Hui Zhao received the Ph. D. degree in Beijing University of Posts and Telecommunications, Beijing, China, in 2017. Since 2017, she has worked at the University of Jinan. Her current research interests include the stability and synchronization of complex dynamical networks (including general complex network, neural network and memristive nueral network), the application of echo state network (ESN) etc.. She has authored almost 50 refereed papers in international journals, and served as reviewer of multiple international journals.
Workshop 11:
Stereo Vision, 3D Reconstruction, Scene Understanding, Environmental Perception
Summary:
Depth estimation is a very popular research direction in the field of computer vision, playing a crucial role in tasks such as robot vision, 3D reconstruction, augmented reality, and autonomous driving. In recent years, depth estimation methods have received widespread attention and in-depth research as low-level visual tasks. Traditional methods use lidar to obtain depth information, but the cost of obtaining dense and accurate depth maps is too high. In contrast, depth estimation methods based on image/vedeo directly estimate scene depth information, without the need for expensive equipment, which have a wider application space. 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 Depth Estimation. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Keywords:
Stereo Vision, Scene Understanding, Monocular Depth Estimation, Multi-view Depth Estimation
   
Chair: Prof. Yongjun Zhang | Guizhou University, China
Yongjun Zhang, received a Ph.D. degree in Computer Science and Technology from Guizhou University. He worked as an associate professor at the school of computer science and technology, Guizhou University. His main research interest is computer vision. He participated in the National Natural Science Foundation, the Innovation Promotion Fund of the Ministry of Education, and the joint innovation project of Guangdong, Hong Kong, and Macao, etc. Based on these projects, he published more than 40 papers in IEEE Trans, CVPR, ACCV and other journals/conferences.
Workshop 12:
Advances on Sensing, Decision-making and Control for Intelligent Robots: Stereo Vision, 3D Reconstruction, Scene Understanding, Environmental Perception
Summary:
With the rapid development of intelligent technology, intelligent robots have attracted increasing interest from the theoretical researches and practical applications. In particular, while complex tasks in the unstructured environment put forward higher requirements for the intelligence of robots, adopting intelligent methods and technologies can effectively improve the robustness and generalization of these tasks. The aim of this workshop is to bring together the latest researches and innovations on sensing, decision-making and control for intelligent robotic systems from both the technology and functionality perspectives.
Keywords:
Intelligent sensing, intelligent control, intelligent decision-making, intelligent robot
   
Chair: Assoc. Prof. Chao Ma | University of Science and Technology Beijing, China
Chao Ma is an associate professor of School of Mechanical Engineering, University of Science and Technology Beijing (USTB), and is the PI of the Multi-modal Neuro Robotics Lab in USTB. He is a senior member of the Chinese Mechanical Engineering Society, a senior member of the Chinese Institute of Command and Control, a member of the IEEE. He obtained a doctor's degree in control science and engineering from Harbin Institute of Technology. His main research fields are in the hybrid intelligent systems, intelligent robot systems, human-computer interaction systems, etc. He has published more than 50 SCI/EI research papers, and published a Springer academic book and served as the conference committee in many international conferences.
   
Co- Chair: Dr. Yidao Ji | University of Science and Technology Beijing, China
Yidao Ji received a Ph.D. degree in Control Science and Engineering from University of Science and Technology Beijing, and now is an assistant professor at the University of Science and Technology Beijing (USTB), China. He has always worked in the research fields of intelligent robot, hybrid control and brain-inspired control. He participated in a few national, provincial projects, meanwhile, he published several SCI journals research papers. He was involved in the 38th Chinese Control Conference as a regular session co-chair.
   
Co-Chair: Dr. Hang Fu | University of Science and Technology Beijing, China
Hang Fu received the B.E. degree and the M.S. degree in Mechanical Engineering in 2017 and 2020 respectively, from University of Science and Technology Beijing, Beijing, China. He is currently working toward PhD. degree in Mechanical Engineering in University of Science and Technology Beijing, China. His research interests in hybrid systems, Markov jump systems, switched systems, and intelligent robotic systems.
Workshop 13:
AI in Medical Image Analysis
Summary:
Provides a forum for the dissemination of new research results in the field of medical and biological image analysis, with special emphasis on efforts related to the applications of computer vision, virtual reality and robotics to biomedical imaging problems. The highest quality, original papers that contribute to the basic science of processing, analyzing and utilizing medical and biological images are welcomed.
Keywords:
Medical images processing；landmark detection；deep learning；convolutional neural networks
   
Chair: Assoc. Prof. Qing Chang | East China University of Science and Technology, China
Qing Chang received the BS and the MS degree in Automatic Control，and PhD degree in Navigation, guidance, and control from Northwestern Polytechnic University (NWPU), in1997,2000 and 2003. She is an Associate Professor from East China University of Science and Technology Shanghai, China. Her research interests include optical imaging and recognition, biomedical image analysis, with an emphasis on computational modeling of high-level vision.
Workshop 14:
Multi-modal Domain-adaptation
Summary:
Multi-modal learning is a task of using different types of data such as text images, video and audio. It is associated with information from different modes as input and mapped to the same feature space for interpretation and inference. At present, the domain adaptive problem is one of the big challenges of multi-modal learning, in the model training and reasoning link researchers assume that training set and test set distribution is consistent, but in the actual problem, test environment is often different with the training data, the fitting problem, so the multi-modal field adaptation. 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 Blockchains and Privacy-Preserving. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical case reviews.
Keywords:
Multi-modal learning ,transfer learning, domain adaptation ,Multi-modal domain-adaptation

Chair: Prof. 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. She has published 15 SCI journals, 4 invention patents. Her research interests are in Artificial Intelligence, Machine Learning, CV, Data/Text/Web Mining. Her professional services are as follows 
  • Methods(April 21, 2020-present) - Guest editors (IF：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) 2020- Program committee 
  • 2021 International Conference On Computational Intelligence and Security – Special co-Chair 
  • The 2nd International Conference on Robotics, Automation and Intelligent Control（ICRAIC 2022）---Peer Reviewer 
  • The 2nd International Conference on Image, Vision and Intelligent Systems (ICIVIS 2022)—Workshop 1 co-Chair 
  • The 24th IEEE International Conference on High Performance Computing & Communications (HPCC-2022)--- Special Session: Applications of Data Science and Emerging Technologies----PC 
  • IEEE International Conference Bioinformatics and Biomedicine(BIBM) 2022- Program committee 
  • 2023 IEEE International Conference on Big Data (IEEE BigData 2023)--workshop chair 
  • 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023)--workshop chair 
  • 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023)--Program committee
Workshop 15:
Wisdom Medical and Health Research Progress and Applications
Summary:
With the continuous improvement of material life, people's pursuit of health demand is more and more intense. The cross integration of machine learning and traditional Chinese medicine has promoted the further development of intelligent medical treatment and intelligent health care.The application innovation of machine learning in medical health care has also attracted the attention of scholars in the field of artificial intelligence and life medicine, and fruitful results have emerged in recent years.  However, due to the particularity of the industry, there are still great challenges in the effectiveness and robustness of intelligent medical treatment and intelligent health care algorithm.  The purpose of this workshop is to provide an exchange platform for discussing these challenges, and it is expected that scholars engaged in this field show the latest research progress and fruitful application results in intelligent medicine and intelligent health care, and strive to promote the development of intelligent medicine and intelligent health care industry through cross integration.  
Keywords:
Wisdom medical, machine learning,robustness, biomedical signal
Chair: Prof. Feng Yuan | Shandong Management University, China
Feng Yuan, female,Ph.D. in Management Science and Industrial Engineering. She has engaged in data mining and traditional Chinese medicine information technology researchfor long term. She is also the Principal of the Key Laboratory of TCM Data Cloud Service in Shandong Province during the 13th Five-Year Plan period, the director of Intelligent Information Processing Professional Committee of Shandong Artificial Intelligence Society, thestanding director of Shandong Artificial Intelligence Society, the member of a council of Shandong Computer Association, executive director of Chronic disease Management Branch of Chinese Ethnic Medicine Association.  In recent years she has high levels of papers published over 20 articles, 5 national invention patents, presided over by national and provincial scientific research project 6 items, including 1 National Social Science Foundation project, 1 Shandong Provincial Key RESEARCH and Development Program (public welfare special project) project. She has won one Natural Science Academic Innovation Award of Shandong Province, one Scientific and Technological Achievement Award of Shandong Traditional Chinese Medicine science and Technology.
  Co-Chair: Prof. Canwei Wang | Shandong Management University, China
Canwei Wang, male, Master, Associate Professor and Assistant DeanofSchool of Information Engineering, Shandong Management University.He has won the title of university-level advanced Individual, member of Shandong Artificial Intelligence Society, deputy secretary general of Occupational Health Intelligence Service Special Committee of Shandong Artificial Intelligence Society, and member of Intelligent Application Branch of Jiangsu Artificial Intelligence Society. His research interest covers machine learning and its applications.  In recent years, he has published 10 academic papers, presided over or mainly participated in 6 department-level and above projects, authorized 1 utility model patent, and is the leader of the university-level teaching team, presided over one provincial-level excellent course, lecturing one provincial-level excellent course.
Co-Chair: Prof. Yixian Fang | Shandong Management University, China
Yixian Fang, Doctor, professor, full-time teacher of School of Information Engineering, Shandong Management University. He has long been engaged in machine learning, smart medicine, big data analysis, image processing, multimodal learning and other research. He has published more than 20 high-level papers, including 15 SCI retrieval papers, accepted 1 national invention patent, presided over and participated in 10 national and provincial scientific research and horizontal projects.  Guided students to participate in Shandong University Student Mathematics Competition, "Huawei Cup" China Graduate Mathematical Contest in Modeling, and won one provincial first prize, one national second prize, and one national third prize.
   Co-Chair: Prof. Weikai He | Shandong Jiaotong University, China
Weikai He, received a Ph.D degree in Acoustics from Shandong University. He worked as a professorat the School of Aeronautics, Shan Dong Jiao tong University. His research interests includeimage processing and computer vision.He participated in the National Natural Science Foundation, Shandong Provincial Higher Educational Youth Innovation Science and Technology Program, etc. Based on these projects, he published more than 10 papers in IEEE Trans and International Journal of Advanced Robotic Systems, etc.
   Co-Chair: Assoc. Prof. Baoxian Jia| Liaocheng University, China
Baoxian Jia, associate professor, doctor and master's supervisor of Liaocheng University, graduated from Tsinghua University in the direction of Big data education in 2023, was selected for the Guangyue Talent Project of Liaocheng University, young and middle-aged experts with outstanding contributions in Liaocheng, young experts in the national business service industry, national business science and technology innovation figures, and vice president of the Computer Academy. He has won the titles of Shandong Province's famous e-commerce teacher, Liaocheng University's graduate education exemplary individual, excellent class teacher, excellent innovation and entrepreneurship instructor, and excellent Communist Party member. Undertake undergraduate and graduate courses in e-commerce technology, introduction to computer science, and advanced operating systems. Mainly engaged in education Big data and artificial intelligence research. Published over 60 academic papers, including 14 SCI/EI/SSCI indexed papers as the first author. Led and completed 1 key research project commissioned by the Ministry of Civil Affairs and 1 national statistical key research project. Led and completed 8 other departmental level research projects. Led and completed 1 horizontal project that was evaluated and accepted by the Department of Education, reaching a leading level in China. Received 13 scientific research awards including the National Business Science and Technology Progress Award, 8 software copyrights, and wrote 3 consulting reports that received affirmative approval from provincial leaders. I have edited 3 textbooks and participated in 4 textbooks. I have guided students in subject competitions and won 16 provincial and ministerial level or above awards. I have led 2 provincial teaching and research projects and won 2 second prizes of Shandong Provincial Teaching Achievement Awards.
Workshop 16:
AI in Computer-aided Cervical Cancer Screening
Summary:
Cervical cancer is one of the most common cancers in women. In 2020, there were about 604,000 women diagnosed with cervical cancer worldwide, and 341,000 died of the disease. A lot of studies show that periodic inspection can reduce the incidence and mortality of cervical cancer. Traditional smear tests require doctors to read about 104 number of cells for each slide under microscopes, which is very labor-intensive and experience-dependent. Artificial intelligence-assisted diagnosis is key for scaling up cervical cancer screening, especially for underdeveloped areas lacking pathologists. But accuracy, generalization, explanation of AI systems in computational cytology remain challenging for clinical-level applications. We welcome researchers interested in this topic, and encourage prospective authors to submit related research papers.
Keywords:
Cervical cancer screening, Computational cytology,Deep learning, AI
Chair: Prof. Shenghua Cheng | Southern Medical University, China
Shenghua Cheng received a Ph.D. degree in Biomedical Photonics from Huazhong University of Science and Technology, China. After the postdoctoral work at Huazhong University of Science and Technology, he joined School of Biomedical Engineering, Southern Medical University, China. His researches focus on computational cytology and AI-assisted cervical cancer screeningsystems. He has hosted 3 projects from NSFC and China Postdoctoral Science Foundation, and participated in the research of key projects from the Ministry of Science and Technology and NSFC. As first or corresponding authors, he has published 7 research articles in NatureCommunications, IEEE TMI, Medical Image Analysis, etc. and has been authorized 8 Chinese invention patents. He was awarded a Silver Distinguished Reviewer of IEEE TMI.
Workshop 17:
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.
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:
Image Restoration and Enhancement
Summary:
Image restoration, enhancement and manipulation are key computer vision tasks, aiming at the restoration of degraded image content, the filling in of missing information, or the needed transformation and/or manipulation to achieve a desired target (with respect to perceptual quality, contents, or performance of apps working on such images). Recent years have witnessed an increased interest from the vision and graphics communities in these fundamental topics of research. Not only has there been a constantly growing flow of related papers, but also substantial progress has been achieved.
Each step forward eases the use of images by people or computers for the fulfillment of further tasks, as image restoration, enhancement and manipulation serves as an important frontend. Not surprisingly then, there is an ever growing range of applications in fields such as surveillance, the automotive industry, electronics, remote sensing, or medical image analysis etc. The emergence and ubiquitous use of mobile and wearable devices offer another fertile ground for additional applications and faster methods. This workshop aims to provide an overview of the new trends and advances in those areas. Moreover, it will offer an opportunity for academic and industrial attendees to interact and explore collaborations.
Keywords:
Image Restoration, Denoising, Deblurring, Image Super-resolution, High Dynamic Range Imaging
Chair: Prof. Qingsen Yan| Northwestern Polytechnical University, China
Qingsen Yan, a Professor with the School of Computer Science, Northwestern Polytechnical University (NPU), Xi’an, China. He received the Ph.D. degree in Computer Science and Technology from Northwestern Polytechnical University in Dec. 2019. Before joining NPU, he was a Senior Research Fellow with the Australian Institute for Machine Learning (AIML), Adelaide, Australian. His current research interests include computer vision, multimedia information processing and content understanding, segmentation, remote sensing image interpretation, artificial intelligence, and deep learning. He has published more than 50 academic papers in important international journals and conferences. He has published more than 20 papers in international journals and conferences as the first author, and 1 paper with ESI high citation, including IJCV, IEEE TIP, IEEE TBD, PR, MedIA, IEEE JBHI, and CVPR (TOP conference in Computer Vision Field). Google Scholar is more than 1,700, and the relevant research results have authorized 7 Chinese invention patents. He was Runner-up of CVPR22 - High Dynamic Range Image Ghosting Competition, Excellent Doctorial Dissertation Award from CSIG and CIE. He served as Program Committee (PC) of AAAI 2022, and reviewer of TPAMI, TIP, TNNLS, TMM, CVPR, ECCV, ICCV and ACCV. He is a member of the Special Committee on Artificial Intelligence and Pattern Recognition of China Computer Society, a member of the Special Committee on Computer-Aided Design and Graphics of China Computer Society, a member of the Special Committee on Computer Vision of China Computer Society, a member of the Special Committee on Detection, Perception and Imaging of China Graphic and Image Society, a member of the Chinese Society of Electronics, and a member of the Asia-Pacific Society of Artificial Intelligence.
Workshop 19:
Image Enhancement and Understanding under Adverse Weather
Summary:
Visual imaging is of great importance in image acquisition and scene understanding for autonomous driving and video surveillance. Unfortunately, the image quality is seriously degraded due to the influence of adverse weather: rain, fog, and snow. Our goal is to bridge the gap between the low-level enhancement and high-level perception by simultaneously enhancing the visualization ability and improving the scene understanding precision. This workshop aims to provide an opportunity to researchers to discuss the latest trends of image enhancement and understanding under adverse weather, and the relevant research papers are welcomed to promote the development of this filed.
Keywords:
Image enhancement, scene understanding, adverse weather
Chair: Assist. Prof. Yi Chang| Huazhong University of Science and Technology
Yi Chang is an Assistant Professor with the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China. He received the B.S. degree in School of Automation from the University of Electronic Science and Technology of China, Chengdu, China, in 2011, the M.S. and the Ph.D. degree from the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, in 2014 and 2019 respectively. Between 2020 and 2022, he was a Post-Doctorate with AI Research Center in PengCheng Lab. His research interests include image enhancement and understanding under adverse conditions, multispectral image processing and neuromorphic event imaging. He has published more than 50 academic papers in CVPR, ICCV, AAAI, TIP, TNNLS (2 papers with ESI high citation). He won the champion of CVPR2023 UG2+ in two tracks: Single Image Deraining and Atmosphere Turbulence Mitigation. He served as Program Committee (PC) of AAAI, and reviewer of TPAMI, IJCV, CVPR, ECCV, and ICCV. He is a member of IEEE, ACM, CSIG, CCF.
Chair: Assoc. Prof. Houzhang Fang| Xidian University, China
Houzhang Fang is an Associate Professor with the School of Computer Science and Technology, Xidian University, Xi’an, China. He received the Ph.D. degree from the School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, in 2014. His research interests include image restoration, object recognition and tracking. He has published over 30 papers in academic journals and conferences. He is a member of IEEE and CCF.
Workshop 20:
Video Analytics: Technology of Searching Objects
Summary:
The article dedicated to video analytics is a technology that uses computer vision methods for the automated acquisition of various data based on analysis sequence of images from video cameras in real-time or from archival records. The task of discovery of dynamic objects is understood as the task of detection and selection of changing areas of the image in a sequence of frames. Accordingly, the detection of a certain object means the choice of one or more detected dynamic objects that have some similar features to a given search object. Features are selected according to the algorithm. The search process object is complicated by affine, projective distortions, overlapping object by other objects, and receiver (sensor) noise. For real practical applications, the task is to process the video sequence at the real speed of receiving the data stream.
Keywords:
Video, objects, methods
Chair: Prof. Beknazarova Saida Safibullayevna | Tashkent University of Information Technologies
Beknazarova Saida Safibullayevna DSc, professor Head of department of Tashkent University of Information Technologies named after Muhammad Al-Khwarizmi
During his scientific-pedagogical Activity, 4 students named after "Beruny", 7 students became winners of the state scholarships named after "Muhammad Al-Khwarizmi", 4 students became winners of the scholarship "Ministry of development of information technologies and communications of the Republic of Uzbekistan". The head of the programmer Girls ' School of the Center for inventive women under the women's wing of Uzbekistan has been carrying out its activities, as well as conducting a review of the projects presented by women in a number of competitions organized by the committee on the use of their knowledge and skills as an expert.
The winner of the Medal of the European Scientific and production consortium "Isaac Newton" ("Isaac Newton"); The 1st place in the international contest of the Russian Federation VIII "Pedagogical Discovery: arysary-2018"; the 1st place in the International contest of outstanding achievements in science and education "recognition" organized by the International Research Center "scientific cooperation".
The winner of the Grand Prix of the women's party of Uzbekistan "Woman of the Year" award in 2018; in the 2008-2009 academic year she was the winner of the state scholarship of the president of the Republic of Uzbekistan in the direction of "technical and informatics". 
At the same time, the winner of the Republican stage in the nomination "The best educator using innovation and information technologies" in the contest "the best pedagogue of the higher education institution" held in our country, was able to become the winner of the contest "young scientists" held by the Ministry of innovation development of the Republic of Uzbekistan.
1. The author have a total of 359 scientific works on scientific research aimed at processing information resources in multimedia systems, creating algorithms and software, modeling processes have been published in foreign and authoritative journals of our country in the collections of conferences. 
2. The author have 5 manuals, 14 monographs, author of 21 software tools created for electronic computing machines, aimed at delivering quality without loss of continuous text, audio, video data in the processing of information, transmission processes, processing audio, video sources, media educational process.
3. ORCID 0000-0001-7708-7616 h-index-3
4. Video technologies, computer graphics, multiplication technologies, audiovisual technologies, projecting informational systems, image processing, audio processing.
Workshop 20:
Deep Learning for Underwater Image Enhancement: Techniques, Applications, and Future Trends
Summary:
In the vast and captivating world beneath the water's surface, capturing high-quality images is a non-trivial challenge. Underwater conditions such as limited visibility, color distortion, and image degradation due to scattering and absorption necessitate sophisticated techniques for image enhancement. With the emergence of deep learning, there has been a significant leap forward in our ability to revitalize underwater imagery and extract valuable information. This workshop aims to explore the intersection of deep learning and underwater image enhancement, bringing together researchers from various disciplines to delve into the latest advancements, techniques, applications, and future trends in this exciting field. Throughout the workshop, we will delve into the fundamental principles of deep learning and its specific applications for underwater image enhancement. We will discuss cutting-edge techniques such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and transfer learning for the underwater domain. By combining theoretical foundations with practical demonstrations and case studies, participants will gain insights into the inner workings of these techniques and their effectiveness in improving underwater image quality. Furthermore, we will explore a range of applications that benefit from enhanced underwater imagery. These applications span scientific research, marine biology, underwater robotics, environmental monitoring, underwater archaeology, and more. By understanding the potential of deep learning in these domains, participants will gain a holistic view of how underwater image enhancement can have a transformative impact on diverse fields. Finally, the workshop will address future trends and open research challenges in the realm of deep learning for underwater image enhancement. Discussions will encompass topics such as dataset creation and annotation, domain adaptation, multi-modal fusion, and the development of robust evaluation metrics. By engaging in these forward-thinking conversations, participants will contribute to shaping the future direction of this rapidly evolving field.
Keywords:
Convolutional Neural Networks, Color Correction, Deep Learning, Dataset Creation and Annotation, Image Enhancement, Generative Adversarial Networks (GANs)
Chair: Assist. Prof. Gunjan Verma | Manav Rachna International Institute of Research and Studies
Gunjan Verma, an Assistant Professor at the School of Computer Applications, Manav Rachna International Institute of Research and Studies (MRIIRS), Faridabad, India. She is pursuing a PhD degree in Computer Science and Technology from GLA University, Mathura, India. Before joining MRIIRS, she was a researcher in Computer Vision and Image Processing lab, at GLA University, India from 2019-2022. Her current research interests include computer vision, object detection, multimedia information processing, underwater image enhancement and interpretation, artificial intelligence, nature-inspired algorithms, and deep learning. She has published many academic papers in international journals (SCI indexed) as well as conferences. She served as a reviewer in various conferences including ISCON-2019, ISCON-2020, FSAET-2021 and FSAET-2022 as well as a reviewer in journals including Multimedia Tools and Applications (MTAP) and Journal of Electronic Imaging.