Workshop
Workshop
Workshop 1

Title1:Big Data and NB-IoT

Summary:
Big data and NB-IoT are important research directions in the computer field. Nowadays, the Internet is generating a large amount of data every moment, it provides people with more and more information services for browsing, searching, socializing, and trading. To obtain various types of information, various information forms a new data combination in the application, which generates a massive amount of data that grows geometrically. The processing of big data is of great significance to promote technological progress, promote economic growth, and facilitate people's lives.
On the other hand, various types of smart terminals are becoming more popular and more convenient, which bring mass data. The interconnection of everything is an inevitable trend, compared with BlueTooth, ZigBee, and other short-range communication technologies, mobile cellular network has features such as wide coverage, portability and large number of connections, which can bring more rich application scenarios and should be the main connection technology of the Internet of Things. As an evolutionary technology of LTE, 5G not only has a peak rate of up to 10Gbps, but also means more connections based on cellular Internet of Things, M2M connection support, and lower latency, which will promote the rapid popularization of APPLICATIONS such as HD video, VoLTE, and Internet of Things. 

Keywords:Cybersecurity, Cyberspace Governance, Security Big Data, Artificial intelligence, International Strategy for Cyberspace, 5G, NB-IoT.


Chair:
Prof. Yang Yi , Sun Yat-sen University, China
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Yang Yi received her B.A. in Electronic Engineering from Fudan University, and her MA.Sc and Ph.D. degree in System Engineering from Northeastern University. Now, she is the Executive Dean of the School of Information Science of Sun Yat-sen University, and meanwhile, she worked as a member at the Key Laboratory of Big Data of Guangdong Province. Her research interests include Pattern Recognition, Computer Vision, Software Engineering, and Numerical Simulation.
She participated in the National Natural Science Foundation, National Key Projects, National Key Research and Development Project, Natural Science Foundation of Guangdong Province. Based on these projects, she published many academic papers.

Prof. Xingcheng Liu , Sun Yat-sen University, China
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Xingcheng Liu received the B.E. and M.E. degrees in Automation at Huazhong University of Science and Technology, and Ph.D. degree in RadioPhysics at Sun Yat-sen University. He is the leader of the major, Electrical Engineering and Automation, in the School of Information Science of Sun Yat-sen University, and meanwhile, he works with the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai). He is a senior member of IEEE and a committee member of the Internet of Things Committee of China Computer Society. He was awarded the KC Wong Fellowship of the Royal Society of the UK for the post-doctoral research at the University of Southampton in 2003. Then, he worked at Oregon State University in the US as a visiting scholar. 
His research interests include the wireless sensor network/Internet of things, channel coding technology, intelligent algorithm and information processing, and new wireless communication technology. He finished and/or currently works on several research projects funded by the National Natural Science Foundation of China, Guangdong Province, and Guangzhou City. He published more than 150 academic papers, and 2 textbooks, obtained more than 20 invention patents of China.
Prof. Xiao-Dong Li , Sun Yat-sen University, China
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Xiao-Dong Li received the B. S. degree in mathematics from the Department of Mathematics, Shaanxi Normal University, Xian, China, in 1987, the M. Phil. degree in automatic control from the Nanjing University of Science and Technology, Nanjing, China, in 1990, and the Ph. D. in intelligent control from the City University of Hong Kong, Hong Kong, in 2007.
He is a Professor at the School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, China. His research interests include intelligent control, pattern recognition, and neural network.
Prof. En Zou , Sun Yat-sen University, China
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En Zou received his Master and ph.D.degree in Control Engineering from Central South University. Now,she is the Director of the Profession of Electrical Engineering and Automation of Sun Yat-sen University. Her research interests include Automatic Control Theory, Power System Analysis, and Artificial Intelligence. 
She hosts the Natural Science Foundation of Guangdong Province, Science, and Technology Projects of Guangdong Province. Based on these projects, she published many academic papers.
Prof. Zhongli He Sun Yat-sen University, China
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Zhongli He received BA in Electronic Engineering from Hunan University. Now, he is the Associate Professor and Senior Engineer of the School of Information Science of Sun Yat-sen University. His research interests include Electrical Engineering and Automation, NB-IOT, etc.
He participated in The Provincial Department of Education and the university's scientific research projects. Based on these projects, he published many academic papers, has obtained several patents.
Prof. Zhihong Pan , Sun Yat-Sen University, China
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Zhihong Pan received the B.Eng degree from Dongguan University of Technology, Dongguan, China, in 2008, and the M.Eng degree in Jinan University, Guangzhou, China in 2011. he has been an Associate Professor in the School of Information Science at Sun Yat-Sen University since 2015. He has published 25 papers and holds 5 patents, His research interests include internet of things, crowdsourcing, and machine learning.
Workshop 2

Title1 : Image Processing

Summary:
Image processingis the manipulation of the digital data with the help of computer hardware and software to produce digital maps in which the specific information has been extracted and highlighted.It deals with developing a digital system that performs operations on an digital image.The generation and development of digital image processing are mainly affected bythe development of computers, the development of mathematics.

Keywords:Image Denoising, Image Processing, Deep Learning

Chair:
Prof. Changli Li Hohai University, China
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Changli Li was born in Hubei Province, China in 1976. He obtained a Bachelor's Degree in Physics from the Department of Physics, Fudan University in 1999, a Master's Degree in Communication and Information Systems from the School of Telecommunications Engineering, Xidian University in 2004, and a Ph.D. degree in Signal and Information Processing from the School of Electronic Engineering, Xidian University in 2010. Funded by the China Scholarship Council, he conducted collaborative research as a visiting scholar at University of Windsor in Canada from November 2014 to November 2015. In recent years, he has been engaged in the research in theories and appliances of the digital image processing, multi-dimensional signal processing, target recognition and positioning, and radar signal processing. The subject of his research involves a wide range of knowledge, and fully applies the cutting-edge analysis and processing methods of signals and images. Prof. Li has published nearly 50 papers in domestic and foreign academic journals and conferences such as Neural Computing & Applications, Digital Signal Processing, and Journal of Huazhong University of Science and Technology (Natural Science Edition). Six published papers have been indexed by SCI and cited more than 20 times. He has also published 2 monographs and obtained more than 10 national invention patents.
Workshop 3

Title1 Gradient-Driven Parking Navigation Using a Continuous Information Potential Field Based on Wireless Sensor Network

Summary:
Wireless sensor networks can support building and transportation system automation in numerous ways. An emerging application is to guide drivers to promptly locate vacant parking spaces in large parking structures during peak hours. This paper proposes efficient parking navigation via a continuous information potential field and gradient ascent method. Our theoretical analysis proves the convergence of a proposed algorithm and efficient convergence during the first and second steps of the algorithm to effectively prevent parking navigation from a gridlock situation. The empirical study demonstrates that the proposed algorithm performs more efficiently than existing algorithms.

KeywordsIntelligent transportation system、Parking navigationWireless sensor network Information potential field

Chair:
Dr. Wei Wei , Xi'an University of Technology, China
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Wei Wei is an associate professor at School of Computer Science and Engineering, Xi’an University of Technology, Xi'an 710048, China. He is a senior member of IEEE, CCF. He received his Ph.D. and M.S. degrees from Xian Jiaotong University in 2011 and 2005, respectively. He ran many funded research projects as principal investigator and technical members. His research interest is in the area of wireless networks, wireless sensor networks Application, Image Processing, Mobile Computing, Distributed Computing, and Pervasive Computing, Internet of Things, Sensor Data Clouds, etc. He has published around one hundred research papers in international conferences and journals. He is an editorial board member of FGCS, IEEE Access, AHSWN, IEICE, KSII, etc. He is a TPC member of many conferences and a regular reviewer of IEEE TPDS, TVT, TIP, TMC, TWC, JNCA, and many other Elsevier journals.
Workshop 4

Title1:Few-Shot Learning Research and Application

Summary:
In the last decade, deep learning-based methods for visual recognition tasks have reached or even surpassed human beings' level in some scenarios. One crucial point for success is the numerous labeled data. However, it may be a heavy burden of data collection and maintenance under actual circumstances. In contrast, Human has no problem forming the concept of "giraffe" by only taking a glance from a picture in a book or hearing its description as looking like a deer with a long neck. To this end, researchers pay much more attention to addressing the few-shot learning-based tasks in recent years. Multiple kinds of strategies, including metric-learning, meta-learning, semi-supervised learning, have been proposed. 

KeywordsFew-shot learning, Meta-learning, Metric-learning, Semi-supervised learning

Chair:
Prof. Baodi Liu  , China University Of Petroleum, China
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Baodi Liu  received a Ph.D. degree in Information and Communication Engineering from Tsinghua University. He worked as an associate professor at the college of control science and engineering, China University of Petroleum (Huadong). He participated in the National Natural Science Foundation, Natural Science Foundation of Shandong Province. Based on these projects, he published many academic papers.

Workshop 5

Title1:Multi-agent Deep Reinforcement Learning in Games

Summary:
By combining deep learning and reinforcement learning techniques, deep reinforcement learning (deep RL) achieves big successes in a lot of complex decision-making tasks, particularly in games, such as Atari games, the game of Go, and even StarCraft II. A deep RL agent learns like people do, taking in high-dimensional raw data, such as image and sensor input, and refining its predictions and decisions through trial and error. Recently, multi-agent systems attract great attention because of a wide range of potential applications, and they also bring a lot of interesting problems and great challenges, such as distributed learning problem and communication problem among the agents. Another interesting problem is how to understand the behavior of the deep RL agents and improve their interpretability.

Keywords:Deep Learning, Reinforcement Learning, Mulit-agent, Game Theory

Chair:
Prof. Long Han , National University of Defense Technology, China
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Long Han received the Ph.D. degree in Construction Engineering from Tongji University, in 2014. He was a Postdoctoral Researcher in Applied Mathematics. He was a visiting scholar at Warwick University. He is currently an Associate Professor of College of Liberal Arts and Sciences at National University of Defense Technology. He has published two books and eight SCI/EI articles. He has been a director or a member in 10 national grants and projects. He has received the First Prize of Military Science and Technology Progress Award in 2013. His current research interests include reinforcement learning, multi-agent system, and game theory.

Workshop 6

Title 1Intelligent 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, and in recent years business amount of 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’ optimal decision-making problems and or even fails, such as slab design for steel plate production and components material nesting 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 "made in China 2025". 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.


Keywords:Knowledge Engineering, Intelligent Optimization, Big Data, Artificial intelligence, Manufacturing Industry, high-end Equipment Manufacturing, Knowledge, Slab Design


Chair:

ProfZiqiang Li , Xiangtan University, China 

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Ziqiang Li received his Ph.D. degree in Computer Application Technology from Dalian University of Technology. He worked as a member at the school of computer science at Xiangtan University. His research interests include Internet Security, Software Engineering, and Numerical Simulation.

He presided over the National Natural Science Foundation Project (NO:61272294), participated in the National Science and Technology Supporting Program Project (NO: 2012BAF10B04), presided over Natural Science Foundation Projects (NO: 11JJ6050 and 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 published Applied 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 7

Title1: AI Applications in Traditional Chinese Medicine Informatics

Summary:
Traditional Chinese Medicine (TCM) has been used for over 2,000 years to diagnose, treat, and prevent illnesses. It is clinically important to analyze TCM informatics using Artificial Intelligence (AI) technologies. For instance, it will be helpful to use deep learning models, such as Convolutional Neural Network (CNN) or Recurrent Neural Network (RNN), to analyze information of the four methods of diagnosis, and aid doctors of theTCMfor diagnosing illness; another case can be the analysis of ancient books of TCM. Knowledge of diagnosis can be achieved by learning from previous cases by using AI techniques, such as Graph Neural Network (GNN).   

Keywords:  Traditional Chinese Medicine (TCM), Deep Learning, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Graph Neural Network (GNN)

Chair:
Prof. Jiehui Jiang , Shanghai University, China
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Jiehui Jiang received his B.A and MA.Sc degrees in Biomedical Engineering at Shanghai University in 2004 and 2008, and Ph.D degree in Design Engineering from Delft University of Technology, the Netherlands in 2012. Now he is working as PI and head of Biomedical Engineering in the school of Communication and Information Engineering, Shanghai University. His research interests include medical instrument engineering, medical imaging analysis and AI. He has published more than 50 SCI paper, and served as chair or senior member in several domestic and overseas academic organizations. 

Workshop 8


Title 1: Recognizing Text in Image and Video 


Summary:


Keywordspattern recognition, computer vision,digital image, digital video


Chair:

ProfXiwen Zhang  , Beijing Language and Culture University, China

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XiWen Zhang is currently a Professor of Digital Media Department, School of Information Science, in the Beijing Language and Culture University. He worked at the Human-computer interaction Laboratory, Institute of Software, Chinese Academy of Sciences as an associated professor from 2002 to 2007. From 2005 to 2006 he was a Post-doctor advised by Prof. Michael R. Lyu in the Computer Science and Engineering department, the Chinese University of Hong Kong. From 2000 to 2002 he was a Post-doctor advised by Prof. ShiJie Cai in the Computer Science and Technology department, Nanjing University.
Prof. Zhang 's research interests include pattern recognition, computer vision, and their applications in digital image, digital video, as well as digital ink.
Prof. Zhang has published over 60 refereed journal and conference papers in his research areas. His SCI paper are published in Pattern Recognition, IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, Computer-Aided Design. He has published about twenty EI paper.Prof. Zhang received his B.E. in Chemical equipment and machinery from Fushun Petroleum Institute (became Liaoning Shihua University since 2002) in 1995, and his Ph.D. advised by Prof. ZongYing Ou in Mechanical manufacturing and automation from Dalian University of Technology in 2000.
Workshop 9

Title 1: Intelligent manufacturing (industry 4.0) —the development direction of manufacturing industry

Summary:

Intelligent manufacturing is a man-machine integrated intelligent system composed of intelligent machines and human experts. It can carry out intelligent activities in the manufacturing process, such as analysis, reasoning, judgment, conception and decision-making. Artificial intelligence technology is widely used in almost all aspects of the manufacturing process. Expert system technology can be used in engineering design, process design, production scheduling, fault diagnosis, etc. It can also apply advanced computer intelligent methods such as neural network and fuzzy control technology to product formulation, production scheduling, etc. to realize intelligent manufacturing process.With industry 4.0, the position of machine vision technology in industrial automation is becoming more and more important. The continuous innovation of machine vision technology has also promoted the progress of industrial automation, intelligent security and intelligent system. The application of artificial intelligence in industrialization is mostly related to machine vision technology, and now more and more manual operations are replaced by it.


KeywordsIntelligent manufacturing,Artificial intelligence, Industrial Robots,Machine vision


Chair:

Prof. Yuanzhi Wang, School of Computer and Information, Anqing Normal University, China
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Yuanzhi Wang was born in Anhui Province, China, in 1977. Now he is Leading Talent of Anhui Special Support Plan and professor of the School of Computer and Information at Anqing Normal University. His research interests include Industrial Robots and Intelligent Manufacturing, machine vision and image processing. He participated in the National Key Research and Development Program of China, Natural Science Foundation of Anhui Province. Based on these projects, he published academic papers and owns patents for his innovations, who also won The Scientific and Technological Progress Award of Anhui and Prize of China Invention and Entrepreneurship Achievement
Workshop 10

Title 1: Endogenous relationship between Rate-distortion optimization and rate control in Video Coding

Summary:

Hybrid coding framework mainly consists of intra- and inter-prediction, transform and quantization, filter control analysis and entropy coding. With the improvement of the requirement of video quality, the coding optimization mechanism becomes a hot topic, and its goal is to minimize distortion under the code rate budget. Recently, many encoder-only optimization techniques have been developed, which include rate-distortion optimization (RDO) and classical rate control (RC) that adjust variable values such as the quantization parameter (QP) or the Lagrangian multiplier to improve the final image processing effect and performance. However, as a non-core part of the coding standard, RDO has an extremely deep internal relationship with RC. In addition to the encoder optimization technology, the selection of high-quality upsampling and downsampling algorithms for image preprocessing, as well as a series of post-processing techniques to address block effects and artifacts in images, can also directly affect the final image processing effect and performance. At present, coding technology has attracted progressive attention.

KeywordsHybrid coding framework, Rate-distortion optimization, Quantization parameter, Rate control, Image preprocessing, Artifacts

Chair:

Prof. Yimin Zhou, University of Electronic Science and Technology of China
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Yimin Zhou received the B.S., M.S. and Ph.D. degrees in computer science at the College of Computer Science, University of Electronic Science and Technology of China (UESTC), Sichuan, China, in 2003, 2006 and 2009 respectively. Now, he is a Professor at UESTC. He has been a post-doctor majoring in communication and signal processing since 2013. He was a joint Ph.D. student at the University of Central Arkansas from 2007 to 2009 and a visiting scholar at the University of California, Santa Barbara in 2017. In 2007, he started working at UESTC, where he joined the College of Computer Science and Engineering in 2009, participated in the Center for Future Media in 2018, became the dean of the Computer Software and Theory Department in 2018, and was promoted to Chair Professor of Education in 2019. His research interests include image and video coding, streaming and parallel processing, image enhancement, as well as graduate education theory. He has authored or co-authored over 50 papers in journals and conferences. He is very concerned about video encoding standards like VVC, HEVC, IVC and AVS. He owns 15 granted patents and over 20 proposals adopted to the standards like MPEG, IVC and AVS. He instructed students to win IEEE ICIP Video Compression Technology Challenge Winner in 2017, and ChinaMM Competition of Image Compression Post-Processing on Deep Learning in 2018. He won the Science and Technology Award of Chinese Society of Image and Graphics, and the Invention Award from Sichuan Province, China, in 2018.
Workshop 11

Title 1:Visual quality assessment 

Summary:

Since people spend a lot of time consuming streaming videos every day, visual quality assessment (VQA) is thus an essential topic since it reflects the fulfillment of enjoyment or expectation of a service. Moreover, it also can be used for the comparison and optimization of different visual signals. In many image processing tasks (e.g., image acquisition, compression, restoration, transmission, etc.), it is necessary to assess the quality of the output image. The end-user of a visual signal is human, thus the subjective and objective assessments are of vital importance. This workshop is aimed to bring together researchers to focus on VQA both subjectively and objectively.

Keywords:Visual Quality Assessment, Quality of Experience, machine learning

Chair:

DrTonghan Wang, School of Information Engineering, East China University of Technology, China

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Tonghan Wang received his Ph.D. degree in computer science from Southeast University. He is working at School of Information Engineering, East China University of Technology, who is a supervisor for graduate students specialized in Electronics and Communication Engineering pursuing a professional master's degree and a Computer Science and Technology specialty for an academic master's degree, and a member of CCF. His research interests include signal subjective and objective quality assessment, pattern recognition, and artificial intelligence and their applications on signal, especially image quality assessment. He participated in the National Natural Science Foundation of China, sub-project of The National Key Research and Development Program of China, etc. He serves as a reviewer for several journals such as Journal of Visual Communication and Image Representation, Digital Signal Processing, Display, SPIE Journal of Electronic, IEEE Access, etc.
Workshop 12

Title 1: 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 processing and 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. Nianqiang Niu, Shenyang University of technology, China

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Nianqiang 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 of the National Natural Science Foundation of China, Liaoning Natural Science Foundation of China , and Liaoning Province, and published many academic papers.

Workshop 13

Title 1 :Vision Perception and Virtual Reality, Computational intelligence. Cognitive Science

Summary:
In today’s world, a new generation of artificial intelligence, relying on the Internet, big data, and intelligent perception technology, is developing rapidly in depth and breadth and is becoming a new focus of international competition and a new engine of economic development. Through extensive integration with technology and industrial development, artificial intelligence will lead to future technological innovation, promote industrial upgrading, and become a new driving force for information technology and social development. Vision perception technology is an important method to realize intelligence and has important application prospects in the fields of industrial manufacturing, urban transportation, and life services. It is foreseeable that the new generation of vision perception technology will become a research hot spot in the field of artificial intelligence.

Keywords:
Artificial Intelligence Theory. Deep Learning. Pattern Recognition. Machine Learning. Computer Vision. Intelligent Robot. Vision Perception and Virtual Reality. Medical Image Analysis. Computational intelligence. Cognitive Science. Edge Intelligence

Chair:
Prof. Sijie NiuSchool of Information Science and Engineering, University of Jinan, China
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Sijie Niu received B.S. and Ph.D. Degrees from the school of Computer science at Liaocheng University and Nanjing University of Science and Technology in 2007 and 2016, respectively. He was a visiting scholar at Stanford University in 2014. Now he is a Post-doctoral with medical image analysis, UNC. He is currently an associate professor in the School of Information Science and Engineering, University of Jinan, China. His research interests include pattern recognition, machine learning, image processing, and medical image analysis.
Prof. Guoqiang ZhongDepartment of Computer Science and Technology, Ocean University of China
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Guoqiang Zhong received his B.S. degree in Mathematics from Hebei Normal University, Shijiazhuang, China, his M.S. degree in Operations Research and Cybernetics from Beijing University of Technology (BJUT), Beijing, China, and his Ph.D. degree in Pattern Recognition and Intelligent Systems from Institute of Automation, Chinese Academy of Sciences (CASIA), Beijing, China, in 2004, 2007 and 2011, respectively. Between October 2011 and July 2013, he was a Postdoctoral Fellow with the Synchromedia Laboratory for Multimedia Communication in Telepresence, University of Quebec, Montreal, Canada. Between March 2014 and December 2020, he was an associate professor at the Department of Computer Science and Technology, Ocean University of China, Qingdao, China. Since January 2021,he has been a full professor at the Department of Computer Science and Technology, Ocean University of China. He has published 4 books, 4 book chapters, and more than 80 technical papers in the areas of artificial intelligence, pattern recognition, machine learning, and computer vision. His research interests include pattern recognition, machine learning, and computer vision. He has served as Chair/PC member/reviewer for many international conferences and top journals, such as IEEE TNNLS, IEEE TKDE, IEEE TCSVT, Pattern Recognition, Knowledge-Based Systems, Neurocomputing, ACM TKDD, AAAI, AISTATS, ICPR, IJCNN, ICONIP, and ICDAR. He has been awarded outstanding reviewer by several journals, such as Pattern Recognition, Knowledge-Based Systems, Neurocomputing, and Cognitive Systems Research. He has won the Best Paper Award of BICS2019 and the APNNS Young Researcher Award. He is a member of ACM, IEEE, IAPR, APNNS, and CCF, professional committee member of CAAI-PR, CAA-PRMI, and CSIG-DIAR, and trustee of Shandong Association of Artificial Intelligence.
Workshop 14

Title 1 :Intelligent Security and Privacy-Preserving for Open Environments

Summary:
Artificial intelligence is an important part of national strength and will lead the way as a strategic technology in the future. Artificial intelligence technology has promoted the innovation and development of industries such as smart manufacturing, smart logistics, smart healthcare, and smart cities, and has gradually become a key arena for competition among countries. However, a large number of studies and practices have shown that artificial intelligence technology is not yet fully mature, and related intelligent algorithms have security risks, which will cause problems such as data leakage, data forgery, algorithm bottlenecks, privacy security, and ethical dilemmas. These have effects on national security, economic development, and social stability directly or indirectly. Therefore, the issue of intelligent security and data privacy has become a major strategic need of the country. Intelligent security technology can ensure the security of artificial intelligence models through static or dynamic mechanisms so that the system is in a safe state under any circumstances. Intelligent security technology can also ensure that the artificial intelligence system does not leak the user's sensitive data and generates the expected correct results. The discovery and protection of data privacy can effectively reduce the risk of sensitive data leakage and prevent sensitive data of individuals, companies, and even countries from being obtained or used by criminals to cause harm.

Keywords:
Intelligent Security, Privacy Preserving, Artificial Intelligence

Chair:
Prof. Tao WuChongqing University of Posts and Telecommunications (CQUPT), China
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Tao Wu received a Ph.D. degree from the University of Electronic Science and Technology of China. He is currently an associate professor at Chongqing University of Posts and Telecommunications (CQUPT). His research interest involves graph machine learning, intelligent algorithm security, knowledge graph and computing, privacy-preserving of complex data. He has published more than 20 SCI journal articles. The related achievements have been mainly applied in fields such as smart cities, smart grids, and smart cultural museums. He has presided over 8 projects including the sub-projects of the National Key R&D Program, the National Natural Science Foundation of China, the Chongqing Natural Science Foundation, and the Science and Technology Research Program of Chongqing Municipal Education Commission.
Workshop 15

Title 1 :Computer Graphics and Simulation

Summary:
Current Computer Graphics and Simulation have a lot of practical applications for art, architecture, UI/UX development, video games, CGI, animations, augmented reality, physics, biology, chemistry, engineering, academic research, and other disciplines. Recently, deep learning methods have been utilized in the area of computer graphics and simulation e.g. to modeling 3D models or to generating and animating realistic images.

Keywords:
Computer Graphics, Simulation, Multimedia, Scientific Visualization, Human Computer Interfaces

Chair:
Prof. Chih-Kuo YehZhaoqing University, China
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Chih-Kuo Yeh received a Ph.D. degree in Computer Science and Information Engineering from National Cheng-Kung University, Taiwan, in 2015. He was a postdoctoral researcher at National Cheng-Kung University, during 2016-2019. He is currently an associate professor in the School of Computer Science and Software, Zhaoqing University, China. His research interests include computer graphics, computer vision, scientific visualization, and machine learning. He has published many papers on IEEE Transactions on Visualization and Computer Graphics.
Workshop 16

Title 1 :  Agriculture 4.0 ——a disruptive revolution to upgrade traditional agriculture

Summary:
At present, the agricultural area is experiencing a new revolution that is opening up novel research perspectives. The term "Agriculture 4.0" refers to an innovative way of understanding agriculture, where a variety of equipment could be used together to collect data, process data, integrates resources, and finally realize the automation and intellection of farming and cultivation. The development of intelligent agriculture is the realistic demand of traditional agricultural transformation and upgrading.  However, intelligent agriculture in China is still in its infancy. It may take decades to achieve Agriculture 4.0, and it requires the concerted efforts of scientists and engineers during this long-term process.

Keywords: Intelligence Agriculture, Precision Agriculture, Unmanned Aerial Vehicle, Green House, Smart Agriculture Application, Internet of Things, Smart Farming, Deep Learning Networks in Agriculture.

Chair:
Prof. Juan Wen, College of Information and Electrical Engineering, China Agricultural University, China
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Juan Wen received her B.E.degree in information engineering and Ph.D.degree in signal and information technology processing from Beijing University of Posts and Telecommunications. She was a visiting scholar at the University of Florida in 2019 and 2020. She is an associate professor in the Department of Artificial Intelligence of College of Information and Electrical Engineering, China Agricultural University. She is a member of China Artificial Intelligence Association (CAIA). Her research interests include artificial intelligence, machine learning, agricultural information processing, and natural language processing. Based on these projects, she published many academic papers.

Workshop 17

Title 1 :Natural language processing


Summary:


Keywords:


Chair:

Prof. Yatian Shen,Computer College of Henan University, China

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Yatian Shen received a Ph.D. degree in Computing Science from Fudan University, He works in the Computer College of Henan University. His research interests include Natural Language Processing, Deep Learning. 
He participated in the National Natural Science Foundation, Scientific research plan of Shanghai Science and Technology Commission, Key Scientific Research Projects of Colleges and Universities in Henan Province, etc. 
He has published COLING, NLPCC conference and journal papers ,The papers have been cited hundreds of times. He also serves as the reviewer for many referred venues including CIKM, COLING, and EMNLP, etc. 
Workshop 18

Title 1 :Pattern recognition and classification

Summary:
In recent years, pattern recognition has become a hot research branch thanks to the recent advances of machine learning driven by big data. In light of the fast-paced advancements in pattern recognition taking place all over the world, it is of great interest to keep an eye on the state-of-the-art research and development and to facilitate collaboration in multidisciplinary research areas.
The workshop aims to show and share the latest research results in the field of pattern recognition application systems provided by researchers. We encourage prospective authors to submit related distinguished research papers. Please name the title of the submission email with “paper title_workshop title”.

Keywords:
Pattern recognition, feature extraction, image classification, image clustering

Chair:
Prof. Zhonghua Liu, Information Engineering College, Henan University of Science and Technology, China
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Zhonghua Liu is a professor at Information Engineering College, Henan University of Science and Technology. He received his Ph.D. from Nanjing University of Science and Technology in 2011. His research interests include pattern recognition, face recognition, image processing and transfer learning. He has published more than 40 papers in referred journals and conferences. He is also on the reviewer board of several top journals such as KBS, PR, TPDS, TSC, TNNLS, TIIS, etc.
Workshop 19

Title 1 :Challenges and Developments for Person re-identification

Summary:
As the important and interesting research field, personre-identification (Re-ID) was presented and made great progress in recent years. Re-ID has been studied as a specific person retrieval problem across non-overlapping cameras. Given a query person-of-interest, the goal of Re-ID is to determine whether this person has appeared in another place at a distinct time captured by a different camera. The query person can be represented by an image or a video sequence. Due to the urgent demand for public safety and the increasing number of surveillance cameras in university campuses, theme parks, streets, etc., person Re-ID is imperative in intelligent video surveillance system designs. Give its research impact and practical importance, person Re-ID is a fast-growing vision community.
Person Re-ID is a challenging task due to the presence of different viewpoints, varying low-image resolutions, illumination changes, unconstrained poses, occlusions, heterogeneous modalities, etc. With the advancement of deep learning, person Re-ID has achieved inspiring performance on the widely used benchmarks. However, there is still a large gap between the research-oriented scenarios and practical applications. 
The 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 person Re-ID. We encourage prospective authors to submit related distinguished research papers on the subject of both: theoretical approaches and practical schemes. Please name the title of the submission email with “workshop 19 Re-ID: paper title”.

Keywords:
Person re-identification, Deep Learning, Artificial Intelligence, Intelligent Video Surveillance System.

Chair:
Prof. Zhigang Liu, Northeast Petroleum University, China
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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 member of IEEE and CCF, 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 20

Title 1 :Big data analysis, modeling, and prediction

Summary:
In recent year, a lot of data was generated from various areas. With the increasingly widespread collection and processing of big data, there is natural interest in using these data assets to improve decision making. One of the best understood ways to use data to improve decision making is via predictive analytics. This Research Topic intends to provide an international forum for researchers to exchange up-to-date outcomes on AI and DL to address the concerns in big data analysis, modeling, and forecasting.

Keywords:
Artificial intelligence,Machine learning,Deep learning,Big data

Chair:
Prof. Zhiwei Ji, Nanjing Agricultural University, China
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Zhiwei Ji joined the School of Artificial Intelligence in Nanjing Agricultural University (NJAU) on Aug 21, 2020 as Full Professor. Prior to this position, he was Assistant Professor at the University of Texas Health Science Center at Houston (UTHealth), USA. Dr. Ji received his Ph.D. from the Department of Computer Sciences in Tongji University (2016), China and was Postdoctoral research fellow at Wake Forest University School of Medicine (2016-2017), and UTHealth (2017-2018). Dr. Ji has been working on Systems Biology, Bioinformatics, Pattern Recognition, Big data analysis and modeling for over ten years. Currently, he is the direct of center for data science and intelligent computing at NJAU. In the past 5 years, Dr. Ji has published over 40 articles in leading peer-reviewed international journals. Google scholar reported that his citation is over 900 with H-index as 18.
 Workshop 21

Title 1 :Single Object Tracking

Summary:
Single Object Tracking (SOT) is a fundamental and hot topic in the field of computer vision and plays an important role in many applications such as video surveillance, virtual reality, navigation and robotics. The main problem is how to locate a given target (either by manual selection or by automatic detector detection, typically a rectangular box) from continuous image sequences and estimate the target state (such as position, shape, velocity, direction and state information such as motion trajectory. In recent years, with the spread of high-performance computers and the growing demand for video analysis, SOT has developed rapidly and is gaining increasing attention and application in related fields.
Theworkshop aims to show and share the latest research results in the field of visual tracking provided by researchers from academia and industry. 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_workshop title”.

Keywords:
Visual Tracking, Deep Learning, Appearance Model

Chair:
Prof. Huibin Tan, National University of Defense Technology, China
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Huibin Tan received a Bachelor’s degree in Computer Science and Technology from NortheasternUniversity of China in 2014. In 2014-2015, shestudied postgraduate at the School of ComputerScience, National University of Defense Technology,and later became a Ph.D. student through a directPh.D. application. From 2016 to now, she is pursuing the Ph.D. degree at the School of Computer Science, the National University of DefenseTechnology, Changsha, China. Her current researchinterest includes face recognition, visual tracking andrepresentation learning.
 Workshop 22

Title 1 :Target Detection and Recognition Technology

Summary:
With the rapid development of artificial intelligence and machine manufacturing, underwater vehicle target detection technology has also been paid more attention. Deep-sea target detection technology has also become one of the research hotspots in the field of Marine Science. At present, artificial intelligence technology is used to identify and locate targets using in the survey of seabed environment and ecological data. It involves the laying and maintenance of infrastructural infrastructure such as underwater optical cable, environmental inspection of fishing grounds, deep-sea fishing, underwater military missions and research on sustainable development of Marine ecosystems. Therefore, the identification of deep-sea organisms and the research of quantitative algorithms have very important application value and development prospect.
Theworkshop 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 ofunderwater target detection and recognition technology. 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:
Target Detection, Quantitative Recognition Technology, Artificial Intelligence, Marine Benthos

Chair:
Prof. Yong Liu, Qingdao University of Science and Technology, China
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Yong Liu received a Ph.D. degree in computer application technology from Ocean University of China. Her research interests include Computer Vision and Natural Language Processing.
She participated in the National Natural Science Foundation, Natural Science Foundation of Shandong Province, Tackling Key Scientific and Technological Problems in Shandong Province. Based on these projects, she published many academic papers.
 Workshop 23

Title 1 :Smart Systems and Applications Empowered by Computing, Communication and Control (3C) Techniques

Summary:
Smart systems integrate sensing, actuation, signal processing, and control in order to make decisions and create a smart environment. Smart system is the next generation of computing and information system, combining artificial intelligence, machine learning, cyber-physical systems, edge/cloud computing,pervasive/ubiquitous computing, big data, and Internet of Things (IoT) technologies to provide real-time networked information and control, which considerably contributes to the development of the future smart society.
Due to the large diversity of IoT devices and technologies, the major challenges in smart systems include how to design and build integrated smart systems, and how to use computing technologies efficiently and effectively. This workshop provides a platform for researchers and scholars to discuss the ongoing progress of smart systems.When submitting the paper, please specify the subject with “paper title_workshop title(smart systems)”.

Keywords:
Smart Systems, Computing, Communication, Control

Chair:
Prof. Heng Li, Central South University, China
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Heng Li received the bachelor’s and Ph.D. degrees from Central South University, Changsha, China, in 2011 and 2017, respectively. From November 2015 to November 2017, he worked as a Research Assistant at the Department of Computer Science, University of Victoria, Victoria, BC, Canada. In November 2017, he joined the School of Computer Science and Engineering, Central South University, as an Assistant Professor. He is currently an Associate Professor with the School of Computer Science and Engineering, Central South University. His current research interests include smart energy systems and smart factories. Dr. Li was a recipient of the Excellent Ph.D. Thesis Award of Hunan Province in 2019 and the Hunan Natural Science Award and China Railway Academy Science and Technology Award in 2020.

Workshop 24

Title 1 :Complex systems modeling and Hybrid Intelligent System

Summary:
Due to the large scale, non-linearity, coupling, spontaneous order and feedback loop, it is challenging to elaborate the working state and changing rules of the complex systems. Thus, it is difficult to take the whole system as the sum of all the individuals based on reductionism. Also, it cannot derive the property of the system from the entire behavior. Furthermore, since most of the problems in practical applications are complicated, fuzzy and high-dimensional, it makes the existing individual intelligent technique cannot be directly applied to address these problems. Thus, the hybrid intelligent systems have been an emerging field and attracted increasing attention. This workshop aims to summarize the modeling methods of complex systems, the development approaches of hybrid intelligent systems as well as the evaluation criteria, via the analysis of various complex systems modeling methods and hybrid intelligent system theories. As the development of a large number of artificial intelligent algorithms, complex systems modeling and hybrid intelligent system have become hot topics. Currently, it usually depends on the personal experience to construct hybrid intelligent systems. Therefore, Theoretical breakthroughs  are urgently neededin these two fields, and the research of hybrid intelligent system has been further developed.

Keywords:
System Modeling, Intelligence System, Hybrid Intelligence, Intelligent Computing, Intelligent Optimization, Data Processing

Chair:
Prof. Lianghong Wu School of Informationand Electrical Engineering, Hunan University of Science andTechnology, China
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Lianghong Wu received the BSc degree in industrialautomation from the Hunan Universityof Science and Technology, Xiangtan, China, in2001, and the MSc and PhD degrees in controlscience and engineering from Hunan University,Changsha, China, in 2007 and 2011, respectively.From 2015 to 2016, he was a visiting scientistwith the Department of Automatic Control andSystems Engineering, University of Sheffield. Heis currently a professor with the School of Informationand Electrical Engineering, Hunan University of Science andTechnology. His research interests include machine vision, swarm intelligence and intelligent optimization, and their applications.
Prof. Ming LuSchool of Information Electrical and Engineering, Hunan University of Science and Technology,China
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Ming Luvice dean of School of Information Electrical and Engineering, Hunan University of Science and Technology, associate professor of Hunan University of Science and Technology, graduated from Central South University in 2015. His main research subject is Control science and Engineering, and the current work involves modeling, optimization and control in industries process. As a senior member of China Automation Society and expert of Metallurgical process control, Lu has high reputation in his research field. His research on Working condition identification of metallurgical process is in the leading position. He has published over 40 academic papers, more than 20 papers were indexed by SCI or EI. Now, as the project leader he is presiding over the National Natural Science Foundation of China. He has attended 10 research projects and obtained more than 10 patents.
Prof. Zuguo ChenSchool of Information Electrical and Engineering, Hunan University of Science and Technology,China
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Zuguo Chen, Senior Lecturer, Master supervisor, graduated from Central South University, Changsha, Hunan Province, China. He studied under Weihua Gui (Academician of Chinese Academy of Engineering). Over the past ten years, he has been working on big data processing, deep learning, pattern recognition, image processing, knowledge engineering and epidemic spreading behaviors analysis. In 2016, he was awarded as an urgently needed talent in the field of industry and information technology by the Talent Exchange Center of Ministry of Industry and Information Technology of China and I am in charge of or participated in the eight projects, such as Key Research Program from the Ministry of Science and Technology of China, Key Program of National Natural Science of China, Program of National Natural Science of China, China Postdoctoral Science Foundation and Natural Science Youth Foundation of Hunan Province. As the technical leader, he participated in a number of school-enterprise cooperation projects. 
 Workshop 25

Title 1 :Fuzzy Image Processing

Summary:
There are many reasons for image blurring, including optical factors, atmospheric factors, artificial factors, technical factors and so on. It is of great significance to deblur images in daily production and life.To obtain a better treatment effect, different methods are often needed to deal with the fuzziness caused by different reasons. How to use the degraded images to calculate the clear images with good visual effect is a very meaningful practical problem. From the aspect of technology, fuzzy image processing methods are mainly divided into three categories: image enhancement, image restoration and super resolution reconstruction.However, most traditional image restoration and enhancement methods still have shortcomings in complex degradation model modeling, natural image prior characterization, distortion suppression and so on. Although the methods based on deep learning which have been proposed a lot recently can avoid the optimization model and the prior manual design, they often need to be trained on large data sets, and the calculation cost is very high. And the end-to-end mode makes it impossible for people to know how it works.
The 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 Fuzzy Image Processing and understand How to use the degraded images to calculate the clear images. We encourage prospective authors to submit related distinguished research papers on the subject of both: Image enhancement, Image restoration and Super resolution reconstruction. Please name the title of the submission email with “paper title_workshop title”.

Keywords:
Fuzzy Image Processing,Image Enhancement, Image Restoration and Super Resolution Reconstruction

Chair:
Prof. Juan Wang, Hubei University of Technology, China
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Juan Wang received her Ph. D. degree in control science and engineering from Tianjin University. She is an associate professor and master supervisor. Her research interests include artificial intelligence algorithms, machine vision and other aspects of the research.
In recent years, she has presided over one project of National Natural Science Foundation of China, completed one project of Young and Middle-aged Talents of Hubei Province, and one project of Key Laboratory Open Fund of Hubei Province. She has participated in several national and provincial scientific research projects. In 2016 and 2018, she won thesecond prize of Hubei Provincial Science and Technology Progress Award. And in 2017, she won thesecond prize of Hubei Provincial Teaching Achievement Award. Up to now, she has published more than 20 papers in journals and conferences at home and abroad, 10 of which are indexed by SCI/EI, 3 authorized invention patents and 3 software Copyrights.
 Workshop 26

Title 1 :Evolutionary Computation for Machine Learning and Deep Learning

Summary:
Evolutionary computation technique has been widely used for addressing various challenging problems due to its powerful global search ability. There are many complex optimization tasks in the fields of machine learning and data mining such as feature selection, neural architecture search, hyper-parameter search, etc. This workshop aims to collect original papers that develop new evolutionary computation techniques to address any kind of machine learning and data mining tasks.

Keywords:
Evolutionary Deep Learning/Evolving Deep Learning, Machine Learning, Data Mining, Neural Network, Real-world Applications of Evolutionary Computation and Machine Learning

Chair:
Prof. Yu Xue, Nanjing University of Information Science & Technology, China
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Yu Xue received the Ph. D. degree from School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China, in 2013. He is a professor at School of Computer and Software, Nanjing University of Information Science and Technology. He was a visiting scholar in the School of Engineering and Computer Science, Victoria University of Wellington, New Zealand (2016.8-2017.8). He was a research scholar in the Department of Computer Science and Engineering, Michigan State University, the United States of America (2017.10-2018.11). His research interests include Evolutionary Computation, Machine Learning, and Data mining.
 Workshop 27

Title 1 :Image Super-Resolution Algorithm using Feature Map Attention Mechanism

Summary:
To improve the issue of low-frequency and high-frequency components from feature maps being treated equally in existing image super-resolution reconstruction methods, many researchers had proposed improved image super-resolution reconstruction methods using attention mechanism with feature map to facilitate reconstruction from original low-resolution images to multi-scale super-resolution images. For example, the model consists of a feature extraction block, an information extraction block, and a reconstruction module.
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 image understanding. 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_workshop title”.

Keywords:
Image Super-Resolution Reconstruction, Feature Map Attention Mechanism, Multiple Information Extraction, Deep Learning Methods, Multi-Scale Low-Resolution Images

Chair:
Prof. Yuantao ChenChangsha University of Science and Technology, China
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Yuantao Chen received the B.S. degree in Computer Science and Technology from Jianghan Petroleum Institute. He received the M.S. degree in Geodetection and Information Technology from Yangtze University. He received the Ph.D. degree in Control Science and Engineering from Nanjing University of Science and Technology in 2014. He is an associate professor at Changsha University of Science and Technology. His research interests include computer vision, deep learning, pattern recognition, image processing, etc.
To be updated.