Title: Based on IoTs Parking Navigation with Continuous Information Potential Field research

Summary: As Internet of Things(IoTs ) are increasingly being deployed in some important applications, it becomes imperative that we consider application requirements in in-network processes. We intend to use a WSN to aid information querying and navigation within a dynamic and real-time environment. We propose a novel method that relies on the heat diffusion equation to finish the navigation process conveniently and easily. From the perspective of theoretical analysis, our proposed work holds the lower constraint condition. We use multiple scales to reach the goal of accurate navigation. We present a multi-scale gradient descent method to satisfy users’ requirements in WSNs. Formula derivations and simulations show that the method is accurately and efficiently able to solve typical sensor network configuration information navigation problems. Simultaneously, the structure of heat diffusion equation allows more flexibility and adaptability in searching algorithm designs.

Keywords: Internet of Things (IoTs); computer networks; wireless sensor networks (WSNs); navigation process

Chair: Assoc. Prof. Wei WeiXi’an University of Technology, China

Bio: Wei Wei (SM’17) received the M.S. and Ph.D. degrees from Xi’an Jiaotong University, Xi’an, China, in 2005 and 2011, respectively. He is currently an Associate Professor with the School of Computer Science and Engineering, Xi’an University of Technology, Xi’an. In 2022  Wei Wei was presented among "TOP 2% Scientists in the World" by Stanford University for his career achievements. He ran many funded research projects as principal investigator and technical members. His current research interests include the area of wireless networks, wireless sensor networks application, image processing, mobile computing, distributed computing, and pervasive computing, Internet of Things, and sensor data clouds. He has published around 400 research papers in international conferences and journals. Dr. Wei is a Senior Member of ACM and IEEE and the China Computer Federation. He is an Editorial Board Member of the Future Generation Computer System, the IEEE Access, Ad Hoc & Sensor Wireless Sensor Network, the Institute of Electronics, Information and Communication Engineers, and KSII Transactions on Internet and Information Systems. Including top journals special issues, ACM ToSN&TOIT, IEEE Trans on ITS&TII&JBHI,etc. He is a TPC member of many conferences and a regular Reviewer of the IEEE Transactions on Parallel and Distributed Systems, the IEEE Transactions on Image Processing, the IEEE Transactions on Mobile Computing, the IEEE Transactions on Wireless Communications, the Journal of Network and Computer Applications, and many other Elsevier journals.

Co-Chair: Prof. FAN XunliNorthwest University, China

FAN Xunli is currently a Professor with the School of Information Science and Technology, Northwest University, Xi’an. Prof. FAN is ACM members, CCF members, members of the Automation Society, and members of the Construction Robotics Committee of the Automation Society. He is RA from the School of Computing at the University of Nottingham in the UK and a visiting scholar in the Department of Computer Science at the University of Loughborough in the UK. He mainly conducts research on the Internet of Things, artificial intelligence, big data processing, and other related fields. He has published more than 50 research papers, and has led and participated in more than 20 national foreign exchange projects, National Natural Science Foundation of China, and national key research and development projects. He won the first prize of scientific progress Award in Jiangsu Province, and the lead author published more than 20 SCI journal papers, including ESI highly cited (continued to be highly cited for more than 56 months+).
Title: Content-based multimedia analysis

Summary: The goal of this special session is to bring forward recent advancements in content-based multimedia analysis. In addition to multimedia and social media search and retrieval, we wish to highlight related and equally important issues that build on content-based analysis, such as multimedia content management,  user interaction and visualization, media analytics, etc. 

Keywords: Social network, Recommendation Systems,Social Big Data Mining,Knowledge Extraction,User Behavior

Chair: Associate Prof. Jinpeng Chen, School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts & Telecommunications 

Bio: Jinpeng Chen, associate professor and Ph.D. supervisor of Beijing University of Posts and Telecommunications. His research interests include social network analysis, recommendation system, data mining methodologies, machine learning algorithm and information retrieval technique. He has published more than 40 SCI/EI index papers. In addition, he participates in a series of key research projects supported by the National Natural Science Foundation and National Key R&D Program of China.
Title: The Future of India’s Urbanization- Role of IoT in Development of Sustainable Solutions for smart Cities in India 

Summary: The term "smart" refers to a city that is more environmentally friendly, habitable and efficient. The rapid development of wireless communication technologies enabling small and low-cost objects to connect to the internet, as well as the integration of various low-cost smart devices such as sensors and actuators, has resulted in an increase in the deployment of Internet of Things (IoT), where physical objects are changing to smart objects in everyday life. In this avenue, India on a whole has been breakthrough technologies coming around deploying IoT based sustainable solutions for smart cities. The research work envisaged is to address key challenges of a smart city, which include developing smart water, energy and lighting system, integrating EV charging and monitoring load distribution of a transformer and develop communication between the smart water meter and the smart energy meter, smart energy meter and regulatory board via the smart pole, distribution transformer and electricity board via the smart pole and lastly between the master smart pole and slave smart poles for efficient street light management system. This research work also aims to provide additional features such as environment monitoring parameters like temperature, humidity, barometer pressure and specific gas like carbon footprint present in the air, EV charging and SOS function for emergency situations.

Keywords: Internet of Things (IoTs); smart systems; LoRA; integrated master pole

Chair: Associate Prof. MOHANA LAKSHMI JDepartment of Computer Science and  Engineering, Malnad College of Engineering

Bio: Dr. MOHANA LAKSHMI J is currently with the Department of Computer Science and  Engineering, Malnad College of Engineering, Hassan, Karnataka, as Associate Professor. She has over 9 years of academic experience and 1 year of industrial experience. She obtained her B.E. degree from PES College of Engineering, Mandya and M.Tech. Degree from Malnad College of Engineering, Hassan in the year 2010 and 2012 respectively. She completed her doctoral degree in the year 2019 in the field of power electronics and drives. She is a 1st Rank holder and Gold medalist-in her M.Tech programme. Her area of research focus is on power electronics applied to drives control, development of sustainable solutions using IoT for smart cities and development of VR based remote labs. She has received the best project guide award for the project titled “Fabrication of potable solar water purifier” in the year 2018. She has also received best paper presenter award for the research article “Performance analysis of 5-level inverter for Hybrid Distribution Generation System” presented in IEEE conference in 2018. She has received a patent grant for the work “Method and System for performing Experiments in a Remote Laboratory”, from the Indian patent office in April 2021. She has also received “Spot Award” for her contribution in “Variable Frequency Drive” Project at L & T Technology Services. She has delivered keynote lectures in international conference sponsored by IEEE at Thailand and many student development programmes. He patented product has been awarded as the best product award in IEEE Humanitarian Global Conference. She has published several research articles in International Journals/Conferences. She holds membership in various professional societies. She is a senior member of IEEE, and ExCom member of Power and Energy Society, life member ISTE, member - Institute of Engineers, India. She is a reviewer member of various international journals and conferences, including IEEE transactions & Elsevier. She has over 25 research articles, 2 patents and a startup registered in her name.
Title: Cooperative control and optimization of complex networked systems in uncertain environments

Summary: Due to the remarkable advance of information technologies, a large number of engineering and social systems are constructed or emerge by multiple interconnected components with the capability of communication and decision-making. As a result, cooperative control, optimization and decision-making/game of networked systems have attracted increasing attentions in recent years. In many practical networked systems, such as power system, transportation system and communication system, there exist a lot of technical challenges in cooperative control, optimization and game with various constraints. Therein, uncertainties may cause the significant difficulties and challenges in modeling, control, and coordination of networked systems. Although great progress has been made on these topics, many key problems are still unexplored and unsolved in control design for complicated engineering systems and effective decision-making in large-scale social networks. Therefore, this workshop aims to address some core problems of control, optimization and decision-making in networked systems with uncertainties or constraints by coordinating multiple components/agents in a cooperative and efficient manner. Key problems to be solved may include: 1) Distributed optimization/control approaches and decision-making mechanisms to cope with uncertainties. 2) Cooperative control strategies in constrained communication conditions and transmission noises. 3) Performance assessment of uncertainties on system resilience/stability. 4) Cooperative control with coupled constraints or uncertain parameters. 5) Modeling and control design for complicated high-order physical systems with uncertainties. The purposes of this workshop are two-fold. First is to promote academic exchange/discussions and thoughts sharing among researchers in cooperative control and optimization. The second is to provide some recent advances in this field from a variety of perspectives, including distinctive methods, novel modeling approaches, and new applications.

Keywords: Cooperative control, distributed optimization, decision-making/game, networked systems, constraints, uncertainties

Chair: Prof. Chao ZhaiChina University of Geosciences, China

Bio: Chao Zhai (SM’22) is Full Professor at Department of Automatic Control, School of Automation, China University of Geosciences, Wuhan, China. He received the bachelor’s degree in automation engineering from Henan University, China, the master’s degree in control theory and control engineering from Huazhong University of Science and Technology, China, and the PhD in complex systems and control from Institute of Systems Science, Chinese Academy of Sciences, Beijing, China, in 2013. From July 2013 to August 2015, he was a Post-Doctoral Fellow at Department of Engineering Mathematics, University of Bristol, UK. From October 2015 to January 2016, he was a Research Associate at Department of Mechanical Engineering, University of Hong Kong. From February 2016 to October 2019, he was a Research Fellow at Singapore-ETH Centre, ETH Zurich and Nanyang Technological University, Singapore. Since November 2019, he has been affiliated with China University of Geosciences, Wuhan, China. He was TPC members of several international conferences such as IEEE Global Communications Conference: Selected Areas in Communications (2021,2022) and IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (2022), and he received some awards and honors for his academic achievements, including CUG Fellowship, Excellent Master Thesis of Hubei Province, Best Conference Paper Finalist, ICARCV (2010), and so on. His research interests include coverage control of multi-agent system, distributed control, optimal control, resilient systems, power system stability, and social motor coordination. He is a Senior Member of IEEE, and he has over 70 peer-reviewed publications, including 2 monographs.
Title:Urban Profiling with Artificial Intelligence and Big Data

Summary:Urban Profiling is a procedure for modeling to describe specific aspects of the city with multi-source heterogeneous data fusion technology. Urban Profiling helps researchers to better complete the downstream tasks, such as traffic flow prediction, human mobility pattern extraction, e-commerce warehouse inventory, urban functional region division. Thanks to the development of communication technology and sensing technology leading to a huge amount of multi-source heterogeneous data generated on the user side, for example, vehicle trajectory, social platform data, Geographic Information System (GIS) data. Meanwhile, due to the excellent performance of Artificial Intelligence (AI) in processing problems with big data, in recent years, AI technologies and big data analysis, especially Deep Learning, appeared in the field of urban computing. They bring vitality to Urban Profiling while leading to new possibilities for complex applications, intelligent services, and refined governance. The workshop aims to present the state-of-the-art research in the area of Urban Profiling and applications and to provide a forum for experts to disseminate their recent advanced works, views, and ideas on future directions in this field. 

Keywords:urban profiling, artificial intelligence, smart city, big data 

Chair:Prof. Xiangjie Kong, Zhejiang University of Technology (ZJUT), China

Bio: Dr. Xiangjie Kong is currently a Full Professor with the College of Computer Science & Technology, Zhejiang University of Technology (ZJUT), Hangzhou, China. Previously, he was an Associate Professor with the School of Software, Dalian University of Technology (DUT), Dalian, China, where he was the Head of the Department of Cyber Engineering. He is the Founding Director of City Science of Social Computing Lab (The CSSC Lab) ( He is/was on the Editorial Boards of 6 International journals. He has served as the General Co-Chair, Workshop Chair, Publicity Chair or Program Committee Member of over 30 conferences. Dr. Kong has authored/co-authored over 180 scientific papers in international journals and conferences including IEEE TKDE, ACM TKDD, IEEE TNSE, IEEE TII, IEEE TITS, IEEE NETW, IEEE COMMUN MAG, IEEE TVT, IEEE IOTJ, IEEE TSMC, IEEE TETC, IEEE TASE, IEEE TCSS, WWWJ, etc.. 5 of his papers is selected as ESI- Hot Paper (Top 1‰), and 18 papers are ESI-Highly Cited Papers (Top 1%).  His research has been reported by Nature Index and other medias. He has been invited as Reviewers for numerous prestigious journals including IEEE TKDE, IEEE TMC, IEEE TNNLS, IEEE TNSE, IEEE TII, IEEE IOTJ, IEEE COMMUN MAG, IEEE NETW, IEEE TITS, TCJ, JASIST, etc.. Dr. Kong has authored/co-authored three books (in Chinese). He has contributed to the development of 14 copyrighted software systems and 20 filed patents. He has an h-index of 43 and i10-index of 104, and a total of more than 6400 citations to his work according to Google Scholar. He is named in the2019 and 2020 world’s top 2% of Scientists List published by Stanford University. Dr. Kong received IEEE Vehicular Technology Society 2020 Best Land Transportation Paper Award, and The Natural Science Fund of Zhejiang Province for Distinguished Young Scholars. He has been invited as Keynote Speaker at 2 international conferences, and delivered a number of Invited Talks at international conferences and many universities worldwide.  His research interests include big data, network science, and computational social science. He is a Distinguished Member of CCF, a Senior Member of IEEE, a Full Member of Sigma Xi, and a Member of ACM.
Title: Smart control technology in critical infrastructure systems 

Summary: The development of smart city depends heavily on critical infrastructure systems. These backbone systems provide energy supply, transportation, communication and other services that are essential for maintaining the metabolism of urbanized societies. The underlying control of devices and systems are of high importance to maintain the stability and resilience of these infrastructure systems. With access to big and multi-source of data and remarkable advance of smart technology, traditional control technology meets new chance to make systems stronger and smarter. Thus, this workshop aims to design and implement new control approaches that combine classical control theories and smart algorithms for making smart city more resilient. The scope of this workshop will include but is not limited to: 1) intelligent control, computation and optimization; 2) signal processing and information fusion; 3) adaptive control and learning control; 4) fault detection and predictive maintenance, 5) data-driven control and optimization; 6) smart power grid and transportation, etc. 

Keywords: infrastructure system, smart city, control, big data, smart algorithm, smart control, stability, resilience 

Chair: Prof. Hehong Zhang, Fuzhou University, China 

Bio: Hehong Zhang is Full Professor at college of computer science and big data, Fuzhou University, China. He received the PhD in control science and engineering from Nanyang Technological University, Singapore, in 2020. He has ever worked as visiting scholar in RMIT, Australia and ETH, Switzerland. His research interests include active disturbance rejection control, tracking differentiator, signal processing, maglev train, altitude test facility control, multi-agent system, resilient systems, power system. He has over 30 peer-review publications, including 1 monograph. In addition, he hosted a series of key research projects supported by the National/Provincial Natural Science Foundation and National Defense Projects.
Title: Ubiquitous Sensing and Computing for Intelligent Transportation

Summary: The prevalence of Internet of Things (IoT) enables us to gather unprecedented data by exploiting a variety of mobile devices (e.g., wearables, smart-phones and on-board GPS devices) and Automatic Fare Collection (AFC) devices widely deployed in urban transit systems (e.g., subways, buses, and taxis). Such ubiquitous sensing approaches and emerging big data techniques benefit the development and research of Intelligent Transportation Systems (ITS) for the smart city. With the wide available sensing units and powerful data processing & analysis algorithms, tools, and systems, novel data-driven sensing and computing based ITS applications and services become possible and feasible. The aim of this workshop is to get a view of the latest work and advances in the fields of IoT, big data, smart city, and ITS, dealing with new and novel developments in theory, analysis, simulation and modeling, experimentation, demonstration, case studies, field operational tests and deployments. This workshop particularly invites and encourages prospective authors to share their work, findings, perspectives and developments as related to implementation and deployment of advanced and innovative ITS solutions.

Keywords: Internet of Things (IoTs); Intelligent transportation systems (ITS); Ubiquitous sensing; Big data; Smart city.  

Chair: Assoc.Prof. Zhidan Liu, Shenzhen University, China 

Bio: Zhidan Liu is currently an Associate Professor (tenured) in College of Computer Science and Software Engineering, Shenzhen University. He received the B.E. degree in Computer Science and Technology from Northeastern University in June 2009, and the Ph.D. degree in Computer Science and Technology from Zhejiang University in September 2014. Before joining Shenzhen University, he was a postdoctoral research fellow in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He is now heading the Big Data and Urban Computing (BDUC) research group in Shenzhen University. His research interests include Internet of Things, urban computing, mobile computing, and big data analytics. His researches have been published in IEEE/ACM Transactions on Networking (ToN),IEEE Transactions on Mobile Computing (TMC)、IEEE Transactions on Intelligent Transportation System (TITS)、 IEEE Network Magazine、IEEE Internet of Things Journal、ACM MobiSys、IEEE ICDE、ACM/IEEE IPSN、ACM MobiHoc and other top journals/conferences. He was TPC members of several international conferences such as ACM KDD、ACM WSDM、IEEE ICDCS. He received the "Best Paper Award" of IEEE ICPADS 2020. In addition, he ran many funded research projects as principal investigator and technical members. He is a member of IEEE, ACM, and CCF.
Title: Internet of Things application based on digital twin technology  

Summary: As Internet of Things(IoTs ) are increasingly being deployed in some important applications, it becomes imperative that we consider digital twin technology in application requirements. We intend to use a VSS to aid information querying and predicting within a dynamic and real-time environment. We propose a novel method that relies on the digital twin technology to finish the simulation process conveniently and easily. From the perspective of theoretical analysis, our proposed work holds the lower constraint condition. We use multiple scales to reach the goal of accurate application. We present a multi-scale simulation method to satisfy users’ requirements in VSS. Visual digital twinning simulations show that the method is accurately and efficiently able to solve typical problems of experience and prediction on IoTs applications. Simultaneously, the structure of portability allows more flexibility and adaptability in extending IoTs application fields.
Keywords: Internet of Things (IoTs); digital twin technology; Visual System Simulator(VSS)

Chair: Assoc.Prof. Tian Cuihua, Xiamen University of Technology, China 

Bio: Tian Cuihua (ROSS) received the M.S. degrees from Shenyang Ligong University, Shenyang, China, in 2002, and Ph.D. degrees from Northeastern University, Shenyang, China, in 2007. She is currently an Associate Professor with the School of Computer and Information Engineering, Xiamen University of Technology, Xiamen. She ran many funded research projects as principal investigator and technical members. Her current research interests include the area of wireless networks, wireless sensor networks application, Internet of Things application,  cloud computing,algorithm,and development of games. She has published around 30 research papers in international conferences and journals. And She has published three monograph. She presided over the completion of 3 provincial and ministerial projects, a provincial quality course, and a provincial education reform project.
Title: Smart Transportation and Intelligent Driving  

Summary: With the rapid development of artificial intelligence and the increasing popularity of various sensors, intelligent driving is experiencing rapid growth and becoming more widely adopted. Its emergence primarily addresses problems inherent in traditional transportation methods, such as traffic congestion and accidents, while providing people with more convenient and safe travel options. Intelligent driving is becoming the trend and direction for future transportation and travel modes. Currently, most autonomous driving technology relies on a "smart road" system that implements the vehicle-road collaborative information interaction with a three-tier structure consisting of the vehicle, road, and cloud. However, this system heavily relies on high-precision maps and network communication technology. It cannot fully take into account real-time information calculation and transmission. The purpose of this seminar is to explore the latest developments and progress in the field of intelligent driving and transportation and discuss future advancements in independent decision-making and algorithm applications in the field of automatic driving.
Keywords: Intelligent driving; Smart transportation; Intelligent Transportation System.

Chair: Prof. Lyuchao Liao, Fujian University of Technology, China 

Bio: Lyuchao Liao is a postdoctoral fellow of Tsinghua University, a senior member of IEEE, and a senior visiting scholar at the University of Essex, UK. He currently serves as the vice dean of the School of Transportation in Fujian University of Technology. Prof. Liao holds positions such as the director of the Intelligent Driverless Technology Fujian University Engineering Research Center. He actively participates in social services, is a member of the Science Popularization Committee of the Chinese Artificial Intelligence Society, and has been hired by the Provincial Department of Education as a member of the Artificial Intelligence Teaching Steering Committee of primary and secondary schools in Fujian Province. His main research interests include artificial intelligence technology, intelligent driving technology, etc. He has presided over more than 20 projects, including National Natural Science Foundation projects, major science and technology sub-projects in Fujian Province, key science and technology projects in Fujian Province, and horizontal cooperation projects in traffic information engineering. He has published more than 50 SCI/EI papers. He has obtained over 100 invention patents from the United States and national patents for urban spatiotemporal big data processing. He has won one Geneva International Invention Award, participated in preparing many local standards, and won two Fujian Science and Technology Progress Awards. He was selected into the Outstanding Young Scientific Research Talents Program of Fujian Universities in 2014. He was elected into the China Postdoctoral Science and Technology Service Group in 2016. In 2021, he was hired by the Fuzhou Information Society as a "special expert in the field of artificial intelligence".
Title: Security and Layer authentication system in Internet of Things  

Summary: The Internet of Things (IoT) is a network of interconnected devices that can exchange data without human intervention. However, the security of IoT systems is a major concern due to the sensitive information and control mechanisms they may contain. A layered authentication system is one of the ways to secure IoT devices and data. The Internet of Things (IoT) architecture can be divided into different layers that are responsible for different aspects of the IoT system. The following are the commonly accepted layers of the IoT architecture: Device layer, Connectivity layer, Middleware layer, Application layer, Business layer. This workshop's objective is to provide attendees with an overview of the most recent research and developments in the field of IoT security, including fresh and innovative developments in theory, analysis, simulation and modelling, experimentation, demonstration, case studies, field operational tests, and deployments. Since it relates to the implementation and deployment of cutting-edge and creative security solutions, this workshop especially invites and encourages potential writers to contribute their work, discoveries, viewpoints, and innovations.
Keywords: Internet of Things (IoTs); Data Security; layered authentication system; wireless sensor networks (WSNs)

Chair: Assis.Prof. Saurav Verma, MPSTME, NMIMS University, India 

Bio: Saurav Verma   received the B.Tech. degree in electronics & communication engineering from Punjab Technical University, Punjab, India, in 2011 and the M.Tech in electronics and telecommunication engineering from NMIMS university, Mumbai, India in 2013. Currently, pursuing Ph.D. degrees from BIET, Davangere, Visvesvaraya Technological University, Belgavi, Karnatak, India and working as Assistant Professor at the Department of Information Technology, NMIMS University, Mumbai, India. His research interests include Internet of Things (IoT), Data Security, Artificial Intelligence, Machine Learning. Accomplished career as professor with 9 years of experience in teaching, mentoring, and guiding at UG, PG levels. He has more than 18 research articles presented and published at International Conferences and Scopus Journals.
Title: Deep Learning-based IoT Big Data and Streaming

Summary: The intersection of Internet of Things (IoT) and Big Data has created vast opportunities for collecting and analyzing data from various sources. IoT devices generate a large volume of data that is often unstructured, noisy, and continuously streamed, which presents significant challenges for traditional data processing techniques. Deep Learning, a subset of machine learning, has emerged as a powerful tool for extracting insights from big data by automatically learning representations of the data.
This workshop aims to bring together researchers and practitioners from academia and industry to discuss recent advances and challenges in using deep learning for IoT big data and streaming. We invite original research contributions that demonstrate innovative techniques, models, and applications related to deep learning for IoT big data and streaming. Topics of interest include, but are not limited to: - Deep learning algorithms for IoT big data and streaming - Feature extraction and selection in IoT big data and streaming - Anomaly detection and fault diagnosis using deep learning in IoT - Deep learning for predictive maintenance in IoT systems - Real-time analytics for IoT big data and streaming - Deep learning architectures for streaming data - Federated and distributed deep learning for IoT big data and streaming - Edge and fog computing for deep learning-based IoT applications - Privacy, security, and ethical concerns in deep learning-based IoT system - Applications of deep learning in IoT, such as smart cities, healthcare, transportation, and agriculture.
Keywords: Internet of Things (IoTs); IoT data processing; streaming analysis; deep learning

Chair: Assis.Prof. Chenhong Cao, Shanghai University, China 

Bio: Chenhong Cao received the BS and MS degrees in computer science from Northeastern University, China, in 2011 and 2013, respectively, and the Ph.D. degree from Zhejiang University, in 2018. She is currently an assistant professor at Shanghai University. Her research interests include network measurement, the Internet of Things, and wireless and mobile computing. She is a member of the IEEE. In 2020, she was selected for the “Shanghai Sailing Talent Project” and has led a project from the national science foundation of China. She has also participated as a core research member in many national and provincial-level scientific research projects related to the research of the internet of things and wireless sensor networks.
Title: Intelligent Image Processing Technology for Degraded Image in Bad Environments

Summary: Computer vision has wide applications in outdoor traffic navigation, security monitoring, object detection, underwater detection, object recognition, and other fields. A clear image is crucial for computer vision to obtain accurate image information. However, under various bad imaging conditions (such as fog, haze, sand and dust, rain and snow, and weak light and low light environments such as the ocean), images collected by outdoor or underwater computer vision systems often exhibit severe color distortion due to the influence of light and various media, and the scene is blurry and poorly defined, which seriously affects its application and restricts research in related fields. Therefore, people attach great importance to enhance and restore degraded images in various harsh environments through post processing algorithms. In recent years, with the widespread application of deep learning techniques such as deep convolutional neural networks and generative adversarial neural networks in image classification, image recognition, and other fields, the application research of deep learning in image defogging, image rain removal, underwater image clarity, and weak light and low light image enhancement has also received high attention, becoming a research hotspot in this field in recent years. In order to better promote the development of image clarity processing theory, technology, and applications based on deep learning for harsh weather and environments, and to timely share the latest research progress of Chinese scholars in related fields, we plan to organize a special forum on "Intelligent Processing Technology for Severe Environment Images", inviting outstanding scholars in this field to share new theories, technologies, methods, and related typical applications in this field, Build a platform for scholars and graduate students in this field to interact and exchange ideas, explore academic achievements, and collide with academic ideas, promoting exchanges and cooperation among scholars in the field.
Keywords: Image enhancement; Computer vision; Deep learning

Chair: Prof. Shi Zhenghao, Xi'an University of Technology, China 

Bio: Shi Zhenghao, Ph.D., Professor, Doctoral Supervisor, Member of the Academic Committee of Xi'an University of Technology, Distinguished Member of CCF, "500 Talents" in Taizhou City, Zhejiang Province, Chairman of the "Computer Vision Technology Professional Committee" of the Shaanxi Computer Society, Vice Chairman of the "Biomedical Intelligent Computing Professional Committee" of the Shaanxi Computer Society, and Leader of the Research Team for "Intelligent Image Processing and Applications" of Xi'an University of Technology, His main research interests are machine vision, medical image processing and machine learning. He has published and employed 60 academic papers as the lead author or correspondence author. He has won two second prizes of Shaanxi Provincial Science and Technology Progress Award (ranking first), one second prize of Xi'an Science and Technology Progress Award (ranking first), two second prizes of Shaanxi Western Universities Science and Technology Award (ranking first), and won the 2022 "Wiley Wiley High Contribution Author of Open Science in China" award.

Co-Chair:  Prof. Feng Zhao,  Shandong Technology and Business University, China
Bio: Feng Zhao, Ph.D., Professor, Master Supervisor, Deputy dean of the School of Computer Science and Technology at Shandong Technology and Business University, Director of the Digital Intelligent Technology Collaborative Innovation Center for Yellow River Culture and Ecological Protection in shandong universities, Member of the “Granular Computing and Knowledge Discovery Professional Committee” of CAAI (Chinese Association for Artificial Intelligence ), Member of the “knowledge engineering and Distributed Intelligence Professional Committee” of CAAI, Council member of Shandong computer Society amd council member of Shandong Artificial Intelligence Society. His main research interests are Artificial Intelligence,machine learning,medical image processing and financial big data analysis. He has published 30 academic papers in some famous academic journals such as Expert Systems with Applications, IEEE Transactions on Biomedical Engineering and so on. He has won one second prize and one third prize of Shandong Universities Outstanding Scientific Research Award.
Title: Concept Interestingness Cognitive Learning for Social Network Analysis

Summary: The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision, and noisy data from social networks. Thanks to the emerging soft computing techniques, unlike conventional hard computing, which are widely used for coping with the tolerance of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) which is the process of investigating social structures and relevant properties through the use of network and graph theories. In this talk, a novel concept-cognitive learning paradigm for social network analysis will be introduced. Specifically, the representation model of Social Networks using Formal Concept Analysis (FCA) is introduced first. Then, some of our latest research works on topological structures mining and analysis in Social Networks based on concept interestingness are presented. Finally, the relevant FCA-based SNA software packages are summarized.
Keywords: Social Networks, Formal Concept Analysis, Concept Interestingness

Chair: Assoc.Prof. Fei Hao, Shaanxi Normal University, China 

Bio: Dr. Fei Hao received the Ph.D. degree in Computer Science and Engineering from Soonchunhyang University, South Korea, in 2016. Since 2016, he has been with Shaanxi Normal University, Xi'an, China, where he is an Associate Professor. From 2020 to 2022, he was a Marie Sklodowska-Curie Fellow with the University of Exeter, Exeter, United Kingdom. His research interests include social computing, ubiquitous computing, big data analytics, knowledge graph and edge intelligence. He is also a China Regional Director of International Association for Convergence Science and Technology (IACST), an executive director of Shanxi Association of Experts and Scholars (SAES) Information Branch, and an executive director of Shanxi Block-chain Research Association. Dr. Hao holds a world-class research track record of publication in the top international journals and the prestigious conferences. He has published more than 150 papers in the leading international journals and conference proceedings, such as IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Services Computing, IEEE Communications Magazine, IEEE Internet Computing, ACM Transactions on Multimedia Computing, Communications and Applications as well as ACM SIGIR and GlobeCom. In addition, he was the recipient of 6 Best paper awards from CSA 2020, CUTE 2016, UCAWSN 2015, MUE2015, IEEE GreenCom 2013 and KISM 2012 conferences, respectively. He was also the recipient of the Outstanding Service Award at SMMA 2020, FutureTech 2019, DSS2018, and SmartData 2017, the IEEE Outstanding Leadership Award at IEEE CPSCom 2013 and the 2015 Chinese Government Award for Outstanding Self-Financed Students Abroad. Since 2017, he has joined JIPS (Journal of Information Processing Systems) editorial board, where he is currently an associate editor. He is currently an editor of ICT Express journal. And he is an initiator and general/program chair of IEEE DSCI and SMMA. He is also a member of ACM, CCF and KIPS.  
Title: Blockchain-enabled Internet of Things

Summary: The Internet of Things (IoT) devices deployed in various application scenarios will generate massive data with immeasurable value every day. These data often contain the user’s personal privacy information, so there is an imperative need to guarantee the reliability and security of IoT data sharing. The traditional centralized Cloud-based data sharing schemes have to rely on a single trusted third party. Therefore, the schemes suffer from single-point failure and lack of privacy protection and access control for the data. Blockchain is an emerging technique to provide an approach for managing data in a decentralized manner. Especially, the Blockchain-based smart contract technique enables the programmability for data users to access the data. All the interactions are authenticated and recorded by the other participants of the Blockchain network, which is tamper resistant. Therefore, this workshop aims to address key technologies related to Blockchain-enabled Internet of Things. Research issues include, but not limited to: 1) the secure methods to store the data by using Blockchain; 2) the access control mechanisms based on smart contract; 3) data sharing strategies without privacy leaking, etc. Keywords: IoT; Blockchain; data sharing; security; privacy
Keywords: IoT; Blockchain; data sharing; security; privacy

Chair:Professor Yingwen Chen, College of Computer, National University of Defense Technology, China 

Bio: Yingwen Chen received his Ph.D. degree from the National University of Defense Technology(NUDT). He is a professor with the department of Computer Science, College of Computer, National University of Defense Technology China. He is a senior member China Computer Federation. His research interests include Blockchain and algorithms for the Internet of Things. He was a visiting scholar at the George Washington University and a visiting scholar at the University of Delaware in the U.S.A.
Title: Application of swarm intelligence algorithms in IoT systems

Summary: IoT-based systems are complex dynamic aggregations of entities (smart objects) that often lack decentralized control. Swarm intelligence (SI) system is a decentralized, self-organizing algorithm used to solve complex problems with dynamic characteristics, incomplete information and limited computing power. Thus, SI provides a source of inspiration for IoT-based systems. At present, swarm intelligence algorithm is widely used in the Internet of Things and has obvious potential uses. There are many existing swarm intelligence algorithms applied to IoT, such as, AC, ACO, PSO, ABC, BFOA, and BA. With the development of technology in the two major fields, the combination trend of swarm intelligence and IoT-based systems will be clearer, and the selection of appropriate SI algorithms for different IoT-based systems will also be a key point of future work.
Keywords: Swarm intelligence, SI-based algorithms, Internet of Things, Application of SI-based algorithms to IoT

Chair:Professor Gai-Ge Wang,Ocean University of China, China

Bio: Gai-Ge Wang (Member, IEEE) is a professor at Ocean University of China, China. His entire publications have been cited over 10000 times (Google Scholar). The latest Google h-index and i10-index are 58 and 109, respectively. Fifteen and sixty-six papers are selected as Highly Cited Paper by Web of Science, and Scopus, respectively. He was selected as one of “2021 Highly Cited Researchers” by Clarivate. He was selected as one of “2021 and 2020 Highly Cited Chinese Researchers” in computer science and technology by Elsevier. He was selected as one of “MDPI 2021 Most Influential Author Award” in Computer Science and Mathematics by MDPI. He was selected as World’s Top 2% Scientists 2020. He is senior member of SAISE, SCIEI, a member of IEEE, IEEE CIS, and ISMOST. He served as Editors-in-Chief of International Journal of Artificial Intelligence and Soft Computing, Early Career Advisory Board Member of IEEE/CAA Journal of Automatica Sinica (SCI), Editorial Advisory Board Member of Communications in Computational and Applied Mathematics (CCAM), Associate Editor of IJCISIM, an Editorial Board Member of Journal of Computational Design and Engineering, Mathematics, IJBIC, Karbala Int J of Modern Science, and Journal of AIS. He served as Guest Editor for many journals including Mathematics, IJBIC, FGCS, Memetic Computing and Operational Research. His research interests are swarm intelligence, evolutionary computation, and big data optimization.
Title: Application of Image Processing and Multimodal Fusion Algorithms in Medical Diagnosis and Treatment

Summary: As an important part of medical informatization, auxiliary medical treatment has broad prospects for future development. “Artificial intelligence + auxiliary diagnosis and treatment” enables computers to learn and expand the medical knowledge of expert doctors, simulate their thinking and diagnostic experience, and obtain reliable diagnosis and treatment plans. At present, research institutions are mostly committed to the research of medical big data computing at the basic layer and the specific solutions at the product application layer, and there is still a big gap in the development of underlying intelligent algorithms. Therefore, studying image processing and multimodal fusion algorithms has enormous application value and practical significance for medical diagnosis and treatment.
Keywords: auxiliary medical treatment; artificial intelligence; image processing; multimodal Fusion

Chair:Assist. Prof. Aite Zhao, Qingdao University, China

Bio: Dr. Aite Zhao is currently an assistant professor of College of Computer Science and Technology in Qingdao University, member of CCF and ACM. She received her Ph.D. degree in the College of Information Science and Engineering in Ocean University of China. She is a visiting Ph.D. researcher in the School of Informatics, University of Leicester, Leicester, U.K. Her research interests include computer vision, pattern recognition, machine learning, data analysis and robotics. Dr. Zhao has presided many projects, including National Natural Science Foundation, Shandong Provincial Natural Science Foundation, and China Postdoctoral Science Foundation. In the past five years, Dr. Zhao has published more than 20 SCI papers, including IEEE TMM, IEEE TCYB, Knowledge based systems, etc. She collaborated with the University of Cambridge to publish an article in the main issue of the international top journal Nature (IF=42.779) in 2021. She has been invited as Reviewers for numerous journals including IEEE T NEUR SYS REH, IEEE T HUM-MACH SYST, Neurocomputing, Applied Intelligence, etc.
Title: Key technologies of resource optimization allocation based on vehicle-road-cloud
collaboration in Internet of Vehicles Field research

Summary: The Internet of Vehicles(IoV) is the key technology to solve the problems of road safety, traffic efficiency, energy conservation and environmental protection in the future. It is the infrastructure to support the development of the automatic driving industry, and has become a strategic commanding point for major countries in the world. Since 2019, China has successively set up four national-level Internet of Vehicles development pilot zones to explore the commercial scenarios of Internet of Vehicles based on vehicle-road cloud collaboration. Reducing task processing delay and improving system performance by optimizing resource allocation is a major technical requirement for the development of the Internet of Vehicles industry. The scenario of the Internet of Vehicles is complex. It is necessary to consider not only the vehicle operation situation, but also the road status, which brings difficulties and challenges to resource allocation and task scheduling. This workshop aims at the world's leading edge of technology in the Internet of Vehicles industry, and carries out theoretical and methodological research in combination with the major technical requirements for the development of the Internet of Vehicles industry in China. This workshop takes resource optimization allocation problem of the Internet of Vehicles as the research object, and plans to design the expression method of the characteristics of the Internet of Vehicles resources. It plans to propose the task offloading decision scheme, the edge cache scheme, and the edge server deployment scheme based on the collaboration of the vehicle-road cloud under the Internet of Vehicles environment. It is proposed to establish the theoretical framework and technical system of resource optimization allocation based on vehicle-road cloud collaboration under the Internet of Vehicles environment. The expected results of this workshop can provide technical support for solving the bottleneck problem of the Internet of Vehicles and lay the foundation for the large-scale application of the Internet of Vehicles in the future.
Keywords: Internet of Vehicles , vehicle-road-cloud collaboration, cloud-edge-terminal collaborative computing, resource optimization allocation,task offloading decision

Chair: Prof. ZHU Sifeng, Tianjin Chengjian University, China

Bio: ZHU Sifeng received his Ph.D. degrees from Xidian University, Xi’an, China, in 2012. In 2015, he completed postdoctoral research in the State Key Laboratory of Mobile Communications of Southeast University, and in 2017, he completed a study visit at the University of California, San Diego(UCSD). He is currently a Professor with the School of Computer Science and Engineering, Tianjin Chengjian University, Tianjin,China. His current research interests include the area of Internet of Vehicles,Internet of Things, wireless sensor networks application, artificial intelligence algorithm, mobile computing, distributed computing, and other related aspects. He has published around 50+ research papers in  SCI/EI journals. He has led and completed multiple National Natural Science Foundation projects and provincial-level funding projects. In 2017, he was awarded the Henan Province Science and Technology Progress Award. He currently serves as a reviewer for multiple international journals such as "Wireless Networks," "IEEE Trans. on Networking," and "IEEE Wireless communication magazine.
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