| Code | Theme |
| SS 01 | “Smart Manufacturing” - Organized by Mohammadhossein Ghahramani |
| Adaptation and innovation are vitally important to better peacebuilding and success in the modern industrial environment in these changing times. New enabling technologies such as Internet of Things (IoT), Big Data, and Machine Learning are permeating different aspects of the manufacturing industry and can endow associated processes with intelligence. The rapid development and implementation of the mentioned technologies have allowed for various possibilities in technological advancements for different aspects of manufacturing. This special session aims to present the latest advances and developments of new methods, techniques, systems, and tools dedicated to the application of enabling technologies for AI-based manufacturing. The goal is to bring together researchers and practitioners to present efficient scientific and engineering solutions and provide visions for future research and development. | |
| SS 02 | “Photonics for Next Generation Communication and Sensor Networks” - Organized by Ben Wu |
| The goal of this session is to study the photonic devices and system level innovations and their applications in next generation communication and sensor networks. Communication and sensor are key components for cyber physical system. High speed communication ensures the transmission of data between the sensor nodes and the cloud. Smart sensor network ensures accurate information to be collected for the cyber physical system. | |
| SS 03 |
“Model- and Data-Driven Manufacturing and Service Systems” – Organized by Chao-Bo Yan, Xiaolei Xie, Yang Li, Zhi Pei, and Zhiyang Jia |
| The complexity of manufacturing and service systems is embedded through inter-connected components and units and resource-coupled process. Although substantial efforts have been devoted to research and practice of modelling, analysis, design, control, and optimization in manufacturing and service systems, at the advent of Industry 4.0 era, the manufacturing and service systems are more and more complex and operating, managing and maintaining them are more and more challenging. This session focuses on the modeling, analysis, optimization, and control of manufacturing and service systems. The advancement of new information technologies, such as internet of things, big data, cloud and edge computing, and artificial intelligence, which enable both the model-based and data-based methods more powerful, has generated numerous opportunities to tackle the intractable issues. | |
| SS 04 | “Machine Learning in Computer Vision, Vision and Image Processing” -Organized by Ming Fang and Lijin Deng |
| Deep learning has transformed computer vision and image processing in the past few years. As fueled by powerful computational resources and massive amounts of data, deep networks achieve compelling, sometimes even superhuman, performance on a wide range of visual benchmarks. In this session, we bring together researchers from computer vision, machine learning, security, robotics and cognitive science to jointly craft a series of lectures on covering both the basic backgrounds and the most recent progress of machine learning, focusing on computer vision and image processing. | |
| SS 05 | “Safe, Reliable, and Intelligent Cyber-Physical Systems” – Organized by Guanjun Liu, Zizhen Zhang, Liang Qi and Xiwang Guo |
| The theme of this special session includes emerging methods and practices of safe, reliable and intelligent Cyber-Physical Systems. AI and advanced data analytics enhance intelligent operations and management in Cyber-Physical Systems, while complexity exists with inter-connected components and units in the system. This session considers the state-of-the-art research and applications in safe, reliable, and intelligent Cyber-Physical Systems, by bringing together researchers and practitioners from both academia and industry, so as to address significant advancement, expose unsolved challenges, present requirements for integration with new technologies, and provide visions for future research and development. | |
| SS 06 | “Deep Learning for Human-Machine Interaction” – Organized by Mounîm A. El Yacoubi, Hui Yu, Ying Tang, Mehdi Ammi, and Huafeng Qin |
| Thanks to the huge increase of computing power and resources, to today’s ubiquity of the Internet of Things (IoT) and smart devices, and to the breakthroughs in Artificial Intelligence and deep learning over the last years, the field of cyber-physical-social intelligence has witnessed a dramatic development and success stories on a wide range of fields, involving the interaction between humans, machines, and society. The latter include but are not limited to Human-Computer Interaction, Robotics, Digital Twins, AR/VR, social media, e-Health, Education, Cybersecurity, Smart Cities, Smart Manufacturing, Smart Agriculture, Social Sciences, Entertainment. The aim of this special issue is to highlight recent advances and trends of AI/deep learning that made possible such breakthroughs in connection with machine interaction. | |
| SS 07 | “Deep Learning, Neural Computation and Applications for Water Environmental Prediction and Treatment” -Organized by Junfei Qiao, Jing Bi, Gongming Wang, Haitao Yuan, and Ying Tang |
| Water environmental prediction and treatment are important topics in the field of environmental protection. Traditional methods highly rely on the precision of equipment, which is an expensive and time-consuming process, especially for the occasions with spatio-temporal and multiple factors. In comparison to it, data-driven neural computing strategies that often treat dynamic characteristics of water quality parameters as a black box are more suitable. In fact, with the existence of spatio-temporal and multiple factors, water environmental prediction and treatment tend to be supported by big data and intelligent algorithms. Driven by this challenge, recently, deep learning and intelligent neural computing algorithms have attracted a lot of attentions. They specially aim to exploit data-driven strategies, allowing deep neural networks and deep learning techniques to sufficiently learn black-box models, predict and control key water quality parameters. This proposal focuses on the water environmental prediction and treatment from perspectives of deep learning, neural computation and industrial applications. | |
| SS 08 | “Distance experiment for Engineering Education” -Organized by Mingzhang Luo and Xiaolong Yuan |
| The rapid development of information technology and Internet of things (IOT) technology has brought extensive and profound influence to all fields of social life. Especially in the field of education, distance education has broken through the limitation of time and space, and has become an important means to optimize the educational structure, improve the allocation of educational resources, and promote innovative education. This session aims to meet the needs of engineering education for distance experimental teaching, exchange and discuss the latest research results and key technologies of distance experimental teaching mode, remote laboratory system development, and remote laboratory management, so as to provide theoretical and technical support for the development of engineering-oriented distance education. | |
| SS 09 | “Blockchains and their Applications” – Organized by Peiyun Zhang and Aiqing Zhang |
| Blockchains represent a promising infrastructure and technology to realize secure peer-to-peer transactional systems and are attracting considerable attention from the technical, financial, and industrial communities. They have already been applied in many fields, including banking, financial markets, insurance, leasing contracts, smart grids, government services, and health care. Combined with other technologies, they can be used to reduce system risks and decrease operational cost. This special session concerns effective technologies for developing blockchains and trusted applications. | |
| SS 10 | “Recent Advances in Internet of Things Applications” – Organized by Ning Wang |
| The rapid growth in pervasive Internet-of-Things (IoT) is creating a huge demand for novel applications, such as smart home, smart cities, intelligent transportation systems, and smart manufacturing. With the recent advances in artificial intelligence (AI) technologies, a plethora of AI-driven solutions have been proposed for various tasks in IoT systems and shown superior performance. Besides, due to the high speed 5G connectivity and emerging edge computing paradigm, we can offer cheaper, faster, and more efficient service. To this end, it is important to design new algorithms and applications to utilize new opportunities and deal with these problems based on IoT devices. The aim of this special session is to provide a platform for researchers to discuss the recent advances in IoT application design and implementation. | |
| SS 11 | “Cyber-physical Social Intelligence in Emerging Power Systems” – Organized by Jie Li and Yikui Liu |
| The electricity grid has been recognized as the largest and most complex machine ever made, serving the biggest group of customers in the world. Although having been improved and upgraded over the last decades, more frequent blackouts throughout the world clearly indicate that this extremely large-scale complex system continuously faces new challenges that demand fundamental revolution in the physical structure, management policy, and business operation. Indeed, emerging electricity supply and delivery technologies, advanced monitoring, control, and operation strategies, as well as regulation policies are continuously evolving, which are designed and deployed to provide affordable, reliable, and sustainable electric energy to modern life and commerce of our society, ultimately leading to the “Smart” electricity grid. “Smart Grid” is regarded as an enabling engine for our economy, our environment, and our future. proposal targets on soliciting innovative technical and scientific findings and applications of machine/human intelligence to address key challenges of emerging power systems – “Smart Grid”, a vertically integrated cyber-physical social system. | |
| SS 12 | “Data Intelligence” – Organized by Zhijun Fang and Xiaoli Zhao |
| In the era of big data and digital transformation, the value of data has been fully demonstrated in various fields. If knowledge and information cannot be extracted from the data and used effectively, the data itself cannot drive and lead to the success of digital transformation. Therefore, data intelligence was introduced and has become an indispensable key technology to promote digital transformation. It can gain value by analyzing data, processing raw data into information and knowledge, and then translating them into decision or action. This special session aims to promote and reflect recent advances in the field of Data Intelligence and provide a platform for researchers from academic and industry around the world to exchange novel ideas. It will present the latest research endeavors, industry implementations, as well as standard development etc. | |
| SS 13 | “Perception, Control and Optimization for Land Transportation Cyber-Physical Systems” – Organized by Hongjie Liu and Ming Chai |
| This session focuses on understanding the challenges and innovative solutions on perception, control, and optimization problems in land transportation CPS with the help of cutting-edge technologies. We hope to attract high-quality research articles in this field along with review articles that describe the current state of the art. Novel submissions describing practical and theoretical solutions to the challenges in the design, testing and validation phases of land transportation CPS are all welcome. | |
| SS 14 | “Intelligent Control and Estimation” – Organized by Jun Wang |
| Big data mining, deep learning, and reinforcement learning can effectively handle the uncertainty issues with the model structures, parameters, and inputs of control systems, caused by randomness, fuzziness, and chaos. This special session focuses on data mining, deep learning, and other intelligent methods that target the theoretical and practical problems of uncertainty in the process of self-learning, self-reasoning, and self-decision. Also included in this special session are subjects of the estimation and control with incomplete measurement, image-based target autonomous detection, recognition and tracking, multi-agent collaborative control, process optimization, resource allocation of complex systems, identification, and control of smart actuators, etc. | |
| SS 15 | “Sustainability aspects in Cyber-Physical Social Systems” – Organized by Michele Dassisti, Concetta Semeraro, Hervé Panetto, George Weichart, Milan Zdravkovic, Arturo Molina, and Ricardo Jardim-Gonçalves |
| Cyber Physical Social Systems (CPSS) can be defined as a system consisting of cyber space, physical space and social space. Knowledge from cyber space interacts with human space in the real world, as well as the cyber space mapping different facets of the real world. Areas of application of Cyber-Physical Social System (CPSS) in the near future are potentially wide (from energy systems, electricity networks, intelligent production, distributed manufacturing, smart cities etc.). The design and management of CPSS is still a challenging task because of the heterogeneity of components, of software complexity and hardware entities, since there is a lack of effective theories and design approaches to enable an unified structure. Additionally, the effects of a CPSS on sustainable development and performance is still unexplored topic to be faced. As a result, this session aims at bringing together the sustainability aspects and challenges with CPSS to foster convergence, methodologies innovative tools and applications. | |
| SS 16 | “Advanced Optimization for Sustainable Production Automation” – Organized by Shixin Liu, Xiwang Guo, and Ziyan Zhao |
| The sustainable development of scientific and engineering solutions to production automation is becoming an emerging topic to help reduce carbon intensity and other production waste, and to enhance the utilization of energy and natural resources. The recent advancement of intelligence technologies further expands the scope of typical environmentally conscious manufacturing, making modeling, analysis, real-time monitoring, control, and optimization of production and service automation more effective and efficient. The purpose of this session is to bring researchers in this area to discuss their recent development and share their vision for a sustainable future. | |
| SS 17 | "AI methods in FinTech" - Organized by Henry Han |
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With the surge of big data in finance, AI methods are becoming a more and more tool in Fintech to detect trading dynamics, capture trading markers, and pricing securities and its derivatives in a data-driven approach. It pushes finance research moving from model-driven to data-driven. At the same time, it brings new data and challenges in AI. It remains challenges to develop customized AI methods in FinTech. With the rise of blockchain, the problems in Fintech are challenging data science, finance, and even law from all perspectives. In this special session, we bring together researchers from Finance, AI, and analytics to jointly craft a series of lectures on covering both the basic backgrounds and the most recent progress in AI techniques of FinTech. We mainly focus on state-of-the-art findings in FinTech in this special session. |
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| SS 18 | "Learning, Modelling, and Optimization in Human-robot Interaction" - Organized by Chunlin Chen, Huaxiong Li, and Bo Wang |
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Human-robot interaction has been viewed as an important domain of robotics and artificial intelligence. Generally, human-robot interaction can be classified into four interaction paradigms, i.e., robot as a tool, robot as a cyborg extension, robot as an avatar, and robot as a sociable partner. Except for some special purpose, there are a growing number of applications for robots to act as sociable partners, such as elderly care, children education, entertainment, and pedestrian flow optimization. HRI has attracted many researchers to devote to make robots be qualified collaborators, assistants, or partners. In this special session, new developments in the learning, modelling and optimization for human-robot or human-machine interaction are all welcome. Note: The topics include but are not limited to:
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| SS 19 | "Control Technology & Artificial Intelligence Technology and its Possible Applications on Cyber-Physical Systems" - Organized by Mingcong Deng and Shengjun Wen |
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Control technology and artificial intelligence (AI) technology are the fundamental issues of Cyber-Physical Social systems. Recently, more and more researchers have begun to incorporate AI technology into control design subject to improve performance and efficiency of CPS systems. In this SS, papers on the topics of applications and theoretical developments for AI & control technologies are welcome, specially, the topics include but are not limited to: IoT, Machine Learning, Linear & Nonlinear Control Theory, Network Control, Automation, Human-centered Automation, System Integration, Production and Service Automation, Smart Transportation, Healthcare Intelligence, Smart Building/Villages/Cities/Grid. |
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| SS 20 | "Trust in Internet of Things" - Organized by Giancarlo Fortino, Lidia Fotia, Fabrizio Messina, Domenico Rosaci, Giuseppe M.L. Sarnè, and Claudio Savaglio |
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In order to avoid deceptions in IoT, trust-based approaches play a role of social control for determining the best actors to interact. Usually, each member of a community performs direct interactions with a limited set of that community and often for only a small number of times. Therefore, a direct and reliable opinion about someone could be impossible to obtain. For this reason, it is needed to consider the suggestions provided by other IoT members. The accuracy of these opinions enhances as much as the number of the cooperating actors increases but malicious manipulations are possible. Several metrics and techniques to measure trust have been proposed in the literature in a very large variety of papers and survey. Furthermore, some of these approaches deal with the integration of reliability (i.e., information derived by direct experiences) and reputation (i.e., information derived by opinions of others) into a single synthetic measure but leaving to the user the task to set their parameters.
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| SS 21 | "Complex Network Representation" - Organized by MengChu Zhou and Xin Luo |
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Recently, with the widespread utility of information technologies, complex networks are becoming increasingly popular. They are adopted to describe complex relationships across various disciplines, such as social network, biological network and knowledge graph. They are rich of useful knowledge and patterns that shed light on different aspects of social activities such as community structures, information diffusion, and organization patters. Hence, complex network representation (CNR) has been spotlighted as a highly active research field with great success across academic and industrial communities. However, in spite of its great potential, CNR is inherently difficult and confronted with several thorny challenges, such as scalability, structure preserving, data incompleteness and imbalance. There are many open challenges and issues in CNR that need to be resolved to allow its practical usage in real-life applications. This special session focuses on CNR, aiming at gathering state-of-the-art studies addressing related issues. Topics of interest include, but are not limited to:
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