| Special session proposals deadline | July 15, 2023 |
| Paper submission deadline | July 31, 2023 |
| Notification of acceptance | August 15, 2023 |
| Camera-ready copy and author registration | August 31, 2023 |
| Conference | September 22-24, 2023 |
Data-driven machine learning
Dynamic big data machine learning methods and techniques, deep learning,
neural architecture search, reinforcement learning, statistical
relational learning, transfer learning, self-supervised learning,
distributed and federated machine learning, trustworthy machine
learning
Data-driven optimization and decision making
Data-driven optimization algorithms, Bayesian optimization, neural
combinatorial optimization, large-scale and multi-objective
optimization, integration of machine learning and optimization,
data-driven decision paradigm, intelligent scheduling, reinforcement
learning for combinatorial optimization, distributed and federated
optimization, industrial and manufacturing system analysis, and
decision-making
Data-driven modeling and control
Learning and adaptive control, robust control, intelligent control,
optimization-based and optimal control, model predictive control, fault
detection and identification, hybrid intelligent systems, neural
control, fuzzy logic control, networked control, industrial automation,
intelligent transportation systems, environmental monitoring and
control, intelligent manufacturing systems, green communication
systems
Big data analysis and application
Big data storage and mining, data coordination, integration and
processing, big data analytics and metrics, theory and methods of
multi-source big data fusion, data base management systems, big data
service, big data-oriented cloud computing technology, privacy
preserving big data analysis, visual city data analysis, intelligent
transportation data analysis, healthcare data analysis,
bioinformatics
Ting Yang (yangting@tju.edu.cn)
Tianjin University, China
Liyuan Zhao (yuanerzhao@hebut.edu.cn)
Hebei University of Technology, China
Qiqi Liu(qiqi6770304@gmail.com)
Hebei University of Technology, China
Artificial Intelligence (AI) is one of the most subversive science and technology, which has strong processing ability in computational intelligence, cognitive intelligence and cognitive intelligence. At present, the development of AI has entered a new stage, opening a new direction to overcome the drawbacks of traditional solutions for many problems.
With the integration of intermittent renewable energy and large-scale regional interconnection of energy systems, sustainable energy and electric systems have evolved into highly dimensional dynamic large-scale systems. Moreover, the randomness of users’ energy consumption behaviors and the flexible use of active loads also increase the complexity and uncertainty of energy systems. The sustainable energy and electric systems are in real-time dynamic change, and the research of related problems is nonlinear and uncertain. AI is the core support technology of intelligent energy. It has strong optimization processing ability and strong learning ability to deal with high-dimensional and nonlinear problems, which will effectively solve various challenges faced by the sustainable energy and electric systems.
Potential topics of this session include but are not limited to the following:
• Novel theories, concepts, and paradigms of the convergence of AI
• Data-driven decision making in sustainable energy and electric systems
• Data-driven analysis and optimization of energy consumption side
• Data-driven control of new power system
• Data-driven situational awareness of new power system
• Data-driven fault diagnosis and status monitoring of power equipment
• Applications of AI in energy forecast and energy management
• Applications of AI in operation optimization for sustainable energy and electric systems
Important Dates:
Paper submission deadline: June 30, 2023
Notification of acceptance: July 31, 2023
Camera-ready copy and author registration: August 15, 2023
Conference: September 22-24, 2023
Optimization problems are widespread in many real-world fields, including science, engineering, and technology. Despite the many studies on optimization, most current optimization techniques rely on a traditional centralized model. However, with the increasing storage capacity and computational power of edge devices in modern distributed networks, there has been an emergence of decentralized computing such as federated optimization. This novel approach enables optimization to be executed locally at edge devices with minimal communication between them. While this method has great potential, it also presents new challenges and privacy concerns given the large amount of data collected by edge devices.
In addition to standard optimization considerations like maximizing performance, users in some scenarios also care about fairness in decision-making, multi-objective preference or model construction. Thus, developing new algorithmic ideas is paramount for optimizing both overall performance and considerations related to fairness.
Given these considerations, there is currently increased attention to developing secure and privacy-preserving optimization techniques that are also fair-aware. Some of the research topics include security and robustness, privacy-preservation, fairness, verifiability, and transparency when designing optimization algorithms. Notably, significant issues remain unresolved in this field.
It is important to discuss the definitions of security, privacy and fairness concerning optimization since many researchers understand these concepts differently. Finding an appropriate balance between performance optimization alongside privacy/fairness/security guarantees represents another challenge. Additionally, long-standing factors that affect distributed and federated machine learning methods need reevaluation within the context of optimization including non-IID data and enhancing communication efficiency.
Therefore, designing new benchmark problems and performance indicators for evaluating secure, privacy-preserving and fair-aware optimization methods remains key towards addressing open questions on this subject matter.
The aim of this special session is to bring together researchers from different application fields working on optimization and present new solutions to the above-discussed challenges. The special session will focus on new advances, review and discuss the state-of-the-art in the theory, algorithm design, and applications of using secure, privacy-preserving, and fairness-aware solutions in optimization.
Authors are invited to submit papers on one or more of the following topics:
• Privacy-preserving Bayesian optimization
• Privacy-preserving evolutionary algorithm
• Privacy-preserving distributed optimization
• Secure federated data-driven optimization
• Federated surrogate models
• Fairness-aware acquisition function
• Attacks and defenses in optimization
• Fairness-aware multi-objective optimization
• Fairness-aware data-driven optimization
• Fairness-aware federated optimization
• Fairness-aware multi-objective machine learning
• Benchmark problems for secure, privacy-preserving and fairness-aware optimization
• Performance indicators for secure, privacy-preserving and fairness-aware optimization
Dr Yang CHEN (fedora.cy@gmail.com)
School of Electrical Engineering, China University of Mining and Technology, Jiangsu, 221116, China.
Dr Liming YAO(liming.yao@ntu.edu.sg)
School of Mechanical and Aerospace Engineering, Nanyang Technological University, 639798, Singapore.
Dr Xilu WANG(xilu.wang@uni-bielefeld.de)
Faculty of Technology, Bielefeld University, Germany.
Ms Yuping YAN (Ph.D. Candidate)(yupingyan@inf.elte.hu)
Department of Informticas, Eötvös Loránd University, Hungary.
Dr Ying HU(hy200712008@126.com)
School of Computer and information, Anhui Normal University, Anhui, 241002, China.
Dr Qiqi LIU(qiqi6770304@gmail.com)
Department of Artificial Intelligence, Hebei University of Technology, Bielefeld University,Tianjin, 300401, China.
Chaoli Sun (chaoli.sun.cn@gmail.com) [http://www.dscil.cn/people/sun_en.html]
School of Computer Science and Technology, Taiyuan University of Science and Technology
Taiyuan, Shanxi 030024 China.
Handing Wang (hdwang@xidian.edu.cn) [https://faculty.xidian.edu.cn/HandingWang/zh_CN/index/404706/list/index.htm]
Department of Artificial Intelligence, Xidian University
Xi'an, Shaaxi, 710126, China.
Meta-heuristic algorithms, including evolutionary algorithms and swarm optimization, face challenges when solving time-consuming problems, as typically these approaches require thousands of function evaluations to arrive at solutions that are of reasonable quality. Surrogate models, which are computationally cheap, have in recent years gained in popularity in assisting meta-heuristic optimization, by replacing the compute-expense/time-expensive problem during phases of the heuristic search. However, due to the curse of dimensionality, it is very difficult, if not impossible to train accurate surrogate models. Thus, appropriate model management techniques, memetic strategies and other schemes are often indispensable. In addition, modern data analytics involving advance sampling techniques and learning techniques such as semi-supervised learning, transfer learning and active learning are highly beneficial for speeding up evolutionary search while bringing new insights into the problems of interest. This special session aims at bringing together researchers from both academia and industry to explore future directions in this field.
The topics of this special session include but are not limited to the following topics:
• Surrogate-assisted evolutionary optimization for computationally expensive problems
• Adaptive sampling using machine learning and statistical techniques
• Surrogate model management in evolutionary optimization
• Data-driven optimization using big data and data analytics
• Knowledge acquisition from data and reuse for evolutionary optimization
• Computationally efficient evolutionary algorithms for large scale and/or many-objective optimization problems
• Federated data-driven optimization
• Real world applications including multidisciplinary optimization
• Chaoli Sun, Taiyuan University of Science and Technology, China, chaoli.sun.cn@gmail.com
• Rommel G. Regis, Saint Joseph's University, Philadelphia PA, rregis@sju.edu
• Bo Liu, Glyndwr University, UK, b.liu@glyndwr.ac.uk
• Tapabrata Ray, The University of New South Wales, Australia, t.ray@adfa.edu.au
• Xuhua Shi, Ningbo University, China, Shixuhua@nbu.edu.cn
• Handing Wang, Xidian University, China, hdwang@xidian.edu.cn
• Xingyi Zhang, Anhui University, China, xyzhanghust@gmail.com
• Jie Tian, Taiyuan University of Science and Technology, China, tianjie918@163.com
• Tinkle Chugh, University of Exeter, UK, T.Chugh@exeter.ac.uk
• Jakob Bossek, University of Munster, Germany, bossek@wi.uni-muenster.de
• Ivo Couckuyt, Ghent University, Belgium, ivo.couckuyt@ugent.be
• Rodolphe Le Riche, CNRS and Ecole des Mines de Saint Etienne, France, leriche@ emse.fr
• Ilya Loshchilov, University of Freiburg, Germany, ilya.loshchilov@ gmail.com
• Nobuo Namura, Tohoku University, Japan, namura@edge.ifs.tohoku.ac.jp
• Victor Picheny, INRA, France, Victor.Picheny@toulouse.inra.fr
• Bas van Stein, LIACS, Netherlands, bas9112@gmail.com
• Simon Wessing, TU Dortmund, Germany, simon.wessing@tu-dortmund.de
• Saul Zapotecas-martinez, SHINSHU University, Japan, saul.zapotecas@gmail.com
• Kheng Cheng Wai, University Tunku Abdul Rahman, Malaysia, khengcw@utar.edu.my
• Hemant Singh, The University of New South Wales, Australian, h.singh@adfa.edu.au
• Michael T.M. Emmerich, Leiden University, UK, m.t.m.emmerich@liacs.leidenuniv.nl
• Yaochu Jin, University of Surrey, UK, yaochu.jin@surrey.ac.uk
• Zhonghua Han, Northwestern Polytechnical University, China, hanzh@nwpu.edu.cn
• Qi Zhou, Huazhong University of Science & Technology, China, qizhouhust@gmail.com
• Liang Gao, Huazhong University of Science & Technology, China, gaoliang@mail.hust.edu.cn
• Yinan Guo, China University of Mining and Technology, China, nanfly@126.com
• Dunwei Gong, China University of Mining and Technology, China, dwgong@vip.163.com
• Wenyin Gong, China University of Geosciences, China, wygong@cug.edu.cn
• Chunna Li, Northwestern Polytechnical University, China, chunnali@nwpu.edu.cn
• Joshua D. Knowles, University of Birmingham, UK, j.knowles@cs.bham.ac.uk
• Marcus Gallagher, University of Queensland, Australian, marcusg@uq.edu.au
• Kwang-Yong Kim, Inha University, South Korea, kykim@inha.ac.kr
• Aimin Zhou, East China Normal University, China, amzhou@cs.ecnu.edu.cn
• Cheng He, Southern University of Science and Technology, China, chenghehust@gmail.com
• Ye Tian, Anhui University, China, field910921@gmail.com
• Jonathan Fieldsend, University of Exeter, UK, J.E.Fieldsend@exeter.ac.uk
• Hao Wang, Leiden University, UK, h.wang@liacs.leidenuniv.nl
• Ke Li, University of Exeter, UK, K.Li@exeter.ac.uk
• Farooq Akhtar, University of Kotli, Pakistan, farooq.akhtar@uokajk.edu.pk
Chaoli Sun (chaoli.sun.cn@gmail.com, chaoli.sun@tyust.edu.cn ) [http://www.dscil.cn/people/sun_en.html]
School of Computer Science and Technology, Taiyuan University of Science and Technology
Taiyuan, Shanxi 030024 China.
Dr. Chaoli Sun received her B.C. and M.S. degrees in Computer Application Technology from Hohai University, Nanjing, Jiangsu, China, and Ph.D. in Mechanical Design and Theory from Taiyuan University of Science and Technology, Taiyuan, Shanxi, China, in 2011. From September 2014 to September 2016, she was a Postdoctoral Research Fellow in Department of Computer science, University of Surrey. Now she is a Professor in the School of Computer Science and Technology, Taiyuan University of Science and Technology. Her areas of expertise include evolutionary computation, swarm intelligence, self-organized robotic systems, fitness estimation and surrogate assisted evolutionary optimization with application to mechanical structural optimization.
Prof. Sun is an Associate Editor of the IEEE Transactions on Evolutionary Computation, an Associate Editor of the IEEE Transactions on Artificial Intelligence, and an Associate Editor of the Soft Computing Journal. She is also an Editorial Board Member of Complex and Intelligence Systems and an Editorial Board Member of Memetic Computing. She is a member of the Evolutionary Computation Technical Committee of IEEE CIS and a member of the Intelligent Systems Application Technical Committee of IEEE CIS. She was the chair of TF on Data-Driven Evolutionary Optimization of Expensive Problems (2016-2020). She published two monographs and more than 40 first-author papers in international journals and conferences.
Handing Wang (hdwang@xidian.edu.cn) [https://faculty.xidian.edu.cn/HandingWang/zh_CN/index/404706/list/index.htm]
Department of Artificial Intelligence, Xidian University
Xi'an, Shaaxi, 710126, China.
Handing Wang received the B.Eng. and Ph.D. degrees from Xidian University, Xi'an, China, in 2010 and 2015, respectively. She is currently a professor with School of Artificial Intelligence, Xidian University, Xi'an, China. Her research interests include nature-inspired computation, multi-objective optimization, multiple criteria decision making, surrogate-assisted evolutionary optimization, and real-world optimization problems.
Prof. Wang is a member of IEEE Computational Intelligence Society and an Associate Editor of IEEE Transactions on Evolutionary Computation, IEEE Computation Intelligence Magazine, Memetic Compution, Complex & Intelligence System (Springer). She has published more than 30 papers as the first author in international journals and conferences.
Xiaohong Zhang (zhxh@tyust.edu.cn; zhangxh1111@hotmail.com )
Division of Industrial and System Engineering, Taiyuan University of Science and Technology
Taiyuan, Shanxi 030024 China.
Hui shi (huishi@tyust.edu.cn )
Division of Industrial and System Engineering, Taiyuan University of Science and Technology
Taiyuan, Shanxi 030024 China .
Gang Xie (xiegang@tyust.edu.cn; xiegang@tyut.edu.cn)
Shanxi Key Laboratory of Advanced Control and Equipment Intelligence
Taiyuan University of Science and Technology, China.
The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today's manufacturing paradigm to smart manufacturing. Smart manufacturing aims to convert data acquired across the product lifecycle into manufacturing intelligence in order to yield positive impacts on all aspects of manufacturing. Data-driven intelligence and services use artificial intelligence and machine learning tools to analyze and transform massive data into intelligent data insights, which can then be used to improve services and decision making.
Despite data-driven intelligence and services have attracted a lot of attention in intelligent manufacturing, researchers still have many challenges to explore. For example, many problems in machine design lack explicit optimization objectives or are computationally expensive in objective evaluation, resulting in difficulty of achieving a good design. Machine learning approaches have been applied to assist in optimization, which is called data-driven optimization. Model management techniques still play a significantly important role in data-driven optimization. In addition, securing big data techniques, such as secure computations, validation of inputs from endpoints, and privacy-preserving data analytics, plays a significantly important role in guarding both the data and analytics processes against attacks, theft, or other malicious activities that could harm or negatively affect them. Furthermore, data-driven decision making is beneficial to mitigate bias in decisions, avoid fault diagnosis and operation, improve services, and reduce expenses. Finally, the application problems for verifying the efficiency and effectiveness of different approaches are also indispensable.
This special session aims to promote research on data-driven intelligence and services for smart manufacturing.
The topics of this special session include but are not limited to the following topics:
• Data-driven computational intelligence for smart manufacturing
• Big data analytics in product lifecycle
• Data mining in manufacturing
• Deep learning services in the product lifecycle
• Reliable federated learning on manufacturing data
• Intelligent scheduling in manufacturing
• Block chain
• Data-driven industrial diagnosis
• Data-driven industrial forecasting
• Data-driven decision making for smart manufacturing
• Data-driven industrial digitalization
• Real world data-driven industrial applications
• Xiaohong Zhang, Taiyuan University of Science and Technology, China, zhangxh@tyust.edu.cn
• Hui Shi, Taiyuan University of Science and Technology, China, huishi@tyust.edu.cn
• Xiaoyin Nie, Taiyuan University of Science and Technology, China, niexiaoyin900113@163.com
• Lianfeng Li, China Academy of Launch Vehicle Technology, China, buaallf@163.com
• Bing Zhao,Yanshan University, China, zhaobing@ysu.edu.cn
• Zhonghai Ma, Beijing University of technology, China, zm@bjut.edu.cn
• Han Wang, Beihang University, China, wh216@buaa.edu.cn
• Diying Tang, Beihang University, China, tangdiyin@buaa.edu.cn
• Wenjin Zhu, Northwestern Polytechnical University, China, wenjin.zhu@nwpu.edu.cn
• Zhijian Wang, North University of China, China, wangzhijian1013@163.com
• Jingyue Li, Norwegian University of Science and Technology, Norway, jingyue.li@ntnu.no
• Ningyun Lu, Nanjing University of Aeronautics and Astronautics, China, luningyun@nuaa.edu.cn
• Xiaosheng Si, Rocket Force University of Engineering, China, sxs09@mails.tsinghua.edu.cn
• Jiangbin Zhao, Xi'an University of Science and Technology,China zhaojiangbin@xust.edu.cn
• Haitao Liao, University of Arkansas, US, liao@uark.edu
• Yunzheng Zhang, Taiyuan University of Science and Technology, China, zhangyz@tyust.edu.cn
• Jinhe Wang, Taiyuan University of Science and Technology, China, jinhew@tyust.edu.cn
Xiaohong Zhang (zhxh@tyust.edu.cn; zhangxh1111@hotmail.com )
Division of Industrial and System Engineering, Taiyuan University of Science and Technology
Taiyuan, Shanxi 030024 China.
Dr. Xiaohong Zhang received her B.C. and M.S. degrees in Computer Application Technology from Shanxi Normal University, Linfen, Shanxi, China and Shanghai Normal University, Shanghai, China, respectively, and Ph.D. in Mechanical Design and Theory from Taiyuan University of Science and Technology, Taiyuan, Shanxi, China, in 2015. From December 2018 to December 2020, she was a Postdoctoral Research Fellow in Department of industrial engineering, University of Arkansas. Prof. Zhang is now the director of the Division of Industrial and System engineering, Taiyuan University of Science and Technology. Her areas of expertise include data-driven fault diagnosis, fault prediction and health management, predictive maintenance decision, intelligent management information system.
She is currently the reviewer for some academic journals including IEEE Trans. on reliability, IIE Trans., European Journal of Operational Research, Reliability Engineering & System Safety, Computers & Industrial Engineering, Journal of Mechanical Engineering, International Journal of Production Research, and so on. She is also a member of CCF, and a senior member of ORSC.
Hui Shi (huishi@tyust.edu.cn)
College of Electronic Information Engineering, Taiyuan University of Science and Technology
Taiyuan, Shanxi 030024 China .
Dr. Hui Shi received her B.C. degrees in Automation from Taiyuan University of Technology, M.S. degrees in Computer Application Technology from Taiyuan University of Science and Technology, Taiyuan, Shanxi, China. And she received her Ph.D. in Mechanical Design and Theory from Taiyuan University of Science and Technology, Taiyuan, Shanxi, China, in 2015. From November 2019 to November 2020, she was a Postdoctoral Research Fellow in School of Computing and Engineering, University of Huddersfield.
The expertise research areas of Prof. Shi include intelligent information processing data-driven fault diagnosis, fault prediction and health management, she is currently the reviewer for some academic journals including IEEE Trans. on reliability, IIE Trans., Reliability Engineering & System Safety, Computers & Industrial Engineering, Journal of Mechanical Engineering, and so on. She is also a member of CCF, and a member of a council member of Shanxi provincial communication society.
Gang Xie (xiegang@tyust.edu.cn; xiegang@tyut.edu.cn)
Shanxi Key Laboratory of Advanced Control and Equipment Intelligence
Taiyuan University of Science and Technology, China.
Gang Xie obtained his bachelor's degree in automation, master's degree in control theory and control engineering, and doctor's degree in circuits and systems from Taiyuan University of Technology, in 1994, 2002, and 2006 respectively. Professor Xie now works at Taiyuan University of Science and Technology, China. He is the head of Shanxi Key Lab of Shanxi Key Laboratory of Advanced Control and Equipment Intelligence, and Shanxi “1331” Engineering Research Center for Key Technologies of Flat Panel Display Intelligent Manufacturing Equipment. His research interest includes Machine perception, Intelligent Manufacturing Automation System, and Big Data. He has published more than 100 technical papers in academic journals and conferences.
He is currently the reviewer for some academic journals including IEEE Trans., Swarm and Evolutionary Computation, International Journal of Data Science and Analytics, Applied Soft Computing, and so on. He has served at many international conferences as an organizing committee member, such as publication chair for IEEE Big Data Service 2015, 2016, and 2017, CRSSCCWI-CGrC 2013, 2014, 2015, and 2016, IJCRS2015, IEEE CSCloud 2015, RSKT 2014, and GRC2012. He is also a member of IEEE, ACM, and CCF, and a senior member of CAA.