The 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE 2023)




Prof. Zidong Wang

IEEE Fellow

Brunel University London, UK

Member of the Academia Europaea

Member of the European Academy of Sciences and Arts

Academician of the International Academy for Systems and Cybernetic Sciences 

Fellow of the Chinese Association of Automation

Bio: Dr. Zidong Wang is currently Professor of Dynamical Systems and Computing in the Department of Computer Science, Brunel University London, U.K. From 1990 to 2002, he held teaching and research appointments in universities in China, Germany and the UK. Prof. Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published 500+ papers in IEEE Transactions and 120+ papers in Automatica with an H-index of 144. He is a holder of the Alexander von Humboldt Research Fellowship of Germany, the JSPS Research Fellowship of Japan, William Mong Visiting Research Fellowship of Hong Kong. Prof. Wang serves (or has served) as the Editor-in-Chief for Neurocomputing, the Editor-in-Chief for International Journal of Systems Science, and an Associate Editor for 12 international journals including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Neural Networks, IEEE Transactions on Signal Processing, and IEEE Transactions on Systems, Man, and Cybernetics-Part C. He is a Member of the Academia Europaea, a Fellow of the IEEE, a Fellow of the Royal Statistical Society and a member of program committee for many international conferences.

Title: Handling bad data for big data analysis


Abstract: In this talk, we discuss another side of big data analysis, bad data analysis, where the badness means the complexities resulting in the reproducibility issues. Some background knowledge is first introduced on the volatility of the big data analysis, which shows 1) "big" does not necessarily mean "better" and 2) the so-called multi-objective data analysis (against badness) is vitally important in advancing the state-of-the-art. Two examples are used for demonstration of the big data analysis, one for big data from complex networks and the other for big data from gene expression image processing. Finally, conclusions are drawn and some future directions are pointed out.


Prof. Maozhen Li

Brunel University London, UK

Vice-Dean of the NCUT TNE programme/Professor

Bio: Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University London, UK. His research interests are largely in the areas of high-performance computing including cloud computing and edge computing, big data analytics, and intelligent systems with applications in smart grid and smart cities. Recently he has devoted himself to a few topics related to deep neural networks such as trustworthiness, robustness, interpretation and data privacy. He has over 200 scientific publications in these areas including 5 books and 90 peer reviewed journal papers. He is a Fellow of the British Computer Society and the Institution of Engineering Technology (IET).

Title: Preserving Data Privacy in AI

Abstract: The past few years have witnessed a tremendous success in applying AI to classification problems in various areas such as computer vision, speech recognition, nature language processing. This success largely stems from a rapid development of deep learning techniques like deep neural networks (DNNs). However, DNNs are data hungry which always pushes researchers to feed DNNs with more and more training data without considering issues related to data privacy. This talk aims to raise the awareness of data privacy in designing AI algorithms. On one hand, it analyses EU’s General Data Protection Regulation (GDPR) and points out how AI shall comply with GDPR on data privacy. On the other hand, this talk reviews the techniques that can be employed to design privacy-preserving AI algorithms.

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Prof. Lei Shu

Nanjing Agricultural University, China

Bio: Lei Shu is a Professor in Nanjing Agricultural University, China and a “Lincoln Professor” of University of Lincoln, UK. He is also the director of NAU-Lincoln Joint Research Center of Intelligent Engineering. He has published over 500 papers in related conferences, journals, and books in the area of Internet of Things. He has been serving as Editor-in-Chief of Journal of Sensor and Actuator Networks, Specialty Chief Editor of Frontiers in Plant Science - Sustainable and Intelligent Phytoprotection Section, and (or was) associate editors for IEEE Transactions on Industrial Informatics, IEEE Transactions on Industrial Cyber-Physical Systems, IEEE Transactions on Consumer Electronics, IEEE Communications Magazine, IEEE Network Magazine, IEEE Systems Journal, IEEE Access, IEEE/CAA Journal of Automatica Sinica, IEEE Consumer Electronics Magazine, etc. He has served as more than 60 various Co-Chair for international conferences/workshops, e.g., IWCMC, ICC, ISCC, ICNC, Chinacom, IECON, INDIN, ISIE, ICIT, General Co-Chair for Chinacom, Qshine, Collaboratecom, Steering and TPC Chair for InisCom; TPC members of more than 160 conferences, e.g., ICDCS, DCOSS, MASS, ICC, Globecom, ICCCN, WCNC, ISCC, IECON, INDIN, ISIE, ICIT.

Title: Solar Insecticidal Lamp Internet of Things for Smart Agriculture: Vision and Issues

Abstract: As a typical application of physical lure control device, Solar Insecticidal Lamps (SILs) attract pests with the lure lamp and kill them by the high-voltage metal mesh, which has the advantages of low cost, no pollution and self-sufficient energy. Combined with IoTs technology, SILs can be used to collect information on counting the number of killed pests, meteorology, soil moisture and equipment status. This talk will introduce the current on-going researches of SILs and the related future challenging issues towards smart agriculture.

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Prof. Keyang Cheng

Jiangsu University, China

Bio: Keyang Cheng, Executive Dean, Professor, and Doctoral Supervisor of the Cyberspace Security Research Institute of Jiangsu University, is currently a member of the Multimedia Special Committee of the Chinese Computer Society, the Internet of Things Special Committee of the Chinese Computer Society, the Pattern Recognition and Machine Intelligence Special Committee of the Chinese Automation Society, the Pattern Recognition Special Committee of the Chinese Artificial Intelligence Society, and a high-level talent of the "333 Project" in Jiangsu Province. He mainly engaged in research in the fields of artificial intelligence and pattern recognition, led 10 projects such as the National Natural Science Foundation of China, the National Engineering Laboratory Fund of China, and the Jiangsu Provincial Natural Science Foundation. He has published over 50 academic papers in top journals and conferences such as TNNLS, TII, TCSVT, MM, ICDE, and ICME, applied for more than 30 patents and software copyrights, published 3 books, and received Jiangsu Provincial Science and Technology Award twice.

Title: New Progress of Interpretability Theory in Deep Learning

Abstract: With the rapid development of deep neural network theory and technology, the opacity of deep neural network models and the inexplicability of results seriously hinder its application in high-risk fields. Issues such as the "black box" nature of models and unreliable decision-making paths have been explored and studied by relevant researchers in the early stages of deep learning development. Today, there are numerous achievements in the field of interpretability research in deep learning. This report will firstly discuss the measurement and evaluation indicators of interpretability in deep learning. Secondly, it will summarize the current theoretical progress of deep learning interpretability theory in constructing internal interpretable deep learning models and interpreting existing deep learning models. Then it will share the existing interpretable research exploration ideas and methods of our team; Finally, based on the current high-risk application fields, it will look forward to the direction and challenges of future research.


Prof. Zhijun Zhang

IEEE CIS (Computational Intelligence) Guangzhou Chapter,

South China University of Technology, China

Bio: Zhijun Zhang is working as a Full Professor in School of Automation Science and Engineering, South China University of Technology, and chairman of IEEE CIS (Computational Intelligence) Guangzhou Chapter. Zhijun Zhang main research interests include neural networks, robotics, human-robot/computer interaction, and numerical optimization. 

Zhijun Zhang has published more than 100 scientific papers as author or co-author, including 83 SCI journal papers, 40 IEEE Trans regular papers published/accepted by the first author/corresponding author, 2 English book chapters. There were 39 papers in JCR 1 district. Some of the papers are published by SCI-indexed international journals and EI-indexed conferences, such as IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Mechtronics, IEEE Transactions on Cybernetics and so on. In addition, Zhijun Zhang has 14 authorized Patents of Invention and 6 PCT and more than 100 Patents of Invention submitted to Patent Office in China. In his research areas, he has won many awards and scholarships. 

Title: Varying-parameter recurrent neural networks applied to robots and data analysis

Abstract: Everything in nature changes with time is eternal and absolute, while stationary is only relative. Inspired by this fundamental law of nature and based on the neurodynamic approach, Dr. Zhijun Zhang designed and proposed a varying-parameter recurrent neural network. Various forms of varying- parameter recurrent neural networks are designed and derived, and it is theoretically demonstrated that the network has the property of super-exponential convergence in solving time-varying problems and robot motion planning problems. In solving noise-containing problems, this model can effectively suppress noise and has obvious advantages over similar methods. The network model can effectively overcome the limitations of the existing methods in terms of slow convergence and weak robustness in solving time-varying, nonlinear, underdetermined, and multi-solution problems of robot systems in complex environments, and has the advantages of high solution accuracy, fast error convergence, and robustness. In practical systems, this method can be applied to robot motion planning, natural human-robot interaction and flight controller design and many other aspects.