Speech Title: Machine Learning and Granular Computing as a Framework of Design of Models of Quantitative Software Engineering

Abstract: It is evident that with the omnipresence of software systems and their diverse applications quite often concerning critical environments, efficient models of software artifacts, software attributes (for instance, software complexity and maintainability) and software processes (e.g., cost estimation) become of paramount importance with far-reaching practical impact. The nature of software data including their limited number, sparsity, distributed nature and associated with privacy and security constitutes a remarkable challenge when engaging effectively machine learning design framework.
We advocate that in light of the arguments raised above, it is highly desirable that suitable models of software products and processes: (i) are transparent and easily comprehended by users and stakeholders, (ii) exhibit logic fabric that contributes to the elevated level of interpretability, (iii) realize the design based on an orchestrated usage of data and available domain knowledge helpful to customize the key modeling agenda, and (iv) come with well-articulated credibility measures that help efficiently assess the relevance of the models, say associated quality of prediction outcomes.
It is demonstrated that the above-stated requirements are addressed by admitting a suitable level of abstraction of the model and the ensuing results. The detailed discussion is carried by concentrating on rule-based architectures. In this context, several representative conceptual and algorithmic developments in machine learning and Granular Computing are covered: federated learning, granular gradient boosting, and transfer learning (knowledge).

Biography: Witold Pedrycz (Life Fellow, IEEE) received the M.Sc. degree in computer science, the Ph.D. degree in computer engineering, and the D.Sci. degree in systems science from the Silesian University of Technology, Gliwice, Poland, in 1977, 1980, and 1984, respectively.,He is a Professor and the Canada Research Chair of computational intelligence with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada. He is also with the System Research Institute, Polish Academy of Sciences, Warsaw, Poland. He has authored 15 research monographs covering various aspects of computational intelligence, data mining, and software engineering. His current research interests include computational intelligence, fuzzy modeling, granular computing, knowledge discovery and data mining, fuzzy control, pattern recognition, knowledge-based neural networks, relational computing, and software engineering.,Prof. Pedrycz was a recipient of the IEEE Canada Computer Engineering Medal, the Cajastur Prize for Soft Computing from the European Center for Soft Computing, the Killam Prize, and the Fuzzy Pioneer Award from the IEEE Computational Intelligence Society. He is intensively involved in editorial activities. He is an Editor-in-Chief of Information Sciences, WIREs Data Mining and Knowledge Discovery (Wiley), and the International Journal of Granular Computing (Springer). He currently serves as a member of a number of editorial boards of other international journals and is a Former Editor-in-Chief of IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans. He is a Foreign Member of the Polish Academy of Sciences. He is a Fellow of the Royal Society of Canada.(Based on document published on 25 December 2020).

Biography: Shugong Xu is an IEEE Fellow，a professor at Shanghai University. In his 20+ years career in research (over 15 years in industrial research labs), he had over 50 issued US/WO/CN patents and published more than 150 peer-reviewed research papers. He was awarded "National Innovation Leadership Talent" from China government in 2013, IEEE Fellow in 2015. He also won 2017 Award for Advances in Communication from IEEE Communication Society. His current research interests include V2X, wireless communication systems, and machine learning etc.