The 10th International Conference on Fuzzy Systems and Data Mining (FSDM 2024)

Abstract: Correlation analysis has been a powerful paradigm to discover and analyze hidden properties and patterns of large-scale datasets for decades. At now, correlation analysis turns to be a perfect tool for supporting big multidimensional data analysis and mining, with a wide range of relevant properties, including the amenity of supporting meaningfully exploration and discovery of multidimensional ranges kept in such kind of datasets. These operators are thus the basis for several multidimensional big data analytical tools that can be designed and implemented on top of the foundations defined by correlation functions. In line this scientific area, the talk will provide introduction and motivations, models and algorithms, and, finally, best-practices guidelines for effective and efficient implementations of correlation-analysis-based tools over big multidimensional datasets.

Biography: Alfredo Cuzzocrea is Professor of Computer Engineering at the University of Calabria, Rende, Italy. He also covers the role of Excellence Chair in Big Data Management and Analytics at the University of Paris City, Paris, France. He is the Director of the Big Data Engineering and Analytics Lab of the University of Calabria, Rende, Italy. He is also Research Fellow of the National Research Council (CNR), Rome, Italy. His current research interests span the following scientific fields: big data, database systems, data mining, data warehousing, and knowledge discovery. He is author or co-author of more than 750 papers in international conferences (including CIKM, MDM, EDBT, SSDBM, PAKDD, DOLAP), international journals (including TKDE, JCSS, IS, FGCS, INS, JMLR) and international books. He is recognized in prestigious international research rankings, such as: (i) 1st World-Wide Scientist 2020 and 20211 for Research Topic: “OnLine Analytical Processing (OLAP)” by Microsoft Academic, Redmond, WA, USA; (ii) Top 2% World-Wide Scientist 2017, 2018, 2019, 2020 and 2021 by METRICS, Stanford, CA, USA; (iii) Top-100 Italian Scientist in Computer Science and Electronics 2022 and 2023 by Guide2Research, Clifton, NJ, USA; (iv) Top Scientist in Computer Science and Electronics 2019, 2020, 2021, 2022 and 2023 by Guide2Research, Clifton, NJ, USA; (v) Top-100 Researcher in Computer Science 2017-2021 for Research Topic: “Computer Science” by SciVal – Elsevier, Amsterdam, Netherlands; (vi) Top-100 Researcher in Computer Science 2017-2021 for Research Topic: “Theoretical Computer Science” by SciVal – Elsevier, Amsterdam, Netherlands; (vii) Top-100 Researcher in Computer Science 2012-2016 for Research Topic: “Computer Science” by SciVal – Elsevier, Amsterdam, Netherlands; (viii) Top-100 Researcher in Computer Science 2012-2016 for Research Topic: “Theoretical Computer Science” by SciVal – Elsevier, Amsterdam, Netherlands; (ix) Top-100 Italian Scientist in Computer Sciences 2022 by Virtual Italian Academy, Manchester, UK; (x) Top Italian Scientist in Computer Sciences 2016, 2017, 2018, 2019, 2020, 2021 and 2022 by Virtual Italian Academy, Manchester, UK.

Abstract: In recent years, mobile devices can be equipped with increasingly advanced computing capabilities, which opens up countless possibilities for meaningful applications. Traditionally, any cloud-based Machine Learning (ML) approach requires that data be centralized on a cloud-based server/data center. However, this can result in critical issues related to unacceptable latency and communication inefficiency as well as major security and privacy concerns. However, conventional ML technologies still require personal data to be shared. Recently, in light of increasing security and privacy concerns, the concept of Federated Learning (FL) has been introduced. In FL, end devices use their local data to train a local ML model required by the server. In a large, complex mobile edge networks, FL still faces implementation challenges with regard to communicational costs, resource allocation, security, and privacy. In this talk, we begin with an introduction to the background and fundamentals of FL. We then discuss how FL can work to try and preserve privacy while maintaining security of data. Finally, we discuss some open research areas and specific open problems where attendees may be able to make an impact.

Biography: Gautam Srivastava was awarded his B.Sc. degree from Briar Cliff University in U.S.A. in the year 2004, followed by his M.Sc. and Ph.D. degrees from the University of Victoria in Victoria, British Columbia, Canada in the years 2006 and 2012, respectively. He then taught for 3 years at the University of Victoria in the Department of Computer Science, where he was regarded as one of the top undergraduate professors in the Computer Science Course Instruction at the University. From there in the year 2014, he joined a tenure-track position at Brandon University in Brandon, Manitoba, Canada, where he currently is active in various professional and scholarly activities. He was promoted to Professor in January 2023. Dr. G, as he is popularly known, is active in research in the field of Data Mining and Big Data. In his 10-year academic career, he has published a total of 400 papers in high-impact conferences in many countries and in high-status journals (SCI, SCIE) and has also delivered invited guest lectures on Big Data, Cloud Computing, Internet of Things, and Cryptography at many international universities. He is an Editor of several international scientific research journals. He currently has active research projects with other academics in Taiwan (China), Singapore, Canada, Czech Republic, Poland and U.S.A. He is constantly looking for collaboration opportunities with foreign professors and students. For more details about Dr. G., please refer to his personal website at (https://people.brandonu.ca/srivastavag/).

Abstract: Influence maximization is an important problem in the fields of social networks and data mining. Propagation is one of the important properties of this problem. In graph theory, the power domination problem is one of the few problems with propagation properties. This study combines the concepts of influence maximization and power domination problems. We propose some problems with propagation properties. For example, in the k-influence optimization problem, our goal is to find a seed set with the smallest size such that they can spread and influence everyone on the graph through their influence. In the problem, a person is influenced if his/her k friends are influenced. In this research, we consider this propagation property on domination-like problems.

Biography: Sheng-Lung Peng is a Professor at the Department of Creative Technologies and Product Design, and the Dean of the College of Innovative Design and Management, National Taipei University of Business in Taiwan. He received the PhD degree from Computer Science Department of National Tsing Hua University in Taiwan. He is an honorary Professor at Beijing Information Science and Technology University and a visiting Professor at Ningxia Institute of Science and Technology in China. He is also an adjunct Professor at National Dong Hwa University in Taiwan and Kazi Nazrul University in India. In addition, he is also an honorary adjunct professor in School of Management of Sir Padampat Singhania University and in School of Computer Science and School of Business of ITM (SLS) Baroda University. Dr. Peng has edited several special issues at journals, such as Frontiers in Public Health, Journal of Internet Technology, IEEE Internet of Things Magazine, Computers and Electrical Engineering, Journal of Information Science and Engineering, and so on. His research interests are algorithm design in the fields of artificial intelligence, bioinformatics, combinatorics, data mining, and networking.