Keynote Speaker

Keynote Speaker

Prof. Anabela Mesquita

Porto Accounting and Business School / Polytechnic of Porto, Portugal.

Anabela Mesquita is a Professor at the Porto Accounting and Business School / Polytechnic of Porto since 1990. Vice Dean between 2007 - 2018. President of the SPACE European network. Member of the Algoritmi Research Centre (Minho University) and the former Director of CICE (Research Centre for Communication and Education). She is a member of MAERA. President of the External Evaluation Committee for the A3ES (Agency for Evaluation and Accreditation in Higher Education) in the area of Executive Management. She has been (and is) involved in many European and National research projects both as a researcher and as a coordinator. She has published numerous papers in various international journals and conference proceedings. She is a member of the Programme Committee and Scientific Committee of several national and International conferences. She serves as Member of the Editorial Board and referee for IGI Global. She also serves as Associate Editor of the Information Resources Management Journal ( and is co-Editor-in-Chief of the International Journal of Technology and Human Interaction ( and Associate Editor of Helyion ( She has also been evaluator and reviewer for Erasmus+ National Agency and European Commission projects. Her interests include education, elearning, technologies and information systems, knowledge management, innovation and intellectual capital.

Prof. Dr. Reda Alhajj

University of Calgary, Canada

Reda Alhajj is a professor in the Department of Computer Science at the University of Calgary. He published over 500 papers in refereed international journals, conferences and edited books. He served on the program committee of several international conferences. He is founding editor in chief of the Springer premier journal “Social Networks Analysis and Mining”, founding editor-in-chief of Springer Series “Lecture Notes on Social Networks”, founding editor-in-chief of Springer journal “Network Modeling Analysis in Health Informatics and Bioinformatics”, founding co-editor-in-chief of Springer “Encyclopedia on Social Networks Analysis and Mining”, founding steering chair of the flagship conference “IEEE/ACM International Conference on Advances in Social Network Analysis and Mining”, and three accompanying symposiums FAB, FOSINT-SI and HI-BI-BI. He is member of the editorial board of the Journal of Information Assurance and Security, Journal of Data Mining and Bioinformatics, Journal of Data Mining, Modeling and Management; he has been guest editor of a number of special issues and edited a number of conference proceedings. Dr. Alhajj's primary work and research interests focus on various aspects of data science and big data with emphasis on areas like: (1) scalable techniques and structures for data management and mining, (2) social network analysis with applications in computational biology and bioinformatics, homeland security, etc., (3) sequence analysis with emphasis on domains like financial, weather, traffic, energy, etc., (4) XML, schema integration and re-engineering. He currently leads a large research group of PhD and MSc candidates. He received best graduate supervision award and community service award at the University of Calgary. He recently mentored several successful teams, including SANO who ranked first in the Microsoft Imagine Cup Competition in Canada and received KFC Innovation Award in the World Finals held in Russia, TRAK who ranked in the top 15 teams in the open data analysis competition in Canada, Go2There who ranked first in the Imagine Camp competition organized by Microsoft Canada, Funiverse who ranked first in Microsoft Imagine Cup Competition in Canada.

Speech Title:From dynamic data management to view maintenance: Effectiveness of the network model in handling various application domains

Abstract: The network model has been effectively used to tackle many application domains. It is attractive due to its underlying foundation in graph theory which facilitates comprehensive analysis leading to informative and powerful decision supporting mechanisms. Further, data is mostly dynamic leading to the need for effective techniques to manage the changes in data while maintaining its consistency and validity. This talk will focus on the management of data for effective retrieval and update scenarios. Further, the benefit of network analysis in handling social media data for timely decision support will be addressed with real case studies.

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Prof. Wenfeng Wang

Interscience Institute of Management and Technology, India

Dr. Wenfeng Wang is currently a tenured professor by IMT Institute in India and the director of Sino-Indian Joint research center of artificial intelligence and robotics. He is the editor in chief of International Journal of Electrical and Electronics Engineering (IJEEE) and the editor in chief of International journal of Applied Nonlinear Science (IJANS). He is also a professor of Shanghai Institute of Technology. He is the founder of International Academy of Visual Art and Engineering in London and the JWE Technological Research Center in Shanghai. He was selected in 2018 as a key tallent in Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences. He is a reviewer of many SCI journals, including some top journals - Water Research, Science China-Information Sciences, Science of the Total Environment, Nature Computational Science, Environmental Pollution, IEEE Transactions on Automation Science and Engineering and etc. He served as a keynote speaker of AMICR2019, IACICE2020, OAES2020, 3DIT-MSP&DL2020, NAMSP2021 and etc. He is also the scientist in chief of several influential companies (e.g., RealMax, SLAI Lab and etc.). Professor Wang has great will to serve for interested journals and he also knows well about how to develop these journals. His belief is to work at the best for each dream.

Speech Title: A Modified Meta-Learner For Few-Shot Learning

Abstract: Meta-Learning, or so-called Learning to learn, has become another important research branch in Machine Learning. Different from traditional deep learning, meta-learning can be used to solve one-to-many problems and has a better performance in few-shot learning which only few samples are available in each class. In these tasks, meta-learning is designed to quickly form a relatively reliable model through very limited samples. In this paper, we propose a modified LSTM-based meta-learning model, which can initialize and update the parameters of classifier (learner) considering both short-term knowledge of one task and long-term knowledge across multiple tasks. We reconstruct a Compound loss function to make up for the deficiency caused by the separate one in original model aiming for a quick start and better stability, without taking expensive operation. Our modification enables meta-learner to perform better under few-updates. Further experiments conducted on the Mini-ImageNet demonstrate the improved accuracy.