Prof. Fairouz Kamareddine, Heriot-Watt University, UK

Professor of Theoretical Computer Science in Heriot-Watt University in Scotland. Fairouz Kamareddine has been involved in a number of worldwide consultancy assignments, especially on education and research issues working for United Nations and the EU. Her research interest includes Interface of Mathematics, Logic and Computer Science. She has held numerous invited positions at universities worldwide. She has well over 200 published articles and a number of books. She has played leading roles in the development, administration, and implementation of interdisciplinary, international, and academic collaborations and networks.

Speech Title: The paradoxes and the infinite dazzled ancient mathematics and continue to do so today

Abstract:This talk looks at how ancient mathematicians (and especially the Pythagorean school) were faced by problems/paradoxes associated with the infinite which led them to juggle two systems of numbers: the discrete whole/rationals which were handled arithmetically and the continuous magnitude quantities which were handled geometrically. We look at how approximations and mixed numbers (whole numbers with fractions) helped develop the arithmetization of geoemtry and the development of mathematical analysis and real numbers.

 

Gang Li, Qilu University of Technology(Shandong Academy of Sciences), China

Gang Li, Professor and Doctoral Supervisor of Qilu University of Technology, Young Expert of Shandong Province Taishan Scholar, Winner of the Shandong Provincial May Day Labor Medal，ISO/IEC JTC1 (Information Technology) Registered Expert,ISO/IEC JTC1 (Information Technology) Registered Expert,Member of Standardization Principles and Methods Standardization Technical Committee (SAC/TC 286),Deputy Director of the Interconnection of Information Technology Equipment (TC28/SC25).Engaged in big data analysis, digital economy, digital government and other direction research.In the past five years, he has undertaken more than 40 national key research and development, provincial science and technology major projects,led or participated in the release of 35 national standards and 29 local standards, won 9 provincial scientific and ministerial science and technology awards, published more than 30 SCI / EI index papers, published 5 academic monographs, compiled 1 teaching materials, and authorized 21 invention patents

 

William Wei Song, Dalarna University, Sweden

He has been general chair, track chair and program committee chair of international conferences workshops, and symposiums, including World Wide Web, ICCIA 2017-2021, and ISD series. He was keynote speaker at ICKET, WAST, ICCIA, etc. He has been reviewer of many scientific and technology foundations, including ITF (Hong Kong), Vinnova (Sweden), EPSRC (UK), and ESPRIT (EEC) and FP6/7 (EU), and NSFC (China). Prof. Song has published over 150 research papers in international journals including Data and Knowledge Engineering (Elsevier), Journal on Computational Logic, and Information Science, and conferences, including Conceptual Modelling (former Entity Relationships Modelling), CAiSE, WWW, WISE, COMPSAC, and ISD. His research interests cover Database Systems, Conceptual Modelling, Web Science, Semantic Web, Computational Social Network, and Big Data analysis (in intelligent transport, smart cities, and e-Healthcare).

Speech Title: A Russellian (formal and semantic) angle to view the issues in the big data analysis field

Abstract: The methods for big data analysis have been booming for the last decade particularly since the introduction of deep learning algorithms. However, when facing the requirements of interpretation of the analysis results, exact reasoning process, and complex representation of domain knowledge, the conventional learning methods may not provide a satisfactory approach and solution to the requirements given above. As said by Bertrand Russell in his book “Introduction to Mathematical Philosophy”, “… the method is more important than the results, from the point of view of further research; and the method cannot well be explained within the framework …”.
In this talk, I intend to address the above-mentioned issues in the big data analysis procedure from the angles of formality, semantics and logics which could lead to different solutions. Considering the power of AI in logic and the semantic web (SW) in semantics, we propose a coordinate-representational framework for the big data (BD) analysis with the concepts of AI, the SW, and BD, where a fundamental representation of data objects is triples, a term borrowed from SW. In this coordinate, the data-knowledge is viewed as a complex object in hyper-structure. In this hyper-structure, an object is viewed, understood, and interpreted in relativity to the e.g. adjacent objects. In relativity to a complex object, a simple object is considered as an object with coarse granularity. Based on the inter-object relationship representation (still on the triples), data reasoning is done in terms of the equality equation theory and knowledge inference is done in terms of the knowledge graphs (KG) acquired from application domains.
As known to all, LLM is a successful application of deep learning methods in NLP (natural language process), particularly in identifying the “meanings” of text through building up text-meaning patterns with ten-millions of training data. And the connections between the sentences may form semantic interference of meanings, which it is suspicious (unclear or uncertain) whether it can compete logic reasoning. In consequence, with an explanation of the above concepts in an example of Large Language Models (LLM), I attempt to offer a formal definition of the key concepts applied in the data analysis processes and structures and aim to pave a novel road (the coordinate-based representation of the relationships among the BG, the AI, and the SW) toward semantic and knowledge-based interferences and analysis, thus leading to a tremendous improvement of big data analysis.