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

Abstract: The idea of tolerance relation attributed to Zeeman is based on two fundamental concepts: reflexivity and symmetric relation. Although tolerance relations are used to study indiscernibility or indistinguishability phenomena, it has wide range of applications; recent trend has witnessed its application in the field of information system and image analysis. We have recently identified tolerance spaces in the context of quantale-valued convergence spaces, showing that the category of quantale-valued tolerance spaces is isomorphic to the category of quantale-valued convergence spaces. In this talk, we look at function spaces of tolerance spaces and convergence spaces, the role of the degree of transitivity in relation to quantale-valued convergence groups, and their related categorical aspects. Furthermore, we investigate monad and its Eilenberg-Moore category from the perspective of quantale-valued tolerance groups, and convergence groups.
Keywords: Tolerance space, convergence space, probabilistic convergence space, transitive relation, grade of trasitivity, closure operator, function space, group, category theory, monad, Eilenberg-Moore category.

Abstract: The integration of Generative AI with the Metaverse presents a transformative approach to education, which promises to create immersive, personalized learning experiences that transcends the traditional classroom practices. However, this integration also introduces a complex array of challenges that must be addressed to unleash its full potential. The speech explores the different aspects of merging Generative AI with the Metaverse for next-generation education with a focus on technological, pedagogical and ethical aspects.

Abstract: In this talk, we will explore how swarm intelligence algorithms, inspired by the collective behavior of social insects and animals, are being applied to solve complex industrial problems. We will delve into the mathematical foundations of these algorithms, such as Ant Colony Optimization and Particle Swarm Optimization, and illustrate their practical applications across various industries. Through real-world success stories, we will demonstrate how these smart solutions enhance efficiency, reduce costs, and drive innovation in fields like manufacturing, telecommunications, healthcare, and finance. Join us to uncover the transformative power of swarm intelligence and its potential to revolutionize industrial practices.

Abstract: Deep Neural Networks [DNNs] are being integrated into Automated Driving Systems [ADS] to perform complex perception and control problems. However, DNNs are generally challenging or impossible to interpret for the purpose of functional safety [FuSa] or Safety of the intended functionality [SOTIF] assessment. In contrast, physical models of the driving task are generally much easier to explain and assess than the abstract statistical models encoded in a DNN. In this paper, we present a statistical modelling and evaluation workflow that can be easily explained to FuSa and SOTIF assessors. Our workflow uses Bayesian networks [BN] refining fault trees and a physical model of an ADS in a given scenario. The Dominant Factors [DF] that impact the ADS risk can then be identified based on simulations of the physical model and simulations sampled from the BN. The workflow can evaluate under which conditions a tolerable risk target [TRT] can be achieved. We evaluate our proposed workflow in an example high-frequency traffic scenario, a highway cut-in scenario. We compare two methods to identify and confirm the DF for meeting the TRT. The DF found show that a static operating design domain [ODD] definition is insufficient. In the example, if the sense-plan-act control architecture is extended by a dynamic traffic monitoring protection layer, the TRT can be achieved.