Tutorials

Tutorial 1: Approaches, Methods, and Tools for the Engineering and Validation of Cyber-Physical Energy Systems

Organisers: Dr. Thomas L. Strasser
Date: 6th October 2024

Abstract: A driving force for the realisation of a sustainable energy supply is the integration of renewable energy resources. Due to their stochastic generation behaviour, energy utilities are confronted with a more complex operation of the underlying power grids. Additionally, due to technological developments, controllable loads, integration with other energy sources, changing regulatory rules, and market liberalisation, the system’s operation needs adaptation. Proper operational concepts and intelligent automation provide the basis to turn the existing power system into an intelligent entity, a smart grid. While reaping the benefits that come along with those intelligent behaviours, it is expected that system-level developments and testing will play a significantly larger role in realising future solutions and technologies. Proper validation approaches, concepts, and tools have been partly missing until now. This tutorial aims to tackle the above-mentioned requirements by introducing validation methods and tools for validating smart grids and energy systems.

Tutorial 2: Preference-Based Combinatorial Applications

Organisers: Dr. Malek Mouhoub
Date: 6th October 2024

Abstract: Combinatorial problems refer to those applications where we either look for the existence of a consistent scenario satisfying a set of constraints (decision problem), or for one or more good/best solutions meeting a set of requirements while optimising some objectives (optimization problem). These latter objectives include user's preferences that reflect desires and choices that need to be satisfied as much as possible. Moreover, constraints and objectives (in the case of an optimization problem) often come with uncertainty due to lack of knowledge, missing information, or variability caused by events which are under nature's control. Finally, in some applications such as timetabling, urban planning and robot motion planning, these constraints and objectives can be temporal, spatial or both. In this latter case, we are dealing with entities occupying a given position in time and space. Because of the importance of these problems in so many fields, a wide variety of techniques and programming languages from artificial intelligence, computational logic, operations research, and discrete mathematics, are being developed to tackle problems of this kind. While these tools have provided very promising results at both the representation and the reasoning levels, they are still impractical to deal with many real-world applications. Using the Constraint Satisfaction Problem (CSP) formalism, we will explore several exact and approximate solving techniques to address the challenges and limitations listed above. Given that problem modelling is a tedious task requiring strong expertise and background, we will rely on machine learning techniques to automate this process. In this context, we will explore different constraint acquisition and preference learning algorithms.

Tutorial 3: Design of Situation-aware Wearable Computing Systems and Cyber-Physical Systems

Organisers: Giuseppe D’Aniello, Giancarlo Fortino, Francesco Flammini
Date: 6th October 2024

Abstract: Situation Awareness (SA) enables human and artificial agents to understand and react to their environment. The rise of sensor-equipped wearable devices has spurred research into situation-aware wearable computing systems (SA-WCS)—devices that perceive and interpret environmental changes to meet user needs proactively. This tutorial introduces a design methodology based on Endsley's SA model for creating SA-WCS and cyber-physical systems across various domains. It outlines a general-purpose SA-WCS architecture and a hybrid approach combining machine learning and logic-based formalisms, such as context space theory and event description languages, for identifying situations from raw data. Machine learning classifies activities and situations, while logic-based models provide a formal representation of contexts, enhancing classification. Through real-world examples, particularly in healthcare and critical infrastructure, the tutorial highlights the benefits of SA-WCS and SA-CPS over traditional approaches.

Tutorial 4: Introduction to Evolutionary Multi-Objective Optimization

Organisers: Hisao Ishibuchi, Lie Meng Pang
Date: 6th October 2024

Abstract: Multi-objective optimization problems are commonly found in many real-world applications. These problems involve the simultaneous optimization of multiple conflicting objective functions. Consequently, they do not yield a single optimal solution but rather a set of trade-off solutions. The trade-off solutions are often defined using the Pareto dominance relation and are known as Pareto optimal solutions. When mapped to the objective space, they form the Pareto front. Evolutionary Multi-Objective Optimization (EMO) algorithms have been a popular approach for solving multi-objective optimization problems. Thanks to their population-based search nature, EMO algorithms can obtain a set of non-dominated solutions in a single run, which is then used to approximate the Pareto front. This tutorial will give a comprehensive introduction on the fundamental concepts of evolutionary multi-objective optimization including commonly used strategies in designing EMO algorithms. Additionally, in this tutorial, we will cover some recent hot topics and advancements in the field, ensuring audiences are updated on the latest developments in evolutionary multi-objective optimization.

Tutorial 5: AI based Malware Detection

Organisers: Dr. Hemant Rathore, Dr. Mohit Sewak
Date: 6th October 2024

Abstract: Today computing devices like laptops, mobile phones, smart devices, etc., have penetrated very deep into our modern society and have become an integral part of our daily lives. Currently, more than half of the world's population uses computing devices for their professional and personal needs. However, these devices are targeted by malware designers encouraged by profits/gains associated with the attack. According to a recent report, monetary losses due to cybercrime are expected to reach 10 trillion dollars annually by 2025. The primary role in providing defence against malware attacks is developed by the anti-malware community (researchers and the anti-virus industry). Traditionally anti-viruses are based on the signature, heuristic, and behaviour based detection engines. However, these engines are unable to detect next-generation polymorphic and metamorphic malware. Thus researchers have started developing malware detection engines based on machine learning to complement the existing antivirus engines. However, there are many open research challenges in these models like adversarial robustness, explainability, fairness, etc., which we are going to discuss in detail during the tutorial.