Computer Science - Conference Items
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Item Evidence theory-based trust management for the Social Internet of Vehicles(Institute of Electrical and Electronics Engineers (IEEE), 2024-09-09) Shamaeian, Nasrin; Pesch, Dirk; Science Foundation IrelandThe Social Internet of Vehicles (SIoV) is a concept combining the principles of vehicular and social networks, where entities, such as vehicles, drivers, passengers and infrastructure, share information not only for intelligent transportation or cooperative mobility needs, but also using social network principles. Trust in the information exchanged between vehicles in a vehicular network is paramount to achieving safety and reliability of transportation. We propose a trust management model for SIoV that integrates entity trust from direct interactions between vehicles, indirect trust from recommendations, and social trust reflecting the drivers’ social attributes. We utilize Dempster–Shafer Theory to effectively manage inherent uncertainties within this network, enabling robust aggregation of various trust evidences. Our simulation results show the effectiveness of our model in accurately identifying and mitigating malicious entities within the network performing trust-related attacks. Published in: 2024 IEEE 49th Conference on Local Computer Networks (LCN)Item Conformal prediction techniques for electricity price forecasting(Springer Nature Switzerland AG, 2025) O'Connor, Ciaran; Prestwich, Steven D.; Visentin, AndreaIntegrating the erratic production of renewable energy into the electricity grid poses numerous challenges. One approach to stabilising market prices and reducing energy losses due to curtailments is the deployment of batteries. Efficient electricity arbitrage is crucial to make investments in storage systems financially viable; trading solutions to achieve this rely on price forecasting techniques. This study delves into the application of Conformal Prediction (CP) techniques, including Ensemble Batch Prediction Intervals (EnbPI) and Sequential Predictive Conformal Inference for Time Series (SPCI), for generating probabilistic forecasts in the Irish electricity market. Recent advancements in CP have addressed temporal considerations inherent in time series forecasting, eliminating the need for exchangeability assumptions. Our study demonstrates that despite potential efficiency trade-offs, CP methods consistently yield precise and reliable prediction intervals, ensuring comprehensive coverage. We assess the impact of CP on the financial results of a simulated trading algorithm. Monetary outcomes achieved with EnbPI and SPCI outperform those of both split CP and traditional quantile regression models, highlighting the practical superiority of CP in electricity price forecasting.Item A machine learning approach to model counting(Institute of Electrical and Electronics Engineers (IEEE), 2024) Dalla, Marco; Visentin, Andrea; O'Sullivan, Barry; Science Foundation Ireland; European Regional Development FundModel counting (#SAT) is the problem of computing the number of satisfying assignments for a given Boolean formula. It has a significant theoretical and practical interest. Tackling it can be challenging since the number of potential solution grows exponentially with the number of variables. Due to the inherent complexity of the problem, approaches to approximate model counting have been developed as a practical alternative. These methods extract the number of solutions within user-specified tolerance and confidence levels and in a fraction of the time required by exact model counters. However, even these methods require extensive computations, restricting their applicability to relatively small instances. In this paper, we propose a new approximate machine learning model counter that overcome this limitation. Predicting the number of solutions can be seen as a regression problem. We deploy an array of machine learning techniques trained to infer the approximate number of solutions based on statistical features extracted from a SAT propositional formula. Extensive numerical experiments performed on synthetic crafted and benchmark datasets show that learning approaches can provide a good approximation of the number of solutions with a much lower computational time and resource cost than the state-of-the-art approximate and exact model counters. Making it possible to approximate the model count of instances previously out of reach. We then investigated the structural factors that lead to a high model count using AI explainability approaches.Item Machine learning physical fatigue estimation approach based on IMU and EMG wearable sensors(2024) Nair, Suraj P.; Sica, Marco; Tedesco, Salvatore; Visentin, Andrea; Science Foundation IrelandPhysical fatigue refers to a state of exhaustion or reduced capacity for physical performance due to prolonged exertion, repetitive movements, or lack of rest. It is a multifaceted condition that can severely impact performance, especially in activities requiring sustained effort, precision, or concentration. In physical tasks, fatigue manifests as a decrease in muscle strength, coordination, and endurance, leading to diminished performance and an increased risk of injury. Detecting physical fatigue is crucial in a variety of domains: professional sports, collaborative robotics, construction, and more. This research introduces a novel framework for predicting fatigue during shoulder movements using data collected from wearable inertial measurement units and electromyography sensors. By integrating the Borg Scale, a subjective measure of perceived exertion, our approach uniquely combines objective sensor data with user-reported fatigue levels, creating a more holistic fatigue assessment model. The primary aim of this study is to develop a predictive model capable of accurately estimating fatigue, as measured by the Borg Scale. An investigation of the best machine learning algorithm for this task ensures that the chosen method provides the most reliable predictions. Furthermore, by systematically reducing the number of sensors and analyzing the impact on model performance, it is possible to find a minimal sensor configuration that maintains the model’s predictive power while reducing complexity and cost. The Ridge Regression model, after hyperparameter tuning, outperformed other models, achieving a mean absolute error of 2.417 in predicting fatigue. This preliminary study shows the potential of integrating data from different inertial and electromyography sensors for fatigue prediction in shoulder movements, with potential applications in occupational safety.Item Comparison of industrial control system anomaly detection methods(Association for Computing Machinery (ACM), 2024-11-20) Sobonski, Piotr; Roedig, Utz; Science Foundation IrelandIndustrial Control System (ICS) are used to produce goods that must be free of errors. Examples are medicines, medical equipment or vehicle parts. It is essential in such production environments to detect an attack which may aim to compromise goods. While Anomaly Detection (AD) is common to protect Information Technology (IT) infrastructure, it is not yet widely used to protect Operational Technology (OT) elements such as ICS and ultimately production. In this work we analyze the usefulness of different AD algorithms in the context of ICS. We aim to determine if simple statistical methods such as K-Means clustering (K-Means), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Stochastic Gradient Decent (SGD) or Support Vector Machine (SVM) are sufficient or if more advanced Machine Learning (ML) algorithms such as an Autoencoder are necessary to achieve a useful performance. Specifically, we consider real-world constraints such as limited available attack examples in training data and variations in background conditions. We use an evaluation framework called Anomaly Detection Evaluation Framework (ADEF) to model an autoclave manufacturing use case and possible attacks. Using ADEF we benchmark different AD algorithms. Our results show that simple methods perform very well, that large amount of attack examples are un necessary and that fluctuations in environmental conditions pose a significant challenge.