Abstract
In the field of gaming artificial intelligence, selecting the appropriate machine learning approach is essential for improving decision-making and automation. This paper examines the effectiveness of deep reinforcement learning (DRL) within interactive gaming environments, focusing on complex decision-making tasks. Utilizing the Unity engine, we conducted experiments to evaluate DRL methodologies in simulating realistic and adaptive agent behavior. A vehicle driving game is implemented, in which the goal is to reach a certain target within a small number of steps, while respecting the boundaries of the roads. Our study compares Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) in terms of learning efficiency, decision-making accuracy, and adaptability. The results demonstrate that PPO successfully learns to reach the target, achieving higher and more stable cumulative rewards. Conversely, SAC struggles to reach the target, displaying significant variability and lower performance. These findings highlight the effectiveness of PPO in this context and indicate the need for further development, adaptation, and tuning of SAC. This research contributes to developing innovative approaches in how ML can improve how player agents adapt and react to their environments, thereby enhancing realism and dynamics in gaming experiences. Additionally, this work emphasizes the utility of using games to evolve such models, preparing them for real-world applications, namely in the field of vehicles' autonomous driving and optimal route calculation.

Abstract
This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases. Using the Racial Faces in the Wild (RFW) dataset and synthetically added realistic occlusions, we evaluate their effect on the performance of face recognition models trained on the BUPT-Balanced and BUPT-GlobalFace datasets. We note increases in the dispersion of FMR, FNMR, and accuracy alongside decreases in fairness according to Equalized Odds, Demographic Parity, STD of Accuracy, and Fairness Discrepancy Rate. Additionally, we utilize a pixel attribution method to understand the importance of occlusions in model predictions, proposing a new metric, Face Occlusion Impact Ratio (FOIR), that quantifies the extent to which occlusions affect model performance across different demographic groups. Our results indicate that occlusions exacerbate existing demographic biases, with models placing higher importance on occlusions in an unequal fashion across demographics.

Abstract
Automatically generated text has become a popular method for increasing the volume of information used to pre-train audio-visual foundational models. Such data is often leveraged in large volumes of text corpora for pre-training, which can amplify errors caused by incorrectly labelled or inaccurate information. Since it introduces considerable noise, the need for automatic validation strategies for large text collections is becoming particularly important. We present a reference-free method of dialogue evaluation by examining Topic Consistency (TC) within the generated text. Our approach competes with state-of-the-art techniques and provides a more explainable method via questioning. By utilising knowledge databases like ConceptNet, we also explore expanding the topics’ semantic variations for performance improvement. Our contributions include: 1) An objective evaluation methodology for TC; 2) Extensive experiments on four benchmark datasets. Code and data are available on the project page: github.com/lvilaca16/lm-evaluation. © 2025 Copyright held by the owner/author(s)

Abstract
The global shift toward a low-carbon era requires a greater integration of Renewable Energy Sources (RESs) and the phase-out of power plants fueled by non-Renewable Energy Sources (nRESs). Meanwhile, Climate Change (CC) and Extreme Weather Events (EWEs) are increasingly affecting energy systems, introducing new layers of uncertainty in long- and short-term power system planning. This study presents a Multi-Objective Transmission Expansion Planning (MO-TEP) model aimed at improving the flexibility of the ERCOT grid while reducing Greenhouse Gas (GHG) emissions. This model uses real data from Form EIA-860 for RESs penetration and decommissioning of nRESs. The ACTIVSg2000 test case, a synthetic ERCOT-like system, validates the study. The findings emphasize the importance of incorporating high-impact, low-probability (HILP) events into TEP decisions and highlight the critical role of grid flexibility in enhancing resilience. This research contributes to a more reliable and sustainable electricity network capable of adapting to evolving environmental and operational conditions.

Abstract
The advent of large Machine Learning models and the steep increase in the demand for AI solutions occurs at the same point in time in which policies are being enacted to implement more sustainable processes in virtually every sector. This means there is a need for more, better and larger models, which require significant computational resources, while at the same time a call for a decrease in the energy spent in the processes associated to MLOps. In this paper we propose a reduced set of meta-features that can be used to characterize sets of data and their relationship with model performance. We start from a large set of 66 features, and reduce it to only 10 while maintaining the strength of this relationship. This ensures a process of meta-feature extraction and prediction of model performance that is in line with the desiderata of Frugal AI, allowing to develop more efficient ML processes.

Abstract
This paper addresses the persistent rise in motorcycle-related fatalities, even as overall road deaths decline, by introducing an adaptive Fuzzy System based on the Takagi-Sugeno model. The system evaluates parameters such as acceleration and lean angle to classify rider behavior into categories such as normal, aggressive, or dangerous, providing timely feedback aimed at promoting safer driving practices. A key component of this approach is the Local Outlier Factor (LOF) algorithm, which identifies hazardous behaviors by quantifying deviations from standard riding patterns, thereby allowing the establishment of adaptive safety thresholds. By integrating fuzzy logic, the system offers refined decision-making capabilities in complex riding conditions, enhancing active safety systems such as traction and braking controls. This work emphasizes the critical role of behavior-based insights in mitigating accidents, particularly since rider actions are a major contributing factor to motorcycle incidents.