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Publications

Publications by LIAAD

2024

Towards Evaluation of Explainable Artificial Intelligence in Streaming Data

Authors
Mozolewski, M; Bobek, S; Ribeiro, RP; Nalepa, GJ; Gama, J;

Publication
EXPLAINABLE ARTIFICIAL INTELLIGENCE, XAI 2024, PT IV

Abstract
This study introduces a method to assess the quality of Explainable Artificial Intelligence (XAI) algorithms in dynamic data streams, concentrating on the fidelity and stability of feature-importance and rule-based explanations. We employ XAI metrics, such as fidelity and Lipschitz Stability, to compare explainers between each other and introduce the Comparative Expert Stability Index (CESI) for benchmarking explainers against domain knowledge. We adopted the aforementioned metrics to the streaming data scenario and tested them in an unsupervised classification scenario with simulated distribution shifts as different classes. The necessity for adaptable explainers in complex scenarios, like failure detection is underscored, stressing the importance of continued research into versatile explanation techniques to enhance XAI system robustness and interpretability.

2024

More (Enough) Is Better: Towards Few-Shot Illegal Landfill Waste Segmentation

Authors
Molina, M; Veloso, B; Ferreira, CA; Ribeiro, RP; Gama, J;

Publication
ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024)

Abstract
Image segmentation for detecting illegal landfill waste in aerial images is essential for environmental crime monitoring. Despite advancements in segmentation models, the primary challenge in this domain is the lack of annotated data due to the unknown locations of illegal waste disposals. This work mainly focuses on evaluating segmentation models for identifying individual illegal landfill waste segments using limited annotations. This research seeks to lay the groundwork for a comprehensive model evaluation to contribute to environmental crime monitoring and sustainability efforts by proposing to harness the combination of agnostic segmentation and supervised classification approaches. We mainly explore different metrics and combinations to better understand how to measure the quality of this applied segmentation problem. © 2024 The Authors.

2024

Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey

Authors
Jakubowski, J; Strzelecka, NW; Ribeiro, RP; Pashami, S; Bobek, S; Gama, J; Nalepa, GJ;

Publication
CoRR

Abstract

2024

Aequitas Flow: Streamlining Fair ML Experimentation

Authors
Jesus, SM; Saleiro, P; Silva, IOe; Jorge, BM; Ribeiro, RP; Gama, J; Bizarro, P; Ghani, R;

Publication
CoRR

Abstract

2024

Predictive Maintenance for Industry 4.0 & 5.0

Authors
Ribeiro, RP;

Publication
Proceedings of the 1st International Conference on Explainable AI for Neural and Symbolic Methods, EXPLAINS 2024, Porto, Portugal, November 20-22, 2024.

Abstract

2024

Emotion-Enhanced Pain Assessment Protocol

Authors
Alves, B; Almeida, A; Silva, C; Pais, D; Ribeiro, RP; Gama, J; Fernandes, JM; Brás, S; Sebastião, R;

Publication
Human and Artificial Rationalities. Advances in Cognition, Computation, and Consciousness - Third International Conference, HAR 2024, Paris, France, September 17-20, 2024, Proceedings

Abstract
Pain is a highly subjective phenomenon that depends on multiple factors. The common methods used to evaluate pain require the person to be awakened and cooperative, which may not always be possible. Moreover, such methods are subject to non-quantifiable influences, namely the impact of an individual’s emotional state on how pain is perceived or how negative emotions may exacerbate pain perception, while positive emotions may attenuate it. The goal of this study was to conduct a novel protocol for pain induction with emotional elicitation and assess its feasibility. In this protocol, the physiological responses were monitored, and collected, through Electrocardiogram, Electrodermal Activity, and surface Electromyogram signals. Along the protocol, the pain perception was evaluated using a 0–10 numerical rating scale and by registering the time from the pain stimulus beginning to the Pain and Tolerance Thresholds. This study comprised three emotional sessions, negative, positive, and neutral, which were performed through videos of excerpts of terror, comedy, and documentary films, respectively, followed by pain induction using the Cold Pressor Task (CPT). A total of 56 participants performed the study, with a CPT mean time of about 91.70 ± 39.64 s among all the sessions. The conducted protocol was considered feasible and safe as it allowed the collection of physiological data, pain, and questionnaires’ reports from 56 participants, without any harm to them. Moreover, the collected data can be further used to assess how emotional conditions influence pain perception and to provide better emotion-calibrated pain recognition systems based on physiological signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

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