Pain assessment is a critical aspect of healthcare, influencing timely interventions and patient well-being. Traditional pain evaluation methods often rely on subjective patient reports, leading to inaccuracies and disparities in treatment, especially for patients who present difficulties to communicate due to cognitive impairments. Our contributions are three-fold. Firstly, we analyze the correlations of the data extracted from biomedical sensors. Then, we use state-of-the-art computer vision techniques to analyze videos focusing on the facial expressions of the patients, both per-frame and using the temporal context. We compare them and provide a baseline for pain assessment methods using two popular benchmarks: UNBC-McMaster Shoulder Pain Expression Archive Database and BioVid Heat Pain Database. We achieved an accuracy of over 96% and over 94% for the F1 Score, recall and precision metrics in pain estimation using single frames with the UNBC-McMaster dataset, employing state-of-the-art computer vision techniques such as Transformer-based architectures for vision tasks. In addition, from the conclusions drawn from the study, future lines of work in this area are discussed.

Anomaly detection is the identification of events or observations that deviate from the expected behaviour of a given set of data. Its main application is the prediction of possible technical failures. In particular, anomaly detection on supercomputers is a difficult problem to solve due to the large scale of the systems and the large number of components. Most research works in this field employ machine learning methods and regression models in a supervised fashion, which implies the need for a large amount of labelled data to train such systems. This work proposes the use of autoencoder models, allowing the problem to be approached with semi-supervised learning techniques. Two different model training approaches are compared. The former is a model trained with data from all the nodes of a supercomputer. In the latter approach, observing significant differences between nodes, one model is trained for each node. The results are analysed by evaluating the positive and negative aspects of each approach. On the other hand, a replica of the Marconi 100 supercomputer is developed in a virtual reality environment that allows the data from each node to be visualised at the same time.

Recent advances in facial recognition technology have achieved outstanding performance, but unconstrained face recognition remains an ongoing issue. Facial-image-quality-evaluation algorithms evaluate the quality of the input samples, providing crucial information about the accuracy of recognition decisions. By doing so, this can lead to improved results in challenging scenarios. In recent years, significant progress has been made in assessing the quality of facial images. The computation of quality scores has become highly precise and closely correlated with the model results. In this paper, we reviewed and analyzed the existing biases of cutting-edge quality-estimation techniques for face recognition. Our experimentation focused on the quality estimators developed by MagFace, FaceQNet, and SER-FIQ and were evaluated on the CelebA reference dataset. A study of bias in the face-recognition model was conducted by analyzing the quality scores presented in each article. This allowed for an examination of existing biases within both the quality estimators and the face-recognition models.