Technology played a crucial role in combating the COVID pandemic, both in the rapid development of vaccines and the early detection of the virus. Consequently, numerous studies in the medical field have focused on leveraging the power of artificial intelligence for COVID-19 detection. However, in the medical domain, it is essential to have a clear understanding of the processes and algorithms used in decision-making, as these directly impact people’s health. Therefore, efforts have been made to implement explainable artificial intelligence techniques, enabling humans to understand and explain the deep learning algorithms used in disease detection. In this work, we present a novel approach to detecting COVID-19 in chest X-rays using a Variational Autoencoder model to identify lung anomalies. This methodology aims to highlight critical areas of the image, allowing healthcare professionals to identify them more effectively. Additionally, it seeks to provide clearer explanations of the decisions made by the artificial i ntelligence, r educing the complexity of the "black boxes" generated by deep learning neural networks. With this methodology, we hope to improve the effectiveness and reliability of early COVID-19 detection through chest X-rays.

Recognizing variable stars is a task of interest in the astronomy community. Currently, this task has taken advantage of deep learning algorithms. However, these algorithms require a large amount of data to achieve high levels of precision. In this work, self-supervised learning is proposed to improve the classification of variable stars considering a reduced amount of data using recurrent networks. The experiments in Gaia dataset show that the proposed approach allows to improve performance, when compared with traditional initialization schemes, up to 7% and 13% in real databases in semi-supervised learning scenarios. In future work, we propose considering experiments with other variable star databases.

This work investigates the causal correlation between the resolution of a programming guide and academic performance in an introductory programming course at Andrés Bello University in Chile. Specifically, we explore whether completing a guide comprising fifty-two exercises can predict first-year students' performance on the initial test of the course. In particular, we propose utilizing causal modelling framework to analyze and comprehend the impact of programming guides on student performance. The research encompasses a review of pertinent literature, a descriptive examination of collected data, and a discussion on both practical and theoretical implications. The findings aim to enhance strategies for student support and inform decision-making regarding the educational utility of guides

In these days, there are a growing interest in pattern recognition for tasks as prediction of weather events, recommendation of the best route, intrusion detection or face detection. Each of these tasks can be modelled as classification problem, where a common alternative is to use an ensemble model of classification. A well-known example is given by Mixture-of-Experts model, which represents a probabilistic artificial neural network consisting of local experts classifiers weighted by a gate network, and whose combination creates an environment of competition among experts seeking to obtain patterns of the data source. We observe that this architecture assume that one gate influence only one data point, consequently the training can be misguided in real datasets where the data is better explained by multiple experts. In this work, we present a variant of regular Mixture-of-Experts model, which consists of maximizing of the entropy of gate network in addition to classification cost minimization. The results show the advantage of our approach in multiple datasets in terms of accuracy metric. As a future work, we plan to apply this idea to the Mixture-of-Experts with embedded feature selection.

This work describes our experience developing a system to access density and flow of people in large indoor spaces using a network of RGB cameras. The proposed system is based on a set of overlapped and calibrated cameras. This facilitates the use of geometric constraints that help to reduce visual ambiguities. These constraints are combined with classifiers based on visual appearance to produce an efficient and robust method to detect and track humans. In this work, we argue that flow and density of people are low level measurements that need to be complemented with suitable analytic tools to bridge semantic gaps and become useful information for a target application. Consequently, we also propose a set of analytic tools that help a human user to effectively take advantage of the measurements provided by the system. Finally, we report results that demonstrate the relevance of the proposed ideas.

Clustering is a relevant problem in machine learning where the main goal is to locate meaningful partitions of unlabeled data. In the case of labeled data, a related problem is supervised clustering, where the objective is to locate class-uniform clusters. Most current approaches to supervised clus tering optimize a score related to cluster purity with respect to class labels. In particular, we present Labeled K-Means (LK-Means), an algorithm for supervised clustering based on a variant of K-Means that incorporates information about class labels. LK-Means replaces the classical cost function of K-Means by a convex combination of the joint cost associated to: (i) A discriminative score based on class labels, and (ii) A generative score based on a traditional metric for unsupervised clustering. We test the performance of LK-Means using standard real datasets and an application for object recognition. Moreover, we also compare its performance against classical K-Means and a popular K-Medoids-based supervised clustering method. Our experiments show that, in most cases, LK-Means outperforms the alternative techniques by a considerable margin. Furthermore, LK-Means presents execution times considerably lower than the alternative supervised clustering method under evaluation.

Robust category-level object recognition is currently a major goal for the computer vision community. Intra-class and pose variations, as well as, background clutter and partial occlusions are some of the main difficulties to achieve this goal. Contextual information, in the form of object co-occurrences and spatial constraints, has been successfully applied to improve object recognition performance, however, previous work considers only fixed contextual relations that do not depend of the type of scene under inspection. In this work, we present a method that learns adaptive conditional relationships that depend on the type of scene being analyzed. In particular, we propose a model based on a conditional mixture of trees that is able to capture contextual relationships among objects using global information about a scene. Our experiments show that the adaptive specialization of contextual relationships improves object recognition accuracy outperforming previous state-of-the-art approaches.