PAtCH: Proactive Approach to Circumvent Holes in Wireless Sensor Networks.

The occurrence of hole regions in Wireless Sensor Networks is a significant challenge when applying a greedy technique in a geographic routing approach. The local minimum phenomenon is commonly attributed to physical obstacles, energy depletion of the nodes, failures in communication between neighbors, or even the incorrect deployment of the nodes in the sensing field. To address the problem of hole regions, most approaches choose to abandon the traditional greedy forwarding mechanism to temporarily adopt the well-known perimeter routing scheme applied to nearby nodes or along the edge of a region of a hole.

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals.

The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g.

How Validation Methodology Influences Human Activity Recognition Mobile Systems.

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results.