Face mask detection has become a great challenge in computer vision, demanding the coalition of technology with COVID-19 awareness. Researchers have proposed deep learning models to detect the use of face masks. However, the incorrect use of a face mask can be as harmful as not wearing any protection at all. In this paper, we propose a compound convolutional neural network (CNN) architecture based on two computer vision tasks: object localization to discover faces in images/videos, followed by an image classification CNN to categorize the faces and show if someone is using a face mask correctly, incorrectly, or not at all. The first CNN is built upon RetinaFace, a model to detect faces in images, whereas the second CNN uses a ResNet-18 architecture as a classification backbone. Our model enables an accurate identification of people who are not correctly following the COVID-19 healthcare recommendations on face mask use. To enable further global use of our technology, we have released both the dataset used to train the classification model and our proposed computer vision pipeline to the public, and optimized it for embedded systems deployment.

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient’s weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed. For this purpose, both hardware- and software-related criteria were studied for an improved data acquisition through signal coupling and filtering processes. Subsequently, learning algorithms allowed us to estimate the type of footprint biomechanics in preschool and school children volunteers. As a result, the proposed algorithm achieved an overall classification accuracy of 97.2%. A flat feet share of 60% was encountered in a sample of 1000 preschool children. Similarly, flat feet were observed in 52% of a sample of 600 school children.

En los últimos años una de las temáticas más difundidas en diferentes contextos de aplicación es la de los juegos serios, por tanto, a medida que su uso se masifica es conveniente articular de mejor forma los juegos serios en el contexto educativo, de tal manera que, posibiliten el desarrollo de competencias definidas en los planes de estudio y se diseñen centrados en el usuario. En este artículo, se propone como contribución un conjunto de 51 directrices para el diseño, construcción y evaluación de videojuegos serios educativos, las cuales incluyen aspectos relacionados con usabilidad, aprendizaje y el juego. Las directrices fueron empleadas para inspeccionar el videojuego lightbot, con el fin de determinar los aspectos de diseño a mejorar en dicho videojuego. Las directrices propuestas en este trabajo pretenden vincularse en el proceso de desarrollo y en las metodologías para la construcción de videojuegos, de cara a propiciar la generación de videojuegos serios educativos centrados en el usuario y más pertinentes al contexto educativo deseado.

Introduction − Making effort estimation as accurate and suitable for software development projects becomes a fundamental stage to favor its success, which is a difficult task, since the application of these techniques in constant changing agile development projects raises the need to evaluate different methods frequently. Objectives− The objective of this study is to provide a state of the art on techniques of effort estimation in agile software development (ASD), performance evaluation and the drawbacks that arise in its application. Method− A systematic mapping was developed involving the creation of research questions to provide a layout of this study, analysis of related words for the implementation of a search query to obtain related studies, application of exclusion, inclusion, and quality criteria to filter nonrelated studies and finally the organization and extraction of the necessary information from each study. Results− 25 studies were selected; the main findings are: the most applied estimation techniques in agile contexts are: Estimation of Story Points (SP) followed by Planning Poker (PP) and Expert Judgment (EJ). The most frequent solutions supported in computational techniques such as: Naive Bayes, Regression Algorithms and Hybrid System; also, the performance evaluation measures Mean Magnitude of Relative Error (MMRE), Prediction Assessment (PRED) and Mean Absolute Error (MAE) have been found to be the most commonly used. Additionally, parameters such as feasibility, experience, and the delivery of expert knowledge, as well as the constant particularity and lack of data in the process of creating models to be applied to a limited number of environments are the challenges that arise the most when estimating software in agile software development (ASD) Conclusions− It has been found there is an increase in the number of articles that address effort estimation in agile development, however, it becomes evident the need to improve the accuracy of the estimation by using estimation techniques supported in machine learning that have been shown to facilitate and improve the performance of this.

Este trabajo tiene como objetivo desarrollar un estudio de análisis de sentimiento y de texto, con el fin de evaluar la percepción de los asistentes a una feria virtual de emprendimiento, la cual fue diseñada y adaptada a la presencialidad remota con el apoyo de las TIC dentro del curso de “Emprendimiento Empresarial” del Programa de Ingeniería Química de la Universidad de Cartagena. Metodología: Para el desarrollo de este trabajo se definieron cuatro fases metodológicas a saber: diseño y organización de la feria, preparación y adecuación de los datos de las encuestas de percepción, análisis de texto y finalmente análisis de sentimientos sobre los resultados de las preguntas cualitativas. Resultados: El estudio de análisis de sentimientos mostró que las polaridades con mayor contribución por parte de los evaluadores y participantes fueron la neutral y la positiva, quienes resaltaron la creatividad en la organización y aprovechamiento de las TIC. Conclusiones: Se concluyó que la propuesta diseñada para el desarrollo de la feria virtual de emprendimiento tuvo un impacto positivo y puede ser considerado de referencia para su extrapolación en el desarrollo de diferentes eventos académicos durante la pandemia. Así mismo, se concluye que las técnicas de análisis de sentimientos pueden ser adecuadas para el análisis de cuestionarios de percepción en diferentes contextos de aplicación.