La Evaluación Institucional: ¿Qué tiene la Escuela? ¿Qué entrega la Escuela?

La gestión educativa está experimentando cambios importantes. La búsqueda de recursos en el propio nivel de la unidad escolar, la acción competitiva que involucra a la 'empresa educativa', nos presentan situaciones que han hecho cambiar el esquema tradicional que estábamos acostumbrados a observar. Se aumentan las exigencias, los requisitos y el manejo tecnológico es absolutamente necesario. La comunidad se preocupa por la escuela, pero también la enjuicia y presiona. La unidad educativa está siendo vigilada con ojos de eficiencia y de sentido ético. La evaluación institucional aplicada al área de la educación se va a definir como una actividad de investigación y análisis para llegar a verificar logros y deficiencias. Esta evaluación se va a nutrir en fuentes de tipo empírico y científico y como producto de ello, se le valorará para los fines del mejoramiento de la educación. En este trabajo se presentan algunas apreciaciones sobre las características generales que tiene la evaluación institucional de la unidad educativa, junto con la presentación de aspectos metodológicos iniciales

Reconocimiento de objetos y obtención de mapas 3D

La sociedad actual tiene ante sí una diversidad amplia de retos sociales. Entre ellos, destaca la atención a la población en situación de dependencia, un fenómeno que afecta a todas las edades, no sólo a las personas mayores. Los servicios que dan solución a estos problemas están basados en el diseño y uso de robots sociales autónomos en el hogar. Esto implica la integración de diversas tecnologías, así como aportar soluciones a una diversidad de retos tecnológicos que este tipo de sistemas llevan emparejados. En este proyecto se trabajará con el robot Pepper, construido por la empresa Aldebaran Robotics, para el reconocimiento de los objetos del entorno y el desarrollo de un mapa 3D que le permita localizarlos y navegar dentro del hogar. Para la tarea del reconocimiento de objetos se hará uso de una técnica que utiliza el reconocimiento de objetos en imágenes de color mediante Deep Learning y que se apoya en la imagen de profundidad para segmentar el objeto. En cuanto a la realización del mapa 3D, se explorarán técnicas de registro visual y se estudiará el sistema de odometría del robot

Socially Assistive Robots for Older Adults and People with Autism: An Overview

Over one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.This research was funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. It has also been supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

A Hand Motor Skills Rehabilitation for the Injured Implemented on a Social Robot

In this work, we introduce HaReS, a hand rehabilitation system. Our proposal integrates a series of exercises, jointly developed with a foundation for those with motor and cognitive injuries, that are aimed at improving the skills of patients and the adherence to the rehabilitation plan. Our system takes advantage of a low-cost hand-tracking device to provide a quantitative analysis of the performance of the patient. It also integrates a low-cost surface electromyography (sEMG) sensor in order to provide insight about which muscles are being activated while completing the exercises. It is also modular and can be deployed on a social robot. We tested our proposal in two different facilities for rehabilitation with high success. The therapists and patients felt more motivation while using HaReS, which improved the adherence to the rehabilitation plan. In addition, the therapists were able to provide services to more patients than when they used their traditional methodology.This work was funded by a Spanish Government PID2019-104818RB-I00 grant, supported by Feder funds. It was also supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

Permeabilidad del espacio indígena. Discursos de propietarios Mapuche sobre la expansión urbana en el periurbano de Temuco, Araucanía-Chile

Cities are characterized by exerting constant pressure on peri-urban rural land. The logics under which the real estate market and other different agents operate, together with the flexibility of urban planning instruments that regulate the territory, means urban space environments are permanently changing. Temuco, one of the most important intermediate cities in Chile in terms of population numbers, operates under the same logics. However, unlike other Chilean cities, the presence of Indigenous Territory Protection Areas (APTI) associated with Mapuche communities, establishes legal barriers that impede the conventional growth of the city. Likewise, it is possible to see how in recent decades these lands have been permeable to different uses, outside the dimensions supposedly protected by law. This work explores the discourses of Mapuche peri urban landowners regarding the changes that these areas have recently undergone as a result of the city’s expansion. To do this, 20 interviews were conducted with Mapuche landowners from peri-urban areas around Labranza, an urban area of Temuco, which were analyzed under the parameters of the Grounded Theory. Among the results obtained, the following stand out: pressure strategies on this land from different private agents, the loss of ancestral sense of the land by some Mapuche communities that end up selling under different legal loopholes, and the resistance to external interference that still persists in many of them. The latter shows that there are Mapuche resistance strategies not only in territories affected by forestry intervention, but also in those spaces under stress from the rapid growth of cities.Las ciudades se caracterizan por ejercer una constante presión sobre el suelo periurbano rural. Las lógicas bajo las cuales opera el mercado inmobiliario y distintos otros agentes sumado a la flexibilidad de los instrumentos de planificación urbana que regulan el territorio, hace de los entornos urbanos espacios en permanente cambio. Temuco, una de las ciudades intermedias más importantes de Chile en cuanto al número de población, se ha desarrollado a partir de estas mismas lógicas. Sin embargo, y a diferencia de otras ciudades chilenas, la presencia de Áreas de Protección de Territorio Indígena (APTI), asociadas a comunidades mapuche, establece barreras legales que impiden el crecimiento convencional de la ciudad. De igual forma, es posible observar cómo en las últimas décadas, estas tierras han sido permeables a distintos usos, fuera de las dimensiones que supuestamente protege la ley. El presente trabajo explora los discursos de propietarios mapuche de suelo periurbano respecto a los cambios que estas áreas han experimentado en el último tiempo producto de la expansión de la ciudad. Para ello, se realizaron 20 entrevistas a propietarios mapuche de zonas periurbanas aledañas a Labranza, área urbana de Temuco, las cuales fueron analizadas bajo los parámetros de la Teoría Fundamentada. Entre los resultados obtenidos, destacan las estrategias de presión sobre este suelo provenientes de distintos agentes privados, la pérdida de sentido ancestral de la tierra por parte de algunas comunidades mapuche, que terminan vendiendo bajo distintos resquicios legales y la resistencia a la intromisión externa que aún persiste en mucho de ellos. Esto último evidencia que las estrategias de resistencia mapuche no sólo existen en territorios afectados por la intervención forestal, sino también en aquellos espacios que son tensionados por el rápido crecimiento de las ciudades

Accurate Multilevel Classification for Wildlife Images

The most common approaches for classification rely on the inference of a specific class. However, every category could be naturally organized within a taxonomic tree, from the most general concept to the specific element, and that is how human knowledge works. This representation avoids the necessity of learning roughly the same features for a range of very similar categories, and it is easier to understand and work with and provides a classification for each abstraction level. In this paper, we carry out an exhaustive study of different methods to perform multilevel classification applied to the task of classifying wild animals and plant species. Different convolutional backbones, data setups, and ensembling techniques are explored to find the model which provides the best performance. As our experimentation remarks, in order to achieve the best performance on the datasets that are arranged in a tree-like structure, the classifier must feature an EfficientNetB5 backbone with an input size of px, followed by a multilevel classifier. In addition, a Multiscale Crop data augmentation process must be carried out. Finally, the accuracy of this setup is a 62% top-1 accuracy and 88% top-5 accuracy. The architecture could benefit for an accuracy boost if it is involved in an ensemble of cascade classifiers, but the computational demand is unbearable for any real application.This work was funded by the Spanish Government PID2019-104818RB-I00 grant, supported with FEDER funds. It was supported by Spanish grants for PhD studies ACIF/2017/243 and FPU16/00887

A Voxelized Fractal Descriptor for 3D Object Recognition

Currently, state-of-the-art methods for 3D object recognition rely in a deep learning-pipeline. Nonetheless, these methods require a large amount of data that is not easy to obtain. In addition to that, the majority of them exploit features of the datasets, like the fact of being CAD models to create rendered representation which will not work in real life because the 3D sensors provide point clouds. We propose a novel global descriptor for point clouds which takes advantage of the fractal dimension of the objects. Our approach introduces many benefits, such as being agnostic to the density of points of the sample, number of points in the input cloud, sensor of choice, and noise up to a level, and it works on real life point cloud data provided by commercial sensors. We tested our descriptor for 3D object recognition using ModelNet, which is a well-known dataset for that task. Our approach achieves 92.84% accuracy on the ModelNet10, and 88.74% accuracy on the ModelNet40.This work was supported in part by the Spanish Government, with Feder funds, under Grant PID2019-104818RB-I00, and in part by the Spanish Grants for Ph.D. studies under Grant ACIF/2017/243 and Grant FPU16/00887

Three-dimensional reconstruction using SFM for actual pedestrian classification

In recent years, the popularity of intelligent and autonomous vehicles has grown notably. In fact, there already exist commercial models with a high degree of autonomy as regards self-driving capabilities. A key feature for this kind of vehicle is object detection, which is commonly performed in 2D space. This has some inherent issues as an object and the depiction of such an object would be classified as the actual object, which is inadequate since urban environments are full of billboards, printed adverts and posters that would likely make these systems fail. In order to overcome this problem, a 3D sensor could be leveraged, although this would make the platform more expensive, energy inefficient and computationally complex. Thus, we propose the use of structure from motion to reconstruct the three-dimensional information of the scene from a set of images, and merge the 2D and 3D data to differentiate actual objects from depictions. As expected, our approach is able to work with a regular color camera. No 3D sensors whatsoever are required. As the experiments confirm, our approach is able to distinguish between actual pedestrians and depictions of them more than 87% of times in synthetic and real-world tests in the worst scenarios, while the accuracy is of almost 98% in the best case.This work was funded by a Spanish Government PID2019-104818RB-I00 grant, supported by Feder funds. It was also supported by Spanish grants for Ph.D. FPU16/00887. Experiments were made possible by a generous hardware donation from NVIDIA

3D object detection with deep learning

Finding an appropriate environment representation is a crucial problem in robotics. 3D data has been recently used thanks to the advent of low cost RGB-D cameras. We propose a new way to represent a 3D map based on the information provided by an expert. Namely, the expert is the output of a Convolutional Neural Network trained with deep learning techniques. Relying on such information, we propose the generation of 3D maps using individual semantic labels, which are associated with environment objects or semantic labels. So, for each label we are provided with a partial 3D map whose data belong to the 3D perceptions, namely point clouds, which have an associated probability above a given threshold. The final map is obtained by registering and merging all these partial maps. The use of semantic labels provide us a with way to build the map while recognizing objects.This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds, and by grant of Vicerrectorado de Investigación y Transferencia de Conocimiento para el fomento de la I+D+i en la Universidad de Alicante 2016