Nuevo enfoque de la práctica educativa del Programa UNIGIS Girona. Algunas reflexiones sobre las claves del cambio

En los últimos años, se ha debatido mucho sobre el nuevo modelo pedagógico que promueve el nuevo espacio de educación superior. Este artículo recoge la experiencia del Programa UNIGIS de formación a distancia en SIG, que se imparte desde la Universidad de Girona, como ejemplo innovador de la aplicación de una nueva metodología de aprendizaje. La iniciativa es fruto, en gran medida, de las influencias europeas y de los sistemas de aprendizaje que llevan a cabo distintas universidades de nuestro continente. En un estudio a distancia que sustenta el proceso formativo en plataformas e-learning, resulta fundamental que éstas faciliten la implementación del modelo pedagógico sobre el cual se desarrolla el programa de formación, pero sin olvidar el papel clave que desempeñan las personas implicadas.During the last years a new pedagogical model promoted by the European Higher Education Area it has been discussed. This article gathers the experience of UNIGIS Girona distance learning program in GIS, which is organized by University of Girona, as an innovating example of a new learning methodology application. The initiative is due to the European influences and the learning systems that different European universities are carrying out. In a remote study, where educational processes are based on e-learning platforms, is fundamental that these platforms facilitate the opportunity to apply the pedagogical model, but without forgetting the key paper that the implied people play.En els últims anys, s'ha discutit molt al voltant del nou model pedagògic que promou el nou espai europeu d'educació superior. Aquest article recull l'experiència del Programa UNIGIS de formació a distància en SIG, que s'ofereix des de la Universitat de Girona, com a exemple innovador de l'aplicació d'una nova metodologia d'aprenentatge. La iniciativa és fruit, en gran mesura, de les influències europees i dels sistemes d'aprenentatge que duen a terme diverses universitats del nostre continent. En un estudi a distància que dóna suport al procés formatiu en plataformes e-learning, esdevé fonamental que aquestes facilitin l'aplicació del model pedagògic sobre el qual es desenvolupa el programa de formació, però sense oblidar el paper clau que hi tenen les persones implicades

Corporate social responsability of Seur

Treball Final de Grau en Administració d'Empreses. Codi: AE1049. Curs 2019/202

Foveation-based Mechanisms Alleviate Adversarial Examples

We show that adversarial examples, i.e., the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in different image regions. To see this, first, we report results in ImageNet that lead to a revision of the hypothesis that adversarial perturbations are a consequence of CNNs acting as a linear classifier: CNNs act locally linearly to changes in the image regions with objects recognized by the CNN, and in other regions the CNN may act non-linearly. Then, we corroborate that when the neural responses are linear, applying the foveation mechanism to the adversarial example tends to significantly reduce the effect of the perturbation. This is because, hypothetically, the CNNs for ImageNet are robust to changes of scale and translation of the object produced by the foveation, but this property does not generalize to transformations of the perturbation. As a result, the accuracy after a foveation is almost the same as the accuracy of the CNN without the adversarial perturbation, even if the adversarial perturbation is calculated taking into account a foveation

Eccentricity dependent deep neural networks: Modeling invariance in human vision

Humans can recognize objects in a way that is invariant to scale, translation, and clutter. We use invariance theory as a conceptual basis, to computationally model this phenomenon. This theory discusses the role of eccentricity in human visual processing, and is a generalization of feedforward convolutional neural networks (CNNs). Our model explains some key psychophysical observations relating to invariant perception, while maintaining important similarities with biological neural architectures. To our knowledge, this work is the first to unify explanations of all three types of invariance, all while leveraging the power and neurological grounding of CNNs

Conditional Random Fields for Multi-Camera Object Detection

We formulate a model for multi-class object detection in a multi-camera environment. From our knowledge, this is the first time that this problem is addressed taken into account different object classes simultaneously. Given several images of the scene taken from different angles, our system estimates the ground plane location of the objects from the output of several object detectors applied at each viewpoint. We cast the problem as an energy minimization modeled with a Conditional Random Field (CRF). Instead of predicting the presence of an object at each image location independently, we simultaneously predict the labeling of the entire scene. Our CRF is able to take into account occlusions between objects and contextual constraints among them. We propose an effective iterative strategy that renders tractable the underlying optimization problem, and learn the parameters of the model with the max-margin paradigm. We evaluate the performance of our model on several challenging multi-camera pedestrian detection datasets namely PETS 2009 and EPFL terrace sequence. We also introduce a new dataset in which multiple classes of objects appear simultaneously in the scene. It is here where we show that our method effectively handles occlusions in the multi-class case