Opportunities of Base of the Pyramid from the Perspective of Resources and Capabilities

This paper addresses the issue of the Base of the pyramid (BOP) from the perspective of resources and capabilities, analyzing the best strategies to enter the market, making appropriate use of internal resources, which is the main determinant of innovation and organizational capabilities with which have good results, and success stories of Mexican companies were analyzed

Image context for object detection, object context for part detection

Objects and parts are crucial elements for achieving automatic image understanding. The goal of the object detection task is to recognize and localize all the objects in an image. Similarly, semantic part detection attempts to recognize and localize the object parts. This thesis proposes four contributions. The first two make object detection more efficient by using active search strategies guided by image context. The last two involve parts. One of them explores the emergence of parts in neural networks trained for object detection, whereas the other improves on part detection by adding object context. First, we present an active search strategy for efficient object class detection. Modern object detectors evaluate a large set of windows using a window classifier. Instead, our search sequentially chooses what window to evaluate next based on all the information gathered before. This results in a significant reduction on the number of necessary window evaluations to detect the objects in the image. We guide our search strategy using image context and the score of the classifier. In our second contribution, we extend this active search to jointly detect pairs of object classes that appear close in the image, exploiting the valuable information that one class can provide about the location of the other. This leads to an even further reduction on the number of necessary evaluations for the smaller, more challenging classes. In the third contribution of this thesis, we study whether semantic parts emerge in Convolutional Neural Networks trained for different visual recognition tasks, especially object detection. We perform two quantitative analyses that provide a deeper understanding of their internal representation by investigating the responses of the network filters. Moreover, we explore several connections between discriminative power and semantics, which provides further insights on the role of semantic parts in the network. Finally, the last contribution is a part detection approach that exploits object context. We complement part appearance with the object appearance, its class, and the expected relative location of the parts inside it. We significantly outperform approaches that use part appearance alone in this challenging task

Do semantic parts emerge in Convolutional Neural Networks?

Semantic object parts can be useful for several visual recognition tasks. Lately, these tasks have been addressed using Convolutional Neural Networks (CNN), achieving outstanding results. In this work we study whether CNNs learn semantic parts in their internal representation. We investigate the responses of convolutional filters and try to associate their stimuli with semantic parts. We perform two extensive quantitative analyses. First, we use ground-truth part bounding-boxes from the PASCAL-Part dataset to determine how many of those semantic parts emerge in the CNN. We explore this emergence for different layers, network depths, and supervision levels. Second, we collect human judgements in order to study what fraction of all filters systematically fire on any semantic part, even if not annotated in PASCAL-Part. Moreover, we explore several connections between discriminative power and semantics. We find out which are the most discriminative filters for object recognition, and analyze whether they respond to semantic parts or to other image patches. We also investigate the other direction: we determine which semantic parts are the most discriminative and whether they correspond to those parts emerging in the network. This enables to gain an even deeper understanding of the role of semantic parts in the network

Technology and Police: A Way to Create Predicting Policing

Technological development is unstoppable. Police forces are no strangers to this development. In this paper we present the advances in this field of different types of technologies applied to the police function (crime mapping, data mining and big data, social media, drones) and also the application of artificial intelligence to policing. Finally, we reflect on the suitability of these applications and the desirable future through recommendations.2022-2

Capacidades prospectivas y de defensa en la lucha contra el Ciberterrorismo

The cyberattacks and cybercrime (attacks with economic motivation or against national interests) are real every day in Spain and in entire the world. This cyberattack can be a part of cyberterrorism case. The cyberterrorism is the union of terrorism and cyberspace (or technology) to generate fear in the people with an ideological goal. First, we analyze the cyberterrorism threat, and, second, we ask is the preventive capacities (prospective and defense) of the States, with the analysis of Spain capabilities, would be prepared fight against fenomena. We have been verified the different capacities existing in Spain and we have presented their degree of coordination and collaboration. Finaly, conclusions and future implications are discused.Es indudable que los ciberataques y el cibercrimen (disrupciones con motivación económica o de intereses nacionales extranjeros) son una realidad diaria patente y en auge también en España y en el mundo. Dentro de los ciberataques se pueden encontrar casos referidos a ciberterrorismo, es decir, la unión del terrorismo y el ciberespacio (o la tecnología), con la finalidad de generar miedo o intimidar a una sociedad dirigiéndola hacia una meta ideológica. Tras el análisis de la amenaza, la pregunta que nos hacemos es si las capacidades preventivas (prospectivas y de defensa) de los Estados, tras el análisis concreto de España, estarían preparadas para hacer frente a una amenaza de estas características. Aquí hemos podido comprobar las diferentes capacidades existentes en España y conocer su grado de coordinación y colaboración para llegar a una serie de conclusiones sobre su idoneidad en la lucha de este fenómeno en plena expansión

An active search strategy for efficient object class detection

Object class detectors typically apply a window classifier to all the windows in a large set, either in a sliding window manner or using object proposals. In this paper, we develop an active search strategy that sequentially chooses the next window to evaluate based on all the information gathered before. This results in a substantial reduction in the number of classifier evaluations and in a more elegant approach in general. Our search strategy is guided by two forces. First, we exploit context as the statistical relation between the appearance of a window and its location relative to the object, as observed in the training set. This enables to jump across distant regions in the image (e.g. observing a sky region suggests that cars might be far below) and is done efficiently in a Random Forest framework. Second, we exploit the score of the classifier to attract the search to promising areas surrounding a highly scored window, and to keep away from areas near low scored ones. Our search strategy can be applied on top of any classifier as it treats it as a black-box. In experiments with R-CNN on the challenging SUN2012 dataset, our method matches the detection accuracy of evaluating all windows independently, while evaluating 9x fewer windows

Objects as Context for Detecting Their Semantic Parts

We present a semantic part detection approach that effectively leverages object information.We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare to other part detection methods on both PASCAL-Part and CUB200-2011 datasets