Impact of new technologies and social networks on a secondary education theatre project

This paper describes the potential impact of social media and new technologies in secondary education. The case of study has been designed for the drama and theatre subject. A wide set of tools like social networks, blogs, internet, multimedia content, local press and other promotional tools are promoted to increase students’ motivation. The experiment was developed at the highschool IES Al-Satt located in Algete in the Comunidad de Madrid. The students included in the theatre group present a low academic level, 80% of them had previously repeated at least one grade, half of them come from programs for students with learning difficulties and were at risk of social exclusion. This action is supported by higher and secondary education professors and teachers who look forward to implanting networked media technologies as new tools to improve the academic results and the degree of involvement of students. The results of the experiment have been excellent, based on satisfactory opinions obtained from a survey answered by students at the end of the course, and also revealed by the analytics taken from different social networks. This project is a pioneer in the introduction and usage of new technologies in secondary high-schools in Spain.

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%.

Cooperative off-policy prediction of markov decision processes in adaptive networks

We apply diffusion strategies to propose a cooperative reinforcement learning algorithm, in which agents in a network communicate with their neighbors to improve predictions about their environment. The algorithm is suitable to learn off-policy even in large state spaces. We provide a mean-square-error performance analysis under constant step-sizes. The gain of cooperation in the form of more stability and less bias and variance in the prediction error, is illustrated in the context of a classical model. We show that the improvement in performance is especially significant when the behavior policy of the agents is different from the target policy under evaluation.

PUE attack detection in CWSN using collaboration and learning behavior

Cognitive Wireless Sensor Network (CWSN) is a new paradigm which integrates cognitive features in traditional Wireless Sensor Networks (WSNs) to mitigate important problems such as spectrum occupancy. Security in Cognitive Wireless Sensor Networks is an important problem because these kinds of networks manage critical applications and data. Moreover, the specific constraints of WSN make the problem even more critical. However, effective solutions have not been implemented yet. Among the specific attacks derived from new cognitive features, the one most studied is the Primary User Emulation (PUE) attack. This paper discusses a new approach, based on anomaly behavior detection and collaboration, to detect the PUE attack in CWSN scenarios. A nonparametric CUSUM algorithm, suitable for low resource networks like CWSN, has been used in this work. The algorithm has been tested using a cognitive simulator that brings important results in this area. For example, the result shows that the number of collaborative nodes is the most important parameter in order to improve the PUE attack detection rates. If the 20% of the nodes collaborates, the PUE detection reaches the 98% with less than 1% of false positives.

University Networking in Developing Countries based on C4D-Learning

In recent years, the establishment of cooperation networks between universities is one of the most important trends in higher education all over the world. Well recognized local and international university networks have been implemented in most educational institutions. It is common to find associations of various prestigious universities collaborating in a high-­‐technology research project including a very specialized teaching as well. This is the most common cooperation networks among higher education institutions in developed countries. An increasingly common type of networking between developed and developing universities is related to cooperation for development. This is the case of many universities in Africa that are needed for external help in order to improve its capabilities. Numerous memorandums of understanding regarding first world institutions that collaborate with universities in developing countries describe contributions of eventual visiting professors, teaching material and courses. But probably there exist another type of more important, but less explored association, such as networking among developing universities. The new goal, in this case, is not only the excellence but also the mutual development.

Metaphor and Genre: An Approach to improve the learning process of English for Aeronautics

This study suggests a theoretical framework for improving the teaching/ learning process of English employed in the Aeronautical discourse that brings together cognitive learning strategies, Genre Analysis and the Contemporary theory of Metaphor (Lakoff and Johnson 1980; Lakoff 1993). It maintains that cognitive strategies such as imagery, deduction, inference and grouping can be enhanced by means of metaphor and genre awareness in the context of content based approach to language learning. A list of image metaphors and conceptual metaphors which comes from the terminological database METACITEC is provided. The metaphorical terms from the area of Aeronautics have been taken from specialised dictionaries and have been categorised according to the conceptual metaphors they respond to, by establishing the source domains and the target domains, as well as the semantic networks found. This information makes reference to the internal mappings underlying the discourse of aeronautics reflected in five aviation accident case studies which are related to accident reports from the National Transportation Safety Board (NTSB) and provides an important source for designing language teaching tasks. La Lingüística Cognitiva y el Análisis del Género han contribuido a la mejora de la enseñanza de segundas lenguas y, en particular, al desarrollo de la competencia lingüística de los alumnos de inglés para fines específicos. Este trabajo pretende perfeccionar los procesos de enseñanza y el aprendizaje del lenguaje empleado en el discurso aeronáutico por medio de la práctica de estrategias cognitivas y prestando atención a la Teoría del análisis del género y a la Teoría contemporánea de la metáfora (Lakoff y Johnson 1980; Lakoff 1993). Con el propósito de crear recursos didácticos en los que se apliquen estrategias metafóricas, se ha elaborado un listado de metáforas de imagen y de metáforas conceptuales proveniente de la base de datos terminológica META-CITEC. Estos términos se han clasificado de acuerdo con las metáforas conceptuales y de imagen existentes en esta área de conocimiento. Para la enseñanza de este lenguaje de especialidad, se proponen las correspondencias y las proyecciones entre el dominio origen y el dominio meta que se han hallado en los informes de accidentes aéreos tomados de la Junta federal de la Seguridad en el Transporte (NTSB)

Expressive power of binary relevance and chain classifiers based on Bayesian Networks for multi-label classification

Bayesian network classifiers are widely used in machine learning because they intuitively represent causal relations. Multi-label classification problems require each instance to be assigned a subset of a defined set of h labels. This problem is equivalent to finding a multi-valued decision function that predicts a vector of h binary classes. In this paper we obtain the decision boundaries of two widely used Bayesian network approaches for building multi-label classifiers: Multi-label Bayesian network classifiers built using the binary relevance method and Bayesian network chain classifiers. We extend our previous single-label results to multi-label chain classifiers, and we prove that, as expected, chain classifiers provide a more expressive model than the binary relevance method.

Mobile assisted language learning : prototipo de la aplicación UP2B2

La forma de consumir contenidos en Internet ha cambiado durante los últimos años. Inicialmente se empleaban webs estáticas y con contenidos pobres visualmente. Con la evolución de las redes de comunicación, esta tendencia ha variado. A día de hoy, deseamos páginas agradables, accesibles y que nos presenten temas variados. Todo esto ha cambiado la forma de crear páginas web y en todos los casos se persigue el objetivo de atraer a los usuarios. El gran auge de los smartphones y las aplicaciones móviles que invaden el mercado actual han revolucionado el mundo del estudio de los idiomas permitiendo compatibilizar los recursos punteros con el aprendizaje tradicional. La popularidad de los dispositivos móviles y de las aplicaciones ha sido el principal motivo de la realización de este proyecto. En él se realizará un análisis de las diferentes tecnologías existentes y se elegirá la mejor opción que se ajuste a nuestras necesidades para poder desarrollar un sistema que implemente el enfoque llamado Mobile Assisted Language Learning (MALL) que supone una aproximación innovadora al aprendizaje de idiomas con la ayuda de un dispositivo móvil. En este documento se va a ofrecer una panorámica general acerca del desarrollo de aplicaciones para dispositivos móviles en el entorno del e-learning. Se estudiarán características técnicas de diferentes plataformas seleccionando la mejor opción para la implementación de un sistema que proporcione los contenidos básicos para el aprendizaje de un idioma, en este caso del inglés, de forma intuitiva y divertida. Dicho sistema permitirá al usuario mejorar su nivel de inglés mediante una interfaz web de forma dinámica y cercana empleando los recursos que ofrecen los dispositivos móviles y haciendo uso del diseño adaptativo. Este proyecto está pensado para los usuarios que dispongan de poco tiempo libre para realizar un curso de forma presencial o, mejor aún, para reforzar o repasar contenidos ya aprendidos por otros medios más tradicionales o no. La aplicación ofrece la posibilidad de que se haga uso del sistema de forma fácil y sencilla desde cualquier dispositivo móvil del que se disponga como es un smartphone, tablet o un ordenador personal, compitiendo con otros usuarios o contra uno mismo y mejorando así el nivel de partida a través de las actividades propuestas. Durante el proyecto se han comparado diversas soluciones, la mayoría de código abierto y de libre distribución que permiten desplegar servicios de almacenamiento accesibles mediante Internet. Se concluirá con un caso práctico analizando los requisitos técnicos y llevando a cabo las fases de análisis, diseño, creación de la base de datos, implementación y pruebas dentro del ciclo de vida del software. Finalmente, se migrará la aplicación con toda la información a un servidor en la nube. ABSTRACT. The way of consuming content on the Internet has changed over the past years. Initially, static websites were used with poor visual contents. Nevertheless, with the evolution of communication networks this trend has changed. Nowadays, we expect pleasant, accessible and varied topic pages and such expectations have changed the way to create web pages generally aiming at appealing and therefore, attracting users. The great boom of smartphones and mobile applications in the current market, have revolutionized the world of language learning as they make it possible to combine computing with traditional learning resources. The popularity of mobile devices and applications has been the main reason for the development of this project. Here, the different existing technologies will be examined and we will try to select the best option that adapts to our needs in order to develop a system that implements Mobile Assisted Language Learning (MALL) that in broad terms implies an approach to language learning with the help of a mobile device. This report provides an overview of the development of applications for mobile devices in the e-learning environment. We will study the technical characteristics of different platforms and we will select the best option for the implementation of a system that provide the basic content for learning a language, in this case English, by means of an intuitive and fun method. This system will allow the user to improve their level of English with a web interface in a dynamic and close way employing the resources offered by mobile devices using the adaptive design. This project is intended for users who do not have enough free time to make a classroom course or to review contents from more traditional courses as it offers the possibility to make use of the system quickly and easily from any mobile device available such as a smartphone, a tablet or a personal computer, competing with other users or against oneself and thus improving their departing level through different activities. During the project, different solutions have been compared. Most of them, open source and free distribution that allow to deploy storage services accessible via the Internet. It will conclude with a case study analyzing the technical requirements and conducting phases of analysis, design and creation of a database, implementation and testing in the software lifecycle. Finally, the application will be migrated with all the information to a server in the cloud.

Self-organized phase transitions in neural networks as a neural mechanism of information processing.

Transitions between dynamically stable activity patterns imposed on an associative neural network are shown to be induced by self-organized infinitesimal changes in synaptic connection strength and to be a kind of phase transition. A key event for the neural process of information processing in a population coding scheme is transition between the activity patterns encoding usual entities. We propose that the infinitesimal and short-term synaptic changes based on the Hebbian learning rule are the driving force for the transition. The phase transition between the following two dynamical stable states is studied in detail, the state where the firing pattern is changed temporally so as to itinerate among several patterns and the state where the firing pattern is fixed to one of several patterns. The phase transition from the pattern itinerant state to a pattern fixed state may be induced by the Hebbian learning process under a weak input relevant to the fixed pattern. The reverse transition may be induced by the Hebbian unlearning process without input. The former transition is considered as recognition of the input stimulus, while the latter is considered as clearing of the used input data to get ready for new input. To ensure that information processing based on the phase transition can be made by the infinitesimal and short-term synaptic changes, it is absolutely necessary that the network always stays near the critical state corresponding to the phase transition point.

Web-based OERs in Computer Networks

Learning and teaching processes are continually changing. Therefore, design of learning technologies has gained interest in educators and educational institutions from secondary school to higher education. This paper describes the successfully use in education of social learning technologies and virtual laboratories designed by the authors, as well as videos developed by the students. These tools, combined with other open educational resources based on a blended-learning methodology, have been employed to teach the subject of Computer Networks. We have verified not only that the application of OERs into the learning process leads to a significantly improvement of the assessments, but also that the combination of several OERs enhances their effectiveness. These results are supported by, firstly, a study of both students’ opinion and students’ behaviour over five academic years, and, secondly, a correlation analysis between the use of OERs and the grades obtained by students.

Industries and Citizens’ Groups Networks in EU Food Policy: The Emergence of ‘Unholy Alliances’ in Multilevel Governance? Brugges Political Research Paper No. 30, September 2013

Food policy is one the most regulated policy fields at the EU level. ‘Unholy alliances’ are collaborative patterns that temporarily bring together antagonistic stakeholders behind a common cause. This paper deals with such ‘transversal’ co-operations between citizens’ groups (NGOs, consumers associations…) and economic stakeholders (food industries, retailers…), focusing on their ambitions and consequences. This paper builds on two case studies that enable a more nuanced view on the perspectives for the development of transversal networks at the EU level. The main findings are that (i) the rationale behind the adoption of collaborative partnerships actually comes from a case-by-case cost/benefit analysis leading to hopes of improved access to institutions; (ii) membership of a collaborative network leads to a learning process closely linked to the network’s performance; and (iii) coalitions can have a better reception — rather than an automatic better access — depending on several factors independent of the stakeholders themselves.

Designing Regularizers and Architectures for Recurrent Neural Networks

Cette thèse contribue a la recherche vers l'intelligence artificielle en utilisant des méthodes connexionnistes. Les réseaux de neurones récurrents sont un ensemble de modèles séquentiels de plus en plus populaires capable en principe d'apprendre des algorithmes arbitraires. Ces modèles effectuent un apprentissage en profondeur, un type d'apprentissage machine. Sa généralité et son succès empirique en font un sujet intéressant pour la recherche et un outil prometteur pour la création de l'intelligence artificielle plus générale. Le premier chapitre de cette thèse donne un bref aperçu des sujets de fonds: l'intelligence artificielle, l'apprentissage machine, l'apprentissage en profondeur et les réseaux de neurones récurrents. Les trois chapitres suivants couvrent ces sujets de manière de plus en plus spécifiques. Enfin, nous présentons quelques contributions apportées aux réseaux de neurones récurrents. Le chapitre \ref{arxiv1} présente nos travaux de régularisation des réseaux de neurones récurrents. La régularisation vise à améliorer la capacité de généralisation du modèle, et joue un role clé dans la performance de plusieurs applications des réseaux de neurones récurrents, en particulier en reconnaissance vocale. Notre approche donne l'état de l'art sur TIMIT, un benchmark standard pour cette tâche. Le chapitre \ref{cpgp} présente une seconde ligne de travail, toujours en cours, qui explore une nouvelle architecture pour les réseaux de neurones récurrents. Les réseaux de neurones récurrents maintiennent un état caché qui représente leurs observations antérieures. L'idée de ce travail est de coder certaines dynamiques abstraites dans l'état caché, donnant au réseau une manière naturelle d'encoder des tendances cohérentes de l'état de son environnement. Notre travail est fondé sur un modèle existant; nous décrivons ce travail et nos contributions avec notamment une expérience préliminaire.

Learning representations for Information Retrieval

La recherche d'informations s'intéresse, entre autres, à répondre à des questions comme: est-ce qu'un document est pertinent à une requête ? Est-ce que deux requêtes ou deux documents sont similaires ? Comment la similarité entre deux requêtes ou documents peut être utilisée pour améliorer l'estimation de la pertinence ? Pour donner réponse à ces questions, il est nécessaire d'associer chaque document et requête à des représentations interprétables par ordinateur. Une fois ces représentations estimées, la similarité peut correspondre, par exemple, à une distance ou une divergence qui opère dans l'espace de représentation. On admet généralement que la qualité d'une représentation a un impact direct sur l'erreur d'estimation par rapport à la vraie pertinence, jugée par un humain. Estimer de bonnes représentations des documents et des requêtes a longtemps été un problème central de la recherche d'informations. Le but de cette thèse est de proposer des nouvelles méthodes pour estimer les représentations des documents et des requêtes, la relation de pertinence entre eux et ainsi modestement avancer l'état de l'art du domaine. Nous présentons quatre articles publiés dans des conférences internationales et un article publié dans un forum d'évaluation. Les deux premiers articles concernent des méthodes qui créent l'espace de représentation selon une connaissance à priori sur les caractéristiques qui sont importantes pour la tâche à accomplir. Ceux-ci nous amènent à présenter un nouveau modèle de recherche d'informations qui diffère des modèles existants sur le plan théorique et de l'efficacité expérimentale. Les deux derniers articles marquent un changement fondamental dans l'approche de construction des représentations. Ils bénéficient notamment de l'intérêt de recherche dont les techniques d'apprentissage profond par réseaux de neurones, ou deep learning, ont fait récemment l'objet. Ces modèles d'apprentissage élicitent automatiquement les caractéristiques importantes pour la tâche demandée à partir d'une quantité importante de données. Nous nous intéressons à la modélisation des relations sémantiques entre documents et requêtes ainsi qu'entre deux ou plusieurs requêtes. Ces derniers articles marquent les premières applications de l'apprentissage de représentations par réseaux de neurones à la recherche d'informations. Les modèles proposés ont aussi produit une performance améliorée sur des collections de test standard. Nos travaux nous mènent à la conclusion générale suivante: la performance en recherche d'informations pourrait drastiquement être améliorée en se basant sur les approches d'apprentissage de représentations.

Sustainable Development and Collective Learning: Theory and a European Case Study. Bruges European Economic Policy (BEEP) Briefing 7/2004

Network governance of collective learning processes is an essential approach to sustainable development. The first section of the article briefly refers to recent theories about both market and government failures that express scepticism about the way framework conditions for market actors are set. For this reason, the development of networks for collective learning processes seems advantageous if new solutions are to be developed in policy areas concerned with long-term changes and a stepwise internalisation of externalities. With regard to corporate actors’ interests, the article shows recent insights from theories about the knowledge-based firm, where the creation of new knowledge is based on the absorption of societal views. This concept shifts the focus towards knowledge generation as an essential element in the evolution of sustainable markets. This involves at the same time the development of new policies. In this context innovation-inducing regulation is suggested and discussed. The evolution of the Swedish, German and Dutch wind turbine industries are analysed based on the approach of governance put forward in this article. We conclude that these coevolutionary mechanisms may take for granted some of the stabilising and orientating functions previously exercised by basic regulatory activities of the state. In this context, the main function of the governments is to facilitate learning processes that depart from the government functions suggested by welfare economics.

Designing Regularizers and Architectures for Recurrent Neural Networks

Cette thèse contribue a la recherche vers l'intelligence artificielle en utilisant des méthodes connexionnistes. Les réseaux de neurones récurrents sont un ensemble de modèles séquentiels de plus en plus populaires capable en principe d'apprendre des algorithmes arbitraires. Ces modèles effectuent un apprentissage en profondeur, un type d'apprentissage machine. Sa généralité et son succès empirique en font un sujet intéressant pour la recherche et un outil prometteur pour la création de l'intelligence artificielle plus générale. Le premier chapitre de cette thèse donne un bref aperçu des sujets de fonds: l'intelligence artificielle, l'apprentissage machine, l'apprentissage en profondeur et les réseaux de neurones récurrents. Les trois chapitres suivants couvrent ces sujets de manière de plus en plus spécifiques. Enfin, nous présentons quelques contributions apportées aux réseaux de neurones récurrents. Le chapitre \ref{arxiv1} présente nos travaux de régularisation des réseaux de neurones récurrents. La régularisation vise à améliorer la capacité de généralisation du modèle, et joue un role clé dans la performance de plusieurs applications des réseaux de neurones récurrents, en particulier en reconnaissance vocale. Notre approche donne l'état de l'art sur TIMIT, un benchmark standard pour cette tâche. Le chapitre \ref{cpgp} présente une seconde ligne de travail, toujours en cours, qui explore une nouvelle architecture pour les réseaux de neurones récurrents. Les réseaux de neurones récurrents maintiennent un état caché qui représente leurs observations antérieures. L'idée de ce travail est de coder certaines dynamiques abstraites dans l'état caché, donnant au réseau une manière naturelle d'encoder des tendances cohérentes de l'état de son environnement. Notre travail est fondé sur un modèle existant; nous décrivons ce travail et nos contributions avec notamment une expérience préliminaire.