The influence of television stories on narrative abilities in children

This research explores the narrative abilities demonstrated by children aged between 8 and 12 in the production of television stories. The results reveal that not all television stories viewed by children foster the informal education process. One type of story, termed narrativizing, enables children to produce coherent stories which clearly articulate the causal, temporal and motivational relations, as well as the means-end structures, the proximal relations of the intrigue and the distal relations of the plot. Other television stories, denarrativizing stories, tend to induce disarrangements and incoherence at all structural levels of the stories produced by children. This in turn hampers the development of their narrative abilities, which are necessary to the correct development of narrative thought. These results indicate the need to exercise social control over this latter type of fictional television narrative, to which children are exposed throughout their development within the framework of informal education.University of the Basque Country (UPV/EHU), EHU 13/65 Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), GIU 15/14 Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), UFI 11/04 MINECO. Ministerio de Economía y Competitividad, BES-2015-071923 Fondo Social Europeo, BES-2015-07192

Detección de objetos basada en Deep Learning y aplicada a vehículos autónomos

En este trabajo se han utilizado las redes neuronales profundas para el cometido de la detección de objetos en imágenes. En concreto, el trabajo se ha desarrollado en el contexto de los vehı́culos autónomos. Se han entrenado y comparado tres redes Faster-RCNN para la detección de peatones partiendo desde diferentes inicializaciones de sus parámetros para estudiar la influencia de la transferencia del aprendizaje. Se ha analizado un caso práctico de detección de baches en carretera. Se ha recopilado una base de datos, partiendo de repositorios y de etiquetado manual. Se ha explorado la base de datos para inicializar el entrenamiento de una manera más efectiva. Se ha evaluado y comparado el rendimiento de tres modelos Faster-RCNN para la detección de baches con diferentes extractores de caracterı́sticas. El trabajo se ha desarrollado utilizando la librerı́a Tensorflow y los modelos se han probado en el dispositivo Nvidia Drive PX2, el cual está diseñado para la investigación en conducción autónoma

Valores y emociones en narraciones audiovisuales de ficción infantil

El objetivo de este trabajo fue identificar los valores y emociones que se transmiten en los programas televisivos preferidos de niños y niñas de 8 a 12 años, de acuerdo al tipo de estructura. Sobre la base del análisis del consumo mediático referido por los participantes y sus progenitores, así como los índices de audiencia de los programas de ficción infantil, se seleccionaron dos series de ficción televisiva para este grupo de edad («Doraemon» y «Código Lyoko», de estructura narrativa y no-narrativa respectivamente), y se llevó a cabo un análisis de contenido de 86 episodios que fue validado por acuerdo inter-jueces. Los resultados muestran que en ambos programas se exhibe una amplia gama de valores éticos y competenciales, sobre todo valores vitales, mientras que los valores estéticos y trascendentales apenas son representados. Por otra parte, en «Código Lyoko» tienen mayor presencia las emociones complejas y algunas emociones básicas (asombro, ira, alegría y miedo), sin embargo la tristeza aparece en mayor medida en «Doraemon». Destaca que el nivel de empatía representada en los personajes es bajo en ambas series, aunque ligeramente más elevado en «Código Lyoko». La relevancia del estudio radica en el hecho de proveer un procedimiento útil para medir la idoneidad de los contenidos mediáticos respecto a las características psicológicas de la infancia, y contribuir a fundamentar con base sólida los programas de competencia mediática desde las primeras edadesThe aim of this study is to identify which values and emotions are transmitted in the favorite fictional TV programs of children aged between 8 and 12, according to their particular type of structure. Based on the analysis of media consumption reported by participants and their parents, as well as on the ratings of children’s fictional programs, two fictional programs were selected for this age group (Doraemon and Code Lyoko, with a narrative and non-narrative structure, respectively), and a content analysis of 86 episodes was conducted and validated by inter-rater agreement. The results show that a wide range of ethical and competence- based values are conveyed by both programs, although greater emphasis is placed on life-skill values, with aesthetic and transcendental values hardly being represented at all. While more complex emotions and some basic emotions (surprise, anger, happiness and fear) were found to be present in Code Lyoko, sadness was present to a greater extent in Doraemon. The results reveal that the level of empathy represented by the characters in both series is low, although it is slightly higher in Code Lyoko. The relevance of the study lies in the fact that it provides a useful method for measuring the appropriateness of media content in relation to the psychological characteristics of children, and contributes to establishing a solid basis for media literacy programs from early childhoo

Estudio piloto para una investigación descriptiva sobre los programas afectivo-sexuales de los colegios de Madrid

Trabajo fin de grado en EnfermeríaObjetivos. Analizar las características de los programas afectivo-sexuales de diferentes colegios de Madrid. Material y métodos. Estudio piloto descriptivo transversal, efectuado en colegios de Educación Primaria en los distritos Salamanca, Retiro y San Blas-Canillejas del Municipio de Madrid. Se seleccionaron mediante un muestreo no aleatorio intencional. Se recogió la información a través de un cuestionario para obtener los valores de los indicadores y su frecuencia. Mediante tablas de distribución se calcularán frecuencias y porcentajes analizando las diferentes variables por separado. Los datos cuantitativos se analizarán mediante la media, desviación típica, mediana, mínimo y máximo. Los datos cualitativos se describirán con frecuencias absolutas y porcentajes. Resultados. No se pudieron obtener resultados para el estudio debido a que los colegios no participaron en el estudio, asimismo los resultados no sería extrapolables debido al método de muestreo y el tamaño muestral. Conclusiones. Al no participar en el estudio queda patente la falta de implicación de los colegios, sería conveniente alentar a los centros educativos a participar en estudios que impulsen la igualdad de género. Sería conveniente seguir con estudios descriptivos para profundizar en la situación actual.Objective. To analyze affective-sexual education programs features of some of the Madrid’s schools. Material and methods. Descriptive transversal pilot project, made in Primary Education schools of the Retiro, Salamanca and San Blas-Canillejas districts, which were selected through a non-random intentional sampling. Information has been gathered using a questionnaire designed to obtain indicator’s values and frequency. Through tables of distribution, frequencies and percentages will be calculated, analyzing separately different variables. Quantitative data will be analyzed through average, typical deviation, median, minimum and maximum, while qualitative ones will be described with absolute frequencies and percentages. Results. It was not possible to obtain result for the study, owing to lack of cooperation from the schools; anyway, results would not be translatable because of the sample size and the sampling method used. Conclusions. The fact that the schools did not collaborate in the study makes it clear that schools feel scarcely involved with this particular subject. It would be advisable to encourage education centers to take part in studies promoting gender equality. It would also be desirable to go ahead with descriptive studies which dig deeply in the current situation

Habilidades narrativas, valores y relatos personales digitales: una propuesta metodológica para Educación Primaria

La finalidad general de este trabajo es presentar una propuesta metodológica para la educación en valores a través de contenidos mediáticos de ficción, basada en una investigación previa sobre la relación entre el consumo mediático infantil y el desarrollo sociopersonal en Educación Primaria. En la primera parte, se presenta el análisis del índice de habilidad narrativa y la identificación de valores/contravalores en relación a dos tipos de relatos audiovisuales con diferente estructura (narrativa y no narrativa). Han participado 186 alumnos y alumnas de 3º y 6º de Educación Primaria de cuatro centros de la Comunidad Autónoma del País Vasco (CAPV). Se trata de un estudio cuasiexperimental con metodología cualitativa y cuantitativa, que incluye diferentes fases: fase preliminar (índices de audiencia, selección de dos episodios en base a su estructura, análisis de contenido de cada episodio seleccionado); fase experimental (visionado del episodio con estructura narrativa y no narrativa); y fase de análisis de recepción (entrevista semiestructurada en la que se recoge la evocación del relato por parte del alumnado y los valores/contravalores que han percibido en el episodio). Entre los resultados cabe destacar que existen diferencias en la habilidad narrativa y en la identificación de valores/contravalores en función de la estructura del relato de ficción visualizado. La estructura narrativa se asocia con mayores niveles de habilidad narrativa y de identificación de valores/contravalores. En la segunda parte, fundamentada en el estudio realizado, se presentan las fases del proceso a seguir por los profesionales de la educación para promover que el alumnado elabore Relatos Personales Digitales (RDP) como medio para desarrollar las habilidades narrativas y la identificación de valores/contravalores.Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), EHU 13/65 Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), GIU 15/14 Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), UFI 11/04 Ministerio de Economía y Competitividad (Gobierno de España), BES-2015-071923 Fondo Social Europeo, BES-2015-07192

El Proyecto BIOFEP de análisis comparativo, Portugal, España y Francia, de la producción ganadera ecológica en zonas de montaña

Los contactos mantenidos en los arios 2001 y 2002 entre distintas organizaciones de los Pirineos Franceses, el norte de España y la zona norte de Portugal, pusieron en evidencia la existencia de lagunas en la producción ganadera ecológica en zonas de montaña (PGEZM). Esta constatación llevó a plantear un proyecto, de acrónimo BIOFEP, a la convocatoria Interreg IIIB (Sudoeste). que integra a organizaciones de productores, centros de I+D y formación, organismos públicos de las zonas anteriormente descritas

Advances on Time Series Analysis using Elastic Measures of Similarity

A sequence is a collection of data instances arranged in a structured manner. When this arrangement is held in the time domain, sequences are instead referred to as time series. As such, each observation in a time series represents an observation drawn from an underlying process, produced at a specific time instant. However, other type of data indexing structures, such as space- or threshold-based arrangements are possible. Data points that compose a time series are often correlated with each other. To account for this correlation in data mining tasks, time series are usually studied as a whole data object rather than as a collection of independent observations. In this context, techniques for time series analysis aim at analyzing this type of data structures by applying specific approaches developed to leverage intrinsic properties of the time series for a wide range of problems, such as classification, clustering and other tasks alike. The development of monitoring and storage devices has made time se- ries analysis proliferate in numerous application fields, including medicine, economics, manufacturing and telecommunications, among others. Over the years, the community has gathered efforts towards the development of new data-based techniques for time series analysis suited to address the problems and needs of such application fields. In the related literature, such techniques can be divided in three main groups: feature-, model- and distance-based methods. The first group (feature-based) transforms time series into a collection of features, which are then used by conventional learning algorithms to provide solutions to the task under consideration. In contrast, methods belonging to the second group (model-based) assume that each time series is drawn from a generative model, which is then har- nessed to elicit knowledge from data. Finally, distance-based techniques operate directly on raw time series. To this end, these methods resort to specially defined measures of distance or similarity for comparing time series, without requiring any further processing. Among them, elastic sim- ilarity measures (e.g., dynamic time warping and edit distance) compute the closeness between two sequences by finding the best alignment between them, disregarding differences in time, and thus focusing exclusively on shape differences. This Thesis presents several contributions to the field of distance-based techniques for time series analysis, namely: i) a novel multi-dimensional elastic similarity learning method for time series classification; ii) an adap- tation of elastic measures to streaming time series scenarios; and iii) the use of distance-based time series analysis to make machine learning meth- ods for image classification robust against adversarial attacks. Throughout the Thesis, each contribution is framed within its related state of the art, explained in detail and empirically evaluated. The obtained results lead to new insights on the application of distance-based time series methods for the considered scenarios, and motivates research directions that highlight the vibrant momentum of this research area

Advances on Time Series Analysis using Elastic Measures of Similarity

135 p.A sequence is a collection of data instances arranged in an structured manner. When thisarrangement is held in the time domain, sequences are instead referred to as time series. As such,each observation in a time series represents an observation drawn from an underlying process,produced at a specific time instant. However, other type of data indexing structures, such as spaceorthreshold-based arrangements are possible. Data points that compose a time series are oftencorrelated to each other. To account for this correlation in data mining tasks, time series are usuallystudied as a whole data object rather than as a collection of independent observations. In thiscontext, techniques for time series analysis aim at analyzing this type of data structures by applyingspecific approaches developed to harness intrinsic properties of the time series for a wide range ofproblems such as, classification, clustering and other tasks alike.The development of monitoring and storage devices has made time series analysisproliferate in numerous application fields including medicine, economics, manufacturing andtelecommunications, among others. Over the years, the community has gathered efforts towards thedevelopment of new data-based techniques for time series analysis suited to address the problemsand needs of such application fields. In the related literature, such techniques can be divided in threemain groups: feature-, model- and distance- based methods. The first group (feature-based)transforms time series into a collection of features, which are then used by conventional learningalgorithms to provide solutions to the task under consideration. In contrast, methods belonging to thesecond group (model-based) assume that each time series is drawn from a generative model, whichis then harnessed to elicit information from data. Finally, distance-based techniques operate directlyon raw time series. To this end, these latter methods resort to specially defined measures of distanceor similarity for comparing time series, without requiring any further processing. Among them,elastic similarity measures (e.g., dynamic time warping and edit distance) compute the closenessbetween two sequences by finding the best alignment between them, disregarding differences intime gaps and thus focusing exclusively on shape differences.This Thesis presents several contributions to the field of distance-based techniques for timeseries analysis, namely: i) a novel multi-dimensional elastic similarity learning method for timeseries classification; ii) an adaptation of elastic measures to streaming time series scenarios; and iii)the use of distance-based time series analysis to make machine learning methods for imageclassification robust against adversarial attacks. Throughout the Thesis, each contribution is framedwithin its related state of the art, explained in detail and empirically evaluated. The obtained resultslead to new insights on the application of distance-based time series methods for the consideredscenarios, and motivates research directions that highlight the vibrant momentum of this researcharea