La intertextualidad en televisión : cine y publicidad

El presente artículo es una reflexión sobre la influencia del cine en la publicidad, concretamente del séptimo arte en los anuncios televisivos. Se aporta un análisis, no en todos sus sentidos sino en los aspectos más relevantes, para desentrañar la relación que existe entre ambas disciplinas. Dado que el objetivo es amplio y ambicioso, el estudio se centrará en un caso en particular que enlaza directamente la influencia de una película concreta en un anuncio concreto. Podríamos hacer un recorrido por todas las campañas publicitarias basadas en películas de ficción¿ desde Big, de Penny Marshall, a Náufrago, de Robert Zemeckis, La máscara, de Chuck Russell, Nosferatu, de Murnau o, más recientemente, El sexto sentido, de Night Shyamalan. En este caso partimos del concepto de intertextualidad para realizar un análisis del spot 'Cinco años' del coche Mercedes Clase C en el que se encuentran nexos con la película protagonizada por Tom Hanks en 1988, Big. El objetivo, pues, es evidenciar, mediante un análisis narrativo de ambos discursos, que realmente existe dicha intertextualidad. Los protagonistas, la música, los temas tratados, el eslogan son algunas de las claves que señalan que realmente sí existe un vínculo intencionado por parte de los creadores del anuncio de la casa Mercedes, quien una vez más mima al detalle la creatividad de sus campañas para empatizar con el público en general y con su target en particular. Hay que señalar que cuando se descubre la conexión con la película se cierra el círculo para el que fue concebido el spot y la satisfacción -por parte del espectador- y la eficacia -por parte del emisor- es mucho mayor.This article reflects about the influence of cinema in advertising, particularly of cinema in TV ads. It provides an analysis, not in all respects, but the most important aspects to unravel the relationship between the two disciplines. Since the objective is broad and ambitious, the study will focus on a particular case which directly links the influence of a particular film on a particular advertisement. We could do a tour of all advertising campaigns based on fiction movies ... Big, from Penny Marshall, Cast Away, from Robert Zemeckis, The Mask, from Chuck Russell, Nosferatu, from Murnau or, more recently, the sixth sense, from Night Shyamalan. In this case we start from the concept of intertextuality to spot analysis of the 'Five Years' Mercedes C-Class car in which there are links to the movie starring by Tom Hanks in 1988, Big. The goal, then, is to show, through a narrative analysis of both speeches, that there really is such intertextuality. The characters, music, themes, the slogan are some of the key really does show that there is a link intended by the creators of the announcement of the house Mercedes, who once again indulges in detail the creativity of their campaigns to empathize with the public in general and in particular its target. Note that when you discover the connection to the film full circle for which it was conceived the spot and the satisfaction of the viewer-and effectivenessby the issuer, is greate

Cine y publicidad . La intertextualidad en el anuncio de Mercedes clase C

Este artículo es una reflexión sobre la influencia del cine en la publicidad comercial, concretamente del séptimo arte en los spots. Y mucho más puntual, el estudio se centrará en un caso en particular: se trata del comercial ‘Cinco años’ del coche Mercedes clase C en el que se encuentran nexos con la película protagonizada por Tom Hanks, Big. El objetivo del siguiente pretende, mediante un análisis de contenido, evidenciar que realmente existe dicha intertextualidad. Los protagonistas, la música, los temas, el eslogan… son algunas de las claves que apuntan hasta este vínculo intencionado por parte de la casa Mercedes.This article is a reflection about cinema's influence on commercial advertising, specifically the cinema on spots. And much more timely, the research will be based on a particular case: it’s about Mercedes C-class' spot called 'Five years' in which we can find connections the movie starring the actor Tom Hanks, Big, in 1988. The present objective, then, is to prove, through the analysis of the content, that the above-mentioned intertextuality does exist. Main characters, music, matters and slogan… are some of the keys that show that there is really a deliberate link made by the creators of the spot

Toward Knowledge Extraction in Classification of Volcano-Seismic Events: Visualizing Hidden States in Recurrent Neural Networks

ACKNOWLEDGMENT The authors would like to thank the Instituto Andaluz de Geofísica for providing us with the Decepction Island dataset and invaluable geophysical insight.Understanding how deep hierarchical models build their knowledge is a key issue in the usage of artificial intelligence to interpret the reality behind data. Depending on the discipline and models used, such knowledge may be represented in ways that are more or less intelligible for humans, limiting further improvements on the performance of the existing models. In order to delve into the characterization and modeling of volcano-seismic signals, this article emphasizes the idea of deciphering what and how recurrent neural networks (RNNs) model, and how this knowledge can be used to improve data interpretation.The key to accomplishing these objectives is both analyzing the hidden state dynamics associated with their hidden units as well as pruning/trimming based on the specialization of neurons. In this article, we process, analyze, and visualize the hidden states activation maps of two RNN architectures when managing different types of volcano-seismic events. As a result, the class-dependent discriminative behavior of most active neurons is analyzed, thereby increasing the comprehension of the detection and classification tasks. Arepresentative dataset fromthe deception island volcano (Antarctica), containing volcano-tectonic earthquakes, long period events, volcanic tremors, and hybrid events, is used to train the models. Experimental analysis shows how neural activity and its associated specialization skills change depending on the architecture chosen and the type of event analyzed.MINECO under Grant PID2019-106260GB-I00 FEMALEFEDER/Junta de Andalucia-Consejería de Economía y Conocimiento/ Proyecto A-TIC-215- UGR18

Aprendizaje y Servicio en la Universidad de Cádiz. Banco de experiencias I. CURSO 2013/2014

Las 41 experiencias que aquí se presentan, se articularon en dos asignaturas del Grado de Educación Infantil, en concreto en Fundamentos Pedagógicos de las Necesidades en la Infancia, ubicada en 4º curso (asignatura obligatoria), y en Cultura, Políticas y Prácticas Inclusivas en Educación Infantil, de 3º (optativa de la mención de Educación Inclusiva).Este documento es un recopilatorio de acciones de Aprendizaje y Servicio (ApS) en el Grado de Educación Infantil de la Facultad de Ciencias de la Educación de la Universidad de Cádiz. Constituye, por tanto, un Banco de Experiencias que pretende servir de memoria histórica de nuestras actividades, así como, lo que resulta más importante, conformar un material didáctico que facilite la introducción al ApS de otros/as estudiantes y profesorado interesado en esta forma de entender la formación de profesionales desde una perspectiva ética y de compromiso con el entorno. El desarrollo de este documento se enmarca en la Actuación Avalada de Mejora Docente “Actúa” 14/15 de la Universidad de Cádiz que lleva por título “Institucionalización curricular del Aprendizaje y Servicio en los títulos de Educación”.50 página

Materiales de orientación al alumnado en el desarrollo de proyectos de Aprendizaje y Servicio (ApS) Guía de trabajo

Es un documento que ofrece orientaciones al alumnado para el diseño, desarrollo y evaluación de proyectos de Aprendizaje-Servicio.Se trata de una guía en la que se describen las fases a seguir para la elaboración de un proyecto de Aprendizaje-Servicio. Se concretan por cada una de las fases la diferentes tareas a realizar para su completo desarrollo.Total de páginas: 8 Aparece enlace a web de interés

Portafolios de Materiales de orientación al alumnado en el desarrollo de proyectos de Aprendizaje y Servicio (ApS). Guía de trabajo.

Portafolio de documentos para el procedimiento previo y desarrollo de proyectos de Aprendizaje y Servicio (ApS). Se trata compilación de documentos (plantillas) para la elaboración de un proyecto de Aprendizaje-Servicio desde su diseño, desarrollo y cierre. Ayuda a la continuación de Materiales de orientación al alumnado en el desarrollo de proyectos de Aprendizaje y Servicio (ApS) Guía de trabajo

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data

[EN] The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (International Conference on Biomedical and Health Informatics) Benchmark was used. This dataset is composed of 920 sounds of which 810 are of chronic diseases, 75 of non-chronic diseases and only 35 of healthy individuals. As more than 88% of the samples of the dataset are from the same class (Chronic), the use of a Variational Convolutional Autoencoder was proposed to generate new labeled data and other well known oversampling techniques after determining that the dataset classes are unbalanced. Once the preprocessing step was carried out, a Convolutional Neural Network (CNN) was used to classify the respiratory sounds into healthy, chronic, and non-chronic disease. In addition, we carried out a more challenging classification trying to distinguish between the different types of pathologies or healthy: URTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis. We achieved results up to 0.993 F-Score in the three-label classification and 0.990 F-Score in the more challenging six-class classification.SIJunta de Castilla y León throught project LE078G1

Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19

[EN] COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories.S

Heart disease risk prediction using deep learning techniques with feature augmentation

[EN] Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Determining the severity of Parkinson’s disease in patients using a multi task neural network

[EN] Parkinson’s disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson’s severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson’s disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson’s Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson’s disease or non-severe Parkinson’s disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson’s outperforming the state-of-the-art proposals.SIPublicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL