Revista Drom

Universidad de Sevilla. Grado en Periodism

Flamenco Performer’s Perceived Value: Development of a Measurement Index

This paper aims to explain the main elements influencing the perceived value of the flamenco performer. In the framework of cultural economics, it presents a methodology based on two stages: interviews to experts, to identify the different aspects which influence the value of the performer; and surveys to consumers, to measure the valuation of stated aspects. The data from surveys were used for an exploratory factor analysis which results identified three factors that influence the perceived value of the performer: virtuosity, feelings, and influencer-brand. The conclusions also show that among these factors, virtuosity may be used as a synthetic index of performer valuation, since it represents more than 42% of the accumulated total variance. Due to its findings the article is a useful instrument for researchers in cultural economics, art sociology, consumer studies, and empirical aesthetics.JEL Codes - Z1

Introduction

We are pleased to present to the readers of the Scientific Annals of Economics and Business (SAEB) this special issue dedicated to the 1st Workshop on Popular Cultural Economics and Management (WPCEM) organized by the Escuela Universitaria de Osuna-Universidad de Sevilla (Spain) and Pontificia Universidad Javieriana Cali (Colombia) on June 2021. The Workshop aims to discuss the contributions of academic research in economics and management, to the analysis and strengthening of the sectors and activities that integrate local and popular culture. The local and popular adjectives refer to the fact that these sectors and activities, for the most part, come from intangible heritage accumulated in the communities, with strong roots in their territories. The articles stand out for their theoretical and empirical quality. We also want to highlight the variety of the papers where you can observe different perspectives of the Popular Culture developed by a group of researchers from Europe and America

Fregolent, Laura y Nel·lo, Oriol (eds.) (2021). Social Movements and Public Policies in Southern

Obra ressenyada: Laura FREGOLENT, Oriol NEL·LO (eds.) Social Movements and Public Policies in Southern European Cities.Berlín: Springer, 202

La creatividad en las regiones europeas. Estudio comparativo a partir del análisis clúster

En el presente estudio se analizará el rol de la creatividad como factor de desarrollo económico clave para la calidad de vida y el bienestar para la población. Una de las características más representativas de la creatividad es su reparto no homogéneo, ya que tiende a concentrarse en determinados territorios. El objetivo principal de nuestro trabajo consiste en desarrollar un análisis de situación sobre el sistema creativo español respecto al conjunto de las regiones europeas. Para ello aplicaremos el análisis clúster, mediante el cual se clasificará a cada región europea de acuerdo a las siguientes variables: industrias semicreativas, empleo en el sector cultural y creativo, recursos culturales, nivel educativo, clase creativa, calidad de las instituciones y estructura urbana. Los resultados obtenidos permitirán caracterizar y analizar las distintas agrupaciones y, por ende, arrojarán luz sobre la falta de homogeneidad en las regiones estudiadas y sobre las deficiencias que presentan las regiones españolas, lo cual resulta de gran utilidad a la hora de diseñar las políticas culturales a nivel europeo

Silhouette-based human action recognition with a multi-class support vector machine

This paper has been presented at : 9th International Conference on Pattern Recognition Systems (ICPRS 2018)Computer vision systems have become increasingly popular, being used to solve a wide range of problems. In this paper, a computer vision algorithm with a support vector machine (SVM) classifier is presented. The work focuses on the recognition of human actions through computer vision, using a multi-camera dataset of human actions called MuHAVi. The algorithm uses a method to extract features, based on silhouettes. The challenge is that in MuHAVi these silhouettes are noisy and in many cases include shadows. As there are many actions that need to be recognised, we take a multiclass classification ap-proach that combines binary SVM classifiers. The results are compared with previous results on the same dataset and show a significant improvement, especially for recognising actions on a different view, obtaining overall accuracy of 85.5% and of 93.5% for leave-one-camera-out and leave-one-actor-out tests respectively.Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2013-51509) el Ministerio de Educación, cultura y Deporte (CEI-15-17) and Banco Santander

Feature selection using correlation analysis and principal component analysis for accurate breast cancer diagnosis

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%

Niveles de daño económico para Antigastra catalaunalis (Duponchel) en ajonjolí

Ajonjolí-Sésamo - Sesamum indicumMaestría en CienciasMaestrí

PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos

This letter proposes a method for the generation of temporal action proposals for the segmentation of long uncut video sequences. The presence of consecutive multiple actions in video sequences makes the temporal segmentation a challenging problem due to the unconstrained nature of actions in space and time. To address this issue, we exploit the nonaction segments present between the actual human actions in uncut videos. From the long uncut video, we compute the energy of consecutive nonoverlapping motion history images (MHIs), which provides spatiotemporal information of motion. Our proposals from MHIs (PMHI) are based on clustering the MHIs into actions and nonaction segments by detecting minima from the energy of MHIs. PMHI efficiently segments the long uncut videos into a small number of nonoverlapping temporal action proposals. The strength of PMHI is that it is unsupervised, which alleviates the requirement for any training data. Our temporal action proposal method outperforms the existing proposal methods on the Multi-view Human Action video (MuHAVi)-uncut and Computer Vision and Pattern recognition (CVPR) 2012 Change Detection datasets with an average recall rate of 86.1% and 86.0%, respectively.Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement nº 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santande

DA-VLAD: Discriminative action vector of locally aggregated descriptors for action recognition

This paper has been presented at : 25th IEEE International Conference on Image Processing (ICIP 2018)In this paper, we propose a novel encoding method for the representation of human action videos, that we call Discriminative Action Vector of Locally Aggregated Descriptors (DA-VLAD). DA-VLAD is motivated by the fact that there are many unnecessary and overlapping frames that cause non-discriminative codewords during the training process. DA-VLAD deals with this issue by extracting class-specific clusters and learning the discriminative power of these codewords in the form of informative weights. We use these discriminative action weights with standard VLAD encoding as a contribution of each codeword. DA-VLAD reduces the inter-class similarity efficiently by diminishing the effect of common codewords among multiple action classes during the encoding process. We present the effectiveness of DA-VLAD on two challenging action recognition datasets: UCF101 and HMDB51, improving the state-of-the-art with accuracies of 95.1% and 80.1% respectively.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research. We also acknowledge the support from the Directorate of Advance Studies, Research and Technological development (ASR) & TD, University of Engineering and Technology Taxila, Pakistan. Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Unions Seventh Framework Programme for research, technological development and demonstration under grant agreement n 600371, el Ministerio de Economia y Competitividad (COFUND2013-51509) and Banco Santander