Redistribution, capital income taxation and tax evasion

Factor mobility and tax evasión are two phenomena that constraint the effectiveness of redistributive policies now used by the member countries of the European Union. In this paper, a normative analysis of this fact is undertaken using a simple model with two countries and two social classes, where capital is perfectly mobile and labour is immobile. Each country complements the income of its workers, assumed to be poor, with transfers. The latter are financed with two taxes on capital income. The first one, following the origin principie, alters the retum and intemational allocation of capital. The second one, following the residence principie, induces the evasión of capitalists' incomes. Each government chooses the optimal mix of capital taxes that maximizes the welfare of its citizens with no regard on the repercussions on its neighbour country. A numerical exercise is built to examine the sensitivity of the resulting non cooperative equilibrium to the aversión to inequality exhibited by the different governments as well as to the factor endowments of their respective countries

An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification

In predictive tasks, real-world datasets often present di erent degrees of imbalanced (i.e., long-tailed or skewed) distributions. While the majority (the head or the most frequent) classes have su cient samples, the minority (the tail or the less frequent or rare) classes can be under-represented by a rather limited number of samples. Data pre-processing has been shown to be very e ective in dealing with such problems. On one hand, data re-sampling is a common approach to tackling class imbalance. On the other hand, dimension reduction, which reduces the feature space, is a conventional technique for reducing noise and inconsistencies in a dataset. However, the possible synergy between feature selection and data re-sampling for high-performance imbalance classification has rarely been investigated before. To address this issue, we carry out a comprehensive empirical study on the joint influence of feature selection and re-sampling on two-class imbalance classification. Specifically, we study the performance of two opposite pipelines for imbalance classification by applying feature selection before or after data re-sampling. We conduct a large number of experiments, with a total of 9225 tests, on 52 publicly available datasets, using 9 feature selection methods, 6 resampling approaches for class imbalance learning, and 3 well-known classification algorithms. Experimental results show that there is no constant winner between the two pipelines; thus both of them should be considered to derive the best performing model for imbalance classification. We find that the performance of an imbalance classification model not only depends on the classifier adopted and the ratio between the number of majority and minority samples, but also depends on the ratio between the number of samples and features. Overall, this study should provide new reference value for researchers and practitioners in imbalance learning.TIN2017-89517-

Speech emotion recognition via multiple fusion under spatial–temporal parallel network

The authors are grateful to the anonymous reviewers and the editor for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (No. 61702066), the Chongqing Research Program of Basic Research and Frontier Technology, China (No. cstc2021jcyj-msxmX0761) and partially supported by Project PID2020-119478GB-I00 funded by MICINN/AEI/10.13039/501100011033 and by Project A-TIC-434- UGR20 funded by FEDER/Junta de Andalucía Consejería de Transformación Económica, Industria, Conocimiento Universidades.Speech, as a necessary way to express emotions, plays a vital role in human communication. With the continuous deepening of research on emotion recognition in human-computer interaction, speech emotion recognition (SER) has become an essential task to improve the human-computer interaction experience. When performing emotion feature extraction of speech, the method of cutting the speech spectrum will destroy the continuity of speech. Besides, the method of using the cascaded structure without cutting the speech spectrum cannot simultaneously extract speech spectrum information from both temporal and spatial domains. To this end, we propose a spatial-temporal parallel network for speech emotion recognition without cutting the speech spectrum. To further mix the temporal and spatial features, we design a novel fusion method (called multiple fusion) that combines the concatenate fusion and ensemble strategy. Finally, the experimental results on five datasets demonstrate that the proposed method outperforms state-of-the-art methods.National Natural Science Foundation of China 61702066Chongqing Research Program of Basic Research and Frontier Technology, China cstc2021jcyj-msxmX0761MICINN/AEI/10.13039/501100011033: PID2020-119478GB-I00FEDER/Junta de Andalucía A-TIC-434- UGR2

Capacidad Cardiovascular en Adultos Sedentarios Obesos sometidos a prueba de esfuerzo en banda sin fin con Protocolo de Bruce modificado en el Centro de Medicina de la Actividad Física y el Deporte, año 2012

El estilo de vida sedentario es uno de los principales factores de riesgo para enfermedades de alta prevalencia, como la diabetes tipo 2, las enfermedades cardiovasculares, la osteoporosis y algunos cánceres. (1). La asociación del sedentarismo con la actual pandemia de obesidad y con el síndrome metabólico (SM) es clara. En consecuencia, el sedentarismo es un factor asociado con una peor calidad de vida y un incremento de la mortalidad general

An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons

In a recently published paper in JMLR, Demsar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n*n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable p-values in multiple comparison procedures.This research has been supported by the project TIN2005-08386-C05-01. S. García holds a FPU scholarship from Spanish Ministry of Education and Science

Value Co-Creation in Ecotourism: Finding Solutions in a Digital Era

This study aims to identify the main actors in the ecotourism online value co-creation ecosystem. With the ever-increasing demand to which the ecotourism sector has been subject in recent years, there is a need for business models featuring interactive tools that allow ecotourists to participate and co-create their own experiences. Not only will ecotourism businesses and ecotourists participate in this process, but so too will several other stakeholders, becoming key actors in the co-creation of value. Ecotourism, being of a hedonic and experiential nature, requires strategies that are geared towards enjoyment of the joint value co-creation experience. With this finality, we present some factors, such as motivation, that are necessary for the ecotourist's value co-creation, as well as various other aspects that influence their behavior. Our expectation is that value co-creation will have a positive effect on the behavior and experiences of tourists when it comes to developing ecotourism products and services. Specifically, we expect that the implementation of a co-creation strategy will lead to greater satisfaction and loyalty. Accordingly, drawing on the existing literature, we present a theoretical proposal of the antecedents and consequents that affect and enable online value co-creation by ecotourists

Detección y aislamiento robusto de fallas en tiempo finito

Seguridad y confiabilidad en la industria es una prioridad de la actualidad. Una forma de apoyarla es mediante el uso de sistemas de supervisión del desempeño, los cuales incluyen diagnóstico de fallas así como mecanismos de tolerancia de fallas. En este trabajo se propone un algoritmo robusto que converge en tiempo finito para la detección y aislamiento de fallas, permitiendo aumentar la confiabilidad de la detección a partir del tiempo de convergencia, el cual puede ser fijado arbitrariamente. El resultado se logra sin alterar la dinámica propia del residuo. El algoritmo es probado con un modelo de simulación

教师视角下对小学教育资源包容性使用的研究

Addressing the diversity of students from an inclusive perspective requires that schools carry out self-study processes that help them analyse the strengths and weaknesses that hinder the full integration of students. The main objective of this research was to analyse the strengths and obstacles in the use and availability of materials and education resources present in a private school of primary education. A descriptive and quantitative case study was carried out, in which the opinion of 23 professionals was analyzed through the instrument “Guía de Autoevaluación de Centro para la Atención a la Diversidad (ACADI)”, specifically, the “Resources” section. The results showed that the main strengths are the final purpose the centre gives to the resources, the accessibility of its facilities, the engagement of families and the coordination between professionals to respond to the diversity of the centre. The lack of resources for teachers to put their training into practice, the lack of technical support for students who need it to access information, the lack of activities for teacher training, and the lack of consideration given to students’ interests when developing the curriculum were identified as the main barriers to inclusive education. We conclude by stating that identifying the facilitating and hindering elements present in this centre for inclusive education helps us to start progressing on the road to quality education with all and for all.Atender la diversidad del alumnado desde el paradigma de la inclusión requiere que los centros educativos realicen procesos de autorreflexión que les ayuden a analizar cuáles son las fortalezas y debilidades que dificultan la plena participación de todo el alumnado. El objetivo de esta investigación fue analizar las fortalezas y barreras en el uso y disposición de los recursos presentes en un colegio concertado en la etapa de educación primaria. Se realizó un estudio de caso de corte descriptivo y cuantitativo, en el que se analizó la opinión de 23 profesionales encuestados a través del instrumento “Guía de Autoevaluación de Centro para la Atención a la Diversidad (ACADI)”, en concreto, la dimensión referida a “Recursos”. Los resultados mostraron como principales fortalezas la finalidad que el centro da a los recursos existentes en el mismo, la accesibilidad a sus instalaciones, la participación de las familias y la coordinación entre el profesorado para responder a la diversidad del centro. Como barreras para favorecer la inclusión se detectaron la falta de recursos para que el profesorado pueda poner en práctica su formación, la escasez de ayudas técnicas para el alumnado que las requiera, la carencia de actividades de formación para el profesorado, así como la escasa consideración de partir de los intereses del alumnado para construir el currículo. Las conclusiones muestran que conocer los recursos facilitadores y obstaculizadores presentes en este centro, para la inclusión educativa de su alumnado, posibilitará el emprendimiento de líneas de mejora en el camino hacia una educación de calidad con todos y para todos.从包容范式面对学生的多样性需要学校进行一个自我反思的过程，以帮助他们分析阻碍所有学生充分参与的优势和劣势。本研究的目的是分析初等教育阶段半公半私学校资源使用和配置的优势和障碍。 我们进行描述性和定量的案例研究，通过“关注多样性中心（ACADI）自我评估指南”工具对23名受访专业人士的意见进行分析，其中具体的维度为“资源”维度。 结果表明，该中心的主要优势在于其现有资源的目的、设施的可及性、家庭的参与以及教师之间的协调，以应对学校的多样性需求。其阻碍包容性的障碍包括教师缺乏将培训付诸实践的资源、缺乏为有需要的学生提供的技术援助、缺乏对教师的培训活动以及从学生利益出发的考虑有限。学生构建课程。 结论表明，了解该包容性教育中心存在的促进和阻碍因素，有助于我们开始在全民优质教育的道路上取得进展

An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets

In this contribution we carry out an analysis of the rule weights and Fuzzy Reasoning Methods for Fuzzy Rule Based Classification Systems in the framework of imbalanced data-sets with a high imbalance degree. We analyze the behaviour of the Fuzzy Rule Based Classification Systems searching for the best configuration of rule weight and Fuzzy Reasoning Method also studying the cooperation of some pre-processing methods of instances. To do so we use a simple rule base obtained with the Chi (and co-authors’) method that extends the wellknown Wang and Mendel method to classification problems. The results obtained show the necessity to apply an instance preprocessing step and the clear differences in the use of the rule weight and Fuzzy Reasoning Method. Finally, it is empirically proved that there is a superior performance of Fuzzy Rule Based Classification Systems compared to the 1-NN and C4.5 classifiers in the framework of highly imbalanced data-sets.Spanish Projects TIN-2005-08386-C05-01 & TIC-2005-08386- C05-0