Archetypal analysis: an alternative to clustering for unsupervised texture segmentation

Texture segmentation is one of the main tasks in image applications, specifically in remote sensing, where the objective is to segment high-resolution images of natural landscapes into different cover types. Often the focus is on the selection of discriminant textural features, and although these are really fundamental, there is another part of the process that is also influential, partitioning different homogeneous textures into groups. A methodology based on archetype analysis (AA) of the local textural measurements is proposed. AA seeks the purest textures in the image and it can find the borders between pure textures, as those regions composed of mixtures of several archetypes. The proposed procedure has been tested on a remote sensing image application with local granulometries, providing promising results

A Data Science Analysis of Academic Staff Workload Profiles in Spanish Universities: Gender Gap Laid Bare

This paper presents a snapshot of the distribution of time that Spanish academic staff spend on different tasks. We carry out a statistical exploratory study by analyzing the responses provided in a survey of 703 Spanish academic staff in order to draw a clear picture of the current situation. This analysis considers many factors, including primarily gender, academic ranks, age, and academic disciplines. The tasks considered are divided into smaller activities, which allows us to discover hidden patterns. Tasks are not only restricted to the academic world, but also relate to domestic chores. We address this problem from a totally new perspective by using machine learning techniques, such as cluster analysis. In order to make important decisions, policymakers must know how academic staff spend their time, especially now that legal modifications are planned for the Spanish university environment. In terms of the time spent on quality of teaching and caring tasks, we expose huge gender gaps. Non-recognized overtime is very frequent

Finding archetypal patterns for binary questionnaires

Archetypal analysis is an exploratory tool that explains a set of observations as mixtures of pure (extreme) patterns. If the patterns are actual observations of the sample, we refer to them as archetypoids. For the first time, we propose to use archetypoid analysis for binary observations. This tool can contribute to the understanding of a binary data set, as in the multivariate case. We illustrate the advantages of the proposed methodology in a simulation study and two applications, one exploring objects (rows) and the other exploring items (columns). One is related to determining student skill set profiles and the other to describing item response functions

Analysis of Archetypes to Determine Time Use and Workload Profiles of Spanish University Professors

Allocation of time use is important to develop appropriate policies, especially in terms of gender equality. Individual well-being depends on many factors, including how time is spent. Therefore, knowing and analysing the time use and workload of academic staff is relevant for academic policy making. We analyse the responses of 703 Spanish academic staff regarding different activities of paid work and household work (unpaid). We use an innovative machine learning technique in this field, archetype analysis, which we introduce step by step while exploring our data. We identify five profiles, and we examine gender inequalities. The findings indicate that there is a higher prevalence of women in the profiles with a greater workload in household activities and teaching-related activities, but the prevalence is the same in the profile with a greater workload in research activities

La educación en la encrucijada: miradas sociales y progresión pedagógica en las puertas del 2020

Desde diferentes sectores sociales actualmente existe la tentación de exigir que la escuela sea el antídoto social por excelencia. Como consecuencia, las distintas reformas educativas tienden a adoptar decisiones que, a menudo, suelen desembocar en posicionamientos idealistas que no siempre tienen la capacidad de poder ayudar en la compleja tarea de reestructurar y mejorar la educación. En este contexto, la presente ponencia describe los medios que tiene a su alcance la pedagogía para escapar de la paradoja de la individualización totalizante que conlleva el proceso de expansión de las nuevas tecnologías y, a la vez, reflexiona sobre la engañosa protección de la cerrazón en sí misma en la que la pedagogía ha caído, en los últimos tiempos, como respuesta a la globalización. Se llega a la conclusión de que la concreción de pautas, valores y prácticas sobre las maneras mediante las que la existencia humana es capaz de mejorar su situación en el mundo requiere partir de premisas como: dotaciones presupuestarias adecuadas, fomento de la formación del profesorado y actualización de metodologías.Currently, from different social sectors there is the temptation to demand to the school to be the social antidote par excellence. Therefore, the different educational reforms tend to adopt decisions that often lead to idealistic positions that do not always have the capacity to help in the complex task of restructuring and improving education. In this context, the present paper describes the different possibilities that have the pedagogy to escape the paradox of totalizing individualization that involves the process of expansion of new technologies and, at the same time, reflects the tendency to close in itself, in which pedagogy has fallen as a response to globalization. It is concluded that the establishment of guidelines, values and practices on the ways in which human existence is able to improve their situation in the world requires starting from premises such as: adequate budget allocations, promotion of teacher training and updating methodologies

Archetype analysis: A new subspace outlier detection approach

The problem of detecting outliers in multivariate data sets with continuous numerical features is addressed by a new method. This method combines projections into relevant subspaces by archetype analysis with a nearest neighbor algorithm, through an appropriate ensemble of the results. Our method is able to detect an anomaly in a simple data set with a linear correlation of two features, while other methods fail to recognize that anomaly. Our method performs among top in an extensive comparison with 23 state-of-the-art outlier detection algorithms with several benchmark data sets. Finally, a novel industrial data set is introduced, and an outlier analysis is carried out to improve the fit of footwear, since this kind of analysis has never been fully exploited in the anthropometric field.Funding for open access charge: CRUE-Universitat Jaume

La gestión del Big Data en la formación docente

Resumen: esta ponencia presenta el big data como una estructura conformadora de la acción (docente) que va inevitablemente unida a la recolección de información y gracias a la cual se planificarán los sistemas de referencia de las (futuras) prácticas docentes. Aseveramos que el big data no excluye los intereses clásicos de la pedagogía, más bien los ensancha y los actualiza. Nos postulamos, en consecuencia, a favor de una concepción de los datos como eje de desarrollo armónico y en consonancia con la pedagogía. Esto implica la aceptación de la identificación de la información individual de nuestro alumnado (que proviene del procesamiento de los datos) pero también una aproximación intersubjetiva de la realidad

Búsqueda de arquetipos en la relación didáctica del razonamiento proporcional con otros contenidos matemáticos

INNTED Congreso Internacional de Innovación y Tendencias Educativas 202

La mejora en la escuela como eje de desarrollo social y como elemento al servicio de la tarea docente

Ponència presentada al V Congreso Internacional Virtual sobre Desarrollo Económico, Social y Empresarial en Iberoamérica, celebrat del 16 al 30 de junio de 2020, coordinat per la Universidad Autónoma Chapingo, MéxicoPonència presentada al V Congreso Internacional Virtual sobre Desarrollo Económico, Social y Empresarial en Iberoamérica, celebrat del 16 al 30 de junio de 2020, coordinat per la Universidad Autónoma Chapingo, Méxic

Some contributions to archetypal analysis with applications

Aquesta Tesi desenvolupa l’Anàlisi d’Arquetipus (AA) i demostra la seua viabilitat i rigor. Amplia la tipologia de dades amb les quals pot treballar utilitzant, a banda de les dades multivariants contínues, dades binàries i dades funcionals. Aquesta amalgama de dades l’hem treballat implementant AA en diferents bases de dades, donant com a fruit tres investigacions independents. En la primera aportació treballem resultats d’exàmens utilitzant una variant d’AA, l’Anàlisi d’Arquetipoids (ADA) amb dades binàries i funcionals, en la segona aportació presentem un algorisme basat en AA per a treballar la segmentació de textures i en la darrera presentem un altre algorisme per a la cerca de dades atípiques (outliers). En totes aquestes investigacions, s’han utilitzat dades reals i s’ha fet un estudi comparatiu amb altres anàlisis i algorismes més reconeguts. Els resultats obtinguts han demostrat amb escreix la viabilitat i la competència d’AA.This Thesis develops the Archetype Analysis (AA) and demonstrates its viability and rigor. Expands the type of data which can work with, in addition to continuous multivariate data, it will manage binary data and functional data too. This amalgam of data will be used by implementing AA in different databases, resulting in three independent investigations. In the first contribution we work out exam results using a variant of AA, Archetypoids Analysis (ADA) with binary and functional data, in the second contribution we present an AA-based algorithm to work on texture segmentation and in the last one we present another algorithm for the search outliers. In all these investigations, real data have been used and a comparative study has been done with other more recognized analyzes and algorithms. The results obtained have demonstrated the AA feasibility and competence.Programa de Doctorat en Cièncie