Bolsa Familia Program and school enrolment: An analysis of the 2010 Brazilian demographic census

The Bolsa Família Program goal is to promote social development and poverty reduction, through the direct transfer of conditional cash, in association with other social programs. This study aims to analyze whether Bolsa Família had an association with children’s school attendance, which is one of the educational conditions of the program. Our main hypothesis is that children living in households receiving Bolsa Família had greater chances of attending school. Data from the Ministry of Social Development and Combating Famine indicated that children living in households with Bolsa Família had greater school enrolment levels. By using data from the 2010 Demographic Census, collected by the Brazilian Institute of Geography and Statistics (IBGE), some descriptive analyzes and binary logistic regression models were performed for different thresholds of household per capita income. These estimates were made by comparing children who lived in households receiving Bolsa Família to those children not receiving the program. We took into consideration characteristics about the household, mothers, and children. The results were clustered by the municipality of residence of the child. In all income thresholds, children benefi ting from Bolsa Família were more likely to be enrolled in school, compared to children not receiving the benefi t.

La visión artificial, un nuevo aliado para el análisis de imágenes artísticas

En este estudio se evalúa el rendimiento de los métodos de Bag-of-Visualterms (BOV) para la clasificación automática de imágenes digitales de la base de datos del artista Miquel Planas. Estas imágenes intervienen en la ideación y diseño de su producción escultórica. Constituye un interesante desafío dada la dificultad de la categorización de escenas cuando éstas difieren más por los contenidos semánticos que por los objetos que contienen. Hemos empleado un método de reconocimiento basado en Kernels introducido por Lazebnik, Schmid y Ponce en 2006. Los resultados son prometedores, en promedio, la puntuación del rendimiento es aproximadamente del 70%. Los experimentos sugieren que la categorización automática de imágenes basada en métodos de visión artificial puede proporcionar principios objetivos en la catalogación de imágenes y que los resultados obtenidos pueden ser aplicados en diferentes campos de la creación artística.