76 research outputs found

    A Model for Predicting Music Popularity on Streaming Platforms

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    The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify鈥檚 Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today鈥檚 biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%

    Detection and Evaluation of metals in soil under influence of mining by Dispersive Energy X-ray Fluorescence Spectrometry (EDXRF), Lavras do Sul/RS

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    Rocks are the primary source of chemical elements found in the earth. Among them metallic elements, which are distributed in most natural environments and their distribution in soils is widespread. Knowledge of the soil chemical composition will provide subsidies for a prediction of phytotoxicity and possible groundwater contamination. Concentrations of metals, in the right quantities, are essential for maintaining life. Although, if in high quantities (toxicity), can cause damage to plants, animals, and humans. This work consists of a chemical analysis of soil samples in the region of Lavras do Sul / RS, where mineral deposits occur. Until the year 1981 mining activities occupied the area without municipality, resulting in ore and waste deposits at inappropriate places, generating environmental liabilities, contributing to soil chemistry, and consequently affecting public health. Through the energy dispersive X-ray fluorescence spectrometry (EDXRF) method, 20 soil samples were analyzed in two profiles surrounding the municipality. Among them, high concentrations of Zn, Cu, Ce and Cd were detected, possibly contributing to anthropic activities and seasonal fluctuations

    Iniciativa Liblink: actividades y proyectos

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    Esta presentaci贸n contiene: - Iniciativa Liblink. Actividades permanentes. 驴C贸mo intercambiar documentos con las Instituciones ISTEC por medio de la Iniciativa Liblink? - Proyectos propuestos en 2018 - Celsius 3 - Oficina Virtual - Tareas desarrolladas 2018-2019 - Nuevos ProyectosProyecto de Enlace de Biblioteca

    Iniciativa Liblink: actividades y proyectos

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    Esta presentaci贸n contiene: - Iniciativa Liblink. Actividades permanentes. 驴C贸mo intercambiar documentos con las Instituciones ISTEC por medio de la Iniciativa Liblink? - Proyectos propuestos en 2018 - Celsius 3 - Oficina Virtual - Tareas desarrolladas 2018-2019 - Nuevos ProyectosProyecto de Enlace de Biblioteca

    Iniciativa Liblink: actividades y proyectos

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    Esta presentaci贸n contiene: - Iniciativa Liblink. Actividades permanentes. 驴C贸mo intercambiar documentos con las Instituciones ISTEC por medio de la Iniciativa Liblink? - Proyectos propuestos en 2018 - Celsius 3 - Oficina Virtual - Tareas desarrolladas 2018-2019 - Nuevos ProyectosProyecto de Enlace de Biblioteca

    Time series classification with representation ensembles

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    Time series has attracted much attention in recent years, with thousands of methods for diverse tasks such as classification, clustering, prediction, and anomaly detection. Among all these tasks, classification is likely the most prominent task, accounting for most of the applications and attention from the research community. However, in spite of the huge number of methods available, there is a significant body of empirical evidence indicating that the 1-nearest neighbor algorithm (1-NN) in the time domain is 鈥渆xtremely difficult to beat鈥. In this paper, we evaluate the use of different data representations in time series classification. Our work is motivated by methods used in related areas such as signal processing and music retrieval. In these areas, a change of representation frequently reveals features that are not apparent in the original data representation. Our approach consists of using different representations such as frequency, wavelets, and autocorrelation to transform the time series into alternative decision spaces. A classifier is then used to provide a classification for each test time series in the alternative domain. We investigate how features provided in different domains can help in time series classification. We also experiment with different ensembles to investigate if the data representations are a good source of diversity for time series classification. Our extensive experimental evaluation approaches the issue of combining sets of representations and ensemble strategies, resulting in over 300 ensemble configurations.S茫o Paulo Research Foundation (FAPESP) (grant #2012/08923-8, #2013/26151-5, and #2015/07628-0)CNPq (grant #446330/2014-0 and #303083/2013-1)International Symposium on Advances in Intelligent Data Analysis - IDA (14. 2015 Saint Etienne

    Procesamiento de datos masivos en tiempo real y consumo energ茅tico de sistemas paralelos

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    Los avances tecnol贸gicos de los sistemas de c贸mputo paralelo y distribuido permiten el desarrollo de aplicaciones antes impensadas. Una de nuestras l铆neas de investigaci贸n se enfoca en aplicar estas tecnolog铆as a Unidades de Cuidados Intensivos y Unidades de Vigilancia Intermedia. Buscamos mejorar sustancialmente el rendimiento de ellas con el procesamiento en tiempo real de datos masivos generados por el equipamiento m茅dico y otras fuentes. Adicionalmente, trabajamos en la reducci贸n del consumo energ茅tico de los sistemas de computaci贸n de altas prestaciones, con especial atenci贸n en los mecanismos de tolerancia a fallos. Todas nuestras investigaciones se centran en desarrollar metodolog铆as, modelos y soluciones inform谩ticas para colaborar en la resoluci贸n de problemas que tengan una alta demanda computacional e impacto social. Los trabajos se desarrollan en colaboraci贸n con otras universidades, y un hospital p煤blico de Argentina. La formaci贸n de recursos humanos en estas l铆neas est谩 orientada al nivel de grado, maestr铆a y doctoral.Eje: Procesamiento distribuido y paralelo.Red de Universidades con Carreras en Inform谩tic
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