Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

APLICAÇÃO DO MÉTODO DE BALANÇO DE CALOR NA DETERMINAÇÃO DA TRANSPIRAÇÃO DE MUDAS DE LIMOEIRO

O método de balanço de calor é usado para monitorar fluxo de seiva em plantas de diversas espécies. Neste trabalho, estudou-se o desempenho e aplicabilidade do método, em mudas de limoeiro com diâmetros em torno de 18 mm. Os sensores foram avaliados em plantas envasadas, em casa de vegetação, sob condições naturais de temperatura e radiação solar. O desempenho do método foi avaliado comparando-se o fluxo de massa computado e as medidas gravimétricas de perda de massa, fazendo-se depois, análise de regressão. Os resultados indicam que os sensores tiveram uma precisão de 8% na escala horária e 11% na escala diária. A constante de tempo foi de 12 minutos, o que é um indicativo de que o método é adequado para medir fluxo de seiva em mudas de citros com boa precisão, e suficientemente apropriado para ser usado em aplicações agronômicas e fisiológicas.<br>The heat balance method is widely used to measure sap flow of different plant species. This work focused on the performance and applicability of the method, using 2-year old lemon plants, with 18 mm in diameter, the sensors were evaluated on potted-plants in a greenhouse under natural temperature and solar radiation conditions. The method performance was tested by comparing the computed mass flow with weight losses. A regression equation was used to analyse the data collected. The results showed that sensor precision reached 8% in the hourly scale and 11% in the daily scale. The time constant of 12 minutes indicated that the method was accurate to measure sap flow with good precision. Also we concluded that the method is adequate for agronomic and physiological use

Measurements with silicon photomultipliers of dose-rate effects in the radiation damage of plastic scintillator tiles in the CMS hadron endcap calorimeter

Measurements are presented of the reduction of signal output due to radiation damage for two types of plastic scintillator tiles used in the hadron endcap (HE) calorimeter of the CMS detector. The tiles were exposed to particles produced in proton-proton (pp) collisions at the CERN LHC with a center-of-mass energy of 13 TeV, corresponding to a delivered luminosity of 50 fb-1. The measurements are based on readout channels of the HE that were instrumented with silicon photomultipliers, and are derived using data from several sources: A laser calibration system, a movable radioactive source, as well as hadrons and muons produced in pp collisions. Results from several irradiation campaigns using 60Co sources are also discussed. The damage is presented as a function of dose rate. Within the range of these measurements, for a fixed dose the damage increases with decreasing dose rate