Studying the Performance of Cognitive Models in Time Series Forecasting

Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable, scarce, or only partially relevant. These approaches are based on methods dedicated to preparing experts and then to elicit their opinions about the variables that describe the phenomena under study. In time series forecasting exercises, elicitation processes seek to obtain accurate estimates, overcoming human heuristic biases, while being less time consuming. This paper aims to compare the performance of cognitive and mathematical time series predictors, regarding accuracy. The results are based on the comparison of predictors of the cognitive and mathematical models for several time series from the M3-Competition. From the results, one can see that cognitive models are, at least, as accurate as ARIMA models predictions

APRENDIZADO DE MAQUINA PARA AGRUPAMENTO E ASSOCIACAO DE DADOS DO ENSINO SUPERIOR PUBLICO BRASILEIRO

This work aims to analyze the characteristics of Brazilian public Higher Education Institutions (IES) and to discoverknowledge about students in undergraduate courses at the observed IES. For this, techniques provided by the methodsof Knowledge Discovery in Databases (KDD) and Cross Industry Standard Process for Data Mining (CRISP-DM) wereused on databases published by the Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP) (NationalInstitute of Educational Studies and Research Anísio Teixeira). Unsupervised Machine Learning data clusteringand association experiments were performed in two study scenarios. The first scenario uses the K-Means algorithm tocluster public IES, observing data about expenses, number of professors and technicians, location and administrativecategory of IES, among others. The analysis of the four clusters obtained in the experiment enabled the identification ofsimilarities and dissimilarities between the institutions. The clusters, in addition to data from students of undergraduatecourses at IES (such as age group, length of stay at graduation and form of admission), are considered in the second study scenario, which uses the Apriori algorithm to generate association rules that can base the characterization of the students’ socioeconomic profiles.Este trabalho visa à análise de características de Instituições de Ensino Superior (IES) públicas brasileiras e à descoberta de conhecimento sobre os contextos de formação de discentes de cursos de graduação das IES observadas. Para isso, técnicas previstas pelos métodos de Knowledge Discovery in Databases (KDD) e Cross Industry Standard Process for Data Mining (CRISP-DM) foram empregadas sobre bases de dados publicadas pelo Instituto Nacional de Estudos e Pesquisas Educacionais Anísio Teixeira (INEP). Experimentos de agrupamento e associação de dados, do Aprendizado de Máquina Não Supervisionado, foram executados em dois cenários de estudo. O primeiro cenário usa o algoritmo K-Means para agrupar IES públicas, com a observação de dados sobre despesas, quantidades de docentes e técnicos, localização e categoria administrativa das IES, entre outros. A análise dos quatro grupos obtidos no agrupamento possibilitou a identificação de similaridades e dissimilaridades entre as instituições. Os grupos, além de dados de concluintes de cursos de graduação nas IES (como faixa etária, tempo de graduação e forma de ingresso), são considerados no segundo cenário do estudo, que usa o algoritmo Apriori para geração de regras de associação que podem basear a caracterização dos perfis socioeconômicos dos estudantes

Otimização de acesso em um sistema de integração de dados através do uso de caching e materialização de dados

Sistemas de integração de dados oferecem acesso uniforme sobre fontes de dados heterogêneas e distribuídas. Para fornecer um acesso integrado a diversas fontes de dados, duas abordagens clássicas foram propostas na literatura atual: abordagem materializada e abordagem virtual. Na abordagem materializada, os dados são previamente acessados, integrados e armazenados em um data warehouse e as consultas submetidas ao sistema de integração são processadas nesse repositório sem haver acesso direto às fontes de dados. Na abordagem virtual, as consultas submetidas ao sistema de integração são decompostas em subconsultas endereçadas diretamente às fontes de dados. Os dados obtidos das fontes como resposta a essas subconsultas são integrados e retornados ao usuário solicitante. O nosso trabalho, consiste em criar um ambiente de integração de dados provenientes de múltiplas fontes no ambiente Web o qual combina recursos de ambas as abordagens suportando o processamento de consultas virtuais e materializadas. Um outro recurso de nossa proposta é a inserção de um subsistema de gerenciamento de uma cache para armazenar os resultados das consultas mais freqüentemente submetidas pelo usuário. O ambiente tem recursos de materialização de dados em um data warehouse, e o processo de materialização é feito seletivamente com base na análise e classificação de critérios de qualidade e custo associados aos dados das fontes. Essa seleção criteriosa visa equilibrar melhorias no tempo de resposta das consultas com taxas de custo de manutenção do data warehouse aceitáveis. A partir de uma arquitetura de integração de dados baseada na abordagem virtual, foram incluídos módulos para gerenciamento do data warehouse, gerenciamento da cache e módulos de processamento de consultas sob três formas: virtuais com acesso às fontes de dados, materializadas com acesso ao data warehouse e consultas acessando diretamente a cache. Todos esses recursos são colocados em conjunto visando obter ganhos no desempenho do processamento das consultas no sistema de integraçã

Um Processo Para Gestão De Requisitos Em Desenvolvimento Distribuído De Software

The modality of Development Distributed of Software (DDS) grows every day. However, beside the problems inherent to the processes of conventional development, a distributed team faces other challenges. With an objective of to help these professionals in the actives of management and requirement control, this article propose a process based on two existing processes and with the differential of uses objectives and practices from the Capability Maturity Model Integration (CMMI) for requirements management. The valuation of the process was realized through a field research with professionals of the DDS areas. The results indicate a necessity of utilization of this process by the DDS professionals

BMI, Overweight Status and Obesity Adjusted by Various Factors in All Age Groups in the Population of a City in Northeastern Brazil

Objective: In Brazil, demographic, socioeconomic and epidemiological changes over time have led to a transition in nutritional standards, resulting in a gradual reduction of malnutrition and an increased prevalence of overweight and obese individuals, similar to the situation in developed countries in previous decades. This study assessed the body mass index (BMI) and the prevalence of an overweight status and obesity, adjusted for various factors, in a population in northeastern Brazil including all age groups. Methods: This is a cross-sectional population-based epidemiological study using single sampling procedure composed of levels. Given the heterogeneity of the variable “income” and the relationship between income, prevalence of diseases and nutrition, a stratified sampling on blocks in the first level was used. In this, city districts were classified by income into 10 strata, according to information obtained from IBGE. A systematic sampling was applied on randomly selected blocks in order to choose the residences that would be part of the sample (second level), including 1165 participants from all age groups. Results and Discussion: The prevalence of an overweight status or obesity was adjusted for demographic, socioeconomic and lifestyle variables. When the Chi-square test was applied, a relationship was observed between the prevalence of an overweight status or obesity and the age group, gender, educational level and income of the participants. Regarding lifestyle parameters, only smoking was associated with the prevalence of an overweight status or obesity, in both adults and in the total sample. The results for the following groups were significant (p < 0.05): the age group from 20 to 59 years, when the individual presented an educational level greater than or equal to high school; and the age group ≥ 60 years, when the individual was female. It is noteworthy that educational level and being female were significant in adjusting for the total population as major factors influencing an increased BMI, followed by the variables physical activity and family income. Conclusions: The adjusted results justify the adoption of intervention and prevention policies to combat these clinical conditions for the study population as a whole, particularly directed toward adults with higher education level as well as elderly females

Oleic Acid Induces Lung Injury in Mice through Activation of the ERK Pathway

Oleic acid (OA) can induce acute lung injury in experimental models. In the present work, we used intratracheal OA injection to show augmented oedema formation, cell migration and activation, lipid mediator, and cytokine productions in the bronchoalveolar fluids of Swiss Webster mice. We also demonstrated that OA-induced pulmonary injury is dependent on ERK1/2 activation, since U0126, an inhibitor of ERK1/2 phosphorylation, blocked neutrophil migration, oedema, and lipid body formation as well as IL-6, but not IL-1β production. Using a mice strain carrying a null mutation for the TLR4 receptor, we proved that increased inflammatory parameters after OA challenges were not due to the activation of the TLR4 receptor. With OA being a Na/K-ATPase inhibitor, we suggest the possible involvement of this enzyme as an OA target triggering lung inflammation