Introversion-Extraversion Prediction using Machine Learning

Introversion and extroversion are personality traits that assess the type of interaction between people and others. Introversion and extraversion have their advantages and disadvantages. Knowing their personality, people can utilize these advantages and disadvantages for their benefit. This study compares and evaluates several machine learning models and dataset balancing methods to predict the introversion-extraversion personality based on the survey result conducted by Open-Source Psychometrics Project. The dataset was balanced using three balancing methods, and fifteen questions were chosen as the features based on their correlations with the personality self-identification result. The dataset was used to train several supervised machine-learning models. The best model for the Synthetic Minority Oversampling (SMOTE), Adaptive Synthesis Sampling (ADASYN), and Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) datasets was the Random Forest with the 10-fold cross-validation accuracy of 95.5%, 95.3%, and 71.0%. On the original dataset, the best model was Support Vector Machine, with a 10-fold cross-validation accuracy of 73.5%. Based on the results, the best balancing methods to increase the models’ performance were oversampling. Conversely, the hybrid method of oversampling-undersampling did not significantly increase performance. Furthermore, the tree-like models, like Random Forest and Decision Tree, improved performance substantially from the data balancing. In contrast, the other models, excluding the SVM, did not show a significant rise in performance. This research implies that further study is needed on the hybrid balancing method and another classification model to improve personality classification performance

AGREGAÇÃO DE VALOR ECONÔMICO: UM ESTUDO COMPARATIVO DAS EMPRESAS AMBEV E SOUZA CRUZ

Este estudo versa sobre a importância da avaliação de empresas, haja vista que proporciona aos empresários e diretores condições favoráveis para a compreensão e uma leitura acertada para projetos futuros de crescimento e investimento. Defende-se a ideia que, por meio da avaliação, os dados apresentados pela empresa se tornam mais confiáveis. O processo para a avaliação de empresas não é instantâneo, passa por diversas fases que precisam ser respeitadas para que a avaliação seja eficaz e completa. Descreve-se na fundamentação teórica alguns passos que são essenciais para que uma avaliação de empresas se torne eficiente, entre esses destacam-se a Demonstração de Fluxo de Caixa – DFC e o Economic Value Added – EVA. O primeiro é um controle financeiro que auxilia o administrador a tomar decisões de acordo com o caixa da empresa, por sua vez o EVA, é um indicador de desempenho que representa o valor econômico agregado à empresa. Quanto ao método, trata-se de uma pesquisa quantitativa e descritiva, cujo material estatístico visa a detalhar e comparar os resultados das empresas AMBEV e Souza Cruz. No período analisado, o desempenho das empresas a partir do EVA, foi satisfatório, haja vista que os resultados de ambas foram positivos e crescentes. Portanto, houve agregação de valor econômico no período analisado, ou seja, de 2009 a 2011. Tal fato deve-se ao aumento do retorno sobre o capital investido e, também, pela queda observada no custo, já que na empresa AMBEV houve uma variação de crescimento de 42,11% e, na empresa Souza Cruz, uma variação positiva de 17,56%

Vitamin D Modulates Hematological Parameters and Cell Migration into Peritoneal and Pulmonary Cavities in Alloxan-Diabetic Mice

Background/Aims. The effects of cholecalciferol supplementation on the course of diabetes in humans and animals need to be better understood. Therefore, this study investigated the effect of short-term cholecalciferol supplementation on biochemical and hematological parameters in mice. Methods. Male diabetic (alloxan, 60mg/kg i.v., 10 days) and non diabetic mice were supplemented with cholecalciferol for seven days. The following parameters were determined: serum levels of 25-hydroxyvitamin D, phosphorus, calcium, urea, creatinine, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, red blood cell count, white blood cell count (WBC), hematocrit, hemoglobin, differential cell counts of peritoneal lavage (PeL), and bronchoalveolar lavage (BAL) fluids and morphological analysis of lung, kidney, and liver tissues. Results. Relative to controls, cholecalciferol supplementation increased serum levels of 25-hydroxyvitamin D, calcium, hemoglobin, hematocrit, and red blood cell counts and decreased leukocyte cell counts of PeL and BAL fluids in diabetic mice. Diabetic mice that were not treated with cholecalciferol had lower serum calcium and albumin levels and hemoglobin, WBC, and mononuclear blood cell counts and higher serum creatinine and urea levels than controls. Conclusion. Our results suggest that cholecalciferol supplementation improves the hematological parameters and reduces leukocyte migration into the PeL and BAL lavage of diabetic mice.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Pro-reitoria de Pesquisa da Universidade de Sao Paulo (PRP/USP, Projeto I and Novos Docentes)Univ Sao Paulo, Fac Pharmaceut Sci, Dept Clin & Toxicol Anal, Lab Immunoendocrinol,FCF, Sao Paulo, SP, BrazilUniv Fed Sao Paulo, Dept Med, Rheumatol Div, Sao Paulo, SP, BrazilDepartment of Medicine, Rheumatology Division, Universidade Federal de São Paulo, São Paulo, SP, BrazilFAPESP: 2010/02272-0FAPESP: 2012/23998-4FAPESP: 2013/20904-1FAPESP: 2014/05214-1FAPESP: 2017/05100-4CNPq: 470523/2013-1CNPq: 301617/2016-3Web of Scienc

Efeitos da parede de levedura em dieta úmida na microbiota fecal, na produção de gás e na morfologia intestinal de gatos adultos

The aim of this study is to evaluate the effects of yeast extract (EPL) in the moist diet on the fecal microbiotal, gas production and intestinal morphology of adult cats. Twenty adult cats from both sexes were randomly assigned to four treatments: 1) moist commercial diet (control); 2) control + 0,2% yeast extract dry matter; 3) control + 0,4%; and 4) control + 0,6%. Fecal microbiology and intestinal morphology were performed by radiographic, ultrasound, colonoscopy and intestinal biopsy exams for histology. There were no significant differences (P>0,05) for lactic acid bacteria counts and clostridium-reductor, gas area in the bowel (radiographic), wall thickness of the colon (ultrasound) and colonocytes count/globet cells (histology). Through colonoscopy, changes in characteristics of the intestinal mucosa in animals receiving treatment 4 were noticed. It is concluded that the addition of up to 0.6% EPL had no effect on the parameters evaluated, but further studies are needed to understand the action mechanisms and additive effects for domestic cats.O objetivo deste estudo é avaliar os efeitos do extrato de levedura (EPL) em dietas úmidas sobre a microbiota fecal, a produção de gás e a morfologia intestinal de gatos adultos. Foram utilizados 20 gatos adultos, de ambos os sexos, distribuídos ao acaso em quatro tratamentos: 1) dieta comercial úmida (controle); 2) controle + 0,2% de extrato de levedura em matéria seca; 3) controle + 0,4%; e 4) controle + 0,6%. Foram realizadas a microbiologia fecal, a avaliação da morfologia intestinal por meio de exames radiográficos, ultrassonográficos e de colonoscopia, bem como a biópsia para histologia intestinal. Não foram observadas diferenças significativas (p>0,05) para contagem de bactérias do ácido lático e de clostrídio sulfito redutor, área de gás em alças intestinais (avaliação radiográfica), espessura da parede do cólon (ultrassonografia intestinal) e contagem de colonócitos/células caliciformes (histologia). Por meio da colonoscopia, notaram-se alterações em características de mucosa em animais submetidos ao tratamento 4. Conclui-se que a adição de até 0,6% de EPL não teve efeito sobre os parâmetros avaliados, mas novos estudos são necessários para compreender os mecanismos de ação e os efeitos desse aditivo para gatos domésticos.Universidade de São PauloUniversidade Federal de LavrasUniversidade Estadual Paulista FCAVUniversidade Estadual Paulista FCA

Offensive Language Detection Using Soft Voting Ensemble Model

Offensive language is one of the problems that have become increasingly severe along with the rise of the internet and social media usage. This language can be used to attack a person or specific groups. Automatic moderation, such as the usage of machine learning, can help detect and filter this particular language for someone who needs it. This study focuses on improving the performance of the soft voting classifier to detect offensive language by experimenting with the combinations of the soft voting estimators. The model was applied to a Twitter dataset that was augmented using several augmentation techniques. The features were extracted using Term Frequency-Inverse Document Frequency, sentiment analysis, and GloVe embedding. In this study, there were two types of soft voting models: machine learning-based, with the estimators of Random Forest, Decision Tree, Logistic Regression, Naïve Bayes, and AdaBoost as the best combination, and deep learning-based, with the best estimator combination of Convolutional Neural Network, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The results of this study show that the soft voting classifier was better in performance compared to classic machine learning and deep learning models on both original and augmented datasets