Endividamento do público universitário da UnB em 2016 : análise e comparação dos graduandos em Ciências Contábeis e outros cursos

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, 2016.Este estudo teve como objetivo analisar o comportamento financeiro do público universitário de quatro cursos da Universidade de Brasília (UnB) e comparar os resultados com aqueles identificados para o curso de Ciências Contábeis. A hipótese considerada é que os alunos de Ciências Contábeis adquirem ao longo do curso maior educação financeira, quando comparados ao comportamento financeiro de alunos de outros cursos. A motivação está em considerar que o curso de Ciências Contábeis apresenta um diferencial positivo ao fornecer o ensino em finanças pessoais e servir de ferramenta ao aplicar o conhecimento contábil no dia a dia do estudante. Foi realizada uma pesquisa bibliográfica para compreender o contexto brasileiro quanto à educação financeira e para identificar os fatores que levam um indivíduo a situação de endividado. A pesquisa foi realizada através da seleção de cursos dentre as onze faculdade da Universidade de Brasília. Foram selecionados quatro cursos, além do curso de Ciências Contábeis, por amostragem aleatória simples, Ciências Econômicas, Enfermagem, Medicina e Pedagogia. Dentre esses cursos foi realizado um survey (levantamento) através de questionário para os calouros e formandos. Foi feita uma comparação entre os calouros e formandos para compreender como a passagem pela universidade inlfuencia na saúde financeira do graduando, através da aquisição de conhecimentos que possam ajudá-los a tomar melhores decisões financeiras ou adquirindo acesso facilitado as linhas de crédito, que possam interfeir de maneira negativa no orçamento. Outra comparação realizada foi entre os cursos, analisando as diferenças socioeconômicas, introdução no mercado de trabalho e comportamentos financeiros que sejam características do curso de cada aluno e influencie sua atitude financeira. Os formandos do curso de ciências contábeis se destacaram por serem os que mais receberam ensino formal em finanças pessoais, em decorrência disso, todos realizam algum meio de controle financeiro, em maioria gastam menos do que ganham e destinam parte de seus recursos a algum investimento. Foi verificado que os fatores de maior influência sobre o endividamento consiste no uso do cartão de crédito, o auxílio recebido dos familiares e inserção no mercado de trabalho

STUDENT PERFORMANCE ANALYZATION AND VISUALIZATION

The invention of this project is to create a user-friendly platform for the user from Edu industry with stable, high performance and cost friendly reputation. Based on the study did at all level of education, student from around the globe learn together; however, all the variant cultures and nationalities result in promoting of different students from all over the world study together; all these different nationalities and cultures result in dissimilar ideas regarding academic success. The fundamentals of this project include the use of Microsoft Office 365 Power BI to create a customized dashboard for students where students can set their own records. With this module, students will be able to collectively assess their performance on a global scale. This project aims to assist the students gain better insight into their academic performance as the information can be used independently or to consult with an academic advisor

Data driven feature extraction for gender classification using multi-script handwritten texts

This paper presents a study on assessing the effectiveness of machine learned features to predict gender of writers from images of handwriting. Pre-trained Convolutional Neural Networks have been employed as feature extractors to discriminate male and female handwriting while classification is carried out using a number of classifiers, Linear Discriminant Analysis (LDA) being the most effective. Feature extraction is carried out by changing the scale of observation using word, patch and page images. Experiments are carried out on English and Arabic handwriting samples of the QUWI database and the realized results demonstrate the effectiveness of machine learned features in predicting gender from handwriting. ? 2018 IEEE.Scopu

Writer identification using VLAD Encoding of the Histogram of Gradient Angle Distribution

The use of computers and automatic systems has enabled scientific researchers to improve the classification rate in the field of writer identification. In our paper, we will propose an identification system based on the use of Histogram of Gradient Angle Distribution (HGAD) in square patches centered around Harris Keypoint locations. A global descriptor per image is calculated subsequently via the VLAD encoding of the local descriptors relating to the histograms of the square patches. The study carried out on two public datasets CVL and BFL made it possible to achieve very interesting identification rates with 99.4% in BFL and 99.7% in CVL

Detecting CO2 anomalies using machine learning: case study of a library

International audienceIndoor air quality is a very important element of a healthy and comfortable environment. The use of low-cost sensors recording CO2 or other substances has become popular in recent years. However, the quantity and complexity of the data measured by these sensors present challenges for identifying anomalies and extracting meaningful information. This is where machine-learning techniques excel. As part of the ACQA project, a micro sensor composed of various air quality sensors was developed and deployed in a university library. This study examines the detection of anomalies in CO2 levels recorded, using various machine-learning models: K-Nearest Neighbors, Random Forest (RF), Gradient Boosting Regressor and Decision Tree Regressor. The models are evaluated based on their accuracy and efficiency in detecting anomalies. The results, quantified using R², RMSE and MAE indicators, and show that the RF model is the most accurate.La qualité de l'air intérieur est un élément très important pour un environnement sain et confortable. L'utilisation des capteurs à faible coût, enregistrant les taux de CO2 ou d’autres substances, s’est répandue ces dernières années. Cependant, la quantité et la complexité des données mesurées posent des problèmes pour l'identification des anomalies et l'extraction d'informations significatives. C'est là que les techniques d'apprentissage automatique excellent. Dans le cadre du projet ACQA, un micro-capteur composé de différents capteurs de qualité de l'air a été développé et déployé dans une bibliothèque universitaire. Cette étude examine la détection d'anomalies dans les niveaux de CO2 enregistrés, en utilisant différents modèles d'apprentissage automatique : KNearest Neighbors, Random Forest (RF), Gradient Boosting Regressor et Decision Tree Regressor. Les modèles sont évalués en fonction de leurs précision et de leur efficacité à détecter des anomalies. Les résultats sont quantifiés à l'aide des indicateurs R², RMSE et MAE, et montrent que le modèle RF est le plus précis

Secure facial recognition in the encrypted domain using a local ternary pattern approach

Automatic facial recognition is fast becoming a reliable method for identifying individuals. Due to its reliability and unobtrusive nature facial recognition has been widely deployed in law enforcement and civilian application. Recent implementations of facial recognition systems on public cloud computing infrastructures have raised strong concerns regarding an individual's privacy. In this paper, we propose and implement a novel approach for facial recognition in the encrypted domain. This allows for facial recognition to be performed without revealing the actual image unnecessarily as the features stay encrypted at all times. Our proposed system exploits the homomorphic properties of the Paillier cryptosystem and performs Euclidean distance calculations using encrypted data. We propose to represent the images using a radial Local Ternary Pattern approach where a higher than proposed radius is used to extract the image features. Our proposed system has been evaluated using two publicly available datasets and has also been compared against the previously used eigenface approach in the encrypted domain and the obtained results justify the feasibility of the proposed system. © 2021 Elsevier Lt