MACIEL CALEBE VIDAL
Lattes
Google ScholarUnidades Organizacionais
Resumo profissional
person.page.researcharea
Nome para créditos
14 resultados
Resultados da Pesquisa
Agora exibindo 1 - 10 de 14
Trabalho de Conclusão de Curso Development of a dashboard for real-time student performance data visualization(2024) Carvalho, Arthur Ferreira; Carreras, Natália Queiroz Menezes; Mahfuz, Pedro OsbornThis project is designed to provide instructors who use the PrairieLearn platform with visual tools and insights into students’ performance by creating and displaying said information in an external dashboard. The data used to create the dashboard is pulled from PrairieLearn’s API. The objective with this is to help instructors model their courses to maximize academic engagement and performance. The PrairieLearn platform offers a dynamic and interactive environment for students to engage with course material, enabling instructors to create customizable quizzes, assignments, and assessments tailored to the evolving educational landscape. With their current system, which provides limited insights by a statistics table that displays average scores and completion times, the project aims to expand the analytical tools available to educators with the creation of this external tool. By integrating features such as performance metric analysis, question score histograms, and assessment completion percentage tracking, the dashboard equips instructors with a detailed view into student performance. This allows for a deeper understanding of assessment effectiveness, enabling educators in identifying learning gaps, adjusting teaching strategies, and customizing content to meet individual student needs more effectively. The anticipated outcome is a user-friendly dashboard, which provides insights into students' learning patterns, allowing instructors to make informed decisions and improve educational outcomes.Artigo Científico Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder(2021) Ribeiro, Adèle Helena; MACIEL CALEBE VIDAL; Sato, João Ricardo; Fujita, AndréGraphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks’ time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models’ parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal’s application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain’s right to the left hemisphere is different between ASD and controls.Trabalho de Conclusão de Curso Development of an Administrator Web App for subscriptions’ management and a Mobile App for unlocking E-bikes(2024) Ades, Cesar Ezra; Hadba, Lila Takahashi; Kawahara, Thiago ShigueroThis project aims to develop a Web Application (App) for the Administrator (E-Moving personnel) and an users’ Mobile App for E-Moving, a rental electric bike (E-Bike) company focused on improving urban mobility. This endeavour builds upon a prior Capstone Project (PFE), which developed a control board for electric bicycles in 2023. The current initiative seeks to enhance the E-Moving profitability and control, by being able to block users’ e-bikes and mitigating the risks of theft and default. The project, a collaboration between students from Insper (São Paulo, Brazil) and Texas A&M (Texas, United States), involves Insper students developing two Apps in accordance with the requirements established by the previous project and those of the Texas A&M students. Notably, the hardware component is being developed by Texas A&M students. The project is structured into five primary segments: (i) Web App and Mobile App screen flowchart, (ii) Web App and Mobile App screen design and front-end implementation, (iii) Bluetooth connection with E-bike, (iv) Database integration with both Web App and Mobile App front-end, and (v) Integration of Bluetooth, Web App, Mobile App, and E-bike. This project emphasizes a practical application that enables administrators (E-Moving personnel) to remotely monitor client's E-bikes and for them to manage its E-bike.Trabalho de Conclusão de Curso Search of shipwrecked people using drone swarms (part 2)(2024) Oliveira, Jorás Custódio Campos de; Andrade, Pedro Henrique Britto Aragão; Falcão, Renato Laffranchi; Rodrigues, Ricardo RibeiroThe project's purpose is to iterate on the given multi-agent Drone Swarm Search Environment (DSSE) and research into Reinforcement Learning methods. The DSSE was created with the direct purpose of using reinforcement learning algorithms to train swarms of drones to execute autonomous maritime search and rescue missions of shipwrecked people in the ocean. The environment simulates the movement of persons-in-water (PIW) considering the ocean's dynamic circumstances and calculates a dynamic map of probabilities to be given to the agents, with two distinct environments, one for rescue scenarios with simulated PIW and a second expanding on state-of-the-art research for maritime coverage search path planning. The DSSE facilitates the training and visualization of drone behavior, the project emphasizes continuous improvement and open accessibility, with the release of the DSSE as an open-source Python package and documentation. The focus is on the continuous improvement of simulation quality and applicability of the environments for research purposes, with development, training and evaluation of Reinforcement learning algorithms to improve the path planning of autonomous agents, for search and rescue maritime scenarios.Artigo Científico Identification of alterations associated with age in the clustering structure of functional brain networks(2018) Guzman, Grover E. C.; Sato, Joao R.; MACIEL CALEBE VIDAL; Fujita, AndreInitial studies using resting-state functional magnetic resonance imaging on the trajectories of the brain network from childhood to adulthood found evidence of functional integration and segregation over time. The comprehension of how healthy individuals’ functional integration and segregation occur is crucial to enhance our understanding of possible deviations that may lead to brain disorders. Recent approaches have focused on the framework wherein the functional brain network is organized into spatially distributed modules that have been associated with specific cognitive functions. Here, we tested the hypothesis that the clustering structure of brain networks evolves during development. To address this hypothesis, we defined a measure of how well a brain region is clustered (network fitness index), and developed a method to evaluate its association with age. Then, we applied this method to a functional magnetic resonance imaging data set composed of 397 males under 31 years of age collected as part of the Autism Brain Imaging Data Exchange Consortium. As results, we identified two brain regions for which the clustering change over time, namely, the left middle temporal gyrus and the left putamen. Since the network fitness index is associated with both integration and segregation, our finding suggests that the identified brain region plays a role in the development of brain systems.Trabalho de Conclusão de Curso Triagem oncogenética modelada por IA(2024) Alves, Gabriel de Araujo; Nishio, Keiya; Rodrigues Filho, Ricardo Mourão; Costa, Sarah Azevedo Pimenta daA Triagem Oncogenética por meio de questionário modelado por Inteligência Artificial (IA) representa uma inovação significativa no diagnóstico do câncer hereditário. Este estudo propõe uma abordagem que se baseia na análise criteriosa de parâmetros de história familiar para a seleção eficiente de pacientes aptos a realizar testes genéticos, sendo reconhecida como a estratégia mais custo-efetiva disponível atualmente. Com uma extensa base de dados de aproximadamente 10 mil pacientes atendidos e testados nos últimos quatro anos pelo A.C. Camargo, esta pesquisa se apoia em uma metodologia robusta, envolvendo a implementação de modelos probabilísticos e de inferência. Um dos pontos-chave deste estudo é a possibilidade de utilização do pacote PanelPro, desenvolvido pelo laboratório Bayesmendel de Harvard, que oferece ferramentas avançadas para análise de dados genéticos. Além disso, o projeto se beneficia da expertise acumulada ao longo dos anos pelo A.C. Camargo em Oncogenética. Ao analisar uma amostra representativa de 4 mil pacientes, juntamente com os resultados de testes genéticos positivos e negativos, busca-se identificar os parâmetros que maximizam a sensibilidade, especificidade e a área sob a curva ROC (AUC), a fim de automatizar o processo de seleção de pacientes para testes genéticos. A implementação bem-sucedida desta ferramenta tem o potencial de revolucionar a prática clínica, permitindo um encaminhamento mais eficiente e direcionado aos pacientes dos centros de referência (CRs) do A.C. Camargo para o Departamento de Oncogenética. Além disso, essa abordagem pode contribuir significativamente para a identificação precoce de indivíduos com predisposição genética ao câncer, possibilitando intervenções preventivas e terapêuticas mais eficazes. Em última análise, esse avanço tecnológico promete impactar positivamente a qualidade de vida e o prognóstico dos pacientes e suas famílias, representando um importante passo rumo à medicina personalizada e de precisão no combate ao câncer hereditário.Artigo Científico A Software to Compare Clusters between Groups and Its Application to the Study of Autism Spectrum Disorder(2017) MACIEL CALEBE VIDAL; Sato, João R.; Balardin, Joana B.; Takahashi, Daniel Y.; Fujita, AndréUnderstanding how brain activities cluster can help in the diagnosis of neuropsychological disorders. Thus, it is important to be able to identify alterations in the clustering structure of functional brain networks. Here, we provide an R implementation of Analysis of Cluster Variability (ANOCVA), which statistically tests (1) whether a set of brain regions of interest (ROI) are equally clustered between two or more populations and (2) whether the contribution of each ROI to the differences in clustering is significant. To illustrate the usefulness of our method and software, we apply the R package in a large functional magnetic resonance imaging (fMRI) dataset composed of 896 individuals (529 controls and 285 diagnosed with ASD—autism spectrum disorder) collected by the ABIDE (The Autism Brain Imaging Data Exchange) Consortium. Our analysis show that the clustering structure of controls and ASD subjects are different (p < 0.001) and that specific brain regions distributed in the frontotemporal, sensorimotor, visual, cerebellar, and brainstem systems significantly contributed (p < 0.05) to this differential clustering. These findings suggest an atypical organization of domain-specific functionbrain modules in ASD.Trabalho de Conclusão de Curso Implementação de empréstimos com garantia utilizando DeFi(2024) Onishi, Gabriel Hideki Stanzani; von Dannecker, Pedro Santos Rocha; Coelho, Rodrigo Guimarães; Lee, Willian Kenzo AsanumaEste projeto tem como objetivo implementar uma solução de empréstimos com garantia em ativos digitais combinando diferentes frentes. Primeiramente, houve o desenvolvimento de um smart contract, responsável pela lógica de negócio e servindo como intermediário entre mútuo e mutuário. A fim de facilitar a interação entre o usuário final e o contrato, também se criou uma plataforma para clientes gerirem seus empréstimos através de suas carteiras digitais. Por fim, construiu-se uma plataforma administrativa para acompanhamento dos contratos existentes. O projeto tem como propósito servir como prova de conceito para esse tipo de serviço financeiro, buscando testar a sua viabilidade tecnológica. Realizado em parceria com o braço de inovação do Banco Bradesco, a iniciativa faz parte do plano da instituição de se posicionar estrategicamente frente os planos de lançamento do DREX pelo Banco Central, com expectativa de lançamento para os próximos anos.Trabalho de Conclusão de Curso Desenvolvimento de plataforma de backtesting para testes de modelo de alta frequência(2024) Sutton, Jonathan; Oliveira, Luís Antônio Bordignon de; Israel, Ricardo; Riccetti, Vitor Fortes GiulianoEste projeto visa o desenvolvimento de uma plataforma de backtesting para alta frequência destinada à avaliação de modelos para a previsão de retornos em contratos futuros. A plataforma será utilizada pelos responsáveis da área Quant da Legacy Capital. Para cumprir com objetivo, foram utilizados raw market data de ordens e negociações realizadas na Bolsa de Mercadorias & Futuros (B3), onde após manipulados e realizados os devidos cálculos, servem como entrada para os modelos. Os modelos escolhidos foram Autorregressivo Exógena (ARX), que é uma extensão dos modelos de regressão linear básica utilizada para séries temporais, Long Short Term Memory (LSTM), que se configura como uma arquitetura de Rede Neural Recorrente (RNN), Multilayer Perceptrons (MLP), que está dentro das Redes Neurais Artificiais (RNA), LSTM-MLP, uma junção da arquitetura de Redes Neurais Artificias (RNA) com Rede Neural Recorrente (RNN) e finalmente o Gradient Boosting Regressor (XGB), que funciona como um processo de árvores de decisão sequencial. A plataforma será projetada para ser flexível, permitindo a incorporação de diversos modelos e para ajuste de parâmetros conforme determinado necessário pelo contratante, com o objetivo de aprimorar as capacidades de previsão e a eficácia dos resultados advindo da plataforma.Artigo Científico Correlation between graphs with an application to brain network analysis(2017) Fujita, André; Takahashi, Daniel Yasumasa; Balardin, Joana Bisol; MACIEL CALEBE VIDAL; Sato, João RicardoThe global functional brain network (graph) is more suitable for characterizing brain states than local analysis of the connectivity of brain regions. Therefore, graph-theoretic approaches are natural methods to use for studying the brain. However, conventional graph theoretical analyses are limited due to the lack of formal statistical methods of estimation and inference. For example, the concept of correlation between two vectors of graphs has not yet been defined. Thus, the introduction of a notion of correlation between graphs becomes necessary to better understand how brain sub-networks interact. To develop a framework to infer correlation between graphs, one may assume that they are generated by models and that the parameters of the models are the random variables. Then, it is possible to define that two graphs are independent when the random variables representing their parameters are independent. In the real world, however, the model is rarely known, and consequently, the parameters cannot be estimated. By analyzing the graph spectrum, it is shown that the spectral radius is highly associated with the parameters of the graph model. Based on this, a framework for correlation inference between graphs is constructed and the approach illustrated on functional magnetic resonance imaging data on 814 subjects comprising 529 controls and 285 individuals diagnosed with autism spectrum disorder (ASD). Results show that correlations between the default-mode and control, default-mode and somatomotor, and default-mode and visual sub-networks are higher in individuals with ASD than in the controls.