https://repositorio.ufu.br/handle/123456789/18919 2026-10-09T13:32:38Z 2026-10-09T13:32:38Z https://repositorio.ufu.br/handle/123456789/50544 2026-10-07T06:21:05Z 2026-09-08T00:00:00Z

Title: Módulo de avaliação semiautomática do sistema de candidatura em processos seletivos da pós-graduação Abstract: Graduate program admission processes involve critical stages of application submission and documentary and curricular evaluation, the latter of which is frequently carried out manually, potentially leading to inconsistencies, human error, low traceability and high operational effort. In the context of the Universidade Federal de Uberlândia (UFU), the SIPPGCO-FACOM system automated the application submission stage; however, the evaluation phase still presented limitations related to standardization and efficiency. This work presents the development of a semi-automatic evaluation module integrated into the existing system, aimed at supporting the evaluation committee in performing repetitive and systematic tasks, leaving to the evaluators only those that depend on judgement of merit. The solution encompasses an initial document screening stage, in which every required document is individually reviewed and each rejection is justified; the automatic calculation of curricular scores based on the rules defined in the selection notice; evaluation stages with no associated document, such as the interview, whose grades are recorded by the committee and combined through a weighted sum; manual adjustments with immediate score reprocessing; the generation of the candidate ranking, which combines the cut-off score and the number of positions available in each research line; and the production of structured reports, thereby promoting greater transparency, consistency, and auditability of the process. The adopted architecture follows a layered approach, composed of an Angular web application and a REST API built with TypeScript and NestJS, persisting data in a PostgreSQL database shared with the submission system. The system addresses functional and non-functional requirements related to usability, performance, security, and reliability — including the segregation between the coordination and evaluation committee profiles, a complete audit trail and data protection compliance guidelines — and was validated through automated unit and integration tests, complemented by functional testing of the user interface. The solution increases decision traceability and standardizes validations, while maintaining the flexibility required for evaluation committee intervention, and its expected benefits — reduced manual effort and fewer inconsistencies — remain to be confirmed through use in real admission processes. Furthermore, this work presents a reusable and adaptable architecture applicable to other evaluation contexts in academic admission processes.

2026-09-08T00:00:00Z https://repositorio.ufu.br/handle/123456789/50535 2026-10-06T06:20:36Z 2026-09-11T00:00:00Z

Title: Modelo de propagação da COVID-19 em ambientes fechados baseado em autômatos celulares Abstract: COVID-19 has highlighted the importance of understanding the spread of contagious diseases in confined environments, where the proximity and movement of people favor viral transmission. This work aims to develop and evaluate an epidemiological model based on a cellular automaton to simulate the spread of COVID-19 in controlled environments, taking the study by Cavalcante et al. (2021) as a reference. The reference model was understood and implemented in the C language, with the addition of a real-time visualization through the raylib library. The cellular automaton was implemented to represent an SI (Susceptible and Infected) epidemiological model based on the Moore neighborhood and on the pseudorandom movement of individuals throughout the monitored environment. The evaluation was carried out in three stages: reproduction of the experiments from the reference paper, variation of parameters, and variation of the environment layout. The results reproduced the behavior reported in the reference, confirming that both an increase in the number of individuals and in the transmission rate intensify the contagion, while reduced populations result in almost nonexistent transmission. Furthermore, keeping the population density constant, it was found that the spatial structure of the environment does not influence the final outcome of the simulation. These results show that the developed model is able to satisfactorily reproduce the transmission dynamics of COVID-19 in confined environments, reinforcing the importance of social distancing as a control measure.

2026-09-11T00:00:00Z https://repositorio.ufu.br/handle/123456789/50471 2026-09-29T06:18:39Z 2026-07-24T00:00:00Z

Title: Geração de números pseudoaleatórios em sistemas quânticos caóticos aplicada à criptografia de imagens

2026-07-24T00:00:00Z https://repositorio.ufu.br/handle/123456789/50384 2026-09-23T06:25:05Z 2026-09-16T00:00:00Z

Title: Identificação de usuários maliciosos de bitcoin com floresta randômica Abstract: The expansion and consolidation of Bitcoin as a decentralized financial system have introduced complex challenges to public safety and regulatory compliance, primarily due to pseudo-anonymity exploited by malicious actors for illicit activities such as money laundering, darknet market operations, and ransomware extortions. This study aimed to develop and evaluate a supervised machine learning model capable of classifying transactions and addresses on the Bitcoin network for the automated identification of malicious entities. The methodology utilized public records extracted from the Blockchair platform—spanning blocks 600,000 to 605,999, integrated with ground-truth labels from the Bitcoin Address Behavior Dataset (BABD-13). A Random Forest classifier was trained using balanced class weighting, followed by a contamination heuristic and adress majority voting aggregation. Feature importance analysis demonstrated that economic metrics, particularly transaction fees (fee_per_kwu and fee_per_kb) and transaction amounts in fiat currency, were the most decisive indicators of suspicious behavior. At the transaction level, the model achieved 100% accuracy, a macro precision of 96%, and a recall of 84% for illicit activities. By incorporating the address-level majority voting scheme (θ = 0.5), the system eliminated false negatives, achieving a perfect recall of 100% on malicious addresses and an overall accuracy of 99%, confirming its practical viability for digital forensics and compliance audits within blockchain ecosystems.

2026-09-16T00:00:00Z