https://repositorio.ufu.br/handle/123456789/18921 2026-05-30T17:20:16Z 2026-05-30T17:20:16Z https://repositorio.ufu.br/handle/123456789/48693 2026-05-13T06:23:42Z 2026-01-27T00:00:00Z

Title: Ponto de Troca de Internet do Futuro (FIXP) - habilitando Interconexão de arquiteturas de internet do futuro Abstract: The Internet was designed to meet needs different from those of today. Current applications impose new demands on the network that it cannot meet, given its limitations. One of the difficulties of the Internet is the lack of flexibility to deploy new network-layer services, which limits its evolution. This scenario motivated the development of several Future Internet Architectures (FIAs). These are disjoint architectures that propose, through their designs, to offer mechanisms that advance various aspects of communication, such as flexibility in the network layer, mobility, multicasting, quality of service (QoS), connectivity, quality of experience (QoE), security, among others. Despite the demand, the lack of implementation of these new architectures in high-performance networks makes it difficult to assess the maturity of their communication characteristics for specific scenarios and applications. On the other hand, due to the ubiquity of the traditional Internet, replacing the TCP/IP architecture is impractical. A single communication network or logical exchange point that integrates FIAs and the TCP/IP architecture without compromising its architectural principles (resources and mechanisms) is a promising approach to meeting the demands of current and future applications. This work presents the Future Internet Exchange Point (FIXP) and positions it as a candidate for this single network or logical exchange point that allows the coexistence of different Internet architectures; it also provides qualitative and quantitative analysis of FIXP: its capacity for evolution and incorporation of new architectures, the feasibility of implementing FIXP in the current networks, and the performance of application communication of IP and ETArch architectures in the FIXP infrastructure, taking into account the quality of user experience. Evaluations demonstrate the FIXP architecture conceptually by executing selected use cases in which applications from different FIAs coexist in the same communication environment. In addition, the work presents the details of the integration of the ETArch and IP architectures in the FIXP network, aiming to facilitate, in the future, the incorporation of other Internet architectures in this infrastructure. Regarding related work, the collection allows comparison with these works and with FIXP, positioning it as the state of the art, as the only infrastructure that does not add overhead to switching devices through interoperability processes and/or protocol stack development. Furthermore, its architectural principles differ from those of related work; therefore, the difficulty of integrating new FIAs into FIXP is low to medium relative to that of related work. These two facts, the lack of data plan overhead and the low or medium difficulty level of integrating new FIAs, facilitate the implementation of FIXP in current networks. The work also presents FIXP’s view of multi-architecture applications. These applications use different Internet architectures for their services. The use cases that demonstrate FIXP’s support for these applications simulate the execution of microservices within the same application or virtual machine, which is common nowadays. The difference is that microservices in a multi-architecture application can use frameworks from different FIAs. Notes: O documento inclui um apêndice (Apêndice A) que apresenta parte da implementação dos switches FIXP.

2026-01-27T00:00:00Z https://repositorio.ufu.br/handle/123456789/48484 2026-03-05T06:19:58Z 2026-02-06T00:00:00Z

Title: Classificação de imagens histológicas utilizando algoritmo polinomial de base hermite e regularizador Abstract: Diagnosis support systems are crucial for the analysis of histological images, but their performance depends on the extraction of robust features. Although fractal geometry and eXplainable Artificial Intelligence (XAI) descriptors hold relevance, recent studies point to the superiority of features extracted by deep learning. Therefore, this work proposes a classification system for histological images based on the Hermite Polynomial (HP) algorithm, associated with multi-source descriptors (fractal geometry, deep learning, and XAI). To handle the high dimensionality of the data, the system integrates a LASSO regularizer for feature selection. The computational complexity inherent to HP, in turn, is overcome through a parallel implementation that distributes the training and evaluation process of multiple feature subsets among CPU cores. The experimental evaluation, conducted on binary and multi-class databases, demonstrated the superiority of descriptors extracted from the ResNet-50 (RN50) network in a comparative analysis with fractal and XAI-based approaches. With the RN50 descriptors, the methodology achieved a performance with accuracy (ACC) of up to 100% and an imbalanced accuracy measure (IAM) of 1.00 in binary data classification scenarios. In multi-class databases, the ACC value was higher than 98% and the IAM was 0.89. Additionally, in robustness tests against noise, the proposed system maintained its superiority, presenting significantly higher accuracy than other machine learning algorithms even with 50% noise fin the attributes, in both binary and multi-class scenarios. The results consolidate the combination of the HP classifier with regularization and RN50 descriptors as a high-precision and robust approach, with the potential to assist the decision-making of specialists in clinical practice.

2026-02-06T00:00:00Z https://repositorio.ufu.br/handle/123456789/48048 2026-01-21T06:17:52Z 0008-08-08T00:00:00Z

Title: HESMM - um modelo para o desenvolvimento de pessoas com pensamento crítico de alto nível Abstract: Teaching in the area of Computing has evolved over time, forced in part by frequent innovations in information technology. To meet the growing need for human talent with a high capacity for logical-mathematical reasoning, among some initiatives, Programming Marathons emerged. These are competitions in which participants focus on solving computational challenges using algorithm resolution and implementation techniques. These events, practiced from regional to global scope and experienced by thousands of students, have become very popular in the university community and even in high school because they use a demanding and efficient format based on problems validated by teachers assisted by software robots. With the intention of contributing to the improvement of teaching in the area of Computing, an effort was made to organize, train, participate and collaborate in this movement. It is hoped that the experience gained can support new proposals for education. To generate a result with a high level of consistency, we selected marathon runners with significant results and, using qualitative research as a method, we worked on collecting information and subsequently analyzing the responses through a questionnaire. From this, the premises were outlined and served as the basis for defining a computing teaching model suitable for the new world named HESMM- High-end Shapers and Makers Model of Critical Thinking People. As this work evolved, it became clear that, based on the experience of former competitors who stood out in programming competitions, we can identify routine patterns and generate precepts for proposals to improve current teaching models. The results highlight that the discipline in the study, with adequate planning of schedules and practice in simulators, such as online judge systems, are fundamental for solid learning aligned with contemporary demands.

0008-08-08T00:00:00Z https://repositorio.ufu.br/handle/123456789/47982 2026-01-08T06:22:38Z 2025-12-08T00:00:00Z

Title: Recomendação automática de atividades e rastreamento de competências do sujeito complexo em ambientes virtuais de ensino e aprendizagem Abstract: A persistent challenge in Virtual Learning Environments (VLEs) is the lack of mechanisms to track, over time, higher-order competencies—such as autonomy, cooperation and metacognition—despite the abundance of interaction traces recorded in these platforms. Such tracking can support formative and personalized study guidance. In this context, this thesis develops an automatic pedagogical activity recommender to monitor and foster complex subject competencies in Distance Education. The work was developed in two integrated stages. First, multiple Moodle activities were constructed and refined through competency labeling based on seven complex-subject competencies, supported by quantitative analysis, resulting in an activity×competency matrix validated by educators experienced with Moodle. Second, the M-COMPASS recommender was designed by adapting Dynamic Key-Value Memory Networks (DKVMN), incorporating dynamic memory and attention mechanisms. The main modifications include integrating students’ behavioral signals via SQL queries and introducing competency-level attention, which also grounds a new student model. Experiments conducted in three courses within a Computing Teacher Education program provided consistent evidence: (i) adherence to recommendations is associated with better performance (positive intra-module and overall correlations); (ii) competency-level dose–response patterns, with gains observed both in the short term and cumulatively across modules; and (iii) training stability, indicated by a regular decrease in loss and interpretable individual cases via competency attention and radar charts. The analyses also supported an instrument-improvement cycle (Ridge+LOO diagnostics over SQL queries), reducing redundancies and correcting constant columns. Overall, the thesis contributes a full Moodle→SQL→student model→explainable recommendation pipeline aligned with Complex Thinking Theory, and a DKVMN adaptation for multiple competencies per activity with pedagogical justifications.

2025-12-08T00:00:00Z