https://repositorio.ufu.br/handle/123456789/18920 Wed, 07 Oct 2026 23:23:41 GMT 2026-10-07T23:23:41Z https://repositorio.ufu.br/handle/123456789/50540 Title: Internet quântica: uma análise da evolução do conhecimento científico Abstract: The Quantum Internet has been recognized as one of the key technologies under development for the next generation of communication networks, as it exploits properties of quantum mechanics—such as superposition and entanglement—to enable new models of communication, security, and distributed computing. In this context, this dissertation aims to characterize the evolution of the Quantum Internet through an analysis of scientific knowledge over time. To this end, articles indexed in the Web of Science and Scopus databases were analyzed. This dataset was used to characterize scientific knowledge in the field through descriptive statistics and to investigate the evolution of knowledge based on citation network analysis and the identification of knowledge pathways. The results showed an increase in scientific knowledge on the topic, concentrated primarily in highly influential journals and in research groups located in China, Europe, and the United States. The knowledge pathway made it possible to analyze the scientific evolution of the Quantum Internet across three distinct layers: foundational, intermediate, and emerging. A transition was observed from studies focused on quantum communication theory to research directed toward the development of architectures, protocols, experimental networks, and distributed applications. It is concluded that the Quantum Internet has established itself as a rapidly growing field, with advances demonstrating maturity in research related to the development of large-scale quantum networks. Tue, 25 Aug 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50540 2026-08-25T00:00:00Z https://repositorio.ufu.br/handle/123456789/50406 Title: Previsão de geração de energia fotovoltaica baseada em aprendizado de máquina com monitoramento de degradação e aprendizado incremental Abstract: The increasing participation of photovoltaic energy in the electrical grid highlights the need for forecasting methods capable of providing accurate estimates of power generation, supporting the planning and operation of power systems. However, predictive models may experience concept drift over time due to changes in data characteristics, requiring continuous adaptation. In this context, this study proposes an adaptive architecture for photovoltaic power forecasting based on machine learning, integrating concept drift monitoring and incremental learning. Initially, different preprocessing strategies, predictive models, and hyperparameter optimization methods were evaluated using historical data from a photovoltaic power plant located in Minas Gerais, Brazil. The evaluated models included Persistence, ARIMA, Ridge Regression, MLP, and LSTM networks. Subsequently, a second dataset was used to evaluate the effects of cyclical features, the removal of weakly correlated variables, and the exclusion of nighttime data. Finally, four adaptive configurations combining the CUSUM and ADWIN concept drift detectors with the finetuning and EWC incremental learning strategies were evaluated. The results showed that mean aggregation, Min-Max normalization, and LSTM models achieved the best predictive performance, particularly for forecasting horizons longer than 15 minutes. Excluding nighttime data improved model learning, whereas the inclusion of cyclical features did not provide consistent performance gains. Among the evaluated adaptive architectures, the ADWIN + EWC combination achieved the best performance, reducing the frequency of model updates while preserving predictive accuracy over time. Furthermore, incremental learning enabled models trained with limited data to progressively adapt, demonstrating its potential for real-world applications. Fri, 28 Aug 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50406 2026-08-28T00:00:00Z https://repositorio.ufu.br/handle/123456789/50340 Title: Domain-specific small language models for creative text generation: a case study on the backrooms Abstract: Large Language Models (LLMs) occupy a prominent place among Machine Learning models that have been revolutionizing modern life as an indispensable support tool in the most diverse areas of human activity. Nevertheless, the popularization of large LLMs faces a significant limiting challenge: the high cost associated with their use, whether through expensive hardware, cloud processing or paid APIs. In this light, the main objective of this work is to propose an approach to generate smaller language models, which can be trained in more modest hardware and that are effective in generating texts related to specific domains. The case study chosen for that are The Backrooms, an online legend which describes an alternative reality with different levels that are described with very unique, surreal and rich descriptions. For this, a Backrooms dataset, was created and used to produce two small language models (SLMs) to generate Backrooms level descriptions: Backrooms-Llama, conceived from the fine-tuning of the open source pretrained model Llama 1B; and Tiny Backrooms, a decoder-only architecture pretrained from scratch and then fine-tuned. The performance of these two models was evaluated against the famous and much larger DeepSeek-V3. The generated level descriptions of the three models were evaluated by using chatGPT 4o-mini as judge and human judges. Additionally, a brand new evaluation method, which consists on generating pictures with the output of each competitor model and, then, evaluating these pictures alongside their descriptions with chatGPT 4o, was proposed. The results found are very promising, showing that, even though DeepSeek performed better in general, Backrooms-Llama and Tiny Backrooms, operating in a much more modest architecture, also got mainly relevant evaluations and were able to learn how to generate Backrooms level descriptions. Such results indicate that, in creative generative tasks, a lower cost domain-specific SLM, when properly optimized, can achieve performance comparable to that of consecrated LLMs. Thu, 18 Jun 2026 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50340 2026-06-18T00:00:00Z https://repositorio.ufu.br/handle/123456789/50172 Title: Explorando o uso de árvores B+ na indexação de dados por similaridade Abstract: Similarity queries are beneficial for retrieving complex data (such as multimedia data: images, videos, and audios), for which order relationships are not significant. In many application domains, ordering results based on distance calculations makes the data recovery process more intuitive. The optimization of operations involving similarity calculations are usually given by indexing data on methods, called Metric Access Methods (MAM). Despite the development that occurred over the last two decades, the presence of a large number of dimensions in the data degrades the performance of existing methods. In this context, this dissertation presents a new metric access method for similarity queries, such as range queries and nearest neighbor queries, through adding reference pivots to structures called B + trees. Experimental results performed with different real datasets demonstrate the effectiveness of the presented MAM, called GroupSim+. Fri, 29 Nov 2019 00:00:00 GMT https://repositorio.ufu.br/handle/123456789/50172 2019-11-29T00:00:00Z