http://repositorio.ufc.br/handle/riufc/533 Mon, 25 May 2026 12:07:07 GMT 2026-05-25T12:07:07Z https://repositorio.ufc.br:443/retrieve/450a26d4-61c6-4c8f-bdb8-a92e21c8ce8e/Comunidade_CQUIXADA_RI-moldurado.jpg http://repositorio.ufc.br/handle/riufc/533 http://repositorio.ufc.br/handle/riufc/86376 Título: Planejamento temporal para gerenciamento de dispositivos de internet das coisas em um campus universitário Autor(es): Umbelino, Wellington de Sousa Abstract: Energy costs in public buildings in Brazil constitute a critical problem, especially in light of the consumption reduction targets established by the federal government. Several measures have been adopted to mitigate this scenario, such as awareness campaigns aimed at building occupants. However, there are still opportunities for improvement, particularly with the adoption of technologies such as the Internet of Things (IoT), which enable basic levels of automation. For a more effective approach, it is necessary to consider not only device automation, but also factors such as environment occupancy, user comfort, and variations in energy prices. Different Artificial Intelligence approaches, such as model predictive control, machine learning, and automated planning, have been explored to address this problem. In this context, this work proposes the application of the Temporal Planning technique for the management of IoT devices, through the integration between a communication system of an IoT network and a planning system capable of performing automated reasoning. As a result, an architecture is proposed that enables the control of electrical equipment in a university campus at the Federal University of Ceará. Tipo: Dissertação Thu, 01 Jan 2026 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/86376 2026-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/86151 Título: Avaliando o desempenho de grandes modelos de linguagem na realização de provas de dedução natural em lógica proposicional e lógica de predicados Autor(es): Martins, Francisco Leonardo Batista Abstract: The growing use of conversational agents has sparked increasing interest among researchers, educators, and educational institutions worldwide. Moreover, with the recent popularization of Large Language Models (LLMs), numerous studies have been conducted to explore the use of these tools to support the teaching and learning process. These systems are capable of understanding and processing vast amounts of data, which allows them, for example, to provide individualized support to students in solving exercises. However, it is essential to consider that such systems generally employ reasoning processes based on statistical methods, such as machine learning algorithms, which may produce incorrect answers in tasks requiring logical reasoning. This study aims to evaluate the ability of Large Language Models (LLMs) to solve Natural Deduction exercises in Propositional Logic and Predicate Logic. To this end, experiments were carried out using the GPT 4.1-mini, GPT 4o, and GPT 3.5turbo models to solve natural deduction exercises. These experiments were conducted a priori, without training, and subsequently, further experiments were performed on the trained GPT 4.1-mini model to assess potential performance improvements. The results indicate a significant improvement after training, although the model still exhibits a considerable number of errors. Therefore, if employed for this task, its use is recommended with due caution. Tipo: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/86151 2025-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/85336 Título: Recomendações para o desenvolvimento de sistemas para pessoas surdas Autor(es): Silva, Lucas de Oliveira da Abstract: This work addresses the challenge of developing effective and inclusive computational systems for the Deaf community, which is often underrepresented in design processes. The research investigates how to actively integrate Deaf individuals into the system lifecycle, respecting their communicational and cultural specificities, through Participatory Design (PD). The main objective was to propose, apply, and evaluate a set of recommendations – called the "Guia da Lilla"– to support development teams in designing systems that are accessible and user-centered for Deaf users. The adopted methodology included a Systematic Literature Review (SLR) to map challenges, methods, practices, and requirements; an analysis of the practical experience of the startup Bilíngua; the iterative development of the Guia da Lilla in both textual and interactive digital formats; and its evaluation in two stages: an investigative analysis with experts and an exploratory study applying the Guide in the real development of a governmental management platform, involving Deaf and hearing participants in PD sessions and interviews. The results identified crucial challenges in applying PD with the Deaf community, such as communication barriers and the need for cultural and methodological adaptation, while also consolidating essential Human-Computer Interaction (HCI) practices, such as prioritizing visual elements and providing multimodal feedback. The practical application demonstrated the usefulness of the Guia da Lilla as a structuring tool in the PD process, resulting in concrete accessibility improvements in the developed system and the effective inclusion of Deaf individuals as the system’s primary focus. It is concluded that PD is fundamental for creating inclusive technologies, and the Guia da Lilla represents the main contribution of this research, offering a validated methodological resource for developing systems WITH and FOR the Deaf community. Tipo: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/85336 2025-01-01T00:00:00Z http://repositorio.ufc.br/handle/riufc/84668 Título: AromaLIA: a language-independent approach to detect test smells Autor(es): Silva, Publio Blenilio Tavares Abstract: Test smells are indicators of poor practices or suboptimal decisions made during test-code development, which can undermine its maintainability and evolution. This topic has gained increasing relevance in recent years, and numerous studies have proposed techniques to detect test smells. However, most existing solutions support only a very limited set of programming languages, often just one. As a result, applying test smell detection to a new language typically requires developing an entirely new solution, even when similar problems have already been addressed elsewhere. In this context, the objective of this work is to propose a languageindependent approach for detecting test smells. To understand how this topic has been explored in the literature, we conducted a Systematic Mapping Study (SMS) analyzing 117 studies published up to August 2025. From this review, we identified, among other aspects, the most frequently discussed test smells across multiple programming languages and those considered most critical, that is, the ones that require the greatest attention from the community. Based on these findings, we developed our approach and evaluated its effectiveness in C#, Java, JavaScript, TypeScript, and Python across 10 different test smells, comparing it with existing language-specific techniques. The results were promissing: our solution demonstrated superior effectiveness, achieving 97% precision, 96% recall, and a 97% F1-score. To support practical adoption, we also implemented and released a test smell detection tool built upon our languageindependent methodology. The outcomes of this work have the potential to greatly simplify the incorporation of new programming languages into test smell detection workflows, enabling the reuse of prior efforts across diverse ecosystems. This benefits both industry practitioners seeking high-quality testing tools and researchers interested in studying test smells in additional languages. Tipo: Dissertação Wed, 01 Jan 2025 00:00:00 GMT http://repositorio.ufc.br/handle/riufc/84668 2025-01-01T00:00:00Z