https://repositorio.ufba.br/handle/ri/34151 2026-05-17T04:36:07Z https://repositorio.ufba.br/handle/ri/44334 Process checklist: checklist orientado à transparência para inspeção de processos BPMN Santos, Juliana Conceição Maciel, Rita Suzana Pitangueira In recent years there has been an interest in quality assurance processes for Process models in BPMN. This can be achieved through inspection, a static analysis technique that demonstrates potential for identifying problems in software artifacts. Inspection of process models with checklists, although still little explored in the process literature, is an important instrument to support the inspection process in detecting defects, aiming at the quality of the artifacts. The complexity of process models, the lack of studies on inspection performed by humans and the search for quality of the models have driven this research, focusing on quality Transparency. The transparency of BPMN models contributes to the description of process models, improving understanding and comprehension, benefiting not only internal efficiency such as management and communication, but also resulting in strategic benefits, such as good reputation, credibility and the sharing of quality information. Thus, by evaluating the items of the checklist that supports the assessment of the quality of the models, systematically and adding the knowledge of the Transparency Catalog, in the checklist of an organization in the justice sector, opportunities were identified to evolve the practices of verification of process models in BPMN. This master's dissertation aims to propose a Checklist (Process Checklist), an inspection tool focused on transparency, to enhance the quality of process models in BPMN, identifying quality problems in the model. For this purpose, a literature review was initially carried out to identify studies on inspection of BPMN process models. The checklist, developed for human use, was evaluated anonymously by process modeling experts. Based on the results of the first evaluation of the usability, efficiency and effectiveness of the Process Checklist, carried out by five experts, opportunities for improvement were identified that were implemented to ensure transparent, objective, reliable and quality process models. In the second performance evaluation, the Process Checklist was more effective in ensuring model quality compared to BPCheck. Universidade Federal da Bahia Dissertação 2025-06-05T00:00:00Z https://repositorio.ufba.br/handle/ri/44119 Paperman - um sistema de recomendação de artigos científicos. Sant'Ana Filho, Marcos Vinícius Queiroz de Durão, Frederico Araújo The search for references and related work in scientific research can be an exhausting process, consuming an average of 4 hours per week for researchers. The abundance of sources and repositories makes it even more challenging to verify the veracity and reliability of these works, hindering the selection of high-quality, relevant research and leading to the disposal of half of the collected samples, negatively impacting productivity. Considering this scenario, the objective of this study is to plan and develop a platform to facilitate the initial stages of research through recommendation systems, models based on the researcher's profile, and data post-processing. The proposed system, called Paperman, employs natural language processing and machine learning techniques to analyze researchers' publication history and generate personalized recommendations for scientific articles. The system architecture includes an API for data collection and processing, integrations with external services such as ORCID and DBLP, and a browser extension that presents recommendations intuitively. Experimental results demonstrate the system's effectiveness, with metrics such as MRR of 0.8 and nDCG@5 of 0.9407, indicating high relevance of generated recommendations. The study contributes to the field of educational recommendation systems, offering a practical solution to optimize the literature review process and discovery of related works in scientific research. The Paperman system addresses common challenges in academic research, such as information overload and the need for efficient discovery of relevant publications, by leveraging the researcher's own profile and publication history to provide tailored recommendations. Universidade Federal da Bahia Dissertação 2025-11-27T00:00:00Z https://repositorio.ufba.br/handle/ri/43039 Tetris: sn SLA-aware application placement strategy in the Edge-Cloud continuum. Almeida, Lucas Mascarenhas Peixoto, Maycon Leone Maciel An Edge-Cloud Continuum integrates edge and cloud resources to deliver a flexible and scalable infrastructure. While this paradigm reduces latency and enhances scalability, the heterogeneous nature of these environments introduces challenges such as resource fragmentation and inefficient placement. Many existing approaches assume high resource availability and homogeneous infrastructures, overlooking fragmentation, application drops, and the interplay between performance metrics. This work introduces Tetris, an edge-cloud continuum application placement strategy inspired by the iconic puzzle game, designed to operate in a heterogeneous and complex edge–cloud continuum environment. It prioritizes tasks based on urgency, workload diversity, and resource availability. Compared to state-of-the-art approaches, Tetris reduces latency SLA violations by approximately 75%, while maintaining zero drop occurrences. Unlike proximity-based methods that focus on placing tasks near users, Tetris’s key strength lies in avoiding resource fragmentation, simplifying the placement problem, and improving overall system resilience. Additionally, clustering analysis reveals strong correlations between fragmentation and service degradation, reinforcing the importance of balanced resource allocation. These findings contribute to the development of more resilient, efficient, and explainable edge–cloud systems, with improved QoS and QoE for end-users. Universidade Federal da Bahia Dissertação 2025-09-09T00:00:00Z https://repositorio.ufba.br/handle/ri/42532 Modelagem causal para estudo de viés racial em sistemas de detecção de face. Almeida Junior, Ariel Menezes de Rios, Ricardo Araújo Face detection and recognition algorithms have been widely adopted in a diversity of applications, such as social networks that automatically detect and recognize every person present in published images. However, with the growing adoption of Artificial Intelligence (AI) algorithms in general, questions related to the existence of biases began to arise. In many situations, it was found that there were biases affecting historically oppressed minorities. As an example, there was racial bias in many facial recognition systems used by American police, which led to the suspension of the use of this technology in some states, the discontinuation of development in some companies, such as IBM, and researchers to ask their colleagues to stop working on these systems because of the impact on people of different races and ethnicities. The aforementioned problem motivates the study and evaluation of the existence of bias in a fraud detection, AI-based, system used by the public transportation in Salvador (Brazil). Taking into account the fact that Salvador is the Brazilian city with the highest percentage of black people, any error can affect a significant number of users, leading to a high number of false positives. In previous studies developed by the research group, statistical tests were performed to verify if there is a correlation between the error rate and race and gender. The results indicated the existence of this correlation, that is, there is a higher error rate in face detection of black or brown users and women. From these results, a main question motivates the development of this project: Is there, in fact, a causal relationship between race and the error rate in detection? To evaluate this question, a causal model was developed to analyze the influence of skin color on the face detection system used in Salvador’s public transportation. Universidade Federal da Bahia Dissertação 2024-05-28T00:00:00Z