https://ri.ufs.br/jspui/handle/riufs/2439 Sun, 06 Sep 2026 22:28:07 GMT 2026-09-06T22:28:07Z http://ri.ufs.br:80/retrieve/1af663d6-1c95-4b50-904f-7b6c109edabf/procc.jfif https://ri.ufs.br/jspui/handle/riufs/2439 https://ri.ufs.br/jspui/handle/riufs/25815 Título: Investigação de métodos para estimação de pesos de relevância para agrupamento de dados relacionais com múltiplas visões Autor(es): Silva, Vitória Teles da Abstract: Multiview data clustering is characterized as the challenge of grouping similar data and separating dissimilar data by using different representations, such as attribute vectors, feature matrices, graphs, or images, whichtogetherprovidecomplementaryinformation.Thisapproachisappliedin real-world scenarios such as social network analysis, medical image classification, recommender systems, and multimedia data integration. Data clustering is one of the most relevant tools for data analysis, and its importance is related to the faithful representation of real-world data, with the combined use of multiple views providing greater accuracy when compared to the use of a single view. The experiments show that the performance of multiview clustering algorithms strongly depends on the distance metrics and weight estimation strategies. While MRDCA-RWL is more sensitive to the adopted distance metric, methods such as MVKKM and MVSC exhibit a strong joint dependence on both distance metrics and weights, reinforcing the importance of selecting appropriate parameters. Given the above, this work addresses the need for a proper interpretation of the use of multiview data in the context of contemporary applications, aiming to investigate different methods for estimating relevance weights. These weights are then applied to the different views in the context of relational data clustering, discussing both the quality of the results and the algorithmic efficiency in comparison with the complexity of competing solutions. Based on this, the experimental results demonstrate the influence of the methodological choices on the behavior of the algorithms, with MRDCA-RWL exhibiting more stable behavior, whereas MVKKM,MVSC,andTW-KMdemonstrate sensitivity to configuration changes. Thu, 23 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25815 2026-07-23T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25810 Título: A comparative survey of ARM and RISC-V: architectures, applications and challenges Autor(es): Meira, Andre Reges Souza Abstract: The selection of a processor instruction set architecture (ISA) has become a foundational engineering decision that shapes the performance, power efficiency, security, and supply-chain independence of modern computing systems. Three converging trends make a renewed comparison of ARM and RISC-V especially timely: the expansion of computing into safety- and securitycritical domains governed by standards such as ISO 26262 and IEC 61508; the rise of RISC-V as the first credible open alternative to ARM, with over ten billion cores shipped by 2024; and the growing demand for hardware auditability following microarchitectural attacks such as Spectre and Meltdown and supply-chain compromises such as the XZ Utils backdoor. In this master’s thesis, we provide a comprehensive, parallel-structured comparative survey of the ARM and RISC-V architectures, spanning ISA design and evolution, microarchitecture, vector processing, and virtualization, together with the application domains of robotics, artificial intelligence, and hardware security — dimensions that existing ISA-level, benchmarking, and industry analyses address only in isolation. The work is an architectural survey rather than an experimental benchmarking study, grounded in ISA specifications, peer-reviewed literature from IEEE, ACM, USENIX, and Springer venues, vendor datasheets and safety documentation, and international functional-safety standards, and organised as a chapter-by-chapter parallel evaluation of the two architectures. The analysis finds that the gap between ARM and RISC-V is increasingly one of ecosystem maturity rather than architectural capability: ARM offers a vertically integrated, broadly deployed ecosystem with hardware-enforced security defaults (PAC, MTE, BTI, CCA) and certified safety cores, whereas RISC-V offers openness, amenability to formal verification, and license-free customisation, with ISO 26262 ASIL D cores now emerging. For artificial-intelligence workloads, RISC-V demonstrates up to fourfold superior energy efficiency through custom extensions, while ARM delivers up to fifteenfold faster inference on complex networks through a mature toolchain; these figures are best-case, implementation-dependent trends rather than fixed architectural verdicts. Across robotics and security, architecture selection proves application- and certificationdependent rather than absolute, and the frontier is shifting from a binary ARM-versus-RISC-V choice toward heterogeneous designs that compose both architectures with domain-specific accelerators on a single platform. Wed, 22 Jul 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25810 2026-07-22T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25623 Título: Modelagem composicional com regressão Dirichlet: correção de viés por bootstrap e teste para seleção de modelos não-encaixados Autor(es): Santos, Jesy Karolayne Sales dos Abstract: This study aims to investigate the effectiveness of bias correction via the Bootstrap technique in Dirichlet regression models, and to adapt hypothesis tests for non-nested models, such as the J and MJ tests, to the Dirichlet regression context, in order to support the selection of competing models. Regression models are fundamental in statistical analysis, enabling the description and prediction of relationships between variables. In particular, the Dirichlet regression model is suitable for compositional data, which are characterised by components that represent proportional parts of a whole. Parameter estimation in this model using maximum likelihood may exhibit significant bias in small samples, compromising inferential quality. Thus, this work proposes to evaluate the performance of bias correction through the Bootstrap method, in both its parametric and non-parametric versions, by comparing it with the original estimators through Monte Carlo simulations. The results demonstrate that the resampling technique is effective in reducing bias, with a variable impact on the mean squared error. Regarding the model selection procedure, the Monte Carlo experiments demonstrated that the proposed adaptation of the MJ test, based on the choice of the maximum p-value among competing models, yielded null rejection rates close to the nominal significance levels and a high frequency of correctly selecting the true model. Fri, 26 Jun 2026 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25623 2026-06-26T00:00:00Z https://ri.ufs.br/jspui/handle/riufs/25476 Título: Uma arquitetura big data inteligente para auditoria na saúde Autor(es): Santos, Helder Prado Abstract: Context: The auditing of public health procurement, particularly for Orthotics, Prosthetics, and Special Materials (OPME), is a critical process hindered by data inconsistency in documents such as invoices. This lack of standardization leads to inefficiencies and creates vulnerabilities for irregularities. Although Artificial Intelligence (AI) solutions have already demonstrated effectiveness in small-scale item classification, their nationwide application is impeded by the absence of an infrastructure capable of processing a massive volume of data in a scalable, robust, and economically viable manner. Objective: This work aims to overcome this barrier on two fronts: first, to characterize the state of the art in Big Data architectures for healthcare, identifying the fundamental approaches, tools, and concepts for their construction; second, to present ALIAS (Architecture for Large-scale Intelligent Auditing of Healthcare Systems), a detailed, replicable, and open-source technical blueprint designed to democratize the development of data platforms and support the complete lifecycle of AI solutions (MLOps). Method: The methodology adopted a two-phase approach. First, a Systematic Literature Mapping consolidated the state of the art. These findings then guided the design, implementation, and evaluation of the ALIAS blueprint. Its effectiveness and applicability were investigated through a concrete case study: the large-scale classification of OPME items from invoices, scaling a pre-existing AI solution to a nationwide data volume and seeking evidence of its performance in a real-world auditing scenario. Results: The systematic mapping analyzed 219 articles and selected 16 primary studies, which guided the design of ALIAS. The practical application of the architecture overcame previous processing barriers, enabling large-scale analysis that had previously failed due to memory exhaustion. The adoption of the Parquet format reduced storage by approximately 80%, and data partitioning accelerated queries by orders of magnitude. Crucially, the architecture established an efficient MLOps workflow, which democratized access to distributed analysis and drastically reduced the cycle between experimentation and production. Conclusion: The research presents evidence that the ALIAS blueprint constitutes a robust and financially viable solution for public institutions to implement their own data analysis and AI platforms. By promoting technological sovereignty and offering a practical guide whose effectiveness has been demonstrated, this work empowers organizations to optimize complex processes such as OPME auditing, establishing the foundation for future innovations and ensuring greater transparency, efficiency, and quality in public health management. Wed, 30 Jul 2025 00:00:00 GMT https://ri.ufs.br/jspui/handle/riufs/25476 2025-07-30T00:00:00Z