DSpace Communidade:https://repositorio.uema.br/jspui/handle/123456789/19152026-07-03T20:18:20Z2026-07-03T20:18:20ZSistemas de gerenciamento e monitoramento de rack’s outdoors de telecomunicações, baseado em internet of thingshttps://repositorio.uema.br/jspui/handle/123456789/62472026-07-02T18:25:29Z2022-10-31T00:00:00ZTítulo: Sistemas de gerenciamento e monitoramento de rack’s outdoors de telecomunicações, baseado em internet of things
Abstact: The present work aims to correlate concepts, technologies and application of a solution
based on embedded systems, Internet Of Things and 5G, to monitor and ensure data
persistence in telecommunications companies. First, a survey of the current scenario of the
Telecommunications industry is carried out. In addition, they present intrinsic problems, in
which consumers are the most affected, such as the lack of reliability and interactivity of the
product offered by energy concessionaires. The bibliographic research was through a literary
review based on articles, books and works by several authors from the period 2019 to 2022.
The collection of information on the topic took place through the databases: Google Scholar
and IEEE. In this way, conceptual information was presented on technologies such as ESP32,
5G, sensing and especially the MQTT protocol, for long-distance solutions, which belong to
the concept of Internet of Things (IoT). Therefore, we propose the development of a device
capable of monitoring and assisting telecommunications companies in the management to
maintain their external assets.2022-10-31T00:00:00ZProcessamento de documentos jurídicos longos: comparação e avaliação de métodos baseados em Modelos de Linguagemhttps://repositorio.uema.br/jspui/handle/123456789/62362026-07-01T17:56:18Z2025-11-12T00:00:00ZTítulo: Processamento de documentos jurídicos longos: comparação e avaliação de métodos baseados em Modelos de Linguagem
Abstact: The Brazilian legal system faces a structural scenario of accumulating lawsuits, which compromises its efficiency and adherence to the constitutional principle of a reasonable trial duration. As of August 2024, the number of pending cases exceeded 80 million, according to data from the Judiciary's National Database. This substantial volume of litigation directly impacts the speed and effectiveness of judicial service delivery, necessitating the development of technological tools capable of supporting the management, screening, and comprehension of legal documents—documents whose length and complexity challenge traditional human and automated analysis workflows. In this context, understanding how advanced text processing techniques can contribute to streamlining judicial activity lies at the heart of this dissertation. Existing applications in the field, such as BumbaBERT, show promising results for optimizing procedural workflows but remain constrained by the structural limitations of their underlying Transformer architecture, particularly due to high computational complexity. To address this issue, this dissertation project proposes and evaluates a set of strategies aimed at the efficient processing of long documents, using initial complaints linked to Incidents for the Resolution of Repetitive Demands (IRDR) as a case study. Building on gaps identified in the literature and the practical motivation stemming from the UEMA-TJMA technical cooperation agreement—specifically regarding the difficulty of adapting language models to extensive legal texts—the methodological process was guided by the Data Science Trajectories (DST) framework. This approach provided a foundation for understanding the domain and planning solutions, as well as for identifying a taxonomy of methods capable of organizing the field of automatic long-document classification into three categories: truncation methods derived from baselines (e.g., BumbaBERT, LegalBERT-PT); ...decomposition-recomposition (e.g., ToBERT) and content synthesis based on sentence selection strategies (e.g., TextRank, LexRank, SBERT, LlaMa). Based on this framework, empirical experimentation and statistical validation were conducted. Consequently, the study involved implementing and comparing eight architectures based on fine-tuning BumbaBERT—totaling 40 experiments—that considered performance metrics such as accuracy, F1-score, precision, and recall; computational efficiency indicators such as time, inference speed, and memory usage; statistical significance tests; and practical implementation feasibility. The results demonstrated that hierarchical architectures outperform content synthesis-based approaches, achieving a better balance between precision and stability, albeit at a higher computational cost. This finding reinforces the importance of preserving the integral argumentative structure of legal texts to ensure interpretive consistency and the reliability of automated classifications. Thus, the work contributes scientifically to the advancement of natural language processing in the legal domain by demonstrating how established strategies can be reinterpreted and adapted to address the linguistic and structural specificities of Brazilian legal texts. From a technological and institutional perspective, the study offers a reproducible artifact capable of integration into the TJMA’s automation system, thereby contributing to reduced case processing times and the strengthening of digital transformation policies in the public sector. Finally, regarding the social dimension, the study reaffirms the
role of digital transformation as a tool for democratizing access to justice, fostering innovation that combines technical precision, ethical responsibility, and a commitment to the public interest.2025-11-12T00:00:00ZCiência de dados aplicada à gestão fiscal: um estudo sobre a inadimplência do Imposto Predial e Territorial Urbano em São Luís-MAhttps://repositorio.uema.br/jspui/handle/123456789/62352026-07-01T20:08:42Z2026-02-27T00:00:00ZTítulo: Ciência de dados aplicada à gestão fiscal: um estudo sobre a inadimplência do Imposto Predial e Territorial Urbano em São Luís-MA
Abstact: The Urban Property Tax (IPTU) is one of the main sources of own-source revenue for
Brazilian municipalities; however, its collection is often affected by high levels of tax
delinquency and weaknesses in cadastral records. In the municipality of São Luís,
there is a significant gap between the revenue potential and the amounts effectively
collected. Within this context, this study investigates the factors associated with IPTU
delinquency through the application of data science techniques and machine learning
algorithms. Cadastral and financial IPTU data for the period from 2019 to 2023 were
used, combining descriptive analyses, supervised modeling, and unsupervised
methods. The initial diagnosis revealed structural delinquency, with approximately 70%
of properties without tax exemptions remaining delinquent five years after the tax
assessment, and about 45% of the assessed value not collected by the end of the
statutory limitation period. In the supervised modeling stage, a multiclass classifier
based on the XGBoost algorithm was applied, enabling the distinction between
compliant, partially compliant, and delinquent properties, even in a context of strong
class imbalance. The results indicate that tax delinquency is strongly associated with
poor cadastral quality, particularly the absence of a valid CPF/CNPJ, as well as
variables related to urban infrastructure, property construction characteristics, and
spatial location. The clustering analysis using the K-prototypes algorithm revealed a
high concentration of revenue potential within a small number of groups of delinquent
properties, characterized by higher average IPTU values and greater operational
feasibility for collection efforts. In addition, the identification of atypical patterns and
cadastral inconsistencies demonstrated the potential of these approaches to support
the improvement of property records and the prioritization of administrative actions.
It is concluded that IPTU delinquency in São Luís is predominantly driven by structural,
cadastral, and territorial factors. The findings show that the integrated use of machine
learning techniques can significantly contribute to the improvement of municipal tax
administration, promoting greater revenue efficiency and tax fairness without the need
to increase tax rates.2026-02-27T00:00:00ZUniversidade VOIP: caso de usuário posto de combustívelhttps://repositorio.uema.br/jspui/handle/123456789/62132026-07-01T15:35:00Z2025-01-01T00:00:00ZTítulo: Universidade VOIP: caso de usuário posto de combustível
Abstact: Telephony continues to be a fundamental element in the operations of any company today,
and this is for very clear and practical reasons. It not only ensures direct and reliable
communication between employees, clients, and partnersŮhelping to solve problems
immediately, close deals without wasting time, and provide instant customer supportŮbut
it also perfectly Ąts the well-known business maxim: ŞTime is money.Ť For many executives
and managers, investing in eicient telephony is synonymous with agility, as it allows
teams to handle urgent demands quickly, avoiding delays that can be costly in terms of
productivity and lost opportunities.
Although the use of traditional telephony has declined due to the growth of messaging
and internet-based calling applications, voice communication has not lost its relevance.
What has occurred is a transformation in the medium used: technology has migrated from
Ąxed lines and analog exchanges to digital platforms, which are more Ćexible and better
adapted to the corporate environment.
In recent years, the widespread adoption of high-speed internet has enabled voice to be
transmitted as data packets, paving the way for Voice over IP (VoIP) solutions. This change
marked a milestone for companies of all sizes, as it allowed cost reductions, integration of
advanced features such as call recording and call queues, and the scalability necessary to
support organizational growth.
Despite the strong presence of communication applications in peopleŠs daily lives, in
business contexts they still present limitations, such as the lack of integration with internal
systems and the diiculty of maintaining security and contact privacy. For this reason,
corporate telephony based on VoIP has consolidated itself as the most eicient alternative,
combining cost savings, reliability, and intelligent call management features.
In summary, telephony has not ceased to be important; on the contrary, it has evolved
and adapted to new demands. Today, more than just a voice channel, it integrates with
companiesŠ data networks and digital tools, becoming a fundamental part of modern
technological infrastructure2025-01-01T00:00:00Z