Mestrado em Informática
URI Permanente para esta coleção
Nível: Mestrado Acadêmico
Ano de início:
Conceito atual na CAPES:
Ato normativo:
Periodicidade de seleção:
Área(s) de concentração:
Url do curso:
Navegar
Submissões Recentes
- ItemHeurística híbrida para o problema de roteamento de veículos com carregamento tridimensional, janelas de tempo e objetivos hierárquicos(Universidade Federal do Espírito Santo, 2025-11-10) Pimentel, Wesley Pereira; Amaral, André Renato Sales ; https://orcid.org/0000-0001-7344-3994; http://lattes.cnpq.br/4695002674556067; https://orcid.org/0000-0002-2847-4078; http://lattes.cnpq.br/9868402229168513; Boeres, Maria Claudia Silva ; https://orcid.org/0000-0001-9801-2410; http://lattes.cnpq.br/0528154281423964; Lorenzoni, Luciano Lessa ; https://orcid.org/0000-0003-4859-7750; http://lattes.cnpq.br/7959495705859101This work introduces and formalizes the Three-Dimensional Loading Vehicle Routing Problem with Time Windows and Hierarchical Objectives (3L-VRPTWH). The singularity of the problem lies in its lexicographical objective function, which successively prioritizes the minimization of the number of vehicles, total distance, and waiting time, and in the integration of a robust set of operational constraints. Such constraints include meeting pre-defined time windows, vehicle weight capacity, LIFO (Last In, First Out) unloading policy, and load stability requirements, such as minimum support area and maximum stackable weight. The proposed method consists of a two-stage hybrid heuristic: a pre packing phase with Simulated Annealing to estimate the length space occupied by each customer, followed by an Adaptive Large Neighborhood Search for route optimization. The approach is enhanced by mechanisms such as Adaptive Container Length and the Total Compaction Algorithm, which intensify the search for solutions with more customers per route. Computational experiments, carried out on a set of instances adapted from the literature, confirmed the relevance of the hierarchical formulation, evidencing consistent trade-offs between objectives. The results demonstrate that the method is capable of generating robust and adaptable solutions, reconciling operational efficiency and practical feasibility in complex logistical scenarios. The main contribution of this work is, therefore, the formalization as an optimization problem and the proposal of a solution method, offering a robust tool for logistical scenarios that demand the optimization of multiple criteria with well-defined strategic priorities
- ItemHeurística híbrida para o problema de roteamento de veículos com carregamento tridimensional, janelas de tempo e objetivos hierárquicos(Universidade Federal do Espírito Santo, 2025-11-10) Pimentel, Wesley Pereira; Amaral, André Renato Sales; https://orcid.org/0000-0001-7344-3994; http://lattes.cnpq.br/4695002674556067; https://orcid.org/0000-0002-2847-4078; http://lattes.cnpq.br/9868402229168513; Boeres, Maria Claudia Silva ; https://orcid.org/0000-0001-9801-2410; http://lattes.cnpq.br/0528154281423964; Lorenzoni, Luciano Lessa; https://orcid.org/0000-0003-4859-7750; http://lattes.cnpq.br/7959495705859101This work introduces and formalizes the Three-Dimensional Loading Vehicle Routing Problem with Time Windows and Hierarchical Objectives (3L-VRPTWH). The singularity of the problem lies in its lexicographical objective function, which successively prioritizes the minimization of the number of vehicles, total distance, and waiting time, and in the integration of a robust set of operational constraints. Such constraints include meeting pre-defined time windows, vehicle weight capacity, LIFO (Last In, First Out) unloading policy, and load stability requirements, such as minimum support area and maximum stackable weight. The proposed method consists of a two-stage hybrid heuristic: a pre packing phase with Simulated Annealing to estimate the length space occupied by each customer, followed by an Adaptive Large Neighborhood Search for route optimization. The approach is enhanced by mechanisms such as Adaptive Container Length and the Total Compaction Algorithm, which intensify the search for solutions with more customers per route. Computational experiments, carried out on a set of instances adapted from the literature, confirmed the relevance of the hierarchical formulation, evidencing consistent trade-offs between objectives. The results demonstrate that the method is capable of generating robust and adaptable solutions, reconciling operational efficiency and practical feasibility in complex logistical scenarios. The main contribution of this work is, therefore, the formalization as an optimization problem and the proposal of a solution method, offering a robust tool for logistical scenarios that demand the optimization of multiple criteria with well-defined strategic priorities.
- ItemAnálise de arquiteturas baseadas em transformers na transcrição de fala e descrição de áudio de fundo simultâneos em cenários sonoros mistos(Universidade Federal do Espírito Santo, 2025-03-26) Silva, João Vitor Roriz da; Boldt, Francisco de Assis; https://orcid.org/0000-0001-6919-5377; http://lattes.cnpq.br/0385991152092556; Badue, Claudine Santos; https://orcid.org/0000-0003-1810-8581; http://lattes.cnpq.br/1359531672303446; https://orcid.org/; http://lattes.cnpq.br/8121638031129636; Souza, Alberto Ferreira de; https://orcid.org/0000-0003-1561-8447; http://lattes.cnpq.br/7573837292080522; Paixão, Thiago Meireles; https://orcid.org/0000-0003-1554-6834; http://lattes.cnpq.br/2961730349897943This work investigates how two specialized neural networks—a speech transcription model (Whisper) and a general audio captioning model (Prompteus)—can be jointly leveraged to process mixed audio inputs containing both speech and non-speech events. We construct the Clotho Voice dataset by merging speech recordings from the Common Voice 5.1 corpus and general sounds from the Clotho 2.1 dataset. Through a series of controlled experiments, we examine how each model’s performance degrades when presented with overlapping speech and background sounds. Results show that Whisper excels at transcription when speech dominates the input signal, yet its accuracy diminishes in the presence of substantial non speech noise. Conversely, Prompteus demonstrates high performance in purely background oriented settings but exhibits a decline in descriptive capability as speech levels increase. We also highlight how preprocessing steps—such as normalization and resampling—impact borderline cases, revealing that subtle audio features are crucial for robust event detection in challenging acoustic environments. Our findings underscore the importance of tailored training and data augmentation strategies to mitigate performance loss in mixed audio scenarios. By integrating the complementary strengths of speech-focused and background focused models, we offer a pathway toward more comprehensive audio understanding systems suitable for noisy, real-world applications, including industrial automation and assistive technologies. This research paves the way for developing hybrid frameworks that capture both spoken language and context-rich environmental cues in a single, unified approach
- ItemTRAJES: um arcabouço para geração e avaliação de modelos de predição de trajetórias veiculares(Universidade Federal do Espírito Santo, 2024-12-12) Krohling, Breno Aguiar; Comarela, Giovanni Ventorim; Mota, Vinícius Fernandes Soares; https://orcid.org/0000-0002-8341-8183; Dias, Diego Roberto Colombo; Rettore, Paulo Henrique LopesVehicle trajectories prediction enables traffic management optimization and facilitates solutions that require knowledge of where a vehicle, or its driver, is heading. To use such information on a large scale, it is necessary to employ models capable of generalizing complex movement patterns across an entire region or city. To achieve this, an end-to-end framework called TRAJES (Trajectory Estimator) was proposed to generate models from urban vehicle mobility data, using trajectories consisting only of geolocation information. The model generation and selection are based on concrete metrics, such as the actual distance between predicted and real points, and the proposed Hit Race Accuracy metric, which evaluates model performance based on regions of interest throughout the entire city. The framework was employed to create models capable of predicting vehicle positions in both the near and distant future, tested on real-world datasets collected in the cities of Porto and San Francisco. The results demonstrated the ability to generalize effective models for both prediction scenarios, indicating their viability as an intermediate step for external solutions, particularly those requiring knowledge of a vehicle’s future region.
- ItemAnalysis of bias in GPT language models through fine-tuning with anti-vaccination speech(Universidade Federal do Espírito Santo, 2024-12-02) Turi, Leandro Furlam; Badue, Claudine; Souza, Alberto Ferreira de; https://orcid.org/0000-0003-1561-8447; Pacheco, Andre Georghton Cardoso; Almeida Junior, Jurandy Gomes deWe examined the effects of integrating data containing divergent information, particularly concerning anti-vaccination narratives, in training a GPT-2 language model by fine-tuning it using content from anti-vaccination groups and channels on Telegram. Our objective was to analyze the model’s ability to generate coherent and rationalized texts compared to a model pre-trained on OpenAI’s WebText dataset. The results demonstrate that fine-tuning a GPT-2 model with biased data leads the model to perpetuate these biases in its responses, albeit with a certain degree of rationalization, highlighting the importance of using reliable and high-quality data in the training of natural language processing models and underscoring the implications for information dissemination through these models. We also explored the impact of data poisoning by incorporating anti-vaccination messages combined with general group messages in different proportions, aiming to understand how exposure to biased data can influence text generation and the introduction of harmful biases. The experiments highlight the change in frequency and intensity of anti-vaccination content generated by the model and elucidate the broader implications for reliability and ethics in using language models in sensitive applications. This study provides social scientists with a tool to explore and understand the complexities and challenges associated with misinformation in public health through the use of language models, particularly in the context of vaccine misinformation.