Doutorado em Ciência da Computação

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Redes de telecomunicações convergentes: modelagem e implementação de arquitetura para infraestruturas pós-5G
(Universidade Federal de Goiás, 2024-09-25) Macedo, Ciro José Almeida; Both, Cristiano Bonato; http://lattes.cnpq.br/2658002010026792; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Martins, Joberto Sérgio Barbosa; Oliveira Júnior, Antonio Carlos de; Klautau Júnior, Aldebaro Barreto da Rocha; Alberti, Antônio Marcos
The evolution of cellular mobile networks has been guided by directives specified by institutions such as 3GPP, aimed at supporting demanding services. Simultaneously, non3GPP wireless communication technologies have also evolved and play a significant role in various contexts. These technologies are essential for connectivity in diverse scenarios where long-range communication and low power consumption are crucial. Recent studies have shown that the integration and harmonious coexistence of 3GPP and non-3GPP technologies are vital in the context of post-5G networks, enhancing ubiquitous and seamless connectivity. In this context, the present thesis investigated the feasibility of converging non-3GPP communication technologies with the 5G core, dividing the investigation into two phases. In the first phase, an architecture was proposed to integrate these technologies. In the second phase, a functional prototype of this architecture was built to conduct experiments demonstrating its viability in different use cases. The thesis conducted a detailed technical analysis, offering a comprehensive view of the benefits of convergence for consumers and infrastructure providers. Significant gaps were identified that still need to be addressed in post-5G/6G networks, such as the current inability to monitor non-3GPP networks by the 5G infrastructure operator. Some of these gaps were explored and investigated in the context of the solution proposed in this thesis
MemoryGraph: uma proposta de memória para agentes conversacionais utilizando grafo de conhecimento
(Universidade Federal de Goiás, 2024-09-25) Oliveira, Vinicius Paulo Lopes de; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Galvão Filho, Arlindo Rodrigues; Silva, Nadia Felix Felipe da; Pereira, Fabíola Souza Fernandes; Fanucchi, Rodrigo Zempullski
With the advancement of massive language models applied to natural language processing, many proposals have become viable, such as the use of conversational agents applied to various everyday tasks. However, these models still have limitations in both the integration of new knowledge and the representation and retrieval of that knowledge, being constrained by costs, execution time, and training. Furthermore, their black-box nature prevents the direct manipulation of knowledge, mainly due to the vector representation that indirectly represents it, making the control and explanation of their results more difficult. In contrast, knowledge graphs allow for a rich and explicit representation of relationships between real-world entities. Despite the challenges in their construction, studies indicate that these can complement each other to produce better results. Therefore, the objective of this research is to propose a memory system for conversational agents based on massive language models through the combination of explicit knowledge (knowledge graphs) and implicit knowledge (language models) to achieve better semantic and lexical representation. This methodology was called MemoryGraph and is composed of three processes: graph construction, graph search, and user representation. Various knowledge graph construction workflows were proposed and compared, considering their costs and influences on the final result. The agent can search for information in this base through various search proposals based on RAG, referred to here as GraphRAG. This search methodology was evaluated by humans in five proposed question scenarios, showing superior average results in all five proposed search approaches (29% in the best approach). In addition, six RAG metrics, evaluated by a massive model, were applied to the proposed application results from two popular datasets and one composed of diabetes guidelines, showing superior results in all datasets. Furthermore, a method for long-term user representation, called user_memory, was proposed, demonstrating 93% retention of user information. To reinforce this result, case studies were conducted, demonstrating the agent's ability to personalize the user experience based on past experiences, increasing the speed of information delivery and user satisfaction. The results demonstrate that the MemoryGraph paradigm represents an advance over vector representation in environments where richer, temporal, and mutable contextualization is necessary. It also indicates that the integration of knowledge graphs with massive language models, especially in the construction of long-term memory and rich contextualization based on past experiences, can represent a significant advance in creating more efficient, personalized conversational agents with enhanced capacity for retaining and utilizing information over time.
IVF/NSGA-III: Uma Metaheurística Evolucionária Many-Objective com Busca Guiada por Balizas e Fertilização In Vitro
(Universidade Federal de Goiás, 2024-04-11) Sampaio, Sávio Menezes; Camilo Junior, Celso Gonçalves; http://lattes.cnpq.br/6776569904919279; Camilo Junior; Camilo Junior, Celso Gonçalves; Lima Neto, Fernando Buarque de; Leite, Karla Tereza Figueiredo; Rodrigues, Vagner José do Sacramento; Oliveira, Sávio Salvarino Teles de
Sampaio, Sávio Menezes. The In Vitro Fertilization Genetic Algorithm (IVF/GA) demonstrates robust applicability to single-objective optimization problems, particularly those that are complex and multimodal. This work proposes the expansion of the IVF method to many-objective optimization, which deals with more than three simultaneous objectives. The study introduces new activation criteria, selection, assisted exploration, and transfer mechanisms, consolidating innovation through the integration of the IVF method with NSGA-III, here referred to as IVF/NSGA-III. This approach incorporates the Beacon-Guided Search strategy in a Steady State configuration, aiming to overcome the inherent challenges of many-objective optimization. It focuses on dynamic convergence to promising regions of the solution space and adopts an adaptive scale factor within the context of Differential Evolution, providing an alternative methodology to conventional intensification methods. Experiments conducted with the many-objective benchmarks DTLZ, MaF, WFG show that IVF/NSGA-III significantly enhances performance compared to the standard NSGA-III algorithm across various tested problems, validating its potential as a valuable contribution to the field of Many-Objective Evolutionary Algorithms (MOEAs). The study suggests new directions for the development of many-objective memetic strategies and offers significant insights for researchers seeking more effective and adaptable optimization methods.. Goiânia-GO, 2024. 220p. PhD. Thesis Relatório de Graduação. Instituto de Informática, Universidade Federal de Goiás.
Framework para sistemas de recomendação baseados em neural contextual Bandits com restrição de justiça
(Universidade Federal de Goiás, 2024-06-03) Santana, Marlesson Rodrigues Oliveira de; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Rosa, Thierson Couto; Carvalho, Cedric Luiz De; Araújo, Aluizio Fausto Ribeiro; Veloso, Adriano
The advent of digital businesses such as marketplaces, in which a company mediates a commercial transaction between different actors, presents challenges to recommendation systems as it is a multi-stakeholder scenario. In this scenario, the recommendation must meet conflicting objectives between the parties, such as relevance versus exposure, for example. State-of-the-art models that address the problem in a supervised way not only assume that the recommendation is a stationary problem, but are also user-centered, which leads to long-term system degradation. This thesis focuses on modeling the recommendation system as a reinforcement learning problem, through a Markovian decision-making process with uncertainty where it is possible to model the different interests of stakeholders in an environment with fairness constraints. The main challenges are the need for real interactions between stakeholders and the recommendation system in a continuous cycle of events that enables the scenario for online learning. For the development of this work, we present a model proposal, based on Neural Contextual Bandits with fairness constrain for multi-stakeholder scenarios. As results, we present the construction of MARS-Gym, a framework for modeling, training and evaluating recommendation systems based on reinforcement learning, and the development of different recommendation policies with fairness control adaptable to Neural models. Contextual Bandits, which led to an increase in fairness metrics for all scenarios presented while controlling the reduction in relevance metrics.
Design de experiência aplicado a times
(Universidade Federal de Goiás, 2024-10-18) Alves, Leonardo Antonio; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Ferreira, Deller James; Lucena, Fábio Nogueira de; Dias, Rodrigo da Silva; Federson, Fernando Marques
Despite recent advances, current Gamification methodologies still face challenges in effectively personalizing learning experiences and accurately assessing the development of specific competencies. This thesis presents the Marcta Autonomy Framework (MAF), an innovative framework that aims to overcome these limitations by increasing team members’ motivation and participation while promoting personal development and skills through a personalized experience.The MAF, consisting of six phases (Planning, Reception, Advancement, Feedback, Process Evaluation, and Lessons and Adjustments), guides the development of activities with both intrinsic and extrinsic rewards. The research was applied in two academic case studies: a Software Factory and an Introduction to Programming course for students of the Bachelor’s degree in Artificial Intelligence. Using a qualitative approach, including interviews and observations, the results demonstrate that the MAF significantly enhances the development of personal skills. The analysis suggests that the framework can be applied both within a course and in a specific discipline. The main contribution of the MAF lies in its ability to provide a structured roadmap for planning and evaluating pedagogical actions focused on Personal Skills Development.Furthermore, the framework leverages easily capturable data through observation, context, and evaluations. It is concluded that the MAF stands as a personalized and affective Gamification solution for Experience Design in Learning, promoting Personal Skills Development in both academic and corporate contexts.
Escolha de parâmetros aplicados a modelos inteligentes para o incremento da qualidade do aprendizado de sinais de EEG captados por dispositivo de baixo custo
(Universidade Federal de Goiás, 2024-07-10) Silva, Uliana Duarte; Felix, Juliana Paula; http://lattes.cnpq.br/3610115951590691; Nascimento, Hugo Alexandre Dantas do; http://lattes.cnpq.br/2920005922426876; Nascimento, Hugo Alexandre Dantas do; Pires, Sandrerley Ramos; Carvalho, Sérgio Teixeira de; Carvalho, Sirlon Diniz de; Melo, Francisco Ramos de
Since the creation of the first electroencephalography (EEG) equipment at the beginning of the 20th century, several studies have been carried out based on this technology. More recently, investigations into machine learning applied to the classification of EEG signals have started to become popular. In these researches, it is common to adopt a sequence of steps that involves the use of filters, signal windowing, feature extraction and division of data into training and test sets. The choice of parameters for such steps is an important task, as it impacts classification performance. On the other hand, finding the best combination of parameters is an exhaustive work that has only been partially addressed in studies in the area, particularly when considering many parameter options, the progressive growth of the training set and data acquired from low-cost EEG equipment. This thesis contributes to the area by presenting an extensive research on the choice of parameters for processing and classifying of EEG signals, involving both raw signals and specific wave data collected from a low-cost equipment. The EGG signals acquisition was done with ten participants, who were asked to observe a small white ball that moved to the right, left or remained stationary. The observation was repeated 24 times randomly and each observation situation lasted 18 seconds. Different parameter settings and machine learning methods were evaluated in classifying EEG signals. We sought to find the best parameter configuration for each participant individually, as well as obtain a common configuration for several participants simultaneously. The results for the individualized classifications indicate better accuracies when using data from specific waves instead of raw signals. Using larger windows also led to better results. When choosing a common parameter combination for multiple participants, the results indicate a similarity to findings when looking for the best parameters for individual participants. In this case, the parameter combinations using data from specific waves showed an average increase of 8.69% with a standard deviation of 4.02%, while the average increase using raw signals was 7.82% with a standard deviation of 2.81%, when compared to general average accuracy results. Still in the case of the parameterization common to several participants, the maximum accuracies using data from specific waves were higher than those obtained with the raw signals, and the largest windows appeared among the best results.
Soluções baseadas em aprendizado de máquina para alocação de recursos em redes sem fio de próxima geração
(Universidade Federal de Goiás, 2024-05-06) Lopes, Victor Hugo Lázaro; Klautau Júnior, Aldebaro Barreto da Rocha; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Klatau Júnior, Aldebaro Barreto da Rocha; Rocha, Flávio Geraldo Coelho; Silva, Yuri Carvalho Barbosa; Rezende, José Ferreira de
5G and beyond networks have been designed to support challenging services. Despite important advances already introduced, resource allocation and management methods remain critical tasks in this context. Although resource allocation methods based on exact optimization have a long history in wireless networks, several aspects involved in this evolution require approaches that can overcome the existing limitations. Recent research has shown the potential of AI/ML-based resource allocation methods. In this approach, resource allocation strategies can be built based on learning, in which the complex relationships of these problems can be learned through the experience of agents interacting with the environment. In this context, this thesis aimed to investigate AI/MLbased approaches for the development of dynamic resource allocation and management methods. Two relevant problems were considered, the rst one related to user scheduling and the allocation of radio resources in multiband MIMO networks, and the second one focused on the challenges of allocating radio, computational, and infrastructure resources involved in the VNF placement problem in disaggregated vRAN. For the rst problem, an agent based on DRL was proposed. For the second problem, two approaches were proposed, the rst one being based on an exact optimization method for dening the VNF placement solution, and the second one based on a DRL agent for the same purpose. Moreover, components adhering to the O-RAN architecture were proposed, creating the necessary control for monitoring and dening new placement solutions dynamically, considering aspects of cell coverage and demand. Simulations demonstrated the feasibility of the proposals, with important improvements observed in different metrics.
Reflexo pupilar à luz como biomarcador para identificação de glaucoma: avaliação comparativa de redes neurais e métodos de aprendizado de máquina
(Universidade Federal de Goiás, 2024-08-22) Pinheiro, Hedenir Monteiro; Costa, Ronaldo Martins da; http://lattes.cnpq.br/7080590204832262; Costa, Ronaldo Martins da; Matsumoto, Mônica Mitiko Soares; Camilo, Eduardo Nery Rossi; Papa, João Paulo; Barbosa, Rommel Melgaço
The study of retinal ganglion cells, their photosensitivity characteristics, and their relationship with physical and cognitive processes has driven research on the pupillary reflex. Controlled by the Autonomic Nervous System (ANS), dilation (mydriasis) and contraction (miosis) are involuntary reflexes. Variations in pupil diameter may indicate physical or cognitive changes in an individual. For this reason, the pupillary reflex has been considered an important biomarker for various types of diagnoses. This study aimed to improve the automated identification of glaucoma using data from the pupillary light reflex. A comparative analysis between neural networks and classical techniques was performed to segment the pupillary signal. In addition, the performance of various data processing methods was evaluated, including filtering techniques, feature extraction, sample balancing, and feature selection, analyzing their effects on the classification process. The results show an accuracy of 73.90% in the overall classification of glaucoma, 98.10% for moderate glaucoma classification, and 98.73% for severe glaucoma, providing insights and guidelines for glaucoma screening and diagnosis through the signal derived from the pupillary light response
Future-Shot: Few-Shot Learning to tackle new labels on high-dimensional classification problems
(Universidade Federal de Goiás, 2024-02-23) Camargo, Fernando Henrique Fernandes de; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Galvão Filho, Arlindo Rodrigues; Vieira, Flávio Henrique Teles; Gomes, Herman Martins; Lotufo, Roberto de Alencar
This thesis introduces a novel approach to address high-dimensional multiclass classification challenges, particularly in dynamic environments where new classes emerge. Named Future-Shot, the method employs metric learning, specifically triplet learning, to train a model capable of generating embeddings for both data points and classes within a shared vector space. This facilitates efficient similarity comparisons using techniques like k-nearest neighbors (\acrshort{knn}), enabling seamless integration of new classes without extensive retraining. Tested on lab-of-origin prediction tasks using the Addgene dataset, Future-Shot achieves top-10 accuracy of $90.39\%$, surpassing existing methods. Notably, in few-shot learning scenarios, it achieves an average top-10 accuracy of $81.2\%$ with just $30\%$ of the data for new classes, demonstrating robustness and efficiency in adapting to evolving class structures
Abordagem de seleção de características baseada em AUC com estimativa de probabilidade combinada a técnica de suavização de La Place
(Universidade Federal de Goiás, 2023-09-28) Ribeiro, Guilherme Alberto Sousa; Costa, Nattane Luíza da; http://lattes.cnpq.br/9968129748669015; Barbosa, Rommel Melgaço; http://lattes.cnpq.br/6228227125338610; Barbosa, Rommel Melgaço; Lima, Marcio Dias de; Oliveira, Alexandre César Muniz de; Gonçalves, Christiane; Rodrigues, Diego de Castro
The high dimensionality of many datasets has led to the need for dimensionality reduction algorithms that increase performance, reduce computational effort and simplify data processing in applications focused on machine learning or pattern recognition. Due to the need and importance of reduced data, this paper proposes an investigation of feature selection methods, focusing on methods that use AUC (Area Under the ROC curve). Trends in the use of feature selection methods in general and for methods using AUC as an estimator, applied to microarray data, were evaluated. A new feature selection algorithm, the AUC-based feature selection method with probability estimation and the La PLace smoothing method (AUC-EPS), was then developed. The proposed method calculates the AUC considering all possible values of each feature associated with estimation probability and the LaPlace smoothing method. Experiments were conducted to compare the proposed technique with the FAST (Feature Assessment by Sliding Thresholds) and ARCO (AUC and Rank Correlation coefficient Optimization) algorithms. Eight datasets related to gene expression in microarrays were used, all of which were used for the cross-validation experiment and four for the bootstrap experiment. The results showed that the proposed method helped improve the performance of some classifiers and in most cases with a completely different set of features than the other techniques, with some of these features identified by AUC-EPS being critical for disease identification. The work concluded that the proposed method, called AUC-EPS, selects features different from the algorithms FAST and ARCO that help to improve the performance of some classifiers and identify features that are crucial for discriminating cancer.
Acelerando florestas de decisão paralelas em processadores gráficos para a classificação de texto
(Universidade Federal de Goiás, 2022-09-12) Pires, Julio Cesar Batista; Martins, Wellington Santos; http://lattes.cnpq.br/3041686206689904; Martins, Wellington Santos; Lima, Junio César de; Gaioso, Roussian Di Ramos Alves; Franco, Ricardo Augusto Pereira; Soares, Fabrízzio Alphonsus Alves de Melo Nunes
The amount of readily available on-line text has grown exponentially, requiring efficient methods to automatically manage and sort data. Automatic text classification provides means to organize this data by associating documents with classes. However, the use of more data and sophisticated machine learning algorithms has demanded an increasingly computing power. In this work we accelerate a novel Random Forest-based classifier that has been shown to outperform state-of-art classifiers for textual data. The classifier is obtained by applying the boosting technique in bags of extremely randomized trees (forests) that are built in parallel to improve performance. Experimental results using standard textual datasets show that the GPUbased implementation is able to reduce the execution time by up to 20 times compared to an equivalent sequential implementation.
Abordagem de seleção de características baseada em AUC com estimativa de probabilidade combinada a técnica de suavização de La Place
(Universidade Federal de Goiás, 2024-09-28) Ribeiro, Guilherme Alberto Sousa; Costa, Nattane Luíza da; http://lattes.cnpq.br/9968129748669015; Barbosa, Rommel Melgaço; http://lattes.cnpq.br/6228227125338610; Barbosa, Rommel Melgaço; Lima, Marcio Dias de; Oliveira, Alexandre César Muniz de; Gonçalves, Christiane; Rodrigues, Diego de Castro
The high dimensionality of many datasets has led to the need for dimensionality reduction algorithms that increase performance, reduce computational effort and simplify data processing in applications focused on machine learning or pattern recognition. Due to the need and importance of reduced data, this paper proposes an investigation of feature selection methods, focusing on methods that use AUC (Area Under the ROC curve). Trends in the use of feature selection methods in general and for methods using AUC as an estimator, applied to microarray data, were evaluated. A new feature selection algorithm, the AUC-based feature selection method with probability estimation and the La PLace smoothing method (AUC-EPS), was then developed. The proposed method calculates the AUC considering all possible values of each feature associated with estimation probability and the La Place smoothing method. Experiments were conducted to compare the proposed technique with the FAST (Feature Assessment by Sliding Thresholds) and ARCO (AUC and Rank Correlation coefficient Optimization) algorithms. Eight datasets related to gene expression in microarrays were used, all of which were used for the crossvalidation experiment and four for the bootstrap experiment. The results showed that the proposed method helped improve the performance of some classifiers and in most cases with a completely different set of features than the other techniques, with some of these features identified by AUC-EPS being critical for disease identification. The work concluded that the proposed method, called AUC-EPS, selects features different from the algorithms FAST and ARCO that help to improve the performance of some classifiers and identify features that are crucial for discriminating cancer.
Análise multirresolução de imagens gigapixel para detecção de faces e pedestres
(Universidade Federal de Goiás, 2023-09-27) Ferreira, Cristiane Bastos Rocha; Pedrini, Hélio; http://lattes.cnpq.br/9600140904712115; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; http://lattes.cnpq.br/7206645857721831; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; Pedrini, Helio; Santos, Edimilson Batista dos; Borges, Díbio Leandro; Fernandes, Deborah Silva Alves
Gigapixel images, also known as gigaimages, can be formed by merging a sequence of individual images obtained from a scene scanning process. Such images can be understood as a mosaic construction based on a large number of high resolution digital images. A gigapixel image provides a powerful way to observe minimal details that are very far from the observer, allowing the development of research in many areas such as pedestrian detection, surveillance, security, and so forth. As this image category has a high volume of data captured in a sequential way, its generation is associated with many problems caused by the process of generating and analyzing them, thus, applying conventional algorithms designed for non-gigapixel images in a direct way can become unfeasible in this context. Thus, this work proposes a method for scanning, manipulating and analyzing multiresolution Gigapixel images for pedestrian and face identification applications using traditional algorithms. This approach is analyzed using both Gigapixel images with low and high density of people and faces, presenting promising results.
Aplicação de técnicas de visualização de informações para os problemas de agendamento de horários educacionais
(Universidade Federal de Goiás, 2023-10-20) Alencar, Wanderley de Souza; Jradi, Walid Abdala Rfaei; http://lattes.cnpq.br/6868170610194494; Nascimento, Hugo Alexandre Dantas do; http://lattes.cnpq.br/2920005922426876; Nascimento, Hugo Alexandre Dantas do; Jradi, Walid Abdala Rfaei; Bueno, Elivelton Ferreira; Gondim, Halley Wesley Alexandre Silva; Carvalho, Cedric Luiz de
An important category, or class, of combinatorial optimization problems is called Educational Timetabling Problems (Ed-TTPs). Broadly, this category includes problems in which it is necessary to allocate teachers, subjects (lectures) and, eventually, rooms in order to build a timetable, of classes or examinations, to be used in a certain academic period in an educational institution (school, college, university, etc.). The timetable to be prepared must observe a set of constraints in order to satisfy, as much as possible, a set of desirable goals. The current research proposes the use of methods and/or techniques from the Information Visualization (IV) area to, in an interactive approach, help a better understanding and resolution, by non-technical users, of problem instances in the scope of their educational institutions. In the proposed approach, human actions and others performed by a computational system interact in a symbiotic way targeting the problem resolution, with the interaction carried out through a graphical user interface that implements ideas originating from the User Hints framework [Nas03]. Among the main contributions achieved are: (1) recognition, and characterization, of the most used techniques for the presentation and/or visualization of Ed-TTPs solutions; (2) conception of a mathematical notation to formalize the problem specification, including the introduction of a new idea called flexibility applied to the entities involved in the timetable; (3) proposition of visualizations able to contribute to a better understanding of a problem instance; (4) make available a computational tool that provides interactive resolution of Ed-TTPs, together with a specific entity-relationship model for this kind of problem; and, finally, (5) the proposal of a methodology to evaluate visualizations applied to the problem in focus.
Reconhecimento de padrões em imagens radiográficas de tórax: apoiando o diagnóstico de doenças pulmonares infecciosas
(Universidade Federal de Goiás, 2023-09-29) Fonseca, Afonso Ueslei da; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; http://lattes.cnpq.br/7206645857721831; Soares, Fabrízzio Alphonsus Alves de Melo Nunes; Laureano, Gustavo Teodoro; Pedrini, Hélio; Rabahi, Marcelo Fouad; Salvini, Rogerio Lopes
Pattern Recognition (PR) is a field of computer science that aims to develop techniques and algorithms capable of identifying regularities in complex data, enabling intelligent systems to perform complicated tasks with precision. In the context of diseases, PR plays a crucial role in diagnosis and detection, revealing patterns hidden from human eyes, assisting doctors in making decisions and identifying correlations. Infectious pulmonary diseases (IPD), such as pneumonia, tuberculosis, and COVID-19, challenge global public health, causing thousands of deaths annually, affecting healthcare systems, and demanding substantial financial resources. Diagnosing them can be challenging due to the vagueness of symptoms, similarities with other conditions, and subjectivity in clinical assessment. For instance, chest X-ray (CXR) examinations are a tedious and specialized process with significant variation among observers, leading to failures and delays in diagnosis and treatment, especially in underdeveloped countries with a scarcity of radiologists. In this thesis, we investigate PR and Artificial Intelligence (AI) techniques to support the diagnosis of IPID in CXRs. We follow the guidelines of the World Health Organization (WHO) to support the goals of the 2030 Agenda, which includes combating infectious diseases. The research questions involve selecting the best techniques, acquiring data, and creating intelligent models. As objectives, we propose low-cost, high-efficiency, and effective PR and AI methods that range from preprocessing to supporting the diagnosis of IPD in CXRs. The results so far align with the state of the art, and we believe they can contribute to the development of computer-assisted IPD diagnostic systems.
Recomendação de conteúdo ciente de recursos como estratégia para cache na borda da rede em sistemas 5G
(Universidade Federal de Goiás, 2023-10-03) Monção, Ana Claudia Bastos Loureiro; Corrêa, Sand Luz; http://lattes.cnpq.br/3386409577930822; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Cardoso, Kleber Vieira; Corrêa, Sand Luz; Soares, Telma Woerle de Lima; Rosa, Thierson Couto; Fonseca, Anelise Munaretto
Recently, the coupling between content caching at the wireless network edge and video recommendation systems has shown promising results to optimize the cache hit and improve the user experience. However, the quality of the UE wireless link and the resource capabilities of the UE are aspects that impact the user experience and that have been neglected in the literature. In this work, we present a resource-aware optimization model for the joint task of caching and recommending videos to mobile users. We also present a heuristic created to solve the problem more quickly. The goal is to maximize the cache hit ratio and the user QoE (concerning content preferences and video representations) under the constraints of UE capabilities and the availability of network resources by the time of the recommendation. We evaluate our proposed model using a video catalog derived from a real-world video content dataset (from the MovieLens project), real- world video representations and actual historical records of Channel Quality Indicators (CQI) representing user mobility. We compare the performance of our proposal with a state-of-the-art caching and recommendation method unaware of computing and network resources. Results show that our approach significantly increases the user’s QoE and still promotes a gain in effective cache hit rate.
Preditor híbrido de estruturas terciárias de proteínas
(Universidade Federal de Goiás, 2023-08-10) Almeida, Alexandre Barbosa de; Soares, Telma Woerle de Lima; http://lattes.cnpq.br/6296363436468330; Soares , Telma Woerle de Lima; Camilo Junior , Celso Gonoalves; Vieira, Flávio Henrique Teles; Delbem, Alexandre Cláudio Botazzo; Faccioli, Rodrigo Antônio
Proteins are organic molecules composed of chains of amino acids and play a variety of essential biological functions in the body. The native structure of a protein is the result of the folding process of its amino acids, with their spatial orientation primarily determined by two dihedral angles (φ, ψ). This work proposes a new hybrid method for predicting the tertiary structures of proteins called hyPROT, combining techniques of Multi-objective Evolutionary Algorithm optimization (MOEA), Molecular Dynamics, and Recurrent Neural Networks (RNNs). The proposed approach investigates the evolutionary profile of dihedral angles (φ, ψ) obtained by different MOEAs during the minimization process of the objective function by dominance and energy minimization by molecular dynamics. This proposal is unprecedented in the protein prediction literature. The premise under investigation is that the evolutionary profile of dihedrals may be concealing relevant patterns about folding mechanisms. To analyze the evolutionary profile of angles (φ, ψ), RNNs were used to abstract and generalize the specific biases of each MOEA. The selected MOEAs were NSGAII, BRKGA, and GDE3, and the objective function investigated combines the potential energy from non-covalent interactions and the solvation energy. The results obtained show that the hyPROT was able to reduce the RMSD value of the best prediction generated by the MOEAs individually by at least 33%. Predicting new series for dihedral angles allowed for the formation of histograms, indicating the formation of a possible statistical ensemble responsible for the distribution of dihedrals (φ, ψ) during the folding process
Avaliação da qualidade da sintetização de fala gerada por modelos de redes neurais profundas
(Universidade Federal de Goiás, 2023-05-26) Oliveira, Frederico Santos de; Soares, Anderson da Silva; http://lattes.cnpq.br/1096941114079527; Soares, Anderson da Silva; Aluisio, Sandra Maria; Duarte, Julio Cesar; Laureano, Gustavo Teodoro; Galvão Filho, Arlindo Rodrigues
With the emergence of intelligent personal assistants, the need for high-quality conversational interfaces has increased. While text-based chatbots are popular, the development of voice interfaces is equally important. However, the primary method for evaluating voice-based conversational models is mainly done through Mean Opinion Score (MOS), which relies on a manual and subjective process. In this context, this thesis aims to contribute with a new methodology for evaluating voice-based conversational interfaces, with a case study specifically conducted in Brazilian Portuguese. The proposed methodology includes an architecture for predicting the quality of synthesized speech in Brazilian Portuguese, correlated with MOS. To evaluate the proposed methodology, this work included training Text-to-Speech models to create the dataset called BRSpeechMOS. Details about the creation of this dataset are presented, along with a qualitative and quantitative analysis of it. A series of experiments were conducted to train various architectures using the BRSpeechMOS dataset. The architectures used are based on supervised and self-supervised learning. The results obtained confirm the hypothesis raised that pre-trained models on voice processing tasks such as speaker verification and automatic speech recognition produce suitable acoustic representations for the task of predicting speech quality, contributing to the advancement of the state of the art in the development of evaluation methodologies for conversational models.
Controle de admissão para network slicing considerando recursos de comunicação e computação
(Universidade Federal de Goiás, 2023-05-10) Lima, Henrique Valle de; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Corrêa, Sand Luz; http://lattes.cnpq.br/3386409577930822; Corrêa, Sand Luz; Cardoso, Kleber Vieira; Oliveira Júnior, Antônio Carlos de; Costa, Ronaldo Martins da; Both, Cristiano Bonato
The 5G networks have enabled the application of various innovative and disruptive technologies such as Network Function Virtualization (NFV) and Software-Defined Networking (SDN). Together, these technologies act as enablers of Network Slicing (NS), transforming the way networks are operated, managed, and monetized. Through the concept of Slice-as-a-Service (SlaaS), telecommunications operators can monetize the physical and logical infrastructure by offering network slices to new customers, such as vertical industries. This thesis addresses the problem of tenant admission control using NS. We propose three admission control models for NS (MONETS-OBD, MONETS-OBS, and CAONS) that consider both communication and computation resources. To evaluate the proposed models, we compare the results with different classical algorithms from the literature, such as eUCB, e-greedy, and ONETS. We use data from different applications to enrich the analysis. The results indicate that the MONETS-OBD, MONETS-OBS, and CAONS heuristics perform admission control that approaches the set of ideal solutions. We achieve high efficiency with the MONETS-OBD and MONETS-OBS heuristics in controlling tenant admission, reaching acceptance rates of up to 99% in some cases. Furthermore, the CAONS heuristic, which employs penalties, not only achieves acceptance and reward rates close to the optimal solution but also significantly reduces the number of capacity violations. Lastly, the results highlight that the process of slice admission control should consider both communication and computation resources, which are scarce at the network edge. A solution that considers only communication resources can lead to incorrect and unfeasible interpretations, overestimating the capacity of computation resources.
CLAT: arcabouço conceitual e ferramenta de apoio à avaliação da escrita inicial infantil por meio de dispositivos móveis
(Universidade Federal de Goiás, 2022-12-21) Mombach, Jaline Gonçalves; Soares, Fabrizzio Alphonsus Alves de Melo Nunes; http://lattes.cnpq.br/7206645857721831; Soares, Fabrizzio Alphonsus Alves de Melo Nunes; Ferreira, Deller James; Marques, Fátima de Lourdes dos Santos Nunes; Rodrigues, Kamila Rios da Hora; Rocha, Maria Alice de Sousa Carvalho
In childhood literacy, the assessment of initial writing is essential for monitoring learning and consequently planning more effective interventions by educators. However, during the Covid19 pandemic period, early spelling assessments were hampered since the digital tools available did not include some strategic signals, such as visualization of the child's tracing, the reading mode, and the genuine child's thinking about writing. Therefore, as a research problem, we investigate how mobile devices could support the remote child's spelling assessment. Thus, the central goal was to develop an interaction model for mobile devices to promote these writing assignments remotely. Thus, we adopted Design Science Research as a methodological approach. In the study of the problem stage, we conducted a systematic mapping study, a survey with professionals and parents, and we documented the usability requirements. Next, we proposed an artifact for educators to create digital assignments and another to capture the children's tracing and the mode they read. Finally, for validation, we performed concept tests to teachers, children, and a validation experiment in the school ecosystem, involving 92 children and six teachers. The results indicated that children were expressively interested in the resource and could interact satisfactorily on the digital artifact, validating the interaction modeling by registering their writing without significant difficulties. Furthermore, the teachers declared that it is possible to evaluate the children's spelling from the registers visualized on the digital artifact and emphasized the similarity between the interactions promoted by artifacts and the face-to-face environment. The findings of this study contribute to research on digital writing development and new educational resources. At the social level, the proposal also contributes directly to the maintenance of teaching in remote environments while also bringing new possibilities for face-to-face teaching and blended learning.

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