INF - Instituto de Informática

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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.
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.
Implementação de princípios de gamificação adaptativa em uma aplicação mHealth
(Universidade Federal de Goiás, 2023-08-25) Anjos, Filipe Maciel de Souza dos; Carvalho, Sergio Teixeira de; http://lattes.cnpq.br/2721053239592051; Carvalho, Sergio Teixeira de; Mata, Luciana Regina Ferreira da; Berretta, Luciana de Oliveira
This work describes the implementation of a gamified mHealth application called IUProst for the treatment of urinary incontinence through the performance of pelvic exercises for men who have undergone prostate removal surgery. The development of the application followed the guidelines of Framework L, designed to guide the creation of gamified mHealth applications. The initial version of IUProst was exclusively focused on the self-care dimension of Framework L and was released in November 2022. It was used by hundreds of users seeking the treatment provided by the application. Subsequently, the Gamification dimension of Framework L was employed to gamify IUProst. During the process of implementing game elements, it was noted that there were no clear definitions of how to implement the components to allow for gamification adaptation based on user profiles. To address this gap, an implementation model for gamification components was developed to guide developers in creating gamification that could adapt to the user profile dynamics proposed by the adaptive gamification of Framework L. Therefore, the contributions of this research include delivering a gamified mHealth application, analyzing usage data generated by the gamified application, and providing an implementation model for game components that were incorporated into Framework L, enabling the use of components in the context of adaptive gamification. The gamified version of IUProst was published in July 2023 and was used for 30 days until the writing of this dissertation. The results obtained demonstrate that during the gamified month, patients performed approximately 2/3 more exercises compared to the previous two months, reaching 61% of the total exercises performed during the three months analyzed. The data confirmed the hypothesis that game components indeed contribute to patient engagement with the application and also highlighted areas for improvement in the mHealth application.
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
Um catálogo de padrões de requisitos de privacidade baseado na lei geral de proteção de dados pessoais
(Universidade Federal de Goiás, 2024-03-18) Carneiro, Cinara Gomes de Melo; Kudo, Taciana Novo; http://lattes.cnpq.br/7044035224784132; Bulcão Neto, Renato de Freitas; http://lattes.cnpq.br/5627556088346425; Bulcão Neto , Renato de Freitas; Vincenzi , Auri Marcelo Rizzo; Alencar, Wanderley de Souza
[Context] Currently, Brazilian companies are concerned about protecting the personal data of their customers and employees to ensure the privacy of these individuals. This concern arises from the fact that personal data protection is an obligation imposed by the General Data Protection Law (LGPD). Since most organizations store this data digitally to carry out various operations, software must comply with the current legislation. [Problem] According to recent research, a large portion of professionals in the software industry do not have comprehensive knowledge of privacy requirements or the LGPD. [Objective] The objective of this work is to construct and evaluate a Catalog of Privacy Requirement Patterns (CPRP) based on the LGPD. [Method] A method for syntactic analysis of the articles composing the LGPD was defined to extract privacy requirements. These requirements were converted into requirement patterns (RP) using a method for constructing RP catalogs based on the grammar of the Software Pattern Metamodel (SoPaMM), with the support of the Terminal Model Editor (TMed) tool. Finally, two experts in LGPD and Software Engineering evaluated the completeness and correctness of the developed catalog concerning the legislation. [Contributions] The conversion of legal requirements into privacy RPs can assist professionals in eliciting and specifying requirements, as privacy requirements can be reused in various contexts with minor or any modifications.
Classificação de documentos da administração pública utilizando inteligência artificial
(Universidade Federal de Goiás, 2024-04-30) Carvalho, Rogerio Rodrigues; Costa, Ronaldo Martins da; http://lattes.cnpq.br/7080590204832262; Costa, Ronaldo Martins da; Souza, Rodrigo Gonçalves de; Silva, Nádia Félix Felipe da
Public organizations face difficulties in classifying and promoting transparency of the numerous documents produced during the execution of their activities. Correct classification of documents is critical to prevent public access to sensitive information and protect individuals and organizations from malicious use. This work proposes two approachs to perform the task of classifying sensitive documents, using state-of-the-art artificial intelligence techniques and best practices found in the literature: a conventional method, which uses artificial intelligence techniques and regular expressions to analyze the textual content of documents, and an alternative method, which employs the CBIR technique to classify documents when text extraction is not viable. Using real data from the Electronic Information System (SEI) of the Federal University of Goiás (UFG), the results achieved demonstrated that the application of regular expressions as a preliminary check can improve the computational efficiency of the classification process, despite showing a modest increase in classification precision. The conventional method proved to be effective in document classification, with the BERT model standing out for its performance with an accuracy rate of 94%. The alternative method, in turn, offered a viable solution for challenging scenarios, showing promising results with an accuracy rate of 87% in classifying public documents
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.
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.
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.
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
Detecção de posicionamento do cidadão em Projetos de Lei
(Universidade Federal de Goiás, 2024-03-22) Maia, Dyonnatan Ferreira; Silva, Nádia Félix Felipe da; http://lattes.cnpq.br/7864834001694765; Silva, Nádia Félix Felipe da; Pereira, Fabíola Souza Fernande; Fernandes, Deborah Silva Alves
Background: Comments on political projects on the internet reflect the aspirations of a significant portion of the population. The automatic stance detection of these comments regarding specific topics can help better understand public opinion. This study aims to develop a computational model with supervised learning capable of estimating the stance of comments on legislative propositions, considering the challenge of diversity and the constant emergence of new bills. Method: For the domain studied, a specific corpus was constructed by collecting comments from surveys available on the Chamber of Deputies website. The experiments included the evaluation of classic machine learning models, such as Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, and Multilayer Perceptron, in addition to the fine-tuning of BERT language models. Automatic data annotation was also performed using the zero-shot approach based on prompts from the generative GPT-3.5 model, aiming to overcome the difficulties related to human annotation and the scarcity of annotated data, generating approximately three times the size of the manually annotated corpus. Results: The results indicate that the adjusted BERTimbau model surpassed the classic approaches, achieving an average F1- score of 70.4% on unseen topics. Moreover, the application of automatically annotated data in the initial stage of BERTimbau fine-tuning resulted in performance improvement, reaching an F1-score of 73.3%. The results present deep learning models as options with positive performance for the task under the conditions of this domain. Conclusion: It was observed that the ability to generate contextualized representations, along with the number of topics and comments trained, can directly interfere with performance. This makes automatic annotation and the exploration of topic diversity with Transformer architectures, promising approaches for the task
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.
Um modelo de alocação de recursos de rede e cache na borda para vídeo em fluxo contínuo armazenado e 360º
(Universidade Federal de Goiás, 2024-09-04) Oliveira, Gustavo Dias de; Cardoso, Kleber Vieira; http://lattes.cnpq.br/0268732896111424; Correa, Sand Luz; http://lattes.cnpq.br/3386409577930822; Cardoso, Kleber Vieira; Cerqueira, Eduardo Coelho; Oliveira Júnior, Antonio Carlos de
The advancement of immersive technologies, such as Augmented Reality (AR) and Virtual Reality (VR), has introduced significant challenges in the transmission of 360-degree videos, due to the increasing bandwidth and low latency requirements resulting from the large size of video frames used in these technologies. At the same time, video streaming consumption has grown exponentially, driven by technological advances and the widespread use of Internet-connected devices. Efficient transmission of 360-degree videos faces challenges such as the need for up to five times more bandwidth than that required for conventional vídeo high-definition transmissions, as well as stricter latency constraints. Strategies such as video projection slicing and transmitting only the user’s field of view, along with efficient network resource allocation, have been explored to overcome these limitations. To address these challenges, we propose DTMCash, which stands out by using dynamic tiles and combining users’ viewports, effectively tackling transmission in multi-user scenarios. The goal of this work is to develop a model for network and edge cache resource allocation for 360-degree video transmission, focusing on the optimization of these resources. To validate the proposed model, we initially conducted comparative experiments with 6 users, later expanding to 30 users. We also tested performance with different cache sizes and experiments varying user entry times, in addition to evaluating the transmission of different video content. Compared to a state-of-the-art solution, our proposal reduced the aggregate bandwidth consumption of the Internet link by at least 48.2%, while maintaining the same consumption on the wireless link and providing greater efficiency in cache usage
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.
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
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.
Estudo comparativo de comitês de sub-redes neurais para o problema de aprender a ranquear
(Universidade Federal de Goiás, 2023-09-01) Ribeiro, Diogo de Freitas; Sousa, Daniel Xavier de; http://lattes.cnpq.br/4603724338719739; Rosa, Thierson Couto; http://lattes.cnpq.br/4414718560764818; Rosa, Thierson Couto; Sousa, Daniel Xavier de; Canuto, Sérgio Daniel Carvalho; Martins, Wellington Santos
Learning to Rank (L2R) is a sub-area of Information Retrieval that aims to use machine learning to optimize the positioning of the most relevant documents in the answer ranking to a specific query. Until recently, the LambdaMART method, which corresponds to an ensemble of regression trees, was considered state-of-the-art in L2R. However, the introduction of AllRank, a deep learning method that incorporates self-attention mechanisms, has overtaken LambdaMART as the most effective approach for L2R tasks. This study, at issued, explored the effectiveness and efficiency of sub-networks ensemble as a complementary method to an already excellent idea, which is the self-attention used in AllRank, thus establishing a new level of innovation and effectiveness in the field of ranking. Different methods for forming sub-networks ensemble, such as MultiSample Dropout, Multi-Sample Dropout (Training and Testing), BatchEnsemble and Masksembles, were implemented and tested on two standard data collections: MSLRWEB10K and YAHOO!. The results of the experiments indicated that some of these ensemble approaches, specifically Masksembles and BatchEnsemble, outperformed the original AllRank in metrics such as NDCG@1, NDCG@5 and NDCG@10, although they were more costly in terms of training and testing time. In conclusion, the research reveals that the application of sub-networks ensemble in L2R models is a promising strategy, especially in scenarios where latency time is not critical. Thus, this work not only advances the state of the art in L2R, but also opens up new possibilities for improvements in effectiveness and efficiency, inspiring future research into the use of sub-networks ensemble in L2R.
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.
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.
Secure D2Dcaching framework based on trust management and blockchain for mobile edge caching - a multi domain approach
(Universidade Federal de Goiás, 2023-08-18) Rocha, Acquila Santos; Pinheiro, Billy Anderson; http://lattes.cnpq.br/1882589984835011; Borges, Vinicius da Cunha Martins; http://lattes.cnpq.br/6904676677900593; Borges, Vinicius da Cunha Martins; Pinheiro, Billy Anderson; Cordeiro, Weverton Luis da Costa; Carvalho, Sérgio Teixeira de
Device–to-Device communication (D2D), combined with edge caching and mobile edge computing, is a promising approach that allows offloading data from the wireless mobile network. However, user security is still an open issue in D2D communication. Security vulnerabilities remain possible owing to easy, direct and spontaneous interactions between untrustworthy users and different degrees of mobility. This dissertation encompasses the designing of a multi-layer framework that combines diverse technologies inspired in blockchain to come up with a secure multi domain D2D caching framework. Regarding the intra-domain aspect we establish Secure D2D Caching framework inspired on trUst management and Blockchain (SeCDUB) to improve the security of D2D communication in video caching, through the combination of direct and indirect observations. In addition, blockchain concepts were adapted to the dynamic and restricted scenario of D2D networks to prevent data interception and alteration of indirect observations. This adaptation considered the development of a Clustering Approach (CA) that enables scalable and lightweight blockchain for D2D networks. Two different uncertainty mathematical models were used to infer direct and indirect trust values: Bayesian inference and the Theory Of Dempster Shafer (TDS) respectively. Regarding the inter-domain approach we developed Trust in Multiple Domains (TrustMD) framework. This approach combines edge trust storage with blockchain for distributed storage management in a multi layer architecture, designed to efficiently store trust control data in edge across different domains. Regarding the collected results, we performed simulations to test SecDUB’s intra-domain approach. The proposed clustering approach plays a key role to mitigate the SecDuB overhead as well as the consensus time. TrustMD results demonstrated a significant enhancement in goodput, reaching at best, 95% of the total network throughput, whicle SecDUB achieved approximately 80%. Even though there was a 7% increase in D2D overhead, TrustMD effectively keep control of latency levels, resulting in a slight decrease of 1.3 seconds. Hence, the achieved results indicates that TrustMD efficiently manages securitywithout compromising network performance reducing false negative rate up to 31% on the best case scenario. Actually, the combination of SecDUB and TrustMD offers a scalable and effective security solution that boosts network performance and ensures robust protection.

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