Doutorado em Ciência da Computação
URI Permanente para esta coleção
Nível: Doutorado
Ano de início:
Conceito atual na CAPES:
Ato normativo:
Periodicidade de seleção:
Área(s) de concentração:
Url do curso:
Navegar
Submissões Recentes
- ItemCrossing domains for accuracy: in-network stacking of machine learning classifiers(Universidade Federal do Espírito Santo, 2024-06-21) Xavier, Bruno Missi; Ruffini, Marco; https://orcid.org/0000-0001-6220-0065; Martinello, Magnos; https://orcid.org/0000-0002-8111-1719; Pacheco, André; Kirian, Mariam; Pasquini, Rafael; Aparicio, Albert CabellosTraffic management plays a crucial role for this expansive global connectivity. In this context, traffic classification strategically differentiates a range of applications and its requirements. This transformation enhances network agility and facilitates the direct integration of Machine Learning (ML) into the network infrastructure, fundamentally changing traffic management by promoting proactive data processing and analysis within the network. The synergy of Network Softwarization (NS) with ML not only leads to reduced latency and improved load management, but also increases the capacity to effectively handle larger volumes of data. Consequently, networks are better equipped to meet the complex demands of modern digital ecosystems, ensuring robust and efficient connectivity. This thesis delved into the domain of programmable networks, with a specific focus on implementing ML for traffic classification within the network architecture components, including the Radio Access Network (RAN) and programmable data planes. This approach represents a significant departure from the traditional traffic classification techniques, which are typically deployed on end-hosts. It also paves the way for integrating Cross Domain Artificial Intelligence (AI) capabilities within the network, facilitated by multi-view learning. More specifically, we advanced the state-of-the-art with four main contributions: (i) We design a framework named Early Attack Guarding and Learning Engine (EAGLE) as the first defense line against a set of cyber threats. Our framework explores Open Radio Access Network (O-RAN) to collect measurements from the air interface (that is, Physical and Medium Access Control) to identify and early mitigate malicious flows; (ii) We introduce MAP4 that demonstrates the feasibility of deploying ML models (that is, decision trees and Random Forest) in the data plane. To achieve this, we rely on the P4 language to deploy a pre-trained model to accurately classify flows at line rate; (iii) We proposed an In-Network Concept Drift to deal with the dynamic nature of the network traffic. This approach detects changes in traffic distribution by implementing Exponentially Weighted Moving Average (EWMA) overcoming the P4 limitations; (iv) Our Cross-Domain AI integrates multiple layers (RAN and programmable data planes) to form an in-network stacking of ML classifiers under the multi-view learning perspective. This innovative approach overcomes the challenges of a single layer in order to improve the overall accuracy of the classification system.
- ItemA planning pipeline for software systems of autonomous vehicles(Universidade Federal do Espírito Santo, 2023-09-04) Azevedo, Pedro Henrique Vieira de Oliveira; Gonçalves, Claudine Santos Baduê; https://orcid.org/0000-0003-1810-8581; http://lattes.cnpq.br/1359531672303446; http://lattes.cnpq.br/5958251435708396; Komati, Karin Satie; https://orcid.org/0000-0001-5677-4724; http://lattes.cnpq.br/9860697624155451; Santos, Thiago Oliveira dos; https://orcid.org/0000-0001-7607-635X; http://lattes.cnpq.br/5117339495064254; Boeres, Maria Claudia Silva; https://orcid.org/0000-0001-9801-2410; http://lattes.cnpq.br/0528154281423964; Wolf, Denis FernandoThe use of autonomous vehicles on public roads and in industrial environments has been growing in recent years and to achieve the higher level of autonomy, where no human intervention is needed, the vehicle must know the roads of the environment where it will operate, which allows it to travel from an initial location to a desired destination dealing with the uncertainties of the environments, such as static obstacles. In this work, we propose a planning pipeline for autonomous vehicle software systems. The pipeline is composed of an offline process and an online process. The offline process consists of two modules: (i) the Waypoints Editor, used to edit or even create entire new paths using an open-source vector graphics editor. (ii) The Multi-Level Road Network Generator constructs a representation of the environment in two levels of representation where the first one is used to guide the vehicle in the autonomous mission and the second one represents the paths and is used is route computation. The online process consists of three modules: (i) the Route Planner module to compute routes, (ii) the Off-Road Planner module to compute short paths in order to bring the car to the start of the route or to take the car from the end of the route to the final goal, and (iii) the Frenét Frames Path Planner to generate alternative paths to the right and left of the route and, in the presence of static obstacles, overtake them. We evaluated the performance of the proposed planning pipeline through simulations in three different real-world datasets using our Autonomous Vehicle Simulator module. Simulated experimental results showed that the proposed planning pipeline allowed the autonomous vehicle to know the environment as much as the user of the system wants and successfully execute missions from an initial position to a desired destination computing faster routes using the second level of the Multi-Level Road Network, dealing with static obstacles in the environment obeying the overtake safe distance restrictions imposed by the autonomous vehicle system.
- ItemUma plataforma para integração de dispositivos de saúde em sistemas de monitoramento remoto de pacientes(Universidade Federal do Espírito Santo, 2023-07-17) Celestrini, Jordano Ribeiro; Santos, Celso Alberto Saibel; https://orcid.org/0000000232875843; http://lattes.cnpq.br/7614206164174151; https://orcid.org/0000000327148593; http://lattes.cnpq.br/6705424425518474; Pimentel, Maria da Graça Campos; https://orcid.org/0000-0001-8264-5811; http://lattes.cnpq.br/4962820320879891; Gouvea, Sonia Alves; https://orcid.org/000000015180471X; http://lattes.cnpq.br/7268228122543743; Villaca, Rodolfo da Silva; https://orcid.org/0000000280513978; http://lattes.cnpq.br/3755692723547807; Aguilar, Paulo Armando CavalcanteThe increase in the number of people with a chronic health condition has led researchers from different areas to seek alternatives for monitoring these patients outside the hospital environment, in order to monitor them continuously and prevent the worsening of their health status. In this sense, one research topic rising is RPM (Remote Patient Monitoring). This approach allows monitoring the health condition of patients from a distance, helping to prevent new hospitalization episodes, and improving their quality of life, and the care administered by health providers. However, the currently available RPM solutions face some challenges, mainly in the integration of new devices. Usually, solutions are designed for a specific scenario and do not focus on problems related to the heterogeneity of devices available for health monitoring. A solution that provides means for the integration of new devices has the potential to collaborate in advancing the state of the art in research in this context. Therefore, this thesis proposes a solution to support the execution of RPM projects, composed of a reference architecture and its implementation, materialized as the HDash Remote Monitoring Platform. HDash implements the proposed conceptual architecture, providing means for new devices to be easily integrated in order to meet different application scenarios. To evaluate the approach, two case studies were conducted: the first monitoring chronic patients, followed for 20 months, and the second monitoring in remote locations, for 36 months. The studies allowed evaluation of several aspects of the proposed architecture and it is hoped, therefore, that this work can contribute to RPM and that new research in this context will benefit from the artifacts produced in this thesis.
- ItemDetecção automática de doenças em frutos do mamão a partir da análise de imagens por meio de redes neurais profundas(Universidade Federal do Espírito Santo, 2023-07-14) Moraes, Jairo Lucas de; Souza, Alberto Ferreira de; https://orcid.org/0000000315618447; http://lattes.cnpq.br/7573837292080522; https://orcid.org/0000-0002-5111-0811; http://lattes.cnpq.br/8743832227027911; França, Felipe Maia Galvão; https://orcid.org/0000-0002-8980-6208; http://lattes.cnpq.br/1097952760431187; Partelli, Fabio Luiz; https://orcid.org/0000000288300846; http://lattes.cnpq.br/6730543200776161; Komati, Karin Satie; https://orcid.org/0000-0001-5677-4724; http://lattes.cnpq.br/9860697624155451; Oliveira, Elias Silva de; http://lattes.cnpq.br/2210356035827181Horticulture plays an essential role in the economies of various countries, serving as a significant source of income and job creation, particularly in developing nations. Within this sector, papaya holds substantial importance, being cultivated in over 60 countries, including Brazil, which stands as the second-largest producer of this fruit. Papaya is a delicate and climacteric fruit, leading to considerable post-harvest losses, underscoring the pivotal role of early detection and accurate classification of fruit injuries in quality control and loss mitigation. Presently, papaya quality control is conducted manually, demanding exhaustive and repetitive efforts, often necessitating specialized knowledge that may not always be readily available to small-scale farmers or small fruit processing facilities. Given this backdrop, the implementation of autonomous or semi-autonomous system solutions aimed at assisting in papaya quality control, including disease detection and physical damage identification, is highly desirable. Such solutions could effectively mitigate industry losses, offering a more efficient, precise, and reliable approach to ensuring fruit quality and maximizing productivity in the sector. In this thesis, we propose a comprehensive solution spanning from the creation of an unprecedented dataset in the literature to the development of a mobile application. This includes the implementation of novel convolutional neural network (CNN) architectures utilizing the Convolutional Block Attention Module (CBAM). Our dataset comprises more than 23,000 examples of eight types of injuries (Anthracnose, Phytophthora, Chocolate Spot, Sticky Disease, Black Spot, Physiological Spot, Mechanical Damage, and Scar) affecting papaya fruits, alongside examples of healthy fruits. The developed detector achieves a new state-ofthe-art in papaya fruit disease detection, with an average precision (mAP) of 86.2%. This performance significantly surpasses that of human experts, who achieved an average precision of 67.3%. Lastly, we optimized the structure and weights of our detector for use on mobile devices and created a robust mobile application that can run on common smartphones. It can detect diseases in papaya fruits at a rate of up to 6 frames per second without requiring additional resources.
- ItemExames inteligentes: evidenciação estatística de perfis de aprendizagem, composição de banco de itens multidimensionais e personalização de avaliação(Universidade Federal do Espírito Santo, 2023-05-19) Silva, Wesley Pereira da; Oliveira, Elias Silva de; https://orcid.org/0000-0003-2066-7980; http://lattes.cnpq.br/2210356035827181; http://lattes.cnpq.br/8881034997521890; Baduê, Claudine Santos; https://orcid.org/0000-0003-1810-8581; http://lattes.cnpq.br/1359531672303446; Azevedo, Caio Lucidius Naberezny; https://orcid.org/0000-0001-9535-292X; http://lattes.cnpq.br/0856524274837137; Santos, Thiago Oliveira dos; https://orcid.org/0000-0001-7607-635X; http://lattes.cnpq.br/5117339495064254; Guzman, Jorge Luis BazanA common challenge to the areas of knowledge is the construction of teaching strategies that are sufficiently general to suit audiences with varied learning profiles. Usually, the teacher defines his teaching plan according to learning objectives, which are evaluated through the expression of latent traits that denote proficiency on the part of the subject being examined. Thus, intelligent techniques and technological tools are opportune to contribute to increasing the quality of teaching and reducing the teaching effort in the execution of complex activities such as, for example: formulation of assessment items, application of tests and provision of feedback to students. The formal rigor in the creation of instruments for assessment, tabulation and calculation of grades is a key factor to avoid bias in conducting the assessment of learning and estimating the ability of students. Student performance is the first dimension to be considered in the assessment. The grouping of similar performances allows characterizing groups that represent learning profiles. Self-assessment and peer assessment are techniques to stimulate the student’s self-criticism in relation to himself and his classmates, seeking to discourage evaluative biases by encouraging the examinee’s coherence when exercising the role of evaluator. The logistic models derived from Psychometrics allow the quantitative characterization of the evaluation items, allowing the measurement of qualitative aspects, such as: difficulty, discrimination and propensity to kick. With psychometric models, the probability of success of the subject can be predicted when being evaluated with a certain item. Finally, the use of Natural Language Processing provides the selection of items by content similarity with a search expression, which represents a subject to be retrieved in a set of the test items bank. In this way, we seek to propose a method of creating individualized assessment trail, composed of a sequence of activities in a certain order appropriate to the ability of the examinee. Thus, we present an intelligent computerized adaptive test approach, whose execution configuration is adjustable to qualitative, quantitative and/or content teaching strategies related to pre-defined terms. The contribution envisaged with such a proposal is to extrapolate a two-dimensional parameter space of the evaluations, composed of the examinee’s performances and scores achieved by item; for a multidimensional space that considers the characteristics of the items in psychometric and semantic terms, as well as the characteristics of the examinees and historical data of subjects with similar trajectories.