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Made available in DSpace on 2015-03-05T14:01:20Z (GMT). No. of bitstreams: 0 Previous issue date: 23 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho aborda o problema da otimização relacionado ao ciclo de operação de uma insersora automática de componentes (pick-and-place) em máquinas do tipo multi-head. O objetivo consiste em desenvolver uma técnica computacional capaz de encontrar uma boa solução para o problema de otimização, associado ao processo de inserção de componentes. A otimização consiste em resolver de forma conjunta, os Problemas de Escalonamento das Ferramentas, de Escalonamento da Seqüência de Inserção de Componentes e por fim, o Problema da Alocação dos Componentes no Alimentador, visando a redução do tempo total de operação. Como resultados, a técnica computacional permite elevar a produtividade das máquinas onde a mesma é aplicada. Neste trabalho, foi desenvolvida uma aplicação híbrida das metaheurísticas Busca Tabu e Algoritmos Genéticos. Através do uso de uma abordagem diferenciada e utilizando metaheurísticas contemporâneas, bons resultados são apresentados ao longo do trabalho. / This work approaches the optimization Problem related to the operation cycle in an automatic multi-head SMT placement machine (pick-and-place). The objective of this work is developing a computational technique capable to find a good solution for the optimization problem associated with the component insertion process. The optimization process was planned to solve in a joint way the Tools Scheduling Problem, Schedule of Component Insertion Sequence Problem and finally the Component Feeder Allocation Problem. The main objective is to reduce the total operation time. As a result the computational technique can increase the productivity of the machines where it is applied. This study developed a hybrid application of Tabu Search and Genetic Algorithms. The use of a differentiated approach combined with modern metaheuristics, good results are presented in the work.

Background: Phishing attacks continue to pose a significant threat to internetsecurity. One of the most common forms of phishing is through URLs, whereattackers disguise malicious URLs as legitimate ones to trick users into clickingon them. Machine learning techniques have shown promise in detecting phishingURLs, but their effectiveness can vary depending on the approach used.Objectives: The objective of this research is to propose an ensemble of twomachine learning techniques, Convolutional Neural Networks (CNN) and MultiHead Self-Attention (MHSA), for detecting phishing URLs. The goal is toevaluate and compare the effectiveness of this approach against other methodsand models.Methods: a dataset of URLs was collected and labeled as either phishing orlegitimate. The performance of several models using different machine learningtechniques, including CNN and MHSA, to classify these URLs was evaluatedusing various metrics, such as accuracy, precision, recall, and F1-score.Results: The results show that the ensemble of CNN and MHSA outperformsother individual models and achieves an accuracy of 98.3%. Which comparing tothe existing state-of-the-art techniques provides significant improvements indetecting phishing URLs.Conclusions: In conclusion, the ensemble of CNN and MHSA is an effectiveapproach for detecting phishing URLs. The method outperforms existing state-ofthe-art techniques, providing a more accurate and reliable method for detectingphishing URLs. The results of this study demonstrate the potential of ensemblemethods in improving the accuracy and reliability of machine learning-basedphishing URL detection.

Gaze estimation, a vital component in numerous applications such as humancomputer interaction, virtual reality, and driver monitoring systems, is the process of predicting the direction of an individual’s gaze. The predominant methods for gaze estimation can be broadly classified into intrusive and nonintrusive approaches. Intrusive methods necessitate the use of specialized hardware, such as eye trackers, while non-intrusive methods leverage images or recordings obtained from cameras to make gaze predictions. This thesis concentrates on appearance-based gaze estimation, specifically within the non-intrusive domain, employing various deep learning models. The primary focus of this study is to compare the efficacy of Vision Transformers (ViTs), a recently introduced architecture, with Convolutional Neural Networks (CNNs) for gaze estimation on RGB images. Performance evaluations of the models are conducted based on metrics such as the angular gaze error, stimulus distance error, and model size. Within the realm of ViTs, two variants are explored: pure ViTs and hybrid ViTs, which combine both CNN and ViT architectures. Throughout the project, both variants are examined in different sizes. Experimental results demonstrate that all pure ViTs underperform in comparison to the baseline ResNet-18 model. However, the hybrid ViT consistently emerges as the best-performing model across all evaluation datasets. Nonetheless, the discussion regarding whether to deploy the hybrid ViT or stick with the baseline model remains unresolved. This uncertainty arises because utilizing an exceedingly large and slow model, albeit highly accurate, may not be the optimal solution. Hence, the selection of an appropriate model may vary depending on the specific use case. / Ögonblicksbedömning, en avgörande komponent inom flera tillämpningar såsom människa-datorinteraktion, virtuell verklighet och övervakningssystem för förare, är processen att förutsäga riktningen för en individs blick. De dominerande metoderna för ögonblicksbedömning kan i stort sett indelas i påträngande och icke-påträngande tillvägagångssätt. Påträngande metoder kräver användning av specialiserad hårdvara, såsom ögonspårare, medan ickepåträngande metoder utnyttjar bilder eller inspelningar som erhållits från kameror för att göra bedömningar av blicken. Denna avhandling fokuserar på utseendebaserad ögonblicksbedömning, specifikt inom det icke-påträngande området, genom att använda olika djupinlärningsmodeller. Studiens huvudsakliga fokus är att jämföra effektiviteten hos Vision Transformers (ViTs), en nyligen introducerad arkitektur, med Convolutional Neural Networks (CNNs) för ögonblicksbedömning på RGB-bilder. Prestandautvärderingar av modellerna utförs baserat på metriker som den vinkelmässiga felbedömningen av blicken, felbedömning av stimulusavstånd och modellstorlek. Inom ViTs-området utforskas två varianter: rena ViTs och hybrid-ViT, som kombinerar både CNN- och ViT-arkitekturer. Under projektet undersöks båda varianterna i olika storlekar. Experimentella resultat visar att alla rena ViTs presterar sämre jämfört med basmodellen ResNet-18. Hybrid-ViT framstår dock konsekvent som den bäst presterande modellen över alla utvärderingsdatauppsättningar. Diskussionen om huruvida hybrid-ViT ska användas eller om man ska hålla sig till basmodellen förblir dock olöst. Denna osäkerhet uppstår eftersom användning av en extremt stor och långsam modell, även om den är mycket exakt, kanske inte är den optimala lösningen. Valet av en lämplig modell kan därför variera beroende på det specifika användningsområdet.

Dans cette thèse, on s'intéresse aux automates cellulaires en tant que modèle de calcul permettant de reconnaître des langages. Dans un tel domaine, il est toujours difficile d'établir des résultats négatifs, typiquement de prouver qu'un langage donné n'est pas reconnu en une certaine fonction de temps par une certaine classe d'automates. On se focalisera en particulier sur les classes de faible complexité comme le temps réel, au sujet desquelles de nombreuses questions restent ouvertes.Dans une première partie, on propose plusieurs manières d'affaiblir encore les classes de langages étudiées, permettant ainsi d'obtenir des exemples de résultats négatifs. Dans une seconde partie, on montre un théorème d'accélération par automate cellulaire d'un modèle séquentiel, les automates finis oublieux. Ce modèle est une version a priori affaiblie, mais non triviale, des automates finis à plusieurs têtes de lecture. / This document deals with cellular automata as a model of computation used to recognise languages. In such a domain, it is always difficult to provide negative results, that is, typically, to prove that a given language is not recognised in some function of time by some class of automata. The document focuses in particular on the low-complexity classes such as real time, about which a lot of questions remain open since several decades.In a first part, several techniques to weaken further still these classes of languages are investigated, thereby bringing examples of negative results. A second part is dedicated to the comparison of cellular automata with another model language recognition, namely multi-head finite automata. This leads to speed-up theorem when finite automata are oblivious, which makes them a priori weaker than in the general case but leaves them a nontrivial power.