Damped Lyman α systems (DLAs) are useful probes of the chemical enrichment of the universe as they provide accurate abundance measurements of many chemical species. Using a sample of 30 DLAs (with large metal column densities) observed with the High Resolution Echelle Spectrometer on the Keck I telescope, the abundances of several elements (i.e. iron, zinc, chromium, silicon, sulphur, phosphorus, manganese, and boron) are derived and presented. A comparison is drawn between the abundances from these metal-rich DLAs with literature samples encompassing the largest compilation of high resolution observations of other DLAs, and stars from the Milky Way and its satellite galaxies to understand the astrophysical nature of DLAs. Furthermore, the first ever extragalactic study of boron is presented. Using the sample of 30 metal-rich DLAs, two 3σ detections and one near detection 2.97σ) were found. From the comparison of [B/O] and, for the first time, [B/S], with studies in the Milky Way, there appears to be an excess of boron relative to its parent nucleus (oxygen) in these three DLA systems, suggesting that there may be a higher cosmic ray flux in DLAs than in the Milky Way. / Graduate / 0606

Damped Lyman α systems (DLAs) are useful probes of the chemical enrichment of the universe as they provide accurate abundance measurements of many chemical species. Using a sample of 30 DLAs (with large metal column densities) observed with the High Resolution Echelle Spectrometer on the Keck I telescope, the abundances of several elements (i.e. iron, zinc, chromium, silicon, sulphur, phosphorus, manganese, and boron) are derived and presented. A comparison is drawn between the abundances from these metal-rich DLAs with literature samples encompassing the largest compilation of high resolution observations of other DLAs, and stars from the Milky Way and its satellite galaxies to understand the astrophysical nature of DLAs. Furthermore, the first ever extragalactic study of boron is presented. Using the sample of 30 metal-rich DLAs, two 3σ detections and one near detection 2.97σ) were found. From the comparison of [B/O] and, for the first time, [B/S], with studies in the Milky Way, there appears to be an excess of boron relative to its parent nucleus (oxygen) in these three DLA systems, suggesting that there may be a higher cosmic ray flux in DLAs than in the Milky Way. / Graduate / 0606

Les systèmes collaboratifs sont aujourd’hui devenus très populaires et sont de plus en plus utilisés dans de nombreux domaines divers. De fait, les interactions de confiance des differents systèmes sont devenus une priorité. La confiance, en tant que concept informatique, a été étudiée très récemment. Cependant, dans la littérature, très peu d’attention a été portée pour évaluer l’exactitude des interactions entre entités communicantes; même si la plupart des approches se sont basées sur les mesures cumulées de ces valeurs. Pour déterminer, de façon générale, l’exactitude de ces interactions, une approche nommée Monitorage des Réseaux En-Ligne et Distribué (MRED) a été proposée. De plus, des outils prototypes ont été développés pour tester automatiquement les propriétés de confiance entre entités dans des systèmes communicants. MRED est une forme de test passif; elle analyse les réponses des systèmes et teste l’exactitude des interactions en utilisant des traces de réseaux. Comme elle dépend des propriétés à tester, une nouvelle approche a été proposée pour faire l’extraction automatique de propriétés pertinentes que l’ont pourrait, in fine, tester dans un système sous test. Notre approche repose sur le fonctionnement des systèmes de monitorage online. Nous proposons de nouvelles méthodes afin d’améliorer les techniques fournies dans l’état de l’art pour: a) évaluer efficacement les propriétés avec une complexité en temps O($n$), ce en utilisant un Automate Fini Déterministe Prolongée (AFDP); et b) élargir l’expressivité du langage proposé pour exprimer correctement les contraintes systèmes, comme les délais d’attente pour éviter le manque de ressources. Finalement, nous proposons un nouveau cadre flexible utilisable dans de très nombreux domaines, qui permet la définition de caractéristiques de confiance afin d’évaluer les entités dans des contextes différents. De surcroît, avec les évaluations des caractéristiques de confiance, nous proposons un modèle de confiance basé sur l’apprentissage automatique, en résolvant spécifiquement un problème de classification multi-classes et utilisant des Machine à vecteurs de support (SVM). A partir de ces modèles, des expérimentations ont été effectuées en simulant des caractéristiques de confiance pour estimer le niveau de confiance; une précision de plus de 96% a été obtenue / Collaborative systems are growing in use and in popularity. The need to boost the methods concerning the interoperability is growing as well; therefore, trustworthy interactions of the different systems are a priority. Trust as a computer science concept has been studied in the recent years. Nevertheless, in the literature, very little focus is placed on how to assess the correctness of the interactions between the entities; even if most approaches rely on the estimation of trust based on the accumulated measures of these values. To broadly determine the correctness of interactions without targeting a specific domain or application, an approach using Distributed On-line Network Monitoring (DONM) was proposed. Furthermore, a prototype tool-set was developed to automatically test the trust properties. DONM is a form passive testing; it analyzes systems' responses and test the correctness of the interactions via network traces. Since it relies on the stated properties to test, a novel approach was proposed to automatically extract relevant properties to test. Our approach deeply relies on the operation of On-line Monitoring Systems. That is the reason why we propose new methods to enhance the state of the art techniques to: a) efficiently evaluate properties in O(n) time complexity using an Extended Finite State Automata (EFSA) auxiliary data structure; and b) to expand the language expressiveness to properly express the constraints of such systems, such as, timeouts in order to avoid resource starvation. Finally, using the evaluation of the entities' interactions provided by our approach, trust management engines will help trustors to decide with whom and how to interact with other users or applications. We propose a new framework that is flexible for any domain, allowing trustors to define the trust features used to evaluate trustees in different contexts. Furthermore, with the evaluations of the trust features, we propose a trust model which achieves close-to-human inference of the trust assessment, by using a machine learning based trust model, namely solving a multi-class classification problem using Support Vector Machines (SVM). Using the SVM-based trust model, experiments were performed with simulated trust features to estimate trust level; an accuracy of more than 96% was achieved

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