O jazz e a música do mediterrâneo: modos e ritmos de influência árabe num contexto de escrita para Large Ensemble

Dissertação para obtenção do grau de Mestre em Música - Interpretação Artística

Robot visual localization through local feature fusion: an evaluation of multiple classifiers combination approaches

In the last decade, local image features have been widely used in robot visual localization. In order to assess image similarity, a strategy exploiting these features compares raw descriptors extracted from the current image with those in the models of places. This paper addresses the ensuing step in this process, where a combining function must be used to aggregate results and assign each place a score. Casting the problem in the multiple classifier systems framework, in this paper we compare several candidate combiners with respect to their performance in the visual localization task. For this evaluation, we selected the most popular methods in the class of non-trained combiners, namely the sum rule and product rule. A deeper insight into the potential of these combiners is provided through a discriminativity analysis involving the algebraic rules and two extensions of these methods: the threshold, as well as the weighted modifications. In addition, a voting method, previously used in robot visual localization, is assessed. Furthermore, we address the process of constructing a model of the environment by describing how the model granularity impacts upon performance. All combiners are tested on a visual localization task, carried out on a public dataset. It is experimentally demonstrated that the sum rule extensions globally achieve the best performance, confirming the general agreement on the robustness of this rule in other classification problems. The voting method, whilst competitive with the product rule in its standard form, is shown to be outperformed by its modified versions.

Ensemble: an innovative approach to practice computer programming

Currently, the teaching-learning process in domains, such as computer programming, is characterized by an extensive curricula and a high enrolment of students. This poses a great workload for faculty and teaching assistants responsible for the creation, delivery, and assessment of student exercises. The main goal of this chapter is to foster practice-based learning in complex domains. This objective is attained with an e-learning framework—called Ensemble—as a conceptual tool to organize and facilitate technical interoperability among services. The Ensemble framework is used on a specific domain: computer programming. Content issues are tacked with a standard format to describe programming exercises as learning objects. Communication is achieved with the extension of existing specifications for the interoperation with several systems typically found in an e-learning environment. In order to evaluate the acceptability of the proposed solution, an Ensemble instance was validated on a classroom experiment with encouraging results.

Discriminative classifiers for speaker recognition

Speaker Recognition, Speaker Verification, Sparse Kernel Logistic Regression, Support Vector Machine

Machine learning with Lipschitz classifiers

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2010

Pratiquer la religion ensemble : analyse des paroisses et communautés religieuses en Suisse dans une perspective de sociologie des organisations

RESUMÉ DE LA THÈSE EN FRANÇAIS La présente recherche se veut être un examen de la première enquête quantitative menée en Suisse sur les paroisses et communautés religieuses. La recherche vise de à appréhender la dynamique institutionnelle du champ religieux de ce pays. En relation avec une enquête similaire menée aux États-Unis (National Congregations Study, Chaves, 2004) la présente recherche analyse les données récoltées auprès d'un échantillon représentatif de plus de mille responsables spirituels des communautés religieuses de Suisse. Dans la perspective de la sociologie des organisations, elle examine le positionnement des communautés dans le champ institutionnel pour comprendre comment elles s'activent pour se maintenir dans la durée. Les communautés, pour assurer leurs services sur le long terme, sont imbriquées dans des structures confessionnelles avec des contraintes administratives diverses selon leur reconnaissance légale. En conséquence, la dynamique du champ religieux institutionnel est différenciée en trois environnements, selon leur degré de reconnaissance, qui demandent des réponses particulières à chacun pour pouvoir s'adapter et perdurer. Ces trois environnements poussent les groupes qui s'y logent à adopter des structures identiques. Pratiquer la religion ensemble, c'est ainsi se rendre dans une communauté avec une forme de rituel et d'engagement des membres correspondant à la reconnaissance du groupe par la société. Même pratiquée fortuitement, la religion collective est loin d'être un acte fortuit. RESUMÉ DE LA THÈSE EN ANGLAIS Practice the religion together Analysis of parishes and religious congregations in Switzerland in a perspective of sociology of organization This research is intended as a review of the first quantitative survey conducted in Switzerland on parishes and religious communities. The research aims to understand the dynamics of institutional religious field in this country. In connection with a similar survey conducted in the U.S. (National Congregations Study, Chaves, 2004) this research examines data gathered from a representative sample of over a thousand spiritual leaders of religious communities in Switzerland. From the perspective of sociology of organization, it examines the position of communities in the institutional field to understand how they are activated to maintain over time. Communities to ensure their services over the long term, are nested within denominational structures with different administrative constraints according to their legal recognition. Consequently, the dynamics of the religious field is differentiated into three institutional environments according to their degree of recognition, which require specific responses to each in order to adapt and endure. These three environments grow groups staying there to adopt identical structures.

Textural classification of land cover using support vector machines: an empirical comparison with parametric, non parametric and hybrid classifiers in the Bolivian Amazon

Land cover classification is a key research field in remote sensing and land change science as thematic maps derived from remotely sensed data have become the basis for analyzing many socio-ecological issues. However, land cover classification remains a difficult task and it is especially challenging in heterogeneous tropical landscapes where nonetheless such maps are of great importance. The present study aims to establish an efficient classification approach to accurately map all broad land cover classes in a large, heterogeneous tropical area of Bolivia, as a basis for further studies (e.g., land cover-land use change). Specifically, we compare the performance of parametric (maximum likelihood), non-parametric (k-nearest neighbour and four different support vector machines - SVM), and hybrid classifiers, using both hard and soft (fuzzy) accuracy assessments. In addition, we test whether the inclusion of a textural index (homogeneity) in the classifications improves their performance. We classified Landsat imagery for two dates corresponding to dry and wet seasons and found that non-parametric, and particularly SVM classifiers, outperformed both parametric and hybrid classifiers. We also found that the use of the homogeneity index along with reflectance bands significantly increased the overall accuracy of all the classifications, but particularly of SVM algorithms. We observed that improvements in producer’s and user’s accuracies through the inclusion of the homogeneity index were different depending on land cover classes. Earlygrowth/degraded forests, pastures, grasslands and savanna were the classes most improved, especially with the SVM radial basis function and SVM sigmoid classifiers, though with both classifiers all land cover classes were mapped with producer’s and user’s accuracies of around 90%. Our approach seems very well suited to accurately map land cover in tropical regions, thus having the potential to contribute to conservation initiatives, climate change mitigation schemes such as REDD+, and rural development policies.

Active Learning of Very-High Resolution Optical Imagery with SVM: Entropy vs Margin Sampling

An active learning method is proposed for the semi-automatic selection of training sets in remote sensing image classification. The method adds iteratively to the current training set the unlabeled pixels for which the prediction of an ensemble of classifiers based on bagged training sets show maximum entropy. This way, the algorithm selects the pixels that are the most uncertain and that will improve the model if added in the training set. The user is asked to label such pixels at each iteration. Experiments using support vector machines (SVM) on an 8 classes QuickBird image show the excellent performances of the methods, that equals accuracies of both a model trained with ten times more pixels and a model whose training set has been built using a state-of-the-art SVM specific active learning method