The increasing variety of data mining tools offers a large palette of types and representation formats for predictive models. Managing the models then becomes a big challenge, as well as reusing the models and keeping the consistency of model and data repositories. Sustainable access and quality assessment of these models become limited to researchers. The approach for the Data and Model Governance (DMG) makes easier to process and support complex solutions. In this thesis, contributions are proposed towards ensembles of models with a focus on model representation, comparison and usage. Predictive Toxicology was chosen as an application field to demonstrate the proposed approach to represent predictive models linked to data for DMG. Further analysing methods such as predictive models comparison and predictive models combination for reusing the models from a collection of models were studied. Thus in this thesis, an original structure of the pool of models was proposed to represent predictive toxicology models called Predictive Toxicology Markup Language (PTML). PTML offers a representation scheme for predictive toxicology data and models generated by data mining tools. In this research, the proposed representation offers possibilities to compare models and select the relevant models based on different performance measures using proposed similarity measuring techniques. The relevant models were selected using a proposed cost function which is a composite of performance measures such as Accuracy (Acc), False Negative Rate (FNR) and False Positive Rate (FPR). The cost function will ensure that only quality models be selected as the candidate models for an ensemble. The proposed algorithm for optimisation and combination of Acc, FNR and FPR of ensemble models using double fault measure as the diversity measure improves Acc between 0.01 to 0.30 for all toxicology data sets compared to other ensemble methods such as Bagging, Stacking, Bayes and Boosting. The highest improvements for Acc were for data sets Bee (0.30), Oral Quail (0.13) and Daphnia (0.10). A small improvement (of about 0.01) in Acc was achieved for Dietary Quail and Trout. Important results by combining all the three performance measures are also related to reducing the distance between FNR and FPR for Bee, Daphnia, Oral Quail and Trout data sets for about 0.17 to 0.28. For Dietary Quail data set the improvement was about 0.01 though, but this data set is well known as a difficult learning exercise. For five UCI data sets tested, similar results were achieved with Acc improvement between 0.10 to 0.11, closing more the gaps between FNR and FPR. As a conclusion, the results show that by combining performance measures (Acc, FNR and FPR), as proposed within this thesis, the Acc increased and the distance between FNR and FPR decreased.

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Note: Sheet music available upon request.

Savoir si un ensemble est de capacité nulle ou connaître sa dimension capacitaire est une question importante. De nombreux articles (tels que [3], [5], [6]) ont élucidé la question dans le cas de certains ensembles de Cantor. Les K-sets sont des ensembles de R. En particulier, les ensembles de Cantor les plus réguliers, pour lesquels on connaît une condition simple de capacité nulle, sont des K-sets. Ce mémoire a pour but de montrer l'efficacité d'une méthode dans le cadre des ensembles de Cantor et ses limites dans le cadre des K-sets. Il est principalement inspiré de l'article [8].

Les problèmes logiciels sont frustrants et diminuent l’expérience utilisateur. Par exemple, la fuite de données bancaires, la publication de vidéos ou de photos compromettantes peuvent affecter gravement une vie. Comment éviter de telles situations ? Utiliser des tests est une bonne stratégie, mais certains bogues persistent. Une autre solution est d’utiliser des méthodes plus mathématiques, aussi appelées méthodes formelles. Parmi celles-ci se trouve la sémantique dénotationnelle. Elle met la sémantique extraite de vos logiciels préférés en correspondance avec des objets mathématiques. Sur ceux-ci, des propriétés peuvent être vérifiées. Par exemple, il est possible de déterminer, sous certaines conditions, si votre logiciel donnera une réponse. Pour répondre à ce besoin, il est nécessaire de s’intéresser à des théories mathématiques suffisamment riches. Parmi les candidates se trouvent le sujet de ce mémoire : la théorie des domaines. Elle offre des objets permettant de modéliser formellement les données et les instructions à l’aide de relations d’ordre. Cet écrit présente les concepts fondamentaux tout en se voulant simple à lire et didactique. Il offre aussi une base solide pour des lectures plus poussées et contient tout le matériel nécessaire à sa lecture, notamment les preuves des énoncés présentés. / Bugs in programs are definitively annoying and have a negative impact on the user experience. For example, leaks of bank data or leaks of compromising videos or photos have a serious effect on someone’s life. How can we prevent these situations from happening? We can do tests, but many bugs may persist. Another way is to use mathematics, namely formal methods. Among them, there is denotational semantics. It links the semantics of your favorite program to mathematical objects. On the latter, we can verify properties, e.g., absence of bugs. Hence, we need a rich theory in which we can express the denotational semantics of programs. Domain Theory is a good candidate and is the main subject of this master thesis. It provides mathematical objects for data and instructions based on order relations. This thesis presents fundamental concepts in a simple and pedagogical way. It is a solid basis for advanced readings as well as containing all the needed knowledge for its reading, notably proofs for all presented statements.

Em diversos domínios, além das informações sobre os objetos ou entidades que os compõem, existem, também, informaçõoes a respeito das relações entre esses objetos. Alguns desses domínios são, por exemplo, as redes de co-autoria, e as páginas Web. Nesse sentido, é natural procurar por técnicas de classificação que levem em conta estas informações. Dentre essas técnicas estão as denominadas classificação baseada em grafos, que visam classificar os exemplos levando em conta as relações existentes entre eles. Este trabalho aborda o desenvolvimento de métodos para melhorar o desempenho de classificadores baseados em grafos utilizando estratégias de ensembles. Um classificador ensemble considera um conjunto de classificadores cujas predições individuais são combinadas de alguma forma. Este classificador normalmente apresenta um melhor desempenho do que seus classificadores individualmente. Assim, foram desenvolvidas três técnicas: a primeira para dados originalmente no formato proposicional e transformados para formato relacional baseado em grafo e a segunda e terceira para dados originalmente já no formato de grafo. A primeira técnica, inspirada no algoritmo de boosting, originou o algoritmo KNN Adaptativo Baseado em Grafos (A-KNN). A segunda ténica, inspirada no algoritmo de Bagging originou trê abordagens de Bagging Baseado em Grafos (BG). Finalmente, a terceira técnica, inspirada no algoritmo de Cross-Validated Committees, originou o Cross-Validated Committees Baseado em Grafos (CVCG). Os experimentos foram realizados em 38 conjuntos de dados, sendo 22 conjuntos proposicionais e 16 conjuntos no formato relacional. Na avaliação foi utilizado o esquema de 10-fold stratified cross-validation e para determinar diferenças estatísticas entre classificadores foi utilizado o método proposto por Demsar (2006). Em relação aos resultados, as três técnicas melhoraram ou mantiveram o desempenho dos classificadores bases. Concluindo, ensembles aplicados em classificadores baseados em grafos apresentam bons resultados no desempenho destes / In many fields, besides information about the objects or entities that compose them, there is also information about the relationships between objects. Some of these fields are, for example, co-authorship networks and Web pages. Therefore, it is natural to search for classification techniques that take into account this information. Among these techniques are the so-called graphbased classification, which seek to classify examples taking into account the relationships between them. This paper presents the development of methods to improve the performance of graph-based classifiers by using strategies of ensembles. An ensemble classifier considers a set of classifiers whose individual predictions are combined in some way. This combined classifier usually performs better than its individual classifiers. Three techniques have been developed: the first applied for originally propositional data transformed to relational format based on graphs and the second and the third applied for data originally in graph format. The first technique, inspired by the boosting algorithm originated the Adaptive Graph-Based K-Nearest Neighbor (A-KNN). The second technique, inspired by the bagging algorithm led to three approaches of Graph-Based Bagging (BG). Finally the third technique, inspired by the Cross- Validated Committees algorithm led to the Graph-Based Cross-Validated Committees (CVCG). The experiments were performed on 38 data sets, 22 datasets in propositional format and 16 in relational format. Evaluation was performed using the scheme of 10-fold stratified cross-validation and to determine statistical differences between the classifiers it was used the method proposed by Demsar (2006). Regarding the results, these three techniques improved or at least maintain the performance of the base classifiers. In conclusion, ensembles applied to graph-based classifiers have good results in the performance of them

Nesta tese de doutorado apresentamos um estudo sobre o comportamento de diversos sistemas irrevers?veis, caracterizados pela existencia de estados absorventes, atraves de abordagens distintas. Utilizamos aproximacoes de campo medio dinamico, simulacoes numericas usuais, mudanca de ensemble e expanso em serie. Alem disso, mostramos numa parte deste trabalho que a abordagem proposta para o estudo de sistemas irrevers?veis no ensemble em que o numero de part?culas e constante tambem pode ser estendida para sistemas em equil´?brio termodinamico, descrito pela distribuicao de probabilidades de Gibbs. Finalmente mostramos problemas em aberto para trabalhos futuros. / In this PHD thesis, we have presented a study about several nonequilibrium systems with absorbing states by means of different approaches, such as mean-field analysis, usual numerical simulations, analysis in another ensemble and perturbative series expansions. In a specific part of this thesis, we have shown that the approach proposed here for describing nonequilibrium systems in the constant particle number ensemble can also be used to caracterize equilibrium systems, described by Gibbs probability distribution. Finally, we have shown open problems for future researchs.

Fading Points is a piece of music for large wind ensemble. The work is concerned with time perception and is designed to convey to the listener a long temporal progression from slow music to fast music. To this end, the work is written using musical gestures specifically designed for their portrayal of musical time. The work consists of four sections, each of which is described in detail in the analytical part of the thesis. The analysis also describes the rhythmic language, which is created around short rhythmic cells. These cells are based on ratios and are used in all levels of the piece from the surface rhythm to the large formal divisions. The harmonic language is also described. The vertical harmony is derived from a dense chord presented at the beginning of the piece. The horizontal pitch material is created from small pitch cells, also taken from the opening chord. These cells are used in isolation but are also combined to create modes, which are the focus of the latter parts of the piece.