Ma thèse comporte deux grandes parties, la première en biophysique et la seconde en science de l’éducation. La première partie présente des travaux à la frontière entre neurobiologie et microfluidique. Le but de ces travaux est de pouvoir reconstruire et étudier des réseaux complexes de neurones in vitro avec une topologie de connections synaptiques bien contrôlées. Une série de micro-structures mécanique et/ou chimique ont été étudiées pour leur capacité à (i) positionner les corps cellulaires des neurones, (ii) orienter la pousse des neurites, et (iii) différencier les axones des dendrites. Un premier réseau comportant trois populations de neurones connectées en série a été reconstruit à l’intérieur d’un circuit microfluidique. Ce réseau qui mime la voie perforante de l’hippocampe pourra être exploité pour des études en physiologie ou en neuro-dégénerescence. Une méthode entièrement optique de stimulation et d’observation de l’activité neuronal a été mise au point. Elle ouvre de nouvelles portes pour étudier des processus cognitifs complexes dans des systèmes simplifiés in vitro. La seconde partie de mon travail a permis le développement et l’étude de jeux pédagogiques pour l’apprentissage de la chimie en licence. Ces jeux, qui peuvent selon les cas remplacer un cours ou une séance d’exercices, donnent des résultats prometteurs pour l’aide à la compréhension et à la mémorisation de concepts tels que la géométrie des molécules ou la réactivité entre molécules organiques. / My PhD is divided in two parts one on biophysic of neuronal networks and one on science of education. The first part present results at the frontier between neurobiology and microfluidic. The overarching goal of this work was to develop tools and methods to build and study complex neuronal networks controlling the topology of synaptic connexions. Micro-patterning techniques with mechanical and/or chemical constraints were explored regarding their capacity to (i) position cell bodies, (ii) orient neurite outgrowth and (iii) polarize neurons. For the first time, a network comprising three different neuronal populations connected in specified directions was reconstructed in a microfluidic device. This network that mimics the perforant pathway of the hippocampus can be used to study physiological rythms or neurodegenerative processes including Alzheimer’s disease. A novel and fully optical method is presented to stimulate and record neuronal activity in vitro. It opens new routes to study complex cognitive processes in simplified in vitro systems. The second part of my work present the development and assessment of educational games in chemistry at the undergraduate level. These games that can either be used to replace courses or exercises, seem promising to improve the understanding and memorization of chemistry concepts og geometries of molecules and organic reactivity.

As imagens digitais são frequentemente corrompidas por ruídos ou distorcidas pelo processo de aquisição. A teoria dos conjuntos fuzzy e a lógica fuzzy constituem uma alternativa mais adequada para lidar com tais incertezas, em comparação com os sistemas convencionais, baseados na lógica tradicional (crisp). Este trabalho propõe uma nova metodologia para análise de micropadrões de imagens digitais baseada em números fuzzy. Um micropadrão é uma estrutura de níveis de cinza dos pixels de uma vizinhança e pode descrever o contexto espacial da imagem, como borda, textura, linha, canto e padrões mais complexos. Na literatura de visão computacional, algumas abordagens foram desenvolvidas para extrair estas características, tais como Texture Unit (TU), Local Binary Pattern (LBP) e Fuzzy Number Edge Detector (FUNED). O trabalho apresenta um novo método que modela a distribuição dos níveis de cinza de um micropadrão como um conjunto fuzzy, e com base nas funções de pertinência usadas gera códigos-fuzzy que representam o grau de pertinência de cada pixel vizinho com nível de cinza próximo do pixel central. A metodologia proposta é chamada de Local Fuzzy Pattern (LFP) e é aplicada na análise de textura usando a função sigmoide (LFP-s), a função triangular e simétrica (LFP-t) e a função gaussiana (LFP-g) para calcular o grau de pertinência do pixel central em relação à sua vizinhança. Para avaliar o desempenho da técnica proposta foram usados bases de texturas, cujas imagens foram amostradas aleatoriamente. Após processá-las pelas abordagens LFP-s, LFP-t, LFP-g e LBP, foram comparadas as taxas de acertos alcançadas usando a distância Chi-quadrado. Nos experimentos realizados também é avaliado o esforço computacional do LFP, comparando-o com o descritor LBP. Os resultados mostram que o LFP é eficaz na descrição de textura e que supera o LBP nos diferentes testes realizados. Neste trabalho também é demonstrado que a formulação do LFP é uma generalização de técnicas previamente publicadas, como Texture Unit, Local Binary Pattern e FUNED. / Digital images are often corrupted by noise and distorted by the acquisition process. The fuzzy set theory and fuzzy logic are an alternative more appropriate to deal with these uncertainties, in comparison with conventional treatment based on traditional logic (crisp). This work proposes a new methodology for the analysis of micro-patterns of digital images based on fuzzy numbers. A micro-pattern is the structure of the gray-level pixels within a neighborhood and can describe the spatial context of the image, such as edge, texture, line, corner and more complex patterns. In the literature of computer vision, some approaches have been developed to extract these features, such as Texture Unit (TU), Local Binary Pattern (LBP) and Fuzzy Number Edge Detector (FUNED). This work presents a new method that models the distribution of the gray levels of a micro-pattern as a fuzzy set, and based on the membership functions used generates fuzzy-codes that represent the membership degree of each neighbor pixel neighbor with gray-levels near of the central pixel. The proposed methodology is called Local Fuzzy Pattern (LFP) and is applied in the texture analysis by using a sigmoid (LFP-s), a symmetrical triangular (LFP-t) function and Gaussian function (LFP-g) for calculating the membership degree of a central pixel of a neighborhood. To evaluate the performance of the proposed technique were used two database, whose images were randomly sampled. After processing these images by the LFP-s, LFP-t, LFP-g and LBP approaches, it was compared the hit-rate reached by using the Chi-square distance. In the experiments also evaluated the computational effort of the LFP and surpasses the LBP that the different tests. The results show that the LFP-s is efficient to describe texture and that it surpasses the LBP in different tests. This work also demonstrates that the proposed formulation for the LFP is a generalization of previously published techniques such as Texture Unit, LBP and FUNED.

As imagens digitais são frequentemente corrompidas por ruídos ou distorcidas pelo processo de aquisição. A teoria dos conjuntos fuzzy e a lógica fuzzy constituem uma alternativa mais adequada para lidar com tais incertezas, em comparação com os sistemas convencionais, baseados na lógica tradicional (crisp). Este trabalho propõe uma nova metodologia para análise de micropadrões de imagens digitais baseada em números fuzzy. Um micropadrão é uma estrutura de níveis de cinza dos pixels de uma vizinhança e pode descrever o contexto espacial da imagem, como borda, textura, linha, canto e padrões mais complexos. Na literatura de visão computacional, algumas abordagens foram desenvolvidas para extrair estas características, tais como Texture Unit (TU), Local Binary Pattern (LBP) e Fuzzy Number Edge Detector (FUNED). O trabalho apresenta um novo método que modela a distribuição dos níveis de cinza de um micropadrão como um conjunto fuzzy, e com base nas funções de pertinência usadas gera códigos-fuzzy que representam o grau de pertinência de cada pixel vizinho com nível de cinza próximo do pixel central. A metodologia proposta é chamada de Local Fuzzy Pattern (LFP) e é aplicada na análise de textura usando a função sigmoide (LFP-s), a função triangular e simétrica (LFP-t) e a função gaussiana (LFP-g) para calcular o grau de pertinência do pixel central em relação à sua vizinhança. Para avaliar o desempenho da técnica proposta foram usados bases de texturas, cujas imagens foram amostradas aleatoriamente. Após processá-las pelas abordagens LFP-s, LFP-t, LFP-g e LBP, foram comparadas as taxas de acertos alcançadas usando a distância Chi-quadrado. Nos experimentos realizados também é avaliado o esforço computacional do LFP, comparando-o com o descritor LBP. Os resultados mostram que o LFP é eficaz na descrição de textura e que supera o LBP nos diferentes testes realizados. Neste trabalho também é demonstrado que a formulação do LFP é uma generalização de técnicas previamente publicadas, como Texture Unit, Local Binary Pattern e FUNED. / Digital images are often corrupted by noise and distorted by the acquisition process. The fuzzy set theory and fuzzy logic are an alternative more appropriate to deal with these uncertainties, in comparison with conventional treatment based on traditional logic (crisp). This work proposes a new methodology for the analysis of micro-patterns of digital images based on fuzzy numbers. A micro-pattern is the structure of the gray-level pixels within a neighborhood and can describe the spatial context of the image, such as edge, texture, line, corner and more complex patterns. In the literature of computer vision, some approaches have been developed to extract these features, such as Texture Unit (TU), Local Binary Pattern (LBP) and Fuzzy Number Edge Detector (FUNED). This work presents a new method that models the distribution of the gray levels of a micro-pattern as a fuzzy set, and based on the membership functions used generates fuzzy-codes that represent the membership degree of each neighbor pixel neighbor with gray-levels near of the central pixel. The proposed methodology is called Local Fuzzy Pattern (LFP) and is applied in the texture analysis by using a sigmoid (LFP-s), a symmetrical triangular (LFP-t) function and Gaussian function (LFP-g) for calculating the membership degree of a central pixel of a neighborhood. To evaluate the performance of the proposed technique were used two database, whose images were randomly sampled. After processing these images by the LFP-s, LFP-t, LFP-g and LBP approaches, it was compared the hit-rate reached by using the Chi-square distance. In the experiments also evaluated the computational effort of the LFP and surpasses the LBP that the different tests. The results show that the LFP-s is efficient to describe texture and that it surpasses the LBP in different tests. This work also demonstrates that the proposed formulation for the LFP is a generalization of previously published techniques such as Texture Unit, LBP and FUNED.

Software security is an important aspect of ensuring software quality. The goal of this study is to help developers evaluate software security at the early stage of development using traceable patterns and software metrics. The concept of traceable patterns is similar to design patterns, but they can be automatically recognized and extracted from source code. If these patterns can better predict vulnerable code compared to the traditional software metrics, they can be used in developing a vulnerability prediction model to classify code as vulnerable or not. By analyzing and comparing the performance of traceable patterns with metrics, we propose a vulnerability prediction model. Objective: This study explores the performance of code patterns in vulnerability prediction and compares them with traditional software metrics. We have used the findings to build an effective vulnerability prediction model. Method: We designed and conducted experiments on the security vulnerabilities reported for Apache Tomcat (Releases 6, 7 and 8), Apache CXF and three stand-alone Java web applications of Stanford Securibench. We used machine learning and statistical techniques for predicting vulnerabilities of the systems using traceable patterns and metrics as features. Result: We found that patterns have a lower false negative rate and higher recall in detecting vulnerable code than the traditional software metrics. We also found a set of patterns and metrics that shows higher recall in vulnerability prediction. Conclusion: Based on the results of the experiments, we proposed a prediction model using patterns and metrics to better predict vulnerable code with higher recall rate. We evaluated the model for the systems under study. We also evaluated their performance in the cross-dataset validation.