Une architecture pour améliorer la réutilisatibilté des interfaces graphiques

National audienceGraphical interface programming remains a laborious and time-consuming exercise of the bulk of the interaction that usually has to be coded using a programming language. This code, often wordy and not very readable, impacts the reusability of the interfaces and the iterative design, even minor modifications of the user interface requiring it to be substantially modified. We propose an architectural model and an experimental toolkit that makes modifications easier.La programmation des interfaces graphiques reste un exercice laborieux et consommateur en temps l'essentiel de l'interaction devant généralement être codé au moyen d'un langage de programmation. Ce code, souvent verbeux et peu lisible, impacte la réutilisabilité des interfaces et la conception itérative, la moindre modification de l'interface nécessitant qu'il soit substantiellement modifié. Nous proposons un modèle architectural et une boîte à outils expérimentale qui facilitent les modifications et en les rendant peu coûteuses

Application du modèle Entité-Composant-Système à la programmation d'interactions

National audienceThis paper introduces a new GUI framework based on the Entity- Component-System model (ECS), where interactive elements (Entities) can acquire any data (Components). Behaviors are managed by continuously running processes (Systems) which select entities by the components they possess. This model facilitates the handling and reuse of behaviors. It allows to define the interaction modalities of an application globally, by formulating them as a set of Systems. We present Polyphony, an experimental toolkit implementing this approach, detail our interpretation of the ECS model in the context of GUIs, and demonstrate its use with a sample application.Cet article présente un nouveau cadre de conception d’IHM basé sur le modèle Entité-Composant-Système (ECS). Dans ce modèle, les éléments interactifs (Entités) acquièrent librement des données (Composants). Les comportements sont régis par des processus communs s’exécutant continuellement (Systèmes), qui sélection- nent les entités par les composants qu’elles possèdent. Ce modèle favorise la manipulation et la réutilisation des comportements. Il permet de définir globalement les modalités d’interaction d’une application, en les formulant par un ensemble de systèmes. Nous présentons Polyphony, une boîte à outils expérimentale implémen- tant cette approche, détaillons notre interprétation du modèle ECS en contexte IHM, et l’illustrons avec un exemple d’application

Using the djnn framework to create and validate interactive components iteratively

International audienceUsing a real life scenario of aircraft cockpit design, we illustrate how the model-based architecture of the djnn programming framework allows to combine the multidisciplinary and iterative processes of user interface design with the requirements of industrial system development. Treating software programs as hierarchies of interactive components allows to delegate the production of components to multiple actors, each using the tools of their trade. Components can be exchanged in various formats, refined without modifying their surroundings, and undergo automated property verifications before being integrated

Distributed multi-label learning on Apache Spark

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of individual information measures, and a method that selects the subset of features maximizing the geometric mean. The results indicate that each method excels in different scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets confirm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art

Aprendizaje multi-etiqueta distribuido en Apache Spark

This thesis proposes a series of multi-label learning algorithms for classication and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up the multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes the instances across multiple nodes, and an approximate local sensitive hashing method that builds multiple hash tables to index the data. The results indicated that the predictions of the tree-based method are on par with those of an exact method while reducing the execution times in all the scenarios. The aforementioned method is then used to evaluate the quality of a selected feature subset. The optimal adaptation for a multi-label feature selection criterion is discussed and two distributed feature selection methods for multi-label problems are proposed: a method that selects the feature subset that maximizes the Euclidean norm of the individual information measures, and a method selects the subset of features that maximize the geometrical mean. The results indicate that each method excels in di_erent scenarios depending on type of features and the number of labels. Rigorous experimental studies and statistical analyses over many multi-label metrics and datasets con_rm that the proposals achieve better performances and provide better scalability to bigger data than the methods compared in the state of the art.Esta Tesis Doctoral propone unos algoritmos de clasificación y selección de atributos para aprendizaje multi-etiqueta distribuidos implementados en Apache Spark. Cinco estrategias para determinar la arquitectura óptima para acelerar el aprendizaje multi-etiqueta son presentadas. Estas estrategias varían desde la paralelización local utilizando hilos hasta la distribución de la computación utilizando espacios de memoria compartidos o independientes. Ha sido demostrado que la estrategia óptima permite ejecutar cientos de veces más rápido que el método de referencia. Se proponen tres métodos distribuidos de \k nearest neighbors" multi-etiqueta sobre la arquitectura de Spark seleccionada: un método exacto que computa iterativamente las distancias, un método aproximado que usa un árbol para indexar las instancias, y un método aproximado que utiliza tablas hash para indexar las instancias. Los resultados indican que las predicciones del método basado en árboles son equivalente a aquellas producidas por un método exacto a la vez que reduce los tiempos de ejecución en todos los escenarios. Dicho método es utilizado para evaluar la calidad de un subconjunto de atributos. Se discute el criterio para seleccionar atributos en problemas multi-etiqueta, proponiendo: un método que selecciona el subconjunto de atributos cuyas medidas de información individuales poseen la mayor norma Euclídea, y un método que selecciona el subconjunto de atributos con la mayor media geométrica. Los resultados indican que cada método destaca en escenarios diferentes dependiendo del tipo de atributos y el número de etiquetas. Los estudios experimentales y análisis estadísticos utilizando múltiples métricas y datos multi-etiqueta confirman que nuestras propuestas alcanzan un mejor rendimiento y proporcionan una mejor escalabilidad para datos de gran tamaño respecto a los métodos de referencia