Programmation modulaire

Résumé

En informatique, la programmation modulaire reprend l'idée de fabriquer un produit (le programme) à partir de composants (les modules). Elle décompose une grosse application en modules, groupes de fonctions, de méthodes et de traitement, pour pouvoir les développer et les améliorer indépendamment, puis les réutiliser dans d'autres applications. Le développement du code des modules peut être attribué à des (groupes de) personnes différentes, qui effectuent leurs tests unitaires indépendamment. Cette méthode de regroupement permet de réaliser une encapsulation comparable par certains aspects à celle de la programmation objet, et permet l'organisation du code source en unités de travail logiques. Les modules définissent également des espaces de noms utiles lors de leur utilisation. La programmation modulaire n'implique pas l'usage d'un style de ou d'un paradigme de programmation particulier ; les éléments qu'elle structure peuvent être de style objet, impératif ou fonctionnel. L'o

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La programmation orientée objet (POO), ou programmation par objet, est un paradigme de programmation informatique. Elle consiste en la définition et l'interaction de briques logicielles appelées obj

La programmation fonctionnelle est un paradigme de programmation de type déclaratif qui considère le calcul en tant qu'évaluation de fonctions mathématiques. Comme le changement d'état et la mutation

Python (prononcé ) est un langage de programmation interprété, multiparadigme et multiplateformes. Il favorise la programmation impérative structurée, fonctionnelle et orientée objet. Il est doté d'un

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Non-malleable codes are a generalization of classical error-correcting codes where the act of "corrupting" a codeword is replaced by a "tampering" adversary. Non-malleable codes guarantee that the message contained in the tampered codeword is either the original message m, or a completely unrelated one. In the common split-state model, the codeword consists of multiple blocks (or states) and each block is tampered with independently. The central goal in the split-state model is to construct high rate non-malleable codes against all functions with only two states (which are necessary). Following a series of long and impressive line of work, constant rate, two-state, non-malleable codes against all functions were recently achieved by Aggarwal et al. [2]. Though constant, the rate of all known constructions in the split state model is very far from optimal (even with more than two states). In this work, we consider the question of improving the rate of splitstate non-malleable codes. In the "information theoretic" setting, it is not possible to go beyond rate 1/2. We therefore focus on the standard computational setting. In this setting, each tampering function is required to be efficiently computable, and the message in the tampered codeword is required to be either the original message m or a "computationally" independent one. In this setting, assuming only the existence of one-way functions, we present a compiler which converts any poor rate, two-state, (sufficiently strong) non-malleable code into a rate-1, two-state, computational non-malleable code. These parameters are asymptotically optimal. Furthermore, for the qualitative optimality of our result, we generalize the result of Cheraghchi and Guruswami [10] to show that the existence of one-way functions is necessary to achieve rate > 1/2 for such codes. Our compiler requires a stronger form of non-malleability, called augmented non-malleability. This notion requires a stronger simulation guarantee for non-malleable codes and simplifies their modular usage in cryptographic settings where composition occurs. Unfortunately, this form of non-malleability is neither straightforward nor generally guaranteed by known results. Nevertheless, we prove this stronger form of non-malleability for the two-state construction of Aggarwal et al. [3]. This result is of independent interest.

Springer Int Publishing Ag2016

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Modular interface for embedded audio acquisition platforms

Basile Gérard Pierre Bruneau

In this project, I developed a browser based interface for prototyping real-time processing algorithms using embedded multi channel audio acquisition platforms. The interface allows to interact in an easy manner with a GNU/Linux enabled single board computer and to run algorithms in real-time on distant audio streams from a host computer. It creates a bridge between the user’s computer and the audio acquisition board. More precisely, the interface let us to: - change the board configuration; - write Python code; - execute it in real-time on the audio streams; - visualize data (typically outputs of the algorithm). The project is composed of three main components, communicating together using WebSockets: - daemons in C running on the board: they transmit the audio streams and listen for configuration changes; - a Python daemon: it executes Python code sent by the interface; - the interface itself. Finally, a Python module helps the user to access the audio streams, the configuration, and to create data visualizations. This module connects to the C daemons (to receive the streams and the configuration) and to the browser to send data in real-time for visualizations. The technical documentation is included in the Appendix of this report. It is also available online: https://lcav.github.io/easy-dsp/, as well as the code repository: https: //github.com/LCAV/easy-dsp.

2016

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A modular approach to learning manipulation strategies from human demonstration

Aude Billard, Ravin Luis De Souza, Miao Li

Object manipulation is a challenging task for robotics, as the physics involved in object interaction is complex and hard to express analytically. Here we introduce a modular approach for learning a manipulation strategy from human demonstration. Firstly we record a human performing a task that requires an adaptive control strategy in different conditions, i.e. different task contexts. We then perform modular decomposition of the control strategy, using phases of the recorded actions to guide segmentation. Each module represents a part of the strategy, encoded as a pair of forward and inverse models. All modules contribute to the final control policy; their recommendations are integrated via a system of weighting based on their own estimated error in the current task context. We validate our approach by demonstrating it, both in a simulation for clarity, and on a real robot platform to demonstrate robustness and capacity to generalise. The robot task is opening bottle caps. We show that our approach can modularize an adaptive control strategy and generate appropriate motor commands for the robot to accomplish the complete task, even for novel bottles.

Springer Verlag2016

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Springer Int Publishing Ag2016