Best Practices of Convolutional Neural Networks for Question Classification

Question Classification (QC) is of primary importance in question answering systems, since it enables extraction of the correct answer type. State-of-the-art solutions for short text classification obtained remarkable results by Convolutional Neural Networks (CNNs). However, implementing such models requires choices, usually based on subjective experience, or on rare works comparing different settings for general text classification, while peculiar solutions should be individuated for QC task, depending on language and on dataset size. Therefore, this work aims at suggesting best practices for QC using CNNs. Different datasets were employed: (i) A multilingual set of labelled questions to evaluate the dependence of optimal settings on language; (ii) a large, widely used dataset for validation and comparison. Numerous experiments were executed, to perform a multivariate analysis, for evaluating statistical significance and influence on QC performance of all the factors (regarding text representation, architectural characteristics, and learning hyperparameters) and some of their interactions, and for finding the most appropriate strategies for QC. Results show the influence of CNN settings on performance. Optimal settings were found depending on language. Tests on different data validated the optimization performed, and confirmed the transferability of the best settings. Comparisons to configurations suggested by previous works highlight the best classification accuracy by those optimized here. These findings can suggest the best choices to configure a CNN for QC

Renforcement formel et automatique de politiques de sécurité dans des applications Android par réécriture

Autant les applications Android ont réussi à positionner Android parmi les systèmes d'exploitation les plus utilisés, autant elles ont facilité aux créateurs de maliciels de s'introduire et de compromettre ses appareils. Une longue liste de menaces causées par les applications téléchargées vise l'intégrité du système et la vie privée de ses utilisateurs. Malgré l'évolution incessante du système Android pour améliorer son mécanisme de sécurité, le niveau de sophistication des logiciels malveillants a augmenté et s'adapte continuellement avec les nouvelles mesures. L'une des principales faiblesses menaçant la sécurité de ce système est le manque abyssal d'outils et d'environnements permettant la spécification et la vérification formelle des comportements des applications avant que les dommages ne soient causés. À cet égard, les méthodes formelles semblent être le moyen le plus naturel et le plus sûr pour une spécification et une vérification rigoureuses et non ambiguës de telles applications. Notre objectif principal est de développer un cadre formel pour le renforcement de politiques de sécurité dans les applications Android. L'idée est d'établir une synergie entre le paradigme orienté aspect et les méthodes formelles. L'approche consiste à réécrire le programme de l'application en ajoutant des tests de sécurité à certains points soigneusement sélectionnés pour garantir le respect de la politique de sécurité. La version réécrite du programme préserve tous les bons comportements de la version originale qui sont conformes à la politique de sécurité et agit contre les mauvais.As much as they have positioned Android among the most widely used operating systems, Android applications have helped malware creators to break in and infect its devices. A long list of threats caused by downloaded applications targets the integrity of the system and the privacy of its users. While the Android system is constantly evolving to improve its security mechanism, the malware's sophistication level is skyrocketing and continuously adapting with the new measures. One of the main weaknesses threatening smartphone security is the abysmal lack of tools and environments that allow formal specification and verification of application behaviors before damage is done. In this regard, formal methods seem to be the most natural and secure way for rigorous and unambiguous specification and verification of such applications. Our ultimate goal is to formally enforce security policies on Android applications. The main idea is to establish a synergy between the aspect-oriented paradigm and formal methods such as the program rewriting technique. The approach consists of rewriting the application program by adding security tests at certain carefully selected points to ensure that the security policy is respected. The rewritten version of the program preserves all the good behaviors of the original one that comply with the security policy and acts against the bad ones

Natural Language Processing: Emerging Neural Approaches and Applications

This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains