Annotation of Scientific Summaries for Information Retrieval.

International audienceWe present a methodology combining surface NLP and Machine Learning techniques for ranking asbtracts and generating summaries based on annotated corpora. The corpora were annotated with meta-semantic tags indicating the category of information a sentence is bearing (objective, findings, newthing, hypothesis, conclusion, future work, related work). The annotated corpus is fed into an automatic summarizer for query-oriented abstract ranking and multi- abstract summarization. To adapt the summarizer to these two tasks, two novel weighting functions were devised in order to take into account the distribution of the tags in the corpus. Results, although still preliminary, are encouraging us to pursue this line of work and find better ways of building IR systems that can take into account semantic annotations in a corpus

AJACS : APPLYING JAVA TO AUTOMOTIVE CONTROL SYSTEMS

International audienceThis paper presents the conclusions of AJACS (Applying Java to Automotive Control Systems, http://www.ajacs.org), a 2.5 years European initiative 1 including Trialog and PSA Peugeot-Citroën, aiming to specify, develop and demonstrate an open technology allowing the use of Java in deeply embedded automotive control systems running on top of OSEK / OS

Quick Starting Dialog Systems with Paraphrase Generation

Acquiring training data to improve the robustness of dialog systems can be a painstakingly long process. In this work, we propose a method to reduce the cost and effort of creating new conversational agents by artificially generating more data from existing examples, using paraphrase generation. Our proposed approach can kick-start a dialog system with little human effort, and brings its performance to a level satisfactory enough for allowing actual interactions with real end-users. We experimented with two neural paraphrasing approaches, namely Neural Machine Translation and a Transformer-based seq2seq model. We present the results obtained with two datasets in English and in French:~a crowd-sourced public intent classification dataset and our own corporate dialog system dataset. We show that our proposed approach increased the generalization capabilities of the intent classification model on both datasets, reducing the effort required to initialize a new dialog system and helping to deploy this technology at scale within an organization

Experimental and numerical investigation on mixing and axial dispersion in Taylor-Couette flow patterns

Taylor-Couette flows between two concentric cylinders have great potential applications in chemical engineering. They are particularly convenient for two-phase small scale devices enabling solvent extraction operations. An experimental device was designed with this idea in mind. It consists of two concentric cylinders with the inner one rotating and the outer one fixed. Moreover, a pressure driven axial flow can be superimposed. Taylor-Couette flow is known to evolve towards turbulence through a sequence of successive hydrodynamic instabilities. Mixing characterized by an axial dispersion coefficient is extremely sensitive to these flow bifurcations, which may lead to flawed modelling of the coupling between flow and mass transfer. This particular point has been studied using experimental and numerical approaches. Direct numerical simulations (DNS) of the flow have been carried out. The effective diffusion coefficient was estimated using particles tracking in the different Taylor-Couette regimes. Simulation results have been compared with literature data and also with our own experimental results. The experimental study first consists in visualizing the vortices with a small amount of particles (Kalliroscope) added to the fluid. Tracer residence time distribution (RTD) is used to determine dispersion coefficients. Both numerical and experimental results show a significant effect of the flow structure on the axial dispersion

Using collaborative tagging for text classification: from text classification to opinion mining

Numerous initiatives have allowed users to share knowledge or opinions using collaborative platforms. In most cases, the users provide a textual description of their knowledge, following very limited or no constraints. Here, we tackle the classification of documents written in such an environment. As a use case, our study is made in the context of text mining evaluation campaign material, related to the classification of cooking recipes tagged by users from a collaborative website. This context makes some of the corpus specificities difficult to model for machine-learning-based systems and keyword or lexical-based systems. In particular, different authors might have different opinions on how to classify a given document. The systems presented hereafter were submitted to the D´Efi Fouille de Textes 2013 evaluation campaign, where they obtained the best overall results, ranking first on task 1 and second on task 2. In this paper, we explain our approach for building relevant and effective systems dealing with such a corpus

Hacker's guide

Comprendre les dernières techniques de hacking pour agir et se protéger! Quelles sont les techniques des hackers pour asservir votre ordinateur, usurper votre identité, pirater vos mots de passe ou pénétrer dans l'intranet de votre entreprise? Comment se protéger de façon efficace et durable? Toutes les réponses à ces questions sont dans cet ouvrage qui explique chaque méthode de hacking et sa mise en pratique, puis détaille les contre-mesures à appliquer. Cette édition mise à jour vous apporte de nouvelles informations sur les outils logiciels de sécurité et expose les principes de la stéganographie. Elle présente également les coffres forts numériques, les logiciels d'anonymat, le phénomène des botnets ainsi que les méthodes de hacking dans les réseaux sociaux et micro-réseaux. Enfin, elle fait le point sur le droit et le piratage en 2011, prend en compte les dernières trouvailles technologiques, du carding au malware, et vous donne les moyens de vous en protéger. Grâce au Hacker's Guide, vous allez: • Étudier les types de piratage informatique les plus courants (reniflage, défaçage, destruction de contenu, etc.). • Comprendre tous les enjeux de l'évolution de l'identité numérique, ses dangers et les solutions pour continuer à se protéger. • Découvrir les faiblesses de la norme TCP/IP, vous en protéger, et réparer les failles de sécurité. • Utiliser les outils à même de détecter les chevaux de Troie. • Vous prémunir contre le décodage de mots de passe, qu'il s'agisse d'un ordinateur isolé, d'un serveur ou d'un micro-réseau familial ou de PME. • Recourir à des logiciels de cryptage de données pour renforcer la sécurité de votre ordinateur. • Réparer votre PC s'il est trop contaminé

Unsupervised knowledge acquisition for Extracting Named Entities from speech

International audienceThis paper presents a Named Entity Recognition (NER) method dedicated to process speech transcriptions. The main principle behind this method is to collect in an unsupervised way lexical knowledge for all entries in the ASR lexicon. This knowledge is gathered with two methods: by automatically extracting NEs on a very large set of textual corpora and by exploiting directly the structure contained in the Wikipedia resource. This lexical knowledge is used to update the statistical models of our NER module based on a mixed approach with generative models (Hidden Markov Models-HMM) and discriminative models (Conditional Random Field-CRF). This approach has been evaluated within the French ESTER 2 evaluation program and obtained the best results at the NER task on ASR transcripts