Wearable GPS device as a data collection method for travel research

Global Positioning System (GPS) devices are emerging as a potential means to collect improved data on the spatial aspects of personal travel. This paper builds on earlier work by Stopher and others on the use of passive GPS devices, for which additional non-GPS data may be added through a subsequent prompted recall survey. This paper presents sets of rules which can be applied to the raw data acquired by wearable GPS devices to determine the modes of travel used and the trip ends. Experiments have been performed in which the devices were tested for a range of different situations, including collecting data on trains, buses, and ferries, collecting data in urban canyons and also with respect to the cold start phenomenon. The paper also describes the procedures undertaken to download and analyse the data

Automatic Detection and Classification of Argument Components using Multi-task Deep Neural Network

International audienceIn this article we propose a novel method for automatically extracting and classifying argument components from raw texts. We introduce a multi-task deep learning framework exploiting weight parameters trained on auxiliary simple tasks, such as Part-Of-Speech tagging or chunking, in order to solve more complex tasks that require a fine-grained understanding of natural language. Interestingly, our results show that the use of advanced deep learning techniques framed in a multi-task setting enables competing with state-of-the-art systems that depend on handcrafted features

Analyse automatique d’arguments et apprentissage multi-tâches : un cas d’étude

National audienceNous proposons une étude sur l’analyse automatique d’arguments via des techniques d’apprentissage supervisé exploitant le paradigme de l’apprentissage multi-tâches. Nous définissons pour cela une approche multi-tâches à base d’apprentissage profond que nous évaluons sur un cas d’étude spécifique portant sur l’extraction d’arguments dans un corpus de dissertations. Les résultats obtenus permettent de discuter l’intérêt de définir un modèle multi-tâches unique – optimisé sur différents critères en tirant parti de la diversité des tâches d’apprentissage auxquelles il est confronté – par rapport à un ensemble de classifieurs entraînés de manière indépendante et spécifique. Nous montrons en particulier l’impact de l’ajout de tâches auxiliaires de bas niveau, telles que l’étiquetage morpho-syntaxique et l’analyse de dépendances grammaticales, pour l’obtention de classifieurs multi-tâches performants. Nous observons aussi que l’apprentissage multi-tâches permet l’obtention de modèles efficaces de performances semblables à l’état de l’art pour le cas d’étude traité

DÉfi Fouille de Textes 2019: indexation par extraction et appariement textuel

International audienceThis paper presents the contribution of the LGI2P (Laboratoire de Génie Informatique et d'Ingénierie de Production) team from IMT Mines Alès to the DEFT 2019 challenge (DÉfi Fouille de Textes). We detail two approches we devised for the tasks pertaining to (1) the indexing and to (2) the similarity of documents. Said approaches rely on proven and robust techniques from Information Retrieval and Natural Language Processing that have been adapted to the specificities of the corpus (biomedical text) and of the formulation of the tasks. For task 1, we propose an indexing-by-extraction approach applied on the corpus after a normalisation procedure (MAP=0.48) that we will detail further. For task 2, we proposed a similarity-based approach computed on vector representation of the documents (score=0.910) and study the impact of the choice of the similarity metric and of the document representation method on task performance.Cet article présente la contribution de l'équipe du Laboratoire de Génie Informatique et d'Ingénierie de Production (LGI2P) d'IMT Mines Alès au DÉfi Fouille de Textes (DEFT) 2019. Il détaille en particulier deux approches proposées pour les tâches liées à (1) l'indexation et à (2) la similarité de documents. Ces méthodes reposent sur des techniques robustes et éprouvées du domaine de la Recherche d'Information et du Traitement Automatique du Langage Naturel, qui ont été adaptées à la nature spécifique du corpus (biomédical/clinique) et couplées à des mécanismes développés pour répondre aux spécificités des tâches traitées. Pour la tâche 1, nous proposons une méthode d'indexation par extraction appliquée sur une version normalisée du corpus (MAP de 0,48 à l'évaluation) ; les spécificités de la phase de normalisation seront en particulier détaillées. Pour la tâche 2, au-delà de la présentation de l'approche proposée basée sur l'évaluation de similarités sur des représentations de documents (score de 0,91 à l'évaluation), nous proposons une étude comparative de l'impact des choix de la distance et de la manière de représenter les textes sur la performance de l'approche

OperA/ALIVE/OperettA

Comprehensive models for organizations must, on the one hand, be able to specify global goals and requirements but, on the other hand, cannot assume that particular actors will always act according to the needs and expectations of the system design. Concepts as organizational rules (Zambonelli 2002), norms and institutions (Dignum and Dignum 2001; Esteva et al. 2002), and social structures (Parunak and Odell 2002) arise from the idea that the effective engineering of organizations needs high-level, actor-independent concepts and abstractions that explicitly define the organization in which agents live (Zambonelli 2002).Peer ReviewedPostprint (author's final draft

Simultaneous saccharification and fermentation of hydrothermal pretreated lignocellulosic biomass: evaluation of process performance under multiple stress conditions

Industrial lignocellulosic bioethanol processes are exposed to different environmental stresses (such as inhibitor compounds, high temperature, and high solid loadings). In this study, a systematic approach was followed where the liquid and solid fractions were mixed to evaluate the influence of varied solid loadings, and different percentages of liquor were used as liquid fraction to determine inhibitor effect. Ethanol production by simultaneous saccharification and fermentation (SSF) of hydrothermally pretreated Eucalyptus globulus wood (EGW) was studied under combined diverse stress operating conditions (3038 °C, 6080 g of liquor from hydrothermal treatment or autohydrolysis (containing inhibitor compounds)/100 g of liquid and liquid to solid ratio between 4 and 6.4 g liquid in SSF/g unwashed pretreated EGW) using an industrial Saccharomyces cerevisiae strain supplemented with low-cost byproducts derived from agro-food industry. Evaluation of these variables revealed that the combination of temperature and higher solid loadings was the most significant variable affecting final ethanol concentration and cellulose to ethanol conversion, whereas solid and autohydrolysis liquor loadings had the most significant impact on ethanol productivity. After optimization, an ethanol concentration of 54 g/L (corresponding to 85 % of conversion and 0.51 g/Lh of productivity at 96 h) was obtained at 37 °C using 60 % of autohydrolysis liquor and 16 % solid loading (liquid to solid ratio of 6.4 g/g). The selection of a suitable strain along with nutritional supplementation enabled to produce noticeable ethanol titers in quite restrictive SSF operating conditions, which can reduce operating cost and boost the economic feasibility of lignocellulose-to-ethanol processes.The authors thank the financial support from the Strategic Project of UID/BIO/04469/2013 CEB Unit and A Romaní postdoctoral grant funded by Xunta of Galicia (Plan I2C, 2014)