1,721,056 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Neural models for the representation and matching of geotextual objects
No full textStimulée par l'usage intensif des téléphones mobiles, l'exploitation conjointe des données textuelles et des données spatiales présentes dans les objets géotextuels (p. ex. tweets, photos Flickr, critiques de points d'intérêt) est devenue la pierre angulaire à de nombreuses applications utilisées quotidiennement, telles que la gestion de crise, l'assistance touristique ou la recommandation de points d'intérêts (POIs). Du point de vue scientifique, ces tâches reposent de façon critique sur la représentation d'objets spatiaux et la définition de fonctions d'appariement entre ces objets. Dans de précédents travaux, ce problème a principalement été traité au moyen de modèles linguistiques qui reposent sur une estimation coûteuse de probabilité de la pertinence des mots dans les régions spatiales. Cependant, ces approches traditionnelles se sont révélées peu efficaces face aux textes issus des réseaux sociaux. En effet, ces derniers sont généralement de courte longueur, utilisent des mots non conventionnels ou ambiguës et peuvent difficilement être mis en correspondance avec d'autres documents, notamment à cause de l'inadéquation du vocabulaire. De fait, les approches proposées jusqu'à présent conduisent généralement à de faibles taux de rappel et de précision. Les travaux réalisés dans cette thèse s'inscrivent dans ce contexte et visent à réduire la discordance de vocabulaire dans les représentations et l'appariement de tweets géotaggés et de POIs. Nous proposons ainsi de tirer parti des contextes géographiques et de la sémantique distributionnelle pour résoudre la tâche de prédiction sémantique de l'emplacement. Notre travail se compose de deux principales contributions : (1) améliorer les plongements lexicaux pouvant être combinés pour construire des représentations d'objets, grâce aux répartitions spatiales des mots ; (2) exploiter les réseaux de neurones profonds pour réaliser un appariement sémantique de tweets avec des POIs. Concernant l'amélioration des représentations de textes, nous proposons une approche de régularisation a posteriori qui intègre l'information spatiale dans l'apprentissage des plongements lexicaux. L'objectif sous-jacent est de révéler d'éventuelles relations sémantiques locales entre les mots, ainsi que la multiplicité des sens d'un même mot. Pour déceler les spécificités locales des différents sens d'un mot, nous proposons deux solutions, l'une s'appuyant sur une technique de partitionnement spatial, via l'algorithme des k-moyennes, l'autre sur un partitionnement probabiliste à l'aide d'estimation de densités (KDE). Les plongements lexicaux sont ensuite corrigés à l'aide d'une fonction de régularisation qui intègre les répartitions spatiales pour déterminer les relations sémantiques locales entre les mots. [...]Stimulated by the heavy use of smartphones, the joint use of textual and spatial data in space-textual objects (e.g., tweets, Flickr photos, POI reviews) became the mainstay of many applications, such as crisis management, tourist assistance or the finding of places of interest. These tasks are fundamentally based on the representation of spatial objects and the definition of matching functions. In previous work, the problem has been addressed using linguistic models that rely on costly probability estimation of the relevance of words in spatial regions. However, these traditional methods are not very effective when dealing with social network data. These data are usually short, use unconventional or ambiguous words, and are difficult to match with other documents because of vocabulary mismatches. As a result, the proposed approaches generally lead to low recall and precision rates. In this thesis, we focus on tackling the semantic gap in the representation and matching of geotagged tweets and POIs. We propose to leverage geographic contexts and distributional semantics to resolve the semantic location prediction task. Our work consists of two main contributions: (1) improving word embeddings which can be combined to construct object representations using spatial word distributions; (2) exploiting deep neural networks to perform semantic matching between tweets and POIs. Regarding the improvement of text representations, we propose to regularize word embeddings that can be combined to construct object representations. The purpose is to reveal possible local semantic relationships between words and the multiplicity of meanings of the same word. To detect the local specificities of the different meanings, we consider two alternatives. One based on a spatial partitioning method using the k-means algorithm, and the other one based on a probabilistic partitioning using a kernel density estimation (KDE). Word embeddings are then retrofitted using a regularization function that integrates the spatial distributions to compute the local semantic relationships between words. Regarding the use of deep neural networks for the semantic location prediction task, we propose an interaction-based neural model designed for tweet-POI pair matching. Unlike existing architectures, our approach is based on joint learning of local and global interactions between tweet-POI pairs. According to the proposed model, the exact matching signals of the local word-to-word interactions are corrected by a spatial damping factor. Then, these smoothed signals are processed using matching histograms. The local interactions reveal word-pairs patterns similarity driven by spatial information. Global interactions consider the strength of the interaction between the tweet and the POI, both spatially, through a geographical distance between geotextual objects, and semantically, through a semantic proximity of their latent representation
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Datastream Summarization : patterns, Data Cubes and Hierarchies
No full textL'explosion du volume de données disponibles due au développement des technologies de l'information et de la communication a démocratisé les flots qui peuvent être définis comme des séquences non bornées de données très précises et circulant à grande vitesse. Les stocker intégralement est par définition impossible. Il est alors essentiel de proposer des techniques de résumé permettant une analyse a posteriori de cet historique. En outre, un grand nombre de flots de données présentent un caractère multidimensionnel et multiniveaux que très peu d'approches existantes exploitent. Ainsi, l'objectif de ces travaux est de proposer des méthodes de résumé exploitant ces spécificités multidimensionnelles et applicables dans un contexte dynamique. Nous nous intéressons à l'adaptation des techniques OLAP (On Line Analytical Processing ) et plus particulièrement, à l'exploitation des hiérarchies de données pour réaliser cette tâche. Pour aborder cette problématique, nous avons mis en place trois angles d'attaque. Tout d'abord, après avoir discuté et mis en évidence le manque de solutions satisfaisantes, nous proposons deux approches permettant de construire un cube de données alimenté par un flot. Le deuxième angle d'attaque concerne le couplage des approches d'extractions de motifs fréquents (itemsets et séquences) et l'utilisation des hiérarchies pour produire un résumé conservant les tendances d'un flot. Enfin, les catégories de hiérarchies existantes ne permettent pas d'exploiter les connaissances expertes dans le processus de généralisation. Nous pallions ce manque en définissant une nouvelle catégorie de hiérarchies, dites contextuelles, et en proposant une modélisation conceptuelle, graphique et logique d'un entrepôt de données intégrant ces hiérarchies contextuelles. Cette thèse s'inscrivant dans un projet ANR (MIDAS), une plateforme de démonstration intégrant les principales approches de résumé a été mise au point. En outre, la présence de partenaires industriels tels que Orange Labs ou EDF RD dans le projet a permis de confronter nos approches à des jeux de données réelles.Due to the rapid increase of information and communication technologies, the amount of generated and available data exploded and a new kind of data, the stream data, appeared. One possible and common definition of data stream is an unbounded sequence of very precise data incoming at an high rate. Thus, it is impossible to store such a stream to perform a posteriori analysis. Moreover, more and more data streams concern multidimensional and multilevel data and very few approaches tackle these specificities. Thus, in this work, we proposed some practical and efficient solutions to deal with such particular data in a dynamic context. More specifically, we were interested in adapting OLAP (On Line Analytical Processing ) and hierarchy techniques to build relevant summaries of the data. First, after describing and discussing existent similar approaches, we have proposed two solutions to build more efficiently data cube on stream data. Second, we were interested in combining frequent patterns and the use of hierarchies to build a summary based on the main trends of the stream. Third, even if it exists a lot of types of hierarchies in the literature, none of them integrates the expert knowledge during the generalization phase. However, such an integration could be very relevant to build semantically richer summaries. We tackled this issue and have proposed a new type of hierarchies, namely the contextual hierarchies. We provide with this new type of hierarchies a new conceptual, graphical and logical data warehouse model, namely the contextual data warehouse. Finally, since this work was founded by the ANR through the MIDAS project and thus, we had evaluated our approaches on real datasets provided by the industrial partners of this project (e.g., Orange Labs or EDF R&D)
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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