1,721,098 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Variations on the Author
Full text link“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
Full text linkWe 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
Full text linkWe 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
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
New segmentation methods for dental cone-beam computed tomography
No full textLa tomographie à faisceau conique (CBCT) est devenue la modalité de référence pour les praticiens du domaine dentaire. Sa relative nouveauté et ses spécificités impliquent que le domaine du traitement de ces images est peu développé à l’heure actuelle. En partenariat industriel avec Carestream Dental, le premier volet de la thèse a conduit à développer une méthode de segmentation semi-automatique de chaque dent, reposant sur l’utilisation de contraintes de forme et d’intensité, et formulée comme un problème de minimisation d’énergie résolu par coupure de graphe. Les résultats montrent une bonne qualité de segmentation avec un coefficient de Dice moyen de 0, 958. Une application logicielle a été réalisée pour la mise en œuvre de la méthode de segmentation auprès des praticiens, en tenant compte des contraintes techniques et temporelles imposées par le contexte clinique, ainsi que du profil des utilisateurs. Un travail préliminaire d’extension de l’approche par coupure de graphe pour segmenter simultanément plusieurs dents à été réalisé, montrant la nécessité de rendre les contraintes de formes plus adaptées aux images et de modifier la méthode d’optimisation pour atteindre des temps de calcul compatibles avec la pratique clinique. Un second volet prospectif des travaux concerne la constitution d’un modèle structurel de la région maxillo-faciale pour formaliser les connaissances a priori sur les organes et leurs interactions. Ce modèle est un graphe conceptuel où sont représentés les concepts des structures et des relations. En particulier, les relations d’alignement et “le long de” ont été modélisées en 3D dans le cadre des ensembles flous.Cone-Beam computed tomography (CBCT) is the new standard imaging method for dental practitioners. The image processing field of CBCT data is still underdeveloped due to the novelty of the method and its specificities compared to traditional CT. With Carestream Dental as industrial partner, the first part of this work is a new semi-automatic segmentation protocol for teeth, based on shape and intensity constraints, through a graph-cut optimization of an energy formulation. Results show a good quality of segmentation with an average Dice coefficient of 0.958. A fully functional implementation of the algorithm has led to a software available for dentists, taking into account the clinical context leading to temporal and technical difficulties. A preliminary extension to multi-objects segmentation showed the necessity to get more stringent shape constraints as well as a better optimization algorithm to get acceptable computation times. The second part of this thesis, more prospective, is about the creation of a structural model of the maxillo-facial space, to formalize the a priori knowledge on organs and theirs spatial relations. This model is a conceptual graph where structures and relationships are seen as concepts. In particular, the spatial relations “Along” and “Aligned”, modeled in a fuzzy set framework, have been extended to 3D objects
Attaques adverses : un point de vue théorique
No full textCette thèse étudie le problème de classification en présence d’attaques adverses. Une attaque adverse est une petite perturbation humainement imperceptible de l’entrée d’un algorithme, construite pour tromper les meilleurs classifieurs d’apprentissage automatique. En particulier, les réseaux de neurones profonds (« deep learning »), utilisés dans des systèmes critiques d’intelligence artificielle comme les voitures autonomes, présentent des risques considérables avec l’éventualité de telles attaques. Il est d’autant plus surprenant qu’il est très facile de créer des attaques adverses et qu’il est difficile de se défendre contre celles-ci en gardant un haut niveau de précision. La robustesse aux perturbations adverses est encore mal comprise par la communauté scientifique. Dans cette thèse, notre but est de comprendre mieux la nature de ce problème en adoptant un point de vue théorique.This thesis investigates the problem of classification in presence of adversarial attacks. Anadversarial attack is a small and humanly imperceptible perturbation of input designed tofool start-of-the-art machine learning classifiers. In particular, deep learning systems, usedin safety critical AI systems as self-driving cars are at stake with the eventuality of such attacks. What is even more striking is the ease to create such adversarial examples and the difficulty to defend against them while keeping a high level of accuracy. Robustness to adversarial perturbations is a still misunderstood field in academics. In this thesis, we aim at understanding better the nature of the adversarial attacks problem from a theoretical perspective
Efficient algorithms for learning conditional preference networks from noisy data
No full textLa croissance exponentielle des données personnelles, et leur mise à disposition sur la toile, a motivé l’émergence d’algorithmes d’apprentissage de préférences à des fins de recommandation, ou d’aide à la décision. Les réseaux de préférences conditionnelles (CP-nets) fournissent une structure compacte et intuitive pour la représentation de telles préférences. Cependant, leur nature combinatoire rend leur apprentissage difficile : comment apprendre efficacement un CP-net au sein d’un milieu bruité, tout en supportant le passage à l’échelle ?Notre réponse prend la forme de deux algorithmes d’apprentissage dont l’efficacité est soutenue par de multiples expériences effectuées sur des données réelles et synthétiques.Le premier algorithme se base sur des requêtes posées à des utilisateurs, tout en prenant en compte leurs divergences d’opinions. Le deuxième algorithme, composé d’une version hors ligne et en ligne, effectue une analyse statistique des préférences reçues et potentiellement bruitées. La borne de McDiarmid est en outre utilisée afin de garantir un apprentissage en ligne efficace.The rapid growth of personal web data has motivated the emergence of learning algorithms well suited to capture users’ preferences. Among preference representation formalisms, conditional preference networks (CP-nets) have proven to be effective due to their compact and explainable structure. However, their learning is difficult due to their combinatorial nature.In this thesis, we tackle the problem of learning CP-nets from corrupted large datasets. Three new algorithms are introduced and studied on both synthetic and real datasets.The first algorithm is based on query learning and considers the contradictions between multiple users’ preferences by searching in a principled way the variables that affect the preferences. The second algorithm relies on information-theoretic measures defined over the induced preference rules, which allow us to deal with corrupted data. An online version of this algorithm is also provided, by exploiting the McDiarmid's bound to define an asymptotically optimal decision criterion for selecting the best conditioned variable and hence allowing to deal with possibly infinite data streams
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