1,720,969 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
Navigation partagée dans un système autonome cybernétique multi-agents
No full textMa thèse porte sur la navigation partagée entre le système autonome et l'humain. Dans notre recherche, nous mettons l'accent sur la fusion des commandes. Dans notre approche, les deux entités, l'humain et le système autonome, pilotent simultanément le véhicule, et un module acquiert leurs commandes et effectue la fusion des commandes. Cette approche implique l'étude des intentions de l'humain et du système autonome afin d'assurer la fusion la plus appropriée de leurs choix et d'évaluer la prise de décision de chaque entité. L'intention du système autonome est calculée à l'aide d'un contrôleur d’asservissement visuel. La mise en œuvre de l'asservissement visuel repose sur un réseau de deep learning capable de détecter les voies de circulation. Pour le conducteur humain, qui conduit activement et ne peut exprimer son intention en même temps, nous utilisons un modèle basé sur le deep learning pour prédire son intention. La construction de ce modèle a nécessité la création d'un ensemble de données de conduite à l'aide de nos véhicules et le développement d'un modèle récurrent qui intègre des données de divers types. Chacune de ces intentions est ensuite évaluée selon des critères spécifiques, y compris la sécurité, le confort et le contexte, dans le but de guider le processus de fusion vers la sélection de l'intention de la plus haute qualité. Cette quantification est basée sur une analyse de l'état dérivée de la réalisation de ces intentions. Ensuite, nous utilisons la théorie des jeux pour faciliter le processus de fusion, au chaque entité, humain et système autonome, souhaite orienter la commande finale vers son choix.My thesis is based on shared navigation between the autonomous system and the human. In our research, we focus on command fusion. In our approach, both entities, the human and the autonomous system, simultaneously control the vehicle, and a module acquires their commands and performs the command fusion. This approach involves studying the intentions of both the human and the autonomous system to ensure the most appropriate fusion of their choices and to evaluate the decision-making of each entity. The intention of the autonomous system is calculated using a visual servoing controller. The implementation of visual servoing relies on a deep learning network detecting lanes. For the human driver, who actively drives and cannot express their intention simultaneously, we use a deep learning-based model to predict their intention. The construction of this model required the creation of a driving dataset using our vehicles and the development of a recurrent model that integrates data of various types. Each of these intentions is then evaluated according to specific criteria, including safety, comfort, and context, to guide the fusion process towards the selection of the highest quality intention. This quantification is based on a state analysis derived from the realization of these intentions. We then use game theory to facilitate the fusion process, where each entity, human and autonomous system, aims to steer the final command towards their choice
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
Navigation partagée dans un système autonome cybernétique multi-agents
No full textMy thesis is based on shared navigation between the autonomous system and the human. In our research, we focus on command fusion. In our approach, both entities, the human and the autonomous system, simultaneously control the vehicle, and a module acquires their commands and performs the command fusion. This approach involves studying the intentions of both the human and the autonomous system to ensure the most appropriate fusion of their choices and to evaluate the decision-making of each entity. The intention of the autonomous system is calculated using a visual servoing controller. The implementation of visual servoing relies on a deep learning network detecting lanes. For the human driver, who actively drives and cannot express their intention simultaneously, we use a deep learning-based model to predict their intention. The construction of this model required the creation of a driving dataset using our vehicles and the development of a recurrent model that integrates data of various types. Each of these intentions is then evaluated according to specific criteria, including safety, comfort, and context, to guide the fusion process towards the selection of the highest quality intention. This quantification is based on a state analysis derived from the realization of these intentions. We then use game theory to facilitate the fusion process, where each entity, human and autonomous system, aims to steer the final command towards their choice.Ma thèse porte sur la navigation partagée entre le système autonome et l'humain. Dans notre recherche, nous mettons l'accent sur la fusion des commandes. Dans notre approche, les deux entités, l'humain et le système autonome, pilotent simultanément le véhicule, et un module acquiert leurs commandes et effectue la fusion des commandes. Cette approche implique l'étude des intentions de l'humain et du système autonome afin d'assurer la fusion la plus appropriée de leurs choix et d'évaluer la prise de décision de chaque entité. L'intention du système autonome est calculée à l'aide d'un contrôleur d’asservissement visuel. La mise en œuvre de l'asservissement visuel repose sur un réseau de deep learning capable de détecter les voies de circulation. Pour le conducteur humain, qui conduit activement et ne peut exprimer son intention en même temps, nous utilisons un modèle basé sur le deep learning pour prédire son intention. La construction de ce modèle a nécessité la création d'un ensemble de données de conduite à l'aide de nos véhicules et le développement d'un modèle récurrent qui intègre des données de divers types. Chacune de ces intentions est ensuite évaluée selon des critères spécifiques, y compris la sécurité, le confort et le contexte, dans le but de guider le processus de fusion vers la sélection de l'intention de la plus haute qualité. Cette quantification est basée sur une analyse de l'état dérivée de la réalisation de ces intentions. Ensuite, nous utilisons la théorie des jeux pour faciliter le processus de fusion, au chaque entité, humain et système autonome, souhaite orienter la commande finale vers son choix
Navigation partagée dans un système autonome cybernétique multi-agents
No full textMy thesis is based on shared navigation between the autonomous system and the human. In our research, we focus on command fusion. In our approach, both entities, the human and the autonomous system, simultaneously control the vehicle, and a module acquires their commands and performs the command fusion. This approach involves studying the intentions of both the human and the autonomous system to ensure the most appropriate fusion of their choices and to evaluate the decision-making of each entity. The intention of the autonomous system is calculated using a visual servoing controller. The implementation of visual servoing relies on a deep learning network detecting lanes. For the human driver, who actively drives and cannot express their intention simultaneously, we use a deep learning-based model to predict their intention. The construction of this model required the creation of a driving dataset using our vehicles and the development of a recurrent model that integrates data of various types. Each of these intentions is then evaluated according to specific criteria, including safety, comfort, and context, to guide the fusion process towards the selection of the highest quality intention. This quantification is based on a state analysis derived from the realization of these intentions. We then use game theory to facilitate the fusion process, where each entity, human and autonomous system, aims to steer the final command towards their choice.Ma thèse porte sur la navigation partagée entre le système autonome et l'humain. Dans notre recherche, nous mettons l'accent sur la fusion des commandes. Dans notre approche, les deux entités, l'humain et le système autonome, pilotent simultanément le véhicule, et un module acquiert leurs commandes et effectue la fusion des commandes. Cette approche implique l'étude des intentions de l'humain et du système autonome afin d'assurer la fusion la plus appropriée de leurs choix et d'évaluer la prise de décision de chaque entité. L'intention du système autonome est calculée à l'aide d'un contrôleur d’asservissement visuel. La mise en œuvre de l'asservissement visuel repose sur un réseau de deep learning capable de détecter les voies de circulation. Pour le conducteur humain, qui conduit activement et ne peut exprimer son intention en même temps, nous utilisons un modèle basé sur le deep learning pour prédire son intention. La construction de ce modèle a nécessité la création d'un ensemble de données de conduite à l'aide de nos véhicules et le développement d'un modèle récurrent qui intègre des données de divers types. Chacune de ces intentions est ensuite évaluée selon des critères spécifiques, y compris la sécurité, le confort et le contexte, dans le but de guider le processus de fusion vers la sélection de l'intention de la plus haute qualité. Cette quantification est basée sur une analyse de l'état dérivée de la réalisation de ces intentions. Ensuite, nous utilisons la théorie des jeux pour faciliter le processus de fusion, au chaque entité, humain et système autonome, souhaite orienter la commande finale vers son choix
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
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