1,721,022 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
Compréhension de comportements humains 3D par l'analyse de forme de la posture et du mouvement
No full textL'émergence de capteurs de profondeur capturant la structure 3D de la scène et du corps humain offre de nouvelles possibilités pour l'étude du mouvement et la compréhension des comportements humains. Cependant, la conception et le développement de modules de reconnaissance de comportements à la fois précis et efficaces est une tâche difficile en raison de la variabilité de la posture humaine, la complexité du mouvement et les interactions avec l'environnement. Dans cette thèse, nous nous concentrons d'abord sur le problème de la reconnaissance d'actions en représentant la trajectoire du corps humain au cours du temps, capturant ainsi simultanément la forme du corps et la dynamique du mouvement. Le problème de la reconnaissance d'actions est alors formulé comme le calcul de similitude entre la forme des trajectoires dans un cadre Riemannien. Les expériences menées sur quatre bases de données démontrent le potentiel de la solution en termes de précision/temps de latence de la reconnaissance d'actions. Deuxièmement, nous étendons l'étude aux comportements plus complexes en analysant l'évolution de la forme de la posture pour décomposer la séquence en unités de mouvement. Chaque unité de mouvement est alors caractérisée par la trajectoire de mouvement et l'apparence autour des mains, de manière à décrire le mouvement humain et l'interaction avec les objets. Enfin, la séquence de segments temporels est modélisée par un classifieur Bayésien naïf dynamique. Les expériences menées sur quatre bases de données évaluent le potentiel de l'approche dans différents contextes de reconnaissance et détection en ligne de comportements.The emergence of RGB-D sensors providing the 3D structure of both the scene and the human body offers new opportunities for studying human motion and understanding human behaviors. However, the design and development of models for behavior recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, the complexity of human motion and possible interactions with the environment. In this thesis, we first focus on the action recognition problem by representing human action as the trajectory of 3D coordinates of human body joints over the time, thus capturing simultaneously the body shape and the dynamics of the motion. The action recognition problem is then formulated as the problem of computing the similarity between shape of trajectories in a Riemannian framework. Experiments carried out on four representative benchmarks demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Second, we extend the study to more complex behaviors by analyzing the evolution of the human pose shape to decompose the motion stream into short motion units. Each motion unit is then characterized by the motion trajectory and depth appearance around hand joints, so as to describe the human motion and interaction with objects. Finally, the sequence of temporal segments is modeled through a Dynamic Naive Bayesian Classifier. Experiments on four representative datasets evaluate the potential of the proposed approach in different contexts, including recognition and online detection of behaviors
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
Comprensione del comportamento umano 3D attraverso l’analisi di forma del movimento e della posa
No full textThe emergence of RGB-D sensors providing the 3D structure of both the scene and the human body offers new opportunities for studying human motion and understanding human behaviors. However, the design and development of models for behavior recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, the complexity of human motion and possible interactions with the environment. In this thesis, we address this issue in two main phases by differentiating behaviors according to their complexity. We first focus on the action recognition problem by representing human action as the trajectory of 3D coordinates of human body joints over the time, thus capturing simultaneously the body shape and the dynamics of the motion. The action recognition problem is then formulated as the problem of computing the similarity between shape of trajectories in a Riemannian framework. Experiments carried out on four representative benchmarks demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Second, we extend the study to activities by analyzing the evolution of the human pose shape to decompose the motion stream into short motion units. Each motion unit is then characterized by the motion trajectory and depth appearance around hand joints, so as to describe the human motion and interaction with objects. Finally, the sequence of temporal segments is modeled through a Dynamic Naive Bayesian Classifier. Experiments on four representative datasets evaluate the potential of the proposed approach in different contexts, including gesture or activity recognition and online activity detection.L’émergence de capteurs de profondeur capturant la structure 3D de la scène et du corps humain offre de nouvelles possibilités pour l’étude du mouvement et la compréhension des comportements humains. Cependant, la conception et le développement de modules de reconnaissance de comportements à la fois précis et efficaces est une tâche difficile en raison de la variabilité de la posture humaine, la complexité du mouvement et les interactions avec l’environnement. Dans cette thèse, nous abordons cette question en deux étapes principales en différenciant les comportements en fonction de leur complexité. Nous nous concentrons d’abord sur le problème de la reconnaissance d’actions en représentant la trajectoire du corps humain au cours du temps, capturant ainsi simultanément la forme du corps et la dynamique du mouvement. Le problème de la reconnaissance d’actions est alors formulé comme le calcul de similitude entre la forme des trajectoires dans un cadre Riemannien. Les expériences menées sur quatre bases de données démontrent le potentiel de la solution en termes de précision/temps de latence de la reconnaissance d’actions. Deuxièmement, nous étendons l’étude aux activités en analysant l’évolutionde la forme de la posture pour décomposer la séquence en unités de mouvement. Chaque unité de mouvement est alors caractérisée par latrajectoire de mouvement et l’apparence autour des mains, de manière à décrire le mouvement humain et l’interaction avec les objets. Enfin, laséquence de segments temporels est modélisée par un classifieur Bayésien naif dynamique. Les expériences menées sur quatre bases de données évaluent le potentiel de l’approche dans différents contextes comme la reconnaissance de gestes ou d’activités et la détection en ligne d’activités.La diffusione di sensori RGB-D capaci di fornire la struttura 3D sia della scena che del corpo umano offre nuove opportunità per studiare i movimenti dell’uomo e capire i suoi comportamenti. Tuttavia, la progettazione e lo sviluppo di modelli per il riconoscimento dei comportamenti che siano tanto accurati quanto efficienti è un problema competitivo a causa della variabilità delle pose, della complessità del moto e delle possibili interazioni con l’ambiente. In questa tesi si affronta il problema in due passi principali, differenziando i comportamenti in base alla loro complessità. Si pone l’attenzione inizialmente sul problema di riconoscere azioni rappresentandole come traiettorie di coordinate 3D dei giunti del corpo nel tempo, catturando al tempo stesso la forma e le dinamiche di moto. Il problema del riconoscimento delle azioni è poi riformulato come il problema di calcolare le similarità tra la forma delle traiettorie in un manifold Riemanniano. Gli esperimenti effettuati su quattro benchmark dimostrano il potenziale della soluzione proposta in termini di accuratezza/latenza del riconoscimento di azioni. Lo studio è poi esteso al riconoscimento di attività analizzando l’evoluzione della forma delle pose per decomporre il flusso di moto in unità di moto. Ogni unità di moto è quindi caratterizzata dalla traiettoria di moto e da una descrizione della profondità nell’intorno dei giunti delle mani, in modo da descrivere il moto e le interazioni con oggetti. Infine, la sequenza di segmenti temporali è modellata attraverso un classificatore Dynamic Naive Bayesian. Il potenziale dell’approccio proposto è valutato su esperimenti con quattro dataset in contesti diversi, inclusi il riconoscimento di gesti e attività e rilevamento di azioni online
Comprensione del comportamento umano 3D attraverso l’analisi di forma del movimento e della posa
No full textThe emergence of RGB-D sensors providing the 3D structure of both the scene and the human body offers new opportunities for studying human motion and understanding human behaviors. However, the design and development of models for behavior recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, the complexity of human motion and possible interactions with the environment. In this thesis, we address this issue in two main phases by differentiating behaviors according to their complexity. We first focus on the action recognition problem by representing human action as the trajectory of 3D coordinates of human body joints over the time, thus capturing simultaneously the body shape and the dynamics of the motion. The action recognition problem is then formulated as the problem of computing the similarity between shape of trajectories in a Riemannian framework. Experiments carried out on four representative benchmarks demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Second, we extend the study to activities by analyzing the evolution of the human pose shape to decompose the motion stream into short motion units. Each motion unit is then characterized by the motion trajectory and depth appearance around hand joints, so as to describe the human motion and interaction with objects. Finally, the sequence of temporal segments is modeled through a Dynamic Naive Bayesian Classifier. Experiments on four representative datasets evaluate the potential of the proposed approach in different contexts, including gesture or activity recognition and online activity detection.L’émergence de capteurs de profondeur capturant la structure 3D de la scène et du corps humain offre de nouvelles possibilités pour l’étude du mouvement et la compréhension des comportements humains. Cependant, la conception et le développement de modules de reconnaissance de comportements à la fois précis et efficaces est une tâche difficile en raison de la variabilité de la posture humaine, la complexité du mouvement et les interactions avec l’environnement. Dans cette thèse, nous abordons cette question en deux étapes principales en différenciant les comportements en fonction de leur complexité. Nous nous concentrons d’abord sur le problème de la reconnaissance d’actions en représentant la trajectoire du corps humain au cours du temps, capturant ainsi simultanément la forme du corps et la dynamique du mouvement. Le problème de la reconnaissance d’actions est alors formulé comme le calcul de similitude entre la forme des trajectoires dans un cadre Riemannien. Les expériences menées sur quatre bases de données démontrent le potentiel de la solution en termes de précision/temps de latence de la reconnaissance d’actions. Deuxièmement, nous étendons l’étude aux activités en analysant l’évolutionde la forme de la posture pour décomposer la séquence en unités de mouvement. Chaque unité de mouvement est alors caractérisée par latrajectoire de mouvement et l’apparence autour des mains, de manière à décrire le mouvement humain et l’interaction avec les objets. Enfin, laséquence de segments temporels est modélisée par un classifieur Bayésien naif dynamique. Les expériences menées sur quatre bases de données évaluent le potentiel de l’approche dans différents contextes comme la reconnaissance de gestes ou d’activités et la détection en ligne d’activités.La diffusione di sensori RGB-D capaci di fornire la struttura 3D sia della scena che del corpo umano offre nuove opportunità per studiare i movimenti dell’uomo e capire i suoi comportamenti. Tuttavia, la progettazione e lo sviluppo di modelli per il riconoscimento dei comportamenti che siano tanto accurati quanto efficienti è un problema competitivo a causa della variabilità delle pose, della complessità del moto e delle possibili interazioni con l’ambiente. In questa tesi si affronta il problema in due passi principali, differenziando i comportamenti in base alla loro complessità. Si pone l’attenzione inizialmente sul problema di riconoscere azioni rappresentandole come traiettorie di coordinate 3D dei giunti del corpo nel tempo, catturando al tempo stesso la forma e le dinamiche di moto. Il problema del riconoscimento delle azioni è poi riformulato come il problema di calcolare le similarità tra la forma delle traiettorie in un manifold Riemanniano. Gli esperimenti effettuati su quattro benchmark dimostrano il potenziale della soluzione proposta in termini di accuratezza/latenza del riconoscimento di azioni. Lo studio è poi esteso al riconoscimento di attività analizzando l’evoluzione della forma delle pose per decomporre il flusso di moto in unità di moto. Ogni unità di moto è quindi caratterizzata dalla traiettoria di moto e da una descrizione della profondità nell’intorno dei giunti delle mani, in modo da descrivere il moto e le interazioni con oggetti. Infine, la sequenza di segmenti temporali è modellata attraverso un classificatore Dynamic Naive Bayesian. Il potenziale dell’approccio proposto è valutato su esperimenti con quattro dataset in contesti diversi, inclusi il riconoscimento di gesti e attività e rilevamento di azioni online
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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