1,720,955 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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
Détection d'anomalies dans l'IoT : de l'efficacité des données à l'amélioration de la qualité de service
No full textWireless Sensor Networks (WSNs) and the broader Internet of Things (IoT) are vital for data collection and environmental monitoring across various sectors, including medicine, agriculture, manufacturing, and defense. However, these networks face challenges due to outliers, stemming from sensor nodes or harsh deployment conditions. This necessitates effective outlier detection and classification to maintain network quality and application efficiency. Our thesis addresses this issue by offering a literature review, comparative analysis, and innovative solutions for outlier detection and classification in WSNs. Our solutions cover multivariate data, offline/online data, and centralized/distributed clustering, along with advanced techniques for spatial and temporal correlation analysis. We integrate native outlier detection with clustering, ensuring efficient execution and high performance. Simulation results reveal our solutions' effectiveness, improving key metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy Rate (ACC), F1-Score, and Area Under the Curve (AUC).Les réseaux de capteurs sans fil (WSN) et l'Internet des Objets (IoT) sont essentiels pour la collecte d'informations et la surveillance dans divers domaines tels que la médecine, l'agriculture, l'industrie et la défense. Cependant, ces réseaux sont confrontés au défi des valeurs aberrantes, qu'elles proviennent des capteurs ou de l'environnement hostile. Notre thèse aborde cette problématique en proposant une revue de la littérature, une analyse comparative et des solutions innovantes pour détecter et classifier ces valeurs aberrantes dans les WSN.Nos solutions couvrent des données multivariées, des données en ligne/hors ligne, et offrent des méthodes de regroupement centralisées ou distribuées, avec une analyse avancée des corrélations spatiales et temporelles. Nous intégrons des méthodes de détection natives à des algorithmes de regroupement existants. Nos solutions se distinguent par leur efficacité, leur temps d'exécution raisonnable et leur faible complexité, surpassant d'autres travaux. Les résultats de simulations confirment leur efficacité en améliorant plusieurs métriques de détection et de classification des valeurs aberrantes, tels que le taux de détection (DR), le taux d'alarme falsifiée (FAR), le taux de précision (ACC), le F1-score, et l'Aire sous la Courbe (AUC)
Détection d'anomalies dans l'IoT : de l'efficacité des données à l'amélioration de la qualité de service
No full textLes réseaux de capteurs sans fil (WSN) et l'Internet des Objets (IoT) sont essentiels pour la collecte d'informations et la surveillance dans divers domaines tels que la médecine, l'agriculture, l'industrie et la défense. Cependant, ces réseaux sont confrontés au défi des valeurs aberrantes, qu'elles proviennent des capteurs ou de l'environnement hostile. Notre thèse aborde cette problématique en proposant une revue de la littérature, une analyse comparative et des solutions innovantes pour détecter et classifier ces valeurs aberrantes dans les WSN.Nos solutions couvrent des données multivariées, des données en ligne/hors ligne, et offrent des méthodes de regroupement centralisées ou distribuées, avec une analyse avancée des corrélations spatiales et temporelles. Nous intégrons des méthodes de détection natives à des algorithmes de regroupement existants. Nos solutions se distinguent par leur efficacité, leur temps d'exécution raisonnable et leur faible complexité, surpassant d'autres travaux. Les résultats de simulations confirment leur efficacité en améliorant plusieurs métriques de détection et de classification des valeurs aberrantes, tels que le taux de détection (DR), le taux d'alarme falsifiée (FAR), le taux de précision (ACC), le F1-score, et l'Aire sous la Courbe (AUC).Wireless Sensor Networks (WSNs) and the broader Internet of Things (IoT) are vital for data collection and environmental monitoring across various sectors, including medicine, agriculture, manufacturing, and defense. However, these networks face challenges due to outliers, stemming from sensor nodes or harsh deployment conditions. This necessitates effective outlier detection and classification to maintain network quality and application efficiency. Our thesis addresses this issue by offering a literature review, comparative analysis, and innovative solutions for outlier detection and classification in WSNs. Our solutions cover multivariate data, offline/online data, and centralized/distributed clustering, along with advanced techniques for spatial and temporal correlation analysis. We integrate native outlier detection with clustering, ensuring efficient execution and high performance. Simulation results reveal our solutions' effectiveness, improving key metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy Rate (ACC), F1-Score, and Area Under the Curve (AUC)
- …
