1,720,965 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
Author Under Sail The Imagination of Jack London, 1893-1902
No full textIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Intro -- Title Page -- Copyright Page -- Dedication -- Contents -- Acknowledgments -- Introduction -- 1. Spirit Truth -- 2. From Absorption to Theatricality and Back Again -- 3. "I Will Build a New Present" -- 4. Sons as Authors -- 5. Fathers as Publishers -- 6. The Daughter as Author -- 7. Lovers as Authors -- 8. At Sea with the Family -- 9. Yellow News, Yellow Stories -- 10. The Return Home -- Notes -- Bibliography -- Index -- About Jay WilliamsIn Author Under Sail, Jay Williams offers the first complete literary biography of Jack London as a professional writer engaged in the labor of writing. It examines the authorial imagination in London's work, the use of imagination in both his fiction and nonfiction, and the ways he defined imagination in the creative process in his business dealings with his publishers, editors, and agents. In this first volume of a two-volume biography, Williams traverses the years 1893 to 1902, from London's "Story of a Typhoon" to The People of the Abyss. The Jack London who emerges in the pages of Author Under Sail is a writer whose partnership with publishers, most notably his productive alliance with George Brett of Macmillan, was one of the most formative in American literary history. London pioneered many author models during the heyday of realism and naturalism, blurring the boundaries of these popular genres by focusing on absorption and theatricality and the representation of the seen and unseen. London created an impassioned, sincere, and extremely personal realism unlike that of other American writers of the time. Author Under Sail is a literary tour de force that reveals the full range of London as writer, creative citizen, and entrepreneur at the same time it sheds light on the maverick side of machine-age literature.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
Algorithm selection for the graph coloring problem
Full text linkDas Graphenfärbeproblem ist eines der bekanntesten NP-schweren Probleme in der Informatik. Ziel dabei ist es, für einen gegebenen Graphen jedem Knoten eine Farbe zuzuweisen, sodass keine zwei Knoten, welche mittels einer Kante verbunden sind, die gleiche Farbe erhalten und dass die Anzahl der verwendeten Farben minimal ist. Da das Berechnen eine exakte Lösung dieses Problems im schlimmsten Fall eine exponentielle Laufzeit benötigt, wurde im Laufe der Jahre eine Vielzahl an verschiedenen (Meta)Heuristiken für das GCP entwickelt. Viele dieser Methoden weisen gute Erfolge auf, allerdings scheint es, als würden die Ergebnisse sehr oft von der konkreten Instanz abhängen. Dementsprechend ist es schwierig, wenn (in Analogie zu den No Free Lunch Theoremen) nicht sogar unmöglich, einen Algorithmus zu finden, welcher auf allen Graphen optimal ist.Ein Lösungsansatz für dieses Problem wäre, nicht nur einen Algorithmus zu verwenden, sondern, abhängig von der konkreten Instanz, immer den geeignetsten auszuwählen. Bei dieser Herangehensweise, auch bekannt als Algorithm Selection, wird aus einer Menge von Algorithmen anhand bestimmter Attribute einer Instanz derjenige ausgewählt, von welchem auf dieser Instanz das beste Ergebnis prognostiziert wird.Die vorliegende Arbeit befasst sich mit der Anwendung von Techniken des überwachten Lernens als Algorithm Selection für das GCP. Für diesen Zweck stellen wir verschiedene relevante Attribute eines Graphen vor, welche in polynomieller Zeit berechnet werden können. Des Weiteren evaluieren wir die Performance von 7 modernen (Meta)heuristiken auf 1265 öffentlich verfügbaren Instanzen. Die Ergebnisse dieser Experimente zeigen deutlich, dass keine der Heuristiken im Allgemeinen besser als jede andere ist. Zudem beweisen die Experimente, dass auf der einzelnen Untergruppen von Instanzen jeweils ein oder mehrere Algorithmen deutlich bessere Leistung als der Rest erzielen.Im zweiten Teil dieser Arbeit wird die Information über den besten Algorithmus je Instanz mit ihren charakteristischen Attributen kombiniert, um damit 6 verschiedene Klassifikationsalgorithmen zu trainieren. Im Zuge dieser Experimente identifizieren wir erfolgreiche Attributkombinationen und evaluieren, welchen Einfluss verschiedene Attributtransformationstechniken ausüben. Darüber hinaus untersuchen wir, wie eine verringerte Anzahl von Auswahlmöglichkeiten (d.h. das Entfernen von Algorithmen aus der Menge an Lösungsalgorithmen) die Qualität der Vorhersagen verändert.Im letzten Teil vergleichen wir die Performance eines Systems basierend auf Algorithm Selection mit den zugrundeliegenden Heuristiken auf einer Menge eigens erstellter Instanzen.Diese Experimente zeigen eindeutig, dass Algorithm Selection in allen betrachteten Kriterien bessere Ergebnisse als die einzelnen Algorithmen erzielen kann.The graph coloring problem (GCP) is one of the most-studied NP-hard problems in computer science. Given a graph, the task is to assign a color to all vertices such that no vertices sharing an edge receive the same color and that the number of used colors is minimal. In the recent years, various heuristic and exact approaches for this problem have been developed. However, all of them seem to have advantages and disadvantages, which highly depend on the concrete instance on which they are applied. Consequently, designing an algorithm which finds on each graph the best coloring is hard or, by analogy to the No Free Lunch theorems, even impossible.One possibility to achieve a better performance is to predict for each instance the algorithm which achieves the best performance. This task is known as algorithm selection problem: Given a set of algorithms and a set of intrinsic features of a particular instance, select the algorithm which is predicted to show the best performance on that instance.This thesis investigates the application of machine learning techniques to automatic algorithm selection for the GCP. For this purpose, we first present several specific features of a graph, which can be calculated in polynomial time. Then, we evaluate the performance of 7 state-of-the-art (meta)heuristic algorithms for the GCP based on experimental results on 1265 graphs of 3 public available instance sets. The results clearly show that none of the algorithms is superior to all others. In addition, we analyze the behavior of these algorithms on classes of instances with certain attributes. The experiments show that for each of these classes, there exists at least one heuristic which performs clearly better than the rest. In a subsequent step, we use the knowledge about the best-suited algorithm per instance in combination with intrinsic graph features to train 6 classification algorithms. These supervised learning methods are then used to predict for an unseen instance the most appropriate algorithm. For each classifier, we test multiple parameter settings. We further identify relevant subsets of features and investigate the impact of different data-preparation techniques on the performance of the classifiers. In addition, we study the effect of considering only a subset of heuristics on the overall quality of the prediction.For a meaningful comparison with the underlying heuristics, we evaluate our proposed approach on a new generated set of instances. Our experiments show that algorithm selection based on machine learning is able to outperform all considered solvers regarding several performance criteria
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