1,720,987 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
Deep Learning Algorithms for Longitudinal Driving Behavior Prediction: A Comparative Analysis of Convolutional Neural Network and Long–Short-Term Memory Models
Full text linkIn the realm of transportation science, the advent of deep learning has propelled advancements in predicting longitudinal driving behavior. This study explores the application of deep neural network
architectures, specifically long–short-term memory (LSTM) and convolutional neural networks (CNNs), recognized for their effectiveness in handling sequential data. Using a 3-s temporal window that includes past vehicle progress, speed, and acceleration, the proposed model, a hybrid LSTM–CNN architecture, predicts the vehicle’s speed and progress for the next 6 s. The approach achieves
state-of-the-art performance, particularly within a 4 s horizon, but remains competitive even for longer-term predictions. This is achieved despite the simplicity of its input space, which does not include information about vehicles other than the target vehicle. As a result, while its performance may decrease slightly for longer-term predictions due to the lack of environmental information, it
still offers reliable predictions and can be applied effectively in scenarios with partial observability.
The comparative analysis of multilayer perceptron (MLP), LSTM, and one-dimensional CNN architectures highlights the challenges faced by MLP in capturing the complex nonlinearity of driving behavior. LSTM and CNN demonstrate superior performance, with model complexity influencing outcomes. No statistically significant difference is observed in the performance between LSTM and CNN models
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
Erstellung eines sensorbasierten Straßenmodells für das automatisierte Fahren
Full text linkDas Wissen über die unmittelbare Umgebung ist von größter Wichtigkeit für sämtliche Anwendungen in der Robotik und des autonomen Fahrens. Für das autonome Fahren werden diese Informationen stets aus hochgenauen Karten extrahiert, die den Einsatz einer hochpräzisen Lokalisierung voraussetzen. Damit geht die Beschränkung einher, dass das System nur in denjenigen Arealen verwendet werden kann, die zuvor kartiert worden sind. Darüber hinaus sorgen nachträgliche Änderungen der Umgebung dafür, dass die hochgenaue Karte anschließend nicht mehr mit der Realität übereinstimmt und damit ihre Gültigkeit verliert.
Daher widmet sich die vorliegende Arbeit der Problemstellung, wie die unmittelbare Umgebung eines autonom fahrenden Fahrzeugs ohne Zuhilfenahme einer hochgenauen Karte, sondern ausschließlich mit Hilfe von im Fahrzeug verbauter Sensorik erfasst und repräsentiert werden kann. Ausgehend von der „Dempster-Shafer theory of evidence” wird eine neue Methode vorgestellt, um Sensor-Messdaten wie bspw. erkannte Fahrstreifenmarkierungslinien, Punktewolken von einer bildbasierten semantischen Segmentierung, Belegungskarten und andere Verkehrsteilnehmer in einem gridbasierten Umfeldmodell miteinander zu fusionieren und dabei die semantische Bedeutung jeder Gridzelle zu schätzen. Anschließend werden mit Hilfe einer Pfadplanung aus dem gridbasierten Modell potentielle Fahrstreifen extrahiert und in ein geeignetes geometrisches Straßenmodell überführt. Alle entwickelten Methoden wurden mit Sensor-Messdaten aus realen Verkehrssituationen evaluiert und die Leistungsfähigkeit der vorgestellten Lösungen exemplarisch dargestellt.
Im Rahmen der Arbeit entstanden unter anderem 4 Publikationen (Erstautorenschaft), in denen die Forschungsergebnisse veröffentlicht wurden [1, 2, 3, 4]
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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