1,720,954 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
Road detection using LiDAR data acquired from aerial surveys of the Republic of Croatia
No full textCilj ovog rada je primjenom dostupnih ulaznih slojeva – digitalnog ortofoto (DOF) rastera, LiDAR (engl. Light Detection and Ranging) podataka te vektorskog sloja cesta preuzetog s OpenStreetMap-a detektirati i izdvojiti cestovni sloj u urbanom području grada Karlovca. Korišteni softver, eCognition Developer, nudi napredne funkcije za segmentiranje i klasificiranje slika. Za segmentaciju se koristi multirezolucijska segmentacija, pristup temeljen na analizi objekata (engl. Object-based Image Analysis - OBIA). Klasifikacija je nadzirana, što uključuje ručno odabiranje reprezentativnih uzoraka za svaku klasu. Proces klasifikacije koristi srednje vrijednosti ulaznih slojeva i algoritam klasifikatora najbližeg susjeda (engl. k-nearest neighbors - kNN) kako bi dodijelio preostale segmente jednoj od dvije definirane klase: cesta i ne-cesta. Analizom utjecaja pojedinih ulaznih slojeva na rezultate klasifikacije, cilj je dublje razumjeti izazove u izdvajanja cesta u urbanom okruženju te raspraviti moguća rješenja. Kombinacija spektralnih kanala, visinskih podataka i vektorskog sloja cesta pokazala se ključnom za poboljšanje točnosti klasifikacije. Međutim, najveći izazov ostaje pravilna klasifikacija ostalih asfaltiranih površina, poput parkirališta i pločnika, koje su spektralno slične cestama. Iako su neki segmenti ovih površina uspješno eliminirani, varijacije u rezultatima i dalje su prisutne. Ovaj rad pruža temelj za buduća istraživanja i unaprjeđenja u području segmentacije i klasifikacije urbanih cesta, ukazujući na važnost integracije različitih izvora podataka i naprednih algoritama za analizu slika.The aim of this study is to detect and extract the road surfaces in the urban area of Karlovac by applying available input layers – digital orthophoto (DOF) raster, LiDAR data, and a line vector layer of roads obtained from OpenStreetMap. eCognition Developer software was used. It offers advanced functions for image segmentation and classification. Multiresolution segmentation, an approach based on object-based image analysis (OBIA), is used for segmentation. The classification is supervised, which includes manually selecting representative samples for each class. The classification process utilizes the mean values of input layers and the k-nearest neighbor (kNN) classifier algorithm to assign the remaining segments to one of two defined classes: road and non-road. By analyzing the impact of individual input layers on classification results, the goal is to gain a deeper understanding of the challenges in road extraction in urban environments and to discuss possible solutions. The combination of spectral bands, elevation data, and the line vector road layer proved to be crucial for improving classification accuracy. However, the greatest challenge remains the accurate classification of other asphalt surfaces, such as parking lots and sidewalks, which are spectrally similar to roads. Although some segments of these surfaces were successfully eliminated, variations in results are still present. This study provides a base for future research and advancements in the field of urban road segmentation and classification, highlighting the importance of integrating different data sources and advanced image analysis algorithms
Road detection using LiDAR data acquired from aerial surveys of the Republic of Croatia
No full textCilj ovog rada je primjenom dostupnih ulaznih slojeva – digitalnog ortofoto (DOF) rastera, LiDAR (engl. Light Detection and Ranging) podataka te vektorskog sloja cesta preuzetog s OpenStreetMap-a detektirati i izdvojiti cestovni sloj u urbanom području grada Karlovca. Korišteni softver, eCognition Developer, nudi napredne funkcije za segmentiranje i klasificiranje slika. Za segmentaciju se koristi multirezolucijska segmentacija, pristup temeljen na analizi objekata (engl. Object-based Image Analysis - OBIA). Klasifikacija je nadzirana, što uključuje ručno odabiranje reprezentativnih uzoraka za svaku klasu. Proces klasifikacije koristi srednje vrijednosti ulaznih slojeva i algoritam klasifikatora najbližeg susjeda (engl. k-nearest neighbors - kNN) kako bi dodijelio preostale segmente jednoj od dvije definirane klase: cesta i ne-cesta. Analizom utjecaja pojedinih ulaznih slojeva na rezultate klasifikacije, cilj je dublje razumjeti izazove u izdvajanja cesta u urbanom okruženju te raspraviti moguća rješenja. Kombinacija spektralnih kanala, visinskih podataka i vektorskog sloja cesta pokazala se ključnom za poboljšanje točnosti klasifikacije. Međutim, najveći izazov ostaje pravilna klasifikacija ostalih asfaltiranih površina, poput parkirališta i pločnika, koje su spektralno slične cestama. Iako su neki segmenti ovih površina uspješno eliminirani, varijacije u rezultatima i dalje su prisutne. Ovaj rad pruža temelj za buduća istraživanja i unaprjeđenja u području segmentacije i klasifikacije urbanih cesta, ukazujući na važnost integracije različitih izvora podataka i naprednih algoritama za analizu slika.The aim of this study is to detect and extract the road surfaces in the urban area of Karlovac by applying available input layers – digital orthophoto (DOF) raster, LiDAR data, and a line vector layer of roads obtained from OpenStreetMap. eCognition Developer software was used. It offers advanced functions for image segmentation and classification. Multiresolution segmentation, an approach based on object-based image analysis (OBIA), is used for segmentation. The classification is supervised, which includes manually selecting representative samples for each class. The classification process utilizes the mean values of input layers and the k-nearest neighbor (kNN) classifier algorithm to assign the remaining segments to one of two defined classes: road and non-road. By analyzing the impact of individual input layers on classification results, the goal is to gain a deeper understanding of the challenges in road extraction in urban environments and to discuss possible solutions. The combination of spectral bands, elevation data, and the line vector road layer proved to be crucial for improving classification accuracy. However, the greatest challenge remains the accurate classification of other asphalt surfaces, such as parking lots and sidewalks, which are spectrally similar to roads. Although some segments of these surfaces were successfully eliminated, variations in results are still present. This study provides a base for future research and advancements in the field of urban road segmentation and classification, highlighting the importance of integrating different data sources and advanced image analysis algorithms
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
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