1,720,960 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Get PDFWe 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
Searching neural architectures with constraints
Get PDFLAUREA MAGISTRALEÈ innegabile che il Machine Learning (ML) abbia aperto una vasta gamma di opportunità promettenti. AutoML fa un
altro passo avanti automatizzando l’intera pipeline del ML aprendo le porte ad ancora più possibili applicazioni delle
tecniche di ML. In particolare, un metodo di AutoML chiamato Neural Architecture Search (NAS) sta diventando molto
popolare grazie alla sua comprovata efficacia. Questa tecnica mira a scoprire la migliore architettura per una rete neurale
per una specifica task automatizzando la costruzione della rete neurale. Molti algoritmi NAS sono costruiti su una rete
chiamata Once-For-All (OFA), una macro rete che comprende molte possibili configurazioni di reti e che è lo spazio di
ricerca per il nostro algoritmo di ricerca. Il vantaggio principale di questa macro rete è il disaccoppiamento tra ricerca e
addestramento, rendendo possibile dal punto di vista computazionale eseguire la ricerca addestrando la macro rete una
volta e valutando le sottoreti tramite semplice inferenza. Tra questa classe di metodi NAS, MSUNAS (Evolutionary Multi
Objective Surrogate-Assisted NAS) ha mostrato ottimi risultati. Il nostro lavoro propone il miglioramento di MSUNAS
dando vita a un tipo di ricerca di architettura neurale vincolata (CNAS) attraverso l’uso di vincoli che possono essere
personalizzati per scopi specifici, invece dei classici indici di ottimizzazione. Per provare la validità e la varietà di
applicazioni delle nostre modifiche, abbiamo condotto esperimenti sulla nostra CNAS su due casi studio che provengono
dalla letteratura attuale, che sono TinyML e Privacy-Preserving Deep learning with Homomorphic Encryption (PPDL-
HE), e su uno scenario reale. I risultati mostrano che è possibile automatizzare la costruzione di reti neurali efficienti
imponendo vincoli specifici per il nostro assegnato compito con un grande grado di flessibilità in termini di scenari
applicativi.It is undeniable that Machine Learning (ML) has opened up a wealth of
promising opportunities. AutoML takes another step forward by automating the whole
ML pipeline opening the doors to even more possible applications of ML techniques. In
particular, a method of AutoML named Neural Architecture Search (NAS) is becoming
really popular due to its proven effectiveness. This technique aims at discovering the best
architecture for a neural network for a specific need automating the design of the neural
network. Many NAS algorithms are built on top of a Once-For-All (OFA) network,
a supernet that encompasses many configurations of networks that is the search space
for the procedure. The main advantage of this supernet is the decoupling of search
and training, making it computationally feasible to perform the search by training the
supernet once and evaluating the subnetworks through inference. Among this class of NAS
methods, MSUNAS (Evolutionary Multi Objective Surrogate-Assisted NAS) has shown
great results. Our work proposes the enhancement of MSUNAS leading to the setup of a
Constrained Neural Architecture Search (CNAS) through the use of constraints that can
be tailored to specific purposes, instead of the standard optimization indexes. To prove
the validity and the variety of applications of our novelties, we conduct experiments of
the CNAS on two case studies that come from the recent literature, which are TinyML
and Privacy-Preserving Deep Learning with Homomorphic Encryption (PPDL-HE), and
on a real scenario. The results show that it is possible to automatize the construction of
efficient neural networks imposing constraints specific to a given task with a great degree
of flexibility in terms of application scenarios
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