1,720,980 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
RETRIEVAL OF AUTHORITIES AND THEIR EVIDENCE FOR RUMOR VERIFICATION IN ARABIC SOCIAL MEDIA
Full text linkSocial media platforms have become a medium for rapidly spreading rumors along with emerging events. Those rumors may have a lasting effect on users' opinion even after it is debunked, and may continue to influence them if not replaced with convincing evidence. Journalists, or even normal users, who attempt to verify a rumor over social media, try to find a trusted source of evidence that can help them confirm or deny that specific rumor. A strong source of evidence for verifying a rumor is an authority who has the "real knowledge or power" to verify it if asked to. This dissertation contributes towards addressing the problem of rumor verification in social media. We propose augmenting the traditional rumor verification pipeline, which considers the propagation networks and the Web as sources of evidence, by incorporating authorities as another source of evidence. Specifically, in this dissertationwe introduce the problem of rumor verification using evidence from authorities which we believe can help fact-checkers and automated rumor verification systems to find the right authorities and evidence from their Twitter timelines, hence helping in the verification process. First, we propose authority finding in Twitter. We then suggest incorporating those retrieved authorities by detecting their stance towards rumors in Twitter, and retrieving evidence from their timeline tweets. Finally, we propose rumor verification using evidence retrieved from those authorities. To address the problem, we construct and release three datasets targeting the Arabic language namely 1) the first Authority FINding in Twitter (AuFIN) which comprises 150 rumors (expressed in tweets) associated with a total of 1,044 authority accounts and a user collection of 395,231 Twitter accounts (members of 1,192,284 unique Twitter lists), 2) the first Authority STance towards Rumors (AuSTR) which comprises 811 (rumor tweet, authority tweet) pairs relevant to 292 unique rumors, 3) the first Authority- Rumor-Evidence Dataset (AuRED) which comprises 160 rumors expressed in tweets and 692 Twitter timelines of authorities comprising about 34k annotated tweets in total. We propose a hybrid retrieval authority finding model that combines lexical and semantic signals in addition to user profiles and network features. Furthermore, we investigate the usefulness of existing Arabic datasets for stance towards claims for detecting the stance of authorities. Finally, we study the effectiveness of existing factchecking models for evidence retrieval from authorities and rumor verification using the retrieved evidence. Our experimental results suggest that Twitter lists and network features such as followers, and followees count, adopted previously for topic expert finding models, play a crucial role in authority finding; however, they are insufficient. This motivates the need to explore other features to differentiate experts from authorities. Moreover, our proposed hybrid model incorporating lexical, semantic, and user network features achieved a modest performance, 0.41 as precision at depth 1, which indicates that finding authorities is a challenging task, and that there is still room for continued enhancement. Our results also highlighted that adopting existing Arabic stance datasets for claim verification is somewhat useful but clearly insufficient for detecting the stance of authorities. Moreover, we found that AuSTR solely, despite the limited size, can be sufficient for detecting the stance of authorities achieving a performance of 0.84 macro-F1 and 0.78 F1 on debunking tweets. Our investigation on the effectiveness of existing fact-checking (claim verification using evidence from Wikipedia pages) models on our problem highlighted that although evidence retrieval for fact-checking models performrelativelywell on evidence retrieval from authorities, establishing strong baselines achieving 0.70 as recall at depth 5, there is still a big room for improvement. However, existing claim verification for fact-checking models perform poorly on rumor verification using evidence from authorities, 0.42 as macro-F1, no matter how good the retrieval performance is. Moreover, existing fact-checking datasets showed a potential in transfer learning to our problem, however, further investigation using different setups and datasets is required. Furthermore, drawing upon our experiments, we discuss failure factors and make recommendations for future research directions in addressing this problem. Additionally, our approach establishes a strong baseline for future studies targeting automatic rumor verification in social media, and our constructed datasets can facilitate further research on the problem. Finally, our proposed system can be integrated into verification systems, and can be also exploited by fact-checkers or journalists to find trusted sources of evidence
QoE-Aware Resource Allocation For Crowdsourced Live Streaming: A Machine Learning Approach
Full text linkIn the last decade, empowered by the technological advancements of mobile devices
and the revolution of wireless mobile network access, the world has witnessed an
explosion in crowdsourced live streaming. Ensuring a stable high-quality playback
experience is compulsory to maximize the viewers’ Quality of Experience and the
content providers’ profits. This can be achieved by advocating a geo-distributed cloud
infrastructure to allocate the multimedia resources as close as possible to viewers, in
order to minimize the access delay and video stalls.
Additionally, because of the instability of network condition and the heterogeneity of
the end-users capabilities, transcoding the original video into multiple bitrates is
required. Video transcoding is a computationally expensive process, where generally a
single cloud instance needs to be reserved to produce one single video bitrate
representation. On demand renting of resources or inadequate resources reservation
may cause delay of the video playback or serving the viewers with a lower quality. On
the other hand, if resources provisioning is much higher than the required, the
extra resources will be wasted.
In this thesis, we introduce a prediction-driven resource allocation framework, to
maximize the QoE of viewers and minimize the resources allocation cost. First, by
exploiting the viewers’ locations available in our unique dataset, we implement a machine learning model to predict the viewers’ number near each geo-distributed cloud
site. Second, based on the predicted results that showed to be close to the actual values,
we formulate an optimization problem to proactively allocate resources at the viewers’
proximity. Additionally, we will present a trade-off between the video access delay and
the cost of resource allocation.
Considering the complexity and infeasibility of our offline optimization to respond to
the volume of viewing requests in real-time, we further extend our work, by introducing
a resources forecasting and reservation framework for geo-distributed cloud sites. First,
we formulate an offline optimization problem to allocate transcoding resources at the
viewers’ proximity, while creating a tradeoff between the network cost and viewers
QoE. Second, based on the optimizer resource allocation decisions on historical live
videos, we create our time series datasets containing historical records of the optimal
resources needed at each geo-distributed cloud site. Finally, we adopt machine learning
to build our distributed time series forecasting models to proactively forecast the exact
needed transcoding resources ahead of time at each geo-distributed cloud site.
The results showed that the predicted number of transcoding resources needed in each
cloud site is close to the optimal number of transcoding resources
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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