1,720,977 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
Towards Practical and Resource-Efficient Volumetric Video Streaming
No full textVolumetric video offers immersive, six-degrees-of-freedom (6DoF) experiences but presents significant challenges in streaming due to its massive data size, high bitrate demands, and computational complexity. This thesis proposes two systems to address these challenges. First, we present VV-DASH, an end-to-end framework for adaptive volumetric video streaming over DASH. VV-DASH introduces a codec-agnostic segment format (DVV) that consolidates compressed volumetric content into DASH-ready segments, improving streaming throughput by 13.2% and reducing bandwidth demands. It also features a parallel accelerator that enables real-time decoding—achieving up to 183 fps for Draco and 30 fps for V-PCC—while supporting flexible integration with diverse codec pipelines. Next, we introduce TARS, a temporal-spatial adaptive streaming solution for dynamic point cloud videos that reduces temporal redundancy by exploiting inter-frame correlations. TARS employs a Field-of-View (FoV)-aware approach to intelligently avoid the retransmission of redundant regions across consecutive frames, leveraging a specialized Point Cloud Structural Similarity metric for precise similarity assessment. Experiments show that TARS reduces bandwidth usage by up to 66% while maintaining high visual quality (MSE as low as 0.145), and boosts decoding speed by up to 2.42x compared to decoding all I-frames. These results demonstrate the effectiveness of VV-DASH and TARS for enabling practical, real-time, and resource-efficient volumetric video streaming
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
Bi-Modal Deep Neural Network for Gait Emotion Recognition
No full textEmotion Recognition systems can be used for autonomous tasks such as video gaming experiences, medical diagnosis, adaptive education, and smart homes. Several biometric modalities, including face, hands, and voice have been successfully used for emotion recognition tasks. Gait Emotion Recognition (GER) is an emerging domain of research that is focused on identifying the emotional state of a person from gait biometric, which represents the person’s manner of walking. In comparison to the other modalities, gait provides a non-intrusive method to collect data remotely without an expert’s supervision. Moreover, unlike facial expression-based emotion recognition, it does not require high-resolution data for inference. Early works in GER produced limited feature sets and used classical machine learning methodologies to infer emotions, but could not achieve high performance. This thesis proposes powerful architectures based on deep-learning to accurately identify emotions from human gaits. The proposed Bi-Modal Deep Neural Network (BMDNN) architecture utilizes robust handcrafted features that are independent of dataset size and data distribution. The network is based on Long Short-Term Memory units and Multi-Layered Perceptrons to sequentially process raw gait sequences and facilitate feature fusion with the handcrafted features. Lastly, the proposed Bi-Modular Sequential Neural Network (BMSNN) has a low number of parameters and a low inference time, hence making it suitable for deployment in real world applications. The proposed methodologies were evaluated on the Edinburgh Locomotive MoCap Dataset and outperformed all recent state-of-the-art 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
Physically-based animation of plant motions
No full textThe creation of realistic and lifelike plants has been a long-standing challenge in computer graphics. While signifcant progress has been made regarding the generation of plants using procedural methods, there is still a gap in understanding how to simulate their dynamics as effciently and realistically as possible. One of the major challenges in this area is the incorporation of complicated non-inertial effects into plant motion. Previous works tend to either focus on quasistatic simulations - which by defnition assume the absence of non-inertial effects - or ignore secondary motion in their dynamics calculations altogether. Either of these result in incomplete simulations that do not adequately capture the wide range of plant motion observed in nature. This is important because the human eye is keenly critical of inconsistencies in motion, meaning that incomplete models can easily appear off-putting and uncanny. To address these limitations, this thesis proposes a generalized and comprehensive physics model that aims to better capture the dynamics of procedurally-generated plants
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