1,721,006 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
Cross-modal learning from visual information for activity recognition on inertial sensors
Get PDFThe lack of large-scale, labeled datasets impedes progress in developing robust and generalized predictive models for human activity recognition (HAR) from wearable inertial sensor data. Labeled data is scarce as sensor data collection is expensive, and their annotation is time-consuming and error-prone. As a result, public inertial HAR datasets are small in terms of number of subjects, activity classes, hours of recorded data, and variation in recorded environments. Machine learning models, developed using these small datasets, are effectively blind to the diverse expressions of activities performed by wide-ranging populations in the real world, and progress in wearable inertial sensing is held back by this bottleneck for activity understanding. .
But just as Internet-scale text, image and audio data have pushed their respective pattern recognition fields to systems reliable enough for everyday use, easy access to large quantities of data can push forward the field of inertial HAR, and by extension wearable sensing. To this end, this thesis pioneers the idea of exploiting the visual modality as a source domain for cross-modal learning, such that data and knowledge can be transferred across to benefit the target domain of inertial HAR. .
This thesis makes three contributions to inertial HAR through cross-modal approaches. First, to overcome the barrier of expensive inertial data collection and annotation, we contribute a novel pipeline that automatically extracts virtual accelerometer data from videos of human activities, which are readily annotated and accessible in large quantities. Second, we propose acquiring transferable representations about activities, from HAR models trained using large quantities of visual data to enrich the development of inertial HAR models. Finally, the third contribution exposes HAR models to the challenging setting of zero-shot learning; we propose mechanisms that leverage cross-modal correspondence to enable inference on previously unseen classes. .
Unlike prior approaches, this body of work pushes forward the state of the art in HAR not by exhausting resources concentrated in the inertial domain, but by exploiting an existing, resourceful, intuitive, and informative source, the visual domain. These contributions represent a new line of cross-modal thinking in inertial HAR, and suggest important future directions for inertial-based wearable sensing research
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
High performance deep learning on resource constrained platforms
Get PDFAfter a decade of accelerated progress in the different areas of machine learning (ML), it has become virtually impossible to not interact with ML-powered applications and services in our everyday lives. Recommender systems, computational photography, speech translation or, getting directions are only a handful of examples of what can be done from our smartphones or other smart devices, sometimes battery-powered, present in the places where we live, work and, socialize. However, despite the known benefits of running such applications on-device (e.g. privacy, works offline, real-time potential), their reliance on millions (even billions!) of parameters and their increasingly complex execution patterns, have prevented these from leaving the Cloud, where model inference takes place. As a result, only a fraction of ML-powered applications, such as keyword spotting and next-word prediction, remain lightweight enough in terms of memory and compute footprint to run on-device.
Motivated by the urge to deploying more advanced ML-powered applications on commodity devices, the research community has primarily relied on three sets of techniques to lower their runtime costs: pruning, which results in model compression and acceleration during inference by discarding model parameters; quantization, which offers model size savings by representing parameters with fewer bits and, often translates into faster inference when using lower precision arithmetic; and, by making use of lightweight network architecture designs which better map to the target platform's hardware. However, in many cases the aforementioned techniques are insufficient to deliver the required compute, memory and energy savings to run most ML-powered applications on-device. What further optimization avenues would enable the deployment and the efficient execution of complex and better performing models on constrained devices?
This thesis studies this challenging problem from three different perspectives: first, by identifying a new paradigm for compression frameworks where model parameters are generated on-the-fly, enabling inference acceleration by reducing the impact of data movement costs; second, by solving the inherent problem of numerical degradation that has prevented the adoption and deployment of fast Winograd convolutions for models making use of integer arithmetic; and third, by proposing mechanisms that enable the learning of quantized graph neural networks for a variety of applications operating on irregular grids. Each of these contributions materialize into three different frameworks, namely unzipFPGA, wiNAS and Degree-Quant, which are comprehensively evaluated through a wide range of experiments and ablation studies. These contributions push the state of the art on three different fronts and can be used in conjunction with other existing acceleration techniques to collectively enable the deployment of high performance deep learning applications on resource constrained devices
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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