1,720,956 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
An Optimized Hardware System on Chip for a Support Vector Machine Classifier: a Case Study on Melanoma Detection
No full textSupport Vector Machine (SVM) is a robust machine learning model used for efficient classification with high accuracy. SVM is widely utilized for online classification in various embedded applications. However, implementing the SVM classification algorithm for an embedded system or application is challenging, due to intensive and complicated computations required. This increases the importance of implementing SVM on hardware platforms for achieving high performance computing at low cost and power consumption.
Field-Programmable Gate Array (FPGA) is a powerful parallel processing reconfigurable device that is widely used for achieving essential performance of embedded systems, while effectively utilizing hardware resources, offering low cost and low power consumption. Accordingly, FPGA is a promising hardware platform for implementing an efficient embedded SVM classification system, while achieving vital embedded system constraints.
SVM has shown high accuracy for classifying melanoma (skin cancer) clinical images within a computer-aided diagnosis system used by dermatologists to detect melanoma early and save lives. This research aims to develop an optimized FPGA-based SVM classifier to be embedded within a low-cost handheld medical scanning device that runs an embedded SVM-based diagnosis system dedicated to early detection of melanoma in primary care. We aim to consider meeting significant constraints of embedded systems, while achieving efficient classification with high accuracy rate.
A hardware/software co-design for implementing an SVM classifier onto FPGA is proposed to realize melanoma detection on a chip. This SVM implementation achieves efficient melanoma classification on a recent FPGA-based hybrid platform “Zynq SoC” designed using the latest UltraFast High-Level Synthesis design methodology. The hardware implementation results demonstrate classification accuracy of 97.9% and a significant hardware acceleration rate of up to 37x with only 2.7% resource utilization and 1.69 watts for power consumption.
Furthermore, a scalable multi-core architecture is proposed to achieve multi-purpose classification on a single chip/device, which has been validated with a 2-stage cascade classifier implementation with accuracies of 98 % and 73%, to enhance melanoma detection. A simple hardware-friendly design is proposed for the building SVM core of the multi-core architecture, aiming to reduce hardware complexity and optimize implementation results for achieving an efficient classification performance.
A novel dynamic hardware system is also proposed for implementing a cascade SVM classifier on FPGA for early melanoma detection. The hardware implementation results are optimized by using the powerful dynamic partial reconfiguration technology, where very low resource utilization of 1% slices and power consumption of 1.5 watts are achieved.
The implemented SVM classification systems on Zynq SoC using the proposed hardware designs have shown the least power consumption results among other related implementations, in addition to significantly low hardware resource utilization and processing time with significant speedups and high classification accuracy rates at low cost. Consequently, the implemented Zynq systems meet crucial embedded system constraints of high performance and low cost, resource utilization and power consumption, while achieving efficient classification with high classification accuracy, which promises realization of a cost- and energy-efficient handheld medical scanning device for early detection of melanoma
An Optimized Hardware System on Chip for a Support Vector Machine Classifier: A Case Study on Melanoma Detection
No full textSupport Vector Machine (SVM) is a robust machine learning model used for efficient classification with high accuracy. SVM is widely utilized for online classification in various embedded applications. However, implementing the SVM classification algorithm for an embedded system or application is challenging, due to intensive and complicated computations required. This increases the importance of implementing SVM on hardware platforms for achieving high performance computing at low cost and power consumption.
Field-Programmable Gate Array (FPGA) is a powerful parallel processing reconfigurable device that is widely used for achieving essential performance of embedded systems, while effectively utilizing hardware resources, offering low cost and low power consumption. Accordingly, FPGA is a promising hardware platform for implementing an efficient embedded SVM classification system, while achieving vital embedded system constraints.
SVM has shown high accuracy for classifying melanoma (skin cancer) clinical images within a computer-aided diagnosis system used by dermatologists to detect melanoma early and save lives. This research aims to develop an optimized FPGA-based SVM classifier to be embedded within a low-cost handheld medical scanning device that runs an embedded SVM-based diagnosis system dedicated to early detection of melanoma in primary care. We aim to consider meeting significant constraints of embedded systems, while achieving efficient classification with high accuracy rate.
A hardware/software co-design for implementing an SVM classifier onto FPGA is proposed to realize melanoma detection on a chip. This SVM implementation achieves efficient melanoma classification on a recent FPGA-based hybrid platform “Zynq SoC” designed using the latest UltraFast High-Level Synthesis design methodology. The hardware implementation results demonstrate classification accuracy of 97.9% and a significant hardware acceleration rate of up to 37x with only 2.7% resource utilization and 1.69 watts for power consumption.
Furthermore, a scalable multi-core architecture is proposed to achieve multi-purpose classification on a single chip/device, which has been validated with a 2-stage cascade classifier implementation with accuracies of 98 % and 73%, to enhance melanoma detection. A simple hardware-friendly design is proposed for the building SVM core of the multi-core architecture, aiming to reduce hardware complexity and optimize implementation results for achieving an efficient classification performance.
A novel dynamic hardware system is also proposed for implementing a cascade SVM classifier on FPGA for early melanoma detection. The hardware implementation results are optimized by using the powerful dynamic partial reconfiguration technology, where very low resource utilization of 1% slices and power consumption of 1.5 watts are achieved.
The implemented SVM classification systems on Zynq SoC using the proposed hardware designs have shown the least power consumption results among other related implementations, in addition to significantly low hardware resource utilization and processing time with significant speedups and high classification accuracy rates at low cost. Consequently, the implemented Zynq systems meet crucial embedded system constraints of high performance and low cost, resource utilization and power consumption, while achieving efficient classification with high classification accuracy, which promises realization of a cost- and energy-efficient handheld medical scanning device for early detection of melanoma
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
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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