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31.
ABSTRACTLighting a cultural heritage artifact requires balancing visual perception with preventive conservation, by providing the best lighting (in terms of spectral distribution and quantity) to enable the viewer to appreciate details and color, while limiting photo-induced degradation. The paper outlines the methodology applied by a multi-disciplinary team while lighting the Shroud of Turin at its last public exhibition in 2015. The methodology considered the special requirements of the Shroud, including exposure to ultraviolet light, while providing appropriate display conditions that would meet audience expectations. The desired appearance (readability of the body image and color) was defined with the help of Shroud researchers and confirmed by subjective tests, while appropriate light levels for preservation were set in agreement with standard requirements and using knowledge of the degradation of linen in visible and UV light. The installation provided a controlled environment and a managed visitor route to the Shroud, assuring excellent perception of both details and color, with the lowest illuminance level about 15?lx. 相似文献
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Stacy L. Young Manuel D. Pulido Catherine F. Brooks 《Communication Research Reports》2018,35(2):172-177
This study extends instructional communication research on extra-class communication (ECC) by presenting an integrative analysis comparing the explanatory utility of student- and instructor-oriented reasons for student engagement in frequent ECC. Results from 495 college students suggested that student-oriented reasons, specifically the relational and functional student motives, were the only significant, positive predictors of students’ perceptions of frequent ECC engagement. Other student motives (excuse making, participation, sycophancy) and perceptions of instructor approachability did not significantly predict perceived frequency of ECC engagement. 相似文献
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Debasis?GangulyEmail authorView authors OrcID profile Gareth?J.?F.?Jones Aarón?Ramírez-de-la-Cruz Gabriela?Ramírez-de-la-Rosa Esaú?Villatoro-Tello 《Information Retrieval》2018,21(1):1-23
Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents. 相似文献
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ABSTRACTEmploying a number of different standalone programs is a prevalent approach among communication scholars who use computational methods to analyze media content. For instance, a researcher might use a specific program or a paid service to scrape some content from the Web, then use another program to process the resulting data, and finally conduct statistical analysis or produce some visualizations in yet another program. This makes it hard to build reproducible workflows, and even harder to build on the work of earlier studies. To improve this situation, we propose and discuss four criteria that a framework for automated content analysis should fulfill: scalability, free and open source, adaptability, and accessibility via multiple interfaces. We also describe how to put these considerations into practice, discuss their feasibility, and point toward future developments. 相似文献
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