1,720,978 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
Malicious PowerShell Detection Using Attention against Adversarial Attacks
No full textCurrently, hundreds of thousands of new malicious files are created daily. Existing pattern-based antivirus solutions face difficulties in detecting such files. In addition, malicious PowerShell files are currently being used for fileless attacks. To prevent these problems, artificial intelligence-based detection methods have been suggested. However, methods that use a generative adversarial network (GAN) to avoid AI-based detection have been proposed recently. Attacks that use such methods are called adversarial attacks. In this study, we propose an attention-based filtering method to prevent adversarial attacks. Using the attention-based filtering method, we can obtain restored PowerShell data from fake PowerShell data generated by GAN. First, we show that the detection rate of the fake PowerShell data generated by GAN in an existing malware detector is 0%. Subsequently, we show that the detection rate of the restored PowerShell data generated by attention-based filtering is 96.5%
Similar Word Replacement Method for Improving News Commenter Analysis
No full textIn Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 million news comments posted in April 2017. We classified the political stance of each author using a deep learning model (seq2seq), and evaluated how many similar comments each user wrote, as well as how similar each comment was to those posted by other people, using the Jaccard similarity coefficient. However, as our previous model used Jaccard’s similarity only, the meaning of the comments was not considered. To solve this problem, we propose similar word replacement (SWR) using word2vec and a method to analyze the similarity between user comments and classify the political stance of each user. In this study, we showed that when our model used SWR rather than Jaccard’s similarity, its ability to detect similarity between comments increased 3.2 times, and the accuracy of political stance classification improved by 6%
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
Internet News User Analysis Using Deep Learning and Similarity Comparison
No full textNowadays, many Korean users read news from portal sites like Naver and Daum. Users can comment on news articles on such sites, and some try to influence public opinion through their comments. Therefore, news users need to be analyzed. This study proposes a deep learning method to classify each user’s political stance. Further, a method is developed to evaluate how many similar comments each user writes, and another method is developed to evaluate the similarity of a user’s comments with other users’ comments. We collect approximately 2.68 million comments from hundreds of thousands of political news articles in April 2017. First, for the top 100 news users, we classify each user’s political stance with 92.3% accuracy by using only 20% of data for deep learning training. Second, an evaluation of how many similar comments each user writes reveals that six users score more than 80 points. Third, an evaluation of the similarity of each user’s comments to other users’ comments reveals that 10 users score more than 80 points. Thus, based on this study, it is possible to detect malicious commenters, thereby enhancing comment systems used in news portal websites
Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection
No full textEvery day, hundreds of thousands of new malicious files are created. Existing pattern-based antivirus solutions have difficulty detecting these new malicious files. Artificial intelligence (AI)–based malware detection has been proposed to solve the problem; however, it takes a long time. Similarity hash–based detection has also been proposed; however, it has a low detection rate. To solve these problems, we propose k-nearest-neighbor (kNN) classification for malware detection with a vantage-point (VP) tree using a similarity hash. When we use kNN classification, we reduce the detection time by 67% and increase the detection rate by 25%. With a VP tree using a similarity hash, we reduce the similarity-hash search time by 20%
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