• Journal of the Korean Society for Library and Information Science
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• v.31 no.4
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• pp.289-318
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• 1997

The purpose of this study is to identify users behavioral characteristics for using the online catalog opened in May 1996 at Duksung Women's University Library. 278 student users were surveyed from October 4th to 8th in 1996. Major findings are as follows. 1. Most users$(87.4\%)$ prefer the online catalog to the card catalog and regard the online catalog easy to use$(89.6\%)$ 2. $(65.8\%)$ of users are active users who frequently use the online catalog at least 10 times or more per semester. 3. $10.4\%$ of users feel the online catalog difficult because they do not know how to use it. 4. Most users prefer the menu search mode among menu, command and fill-in-blank search modes offered by DISCOVER. The most preferred access points are the title for known-item search and subject headings for subject search. 5. User's attitude toward the online catalog is very favorable$(83.5\%)$, however, the search success rate is rather low $(77.0\%)$ compared to that of the card catalog $(87.0\%)$ 6. The title and author are regarded easy to use among access points offered by DISCOVER. Classification numbers and call numbers are the least easy access points to use. 7. Since users show lack of knowledge of how to use the online catalog, education and training programs on the online catalog use for users are needed. 8. Users showed different search patterns for pursuing different search goals. The most preferred access points are the title for known-item search and subject headings for subject search. These search behaviors are different from those in using the card for both the known-item search and subject search was the title.

• 한국생물공학회:학술대회논문집
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• 2000.11a
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• pp.749-754
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• 2000

PCR primers were designed based on consensus sequences of dTDP-D-glucose 4,6-dehydratase, one of the enzymes involved in the biosynthesis of deoxysugar. The PCR product (360 bp) was obtained from Thermus caldophilus GK24. Colony hybridization was carried out to the cosmid library constructed from T. caldophilus GK24 genomic DNA by the PCR product DNA fragment. We isolated a cosmid clone (pSMTC-1) that was subcloned to call pKCB series plasmid (BamHI fragments), partially sequenced and analyzed. pKCB80 (4.2 kb-BamHI DNA fragment) of them showed ORFs that was orfA, orfB, orfC and orfD. The orfABCD gene cluster is the deosysugar biosynthetic gene ; orfA (glucose-1-phosphate thymidylytransferase), orfB (dTDP-D-glucose 4,6-dehydratase), orfC (dTDP-4-keto-L-rhamnose reductase) and orfD (dTDP-4-keto-6-deoxy-D-glucose 3,5-epimerase). The gene cluster that was related in biosynthesis of dTDP-L-rhamnose was also identified by computer analysis, and we proposed that the biosynthetic pathway of deoxysugar analyzed from DNA sequencing of pKCB80 is from D-glucose-1-phosphate, dTDP-D-glucose, dTDP-4-keto-6-deoxy-D-glucose via dTDP-4-keto-L-rhamnose to dTDP-L-rhamnose.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.45-55
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• 2020

Due to the development of information and communication technology, the number of new / variant malicious codes is increasing rapidly every year, and various types of malicious codes are spreading due to the development of Internet of things and cloud computing technology. In this paper, we propose a malware analysis method based on string information that can be used regardless of operating system environment and represents library call information related to malicious behavior. Attackers can easily create malware using existing code or by using automated authoring tools, and the generated malware operates in a similar way to existing malware. Since most of the strings that can be extracted from malicious code are composed of information closely related to malicious behavior, it is processed by weighting data features using text mining based method to extract them as effective features for malware analysis. Based on the processed data, a model is constructed using various machine learning algorithms to perform experiments on detection of malicious status and classification of malicious groups. Data has been compared and verified against all files used on Windows and Linux operating systems. The accuracy of malicious detection is about 93.5%, the accuracy of group classification is about 90%. The proposed technique has a wide range of applications because it is relatively simple, fast, and operating system independent as a single model because it is not necessary to build a model for each group when classifying malicious groups. In addition, since the string information is extracted through static analysis, it can be processed faster than the analysis method that directly executes the code.