• Proceedings of the Korea Information Processing Society Conference
• /
• 2017.04a
• /
• pp.537-539
• /
• 2017

학술활동 생명주기는 저자, 학술단체 및 정보서비스 제공자의 관점에서 논문투고, 심사, 출판, 데이터베이스 구축, 정보서비스로 정의될 수 있다. 생명주기 단계별로 학술단체들은 온라인 또는 오프라인 도구들을 이용해 저자와 심사위원들간의 커뮤니케이션을 돕고 있으며, 정보서비스 제공기관들은 출판된 학술정보를 수집하고 데이터베이스를 구축하여 정보서비스를 제공하고 있다. 이들 단계들에 표준식별체계를 적용하여 유기적으로 연결해 주면 생명주기 활동에 들어가는 전체적인 시간, 노력, 비용을 절감 할 수 있다. 이 발표에서는 학술활동 생명주기의 이해당사자들의 요구를 전반적으로 만족시키기 위해 개발된 DOI등록 관리 서비스의 설계 및 구현에 대해서 논한다.

• Journal of the Korean Society for Library and Information Science
• /
• v.45 no.3
• /
• pp.235-257
• /
• 2011

In this study, we proposed the FRAD concept model of authority data schema for author, organization and journal titles included in the academic papers. Academic information includes author names, affiliations, publishers, journals and conferences. They are used as access points, and there are multiple relationships among these entities. It is expected that the use of authority data for academic information based on FRAD conceptual model could improve more accurate retrieval of information, systematic management of academic information, and various forms of knowledge representation. In this study, our entity-relationship authority data will be linked to the document, and included the several properties and relationship to identify the object.

• KLA journal
• /
• v.9 no.6
• /
• pp.7-14
• /
• 1968

이글은 경학ㆍ사학ㆍ불교학등 제면에 박식한 중국의 석학인 고 양계초(Liang Chi-Chao 1378~1930)씨의 ‘고서진위여기년대’(대만, 중화서국, 국민 52)를 역한 것이다. 본저는 저자가 그의 만년에 전생을 거쳐 연구한 한 단면을 정리, 국민 16년 2월부터 6월까지 반년간 북경 연경대학에서 이를 강의한 내용이다. 총론에 제기된 고서의 진위식별에 대한 제문제는 동양학연구는 물론, 고서정리와 서지거연구자들의 지침서로 될 것이며, 각론에 소개된 경전을 비롯한 제문헌의 해제는 동양학부문에 있어서의 인문ㆍ사회과학서지연구의 나침반으로 믿어 의심치 않는다.

• Review of KIISC
• /
• v.32 no.5
• /
• pp.21-25
• /
• 2022

디지털 트랜스포메이션(Digital Transformation)으로 인해 소프트웨어에 대한 의존성이 강화되면서 소프트웨어 공급망의 역할이 커지고 있다. 그러나 안전한 소프트웨어 개발 및 이용을 위한 소프트웨어 공급망 보안 관리는 사실상 어려운 실정이다. 공급망이 복잡해질수록 공급망 공격의 유형은 다각화되는 반면 공급망을 구성하는 공급업체 및 구성요소에 대한 식별 및 취약점 분석은 어려워지기 때문이다. 이에 저자는 국내외 소프트웨어 공급망 보안 관리를 위한 기술 동향에 대한 조사 분석을 수행하고 이를 기반으로 향후 적용할 수 있는 공급망 보안 관리 체계의 발전 방향에 대해 작성하였다.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.10
• /
• pp.43-57
• /
• 2022

In this paper, we propose a method of analyzing research papers published by researchers belonging to university departments in the field of disaster & safety for the scientometric analysis of the research status in the field of disaster safety. In order to conduct analysis research, the dataset constructed in previous studies was newly improved and utilized. In detail, for research papers of authors belonging to the disaster prevention and safety engineering type department of domestic universities, institution identification, cited journal identification of references, department type classification, disaster safety type classification, researcher major information, KSIC(Korean Standard Industrial Classification) mapping information was reflected in the experimental data. The proposed method has a difference from previous studies in the field of disaster & safety and data set based on related keyword searches. As a result of the analysis, the type and regional distribution of organizations belonging to the department of disaster prevention and safety engineering, the composition of co-authored department types, the researchers' majors, the status of disaster safety types and standard industry classification, the status of citations in academic journals, and major keywords were identified in detail. In addition, various co-occurrence networks were created and visualized for each analysis unit to identify key connections. The research results will be used to identify and recommend major organizations and information by disaster type for the establishment of an intelligent crisis warning system. In order to provide comprehensive and constant analysis information in the future, it is necessary to expand the analysis scope and automate the identification and classification process for data set construction.

• Journal of Internet Computing and Services
• /
• v.18 no.6
• /
• pp.35-46
• /
• 2017

Recently, attackers using malicious code in cyber security have been increased by attaching malicious code to a mail and inducing the user to execute it. Especially, it is dangerous because it is easy to execute by attaching a document type file. The author analysis is a research area that is being studied in NLP (Neutral Language Process) and text mining, and it studies methods of analyzing authors by analyzing text sentences, texts, and documents in a specific language. In case of attack mail, it is created by the attacker. Therefore, by analyzing the contents of the mail and the attached document file and identifying the corresponding author, it is possible to discover more distinctive features from the normal mail and improve the detection accuracy. In this pager, we proposed IADA2(Intelligent Attack mail Detection based on Authorship Analysis) model for attack mail detection. The feature vector that can classify and detect attack mail from the features used in the existing machine learning based spam detection model and the features used in the author analysis of the document and the IADA2 detection model. We have improved the detection models of attack mails by simply detecting term features and extracted features that reflect the sequence characteristics of words by applying n-grams. Result of experiment show that the proposed method improves performance according to feature combinations, feature selection techniques, and appropriate models.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.9
• /
• pp.373-378
• /
• 2019

Since many programming sources are open online, problems with reckless plagiarism and copyrights are occurring. Among them, source codes produced by repeated authors may have unique fingerprints due to their programming characteristics. This paper identifies each author by learning from a Google Code Jam program source using deep neural network. In this case, the original creator's source is to be vectored using a pre-processing instrument such as predictive-based vector or frequency-based approach, TF-IDF, etc. and to identify the original program source by learning by using a deep neural network. In addition a language-independent learning system was constructed using a pre-processing machine and compared with other existing learning methods. Among them, models using TF-IDF and in-depth neural networks were found to perform better than those using other pre-processing or other learning methods.

• Journal of KIISE:Software and Applications
• /
• v.36 no.4
• /
• pp.306-319
• /
• 2009

A name is a key feature for distinguishing people, but we often fail to discriminate people because an author may have multiple names or multiple authors may share the same name. Such name ambiguity problems affect the performance of document retrieval, web search and database integration. Especially, in bibliography information, a number of errors may be included since there are different authors with the same name or an author name may be misspelled or represented with an abbreviation. For solving these problems, it is necessary to disambiguate the names inputted into the database. In this paper, we propose a method to solve the name ambiguity by using social networks constructed based on the relations between authors. We evaluated the effectiveness of the proposed system based on DBLP data that offer computer science bibliographic information.

• Proceedings of the Korea Contents Association Conference
• /
• 2006.11a
• /
• pp.652-656
• /
• 2006

Bibliographic information retrieval systems require bibliographic data such as authors, organizations, source of publication to be uniquely identified using keys. In particular, when authors are represented simply as their names, users bear the burden of manually discriminating different users of the same name. Previous approaches to resolving the problem of same-name authors rely on bibliographic data such as co-author information, titles of articles, etc. However, these methods cannot handle the case of single author articles, or the case when articles do not have common terms in their titles. To complement the previous methods, this study introduces a classification-based approach using similarity between full-text of articles. Experiments using recent domestic proceedings showed that the proposed method has the potential to supplement the previous meta-data based approaches.

• Korean Business Review
• /
• v.23 no.1
• /
• pp.17-44
• /
• 2010

Even though it is generally recognized that entrepreneurship is complex socio-economic phenomena and entrepreneurial opportunities can be created from economic, social, technological, political and legal forces, entrepreneurship has been discussed in very narrow economic terms and focusing on narrow economic relationships such as consumers, suppliers, owners and employee. However, recently, a lot of researchers talk about social entrepreneurship. How can we explain social entrepreneurs? In our paper, employing stakeholder theory, we try to offer more realistic model to understand entrepreneurial opportunities. We claim that entrepreneurial exploitations can be successful only when entrepreneurs can identify ex post stakeholder inefficiencies. Thus, our efforts focus on the question of where, when, and how stakeholder inefficiencies occur?