• Journal of the Korean Society for Library and Information Science
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• v.48 no.4
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• pp.267-295
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• 2014

The purpose of this study is to analyze empirically relationships between learners' psychological factors, knowledge sourcing behavior and knowledge utilization outcomes and to analyze the mediation effect of social learning and relationships among learners. Another purpose is to understand learners' attitude on social learning and knowledge sourcing behavior. The main results of this study are as follows: First, regression results on relationships among learners' psychological factors, knowledge sourcing behavior, knowledge utilization outcomes show that learners' self-efficacy has a positive effect on social learning activity participation, and goal orientation has a positive influence on group knowledge sourcing and social learning activity participation. Users' experiences of social media has a positive effect on group knowledge sourcing, social learning activity participation and social learning interaction. From a knowledge utilization perspective, published knowledge sourcing positively affects knowledge reuse, knowledge application and knowledge innovation. Dyadic knowledge sourcing has positive influence on knowledge reuse. Group knowledge sourcing affects positively knowledge application and knowledge innovation. Second, social learning activity participation factor has full mediation effect on relationship between learners' goal orientation and group knowledge sourcing, and the relationship between users' experiences of social media and group knowledge sourcing. A relationship among members factor has full mediation effect on the relationship between published knowledge sourcing and knowledge reuse, and relationship between published knowledge sourcing and knowledge innovation. Third, the results of in-depth interview show that learners trust and easily collect knowledge from social network services in general. Also, they get a variety of idea for solving information problem from interaction among members in social learning community.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.377-392
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• 2015

In today's data-driven society, we've been hearing a great deal about the power of Big Data over the last couple of years. At the same time, it has become the most important issue that the problems is caused by the data collection, management and utilization. Moreover, Big Data has a wide applications ranging from situation awareness, decision-making to the area to enable for the foreseeable future with man-made and analysis of data. It is necessary to process data into meaningful information given that the huge amount of structured and unstructured data being created in the private and the public sector, even in disaster management. This data should be public and private sector at the same time for the appropriate linkage analysis for effective disaster management. In this paper, we conducted a literature review and case study efficient Big Data to derive the revitalization of national disaster management. The study obtained data on the role and responsibility of the public sector and the private sector to leverage Big Data for promotion of national disaster management plan. Both public and private sectors should promote common development challenges related to the openness and sharing of Big Data, technology and expansion of infrastructure, legal and institutional maintenance. The implications of the finding were discussed.

• Journal of Korean Society of Transportation
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• v.35 no.3
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• pp.183-196
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• 2017

In this study, policy evaluation scheme was established to encourage the efficient implementation of EcoMobility which has been expanding gradually all around the world. A total of eight evaluation goals and 22 evaluation items are reviewed and suggested based on the three major evaluation categories of "Basic elements for EcoMobility", "Land use and transport system for EcoMobility", and "Implications and impact of EcoMobility". The results of this study are as follows: the result of AHP analysis which reflects only the hierarchical structure shows a high priority in "Elements for EcoMobility promotion", "Eco-fiendly transport infrastructure", and "Safety in transport". While in result of ANP which considered the elements' dependencies, "Eco-fiendly transport Services", "Welfare in transport", and "Environment by transport" have high weights and importances. In conclusion, this study would be useful to make reasonable judgment based on the analysis results of the two techniques in order to ensure reliability in evaluation of EcoMobility policy. Furthermore we have confirmed appropriate evaluation technique between AHP and ANP which is better to reflect the features of EcoMobility.

• Proceedings of the KOR-KST Conference
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• 2001.02a
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• pp.3-29
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• 2001

본 연구에서는 기존의 대중교통 통행배정모형이 고려하지 않거나 미흡하게 반영하였던 차량과 노선의 용량제약을 고려한 모형의 구축을 시도하였다. 일반적으로 대중교통 통행배정에서 수요와 공급의 관계는 수요가 증가하는 것과 무관하다고 받아들여지고 있으나 용량 초과는 통행자들의 경로선택 및 수단선택에 영향을 끼친다고 볼 수 있다. 이러한 용량초과에 의한 혼잡을 반영하기 위해 용량제약식을 포함한 모형을 개발하고 실용운행회수라는 개념을 도입하였다. 또한 기존에 제시된 용량제약을 고려한 모형들이 실제적인 현상을 정확히 모사하는 데에는 한계가 있을 뿐 만 아니라 현실과 다소 거리가 있는 가정이 내재되어 있는 반면 본 연구에서는 가능한 한 최대로 현실 여건을 반영하는 모형을 정립하였다. 본 연구에서 제시한 모형은 동일한 기종점 사이를 운행하는 경쟁노선이 많은 대도시에서 통근·통학을 위한 통행자들이 많은 오전, 오후 첨두시 대중교통 수요를 분석하여 대중교통의 운행관리체계 개선 및 투자계획, 서비스 개선을 위한 수요예측을 하는데 유용하게 사용할 수 있을 것으로 사료된다. 그리고 기존의 대중교통 통행배정의 결과가 현재나 장래의 잠재적인 수요(demand)를 예측하는 방법인데 반하여 본 연구에서 정립된 모형은 주어진 네트워크에서 실제로 통행하고 있는 수요(flow)를 예측함으로써 교통 계획가나 대중교통 운영자의 계획 및 운영정책수립에 합리적인 도움을 줄 수 있다. 또한 현재의 대중교통 시스템 하에서의 통행배정 뿐만 아니라 다른 형태의 용량과 운행특성을 가진 교통 수단이나 노선이 도입되었을 때 공급이 제한적인 경우의 수요 예측에 적합한 모형을 개발하였다.에 대한 규제가 초국가적 차원으로 발전되는 계기를 제공하고 있다. 향후, 담배규제협약안의 세부사안들에 대한 합의과정에서 각 국별로 상당한 이견과 반발이 예상되고 있지만, 협약안의 전체 회원국 투표에서 승인될 경우 각 국가들뿐만 아니라 담배산업과 담배기업들에게 미치는 파급효과가 매우 클 것으로 예상된다. 대부분의 국제협약들이 그러하듯이, 담배규제협약도 그 적용 범위와 수준이 어느 정도로 결정되는지에 따라 각 국가와 기업별 이해관계가 크게 달라지게 되기 때문에 신중한 대응전략이 요구된다고 하겠다.의 화물전용차선의 설치시는 수답렬 교통량의 구성비와 구간 평균교통량에 의하여 그 효과가 다르게 나타남을 알 수 있었다. 따라서 물류비용 절감차원에서의 화물전용차선의 설치는 본 연구에서 나타낸 방법과 같이 수단간의 경제적 편익을 고려한 구간별 시간대별 효과분석을 통하여 정책의 시행여부가 결정되어야 할 것이다. 한편, 화물전용차선의 설치로 인한 물류비용의 절감을 보다 효과적으로 달성하기 위해서는 종합류류 전산망의 시급한 구축과 함께 화물차의 적재율을 높이고 공차율을 낮출 수 있는 운송체계의 수립이 필요한 것으로 판단된다. 그라나 이러한 화물전용차선의 효과는 단기적인 치유책일 수밖에 없기 때문에 물류유통 시설의 확충을 위한 사회간접자본의 구축을 서둘러 시행하여야 할 것이다.으로 처리한 Machine oil, Phenthoate EC 및 Trichlorfon WP는 비교적 약효가 낮았다.>$^{\circ}$E/$\leq$30$^{\circ}$NW 단열군이 연구지역 내에서 지하수 유동성이 가장 높은 단열군

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.67-80
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• 2019

Augmented Reality (AR) is a crucial technology in the Fourth Industrial Revolution that can revolutionize the existing Information and Communication Technology (ICT) market and powerfully create a new market However, it is hard to find the clear answer for AD/PR strategies in the rapidly changing AR market. Thus this research explores the big picture of the AR industry as it pertains to Politics, Economy, Social, and Technology through in-depth interview with seven AR experts who are leading the domestic AR market. The research also analyzes the AR market's Strengths, Weaknesses, Opportunities, and Threats. Furthermore, it looks for strategies to vitalize the advertising and PR industry by analyzing the Contents, Platform, Network, and Devices of the AR ecosystem. The results of the research indicate a need for the government's strengthened policy of supporting the AR market, fostering of pace-setting killer contents, connecting services of several industries through AR platforms, strengthening the network of communication systems such as through 5G, and the commercialization and industrialization of domestic devices in order to vitalize the AR industry in its marketing and PR spheres. Therefore, this research suggests measures to revitalize the marketing and PR industries of the AR ecosystem, which has only recently gotten to its developing stage and provides an academic as well as practical foundation for future research in the field of AR.

• Management & Information Systems Review
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• v.38 no.2
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• pp.209-223
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• 2019

The purpose of this study was to stimulate the online word-of-mouth advertising by developing the concept of word-of-mouth constraint factors and measurement tools in connection with the SNS advertising on social networks. To achieve the objective of this study, this study was conducted in 3 phases. First, the exploratory investigation(target group interview, in-depth interview, and expert interview) was performed to determine the concept and scope of the word-of-mouth constraint based on literature study and qualitative investigation method. Second, the reliability and validity of the measurement questions were verified through the survey in order to refine the developed measurement items. Third, the predictive validity of measurement items was verified by examining the relationship with other major construct concept for which the developed measurement items were different. Based on the results of study, 6 components and a total of 23 measurement questions for those components were derived. Each was called intrapersonal and interpersonal constraint(psychological sensitivity, compensatory sensitivity, and other person assessment), structural constraint(reliability, informativity, and entertainment). We developed the measurement questions related to word-of-mouth constraint based on qualitative study and quantitative study and holistically examined the social and psychological, environmental interruption factors acting as the word-of-mouth constraint factors for SNS advertising in terms of SNS achievements and evaluation from the perspective of word-of-mouth constraint. The results will lead to creation of basic framework for systematic and empirical research on the online word-of-mouth constraint and to achievement of effective SNS word-of-mouth advertising.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.17-26
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• 2003

Recently, as the internet is widespread rapidly among the public, people can use a variety of useful information services through the internet. Accordingly, the protection of information supplied by computer networks 5 has become a matter of primary concern on the whole world. To accede to the realistic demands, it has been worked out some countermeasures to cultivate the experts in information security by the government and many educational facilities. Already the government authority has carried out the each kinds of concerning projects under the framed a policy, Five-Year Development Plan for Information Security Technology. Also, many domestic universities perceives such an international trend, and so they frame their plans to train for the experts in this field, including to found a department with respect to the information security. They are ready to execute their tangible works, such as establishment of educational goal, development of teaching materials, planning curriculum, construction of laboratories and ensuring instructors. Moreover, such universities lead to their students who want to be information security experts to get the fundamental knowledge to lay the foundation for acquiring the information security technology in their bachelor course. In this note, we survey and analyze the curricula of newly-established or member-extended departments with respect to information security fields of some leading universities in the inside and outside of the country, and in conclusion, we propose the effective model of curriculum and educational goal to train the students for the information security experts.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.4
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• pp.179-188
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• 2023

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.333-351
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• 2023

This study investigates user behavior in library spaces through the lens of AI camera analytics. By leveraging the face recognition and tracking capabilities of AI cameras, we accurately identified the gender and age of visitors and meticulously collected video data to track their movements. Our findings revealed that female users slightly outnumbered male users and the dominant age group was individuals in their 30s. User visits peaked between Tuesday to Friday, with the highest footfall recorded between 14:00 and 15:00 pm, while visits decreased over the weekend. Most visitors utilized one or two specific spaces, frequently consulting the information desk for inquiries, checking out/returning items, or using the rest area for relaxation. The library stacks were used approximately twice as much as they were avoided. The most frequented subject areas were Philosophy(100), Religion(200), Social Sciences(300), Science(400), Technology(500), and Literature(800), with Literature(800) and Religion(200) displaying the most intersections with other areas. By categorizing users into five clusters based on space utilization patterns, we discerned varying objectives and subject interests, providing insights for future library service enhancements. Moreover, the study underscores the need to address the associated costs and privacy concerns when considering the broader application of AI camera analytics in library settings.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.109-122
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• 2014

People are nowadays creating a tremendous amount of data on Social Network Service (SNS). In particular, the incorporation of SNS into mobile devices has resulted in massive amounts of data generation, thereby greatly influencing society. This is an unmatched phenomenon in history, and now we live in the Age of Big Data. SNS Data is defined as a condition of Big Data where the amount of data (volume), data input and output speeds (velocity), and the variety of data types (variety) are satisfied. If someone intends to discover the trend of an issue in SNS Big Data, this information can be used as a new important source for the creation of new values because this information covers the whole of society. In this study, a Twitter Issue Tracking System (TITS) is designed and established to meet the needs of analyzing SNS Big Data. TITS extracts issues from Twitter texts and visualizes them on the web. The proposed system provides the following four functions: (1) Provide the topic keyword set that corresponds to daily ranking; (2) Visualize the daily time series graph of a topic for the duration of a month; (3) Provide the importance of a topic through a treemap based on the score system and frequency; (4) Visualize the daily time-series graph of keywords by searching the keyword; The present study analyzes the Big Data generated by SNS in real time. SNS Big Data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. In addition, such analysis requires the latest big data technology to process rapidly a large amount of real-time data, such as the Hadoop distributed system or NoSQL, which is an alternative to relational database. We built TITS based on Hadoop to optimize the processing of big data because Hadoop is designed to scale up from single node computing to thousands of machines. Furthermore, we use MongoDB, which is classified as a NoSQL database. In addition, MongoDB is an open source platform, document-oriented database that provides high performance, high availability, and automatic scaling. Unlike existing relational database, there are no schema or tables with MongoDB, and its most important goal is that of data accessibility and data processing performance. In the Age of Big Data, the visualization of Big Data is more attractive to the Big Data community because it helps analysts to examine such data easily and clearly. Therefore, TITS uses the d3.js library as a visualization tool. This library is designed for the purpose of creating Data Driven Documents that bind document object model (DOM) and any data; the interaction between data is easy and useful for managing real-time data stream with smooth animation. In addition, TITS uses a bootstrap made of pre-configured plug-in style sheets and JavaScript libraries to build a web system. The TITS Graphical User Interface (GUI) is designed using these libraries, and it is capable of detecting issues on Twitter in an easy and intuitive manner. The proposed work demonstrates the superiority of our issue detection techniques by matching detected issues with corresponding online news articles. The contributions of the present study are threefold. First, we suggest an alternative approach to real-time big data analysis, which has become an extremely important issue. Second, we apply a topic modeling technique that is used in various research areas, including Library and Information Science (LIS). Based on this, we can confirm the utility of storytelling and time series analysis. Third, we develop a web-based system, and make the system available for the real-time discovery of topics. The present study conducted experiments with nearly 150 million tweets in Korea during March 2013.