• Journal of Korean Home Economics Education Association
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• v.33 no.4
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• pp.1-13
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• 2021

The purpose of this study is to identify trends and potential themes of research on consumption of children and adolescents for 20 years by analyzing keywords. The keywords of 869 studies on consumption of children and adolescents published in journals listed in Korean Citation Index were analyzed using text mining techniques. The most frequent keywords were found in the order of youth, youth consumers, consumer education, conspicuous consumption, consumption behavior, and character. As a result of analyzing the frequency of keywords by dividing into five-year periods, it was confirmed that the frequency of consumer education was significantly higher betwn 2006 and 2010. Research on ethical consumption has been active since 2011, and research has been conducted on various topics instead of without a prominent keyword during the most recent 5-year period. Looking at the keywords based on the TF-IDF, the keywords related to the environment and the Internet were the main keywords between 2001 and 2005. From 2006 to 2010, the TF-IDF values of media use, advertisement education, and Internet items were high. From 2011 to 2015, fair trade, green growth, green consumption, North Korean defector youths, social media, and from 2016 to 2020, text mining, sustainable development education, maker education, and the 2015 revised curriculum appeared as important themes. As a result of topic modeling, eight topics were derived: consumer education, mass media/peer culture, rational consumption, Hallyu/cultural industry, consumer competency, economic education, teaching and learning method, and eco-friendly/ethical consumption. As a result of network analysis, it was found that conspicuous consumption and consumer education are important topics in consumption research of children and adolescents.

• Journal of Internet Computing and Services
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• v.21 no.1
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• pp.169-178
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• 2020

The reductions of troops/human resources, and improvement in combat power have made Korean Department of Defense actively adapt 4th Industrial Revolution technology (Artificial Intelligence, Big Data). The defense information system has been developed in various ways according to the task and the uniqueness of each military. In order to take full advantage of the 4th Industrial Revolution technology, it is necessary to improve the closed defense datamanagement system.However, the establishment and usage of data standards in all information systems for the utilization of defense big data and artificial intelligence has limitations due to security issues, business characteristics of each military, anddifficulty in standardizing large-scale systems. Based on the interworking requirements of each system, data sharing is limited through direct linkage through interoperability agreement between systems. In order to implement smart defense using the 4th Industrial Revolution technology, it is urgent to prepare a system that can share defense data and make good use of it. To technically support the defense, it is critical to develop Multi Repository Meta-Data Management (MRMM) that supports systematic standard management of defense data that manages enterprise standard and standard mapping for each system and promotes data interoperability through linkage between standards which obeys the Defense Interoperability Management Development Guidelines. We introduced MRMM, and implemented by using vocabulary similarity using machine learning and statistical approach. Based on MRMM, We expect to simplify the standardization integration of all military databases using artificial intelligence and bigdata. This will lead to huge reduction of defense budget while increasing combat power for implementing smart defense.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.

• Journal of the Korea Convergence Society
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• v.8 no.12
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• pp.39-47
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• 2017

The Internet have increased the number of digital web documents related to the history and traditions of Korean Culture. However, users who search for creators or materials related to traditional cultures are not able to get the information they want and the results are not enough. Document classification is required to access this effective information. In the past, document classification has been difficult to manually and manually classify documents, but it has recently been difficult to spend a lot of time and money. Therefore, this paper develops an automatic text classification model of traditional cultural contents based on the data of the Korean information culture field composed of systematic classifications of traditional cultural contents. This study applied TF-IDF model, Bag-of-Words model, and TF-IDF/Bag-of-Words combined model to extract word frequencies for 'Korea Traditional Culture' data. And we developed the automatic text classification model of traditional cultural contents using Support Vector Machine classification algorithm.

• Journal of Korea Multimedia Society
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• v.2 no.3
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• pp.277-288
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• 1999

A distributed multimedia school system is developed for the multimedia classroom at high school and university. The system is designed and implemented for students to improve the learning efficiency through the personalized multimedia contents and pace of learning. The previously developed multimedia information retrieval systems have some limitations on being applied to the multimedia classroom: expensive cost per stream or poor retrieval quality inappropriate for education, unscalability of system and service, unfamiliar proprietary client environment, and difficulty for teachers to use the authoring tools and manage the authored teaching materials. The system we developed overcomes the above problems. It is so scalable as to be applicable not only to a segmented classroom but also to the world wide Internet. The stream server is one of the components of the system: stream servers clients, a service gateway system, and a authoring management system. This paper describes the design and implementation of the stream server. A single stream server can simultaneously playback the multimedia streams as many as clients at one classroom. This is achieved only by the software engine without any changes of the hardware architecture. The systematic coupling with other components gives the scalability of the system and the flexibility of services.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.12
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• pp.894-906
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• 2006

There are many difficulties to develop a mobile contents due to many constraints on mobile environments. It is difficult to make a good mobile contents with only visual reduction of existing contents on wire Internet. Therefore, it is essential to devise the data representation and to develop the authoring tool to meet the needs of the mobile contents market. We suggest the compact mobile contents to learn Chinese characters and developed its authoring tool. The animation which our system produces is realistic as if someone writes letters with pen or brush. Moreover, our authoring tool makes a user generate a Chinese character animation easily and rapidly although she or he has not many knowledge in computer graphics, mobile programming or Chinese characters. The method to generate the stroke animation is following: We take basic character shape information represented with several contours from TTF(TrueType Font) and get the information for the stroke segmentation and stroke ordering from simple user input. And then, we decompose whole character shape into some strokes by using polygonal approximation technique. Next, the stroke animation for each stroke is automatically generated by the scan line algorithm ordered by the stroke direction. Finally, the ordered scan lines are compressed into some integers by reducing coordinate redundancy As a result, the stroke animation of our system is even smaller than GIF animation. Our method can be extended to rendering and animation of Hangul or general 2D shape based on vector graphics. We have the plan to find the method to automate the stroke segmentation and ordering without user input.

• Journal of Digital Convergence
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• v.19 no.2
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• pp.245-251
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• 2021

As the trend of steadily increasing the number of single or double household, there is a growing demand to see who is the outsider visiting the home during the free time. Various models of face recognition technology have been proposed through many studies, and Harr Cascade of OpenCV and Hog of Dlib are representative open source models. Among the two modes, Dlib's Hog has strengths in front of the indoor and at a limited distance, which is the focus of this study. In this paper, a face recognition visitor access system based on Dlib was designed and implemented. The whole system consists of a front module, a server module, and a mobile module, and in detail, it includes face registration, face recognition, real-time visitor verification and remote control, and video storage functions. The Precision, Specificity, and Accuracy according to the change of the distance threshold value were calculated using the error matrix with the photos published on the Internet, and compared with the results of previous studies. As a result of the experiment, it was confirmed that the implemented system was operating normally, and the result was confirmed to be similar to that reported by Dlib.

• Asia pacific journal of information systems
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• v.22 no.2
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• pp.39-69
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• 2012

With the advent of communication technologies including electronic collaborative tools and conferencing systems provided over the Internet, virtual collaboration is becoming increasingly common in organizations. Virtual collaboration refers to an environment in which the people working together are interdependent in their tasks, share responsibility for outcomes, are geographically dispersed, and rely on mediated rather than face-to face, communication to produce an outcome. Research suggests that new sets of individual skill, knowledge, and ability (SKAs) are required to perform effectively in today's virtualized workplace, which is labeled as individual virtual competence. It is also argued that use of online social networking sites may influence not only individuals' daily lives but also their capability to manage their work-related relationships in organizations, which in turn leads to better performance. The existing research regarding (1) the relationship between virtual competence and task performance and (2) the relationship between online networking and task performance has been conducted based on different theoretical perspectives so that little is known about how online social networking and virtual competence interplay to predict individuals' task performance. To fill this gap, this study raises the following research questions: (1) What is the individual virtual competence required for better adjustment to the virtual collaboration environment? (2) How does online networking via diverse social network service sites influence individuals' task performance in organizations? (3) How do the joint effects of individual virtual competence and online networking influence task performance? To address these research questions, we first draw on the prior literature and derive four dimensions of individual virtual competence that are related with an individual's self-concept, knowledge and ability. Computer self-efficacy is defined as the extent to which an individual beliefs in his or her ability to use computer technology broadly. Remotework self-efficacy is defined as the extent to which an individual beliefs in his or her ability to work and perform joint tasks with others in virtual settings. Virtual media skill is defined as the degree of confidence of individuals to function in their work role without face-to-face interactions. Virtual social skill is an individual's skill level in using technologies to communicate in virtual settings to their full potential. It should be noted that the concept of virtual social skill is different from the self-efficacy and captures an individual's cognition-based ability to build social relationships with others in virtual settings. Next, we discuss how online networking influences both individual virtual competence and task performance based on the social network theory and the social learning theory. We argue that online networking may enhance individuals' capability in expanding their social networks with low costs. We also argue that online networking may enable individuals to learn the necessary skills regarding how they use technological functions, communicate with others, and share information and make social relations using the technical functions provided by electronic media, consequently increasing individual virtual competence. To examine the relationships among online networking, virtual competence, and task performance, we developed research models (the mediation, interaction, and additive models, respectively) by integrating the social network theory and the social learning theory. Using data from 112 employees of a virtualized company, we tested the proposed research models. The results of analysis partly support the mediation model in that online social networking positively influences individuals' computer self-efficacy, virtual social skill, and virtual media skill, which are key predictors of individuals' task performance. Furthermore, the results of the analysis partly support the interaction model in that the level of remotework self-efficacy moderates the relationship between online social networking and task performance. The results paint a picture of people adjusting to virtual collaboration that constrains and enables their task performance. This study contributes to research and practice. First, we suggest a shift of research focus to the individual level when examining virtual phenomena and theorize that online social networking can enhance individual virtual competence in some aspects. Second, we replicate and advance the prior competence literature by linking each component of virtual competence and objective task performance. The results of this study provide useful insights into how human resource responsibilities assess employees' weakness and strength when they organize virtualized groups or projects. Furthermore, it provides managers with insights into the kinds of development or training programs that they can engage in with their employees to advance their ability to undertake virtual work.

• Journal of the Korea Society of Computer and Information
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• v.25 no.3
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• pp.243-250
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• 2020

In this paper, we propose an effective software education scheme for universities. The key idea of this software education scheme is to analyze software curriculum of QS world university rankings Top 10, SW-oriented university, and regional main national university. And based on the results, we propose five improvements for the effective SW education method of universities. The first is to enhance the adaptability of the industry by developing courses based on the SW developer's job analysis in the curriculum development process. Second, it is necessary to strengthen the curriculum of the 4th industrial revolution core technologies(cloud computing, big data, virtual/augmented reality, Internet of things, etc.) and integrate them with various fields such as medical, bio, sensor, human, and cognitive science. Third, programming language education should be included in software convergence course after basic syntax education to implement projects in various fields. In addition, the curriculum for developing system programming developers and back-end developers should be strengthened rather than application program developers. Fourth, it offers opportunities to participate in industrial projects by reinforcing courses such as capstone design and comprehensive design, which enables product-based self-directed learning. Fifth, it is necessary to develop university-specific curriculum based on local industry by reinforcing internship or industry-academic program that can acquire skills in local industry field.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.9
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• pp.634-642
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• 2020

With the proliferation of Internet of Things (IoT) devices, using the Linux operating system in various architectures has increased. Also, security threats against Linux-based IoT devices are increasing, and malware variants based on existing malware are constantly appearing. In this paper, we propose a system where the binary data of a visualized Executable and Linkable Format (ELF) file is applied to Local Binary Pattern (LBP) image processing techniques and a median filter to classify malware in a Convolutional Neural Network (CNN). As a result, the original image showed the highest accuracy and F1-score at 98.77%, and reproducibility also showed the highest score at 98.55%. For the median filter, the highest precision was 99.19%, and the lowest false positive rate was 0.008%. Using the LBP technique confirmed that the overall result was lower than putting the original ELF file through the median filter. When the results of putting the original file through image processing techniques were classified by majority, it was confirmed that the accuracy, precision, F1-score, and false positive rate were better than putting the original file through the median filter. In the future, the proposed system will be used to classify malware families or add other image processing techniques to improve the accuracy of majority vote classification. Or maybe we mean "the use of Linux O/S distributions for various architectures has increased" instead? If not, please rephrase as intended.