• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.285-302
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• 2024

For this article, we conducted frequency analysis, topic modeling, and network analysis on eleven texts related to collection development policy found in the National Library of Korea. We deduced the main keywords related to collection development policies and analyzed the relationship between them. We subsequently conducted a pie coefficient analysis to identify the characteristics of collection development policies of university libraries and public libraries by category. The results showed that keywords such as "material," "library," "collection development," "user," and "collection" were the main keywords in frequency analysis and network centrality. Meanwhile, the pie coefficient analysis revealed that keywords such as "university," "construction," "student," "target," and "cost" were prevalent in university libraries, indicating that the academic needs of users and the discussion of digital resources were primary issues, while keywords related to the information needs of various user groups-including "adults," "survey," "feature," and "religion" -appeared in public libraries.

• Journal of Society of Preventive Korean Medicine
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• v.19 no.2
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• pp.159-167
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• 2015

Objective : Recently many kinds of food therapies have sprung up to prevent or manage disease and to promote health. Seeing Korean history, the Korean medical doctors have been applying food therapy based on the thought that the dietary sources were as important as the medicine. Therefore, in this study, we designed a mobile edutainment game prototype with the goal of providing education about healthy food knowledge for users who belongs to different constitutional types. Materials and Method : We adopted the Sasang Constitutional Medicine as the medical background knowledge for our edutainment game design. Based on the user study, we developed the process of edutainment system which is composed of 'My game', 'My constitution', and 'My food'. Among the whole process, we developed a prototype for the core module - the 'My game' part. This prototype used a jumping game for mobile devices that is composed of training, level 1 and level 2 stages. Results : From the target user evaluation, it was proved that 1) in terms of the learning effect of healthy diet, the edutainment game we developed has a significant advantage over the conventional learning media. 2) after playing the edutainment game, the good and bad food identification accuracy based on picture and text format were increased by 44% and 42% respectively, and 3) target users perceived enjoyment while using this prototype, as well as showed positive intention to use this game as edutainment tool in the future. Conclusion : We designed and developed mobile edutainment game prototype to educate healthy food knowledge based on Sasang Constitutional Medicine. Through user evaluation, we proved that our prototype enhanced healthy food knowledge and that user accepted the prototype as a beneficial edutainment tool.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.3
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• pp.1330-1336
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• 2012

The Image retrieval has been study different approaches which are text-based, contents-based, area-based method and sub-image finding. The sub-image retrieval is to find a query image in the target one. In this paper, we propose a novel sub-image retrieval algorithm by Dot-Matrix method to be used in the bioinformatics. Dot-Matrix is a method to evaluate similarity between two sequences and we redefine the problem for retrieval of sub-image to the finding similarity of two images. For the approach, the 2 dimensional array of image converts a the vector which has gray-scale value. The 2 converted images align by dot-matrix and the result shows candidate sub-images. We used 10 images as target and 5 queries: duplicated, small scaled, and large scaled images included x-axes and y-axes scaled one for experiment.

• Journal of Service Research and Studies
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• v.5 no.1
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• pp.71-78
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• 2015

Created, but the development of mobile devices to have a margin of life have appeared in the opposite forces that are considered to be the target of financial crime and attacks them. Financial crime among crimes that target the smartphone SMS phishing, phishing, pharming, phishing, etc. voice and, in particular, a phenomenon that is growing a lot of SMS phishing is by nature a text message to your mobile. Ye Jin proactive rather than post responses in order to be safe from the SMS phishing attack individuals and businesses, and asset protection is even more important in the country. For this, the SMS phishing attack detected in advance and that can block the development program, it is necessary to deploy.

• Journal of Digital Convergence
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• v.12 no.5
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• pp.451-458
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• 2014

The purpose of this paper is to examine website information as well as promotional texts comparing source texts of Korean with translated versions of English, and drew characteristics of tourism texts from a discourse and communicative perspective. This study shows that the website or promotional texts is the first source of information in tourism, which is most referred to by the in-bound tourists, and the information given by the official homepage is most trustful content of Korean tourism. With comparison of source text of Korean with the translated English version, this paper shows that Korean source texts have a tendency to prefer the longer explication and more detailed information on the scenic spots and attractions than the English translations. When it is translated into English, the translated version does not follow the literal way of translation, and is segmented for reader's understanding and adapted following the target language's communicative conventions and the target culture. Consequently, this study supports the adaption in tourism promotional English translation, and ensures that the communicative constraints of tourism, that is, politeness and Grician maxims are preserved even in the written form of communication, translation.

• The KIPS Transactions:PartB
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• v.16B no.6
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• pp.479-488
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• 2009

This paper shows a reinforced construction scheme of training data for improvement of text classification by automatic search of boundary category. The documents laid on boundary area are usually misclassified as they are including multiple topics and features. which is the main factor that we focus on. In this paper, we propose an automated exploring methodology of optimal boundary category based on previous research. We consider the boundary area among target categories to new category to be required training, which are then added to the target category sementically. In experiments, we applied our method to complex documents by intentionally making errors in training process. The experimental results show that our system has high accuracy and reliability in noisy environment.

• Phonetics and Speech Sciences
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• v.7 no.3
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• pp.139-149
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• 2015

This paper aims to analyze pronunciation variations of loanwords produced by Korean and improve the performance of pronunciation modeling of loanwords in Korean by using syllable-based segmentation and phonological knowledge. The loanword text corpus used for our experiment consists of 14.5k words extracted from the frequently used words in set-top box, music, and point-of-interest (POI) domains. At first, pronunciations of loanwords in Korean are obtained by manual transcriptions, which are used as target pronunciations. The target pronunciations are compared with the standard pronunciation using confusion matrices for analysis of pronunciation variation patterns of loanwords. Based on the confusion matrices, three salient pronunciation variations of loanwords are identified such as tensification of fricative [s] and derounding of rounded vowel [ɥi] and [$w{\varepsilon}$]. In addition, a syllable-based segmentation method considering phonological knowledge is proposed for loanword pronunciation modeling. Performance of the baseline and the proposed method is measured using phone error rate (PER)/word error rate (WER) and F-score at various context spans. Experimental results show that the proposed method outperforms the baseline. We also observe that performance degrades when training and test sets come from different domains, which implies that loanword pronunciations are influenced by data domains. It is noteworthy that pronunciation modeling for loanwords is enhanced by reflecting phonological knowledge. The loanword pronunciation modeling in Korean proposed in this paper can be used for automatic speech recognition of application interface such as navigation systems and set-top boxes and for computer-assisted pronunciation training for Korean learners of English.

• The Journal of the Korea Contents Association
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• v.20 no.12
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• pp.13-22
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• 2020

This study aims to find out success factors of crowdfunding on movie projects. For empirical analysis, we collected 583 data of the movie projects from the crowdfunding platform 'Tumblbug'. To figure out the success factors, we examined effects of 10 independent variables on 1 dependent variable. The independent variable includes target amount, project information, reward options, creator funding power, editor recommendation, creator contents power, movie type, number of comments, number of replies, and number of SNS information. The final achievement rate of crowdfunding was set as dependent variable. This study found that the target amount, number of text information, number of video information, editor recommendation, number of backers' reply, and number of SNS information had a significant impact on the achievement rate of the movie crowdfunding project. This study has implications in that it has discovered a variable of editor recommendation and the number of SNS information, and both of them have a positive effect on crowdfunding achievement.

• Journal of The Korean Association For Science Education
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• v.28 no.5
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• pp.471-481
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• 2008

This study investigated connecting errors by students' field independence-dependence in learning chemistry concepts with multiple external representations in current science textbooks. Seventh graders (N=196) at a middle school were assigned to the BL and CL groups, which were respectively taught "Boyle's Law" and "Charles's Law." A field independence-dependence test was administered. After learning the target concept with text and picture emphasizing the particulate nature of matter, a connecting test was also administered. Five types of connecting errors were identified: Insufficient connection, misconnection, rash connection, impossible connection, and failing to connect. 'Failing to connect,' 'Misconnection,' and 'Rash connection' were found to be the frequent types of connecting errors regardless of the target concepts. The frequencies and percentages of the types of connecting errors were not significantly different between the field independent and field dependent students. Educational implications of these findings are discussed.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.