• The KIPS Transactions:PartD
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• v.10D no.6
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• pp.1049-1058
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• 2003

The last few years have been a boom time for the online gaming industry. Internet-based online games have made an increasingly popular form of entertainment. The gaming industry estimates that online gaming players reach over 26 million in 2002. The rapid development of online game-contents and related information technology shall increase the size of industry and have a profound impact on many ways of our living and sociaty. This paper develops the exploratory LISREL model for identifying the factors attecting the players' loyalty for some specific brand of online games. The concept of flow, word of mouth, feedback, challenge, social norms, and online community activities, etc, are all introduced into the model, as the independent variables affecting the loyalty directly and indirectly. Based on data collected from online questionnaire survey, the validity of the model has been tested and interesting conclusions have been developed concerning the relationships between the loyalty, flow, word of mouth and other set of independent vareables. It is hoped that this result might provide the useful guidelines for developing the successful online game contents.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.198-203
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• 2003

We develop the method of estimating the endpoints of speech by jointly using the lip motion (visual speech) and speech being included in multimedia data and then propose a new speech recognition system (SRS) based on that method. The endpoints of noisy speech are estimated as follows : For each test word, two kinds of endpoints are detected from visual speech and clean speech, respectively Their difference is made and then added to the endpoints of visual speech to estimate those for noisy speech. This estimation method for endpoints (i.e. speech interval) is applied to form a new SRS. The SRS differs from the convention alone in that each word model in the recognizer is provided an interval of speech not Identical but estimated respectively for the corresponding word. Simulation results show that the proposed method enables the endpoints to be accurately estimated regardless of the amount of noise and consequently achieves 8 o/o improvement in recognition rate.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.1
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• pp.31-36
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• 2019

A chatbot is an important area of mobile service, which understands informal data of a user as a conversational form and provides a customized service information for user. However, there is still a lack of a service way to fully understand the user's natural language typed query dialogue. Therefore, in this paper, we extract meaningful words, such a region, a food category, and a restaurant name from user's dialogue sentences for recommending a restaurant. and by comparing the extracted words against the contents of the knowledge database that is built from the hashtag for recommending a restaurant in SNS, and provides user target information having statistically much the word-similarity. In order to evaluate the performance of the restaurant recommendation chatbot system implemented in this paper, we measured the accessibility of various user query information by constructing a web-based mobile environment. As a results by comparing a previous similar system, our chabot is reduced by 37.2% and 73.3% with respect to the touch-count and the cutaway-count respectively.

• Journal of Information Technology Services
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• v.19 no.6
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• pp.1-13
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• 2020

Recently, 80% of big data consists of unstructured text data. In particular, various types of documents are stored in the form of large-scale unstructured documents through social network services (SNS), blogs, news, etc., and the importance of unstructured data is highlighted. As the possibility of using unstructured data increases, various analysis techniques such as text mining have recently appeared. Therefore, in this study, topic modeling technique was applied to the Korea Highway Corporation's voice of customer (VOC) data that includes customer opinions and complaints. Currently, VOC data is divided into the business areas of Korea Expressway Corporation. However, the classified categories are often not accurate, and the ambiguous ones are classified as "other". Therefore, in order to use VOC data for efficient service improvement and the like, a more systematic and efficient classification method of VOC data is required. To this end, this study proposed two approaches, including method using only the latent dirichlet allocation (LDA), the most representative topic modeling technique, and a new method combining the LDA and the word embedding technique, Word2vec. As a result, it was confirmed that the categories of VOC data are relatively well classified when using the new method. Through these results, it is judged that it will be possible to derive the implications of the Korea Expressway Corporation and utilize it for service improvement.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.91-101
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• 2011

The purpose of this paper is to quickly identify school parents, teachers, students, community needs and opinions for curriculum organizing and the implementation of an online survey system for operating educational activities. Online survey system should be implemented based on Limesurvey to reduce costs and administrative costs. Limesurvery is available without the development of the separate program and offers the form of web-based template system, complete design, layout. Also, Limesurvey offers basic statistical analysis of survey data. Limesurvey can be executed by installing the program on a web hosting, typing database information. Limesurvey can be made a graph of the statistical results. Besides, Limesurvery can be stored in the form of HTML, Word, Excel, CSV Files and can be stured as basic datas for SPSS or PASW, R data, other statistical processing programs. If we could be operate Limesurvey in the form of open source-based survey program in elementary school, we could be reduced teacher's unnecessary work for statistics and overcame the problem of offline survey system.

• Journal of KIISE
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• v.41 no.9
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• pp.652-665
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• 2014

Many important terminologies in biomedical text are expressed as abbreviations or acronyms. We newly suggest a semantic link topic model based on the concepts of topic and dependency link to disambiguate biomedical abbreviations and cluster long form variants of abbreviations which refer to the same senses. This model is a generative model inspired by the latent Dirichlet allocation (LDA) topic model, in which each document is viewed as a mixture of topics, with each topic characterized by a distribution over words. Thus, words of a document are generated from a hidden topic structure of a document and the topic structure is inferred from observable word sequences of document collections. In this study, we allow two distinct word generation to incorporate semantic dependencies between words, particularly between expansions (long forms) of abbreviations and their sentential co-occurring words. Besides topic information, the semantic dependency between words is defined as a link and a new random parameter for the link presence is assigned to each word. As a result, the most probable expansions with respect to abbreviations of a given abstract are decided by word-topic distribution, document-topic distribution, and word-link distribution estimated from document collection though the semantic dependency link topic model. The abstracts retrieved from the MEDLINE Entrez interface by the query relating 22 abbreviations and their 186 expansions were used as a data set. The link topic model correctly predicted expansions of abbreviations with the accuracy of 98.30%.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.

• The Journal of Korean Medical History
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• v.25 no.2
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• pp.155-193
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• 2012

From the part "歷代醫學姓氏(Historic Doctors)" in "醫林撮要(Uirimchualyo)", the following has been noticed and concluded. 1. Because acupuncture was originated from stony needle, the word "石(sok)" contains the meaning of needle, and from this point on, words like 石(sok), 砭石(pyumsok), 箴石(Jamsok), 鑱石(Chamsok) had been derived. 2. The word 砭石(pyumsok) used in "Hwangjenaekyong(Yellow Emperor's Canon of Medicine or Hwangdineijing)" should be interpreted as acupuncture in a verb form, not a noun form. 3. 鑱石(Chamsok) or 鑱鍼(Chamchim) was used for surgical treatment for tumor, by cutting open tumors and pressing the pus out. Therefore, 砭石(pyumsok), 鑱石(Chamsok) are the same kind of needles, and 鑱鍼(Chamchim) is the tool improved from 鑱石(Chamsok) used in the Bronze Age. 4. Kwakpak put a note on 鑱石(Chamsok) in "山海經(Sanhaekyong)" that reads "it is defined as 砥鍼(Jichim) and treats tumor." This let us know the shape of 石(sok), 砭石(pyumsok), 鑱(Chamsok), 鑱鍼(Chamchim), and the stone that can be used as a surgical tool with slim & sharp shape is obsidian. 5. Because obsidian is only found around Mt. Baekdu and limited area in South Korea & Japan in Asia, it is closely related with the life & medical environment of the tribe "Mt. Baekdu". 6. The development of 鑱鍼(Chamchim) was influenced by surgical treatment used in early stage of civilization, and its origin is traced upto Gochosun dynasty. Korea's own traditional medical knowledge is derived from this surgical treatment skill. 7. Because the acupuncture is originated from Gochosun dynasty, 鍼(chim) was derived from 箴(Jam) of 箴石(Jamsok), 䥠(Chim) & 䥠(Chim) both were used for a time being, and finally settled into 鍼(Chim). 8. The word 針(Chim) showed up at Myung dynasty, and started to be used in Korea from early Chosun dynasty. 9. In the early Chosun dynasty, 鍼(Chim) was used for medical term, and 針(Chim) for non-medical term. In the mid Chosun dynasty, 針(Chim) was used as a term for tool, and 鍼(Chim) as a term for acupunctural medical treatment. 10. Under the order of King Sunjo, Dr. Yesoo Yang published "醫林撮要(Uirimchualyo)", added "醫林撮要續集(Sequel to Uirimchualyo)", and added "歷代醫學姓氏(Historic Doctors)" again which eventually made totally 13 books of "醫林撮要(Uirimchualyo)". In addition, many parts of "醫林撮要續集(Sequel to Uirimchualyo)" were quoted in "東醫寶鑑(Donguibogam)", and influenced much in publishing "Donguibogam". 11. In "歷代醫學姓氏(Historic Doctors)" of "醫林撮要(Uirimchualyo)", the same way in "Donguibogam", referred to 針(Chim) as a term for a needle, and 鍼(Chim) as a term for Acupuncture. 12. From the usage of 針(Chim) & 鍼(Chim), shown in "鄕藥集成方(Hyangyakjipsungbang)", "醫林撮要(Uirimchualyo)" and "東醫寶鑑(Donguibogam)", we can notice the spirit of doctors who tried to take over the legitimacy of Korean tradition, and their elaboration & historical view that expresses confidence on our own medical technology, through the wording 鍼(Chim).

• Journal of the Korea Convergence Society
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• v.12 no.12
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• pp.305-313
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• 2021

This study collected and analyzed big data from 1995 to 2020, focusing on the keywords "unwed mother", "single mother," and "single mom" to present appropriate government support policy directions according to changes in perspectives on unwed mothers. Big data collection platform Textom was used to collect data from portal search sites Naver and Daum and refine data. The final refined data were word frequency analysis, TF-IDF analysis, an N-gram analysis provided by Textom. In addition, Network analysis and CONCOR analysis were conducted through the UCINET6 program. As a result of the study, similar words appeared in word frequency analysis and TF-IDF analysis, but they differed by year. In the N-gram analysis, there were similarities in word appearance, but there were many differences in frequency and form of words appearing in series. As a result of CONCOR analysis, it was found that different clusters were formed by year. This study confirms the change in the perspective of unwed mothers through big data analysis, suggests the need for unwed mothers policies for various options for independent women, and policies that embrace pregnancy, childbirth, and parenting without discrimination within the new family form.

• The Journal of Korean Medicine
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• v.43 no.2
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• pp.61-74
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• 2022

Objectives: This study aimed to explore the short-term effects of bilateral masticatory training using an intraoral device on memory and concentration, which is an advanced form of Gochi, compared to the unilateral form with gum. Methods: Thirty young healthy participants (age, 16-30 years) were screened and randomly assigned to one of two sequences in a crossover design. The participants assigned to sequence A (n=15) performed bilateral mastication using an intraoral device with a total of 300 taps, followed by unilateral mastication using gum with the same number of repetitions and frequency, separated by a 7-day washout period. A reverse order was used for sequence B. The primary and secondary outcomes were the digit span test result and the symbol digit modality test and the word list recall results, respectively, which were conducted before and after each intervention. Results: Symbol digit modality test scores increased by 12.03±8.33 with bilateral mastication, which was significantly higher than that obtained with chewing gum (5.17 points;95% confidence interval: 0.99, 9.34; p<0.05). Changes in the digit span test and word list recall scores were not significantly different between the two groups. In the digit span test forward, symbol digit modality test, and word list recall test, bilateral mastication was not inferior to unilateral mastication in improving memory and concentration. Conclusions: Bilateral masticatory exercises using an intraoral device are not inferior to unilateral mastication with gum for improving memory in healthy young individuals. Further research is needed to determine the efficacy of bilateral masticatory training on cognitive function.