• Journal of Convergence for Information Technology
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• v.12 no.2
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• pp.47-53
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• 2022

Recently, with the change of the intelligent security paradigm, study to apply various information generated from various information security systems to AI-based anomaly detection is increasing. Therefore, in this study, in order to convert log-like time series data into a vector, which is a numerical feature, the CBOW and Skip-gram inference methods of deep learning-based Word2Vec model and statistical method based on the coincidence frequency were used to transform the published ADFA system call data. In relation to this, an experiment was carried out through conversion into various embedding vectors considering the dimension of vector, the length of sequence, and the window size. In addition, the performance of the embedding methods used as well as the detection performance were compared and evaluated through GRU-based anomaly detection model using vectors generated by the embedding model as an input. Compared to the statistical model, it was confirmed that the Skip-gram maintains more stable performance without biasing a specific window size or sequence length, and is more effective in making each event of sequence data into an embedding vector.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.230-236
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• 2001

In this paper, we explored the possibilities of embedding Qualitative Reasoning techniques, the Qualitative Process Theory (QPT), and its implementation in the field of inorganic chemistry. The target field of implementation is Qualitative Chemical Analysis and Laboratory Simulation. By embedding such technique in this education software we aim to combine theory and practice into a single package. The system, are able to generate reasoning and explanation based on chemical theories, helping student in mastering basic chemistry knowledge and practical skill as well. We also review the suitability of embedding QPT techniques into chemistry in general, by comparing some examples from both fields.

• Korean Journal of Acupuncture
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• v.38 no.3
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• pp.122-132
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• 2021

Objectives : This systematic review aims to assess the effectiveness of thread embedding therapy on animal model for rheumatoid arthritis. Methods : Search was conducted in the Cochrane library, MEDLINE (PubMed), EMBASE, the Chinese National Knowledge Infrastructure, Wan-Fang Database, Technology Journal Database, the Korean Studies Information Service System, the Oriental Medicine Advanced Searching Integrated System, the Research Information Sharing Service, the National Digital Science Library, the Korean Traditional Knowledge Portal and the Korea Citation Index. Data were extracted concerning animal model, intervention and rheumatoid arthritis indicator by two independent reviewers. Reporting quality was also evaluated by the ARRIVE (Animal Research: Reporting In Vivo Experiments) guidelines 2.0. Results : One thousand thirty six studies were primarily selected. After screening, 10 studies met the inclusion criteria. 1 study was published 2005, 1 study was published 2010, 1 study was published 2012, 1 study was published 2015, 2 studies were published 2016, 3 studies were published 2017, 1 study was published 2018. The most frequently used acupoints were ST36 and BL23, and were used with an average frequency of 11.8 days. All of these thread embedding therapy studies were effective on behavioral, morphological, immunohistological and hematological indicators to treat of rheumatoid arthritis model. Conclusions : These results demonstrated the effectiveness of thread embedding therapy and suggested the putative mechanism. However, considering the small number of included studies, low reporting quality and differences in study design, further studies with rigorous designs and high reporting quality need to be conducted.

• Journal of Korea Multimedia Society
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• v.25 no.3
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• pp.493-501
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• 2022

This paper is a study on context-sensitive spelling error correction and uses the Korean WordNet (KorLex)[1] that defines the relationship between words as a graph to improve the performance of the correction[2] based on the vector information of the word embedded in the correction technique. The Korean WordNet replaced WordNet[3] developed at Princeton University in the United States and was additionally constructed for Korean. In order to learn a semantic network in graph form or to use it for learned vector information, it is necessary to transform it into a vector form by embedding learning. For transformation, we list the nodes (limited number) in a line format like a sentence in a graph in the form of a network before the training input. One of the learning techniques that use this strategy is Deepwalk[4]. DeepWalk is used to learn graphs between words in the Korean WordNet. The graph embedding information is used in concatenation with the word vector information of the learned language model for correction, and the final correction word is determined by the cosine distance value between the vectors. In this paper, In order to test whether the information of graph embedding affects the improvement of the performance of context- sensitive spelling error correction, a confused word pair was constructed and tested from the perspective of Word Sense Disambiguation(WSD). In the experimental results, the average correction performance of all confused word pairs was improved by 2.24% compared to the baseline correction performance.

• Journal of Science Education
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• v.46 no.1
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• pp.17-29
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• 2022

The purpose of this study is to look qualitatively into how efficiently and reasonably a computer can learn themes related to the Nature of Science (NOS). In this regard, a corpus has been constructed focusing on literature (920 abstracts) related to NOS, and factors of the optimized Word2Vec (CBOW, Skip-gram) were confirmed. According to the four dimensions (Inquiry, Thinking, Knowledge and STS) of NOS, the comparative evaluation on the word-level word embedding was conducted. As a result of the study, according to the previous studies and the pre-evaluation on performance, the CBOW model was determined to be 200 for the dimension, five for the number of threads, ten for the minimum frequency, 100 for the number of repetition and one for the context range. And the Skip-gram model was determined to be 200 for the number of dimension, five for the number of threads, ten for the minimum frequency, 200 for the number of repetition and three for the context range. The Skip-gram had better performance in the dimension of Inquiry in terms of types of words with high similarity by model, which was checked by applying it to the four dimensions of NOS. In the dimensions of Thinking and Knowledge, there was no difference in the embedding performance of both models, but in case of words with high similarity for each model, they are sharing the name of a reciprocal domain so it seems that it is required to apply other models additionally in order to learn properly. It was evaluated that the dimension of STS also had the embedding performance that was not sufficient to look into comprehensive STS elements, while listing words related to solution of problems excessively. It is expected that overall implications on models available for science education and utilization of artificial intelligence could be given by making a computer learn themes related to NOS through this study.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.519-521
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• 2023

온라인 뉴스 플랫폼의 발전은 에코 챔버(echo chamber) 효과와 정치적 양극화를 심화시키며, 이를 완화하기 위한 선행 연구로 뉴스 기사의 정치적 성향을 판단하는 연구가 필요하다. 기존 연구는 외부 지식 그래프를 활용하여 뉴스 기사의 텍스트 정보를 더욱 풍부하게 표현한다. 그러나, 외부 지식을 임베딩하는 지식 그래프 임베딩(knowledge graph embedding, KGE) 방법은 다양하며, 각 KGE 방법이 정치적 성향 예측 정확도에 미치는 효과에 대해서 충분히 연구되지 않았다. 본 논문에서는 정치적 성향 예측에 외부 지식의 활용을 최대화하기 위한 다양한 KGE 방법들의 효과를 분석한다. 실험 결과, 외부 지식 그래프 내의 개체들 간 복잡한 관계를 간단하고 정확하게 표현 가능한 ModE 방법을 활용하는 것이 정치적 성향 예측에 가장 효과적이라는 것을 확인하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2004-2012
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• 2010

This paper assess participant's satisfaction and referring will of embedding study- oriented TEC entrepreneurship program to drive policy implications for domestic entrepreneurship education, such as graduate schools of entrepreneurial management in Korea. The hypothesis test of this research shows that four assumptions given have been accepted among five of them as follows; education content, lecture style, prior knowledge variables with positively correlating to participant's satisfaction and referring will. In particular, the stronger highlights falls on algorithm approach with stressing on iterative and process oriented embedding study relating to education content, case-oriented lecture to lecture style and experience, customer understanding with business plan to prior knowledge.

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.652-657
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• 2018

With the rapid development of e-commerce, many customers can now express their opinion on various kinds of product at discussion groups, merchant sites, social networks, etc. Discerning a consensus opinion about a product sold online is difficult due to more and more reviews become available on the internet. Opinion Mining, also known as Sentiment analysis, is the task of automatically detecting and understanding the sentimental expressions about a product from customer textual reviews. Recently, researchers have proposed various approaches for evaluation in sentiment mining by applying several techniques for document, sentence and aspect level. Aspect-based sentiment analysis is getting widely interesting of researchers; however, more complex algorithms are needed to address this issue precisely with larger corpora. This paper introduces an approach of knowledge representation for the task of analyzing product aspect rating. We focus on how to form the nature of sentiment representation from textual opinion by utilizing the representation learning methods which include word embedding and compositional vector models. Our experiment is performed on a dataset of reviews from electronic domain and the obtained result show that the proposed system achieved outstanding methods in previous studies.

• The Journal of Korean Medicine Ophthalmology and Otolaryngology and Dermatology
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• v.26 no.4
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• pp.43-50
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• 2013

Objectives : This study aims to suggest preliminary standard procedure for face lift and correction of nasolabial folds using thread-embedding (Maeseon) of Korean medicine(KM). Methods : Three KM practitioners of facial correction and rejuvenation who have over hundred case of practice participated in establishment of standard procedure. Standard procedure contains preprocessing, main procedure for correction, and solution of side effects. Results : Standard procedure is comprised of twelve processes with preprocessing and postprocessing. Preprocessing has position, disinfection, and anesthesia. Main process consists of overall structure correction, face lifting, nasolabial folds correction, and mesh making on cheek. Postprocess covers disinfection, edema prevention. Conclusions : To our knowledge, this is the first work to suggest standard procedure of facial rejuvenation using Maeseon. It would contribute to standardized practice in clinical fields and future study of revealing Maeseon's effectiveness.

• Journal of Korean Medicine for Obesity Research
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• v.19 no.1
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• pp.56-67
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• 2019

Objectives: The purpose of this study is to review clinical studies of herb medicine and acupuncture treatment on obesity in menopausal women. Methods: Key words "Obesity", "Menopause", "Herb medicine", "Acupuncture", "Moxibustion", "Catgut embedding" were searched on 9 database systems (PubMed Central, Cochrane Controlled Register of Trials, Embase, China Academic Journals, Korean Traditional Knowledge Portal, Oriental Medicine Advanced Searching Integrated System, Korean Studies Information Service System, National Digital Science Library, DBpia) on April 30th 2019. Results: 1 case report and 17 clinical trials were collected in accordance with the selection and exclusion criteria. Among the 17 trials, 6 were randomized controlled trials, 1 was controlled clinical trial, and 10 were single-arm trials. The types of intervention were herb medicine, electroacupuncture, acupuncture, auricular acupuncture, warm needle acupuncture, moxibustion, laser acupuncture, and catgut embedding. The study design, study results and method of intervention were analyzed. Conclusions: 1 case report described the effectiveness of pharmacopuncture, 4 trials described the effectiveness of herbal medicine, 2 of electroacupuncture, 1 of laser acupuncture, and 2 of catgut embedding. Among the 17 trials, 2 studies showed that herbal medicine treatment was more effective than no treatment or selective serotonin reuptake inhibitors, and 1 study showed that electroacupuncture was more effective than hormone therapy. All of 18 selected studies reported the effectiveness of weight reduction and abdominal obesity reduction after the traditional Korean medicine treatment for obesity in menopausal women.