The Journal of the Institute of Internet, Broadcasting and Communication
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v.19
no.3
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pp.9-15
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2019
Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.
Park, Junhyeok;Lee, Songwook;Lim, Yoonseob;Choi, Jongsuk
KIPS Transactions on Software and Data Engineering
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v.8
no.5
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pp.213-222
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2019
Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.
This study aimed to compare and analyze the number and characteristics of modeling problems in chapters related to function contents in International Baccalaureate Diploma Program (IBDP) mathematics textbooks and Korean high school mathematics textbooks. This study implies how the textbooks contributed to the improvement of students' modeling competency. In this study, three textbooks from IBDP and all nine textbooks from the Korean 2015 revised curriculum were selected. All the problems in textbooks were classified into real-world problems and non-real-world problems. Problems classified as real-world problems were once again divided into word problems and modeling problems according to the need to set up mathematical models. Modeling problems were further categorized into standard applications and good modeling problems depending on whether all the necessary information was included in the problem-solving process. Among the 12 textbooks, the textbook with the most modeling problems was the IBDP textbook, 'Math: Applications and Interpretation', which accounted for 50.41% of modeling problems to the total number of problems. This textbook provided learners with significantly higher modeling opportunities than other IBDP and Korean textbooks, which had 2% and 9% modeling problem ratios. In all 12 textbooks, all problems classified as modeling problems appeared as standard applications, and there were no proper modeling problems. Among the six sub-competencies of mathematical modeling, 'mathematical analysis' and 'interpretation and evaluation of results' sub-competencies appeared the most with very similar number of modeling problems, followed by the 'mathematization'. It is expected that the results of this study will help compare the number and ratio of modeling problems in each textbook and provide a better understanding of which modeling sub-competencies appear to what extent in the modeling problems.
The Journal of the Convergence on Culture Technology
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v.7
no.4
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pp.59-65
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2021
This is a case study on the use of Albert Camus' La Peste, which has gained its popularity in today's generation of post-COVID as well as the use of big data analysis tools for major and elective classes. First, we asked students majoring in French to compare the use of vocabulary and the number of appearances for characters using big data analysis, for about 400 pages of the original text. As a result, we were able to confirm a similar relationship between Camus' Absurdism and the vocabulary used within La Peste, in addition to noting the heavy frequency of resistant characters. Students in elective classes were asked to read the literature in a Korean-translated version to determine the frequency of vocabulary and characters' appearances. Students were able to strongly relate to La Peste due to its commonality between COVID and the plague in the literature. We also received high levels of class satisfaction regarding the use of big data analysis tools. The students showed a positive response both towards choosing La Peste as the work of literature and using big data, the main tool in the Fourth Industrial Evolution. We were able to identify good results even in a non-contact environment, as long as the literature does not rely on traditional methods but rather lectures to reflect current situations.
In this study, a mixed research method that collects and analyzes qualitative and quantitative data together was used to broaden the understanding of young children's faith. First of all, the contents of interviews with 18 research participants were divided into categories, and the frequency of each category was calculated. From 75 statements made in the interview, the importance of each was evaluated with a 5-point Likert scale. The means and standard deviations of the evaluation score were calculated, and sub-factors were identified through exploratory factor analysis. The frequency of categories mentioned in the qualitative interview was in the following order: faith manifested in difficulties, religious activities, Christian education at home, Christian education in the church, love and faith in God, and the formation of a Christian worldview (identity). The statement on the perception of God, theological concepts, religious activities, and Christian education at home and in the church received high scores. On the other hand, statements on developmentally difficult or abstract content, value judgment or conflict resolution based on the Word, and evangelism showed low scores. The sub-factors extracted through factor analysis were faith education through home and church, awareness of God and religious activities, identity based on the gospel, character based on the gospel, and overcoming a crisis through faith. In conclusion, the results of each of the mixed methods of looking at young children's faith were very similar-relationships with God, religious activities, Christian worldview (identity), Christian education at home and church were highlighted, even though various methods were used. This study is meaningful in that it suggests what and how to teach in early childhood Christian education.
This review analyzes research trends related to new drug development using artificial intelligence from 2010 to 2022. This analysis organized the abstracts of 2,421 studies into a corpus, and words with high frequency and high connection centrality were extracted through preprocessing. The analysis revealed a similar word frequency trend between 2010 and 2019 to that between 2020 and 2022. In terms of the research method, many studies using machine learning were conducted from 2010 to 2020, and since 2021, research using deep learning has been increasing. Through these studies, we investigated the trends in research on artificial intelligence utilization by field and the strengths, problems, and challenges of related research. We found that since 2021, the application of artificial intelligence has been expanding, such as research using artificial intelligence for drug rearrangement, using computers to develop anticancer drugs, and applying artificial intelligence to clinical trials. This article briefly presents the prospects of new drug development research using artificial intelligence. If the reliability and safety of bio and medical data are ensured, and the development of the above artificial intelligence technology continues, it is judged that the direction of new drug development using artificial intelligence will proceed to personalized medicine and precision medicine, so we encourage efforts in that field.
Journal of Korea Entertainment Industry Association
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v.13
no.4
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pp.173-184
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2019
The data processing of this study is based on the word data search in social media through textom and the big data analysis is carried out and three areas (2019 Gwangju World Swimming Championships, 2019 Gwangju World Swimming Masters Competition, 2019 World Swimming Championships Problem) was consistently handled through data collection and refinement in the web environment. We applied the collected words to the program of Ucinet6, visualized them, and conducted a CONCOR analysis to grasp the similar relationship of words and to identify the cluster of common factors. As a result of the analysis, the clusters related to the 2019 Gwangju World Swimming Championships mainly consisted of four major areas of recognition and perception, mainly searching for operational aspects related to the swimming championship, and the community related to the 2019 Gwangju World Swimming Masters Competition Is mainly searched for the promotion of the Masters Competition and the aspect of the competition divided into two areas of major recognition and peripheral recognition. The cluster related to the problems of the 2019 Gwangju World Swimming Championships is divided into five areas, And they are mainly searching for the place, operation, institution, event, etc. of the problem of the swimming championship.
We live in two giant pendulum in called 'science' and 'religion'. But science and religion are contained in disparate information with each other, Those two is not easy to achieve convergence. But if you accept the ontological scheme of Great Chain of Being(存在의 大連鎖) in the Perennial philosophy(永遠의 哲學), Debate between religion and science is meaningless 'Great Nest of Being(存在의 대둥지, Great Chain of Being)' is similar to the multiple concentric circles, there are different dimension that the each top level to subsume surrounding the lower level. For example, upper zone 'Mysticism(神秘主義)' includes but transcends(or transcends but includes.) the sub-region theology, psychology, biology and physics. The Perennial philosophy is the great spiritual teachers of the world, philosophers and thinkers have adopted a common worldview, a religious views. Philosophers of the perennial philosophy seem to match the cross-cultural almost unanimous about the general level of the 'Great Nest of Being' for the past 3,000 years. The perennial philosophy made the conclusion that God exists in the world. Several types of view of God existing religions in the world have 'Monotheism(一神論)', 'Pantheism(汎神論)' and 'Panentheism (汎在神論)'. Although traditionally the God of the philosophers is the classical Theism, theological trends of today it is moving in the direction of Panentheism. Panentheism see that god is immanent and transcendent. also Daesoon Thoughts is the position of the Panentheism. so this paper points out the fact that the view of God of the perennial philosophy is precisely consistent with the view of God of Daesoon Thoughts. Wilber says 'envelopment [transcend and include]'. The word translates as 'powol(包越)' in Korean. 'Powol(包越)' means that all the developmental evolution is to surround the sub-region developed into the higher realms. View of the God in the perennial philosophy is 'powol theism(包越的 有神論)'. but 'powol immanent God(包越的 內在神論)' rather than building regulations as 'powol theism(包越的 有神論)'. It would have to be a more accurate representation of it. Because in the existing 'theism(有神論)' the god and humans are thought to exist apart. However, Daesoon Thoughts are deemed to also recognize another universal laws. also Sangje(上帝, the Supreme God) is recognized as a cosmic existence that transcends the laws. This point, as the characteristics of the Daesoon Thoughts, In other religions can not be found. Therefore, More specifically represent(More accurately represent), Sangje of the Daesoon Thoughts can be described as 'powol theism' or 'transcendental and included deism(包越的 理神論)'. Importantly, The idea of God can be captured directly by the discipline. In this sense, In terms of the other religions have no discipline law, the practice [discipline] of the Daesoon Thoughts required in the present age. It has the absoluteness.
Religion is not fixed and permanent and is constantly transforming and changing phenomenon. But in some religions, religious culture happens that emphasize the original and authentic teaching of the founder and removes the other accumulations among the accumulated religious tradition. So some religious communities advocate the original teaching of the founder and insist that they are the orthodox and the others are heresy and make the theory of true transmission etc. Jongtonglon(宗統論) of Daesoonjinrihoe(大巡眞理會) looks similar to the theory of true transmission on the surface. The aim of this paper is the contribution to the understanding of Daesoon thought and the religious culture of Daesoonjinrihoe by studying the characteristics of Jongtonglon. I want to highlight on the characteristics of Jongtonglon by comparing it to Bubmaeklon(法脈論) of Korean Buddhism. Bubmaek(法脈) of Korean Buddhism is well showed in the constitution of Jogaejong(曺溪宗) that represents Korean Buddhism. The constitution says that Jogaejong considers SakyaMuni the main Buddha and Doyi (道義) the founder of Jogaejong and BojoJinul(普照知訥)·TaegoBou (太古普愚) the restorer of tradition and also says that Jogaejong succeedes Cheongheo(淸虛) and Buhyu(浮休). Between SakyaMuni and Doyi, there are several monks of Seocheon(西天)-28Choseol (祖說) and China-6Choseol(祖說). Jinul is highly praised for enhancing the atmosphere of performance of Jogaejong and Bou was regarded as the founder of Jogaejong since late Joseon dynasty. In modern times there were conflicts between Bojojongjoseol(普照宗祖說) and Taegojongjoseol(太古宗祖說), but today's Bubmaek(法脈) of Korean Buddhism was erected after the conflicts was controled. Jongtong of Daesoonjinrihoe was erected by Jo Jengsan(趙鼎山) Doju(道主) who was received Heaven's will through divine revelation. Dojeon(都典) succeeded Jongtong by Doju's will and he didn't say a word when he went to Heaven. So the succession of Jongtong is ended. The first characteristic of Jongtonglon of Daesoonjinrihoe is that the succession of Jongtong was decided by Heaven and has been expected from long ago. The second, Jongtonglon has the sacred characteristic which is not shown in Bubmaeklon. The third, the distinction between Yeonwun(淵源) and Yeonun(緣運) is not appeared in Bubmaeklon. The fourth, Jongtonglon has more anti-syncretic feature than Bubmaeklon. The fifth, Yeonun(緣運) and Bubmaeklon are different in that feature. As Jongtonglon occupies a prominent place in Daesoonjinrihoe, the understanding of it can contribute to grasp the doctrine and culture of Daesoonjinrihoe.
Mun, Seong Min;Kim, Gi Nam;Choi, Gyeong cheol;Lee, Kyung Won
Design Convergence Study
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v.15
no.2
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pp.347-368
/
2016
Recently, researches for the word of mouth(WOM) imply that consumers use WOM informations of products in their purchase process. This study suggests methods using opinion mining and visualization to understand consumers' opinion of each goods and each markets. For this study we conduct research that includes developing domain ontology based on reviews confined to "movie" category because people who want to have watching movie refer other's movie reviews recently, and it is analyzed by opinion mining and visualization. It has differences comparing other researches as conducting attribution classification of evaluation factors and comprising verbal dictionary about evaluation factors when we conduct ontology process for analyzing. We want to prove through the result if research method will be valid. Results derived from this study can be largely divided into three. First, This research explains methods of developing domain ontology using keyword extraction and topic modeling. Second, We visualize reviews of each movie to understand overall audiences' opinion about specific movies. Third, We find clusters that consist of products which evaluated similar assessments in accordance with the evaluation results for the product. Case study of this research largely shows three clusters containing 130 movies that are used according to audiences'opinion.
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