• Trans-
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• v.9
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• pp.37-66
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• 2020

Entering the 21st century, the flow of society and culture is emerging as a cultural phenomenon in which one experiences, enjoys, and experiences on one's own. This trend has emerged as community dance, which has been active since 2010. Community dances can be targeted by anyone and can be divided into children's, adult and senior citizens' dances depending on the characteristics and age of the group, allowing them to work in various age groups. It also refers to all kinds of dances for the happiness and self-achievement of everyone who can promote gender, race and religion health or meet the needs of expression and improve their physical strength at meetings by age group, from preschoolers to senior citizens. Community dance is a dance activity in which everyone takes advantage of their leisure time and voluntarily participates in joyous activities, making it expandable to lifelong education and social learning. It is a voluntary community gathering conducted by experts for the general public. The definition of community dance can be said to be the aggregate of physical activities that enrich an individual's daily life and enhance their social sense to create a bright society, while individuals achieve the goals of health promotion and aesthetic education. In the contemporary community dance, the dance experience in body and creativity as self-expression reflects the happiness perspective by exploring the positive psychological experience and influence of the participants in the process of participation, and participants have continued networking through online offline to enjoy the dance culture. Although research has been conducted in various fields for 10 years since the boom in community dance began, the actual methodology of the program has been insufficient to present the Feldenkrais Method, hoping that it will be used as a methodology necessary for local community dance, and will be used as part of the educational effects and choreography creation methods of artists that can improve the physical functional aspects of dance and give a sense of psychological stability.

• Journal of Korean Philosophical Society
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• v.145
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• pp.91-117
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• 2018

This paper aims to clarify the similarities and differences between the Zhouyi and artificial intelligence. The divination of the Zhouyi is rooted in the oldest system of human knowledge, while artificial intelligence stands at the cutting edge of modern scientific revolution. At first sight, there does not appear to be any association that links the one to the another. However, they share the same ground as seen from a semiotic standpoint because both of them depend on the semiotic system as a means of obtaining knowledge. At least four aspects can be pointed out in terms of similarities. First, artificial intelligence and the Zhouyi use artificial language that consists of semiotic signs. Secondly, the principle that enables divination and artificial intelligence lies in imitation and representation. Thirdly, artificial intelligence and the Zhouyi carry out inferences based on mathematical algorithms that adopt the binary system. Fourth, artificial intelligence and the Zhouyi use analogy as a means of obtaining knowledge. However, those similarities do not guarantee that the Zhouyi could arrive at the scientific certainty. Nevertheless, it can give us important insight into the essence of our civilization. The Zhouyi uses intellect in order to get new information about the unknown world. However, it is hard to know what kind of intellect is involved in the process of divination. Likewise, we do not know the fundamental character of artificial intelligence. The intellect hidden in the unknown subject is a mystic and fearful existence to us. Just as the divination of the Zhouyi inspires the sense of reverence toward the supernatural subject, we could not but have fear in front of the invisible subject hidden in artificial intelligence. In the past, traditional philosophy acknowledged the existence of intellect only in conscious beings. Nonetheless, it becomes evident that human civilization ushers into a new epoch. As Ray Kurzweil mentioned, the moment of singularity comes when artificial intelligence surpasses human intelligence. In my viewpoint, the term of singularity can be used for denoting the critical point in which the human species enters into the new phase of civilization. To borrow the term of Shao Yong(邵雍) in the Northern Song Dynasty, the past civilization belongs to the Earlier Heaven(先天), the future civilization belongs to the Later Heaven(後天). Once our civilization passes over the critical point, it is impossible to go back into the past. The opening of the Later Heaven foretold by the religious thinkers in the late period of Joseon Dynasty was a prophecy in its own age, but it is becoming a reality in the present.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.

• Journal of Preventive Medicine and Public Health
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• v.6 no.1
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• pp.51-63
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• 1973

This paper illustrate residual hearing and socio-medical background on the hearing impaired children, 207 comming to Deaf School. attached to Hankuk Social Work College, Taegu, Korea. The survey was performed through interview with their parents and testing by diagnostic audio-meter (TRIO, AS 105 type) at soundproof room from March 10, to November 28, 1973. The results obtained were as follows. 1) The attendance rate of the compulsory primary school was markedly lower tendency in female than male according to directly proportional to prevalence rate of deafness among them. If was showed the deeper gap in the more superior school (middle and high school). 2) Who entered at the suitable age to each school (six years old to primary school, 12 years to middle and 15 years to high) was 11.3%. And who were enrolled in school age to each school (6-11 years for primary. 12-14 years for middle and 15-17 years for high) was 45.9% (43.7% in male, 50.0% in female). 3) As causative disease, congenital case, were 23.6% included of 13.5% of heredity and 10.1% of troubles during pregnancy; the total acquired cases were 47.9%, it was classified as 11.6% of convulsion from any other diseases, 7.7% of measles, 7.7% of other febrile diseases, 3.4% of drug (the most of streptomycin) intoxication, 2.4% of meningitis, 1.5% of epidemic encephalitis and 31.3% of other diseases; and unknown cases were 28.5%. 4) 31.4% of who included congenital cases lost their hearing within six months old, 11.6% in 6-11 months. 9.7% in 1-2 years old and 14.0% in 2-3years old. Consequently we obtained that the most cases 90.0% were lost their hearing within 3 years after birth. 5) According to qualities of hearing leases the most of cases were perceptive, 197(97.5%), only two cases were conductive, and eight cases were mixed. 6) The status of residual hearing according to average grade of hearing loss. $B(=\frac{a+2b+c}{4}$ as table 13) were as follows. Two cases were normal (one was mute and another was severe speach disorder). Ten cases, moderate. Moderately severe cases were 40 (19.3%). Severe cases, 38(18.4%). Scale out, profound cases, 48 (23.3%). And impossible testing cases because that were infantile or had some mental disorder were 69 (33.3%). 7) The using rate of hearing aides was only 12.0%. Among them who had some more residual hearing and could showed hearing effect with hearing aide have used more many proportionary but who were difficult to expect that effect were rare.