• Cross-Cultural Studies
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• v.41
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• pp.33-56
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• 2015

Trauma studies have provided useful models for dealing with the catastrophic and disastrous events that an individual and collective group experience. Most important of all, the perspective of post-structuralist trauma study, including Cathy Caruth, became a paradigmatic model and it has been applied to almost all contexts of life. The perspective of this study model, which is called an "event-based model of trauma," focuses on the literal registration of the traumatic event and the accurate and immediate recall of the past. The person directly involved in the event becomes the passive bearer transmitting the truth of a traumatic event. From this perspective, the traumatic subject only undergoes and endures the event and cannot play an active role in constructing trauma and dealing with it. Eventually, the truth of trauma has to be obtained at the cost of the traumatic subject's autonomy and the possibility of his/her agency. The problem here is that the truth, which is reencountered through the literal return of the past, obtained at the cost of the subject's autonomy, strikes a rather fatal blow to the person, than gives help for resolving many of matters surrounding traumatic experience and curing trauma. This suggests that the active way of dealing with trauma on the part of the traumatic subject, rather than the traumatic event itself, is demanded. Furthermore, because more recently, images of disastrous events were viewed "live" by audiences and an immediacy to the event is replicated in public discourse about them, the event becomes more immediately traumatic and there is a more strong presumption that people regard themselves as traumatic victims than before. This is the reason that we must explore an active way dealing with trauma on more human position at this time. This essay aims to examine the limits of the paradigmatic model of trauma study, an "event-based model of trauma," critically through a literary, theoretical text in which it reveals how the literal return of the traumatic past have a fatal effect on the victim; and hopes to suggest "the narrative memory" as a way to deal with trauma from a more humanistic perspective.

• 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.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• (The) Research of the performance art and culture
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• no.25
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• pp.57-89
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• 2012

The performers preparing for a performance usually experience the process of mental transference, contacting with text (drama) for the first time. It is movement from their everyday life to space in the play, when they try to break the wall between cast and themselves. The transference happens actually at the physical space, such as a dressing room, wing, (place just before appearing at a stage), and a stage (place to contact with audience). Performers keep moving among each psychological and physical space repeatedly, until the performance finishes totally. The transference means moving to each space to another, and the mental borderline means the point of mental change the performers experience during the process of transference. The mental borderline can be guessed to exist through mental aspects the performers feel when they move from each space to another. The most typical example, that shows performer's mental borderline well, is stage fright shown as tension, or anxiety among the variety of aspects. According to a research, the most performers experience that kind of mental aspect just before appearing at a stage. The study on it is already referred by my article.' A Study on Korean Performer's Stage Fright. This study aims at examining the relationship among psychological and physical space the performers experience, mental borderline when transferring and penetrating those spaces, and performer's mental change First, the concept of mental borderline is to be understood totally with preceded research. And the space the performers experience and mental borderline at transference are to be reorganized. Secondly, the area of transference in the process of performance is to be reclassified into physical and mental space. Third, analyzing the actual case of performers experiencing the mental borderline, the diversified use should be searched to make use of mental borderline as a positive element. The psychological symptoms, performers experience in the performance, can have positive consequence beside negative one. The tension occurring at the area of borderline is positive, and it can be the actual borderline for the performers. It will be researched how the performers change at the mental borderline, the state of mind is maintained, and they perform in an overall performance, through the study on the relationship between the transference and the mental borderline. And the stress and concentration caused by stage fright, and shyness will be confirmed, and the positive element of a stage, which is used as various defense mechanism.

• The Journal of Korean Philosophical History
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• no.31
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• pp.241-262
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• 2011

In this paper we show how the perception of heavenly principle(天理) and definition of individual desires(私慾) in the five commentaries on Daodejing(道德經) was changed over time. The five commentaries on Daodejing(道德經) composed during $Chos{\breve{o}}n$ are 'Sooneon(醇言) by Lee, Yul-gock (李珥,1536~1584), 'SinJoo-DoDuckKyung (新註道德經) - or New Commentary on Daodejing(道德經) - by Park, Se-dang(朴世堂,1629~1703), 'Dodukjigi(道德指歸)' by Suh, Myoung-euing(徐命膺,1716~1787), 'Chowondamro (椒園談老)' by Lee, Chung-ik(李忠翊,1744~1816), and 'Jungro(訂老)' by Hong, Suk-joo (洪奭周,1774~1842). The course of history in understanding the book, "Daodejing(道德經)," demonstrated that by the late of $Chos{\breve{o}}n$ Dynasty in the 18th century, the notion of 'the moral law for the community' has changed. Neither Suh, Myoung-euing nor Lee, Chung-ik emphasized 'the necessity for the truth of the heavens.'Instead, they focused more on the 'individuals' who followed the moral law than the moral law itself. They did not see the individual desire as the object that had to be discarded. Within the context of this framework, the individual's role had changed from the person who had to be obedient to the law to the subject who judged the moral law all by him/herself. This process of breaking up 'the goodness of the community' led the $Chos{\breve{o}}n$ Dynasty of the 18th century in the transition period to the modern era. In other words, it was the time when the introspection of the 'moral law' prevailed in the $Chos{\breve{o}}n$ Dynasty occurred naturally and spontaneously among the Confucian scholars, which implied the reconceptualization of the 'self-awareness' or 'the point of view on the individual's self-desire' was occurred in the context of academic development during the late $Chos{\breve{o}}n$ Dynasty.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.75-88
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• 2021

The film won the Golden Palm Award at the Cannes Film Festival, and received the Academy Award for a non-English-speaking film in February 2020, respectively. It has received a monumental evaluation in the world film history. Overall, this film is about class conflict, and critics evaluate the theme of the film as "badly twisted class gap" and "anger from class." The film expresses an intrinsic conflict embodied in culture as a "tragedy in which no bad person appears," rather than the dichotomous composition of the classical class struggle from Marxism. In other words, this can be seen as expressing the substrated class relationship of the modern society that Pierre Bourdieu had argued. This film has been focused as a controversial target under Korea society with excess of ideology. Politics used to adopt the keyword, 'parasite', for political disputes not only in culture contents world. Paradoxically socialism China did not allow to release film 'Parasite.' On the other hand, Lee O-Yong argues that the movie "Parasite" does not look at social phenomena through a dichotomous perspective, but is viewed through a "double perspective" and evaluates that it does not lose eyes looking at humans through tension. This view is based upon 'Vital Capitalism'. Lee. O-Yong looks at the movie "Parasite" from the perspective of "Vital Capitalism". The theory of Vital Capitalism does not seek to find the root of historical development in class struggle conflicts, but rather figuring out history and society pays attention onto the intrinsic characteristics of life, Topophilia, Neophilia, and Biophilia. Lee Eo-ryeong argues that the development of civilization theory evolved from the stage of Hobbes' Darwinism or predatism to the stage of host vs. parasite of Michel Serres, and onto the stage of Margulis's 'Win-Win (inter-dependence)'. In this paper, after overview of vital capital concept and preceeding research, re-interpretations were tried onto scenes based upon fields from habitus, culture capital. This exploration looks for a alternative for excess of ideology in Korea society.

• Journal of Popular Narrative
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• v.26 no.3
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• pp.221-254
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• 2020

This paper finds a solution in the social context which cannot be explained thoroughly by well-timed release date, revival of comedy films, and the attraction of Lee Byeong-heon's comedy etc. while it throws question of how the film, Extreme Job captivated 16 million audience. The incredible hits of Extreme Job cannot be explained by analyzing the text alone. After this essay investigates a function and a role of comedy as a public sphere, it examines people's desires and wishes in the comedy and other genres since 2008 when the conservative government has seized power. Since 2008 a series of dark tone's action thriller, social problem film, and disaster film have emerged, these genres showed absence of public security, crisis of democracy and criticism against rulling class. On the other hand, hit comedy films have showed escapism such as weepie, nostalgia, and fantasy at the same time, generally. Although Veteran (2015) is not full-blown comedy, after this film's big success, "comic mode" has gradually revived. A light tone's films which are truer to genre rules has started representing the wishes of people toward social reforms and changes. Meanwhile, "Candlelight Protest" served as a momentum to recover the democracy which has been in crisis, but it could not lead changes in economic and daily lives. Exreme Job can be read as a question how we will survive since "Candlight Protest." The lives of detectives as self-employed workers who has taken over a fried chicken restaurant for going undercover are appearances of ordinary persons who must survive in the edless conpetition. Furthermore, this film shows a dream of a "great success myth" which becomes well-known as a famous restaurant and a self-management such as brand-naming and an exapansion of franchise business. We can read ganster's chicken franchises as a huge distribution industry which disturbs market system by delivering drugs secretly. While applauses that we give to the police having identities of self-employed workers which sweeps the ganster are giving support to oridinary neighborhood like us, they are also wishes of people who long for the restoration of publicness of police in the market which is becoming increasingly privatized today. A significance of this essay is to examine Extreme Job in terms of the geography of film genres and the revival of comic mode sicne 2008 at the macro level, and is to read the film in the perspective of the problems of economic and daily lives which has been still unsolved since "Candlelight Protest" at the micro level.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.251-273
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• 2022

Recently, research on applying text analysis to deep learning has steadily continued. In particular, researches have been actively conducted to understand the meaning of words and perform tasks such as summarization and sentiment classification through a pre-trained language model that learns large datasets. However, existing pre-trained language models show limitations in that they do not understand specific domains well. Therefore, in recent years, the flow of research has shifted toward creating a language model specialized for a particular domain. Domain-specific pre-trained language models allow the model to understand the knowledge of a particular domain better and reveal performance improvements on various tasks in the field. However, domain-specific further pre-training is expensive to acquire corpus data of the target domain. Furthermore, many cases have reported that performance improvement after further pre-training is insignificant in some domains. As such, it is difficult to decide to develop a domain-specific pre-trained language model, while it is not clear whether the performance will be improved dramatically. In this paper, we present a way to proactively check the expected performance improvement by further pre-training in a domain before actually performing further pre-training. Specifically, after selecting three domains, we measured the increase in classification accuracy through further pre-training in each domain. We also developed and presented new indicators to estimate the specificity of the domain based on the normalized frequency of the keywords used in each domain. Finally, we conducted classification using a pre-trained language model and a domain-specific pre-trained language model of three domains. As a result, we confirmed that the higher the domain specificity index, the higher the performance improvement through further pre-training.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• (The) Research of the performance art and culture
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• no.38
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• pp.143-191
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• 2019

This research argues that Pansori had patrons in its development. Patrons are commonly discussed aspect of history of any art form. Pansori is no exception. While Pansori originally began as the art of the common people, Yangban class became the primary audience. This paper examines the role of royal family of Choson dynasty in development of Pansori. Heungseon Daewongun (흥선대원군) in particular was a Pansori aficionado. The record around Daewongun's involvement to Pansori proves that heavy monetary investment was made. He hosted Pansori competitions and sponsored creation of Pansori tradition, Boseong Sori (보성소리) and Gangsanje (강산제). Also the aspect of Pansori patronage lies not just in Yangban class, but also in Jung'in class, which is roughly analoguous to European bourgeois in that they were not of Yangban class, but had gained monetary status, and had aesthetics of both Yangban and commoner class. I argue that Heungseon Daewongun's ties to the Jung'in class is reflected in his actions towards Pansori artists. The traditions he had sponsored have important characteristics, including sophisticated lyrics heavily utilizing Classical Chinese poetry, highly artistic musical composition, and conservative Confucian ethics. Those characteristics indicate that the Pansori traditions sponsored by the royal patrons have changed to cater to their artistic taste and philosophy. This paper conducts a textual comparative analysis between Gangsanje Pansori Jeokbyeokga (강산제 판소리 적벽가), Dongpyeonje's Pansori Jeokbyeokga (동편제 판소리 적벽가), and Seopyeonje Pansori Jeokbyeokga, who share the same plot yet offers a stark differences in tone, philosophy, and sense of humor. Daewongun was a primary sponsor of Pansori, which proves that Yangban class and the royal family have played important role as patrons of Pansori.