• Journal of Intelligence and Information Systems
• /
• v.22 no.2
• /
• pp.33-56
• /
• 2016

Many researchers have focused on developing bankruptcy prediction models using modeling techniques, such as statistical methods including multiple discriminant analysis (MDA) and logit analysis or artificial intelligence techniques containing artificial neural networks (ANN), decision trees, and support vector machines (SVM), to secure enhanced performance. Most of the bankruptcy prediction models in academic studies have used financial ratios as main input variables. The bankruptcy of firms is associated with firm's financial states and the external economic situation. However, the inclusion of qualitative information, such as the economic atmosphere, has not been actively discussed despite the fact that exploiting only financial ratios has some drawbacks. Accounting information, such as financial ratios, is based on past data, and it is usually determined one year before bankruptcy. Thus, a time lag exists between the point of closing financial statements and the point of credit evaluation. In addition, financial ratios do not contain environmental factors, such as external economic situations. Therefore, using only financial ratios may be insufficient in constructing a bankruptcy prediction model, because they essentially reflect past corporate internal accounting information while neglecting recent information. Thus, qualitative information must be added to the conventional bankruptcy prediction model to supplement accounting information. Due to the lack of an analytic mechanism for obtaining and processing qualitative information from various information sources, previous studies have only used qualitative information. However, recently, big data analytics, such as text mining techniques, have been drawing much attention in academia and industry, with an increasing amount of unstructured text data available on the web. A few previous studies have sought to adopt big data analytics in business prediction modeling. Nevertheless, the use of qualitative information on the web for business prediction modeling is still deemed to be in the primary stage, restricted to limited applications, such as stock prediction and movie revenue prediction applications. Thus, it is necessary to apply big data analytics techniques, such as text mining, to various business prediction problems, including credit risk evaluation. Analytic methods are required for processing qualitative information represented in unstructured text form due to the complexity of managing and processing unstructured text data. This study proposes a bankruptcy prediction model for Korean small- and medium-sized construction firms using both quantitative information, such as financial ratios, and qualitative information acquired from economic news articles. The performance of the proposed method depends on how well information types are transformed from qualitative into quantitative information that is suitable for incorporating into the bankruptcy prediction model. We employ big data analytics techniques, especially text mining, as a mechanism for processing qualitative information. The sentiment index is provided at the industry level by extracting from a large amount of text data to quantify the external economic atmosphere represented in the media. The proposed method involves keyword-based sentiment analysis using a domain-specific sentiment lexicon to extract sentiment from economic news articles. The generated sentiment lexicon is designed to represent sentiment for the construction business by considering the relationship between the occurring term and the actual situation with respect to the economic condition of the industry rather than the inherent semantics of the term. The experimental results proved that incorporating qualitative information based on big data analytics into the traditional bankruptcy prediction model based on accounting information is effective for enhancing the predictive performance. The sentiment variable extracted from economic news articles had an impact on corporate bankruptcy. In particular, a negative sentiment variable improved the accuracy of corporate bankruptcy prediction because the corporate bankruptcy of construction firms is sensitive to poor economic conditions. The bankruptcy prediction model using qualitative information based on big data analytics contributes to the field, in that it reflects not only relatively recent information but also environmental factors, such as external economic conditions.

• Journal of The Korean Ophthalmological Society
• /
• v.54 no.9
• /
• pp.1445-1451
• /
• 2013

Purpose: To report long-term changes in the average retinal nerve fiber layer (RNFL) thickness in 2 patients who had intravitreal bevacizumab (IVB) injection for diabetic papillopathy. Case summary: A 36-year-old patient with diabetes complained of decreased visual acuity (20/200) in the right eye. The fundus examination showed optic disc swelling in both eyes. The average RNFL thickness based on optical coherence tomography (OCT) increased to $278{\mu}m$ and Goldmann perimetry showed nasal visual field defect in the right eye. The IVB was injected into the right eye. Three weeks after the IVB injection, RNFL thickness decreased to $135{\mu}m$ and visual acuity improved to 20/25 in the right eye. However, RNFL thickness increased from 126 to $207{\mu}m$ and visual acuity decreased to 20/32 in the left eye. Thus, IVB was injected into the left eye. In week 3, RNFL thickness decreased to $147{\mu}m$ and visual acuity improved to 20/20 in the left eye. At 12 months after IVB injection, RNFL thickness was $87{\mu}m$ in the right eye and $109{\mu}m$ in the left eye. A 57-year-old patient with diabetes complained of decreased visual acuity (20/200) and showed optic disc swelling in the right eye. The average RNFL thickness increased to $252{\mu}m$ and Goldmann perimetry showed an enlarged blind spot in the right eye. IVB was injected into the right eye. After 3 weeks, RNFL thickness decreased to $136{\mu}m$ and visual acuity improved to 20/70 in the right eye. Six months after IVB injection, RNFL thickness was $83{\mu}m$ in the right eye. Conclusions: Visual acuity progressively improved within 3 weeks and RNFL thickness measured by spectral domain OCT showed progressive thickness reduction in 2 cases of diabetic papillopathy patients who had IVB injections.

• Korean Journal of Soil Science and Fertilizer
• /
• v.36 no.3
• /
• pp.134-144
• /
• 2003

A rhizobacterium Pseudomonas cholororaphis O6 produced several secondary metabolites, such as phenazines, protease, and HCN that may be involved in inhibition of the growth of phytopathogenic fungi. In field study, P. chlororaphis O6 treatment on wheat seed suppressed root rot disease caused by Fusarium culmorum. The major organic acids of cucumber root exudates were fumaric acid, malic acid, benzoic acid, and succinic acid. Glucose and fructose were major monosaccharides in cucumber root exudates. The total amount of organic acids was ten times higher than that of the sugars. P. chlororaphis O6 grew well on cucumber root exudates. The dctA gene of P. chlororaphis O6 consisted of a 1,335 bp open reading frame with a deduced amino acid sequence of 444 residues, corresponding to a molecular size of about 47 kD and pI 8.2. The deduced dctA sequence has ten putative transmembrane domains, as expected of a membrane-embedded protein. Our results indicated that organic acids in cucumber root exudates may play an important role in providing nutrient source for root colonization of biological control bacteria, and the dctA gene of P. chlororaphis O6 may be an important bacterial trait that is involved in utilization of root exudates.

• The Korean Journal of Archival Studies
• /
• no.24
• /
• pp.3-40
• /
• 2010

In recent time, archival science has been in charge of positively preserving and handling with valuable things, as well as managing established ones, However, even though several archival methodologies that manage contexts among tasks, organizations and subjects exist nowadays, there is a lack of theoretical methodology on archiving focusing on valuable things. In this sense, this article dealt with a theoretical methodology which carries out archiving valuable things and represents it based on the value of records. Also, this paper, which covers a methodology that carries out archiving and representing one focusing on the value of the one to preserve, is divided into three chapters: putting in, managing, and viewing. To begin with, in the chapter of purring in, the methodology of documentation based on a strategy to distinguish and represent the value of the valuable things were explained. In addition, the article tried to explain the definition of how the valuable things based on the value of it can be put in, and presented how to divide the one for representation into the objet and the activity so as to provide an effective approach. At the same time, as this paper took an approach to the value of the one, it proposed a way to be able to do archiving effectively by applying a representation unit which has its own value. Secondly, in the chapter of managing, representation class and metadata for managing with a representable structure was considered. Metadata categories were illustrated in order to present the class from individual records to final representation valuable things and to make representation with ease. Furthermore, in the chapter of viewing, the process of representation using theoretically archived records was explained. In fact, viewing is the descriptive domain in general, yet this paper focused on the conceptional part. As a consequence, in this paper, a series of process was considered, which starts from how the subject of representation was archived to managing it. Moreover, the process has a meaning by itself in that it gives a practical method to be applied. Finally, the paper suggested that the argumentation on representation be expanded in the field of archival science so as to present theoretical grounds in this sort of work.

• Journal of Korean Theatre Studies Association
• /
• no.52
• /
• pp.189-228
• /
• 2014

The purpose of this research was to summarize Korea and China's researches on Noh and to examine main domain in this field, by investigating the academic books and articles published in two countries. In 1960s, since Nohgaku has been introduced to China, academic articles on Zeami's theories and aesthetics have emphasized on aesthetic characteristics of Chinese plays and Japanese Nohgaku through the similarities of oriental plays. The number of researches on Kabuki is almost twice as that of researches on Noh in China. While most researches on Kabuki were compared with the styles and music of Pecking Opera and the theatrical theories of liyu[李漁], those on Noh has been highlighted the comparative studies on $Y{\bar{o}}kyoku$[謠曲], Chinese Noh plays. The main difference among the researches on $Y{\bar{o}}kyoku$ in Korea and China was the material regarding characters of Noh. Because song yuanzaju[宋 元雜劇]and Nohgaku in Chinese-Japanese plays were the mature form of the classic plays and those were representative of traditional nation plays, this researches tried to ascertain the cultural origins of two countries regarding the aesthetic characteristics by referencing lyrical and narrative features[曲詞] of yuanzaju[元雜劇]and the classic waka of Nohgaku. While the comparative studies on Noh and song yuanzaju and kunqu[昆劇] in China were prevalent, national researches have emphasized on the inner world of the main character and dramaturgy through the verbal description of Noh. Especially, this research tried to investigate the inner world of the main character and the intention of the writers through the verbal description of Noh authorized in the history of the works. Also, the researches on Buddhism in the Middle Ages and religious background were examined significantly. In addition, the $Y{\bar{o}}kyoku$ has influenced on European modern playwrights and the comparative studies between the materials of $Y{\bar{o}}kyoku$ and Western modern plays were concerned. In Korea, the comparative studies on Noh between Korea abd Japan has been most focused on the origin theory of Noh. The fact that appearance theory of Noh had originated from Sangaku was common opinion among Korean, Chinese, and Japanese scholars. However, they are agree with the opinion that according to the formation of the different genres, Noh's mainstream was different among three countries despite of the same origin. Yuan drama and Noh play have the same origin, but different branch. In relation to the Noh's origin theory, there are literature comparative studies in religious background, the studies presumed the origin of instrumental music related to those in mask plays, and the comparative studies between Korean mask plays and $ky{\bar{o}}gen$ of Nohgaku. Kyogen is the Comedy inserted among the stories in Nohgaku performed in just one day. Therefore, $ky{\bar{o}}gen$ must be discussed separately from the relations of 'shite[任手]'s inner action veiled with masks. This research figured out that the lacking points of the two countries' researches were the acting methods of Noh. Academic articles written by foreign scholars studying Korean and Chinese theatres should be included when this issue will be dealt with. In Korea and China, translation studies and writings regarding Nohgaku have studied by those who are major in Japanese literature or oriental literature. This case is the same in Korea in that scholars whose speciality is not theatre, but Japanese literature has studied. Therefore, this present study can give a good grasp of whole tendency on Nohgaku's research in theatre fields.

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

• Journal of Intelligence and Information Systems
• /
• v.28 no.2
• /
• pp.127-146
• /
• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Korean Association of Arts Management
• /
• no.58
• /
• pp.5-47
• /
• 2021

This paper is a story about five middle-aged men in their 50s who suddenly began their photographic activities as they reached middle age. In the perspective of Borudieu's cultural capital theory, this study observes five men in their 50s by implementing in-depth interviews about the motivation behind taking photographs, the experience of photography activities, and the rewards of these activities. The theory has undergone a theoretical revision with the criticism that factors other than the class can be influential. Based on these ideas, I have proceeded my study by preferentially grasping the notion of the 'field' in accordance with the specific history of Korean society. Therefore, this study sought to more specifically understand the various photographic activities of middle-class men in their 50s by referring Coskuner-Balli and Thompson's argument(2013), which revised 2018's cultural captial theory and proposed the concept of 'subordinate cultural capital' and 'leisure capital' who proposed by Backlund, E. A. & Kuentzel, W. F.(2013). As a middle-class men in their 50s, research participants have grown up and worked in a social atmosphere where economic capital is recognized as an individual's ability. However, they are faced with the value that the knowledge and taste towards culture and arts is one's identity. In addition to the subjective deprivation that arises from this situation, the lifespan characteristic of their age that it is on the brink of the old age appeared to have influenced them to put their psychological motivation immediately into practice. Economic capital was the main conversion terms to move form interest to practice, which includes 'time' as a resource as well as money. With the cultural practices being expanded since their creation of photographs, the reason that these expansions can be maintained more actively lies in their identity as 'cultural artist' that is consolidated in new relationships in the sharing of photographic activities. In this way, photographic activities grant a symbolic status of 'a middle-aged man who actively builds and expresses his identity' through the conversion of accumulating cultural capital and the conversion into social capital. Furthermore, the recognized scope of the symbolic capital acquired by the research participants is in the domain of the private life that is family and acquaintance. Especially, they were gaining a great psychological reward from their children's recognition that they are not just a 'breadwinner' but 'dad who cultivates himself with a culture and arts'. Accordingly, by considering that 'generation' other than class can be a meaningful discussion point when understanding Korea society from the perspective of cultural theory, this study is meaningful that a more flexible understanding of cultural theory can give a glimpse into the possibility of a more specific and diverse approach that will arise in the discussion of culture and arts education.

• Information Systems Review
• /
• v.25 no.4
• /
• pp.131-159
• /
• 2023

Recently, amidst the backdrop of the COVID-19 pandemic shifting towards an endemic phase, there has been a rise in discussions and debates about the future of the metaverse. Simultaneously, major metaverse platforms like Roblox have been launching services integrated with generative AI, and Apple's mixed reality hardware, Vision Pro, has been announced, creating new expectations for the metaverse. In this situation where the outlook for the metaverse is divided, it is crucial to diagnose the metaverse from an ecosystem perspective, examine its key ecological features, driving forces for development, and future possibilities for advancement. This study utilized Wang's (2021) Information Ecology Theory (IET) framework, which is representative of ecosystem research in the field of Information Systems (IS), to derive the Metaverse Industrial Ecosystem (MIE). The analysis revealed that the MIE consists of four main domains: Tech Landscape, Category Ecosystem, Metaverse Platform, and Product/Service Ecosystem. It was found that the MIE exhibits characteristics such as digital connectivity, the integration of real and virtual worlds, value creation capabilities, and value sharing (Web 3.0). Furthermore, the interactions among the domains within the MIE and the four characteristics of the ecosystem were identified as driving forces for the development of the MIE at an ecosystem level. Additionally, the development of the MIE at an ecosystem level was categorized into three distinct stages: Narrow Ecosystem, Expanded Ecosystem, and Everywhere Ecosystem. It is anticipated that future advancements in related technologies and industries, such as robotics, AI, and 6G, will promote the transition from the current Expanded Ecosystem level of the MIE to an Everywhere Ecosystem level, where the connection between the real and virtual worlds is pervasive. This study provides several implications. Firstly, it offers a foundational theory and analytical framework for ecosystem research, addressing a gap in previous metaverse studies. It also presents various research topics within the metaverse domain. Additionally, it establishes an academic foundation that integrates concept definition research and impact studies, which are key areas in metaverse research. Lastly, referring to the developmental stages and conditions proposed in this study, businesses and governments can explore future metaverse markets and related technologies. They can also consider diverse metaverse business strategies. These implications are expected to guide the exploration of the emerging metaverse market and facilitate the evaluation of various metaverse business strategies.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.1
• /
• pp.67-83
• /
• 2023

The social venture start-up phenomenon is found from the perspectives of social enterprise and for-profit enterprise. This study aims to fundamentally explore the start-up phenomenon of social ventures from these two perspectives. Considering the lack of prior research that researched both social and commercial perspectives at the same time, this paper analyzed using grounded theory approach of Strauss & Corbin(1998), an inductive research method that analyzes based on prior research and interview data. In order to collect data for this study, eight corporate representatives currently operating social ventures were interviewed and data and phenomena were analyzed. This progressed to a theoretical saturation where no additional information was derived. The analysis results of this study using the grounded theory approach are as follows. As a result of open coding and axial coding, 147 concepts and 70 subcategories were derived, and 18 categories were derived through the final abstraction process. In the selective coding, 'expansion of social venture entry in the social domain' and 'expansion of social function of for-profit companies' were selected as key categories, and a story line was formed around this. In this study, we saw that it is necessary to conduct academic research and analysis on the competitive factors required for companies that pursue the values of two conflicting relationships, such as social ventures, to survive with competitiveness. In practice, concepts such as collaboration with for-profit companies, value combination, entrepreneurship competency and performance improvement, social value execution competency reinforcement, communication strategy, for-profit enterprise value investment, and entrepreneur management competency were derived. This study explains the social venture phenomenon for social enterprises, commercial enterprises, and entrepreneurs who want to enter the social venture field. It is expected to provide the implications necessary for successful social venture startups.