• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.149-156
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• 2022

In the current COVID-19 pandemic, fake news and misinformation related to COVID-19 have been causing serious confusion in our society. To accurately detect such fake news, social context-based methods have been widely studied in the literature. They detect fake news based on the social context that indicates how a news article is propagated over social media (e.g., Twitter). Most existing COVID-19 related datasets gathered for fake news detection, however, contain only the news content information, but not its social context information. In this case, the social context-based detection methods cannot be applied, which could be a big obstacle in the fake news detection research. To address this issue, in this work, we collect from Twitter the social context information based on CoAID, which is a COVID-19 news content dataset built for fake news detection, thereby building CoAID⁺ that includes both the news content information and its social context information. The CoAID⁺ dataset can be utilized in a variety of methods for social context-based fake news detection, thus would help revitalize the fake news detection research area. Finally, through a comprehensive analysis of the CoAID⁺ dataset in various perspectives, we present some interesting features capable of differentiating real and fake news.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.2
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• pp.237-253
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• 2022

This article offers an account of how regional development policy in Korea has evolved under the influence of actor-networks comprising the cultural circuit of soft capitalism. In so doing, the roles played by transnational actor-networks forged between global consulting firms and national business media are emphasized. For this discussion, the waning of spatial Keynesianism in the country is contextualized in the first place, with particular attention to changing planning goals of key regional development policies including consultancies, influential policy gurus (e.g., Michael Porter and Richard Florida), and local business media outlet Maekyong are found to be key movers and shakers in the transition. These empirical findings call for striking a balance between dominant structuralist accounts and emerging actor-oriented approaches, and also help shed a new light on the dualistic conceptualization of managerialist and entrepreneurial governance in a way that the latter may be a new form of the former.

• Korean Journal of Plant Resources
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• v.35 no.5
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• pp.574-585
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• 2022

This study was conducted to quantify chlorogenic acid content and evaluate biological activity, such as antioxidant, antibacterial, anti-inflammatory, and digestive enzyme activity of Aralia elata ethanol extract (AEE). The SC₅₀ of DPPH and ABTS radical scavenging activities of AEE were 4.79±0.05 mg/mL, 5.79±0.05 mg/mL; total polyphenol and total flavonoid contents were 170.0±1.8 mgGAE/g, 105.5±4.1 mgQE/g, respectively. Nitric oxide (NO) was increased in RAW 264.7 cells and Caco-2 cells with treatment of LPS, and production of NO was inhibited by AEE in a concentration-dependent manner. Production of NO was reduced by 60.0±1.1% in RAW 264.7 cells and 50.7±2.8% in Caco-2 cells at of AEE. Similarly, the production of inflammatory cytokines (TNF-α, IL-1β and IL-6) was inhibited in a concentration dependent manner. Antibacterial activity increased as the dose concentration of AEE increased, and the MIC was 75 mg/mL for L. monocytogenes, and 100 mg/mL for S. typhimurium and H. pylori. In addition, amylase and protease enzyme activity was observed in AEE and increased enzyme activity was observed according to the concentration of the extract. AEE contained 7.06±0.01 mg/g of chlorogenic acid. As a result of the experiment, it is judged that it can be used as basic data for the development of health food using Aralia elata.

• Knowledge Management Research
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• v.23 no.3
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• pp.129-152
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• 2022

As a key infrastructure for digital transformation, the spread of data, network, artificial intelligence (D.N.A.) fields and the emergence of promising industries are laying the groundwork for active digital innovation throughout the economy. In this study, by applying the text mining methodology, major topics were derived by using the abstract, publication year, and research field of the study corresponding to the SCIE, SSCI, and A&HCI indexes of the WoS database as input variables. First, main keywords were identified through TF and TF-IDF analysis based on word appearance frequency, and then topic modeling was performed using g-DMR. With the advantage of the topic model that can utilize various types of variables as meta information, it was possible to properly explore the meaning beyond simply deriving a topic. According to the analysis results, topics such as business intelligence, manufacturing production systems, service value creation, telemedicine, and digital education were identified as major research topics in digital transformation. To summarize the results of topic modeling, 1) research on business intelligence has been actively conducted in all areas after COVID-19, and 2) issues such as intelligent manufacturing solutions and metaverses have emerged in the manufacturing field. It has been confirmed that the topic of production systems is receiving attention once again. Finally, 3) Although the topic itself can be viewed separately in terms of technology and service, it was found that it is undesirable to interpret it separately because a number of studies comprehensively deal with various services applied by combining the relevant technologies.

• Korean small business review
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• v.43 no.1
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• pp.165-186
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• 2021

Startups are undergoing a change throughout the growth process of startups that appear in existing studies as they move away from the existing B2B or B2C frame and expand their target customer groups to O2O, C2C. In this regard, a new type of startup known as unicorns, a unicorn which has grown rapidly in a short period of time, is being created by successfully attracting government support and external investment in recognition of the potential value of the startup. This study examined the relationship between investment attraction and growth after founding for five representative unicorns in the U.S. and Korea. As a result, it was found that private investment in Korea is passive and defensive, and is attracted after the Series A stage, compared to the U.S., where the growth potential of the startup ecosystem is positively evaluated. In addition, it found that government's support policy throughout the startup's growth process is an abstract and comprehensive policy focusing on initial funding for startups. Therefore, it was suggested that the scope of government policies should be expanded to forster startups as unicorns, and that it is necessary to establish and implement differentiated support policies for each growth of the scale-up of startups. This study is significant in that it presented the criteria for the growth stage and support of startups as well as policy support for scale-up through practical case analysis of unicorns.

• Korean small business review
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• v.42 no.1
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• pp.107-133
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• 2020

Many researchers conducted studies on the relationship between entrepreneur's characteristic, capability, strategy and performance of new venture. However, the development of scientific technique and the complexity of the business environment have stimulated entrepreneurial teams rather than individuals. Therefore, the necessity of theoretical and practical study on the effect of the characteristics of an entrepreneurial team on the new venture companies was suggested. Initial research on entrepreneurial team diversity has primarily addressed the impact of demographic diversity on performance. In order to verify the research model of this study, 287 delegates of new venture companies that participated in the projects at the 18 Centers for Creative Economy & Innovation in 17 regions of the country conducted validity and reliability test based on the questionnaire to which they answered. The result shows that only gender diversity among demographic diversity affected non-financial performance. Information diversity influenced career diversity on financial performance and diversity in education on non-financial performance. Also, the higher the previous sharing experience, the better the financial performance. Value diversity has negative effect on both financial and non-financial performance. Based on the results, theoretical and practical implications are derived. Also suggested are methodological limitations and future research directions.

• Journal of the Korean Association of Geographic Information Studies
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• v.25 no.2
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• pp.59-71
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• 2022

This study considered regional differences in the university-industry collaboration of Korean universities and performed cluster analysis to identify the spatial range with high university-industry collaboration connectivity. By university establishment type, it was found that the university-industry collaboration capacity of the major national university was superior overall, especially in the technology transfer & commercialization sector and the infrastructure sector, compared to private universities and general national universities. The spatial pattern of university-industry collaboration capacity showed relatively clear differences by city and province. In terms of university-industry collaboration capacity by sector, it was confirmed that the regional gap was not large in the talent training sector and the infrastructure sector, but the regional gap was relatively large in the technology transfer & commercialization sector and the start-up sector. As a result of the cluster analysis to identify a spatial range with high connectivity in terms of similarity and spatial proximity of university-industry collaboration patterns, it is divided into 15 clusters. It is found that most of major national universities are included in one of 15 clusters where all sectors of university-industry collaboration are strong. Therefore, as a policy measure to achieve regional innovative growth through enhancing the effectiveness of university-industry collaboration, we propose the establishment of a hub & spoke network-type collaboration system in which a major national university acts as a hub and nearby local universities play a spoke role.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.101-113
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• 2021

In this paper, we introduce a speaker overlap system and look at the process of converting the existed system on the specific framework of artificial intelligence. Speaker overlap is when two or more speakers speak at the same time during a conversation, and can lead to performance degradation in the fields of speech recognition or speaker recognition, and a lot of research is being conducted because it can prevent performance degradation. Recently, as application of artificial intelligence is increasing, there is a demand for switching between artificial intelligence frameworks. However, when switching frameworks, performance degradation is observed due to the unique characteristics of each framework, making it difficult to switch frameworks. In this paper, the process of converting the speaker overlap detection system based on the Keras framework to the pytorch-based system is explained and considers components. As a result of the framework switching, the pytorch-based system showed better performance than the existing Keras-based speaker overlap detection system, so it can be said that it is valuable as a fundamental study on systematic framework conversion.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.261-268
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• 2022

No language can be separated from other languages and exist independently. When a language comes in contact with a foreign culture, they continuously affect each other and bring changes. Hallyu boom(Korean wave), which was derived from the emergence of K-drama and K-pop due to rapid developments in global scientific technologies and digitization after the 90's, affected the Chinese language. As a result, neologisms that are derived from the Korean language are being commonly used for making exchanges and becoming social buzzwords. Neologisms derived from Korean reflect the effects and results of language contact between the two languages. We examined the background and cause of Chinese neologisms derived from Korean based on the sociocultural factors and psychological necessity, and explained neologisms by using four categories of transliteration, liberal translation, borrowing Korean-Chinese characters and others. Despite having the issue of being anti-normative during the process of coining new words, neologism enriches Chinese expressions and is a mirror for social culture that reflects the opinions and understandings of young Chinese people who pursue novelty, change, innovation and creativity in linguistic aspects. We hope that it will serve as an opportunity for the young people in Korea and China to change their perceptions and become more friendly by understanding each other's language, culture and by communicating. We also expect to provide assistance in regard to teaching and learning the applications of Korean-Chinese language fusion at Chinese education fields.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.315-324
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• 2022

In this paper, we propose KoEPT, a Transformer-based generative model for automatic math word problems solving. A math word problem written in human language which describes everyday situations in a mathematical form. Math word problem solving requires an artificial intelligence model to understand the implied logic within the problem. Therefore, it is being studied variously across the world to improve the language understanding ability of artificial intelligence. In the case of the Korean language, studies so far have mainly attempted to solve problems by classifying them into templates, but there is a limitation in that these techniques are difficult to apply to datasets with high classification difficulty. To solve this problem, this paper used the KoEPT model which uses 'expression' tokens and pointer networks. To measure the performance of this model, the classification difficulty scores of IL, CC, and ALG514, which are existing Korean mathematical sentence problem datasets, were measured, and then the performance of KoEPT was evaluated using 5-fold cross-validation. For the Korean datasets used for evaluation, KoEPT obtained the state-of-the-art(SOTA) performance with 99.1% in CC, which is comparable to the existing SOTA performance, and 89.3% and 80.5% in IL and ALG514, respectively. In addition, as a result of evaluation, KoEPT showed a relatively improved performance for datasets with high classification difficulty. Through an ablation study, we uncovered that the use of the 'expression' tokens and pointer networks contributed to KoEPT's state of being less affected by classification difficulty while obtaining good performance.