• 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.

• 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.

• 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.

• Journal of Service Research and Studies
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• v.10 no.4
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• pp.57-70
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• 2020

Globally, cloud service is a core infrastructure that improves industrial productivity and accelerates innovation through convergence and integration with various industries, and it is expected to continuously expand the market size and spread to all industries. In particular, due to the global pandemic caused by COVID-19, the introduction of cloud services was an opportunity to be recognized as a core infrastructure to cope with the untact era. However, it is still at the preliminary stage for market expansion of cloud service in Korea. This paper aims to empirically analyze how cloud services can be accepted by users by each industry through extended Technology Acceptance Model(TAM), and what factors influence the acceptance and avoidance of cloud services to users. For this purpose, the impact and factors on the acceptance intention of cloud services were analyzed through the hypothesis test through the proposed extended technology acceptance model. The industrial sector selected four industrial sectors of education, finance, manufacturing and health care and derived factors by examining the parameters of TAM, key characteristics of the cloud and other factors. As a result of the empirical analysis, differences were found in the factors that influence the intention to accept cloud services for each of the four industry sectors, which means that there is a difference in perception of the introduction or use of cloud services by industry sector. Eventually it is expected that this study will not only help to understand the intention of using cloud services by industry, but also help cloud service providers expand and provide cloud services to each industry.

• Geophysics and Geophysical Exploration
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• v.25 no.4
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• pp.203-213
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• 2022

To understand an underlying geological structure via seismic imaging, the velocity information of the subsurface medium is crucial. Although the full-waveform inversion (FWI) method is considered useful for estimating subsurface velocity models, 3D FWI needs a lot-of computing power and time. Herein, we compare the calculation efficiency and accuracy of frequency-domain 2D and 3D acoustic FWIs. Thereafter, we demonstrate that the artifacts from 2D approximation can be partially suppressed via frequency-domain 2D FWI by employing diffraction angle filtering (DAF). By applying DAF, which employs only big reflection angle components, the impact of noise and out-of-plane reflections can be reduced. Additionally, it is anticipated that the DAF can create long-wavelength velocity structures for 3D FWI and migration.