• KIPS Transactions on Software and Data Engineering
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• v.5 no.11
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• pp.541-548
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• 2016

The use of social media is already essential as a source of information for listening user's various opinions and monitoring. We define social 'risks' that issues effect negative influences for public opinion in social media. This paper aims to discriminate various linguistic features and reveal their effects for building an automatic classification model of social risks. Expecially we adopt a word embedding technique for representation of linguistic clues in risk sentences. As a preliminary experiment to analyze characteristics of individual features, we revise errors in automatic linguistic analysis. At the result, the most important feature is NE (Named Entity) information and the best condition is when combine basic linguistic features. word embedding, and word clusters within core predicates. Experimental results under the real situation in social bigdata - including linguistic analysis errors - show 92.08% and 85.84% in precision respectively for frequent risk categories set and full test set.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.1
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• pp.74-86
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• 2019

As many existing research fields are matured academically, researchers have encountered numbers of academic, social and other problems that cannot be addressed by internal knowledge and methodologies of existing disciplines. Earlier, pioneers of researchers thus are following a new paradigm that breaks the boundaries between the prior disciplines, fuses them and seeks new approaches. Moreover, developed countries including Korea are actively supporting and fostering the convergence research at the national level. Nevertheless, there is insufficient research to analyze convergence trends in national R&D support projects and what kind of content the projects mainly deal with. This study, therefore, collected and preprocessed the research proposal data of National Research Foundation of Korea, transforming the proposal documents to term-frequency matrices. Based on the matrices, this study derived detailed research topics through Latent Dirichlet Allocation, a kind of topic modeling algorithm. Next, this study identified the research topics each proposal mainly deals with, visualized the convergence relationships, and quantitatively analyze them. Specifically, this study analyzed the centralities of the detailed research topics to derive clues about the convergence of the near future, in addition to visualizing the convergence relationship and analyzing time-varying number of research proposals per each topic. The results of this study can provide specific insights on the research direction to researchers and monitor domestic convergence R&D trends by year.

• Journal of Digital Convergence
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• v.19 no.3
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• pp.161-166
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• 2021

The macroeconomic concept represents the movement of a country's economy, and it affects the overall economic activities of business, government, and households. In the macroeconomy, by looking at changes in national income, inflation, unemployment, currency, interest rates, and raw materials, it is possible to understand the effects of economic actors' actions and interactions on the prices of products and services. The US Federal Reserve System (FED) is leading the world economy by offering various stimulus measures to overcome the corona economic recession. Although the stock price continued to decline on March 20, 2020 due to the current economic recession caused by the corona, the US S&P 500 index began rebounding after March 23 and to 3,694.62 as of December 15 due to quantitative easing, a powerful stimulus for the FED. Therefore, the FED's economic stimulus measures based on macroeconomic indicators are more influencing, rather than judging the stock price forecast from the corporate financial statements. Therefore, this study was conducted to reduce losses in stock investment and establish sound investment by analyzing the FED's economic stimulus measures and its effect on stock prices.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.155-161
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• 2022

The outbreak of the Covid 19 increased the interest on the 'Virtual Tourism. In this research the key word related to "Virtual Tourism" was collected through the search engine and was analyzed through the data mining method such as Log-odds ratio, Frequency, and network analysis. It is clear that the information and communication dependency increased in the field of "Virtual Tourism" after Covid 19 and also the trend have changed from "securement of the contents diversity" to "project related to economic recovery." Since the demands for the "Virtual Reality" such as metaverse is increasing, there should be an economic and circular structure in which the government establishing a related policy and the funding plan based on the research, local government and the private companies planning and producing discriminate contents focusing on AISAS(Attension, Interest, Search, Action, Share) aand the research institutions and universities developing, applying, assessing and commercializing the technology.

• Journal of Environmental Impact Assessment
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• v.31 no.3
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• pp.173-183
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• 2022

The purpose of this study is to explore the purpose of introducing the system and the possibility of development by comparing the period before and after the implementation of the Korean Important Agricultural Heritage System (KIAHS) using big data. In terms of perception related to Ulleungdo Island, keywords related to accessibility were derived as higher keywords before and after designation, and in particular, keywords such as various approaches and new ports could be found after designation. It can be seen that positive perception increased after the designation of KIAHS, and the perception of good increased particularly. In addition, the exact name of wild greens and keywords for volcanic island appeared in common, but it was confirmed that the influence increased in the results of the centrality analysis after the designation. In other words, it was found that the designation of KIAHS was helpful in preserving traditional knowledge and developing traditional agricultural culture using it.

• Journal of Radiation Protection and Research
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• v.47 no.2
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• pp.67-76
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• 2022

Background: We conducted a cross-sectional study of residents within and outside Fukushima Prefecture to clarify their perceptions of the need for smartphone applications (apps) for explaining exposure doses. The results will lead to more effective methods for identifying target groups for future app development by researchers and municipalities, which will promote residents' understanding of radiological situations. Materials and Methods: In November 2019, 400 people in Fukushima Prefecture and 400 people outside were surveyed via a web-based questionnaire. In addition to basic characteristics, survey items included concerns about radiation levels and intention to use a smartphone app to keep track of exposure. The analysis was conducted by stratifying responses in each region and then cross-tabulating responses to concerns about radiation levels and intention to use an app by demographic variables. The intention to use an app was analyzed by binomial logistic regression analysis. Text-mining analyses were conducted in KH Coder software. Results and Discussion: Outside Fukushima Prefecture, concerns about the medical exposure of women to radiation exceeded 30%. Within the prefecture, the medical exposure of women, purchasing food products, and consumption of own-grown food were the main concerns. Within the prefecture, having children under the age of 18, the experience of measurement, and having experience of evacuation were significantly related to the intention to use an app. Conclusion: Regional and individual differences were evident. Since respondents differ, it is necessary to develop and promote app use in accordance with their needs and with phases of reconstruction. We expect that a suitable app will not only collect data but also connect local service providers and residents, while protecting personal information.

• Smart Media Journal
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• v.11 no.8
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• pp.9-20
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• 2022

With the rise of the metaverse industry worldwide, relevant national strategies and nurturing systems have been prepared in Korea. As the complexity of policies increases, the importance of establishing data-based policymkaing is growing, and studies diagnosing national R&D trends in the metaverse field are still lacking. Therefore, this paper collected NTIS national R&D information for 9,651 R&D projects promoted from 2002 to 2020. And this study looked at the current status and identified major topics based on the topic modeling, and considered time-series changes in the topics. Eleven major topics of R&D tasks in the metaverse field were derived, hot topics were service/content/platform development and medical/surgical fields of application fields, and cold topics were urban/environment/spatial information fields. Strategic R&D Management, metaverse-related laws, and institutional studies were proposed as policy directions.

• Imaging Science in Dentistry
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• v.52 no.1
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• pp.1-9
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• 2022

Purpose: This systematic review aimed to compare assessments of the healing of periapical endodontic surgery using conventional radiography and cone-beam computed tomography (CBCT). Materials and Methods: This review of clinical studies was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. All articles published from 1990 to March 2020 pertaining to clinical and radiographic healing assessments after endodontic surgery using conventional radiography and CBCT were included. The question was "healing assessment of endodontic surgery using cone-beam computed tomography." The review was conducted by manual searching, as well as undertaking a review of electronic literature databases, including PubMed and Scopus. The studies included compared radiographic and CBCT assessments of periapical healing after periapical endodontic surgery. Results: The initial search retrieved 372 articles. The titles and abstracts of these articles were read, leading to the selection of 73 articles for full-text analysis. After the eligibility criteria were applied, 11 articles were selected for data extraction and qualitative analysis. The majority of studies found that CBCT enabled better assessments of healing than conventional radiography, suggesting higher efficacy of CBCT for correct diagnosis and treatment planning. A risk of bias assessment was done for 10 studies, which fell into the low to moderate risk categories. Conclusion: Three-dimensional radiography provides an overall better assessment of healing, which is imperative for correct diagnosis and treatment planning.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.845-853
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• 2022

As the memory capacity of smartphone increases, the type and amount of privacy stored in the smartphone is also increasing. but recently there is an increasing possibility that various personal information such as photos and videos of smartphones may be leaked due to malicious apps by malicious attackers or other people such as repair technicians. This paper analyzed and studied the security and vulnerability of these vault apps by analyzing the cryptography algorithm and data protection function. We analyzed 5.3.7(June 13, 2022) and 3.3.2(December 30, 2020) versions of AppLock, the most downloaded information-hidding apps registered with Google Play, and found various vulnerabilities. In the case of access control, there was a vulnerability in that values for encrypting patterns entered by users were hardcoded into plain text in the source code, and encrypted pattern values were stored in xml files. In addition, in the case of the vault function, there was a vulnerability in that the files and log files for storing in the vault were not encrypted.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.57-64
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• 2023

This study analyzed the perceptions of elementary and secondary school teachers regarding the importance of SW/AI education in fostering students' fundamental knowledge and the necessity of integrating SW/AI into education. A total of 830 elementary and secondary school teachers were selected as study subjects using the judgment sampling method. The analysis of survey data revealed that elementary and secondary teachers exhibited a strong awareness of the importance and necessity of SW/AI education, irrespective of school characteristics, region, educational experience, or prior involvement in SW and AI education. Nevertheless, the primary reasons for not implementing SW/AI education were identified as excessive workload and a lack of pedagogical expertise. An analysis of opinions on the essential conditions for implementing SW/AI education revealed that workload reduction, budget support, teacher training to enhance teacher competency, content distribution, expansion of subject-linked courses, and dedicated instructional time allocation were the major influencing factors. These findings indicate a significant demand for comprehensive instructional support and teacher capacity-building programs.