• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.527-535
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• 2023

To develop experts capable of responding to cyber security incidents, numerous institutions have established cyber training facilities to cultivate security professionals equipped with effective defense strategies. However, these challenges such as limited resources, scenario-based content development, and cost constraints. To address these issues, this paper proposes a virtual infrastructure variation generation framework. It provides customized, diverse IT infrastructure environments for each organization, allowing cyber defense trainers to accumulate a wide range of experiences. By leveraging Infrastructure-as-Code (IaC) containers and employing Word2Vec, a natural language processing model, mutable code elements are extracted and trained, enabling the generation of new code and presenting novel container environments.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.19 no.2
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• pp.165-186
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• 2008

The purpose of this study is to indicate a case that applied forming a instructional community-establishing instructional strategies-developing learning and assessment toolkits as a connective strategy between information literacy and curricula to foster the Library-Assisted instruction(LAI). In this case a new instructional community consisted of a teacher librarian, a korean language teacher, a korean history teacher and a korean geography teacher of the 10th grade. The community developed integrated learning topics connected with subject-specific classes and designed LAI's instructional strategy. Finally learning and assessment toolkits containing information problem solving and resources using model were designed by this community. This method can provide students with enough time in using resources and teach information literacy systematically. Also it will help teacher librarians improve leadership and partnership in the school community.

• Safety and Health at Work
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• v.14 no.3
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• pp.332-339
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• 2023

Background: The aim of the study was to test the validity and reliability of the Perceived Future Decent Work Securement Scale for Turkish nursing students. Methods: A cross-sectional, methodological study design was used. The study was carried out at three nursing undergraduate programs in Turkey during the academic year of 2020-2021 with 336 senior nursing students. Language validity and content validity analyses were performed for the scale adaptation, followed by confirmatory factor analysis (CFA) for construct validity. The reliability of the scale was determined using the test-retest and Cronbach's alpha internal consistency coefficient. Results: The scale-content validity index score was 0.988. In CFA, all goodness-of-fit indices verified the acceptable fit of the model; its root mean square error of approximation was 0.076; the normed fit index was 0.909; the standardized mean square residual was 0.097; the relative fit index was 0.881; the goodness-of-fit index was 0.915; the adjusted goodness-of-fit index was 0.872 and χ²/df = 2.932. The overall reliability was α = 0.86. The item-total correlations of the scale were above the acceptable level, and the test-retest analysis had a high correlation. The access to healthcare (14.68, SD 3.53) obtained the highest average score, and the adequate compensation (8.52, SD 3.76) was the lowest rated by the senior nursing students. Conclusion: The Perceived Future Decent Work Securement Scale is a valid and reliable scale to assess nursing students' future decent work securement.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.509-515
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• 2023

Despite the continued development of alternative energies, fuel consumption is increasing. In particular, the price of gasoline fluctuates greatly according to fluctuations in international oil prices. Gas stations adjust their gasoline inventory to respond to gasoline price fluctuations. In this study, news datasets is used to analyze the gasoline consumption patterns through fluctuations of the gasoline inventory. First, collecting news datasets with web crawling. Second, summarizing news datasets using KoBART, which summarizes the Korean text datasets. Finally, preprocessing and deriving the fluctuations factors through N-Gram Language Model and TF-IDF. Through this study, it is possible to analyze and predict gasoline consumption patterns.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.489-499
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• 2023

As sharing of analyst reports became widely available, reports generated by analysts have become a useful tool to reduce difference in financial information between market participants. The quantitative information of analyst reports has been used in many ways to predict stock returns. However, there are relatively few domestic studies on the prediction power of text information in analyst reports to predict stock returns. We test stock return predictability of text in analyst reports by creating variables representing the TONE from the text. To overcome the limitation of the linear-model-assumption-based approach, we use the random-forest-based F-test.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.528-535
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• 2022

Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.

• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.154-160
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• 2023

Recently, with the activation of eBook services, books are being published simultaneously as physical books and digitized eBooks. Paper books are more expensive than e-books due to printing and distribution costs, so demand for relatively inexpensive e-books is increasing. There are cases where previously published physical books cannot be digitized due to the circumstances of the publisher or author, so there is a movement among individual users to digitize books that have been published for a long time. However, existing research has only studied the advancement of the pre-processing process that can improve text recognition before applying OCR technology, and there are limitations to digitization depending on the condition of the book. Therefore, support for book digitization services depending on the condition of the physical book is needed. need. In this paper, we propose a method to support digitalization services according to the status of physical books held by book owners. Create images by scanning books and extract text information from the images through OCR. We propose a method to recover text that cannot be extracted depending on the state of the book using BERT, a natural language processing deep learning model. As a result, it was confirmed that the recovery method using BERT is superior when compared to RNN, which is widely used in recommendation technology.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.213-220
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• 2011

In the current construction industry where various stakeholders take part, BIM Data exchange using standard format can provide a more efficient working environment for related staffs during the life-cycle of the building. Currently, the formats used to exchange the data from 3D-CAD application to structure energy analysis at the design stages are IFC, the international standard format provided by IAI, and gbXML, developed by Autodesk. However, because of insufficient data compatibility, the BIM data produced in the 3D-CAD application cannot be directly used in the energy analysis, thus there needs to be additional data entry. The reasons for this are as follows: First, an IFC file cannot contain all the data required for energy simulation. Second, architects sometimes write material names on the drawings that are not matching to those in the standard material library used in energy analysis tools. DOE-2.2 and Energy Plus are the most popular energy analysis engines. And both engines have their own material libraries. However, our investigation revealed that the two libraries are not compatible. First, the types and unit of properties were different. Second, material names used in the library and the codes of the materials were different. Furthermore, there is no material library in Korean language. Thus, by comparing the basic library of DOE-2, the most commonly used energy analysis engine worldwide, and EnergyPlus regarding construction materials; this study will analyze the material data required for energy analysis and propose a way to effectively enter these using semantic web's ontology. This study is meaningful as it enhances the objective credibility of the analysis result when analyzing the energy, and as a conceptual study on the usage of ontology in the construction industry.

• International Journal of Computer Science & Network Security
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• v.24 no.6
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• pp.207-215
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• 2024

Cloud computing is now used by most companies, business centres and academic institutions to embrace new computer technology. Cloud Service Providers (CSPs) are limited to certain services, missing some of the assets requested by their customers, it means that different clouds need to interconnect to share resources and interoperate between them. The clouds may be interconnected in different characteristics and systems, and the network may be vulnerable to volatility or interference. While information technology and cloud computing are also advancing to accommodate the growing worldwide application, criminals use cyberspace to perform cybercrimes. Cloud services deployment is becoming highly prone to threats and intrusions. The unauthorised access or destruction of records yields significant catastrophic losses to organisations or agencies. Human intervention and Physical devices are not enough for protection and monitoring of cloud services; therefore, there is a need for more efficient design for cyber defence that is adaptable, flexible, robust and able to detect dangerous cybercrime such as a Denial of Service (DOS) and Distributed Denial of Service (DDOS) in heterogeneous cloud computing platforms and make essential real-time decisions for forensic investigation. This paper aims to develop a framework for digital forensic for the detection of cybercrime in a joined heterogeneous cloud setup. We developed a Digital Forensics model in this paper that can function in heterogeneous joint clouds. We used Unified Modeling Language (UML) specifically activity diagram in designing the proposed framework, then for deployment, we used an architectural modelling system in developing a framework. We developed an activity diagram that can accommodate the variability and complexities of the clouds when handling inter-cloud resources.

• The Mathematical Education
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• v.63 no.2
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• pp.139-163
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• 2024

The purpose of this study is to examine the capabilities of ChatGPT as a tool for supporting students in generating mathematical arguments that can be considered proofs. To examine this, we engaged students enrolled in a mathematics pathways course in evaluating and revising their original arguments using ChatGPT feedback. Students attempted to find and prove a method for the area of a triangle given its side lengths. Instead of directly asking students to prove a formula, we asked them to explore a method to find the area of a triangle given the lengths of its sides and justify why their methods work. Students completed these ChatGPT-embedded proving activities as class homework. To investigate the capabilities of ChatGPT as a proof tutor, we used these student homework responses as data for this study. We analyzed and compared original and revised arguments students constructed with and without ChatGPT assistance. We also analyzed student-written responses about their perspectives on mathematical proof and proving and their thoughts on using ChatGPT as a proof assistant. Our analysis shows that our participants' approaches to constructing, evaluating, and revising their arguments aligned with their perspectives on proof and proving. They saw ChatGPT's evaluations of their arguments as similar to how they usually evaluate arguments of themselves and others. Mostly, they agreed with ChatGPT's suggestions to make their original arguments more proof-like. They, therefore, revised their original arguments following ChatGPT's suggestions, focusing on improving clarity, providing additional justifications, and showing the generality of their arguments. Further investigation is needed to explore how ChatGPT can be effectively used as a tool in teaching and learning mathematical proof and proof-writing.