Hye-Yeon Shim;MinSeo Kweun;DaYoung Yoon;JiYoung Seo;Il-Gu Lee
Journal of the Korea Institute of Information Security & Cryptology
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v.34
no.2
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pp.207-216
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2024
As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.
Aging and damaged underground utilities cause cavity and ground subsidence under roads, which can cause economic losses and risk user safety. This study used infrared cameras to assess the thermal characteristics of such cavities and evaluate their reliability using a CNN algorithm. PVC pipes were embedded at various depths in a test site measuring 400 cm × 50 cm × 40 cm. Concrete blocks were used to simulate road surfaces, and measurements were taken from 4 PM to noon the following day. The initial temperatures measured by the infrared camera were 43.7℃, 43.8℃, and 41.9℃, reflecting atmospheric temperature changes during the measurement period. The RP algorithm generates images in four resolutions, i.e., 10,000 × 10,000, 2,000 × 2,000, 1,000 × 1,000, and 100 × 100 pixels. The accuracy of the CNN model using RP images as input was 99%, 97%, 98%, and 96%, respectively. These results represent a considerable improvement over the 73% accuracy obtained using time-series images, with an improvement greater than 20% when using the RP algorithm-based inputs.
Journal of The Korean Association For Science Education
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v.27
no.9
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pp.907-918
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2007
This study was conducted to explore key factors of expertise development of talented scientists who achieved outstanding research performance according to the stages of expertise development and dimensions of individual-domain-field. To fulfill the research purpose, 31 domestic scientists who were awarded major prizes in the field of science were interviewed in-depth from March to September, 2007. Stages of expertise development were analyzed in light of Csikszentmihalyi's IDFI (individual-domain-field interaction) model. Self-directed learning, multiple interests and finding strength, academic and liberal home environment, and meaningful encounter were major factors affecting expertise development in the exploration stage. In the beginner stage, independence, basic knowledge on major, and thirst for knowledge at university affected expertise development. Task commitment, finding flow, finding their field of interest and lifelong research topic, and mentor in formal education were the affecting factors in the competent stage. Finally, placing priority, communication skills, pioneering new domain, expansion of the domain, and evaluation and support system affected talented scientists' expertise development in the leading stage. The meaning of major patterns of expertise development were analyzed and described. Based on these analyses, educational implications for nurturing scientists were suggested.
Journal of The Korean Association For Science Education
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v.28
no.1
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pp.75-88
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2008
The purpose of this study was to understand the factors affecting interactions as well as the students' learning process in small group activities. For this purpose, the changes and characteristics of students' interactions in scientific inquiry experiments were analyzed. This study focused on 2 homogeneous small groups of eighth graders. Students were involved in 13 inquiry experiments for one year and students' interactions in each experiments were observed and recorded using video/audio and the data recorded were transcribed. The analysis of data was based on the method of making a note by looking on and listening to the data repeatedly. Changes in the interactions of the two homogeneous groups differ remarkably. In small group A, owing to the conflicts of students' emotions, learning through social interactions became to be impossible. On the other hand, the interactions in small group B became more active. It seems that this changes are affected largely by the existence of peers who are able to mediate different opinions or feelings among group members. In general, middle school students were poor at receiving peers' opinion, cared a lot about writing reports. The less able students tended to be placed at a disadvantageous position in experiment lessons emphasizing social interactions. Four factors that affected the change of interactions were identified: Is the aim of experiments the understanding or completion of report? Is there any attitude towards peers' suggestions? Is there a disposition to care about peers? Is there any peer to mediate on peers' opinions or feelings? Educational implications of the progression of activities emphasizing interactions and the organization of grouping were drawn.
Journal of The Korean Association For Science Education
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v.28
no.4
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pp.282-290
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2008
The Research and Education (R&E) program was a year-long, apprenticeship and research-based program that was guided by mentors who are scientists or science teachers. The objective of the R&E program was to help scientifically gifted students in Korea Science Academy (KSA) and Science High Schools (SHS) to enhance abilities in creative thinking, scientific inquiry, problem solving, positive attitude towards scientists, and promoting cooperative research and interests in science and technology. In this study, the impact of the R&E program on the goals of 182 gifted college students in KAIST was evaluated using Likert-type items and multiple-choice method approach that provided a more comprehensive evaluation of the program's impact on science attitudes, creative thinking, scientific inquiry, and interests in science and technology. The results indicated a positive impact on cooperative research, gaining knowledge on the research topic, attitude towards scientists, interest in science and technology, scientific inquiry, and creative thinking in that order. There were rather remarkable and meaningful differences in science inquiry (p<.05), and scientific knowledge (p<.01), between the two groups of KAIST freshmen who came from SHS and KSA in 2006. Implications for science apprenticeship or a research-based mentorship program and their respective evaluations are also discussed.
As cloud services and deployment models become diverse, there are a growing number of cloud computing selection options. Therefore, financial companies need a methodology to select the appropriated cloud for each financial computing system. This study adopted the Balanced Scorecard (BSC) framework to classify factors for the introduction of cloud computing in financial companies. Using Analytic Hierarchy Process (AHP), the evaluation items are layered into the performance perspective and the cloud consideration factor and a comprehensive decision model is proposed. To verify the proposed research model, a system of financial company is divided into three: account, information, and channel system, and the result of decision making by both financial business experts and technology experts from two financial companies were collected. The result shows that some common factors are important in all systems, but most of the factors considered are very different from system to system. We expect that our methodology contributes to the spread of cloud computing adoption.
In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.
Journal of The Korean Association For Science Education
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v.31
no.5
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pp.788-800
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2011
The purpose of this study was to investigate the elementary school teachers' perception of science writing. In this study, 10 elementary school teachers who have taught in the 3rd or 4th grade science lesson in 2010 were selected. Researchers constructed interview guide in three parts including the teachers' understanding of science writing, the status of science writing teaching and the difficulties of science writing in their classes. For the investigation, semi-structured in-depth interviews with 10 elementary school teachers were conducted individually. The results showed that the elementary school teachers were unfamiliar with the word ‘science writing’ and considered science writing as a writing using science learning contents. Also, they think that teaching science writing in their science lessons was not needed and didn't assess and provide detailed feedback with the students' written works. Most teachers needed teaching materials and assessment tools for science writing. To develop elementary teachers' understanding of the value and use of writing for learning in science, they will need to participate in science writing programs for in-service teachers and various teaching materials and assessment tools should also be developed.
Journal of the Korean Institute of Landscape Architecture
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v.37
no.2
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pp.1-13
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2009
The purpose of this study was to look into the feasibility of site suitability focused on the potential for environmentally- and water-friendly recreation area development in a wide area(Nakdong River 35km) and to study new methods for providing basic data in regard to the recreation planning over a wide area as well as in application to other sites. The results of this study are as follows. Through classification by mesh method, the site of this study was classified into 42 grids, and by means of the analysis of evaluation indicators, 20 indicators were established and sorted into 4 types of significant recreation activity. According to the results of the analysis for each recreation activity type, there were 8 essentials for water-friendly recreation activity types based on water use while water-friendly recreation types for static activity included 12 sub-essentials. As a result of the first evaluation(the minimum required evaluation) by each classified grid, 32 of the 42 total grids were implemented by the minimum requirements. These grids were usually distributed evenly through the whole site. In terms of the second evaluation(specific site evaluation) results, 6 grids were highly suitable for recreational nature experiences and landscape ecological learning, 4 grids for developing water-friendly recreation for exercise, 1 grid for building water-friendly recreation based on water use, and 4 grids for planning water-friendly recreation for static activity. The results of the grid evaluation of this study could be extended to contiguous grids or reduced. Actual planning for a water-friendly recreation area must change the grid shape or size through boundary adjustments.
Journal of the Korea Academia-Industrial cooperation Society
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v.18
no.1
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pp.262-268
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2017
The energy consumption of buildings is approximately 20.5% of the total energy consumption, and the interest in energy efficiency and low consumption of the building is increasing. Several studies have performed energy analysis and evaluation. Energy analysis and evaluation are effective when applied in the initial design phase. In the initial design phase, however, the energy performance is evaluated using general level information, such as glazing area and surface area. Therefore, the evaluation results of the detailed design stage, which is based on the drawings, including detailed information of the materials and facilities, will be different. Thus far, most studies have reported the analysis and evaluation at the detailed design stage, where detailed information about the materials installed in the building becomes clear. Therefore, it is possible to improve the accuracy of the energy environment analysis if the energy environment information generated during the life cycle of the building can be established and accurate information can be provided in the analysis at the initial design stage using a probability / statistical method. On the other hand, historical data on energy use has not been established in Korea. Therefore, this study performed energy environment analysis to construct the energy environment historical data. As a result of the research, information classification system, information model, and service model for acquiring and providing energy environment information that can be used for building lifecycle information of buildings are presented and used as the basic data. The results can be utilized in the historical data management system so that the reliability of analysis can be improved by supplementing the input information at the initial design stage. If the historical data is stacked, it can be used as learning data in methods, such as probability / statistics or artificial intelligence for energy environment analysis in the initial design stage.
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