• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.333-351
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• 2023

This study investigates user behavior in library spaces through the lens of AI camera analytics. By leveraging the face recognition and tracking capabilities of AI cameras, we accurately identified the gender and age of visitors and meticulously collected video data to track their movements. Our findings revealed that female users slightly outnumbered male users and the dominant age group was individuals in their 30s. User visits peaked between Tuesday to Friday, with the highest footfall recorded between 14:00 and 15:00 pm, while visits decreased over the weekend. Most visitors utilized one or two specific spaces, frequently consulting the information desk for inquiries, checking out/returning items, or using the rest area for relaxation. The library stacks were used approximately twice as much as they were avoided. The most frequented subject areas were Philosophy(100), Religion(200), Social Sciences(300), Science(400), Technology(500), and Literature(800), with Literature(800) and Religion(200) displaying the most intersections with other areas. By categorizing users into five clusters based on space utilization patterns, we discerned varying objectives and subject interests, providing insights for future library service enhancements. Moreover, the study underscores the need to address the associated costs and privacy concerns when considering the broader application of AI camera analytics in library settings.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.4
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• pp.75-95
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• 2023

This study investigated the overall perceptions of pre-service school librarian teacher on the current education practicum through semi-structured in-depth interviews and suggested improvements to the educational practicum system. For this purpose, interview data were collected from 28 pre-service school librarian teacher (6 teachers' colleges, 14 taking teaching qualification courses, and 8 graduate school of education) who participated in educational practicum in school libraries, and a research method that combines qualitative analysis techniques with text network analysis was applied. The results of the study showed that pre-service school librarian teacher believe that educational practicum can prepare them for various field experiences and cultivate their ability to cope with situations they will encounter in the future. Through qualitative inquiry, we were able to identify their perceptions of school field practicum as a whole, their perceptions of the school field practicum, and their perceptions of educational service activities. Based on this, to improve the current problems of educational practice, we suggested expanding the period of school internship program, distributing the time, establishing a full-time practice system, having continuous discussions with field teachers, and developing a systematic school field practicum.

• Journal of the Korea Computer Graphics Society
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• v.29 no.5
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• pp.21-30
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• 2023

Accurate segmentation of the kidney tumor is necessary to identify shape, location and safety margin of tumor in abdominal CT images for surgical planning before renal partial nephrectomy. However, kidney tumor segmentation is challenging task due to the various sizes and locations of the tumor for each patient and signal intensity similarity to surrounding organs such as intestine and spleen. In this paper, we propose a semi-supervised learning-based mean teacher network that utilizes both labeled and unlabeled data using a kidney local guided map including kidney local information to segment small-sized kidney tumors occurring at various locations in the kidney, and analyze the performance according to the kidney tumor size. As a result of the study, the proposed method showed an F1-score of 75.24% by considering local information of the kidney using a kidney local guide map to locate the tumor existing around the kidney. In particular, under-segmentation of small-sized tumors which are difficult to segment was improved, and showed a 13.9%p higher F1-score even though it used a smaller amount of labeled data than nnU-Net.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.5
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• pp.827-846
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• 2023

Recently, various information technologies such as network communication and sensors have begun to be integrated into weapon systems that were previously operated in stand-alone. This helps the operators of the weapon system to make quick and accurate decisions, thereby allowing for effective operation of the weapon system. However, as the involvement of the cyber domain in weapon systems increases, it is expected that the potential for damage from cyber attacks will also increase. To develop a secure weapon system, it is necessary to implement built-in security, which helps considering security from the requirement stage of the software development process. The U.S. Department of Defense is implementing the Risk Management Framework Assessment and Authorization (RMF A&A) process, along with the introduction of the concept of cybersecurity, for the evaluation and acquisition of weapon systems. Similarly, South Korea is also continuously making efforts to implement the Korea Risk Management Framework (K-RMF). However, so far, there are no cases where K-RMF has been applied from the development stage, and most of the data and documents related to the U.S. RMF A&A are not disclosed for confidentiality reasons. In this study, we propose the method for inferring the composition of the K-RMF based on systematic threat analysis method and the publicly released documents and data related to RMF. Furthermore, we demonstrate the effectiveness of our inferring method by applying it to the naval battleship system.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.67-76
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• 2023

Although deep learning models are making innovative achievements in the field of computer vision, the problem of vulnerability to adversarial examples continues to be raised. Adversarial examples are attack methods that inject fine noise into images to induce misclassification, which can pose a serious threat to the application of deep learning models in the real world. In this paper, we propose a model that detects adversarial examples using differences in predictive values between edge-learned classification models and underlying classification models. The simple process of extracting the edges of the objects and reflecting them in learning can increase the robustness of the classification model, and economical and efficient detection is possible by detecting adversarial examples through differences in predictions between models. In our experiments, the general model showed accuracy of {49.9%, 29.84%, 18.46%, 4.95%, 3.36%} for adversarial examples (eps={0.02, 0.05, 0.1, 0.2, 0.3}), whereas the Canny edge model showed accuracy of {82.58%, 65.96%, 46.71%, 24.94%, 13.41%} and other edge models showed a similar level of accuracy also, indicating that the edge model was more robust against adversarial examples. In addition, adversarial example detection using differences in predictions between models revealed detection rates of {85.47%, 84.64%, 91.44%, 95.47%, and 87.61%} for each epsilon-specific adversarial example. It is expected that this study will contribute to improving the reliability of deep learning models in related research and application industries such as medical, autonomous driving, security, and national defense.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.556-576
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• 2023

This study attempted to find a way to utilize idle spaces in rural areas as a way to respond to rural extinction. Based on the keywords "startup," "youth start-up," and "youth start-up+rural," start-up+rural," the study sought to identify the perception of idle facilities in rural areas through the keywords "Idle facilities" and "closed schools." The study presented basic data for policy direction and plan search by reviewing frequency analysis, major keyword analysis, network analysis, emotional analysis, and domestic and foreign cases. As a result of the analysis, first, it was found that idle facilities and school closures are acting importantly as factors for regional regeneration. Second, in the case of youth startups in rural areas, it was found that not only education on agriculture but also problems for residence should be solved together. Third, in the case of young people, it was confirmed that it was necessary to establish digital utilization for agriculture by actively starting a business using digital. Finally, in order to attract young people and revitalize the region through best practices at home and abroad, policy measures that can serve as various platforms such as culture and education as well as startups should be presented in connection with local residents. These results are significant in that they presented implications for youth start-ups in rural areas by reviewing start-up recognition for the influx of young people as one of the alternatives for the use of idle facilities and regional regeneration, and if additional solutions are presented through field surveys, they can be used to set policy goals that fit the reality.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.4
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• pp.149-182
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• 2023

This study was conducted for the purpose of suggesting ways to improve KMLA's support programs to strengthen the capabilities of domestic medical librarians. To this end, after investigating the roles of professional associations, common roles were extracted and categorized into five areas, and then the support programs of MLA and KMLA were compared and analyzed. As a result, six suggestions can be made as ways to improve KMLA's support program. First, policy development is needed to officially and continuously support medical librarians. Second, in the area of (continued) education and training programs, educational support in professional areas required in the medical field is needed, as well as education for the role of the IRB committee. Third, in terms of research and publication support, various support for research funds and training programs is required. Fourth, in terms of network and cooperation support, mentoring, leadership, and fellowship programs are needed, and improvements to domestic and international partnerships and support for various awards should also be planned. Fifth, in terms of professional certification and various certificate systems, it is necessary to provide support for obtaining degrees in related fields and to issue certificates through support for various courses. Sixth, KMLA's active internal and external promotion and activities should be carried out to attract donations.

• Journal of Korean Society of Disaster and Security
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• v.16 no.2
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• pp.85-98
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• 2023

Flood is one of the most frequent natural disasters worldwide. In Korea, the probability of urban flooding is greatly increasing due to complex factors such as global warming, an increase in impervious areas, and limitations in expanding water supply facilities in existing urban areas. However, large-scale civil engineering works to prevent urban inundation are socially and economically difficult to obtain national consent. Recently the importance of resilience, which is the ability to return to the original state after a disaster through rapid recovery while preparing for natural disasters to a level that the local community can afford socially and economically, is increasing. Accordingly, various studies on urban resilience have been conducted, but the resilience measurement method related to the lifeline that provides essential services of the city is insufficient. However, among lifelines, road networks are important facilities for the transportation of recovery resources and rapid recovery in the event of a natural disaster, so road networks are a major factor that must be considered when measuring the degree of recovery of a city in the field of natural disasters. Therefore, this study proposes a recovery evaluation method considering the characteristics of resilience and road networks in the urban flooding field and analyzes the effect of road networks on urban resilience.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.5
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• pp.15-30
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• 2023

Recently, generative models based on the Transformer architecture, such as ChatGPT, have been gaining significant attention. The Transformer architecture has been applied to various neural network models, including Google's BERT(Bidirectional Encoder Representations from Transformers) sentence generation model. In this paper, a method is proposed to create a text binary classification model for determining whether a comment on Korean movie review is positive or negative. To accomplish this, a pre-trained multilingual BERT sentence generation model is fine-tuned and transfer learned using a new Korean training dataset. To achieve this, a pre-trained BERT-Base model for multilingual sentence generation with 104 languages, 12 layers, 768 hidden, 12 attention heads, and 110M parameters is used. To change the pre-trained BERT-Base model into a text classification model, the input and output layers were fine-tuned, resulting in the creation of a new model with 178 million parameters. Using the fine-tuned model, with a maximum word count of 128, a batch size of 16, and 5 epochs, transfer learning is conducted with 10,000 training data and 5,000 testing data. A text sentiment binary classification model for Korean movie review with an accuracy of 0.9582, a loss of 0.1177, and an F1 score of 0.81 has been created. As a result of performing transfer learning with a dataset five times larger, a model with an accuracy of 0.9562, a loss of 0.1202, and an F1 score of 0.86 has been generated.

• Journal of Korean Elementary Science Education
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• v.42 no.4
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• pp.591-604
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• 2023

This study investigated the impact of elementary science classes using metaverse on the academic achievement, positive experience in science, and digital literacy of elementary school students. In addition, we examined their perceptions. The respondents were derived from two classes in the sixth grade at an elementary school in Gyeonggi-do, who were selected designated as the experimental (n=29 students) and comparative (n=29) groups, respectively. Across five lessons under the "Plant Structure and Function" unit, the experimental group conducted science classes using the metaverse, whereas the comparative group conducted general textbook-based classes. To investigate instructional effects, the study performed ANCOVA using the pre-test score as a covariate, a survey on the perception of students about science classes using metaverse, and conducted interviews with a number of subjects. The result demonstrated that science classes using metaverse exerted no significant effect on scientific academic achievement and digital literacy. However, the study observed a statistically significant effect on science learning emotion which is a sub-element of positive experiences in science. The students were positively aware of science classes using metaverse in terms of interesting and diverse activities, and free expression of inquiry results and perceived the instability of smart devices and network connections as regrettable. Finally, the study posed the implications of the use of metaverse in science classes.