• Journal of the Korean Society for Library and Information Science
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• v.58 no.1
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• pp.123-149
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• 2024

The purpose of this study is to present a plan to simplify the issuance of public library loan membership cards for children under the age of 14, and to devise measures to promote the convenience of using public libraries and to promote their use. To this end, related laws and systems, related services, and systems were analyzed. The issuance cases for each type were derived and analyzed by thoroughly investigating the procedure for issuing loan membership cards for children under the age of 14 in 1,211 public libraries nationwide, and focus group interviews were conducted. Based on this, the "double diamond model" was employed among service design methodologies to propose step-by-step guidelines for simplifying the procedure for issuing public library loan membership cards for children under the age of 14, as well as improving the environment, such as the roles of stakeholders, laws, and systems.

• Knowledge Management Research
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• v.24 no.4
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• pp.43-61
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• 2023

Businesses recognize the importance of empathy among members for achieving organizational goals. Accordingly, they have developed and implemented communication programs aimed at enhancing mutual understanding between the MZ generation and the older generation. However, recent communication programs conducted by businesses differ in that they involve compulsory participation driven by the organization. This study sought to empirically examine their effectiveness. Data was collected from 697 participants in communication programs to validate the proposed research model, which was empirically tested through regression analysis. The results of the analysis confirmed the effectiveness of communication programs even in non-voluntary situations and highlighted intergenerational perception differences. The findings of this study emphasize the significant role of communication and empathy within organizations. Consequently, they have impacted the development of communication strategies and culture within organizations, and are expected to provide theoretical and practical insights valuable to researchers and practitioners interested in intergenerational perception differences from a knowledge management perspective.

• Knowledge Management Research
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• v.25 no.2
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• pp.1-18
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• 2024

With the increasing demand for and utilization of large language models (LLMs), the risk of user sensitive information being inputted and leaked during the use of LLMs also escalates. Typically recognized as a tool for mitigating the hallucination issues of LLMs, knowledge graphs, constructed independently from LLMs, can store and manage sensitive user information separately, thereby minimizing the potential for data breaches. This study, therefore, presents a knowledge graph-based chatbot that transforms user-inputted natural language questions into queries appropriate for the knowledge graph using LLMs, subsequently executing these queries and extracting the results. Furthermore, to evaluate the functional validity of the developed knowledge graph-based chatbot, performance tests are conducted to assess the comprehension and adaptability to existing knowledge graphs, the capability to create new entity classes, and the accessibility of LLMs to the knowledge graph content.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.163-174
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• 2024

Currently, the urban railway operating agency is improving the emergency telephone in operation into an IP-based "trackside integrated interface communication facility" that can support a variety of additional services in order to quickly respond to emergency situations within the tunnel. This study is based on this Analyze the needs of various stakeholders regarding the design of a system architecture that establishes an IoT sensor network environment to detect abnormal situations in the tunnel and transmits the collected information to the control center to predict disaster situations in advance, and defines the system requirements. In addition, a scenario model for disaster response was provided through the presentation of a service model. Through this, the perspective of responding to urban railway disasters changes from reactive response to proactive prevention, thereby ensuring safe operation of urban railways and preventing major industrial accidents.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.35 no.2
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• pp.157-183
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• 2024

This study aims to develop a research methodology course for graduate-level students using an information-seeking behaviour model of distance learners. Based on a case study and structured survey, the factors that motivate and hinder information-seeking behaviours were identified. The motivating factor for students seeking information through the research methodology course was the necessity to obtain a master's degree, while the hindering factor was the challenge of balancing work and study. The course was developed by leveraging motivational factors and addressing hindering factors. The results of this study can serve as foundational data for understanding students' information-seeking behaviour and establishing teaching and learning strategies to enhance students' information-seeking skills when developing online courses.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.143-156
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• 2024

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

• The Journal of the Convergence on Culture Technology
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• v.10 no.6
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• pp.165-174
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• 2024

This study aims to develop a curriculum that effectively applies gamification in educational settings by analyzing the theoretical principles of gamification and various educational techniques. The course titled "Understanding and Application of Gamification" was developed and conducted in a mixed online/offline format, including practical activities where students developed and presented their own gamification strategies. The results showed high participation rates and positive evaluations from students, attributed to increased voluntary engagement and the cultivation of creative problem-solving skills through game-based exercises. This gamification education model can be applied to various educational contexts, and future research should focus on continuous improvement through additional educational cases. Gamification education is expected to foster integrative thinking across various fields and enhance creative problem-solving skills for addressing social issues. Moreover, such classroom activities will promote voluntary and active participation in classes, and motivate both internal and external engagement for personal goals through the gamification of social phenomena.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.305-310
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• 2024

With the emergence of generative AI, a new era of coexistence with humanity has begun. The vast data-driven learning capabilities of AI are being utilized in various industries to achieve a level of productivity distinct from human learning. However, AI also manifests societal phenomena such as technophobia. This study aims to analyze collaborative AI models based on an understanding of AI and identify areas within the jewelry industry where these models can be applied. The utilization of collaborative AI models can lead to the acceleration of idea development, enhancement of design capabilities, increased productivity, and the internalization of multimodal functions. Ultimately, AI should be used as a collaborative tool from a utilitarian perspective, which requires a proactive, human-centric mindset. This research proposes collaborative AI strategies for the jewelry business, hoping to enhance the industry's competitiveness.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.5
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• pp.61-67
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• 2024

In recent years, artificial intelligence and deep learning technologies have made significant advances, and the BERT model has been recognized for its excellent contextual understanding in natural language processing based on the transformer architecture. This performance has the potential to take traditional recommendation systems to the next level. In this study, we adopt an approach that combines a collaborative filtering approach with a deep learning model to improve the performance of recommendation systems. Specifically, we implemented a system that uses BERT to analyze the sentiment of user reviews and embed users based on these review sentiments to find and recommend users with similar tastes. In the process, we also utilized Elasticsearch, an open-source search engine, for quick search and retrieval of recommended results. The approach of analyzing users' textual data to increase the accuracy and personalization of recommendations will play an important role in improving the user experience on various online services in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.6
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• pp.1335-1344
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• 2024

Containers have become the standard for delivering cloud-native services, leveraging their scalability, portability, and resource efficiency. Simultaneously, they have become targets for various security attacks exploiting misconfigurations and vulnerabilities, particularly in network policies. In complex cloud-native environments, manual policy management is prone to errors, and existing research on policy generation automation has limitations in accuracy. This paper presents Kunerva+, a highly automated intelligent network policy generation framework. It operates through an enhanced intent-based approach using natural language processing and fine-tuned large language models, generating network policies without the need to understand complex configurations. We have also devised a multi-stage validation process to fundamentally prevent misconfigurations in network policy enforcement. The evaluation results show that the most improved fine-tuned LLM achieved a 360% increase in BLEU score and 233% in ROUGE-2 score compared to the baseline model, demonstrating the potential and effectiveness of intent-based generation.