• Journal of the Korea Convergence Society
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• v.13 no.1
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• pp.167-178
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• 2022

This study attempted to confirm the effect of the user's characteristics on perceived ease of use, perceived usefulness, willingness to pay, flow and addiction to OTT service platform iQIYI users. Researchers believe that as the number of OTT service users who are willing to pay increases, research on the influencing factors and the cause of addiction is needed. As a result of the study, first, it was found that perceived usefulness affects willingness to pay, second, use intention and willingness to pay affect flow, and third, willingness to pay and flow affect addiction. This study considered that it has theoretical and practical value in that the route to flow and addiction of OTT service users was set and tested with intention to use and pay. Researchers hope that the study will expand in terms of willingness to pay and use experience of OTT services in the future.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.369-372
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• 2021

These days, people spend more time sitting at a desk for studies or work. Also, because people continue to use computers, smartphones, and tablet PCs often during break times, their posture is getting worse. Maintaining a position of bad posture for an extended period of time causes problems with the musculoskeletal system related to the neck, shoulders, and spine. Additionally, problems such as physical fatigue and posture deformation are predicted to expand to a wide range of age groups. Therefore, the core function of the system we are developing is to ensure correct sitting posture and to receive alert notifications via the created mobile application. To create the system, a flex sensor, pressure sensor, and tilt sensor are attached to a chair and utilized. The flex sensor detects and compares the amount of bending in the chair's posture and transmits this value to an Arduino Uno R3 board. Additionally, information such as body balance and incline angle are collected to determine whether or not the current sitting posture is correct. When the posture is incorrect, a notification is sent through the mobile application to indicate to the user and the monitoring app that their posture is not correct. The system proposed in this study is expected to be of great help in future posture-related research.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.2
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• pp.241-269
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• 2023

This study developed a scale for measuring publicness and examined the differences in perception of publicness according to library usage experience, personal characteristics of local residents, and types of library services. The survey was conducted on 15 local public libraries in 5 districts of Seoul, targeting library users and local residents. As a result, it was found that the publicness of libraries is composed of three factors: participatory responsiveness, procedural fairness, and situational equality, which demonstrate different aspects formed through the interaction between library users and local residents in the local community. The study derived ways to enhance publicness and presented in detail which aspect of publicness needs to be enhanced according to library usage experience and service period, local residents' occupational environment and experience of local activities, and types of library services. The study suggests that when service experiences that enhance publicness are effectively provided, more local residents can benefit from them, and the value of the library's existence can be demonstrated.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.33-43
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• 2022

Labeled graphs are used to represent entities, their relationships, and their structures in real data such as knowledge graphs and protein interactions. With the rapid development of IT and the explosive increase in data, there has been a need for a subgraph matching technology to provide information that the user is interested in. In this paper, we propose an approximate Top-k labeled subgraph matching scheme that considers the semantic similarity of labels and the difference in graph structure. The proposed scheme utilizes a learning model using FastText in order to consider the semantic similarity of a label. In addition, the label similarity graph(LSG) is used for approximate subgraph matching by calculating similarity values between labels in advance. Through the LSG, we can resolve the limitations of the existing schemes that subgraph expansion is possible only if the labels match exactly. It supports structural similarity for a query graph by performing searches up to 2-hop. Based on the similarity value, we provide k subgraph matching results. We conduct various performance evaluations in order to show the superiority of the proposed scheme.

• Information Systems Review
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• v.21 no.2
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• pp.91-115
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• 2019

The updates in the mobile application market have been widely used as strategic marketing tools. It has been used to (i) improve the visibility of an application, (ii) enhance the value of an applicationand strengthen the loyalty of current users, and (iii) expand the market by adding additional features or functions. A number of studies and anecdotal evidence have highlighted the importance of these updates in the mobile application market. However, not all updates in the mobile application market have been successful. Snapchat, for instance, lost 3 million users in three months after it rolled out a major update in November 2017. In this study, we investigate the impact of updates on the usage patterns of users in the mobile application market using MAU (Monthly Active Users), usage frequency, and usage time that we collected from a company that provides mobile application usage statistics.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.55-66
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• 2024

The purpose of this study was to identify the experience factors (4Es) of live commerce that provide differentiated value for consumers based on Pine & Gilmore's experience economy and analyze the relationships between experience factors, flow, attitude, and purchase intention. This study conducted an online survey to collect live commerce user responses and employed a path analysis to test the research hypotheses, using Smart PLS 4.0. The results of the study were as follows: 1) all experience factors had significant effects on flow, 2) both entertainment experience and educational experience had significant effects on attitude, while esthetic experience and escapist experience did not have significant effects on attitude, 3) flow had a significant effect on attitude and purchase intention respectively, and 4) attitude had a significant effect on purchase intention. The findings of the study are expected to provide the implications for further research and marketing strategies grounded on the understanding of the experience factors in live commerce.

• Journal of Cardiovascular Imaging
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• v.31 no.3
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• pp.135-141
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• 2023

BACKGROUND: Global longitudinal strain (GLS) is an accurate and reproducible parameter of left ventricular (LV) systolic function which has shown meaningful prognostic value. Fast, user-friendly, and accurate tools are required for its widespread implementation. We aim to compare a novel web-based tool with two established algorithms for strain analysis and test its reproducibility. METHODS: Thirty echocardiographic datasets with focused LV acquisitions were analyzed using three different semi-automated endocardial GLS algorithms by two readers. Analyses were repeated by one reader for the purpose of intra-observer variability. CAAS Qardia (Pie Medical Imaging) was compared with 2DCPA and AutoLV (TomTec). RESULTS: Mean GLS values were -15.0 ± 3.5% from Qardia, -15.3 ± 4.0% from 2DCPA, and -15.2 ± 3.8% from AutoLV. Mean GLS between Qardia and 2DCPA were not statistically different (p = 0.359), with a bias of -0.3%, limits of agreement (LOA) of 3.7%, and an intraclass correlation coefficient (ICC) of 0.88. Mean GLS between Qardia and AutoLV were not statistically different (p = 0.637), with a bias of -0.2%, LOA of 3.4%, and an ICC of 0.89. The coefficient of variation (CV) for intra-observer variability was 4.4% for Qardia, 8.4% 2DCPA, and 7.7% AutoLV. The CV for inter-observer variability was 4.5%, 8.1%, and 8.0%, respectively. CONCLUSIONS: In echocardiographic datasets of good image quality analyzed at an independent core laboratory using a standardized annotation method, a novel web-based tool for GLS analysis showed consistent results when compared with two algorithms of an established platform. Moreover, inter- and intra-observer reproducibility results were excellent.

• Steel and Composite Structures
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• v.52 no.2
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• pp.145-163
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• 2024

This paper presents six novel hybrid machine learning (ML) models that combine support vector machines (SVM), Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), extreme gradient boosting (XGB), and categorical gradient boosting (CGB) with the Harris Hawks Optimization (HHO) algorithm. These models, namely HHO-SVM, HHO-DT, HHO-RF, HHO-GB, HHO-XGB, and HHO-CGB, are designed to predict the ultimate strength of both rectangular and circular reinforced concrete (RC) columns. The prediction models are established using a comprehensive database consisting of 325 experimental data for rectangular columns and 172 experimental data for circular columns. The ML model hyperparameters are optimized through a combination of cross-validation technique and the HHO. The performance of the hybrid ML models is evaluated and compared using various metrics, ultimately identifying the HHO-CGB model as the top-performing model for predicting the ultimate shear strength of both rectangular and circular RC columns. The mean R-value and mean a20-index are relatively high, reaching 0.991 and 0.959, respectively, while the mean absolute error and root mean square error are low (10.302 kN and 27.954 kN, respectively). Another comparison is conducted with four existing formulas to further validate the efficiency of the proposed HHO-CGB model. The Shapely Additive Explanations method is applied to analyze the contribution of each variable to the output within the HHO-CGB model, providing insights into the local and global influence of variables. The analysis reveals that the depth of the column, length of the column, and axial loading exert the most significant influence on the ultimate shear strength of RC columns. A user-friendly graphical interface tool is then developed based on the HHO-CGB to facilitate practical and cost-effective usage.

• Advanced Industrial SCIence
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• v.3 no.3
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• pp.8-13
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• 2024

Zero Knowledge Proof (ZKP) is an innovative decentralized technology designed to enhance the privacy and security of virtual currency transactions. By ensuring that only the necessary information is disclosed by the transaction provider, ZKP protects the confidentiality of all parties involved. This ensures that both the identity of the transacting parties and the transaction value remain confidential.ZKP not only provides a robust privacy function by concealing the identities and values involved in blockchain transactions but also facilitates the exchange of money between parties without the need to verify each other's identity. This anonymity feature is crucial in promoting trust and security in financial transactions, making ZKP a pivotal technology in the realm of virtual currencies. In the context of the Fourth Industrial Revolution, the application of ZKP contributes significantly to the comprehensive and stable development of financial services. It fosters a trustworthy user environment by ensuring that transaction privacy is maintained, thereby encouraging broader adoption of virtual currencies. By integrating ZKP, financial services can achieve a higher level of security and trust, essential for the continued growth and innovation within the sector.

• Journal of Korean Society of Archives and Records Management
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• v.24 no.3
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• pp.145-156
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• 2024

This study explores the integration of cutting-edge technology with records management, aiming to create new value not only in work processes but also in record information services. The research focuses on the case of constructing an "AI-based cultural heritage research record learning data and search system," carried out by the National Research Institute of Cultural Heritage (NRICH) Archives, and analyzes user satisfaction results. "Discovering Cultural Heritage with ZOOM" is a system designed to proactively predict research data demand by constructing big data (learning data) from images (675,338 items) contained in 1,421 volumes of publications in the cultural heritage field, spanning from 1973 to the present, and simultaneously presenting 50 similar images. This initiative aims to foster change and development in the field of records management and cultural heritage in response to the Fourth Industrial Revolution's advanced technologies. It is expected to provide valuable information to researchers, practitioners, and the general public alike.