• Asia Marketing Journal
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• v.25 no.3
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• pp.99-110
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• 2023

Chatbot services have become an essential communication tool for interacting with consumers in e-commerce. To understand consumer behavior in the context of chatbot services, we apply the Theory of Planned Behavior (TPB) to analyze continuance intention to use and additional predictors to explain behavioral intention. An analysis of data collected from 300 digital shopping users who had experienced chatbot services revealed that an extended TPB model holds for the continuous use of chatbot services, driven by both interaction and information quality. Accordingly, these ndings provide a better understanding of consumer behavior toward chatbot services and valuable insights into digital customer relationship management.

• Journal of Computing Science and Engineering
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• v.6 no.3
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• pp.193-206
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• 2012

Keys play a fundamental role in all data models. They allow database systems to uniquely identify data items, and therefore, promote efficient data processing in many applications. Due to this, support is required to discover keys. These include keys that are semantically meaningful for the application domain, or are satisfied by a given database. We study the discovery of keys from SQL tables. We investigate the structural and computational properties of Armstrong tables for sets of SQL keys. Inspections of Armstrong tables enable data engineers to consolidate their understanding of semantically meaningful keys, and to communicate this understanding to other stake-holders. The stake-holders may want to make changes to the tables or provide entirely different tables to communicate their views to the data engineers. For such a purpose, we propose data mining algorithms that discover keys from a given SQL table. We combine the key mining algorithms with Armstrong table computations to generate informative Armstrong tables, that is, key-preserving semantic samples of existing SQL tables. Finally, we define formal measures to assess the distance between sets of SQL keys. The measures can be applied to validate the usefulness of Armstrong tables, and to automate the marking and feedback of non-multiple choice questions in database courses.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.5
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• pp.1205-1219
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• 2019

As the ability to use data becomes competitive power in the data-driven economy, the effort to create economic value by using personal data is emphasized as much as to protect personal data. EU's PSD2(the second Payment Service directive) became the initiative of the Open Banking trends all over the world, as it is the Mydata policy which protects the data subject's right by empowering the subject to control over the personal data with the right to data portability and promotes personal data usages and transfer. Korean government is now fast adopting EU's PSD2 in financial sector, but there is growing concerns in personal data abuse and misuse, and data breach. This study analyzes domestic financial Mydata policy in comparison with EU's PSD2 and focus on Personal information life-cycle risks of financial Mydata policy. Some suggestions on how to promote personal information and privacy in domestic financial Mydata Policy will be given.

• Computers and Concrete
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• v.6 no.2
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• pp.155-165
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• 2009

Splitting tensile strength (STS) of high-performance concrete (HPC) is one of the important mechanical properties for structural design. This property is related to compressive strength (CS), water/binder (W/B) ratio and concrete age. This paper presents a clustering-based fuzzy model for the prediction of STS based on the CS and (W/B) at a fixed age (28 days). The data driven fuzzy model consists of three main steps: fuzzy clustering, inference system, and prediction. The system can be analyzed directly by the model from measured data. The performance evaluations showed that the fuzzy model is more accurate than the other prediction models concerned.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.6
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• pp.738-744
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• 2018

This paper proposes a low cost hot swap operation circuit of a parallel operation power supply for servers. Hot swap function for server power system is essential in 24 hour operation system such as internet data center, server, factory and etc. Server power supplies used in internet data centers have two or more parallel operations with the hot swap operation. However, the cost of the power supply is high because the controller IC for hot swap operation is very expensive. Therefore, this paper proposes a parallel-operated power supply with a low-cost hot swap controller for servers. The proposed system can operate hot swap function by using discrete devices and reduce the cost by more than 50%. A 1.2kW prototype system is implemented to verify the proposed low cost hot swap controller.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.309-316
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• 2018

The main purpose of this study is to understand how cognitive factors influence the way people interact with information/information systems, by conducting comprehensive and in-depth literature reviews and a theoretical synthesis of related research. Adaptive systems have been built around an individual user's characteristics, such as interests, preferences, knowledge and goals. Individual differences in the ability to use new information and communication technology have been an important issue in all fields. Performance differences in utilizing new information and communication technology are sufficiently predictable that we can begin to coordinate them. Therefore, it is necessary to understand cognitive mechanisms to explain differences between individuals as well as the levels of performance. The theoretical synthesis from this study can be applied to design intelligent (i.e., human friendly) systems in our everyday lives. Further research should explore optimization design for user, by integrating user's individual traits (such as emotion and intent) and system modules to improve the interactions of human-system in data-driven environments.

• Journal of Smart Tourism
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• v.2 no.1
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• pp.33-39
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• 2022

This research explores the public's perceptions of the appropriateness of the use of robots in museums and galleries. Using data from an international survey of 1589 participants, the data show that the perceived appropriateness of robot implementation in museums and galleries is driven largely by perceptions of the usefulness and emotional skills of robotic technologies, and their perceived advantages compared to human employees. Additionally, the findings suggest that the general attitudes towards service robots in tourism shape the attitudes towards robots in museums and galleries in particular. Furthermore, the findings reveal that the demographic characteristics of visitors are not related to their perceptions of robots in museums and galleries.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.307-313
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• 2024

This study explores the integration of Explainable Artificial Intelligence (XAI) and network science in healthcare, focusing on enhancing healthcare data interpretation and improving diagnostic and treatment methods. Key methodologies like Graph Neural Networks, Community Detection, Overlapping Network Models, and Time-Series Network Analysis are examined in depth for their potential in patient health management. The research highlights the transformative role of XAI in making complex AI models transparent and interpretable, essential for accurate, data-driven decision-making in healthcare. Case studies demonstrate the practical application of these methodologies in predicting diseases, understanding drug interactions, and tracking patient health over time. The study concludes with the immense promise of these advancements in healthcare, despite existing challenges, and underscores the need for ongoing research to fully realize the potential of AI in this field.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.328-342
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• 2022

The Internet of Things (IoT) is a technology that offers lucrative services in various industries to facilitate human communities. Important information on people and their surroundings has been gathered to ensure the availability of these services. This data is vulnerable to cybersecurity since it is sent over the internet and kept in third-party databases. Implementation of data encryption is an integral approach for IoT device designers to protect IoT data. For a variety of reasons, IoT device designers have been unable to discover appropriate encryption to use. The static support provided by research and concerned organizations to assist designers in picking appropriate encryption costs a significant amount of time and effort. IoTES is a web app that uses machine language to address a lack of support from researchers and organizations, as ML has been shown to improve data-driven human decision-making. IoTES still has some weaknesses, which are highlighted in this research. To improve the support, these shortcomings must be addressed. This study proposes the "IoTES with Security" model by adding support for the security level provided by the encryption algorithm to the traditional IoTES model. We evaluated our technique for encryption algorithms with available security levels and compared the accuracy of our model with traditional IoTES. Our model improves IoTES by helping users make security-oriented decisions while choosing the appropriate algorithm for their IoT data.

• Smart Structures and Systems
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• v.30 no.6
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• pp.687-701
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• 2022

Inevitable response loss under complex operational conditions significantly affects the integrity and quality of measured data, leading the structural health monitoring (SHM) ineffective. To remedy the impact of data loss, a common way is to transfer the recorded response of available measure point to where the data loss occurred by establishing the response mapping from measured data. However, the current research has yet addressed the structural condition changes afterward and response mapping learning from a small sample. So, this paper proposes a novel data driven structural response reconstruction method based on a sophisticated designed generating adversarial network (UAGAN). Advanced deep learning techniques including U-shaped dense blocks, self-attention and a customized loss function are specialized and embedded in UAGAN to improve the universal and representative features extraction and generalized responses mapping establishment. In numerical validation, UAGAN efficiently and accurately captures the distinguished features of structural response from only 40 training samples of the intact structure. Besides, the established response mapping is universal, which effectively reconstructs responses of the structure suffered up to 10% random stiffness reduction or structural damage. In the experimental validation, UAGAN is trained with ambient response and applied to reconstruct response measured under earthquake. The reconstruction losses of response in the time and frequency domains reached 16% and 17%, that is better than the previous research, demonstrating the leading performance of the sophisticated designed network. In addition, the identified modal parameters from reconstructed and the corresponding true responses are highly consistent indicates that the proposed UAGAN is very potential to be applied to practical civil engineering.