• Journal of Communications and Networks
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• v.18 no.3
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• pp.429-438
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• 2016

In this paper, we investigate an interference mitigation scheme by antenna selection in device-to-device (D2D) communication underlaying downlink cellular networks. We first present the closed-form expression of the system achievable rate and its asymptotic behaviors at high signal-to-noise ratio (SNR) and the large antenna number scenarios. It is shown that the high SNR approximation increases with more antennas and higher ratio between the transmit SNR at the base station (BS) and the D2D transmitter. In addition, a tight approximation is derived for the rate and we reveal two thresholds for both the distance of the D2D link and the transmit SNR at the BS above which the underlaid D2D communication will degrade the system rate. We then particularize on the small cell setting where all users are closely located. In the small cell scenario, we show that the relationship between the distance of the D2D transmitting link and that of the D2D interfering link to the cellular user determines whether the D2D communication can enhance the system achievable rate. Numerical results are provided to verify these results.

• Human Ecology Research
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• v.58 no.4
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• pp.497-515
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• 2020

The use of mobile payment services has recently increased in South Korea. Mobile payments allow consumers to purchase items digitally, using a mobile card in an app affiliated with a payment service. This study explores the predictors of intention to adopt mobile payment services. The study employed an A(affective)-B(behavioral)-C(cognitive) model with two antecedent variables: cognitive (perceived usefulness, perceived risk, perceived ease of use, and perceived herding behavior) and affective (satisfaction with the status quo, innovation resistance) responses. An online survey of 405 non-users of mobile payment services aged 20 to 49 years was conducted. The study used SPSS 23.0 for descriptive analysis and Amos 23.0 for confirmatory factor analysis and structural equation modelling. The results are as follows. First, perceived usefulness, perceived risk, and perceived herding behavior significantly influenced innovation resistance. Second, perceived herding behavior significantly influenced subjective norms. Third, innovation resistance and subjective norms significantly influenced the intention to adopt mobile payment services. The findings suggest that the A-B-C model can be useful in understanding consumers' adoption and resistance behaviors and that cognitive and affective responses are important antecedent variables affecting the decision to adopt mobile payment services.

• Industrial Engineering and Management Systems
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• v.7 no.1
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• pp.51-56
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• 2008

With growing demand for zero defects, predicting reliability of software systems is gaining importance. Software reliability models are used to estimate the reliability or the number of latent defects in a software product. Most reliability models to estimate the reliability of software in the literature are based on the development lifecycle stages. However, in the maintenance phase, the software needs to be corrected for errors and to be enhanced for the requests from users. These decrease the reliability of software. Software Reliability Growth Models (SRGMs) have been applied successfully to model software reliability in development phase. The software reliability in maintenance phase exhibits many types of systematic or irregular behaviors. These may include cyclic behavior as well as long-term evolutionary trends. The cyclic behavior may involve multiple periodicities and may be asymmetric in nature. In this paper, SGRM has been adapted to develop a reliability prediction model for the software in maintenance phase. The model is established using maintenance data from a commercial shop floor control system. The model is accepted to be used for resource planning and assuring the quality of the maintenance work to the user.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.448-448
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• 2018

In this study, the regression models (Load ESTimator and eight-parameter model) were evaluated to estimate instantaneous pollutant loads under various criteria and optimization methods. As shown in the results, LOADEST commonly used in interpolating pollutant loads could not necessarily provide the best results with the automatic selected regression model. It is inferred that the various regression models in LOADEST need to be considered to find the best solution based on the characteristics of watersheds applied. The recently developed eight-parameter model integrated with Genetic Algorithm (GA) and Gradient Descent Method (GDM) were also compared with LOADEST indicating that the eight-parameter model performed better than LOADEST, but it showed different behaviors in calibration and validation. The eight-parameter model with GDM could reproduce the nitrogen loads properly outside of calibration period (validation). Furthermore, the accuracy and precision of model estimations were evaluated using various criteria (e.g., $R^2$ and gradient and constant of linear regression line). The results showed higher precisions with the $R^2$ values closed to 1.0 in LOADEST and better accuracy with the constants (in linear regression line) closed to 0.0 in the eight-parameter model with GDM. In hence, based on these finding we recommend that users need to evaluate the regression models under various criteria and calibration methods to provide the more accurate and precise results for pollutant load estimations.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.6
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• pp.1319-1328
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• 2018

A blockchain system developed for crypto-currency has attractive characteristics, such as de-centralization, distributed ledger, and partial anonymity, making itself adopted in various fields. Among those characteristics, partial anonymity strongly assures privacy of users, but side effects such as abuse of crime are also appearing, and so countermeasures for circumventing such abuse have been studied continuously. In this paper, we propose a machine-learning based method for classifying smart contracts in Ethereum regarding their functions and design patterns and for identifying user behaviors according to them.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.1
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• pp.17-23
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• 2019

Nowadays, the concept of outdoor campus is different from the past. U-Campus with a well-developed high-speed computer network is no longer a constraint on the campus interior and exterior spaces. From this point of view, today's large-scale university outdoor spaces need to be changed from a simple green space. The university campus outdoor spaces need to be changed into a new concept space. This study analyzed the changes in academic activities and preferences of college students who are users of university campus outdoor spaces and it is aimed at space planning that reflects the preference. The university campus should be remodified through changes in students' behaviors. Participants in this study were four different departments students (Social science, Physical education, Natural science and Engineering). The preference results of 17 items were analyzed. As a result of this preference analysis, we found that there is a difference in preference among students belonging to four different departments students. In conclusion, this study will propose that the preferences of each college should be considered in planning the outdoor rest spaces of university campus.

• Human Ecology Research
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• v.57 no.1
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• pp.1-13
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• 2019

Internet-based financial services are being increasingly integrated into consumers' daily lives. Internet-only banks have emerged as a powerful tool accelerating financial inclusion. This study investigates the satisfaction and continuous use intention predictors for Internet-only banks. We employed an extended post-acceptance model and used six antecedent factors that included perceived usefulness, perceived ease of use, privacy risk, functional risk, subjective norms, and network externality. All 351 participants used Internet-only banks and were 20-40 years of age. A self-administration online survey was conducted. SPSS 23.0 analyzed the frequency, description, and multiple regression analysis. The results of current study are as follows. The education, perceived usefulness, perceived ease of use, and network externality positively influenced the satisfaction of Internet-only banks. Privacy risk negatively influenced satisfaction with Internet-only banks. Perceived ease of use, subjective norm, network externality, and satisfaction positively influenced the continuous use intention of Internet-only banks. The results of our study provide a better explanation of important factors that could enhance the understanding of satisfaction and continuous use intention for Internet-only banks. Furthermore, this study extends the antecedent variables to the knowledge of financial services and enlarges the understanding of users' post-adoption behaviors.

• ETRI Journal
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• v.43 no.1
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• pp.40-52
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• 2021

DNS (Domain Name System) tunnels almost obscure the true network activities of users, which makes it challenging for the gateway or censorship equipment to identify malicious or unpermitted network behaviors. An efficient way to address this problem is to conduct a temporal-spatial analysis on the tunnel traffic. Nevertheless, current studies on this topic limit the DNS tunnel to those with a single protocol, whereas more than one protocol may be used simultaneously. In this paper, we concentrate on the refined identification of two protocols mixed in a DNS tunnel. A feature set is first derived from DNS query and response flows, which is incorporated with deep neural networks to construct a regression model. We benchmark the proposed method with captured DNS tunnel traffic, the experimental results show that the proposed scheme can achieve identification accuracy of more than 90%. To the best of our knowledge, the proposed scheme is the first to estimate the ratios of two mixed protocols in DNS tunnels.

• International Journal of Computer Science & Network Security
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• v.21 no.6
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• pp.17-22
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• 2021

The attack trend on end-users via mobile devices is increasing in both the danger level and the number of attacks. Especially, mobile devices using the Android operating system are being recognized as increasingly being exploited and attacked strongly. In addition, one of the recent attack methods on the Android operating system is to take advantage of Android Package Kit (APK) files. Therefore, the problem of early detecting and warning attacks on mobile devices using the Android operating system through the APK file is very necessary today. This paper proposes to use the method of analyzing abnormal behavior of APK files and use it as a basis to conclude about signs of malware attacking the Android operating system. In order to achieve this purpose, we propose 2 main tasks: i) analyzing and extracting abnormal behavior of APK files; ii) detecting malware in APK files based on behavior analysis techniques using machine learning or deep learning algorithms. The difference between our research and other related studies is that instead of focusing on analyzing and extracting typical features of APK files, we will try to analyze and enumerate all the features of the APK file as the basis for classifying malicious APK files and clean APK files.

• Journal of Information Technology Applications and Management
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• v.28 no.1
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• pp.13-31
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• 2021

The purpose of this study is to find the factors that influence big data characteristics on decision satisfaction and utilization behavior, analyze the extent of their influence, and derive differences from existing studies. To summarize the results of this study, First, the study found that among the three categories that classify the characteristics of big data, qualitative attributes such as representation, purpose, interpretability, and innovation in the value innovation category greatly enhance decision confidence and decision effectiveness of decision makers who make decisions using big data. Second, the study found that, among the three categories that classify the characteristics of big data, the individuality properties belonging to the social impact category improve decision confidence and decision effectiveness of decision makers who use big data to make decisions. However, collectivity and bias characteristics have been shown to increase decision confidence, but not the effectiveness of decision making. Third, the study found that among the three categories that classify the characteristics of big data, the attributes of inclusiveness, realism, etc. in the integrity category greatly improve decision confidence and decision effectiveness of decision makers who make decisions using big data. Fourth, it was analyzed that using big data in organizational decision making has a positive impact on the behavior of big data users when the decision-making confidence and finally, decision-making effect of decision-makers increases.