• Korean journal of communication and information
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• v.39
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• pp.224-249
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• 2007

This paper tried to examine a relationship between demographic factors(sex, age) and online flaming, and between personality trait(verbal aggression) and flaming. Further, the current study researched the impact of message credibility on significant explaining variables which increase hostile aggressive communication. The research data were collected by a reliable online research center and analysed through SPSS 12.1 software. According to the results, age, sex, and verbal aggression trait were statistically significantly related with flaming. Especially, man, teen age, and the people having high verbal aggression had more frequent flaming behavior than women, other aged people, people with low verbal aggression trait. On the other hand, the message credibility was partially associated with personality trait. The persons who had high verbal aggression trait showed fewer flaming when they met high credible message. This fact might imply that the quality of online contents could decrease online flaming.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.157-165
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• 2015

With the ever-improving performance of smartphone cameras and the universal dissemination of cloud services, users can now record and store the events in their daily lives more easily and conveniently. The advent of lifelogging technology has been changing the uses as well as the paradigm of internet services, and emphasizing the importance of services being personalization. As the amount of lifelog data becomes vast, it requires an efficient way to manage and store such vast information. In this paper, we propose an low power lifelog management scheme based on user movement behaviors in wireless networks. In order to reduce the power consumption of a smartphone, in our proposal, frequency of data collection and transfer can be dynamically adjusted based on a user's movement pattern. The analytical results show that our approach achieves better performance than that of the existing lifelog management scheme.

• Information Systems Review
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• v.8 no.2
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• pp.119-138
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• 2006

Association Rule Mining has been successfully used for mining knowledge in static environment but it provides limited features to discovery time-dependent knowledge from multi-point data set. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different multi-point snapshots. This paper proposes a procedure named 'Recursive Change Mining' for detecting continuous change of customer purchase behavior. The Recursive Change Mining Procedure is basically extended association rule mining and it assures to discover continuous and repetitive changes from data sets which collected at multi-periods. A case study on L department store is also provided.

• Journal of the Korea Society of Computer and Information
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• v.18 no.3
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• pp.35-45
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• 2013

MANET has a weak point because it allows access from not only legal nodes but also illegal nodes. Most of the MANET researches had been focused on attack on routing path or packet forwarding. Nevertheless, there are insuffcient studies on a comprehensive approach to detect various attacks on malicious nodes at packet forwarding processes. In this paper, we propose a technique, named DTecBC (detection technique of malicious node behaviors based on collaboration), which can handle more effciently various types of malicious node attacks on MANET environment. The DTecBC is designed to detect malicious nodes by communication between neighboring nodes, and manage malicious nodes using a maintain table. OPNET tool was used to compare with Watchdog, CONFIDANT, SRRPPnT for verifying effectiveness of our approach. As a result, DTecBC detects various behaviors of malicious nodes more effectively than other techniques.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.225-232
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• 2014

Supply of a smart device has been made with the development of communication networks and growing desire of people to improve the quality of life, the service industry about it have been made with smart devices. Among them, there are various services that can help people change their behavior to healthy life. But it does not have a large impact on the behavior change of performer because its been developed as a general process. Therefore in this study examine development situation about current behavior change applications through application markets. Development current situation can be separated by process to two patterns. As a result we propose behavioral change model that includes integrated change model that one of the behavior theory model, accomplish the goal process of goal setting theory and habit formation phase for automate the changes. Also, we provide appropriate service according to the model that leads motivation, sustainability of the action and positive behavior change to performer. We can expect in many aspects such as ability and motivation than existing service.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.163-170
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• 2021

Smart-textronics technology which enables functional textiles has recently been applied in various fields such as smart clothes, smart home and smart health care, and a variety of smart-textronics products have been developed. In this context, the smart-textronics product development process is proposed based on the systematic participatory design method in this paper. The proposed method consists of two phases: in-depth interviews and analyzing. In the phase of in-depth interviews, participants are asked to create journey maps that include activities, pain points and emotional status and to generate solution ideas with sketches and simple prototypes. In the analyzing phase, design researchers investigate the participants' journey maps, and create personas by identifying critical characteristics with the behavior pattern analysis. Then, each persona's needs are linked with value elements of the E3 value framework. Finally, pre-survey was conducted to identify smart-textronics market and a smart sofa design is proceeded as the case study to show the applicability of the proposed method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.2
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• pp.225-238
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. In this paper, we propose a framework for user behavior analysis for bot detection in online games. Specifically, we focus on party play that reflects the social activities of gamers: In a Massively Multi-user Online Role Playing Game (MMORPG), party play log includes a distinguished information that can classify game users under normal-user and abnormal-user. That is because the bot users' main activities target on the acquisition of cyber assets. Through a statistical analysis of user behaviors in game activity logs, we establish the threshold levels of the activities that allow us to identify game bots. Also, we build a knowledge base of detection rules based on this statistical analysis. We apply these rule reasoner to the sixth most popular online game in the world. As a result, we can detect game bot users with a high accuracy rate of 95.92%.

• Korea journal of population studies
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• v.10 no.2
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• pp.17-45
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• 1987

This study contributes to understanding women's labor market behavior by focusing on a particular set of labor force transitions - labor force withdrawal and entry during the period surrounding the first birth of a child. In particular, this study provides a dynamic analyses, using longitudinal data and event history analysis, to conceptualize labor force behaviors in a straightforward way. The main research question addresses which factors increase or decrease the hazard rates of leaving and entering the labor market. This study used piecewise Gompertz model, following the guide of the non-parametric analysis on the hazard rates, which allowed relatively detailed description on the distribution of timing of leave and entry to the labor market as parameters of interest. The results show that preferences and structural variables, as well as economic considerations, are very important factors to explain the labor market behavior of women in the period surrounding childbirth.

• Journal of KIISE:Computer Systems and Theory
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• v.35 no.5
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• pp.213-222
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• 2008

This paper is related to color image segmentation and textile texture mapping for the 2D virtual wearing system. The proposed system is characterized as virtually wearing a new textile pattern selected by user to the clothing shape section, based on its intensity difference map, segmented from a 2D clothes model image using color image segmentation technique. Regardless of color or intensity of model clothes, the proposed system is possible to virtually change the textile pattern or color with holding the illumination and shading properties of the selected clothing shape section, and also to quickly and easily simulate, compare, and select multiple textile pattern combinations for individual styles or entire outfits. The proposed system can provide higher practicality and easy-to-use interface, as it makes real-time processing possible in various digital environment, and creates comparatively natural and realistic virtual wearing styles, and also makes semi-automatic processing possible to reduce the manual works to a minimum. According to the proposed system, it can motivate the creative activity of the designers with simulation results on the effect of textile pattern design on the appearance of clothes without manufacturing physical clothes and, as it can help the purchasers for decision-making with them, promote B2B or B2C e-commerce.

• Journal of Internet Computing and Services
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• v.6 no.5
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• pp.45-56
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• 2005

The weak foundation of the computing environment caused information leakage and hacking to be uncontrollable, Therefore, dynamic control of security threats and real-time reaction to identical or similar types of accidents after intrusion are considered to be important, h one of the solutions to solve the problem, studies on intrusion detection systems are actively being conducted. To improve the anomaly IDS using system calls, this study focuses on neural networks learning using the soundex algorithm which is designed to change feature selection and variable length data into a fixed length learning pattern, That Is, by changing variable length sequential system call data into a fixed iength behavior pattern using the soundex algorithm, this study conducted neural networks learning by using a backpropagation algorithm. The backpropagation neural networks technique is applied for anomaly detection of system calls using Sendmail Data of UNM to demonstrate its performance.