• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.83-90
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• 2004

Strategic games are missing special qualities of genre these days. Game engines neither reason about behaviors of computer objects nor have learning ability that can prepare countermeasure in variously command user's strategy. This paper suggests a strategic game engine that applies non-monotonic reasoning and inductive machine learning. The engine emphasizes three components -“user behavior monitor”to abstract user's objects behavior,“learning engine”to learn user's strategy,“behavior display handler”to reflect abstracted behavior of computer objects on game. Especially, this paper proposes two layered-structure to apply non-monotonic reasoning and inductive learning to make behaviors of computer objects that learns strategy behaviors of user objects exactly, and corresponds in user's objects. The engine decides actions and strategies of computer objects with created information through inductive learning. Main contribution of this paper is that computer objects command excellent strategies and reveal differentiation with behavior of existing computer objects to apply non-monotonic reasoning and inductive machine learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.4
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• pp.17-22
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• 2020

Behavior-based authentication technology, which is currently being researched a lot, requires a long extraction of a lot of data to increase the recognition rate of authentication compared to other authentication technologies. This paper uses the touch sensor and the gyroscope embedded in the smartphone in the Android environment to measure five times to the user to use only the minimum data that is essential among the behavior feature data used in the behavior-based authentication study. By requesting, a total of six behavior feature data were collected by touching the five touch screen, and the mean value was calculated from the changes in data during the next touch measurement to measure the cosine similarity between the value and the measured value. After generating the allowable range of cosine similarity by performing, we propose a user behavior based authentication method that compares the cosine similarity value of the authentication attempt data. Through this paper, we succeeded in demonstrating high performance from the first EER of 37.6% to the final EER of 1.9% by adjusting the threshold applied to the cosine similarity authentication range even in a small number of feature data and experimenter environments.

• Journal of Korea Multimedia Society
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• v.16 no.8
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• pp.962-971
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• 2013

In this paper, we propose a method to infer the user's behavior and situation through collected data from multi-sensor equipped with a smart clothing and it was implemented as a smart-phone App. This smart-clothes is able to monitor wearer users' health condition and activity levels through the gyro, temp and acceleration sensor. Sensed vital signs are transmitted to a bluetooth-enabled smart-phone in the smart-clothes. Thus, users are able to have real time information about their user condition, including activities level on the smart-application. User context reasoning and behavior determine is very difficult using multi-sensor depending on the measured value of the sensor varies from environmental noise. So, the reasoning and the digital filter algorithms to determine user behavior reducing noise and are required. In this paper, we used Multi-black Filter and SVM processing behavior for 3-axis value as a representative value of one.

• Journal of Digital Convergence
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• v.19 no.6
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• pp.1-9
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• 2021

Artificial intelligence (AI) devices are rapidly emerging as a core platform of next-generation information and communication technology (ICT), this study investigated consumer usage behavior and user experience through AI devices that are widely applied to consumers' daily lives. To this end, data was collected from 600 consumers with experience in using AI devices were derived to recognize the attributes and behavior of AI devices. The analysis results are as follows. First, music listening was the most used among various attributes and it was found that simple functions such as providing weather information were usefully recognized. Second, the main devices used by AI device users were identified as AI speakers, smartphone, PC and laptops. Third, associative images of AI devices appeared in the order of fun, useful, novel, smart, innovative, and friendly. Therefore, practical implications are suggested to contribute to provision of user services using AI devices in the future by analyzing usage behaviors that reflect the characteristics of AI devices.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.7
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• pp.319-326
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• 2016

Given the attributes of mobile apps that shorten the time to make purchase decisions while enabling easy purchase cancellations, usability can be regarded to be a highly prioritized quality attribute among the diverse quality attributes that must be provided by mobile apps. With that backdrop, mobile app developers have been making great effort to minimize usability hampering elements that degrade the merchantability of apps in many ways. Most elements that hamper the convenience in use of mobile apps stem from those potential errors that occur when GUIs are designed. In our previous study, we have proposed a technique to analyze the usability of mobile apps using user behavior logs. We proposes a technique to detect usability hampering elements lying dormant in mobile apps' GUI models by expressing user behavior logs with finite state models, combining user behavior models extracted from multiple users, and comparing the combined user behavior model with the expected behavior model on which the designer's intention is reflected. In addition, to reduce the burden of the repeated test operations that have been conducted by existing developers to detect usability errors, the present paper also proposes a mobile usability error detection automation tool that enables automatic application of the proposed technique. The utility of the proposed technique and tool is being discussed through comparison between the GUI issue reports presented by actual open source app developers and the symptoms detected by the proposed technique.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.573-578
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• 2008

Recently, we should control various devices such as TV, audio, DVD player, video player, and set-top box simultaneously to manipulate home theater system. To execute the function the user want in this situation, user should know functions and positions of the buttons in several remote controllers. Normally, people feel difficult due to these realistic problems. Besides, the number of the devices that we can control shall increase, and people will confuse more if the ubiquitous home environment is realized. Therefore, user adaptive interface that provides the summarized functions is required. Moreover there can be a lot of mobile and stationary controller devices in ubiquitous computing environment, so user interface should be adaptive in selecting the functions that user wants and in adjusting the features of UI to fit in specific controller. To implement the user and controller adaptive interface, we modeled the ubiquitous home environment and used modeled context and device information. We have used Bayesian network to get the degree of necessity in each situation. Behavior selection network uses predicted user situation and the degree of necessity, and it selects necessary functions in current situation. Selected functions are used to construct adaptive interface for each controller using presentation template. For experiments, we have implemented ubiquitous home environment and generated controller usage log in this environment. We have confirmed the BN predicted user requirements effectively as evaluating the inferred results of controller necessity based on generated scenario. Finally, comparing the adaptive home UI with the fixed one to 14 subjects, we confirmed that the generated adaptive UI was more useful for general tasks than fixed UI.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2005.11a
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• pp.63-66
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• 2005

The purpose of this study is to suggest the modeling of ubiquitous environments according to the analysis of user's behaviors focused on the living area within the house. Recently, with appearing 'Ubiquitous environments', the applications of ubiquitous technologies on the our environment adopt a new paradigm. This new paradigm leads to the possibility of creating more intellectual dwelling environment according to user's behaviors. This paper suggests to change our dwelling by considering both engineering technology and the character of dwelling, because the house should provide causes humans with comfortability. Therefore, we need to understand user's behaviors in the dwelling, towards user friendly environment. Also, it very important for us to configure proper sensors and technologies by the priority based on user's behaviors. To sum up, this study is aimed to analyze user's behaviors and then, to suggest the guideline for users offering optimal ubiquitous environments.

• Journal of Information Technology Applications and Management
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• v.19 no.4
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• pp.41-59
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• 2012

This study examines the roles of user resistance and social influences on the acceptance and continuous usage of smartphones at different stages of adoption. The respondents were classified into three groups according to their innovation adoption stage : non-user group, the potential user group and the trial user group. Theories relevant to user resistance, social influences including normative social influences and informational social influences, as well as user adoption and continuance behavior were reviewed and integrated into our research model. In order to verify the proposed structured equation model, we conducted an online survey by targeting mobile phone users and collected data to be analyzed through a partial least squares (PLS) test. This study tested whether there exists differences in the effects of user resistance and different types of social influence on user's adoption or continuance intetion among these three groups. The results showed that user resistance exists in all adopter groups and that it has significant negative influences on intention to use a smartphone. The findings also revealed that user resistance can be enhanced or resolved by two types of social influence; informational social influence resolves user resistance regardless of the adopter category, while normative social influence enhances the user resistance of potential users. Furthermore, the findings show that social influence regardless of the type positively affects user intention. Several theoretic and practical implications pertaining to the results are discussed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.2
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• pp.181-192
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• 2013

There have been various techniques to detect and control document leakage; however, most techniques concentrate on document leakage by outsiders. There are rare techniques to detect and monitor document leakage by insiders. In this study, we observe user's document reading behavior to detect and control document leakage by insiders. We make each user's document reading patterns from attributes gathered by a logger program running on Microsoft Word, and then we apply the proposed system to help determine whether a current user who is reading a document matches the true user. We expect that our system based on document reading behavior can effectively prevent document leakage.

• Proceedings of the Korea Contents Association Conference
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• 2005.11a
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• pp.529-532
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• 2005

In anomaly intrusion detection, how to model the normal behavior of activities performed by a user is an important issue. To extract the normal behavior as a profile, conventional data mining techniques are widely applied to a finite audit data set. However, these approaches can only model the static behavior of a user in the audit data set This drawback can be overcome by viewing the continuous activities of a user as an audit data stream. This paper proposes a new clustering algorithm which continuously models a data stream. A set of features is used to represent the characteristics of an activity. For each feature, the clusters of feature values corresponding to activities observed so far in an audit data stream are identified by the proposed clustering algorithm for data streams. As a result, without maintaining any historical activity of a user physically, new activities of the user can be continuously reflected to the on-going result of clustering.