• KIPS Transactions on Software and Data Engineering
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• v.2 no.9
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• pp.629-642
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• 2013

Recently, with the advent of smart phones, it brought many changes in lives of modern people. Especially, application utilizing the sensor information of smart phone, which provides the service adapted by user situations, has been emerged. Sensor data of smart phone can be used for recognizing the user situation, Because it is closely related to the behavior and habits of the user. currently, GPS sensor one of mobile sensor has been utilized a lot to recognize basic user activity. But, depending on the user situation, activity recognition system cannot receive GPS signal, and also not collect received data. So utilization is reduced. In this paper, for solving this problem, we suggest a method of user activity recognition that focused on the accelerometer sensor data using smart phone. Accelerometer sensor is stable to collect the data and it's sensitive to user behavior. Finally this paper suggests a noble approach to use state transition diagrams which represent the natural flow of user activity changes for enhancing the accuracy of user activity recognition.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.404-414
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• 2010

The primary purpose of this paper is to identify the influencing factors on the post acceptance behavior of user in internet banking. For this purpose, a research model and hypotheses are developed based on the relevant literature reviews. Data have been collected from 248 users who have used internet banking and the research hypotheses were tested by covariance structural model analysis. The results of this empirical study are summarized as follows. First, security, confirmation, and perceived ease of use have positive influence upon user's perceived usefulness. Second, security, economy efficiency, and confirmation have positive influence upon user's satisfaction. Third, loyalty incentives have positive influence upon continuance intention. Lastly, user's perceived usefulness have positive effect on the satisfaction, and user's perceived usefulness and satisfaction positively related to continuance intention in internet banking. The findings have significant implications for internet banking service providers.

• The KIPS Transactions:PartC
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• v.10C no.7
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• pp.857-866
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• 2003

For detecting an intrusion based on the anomaly of a user's activities, previous works are concentrated on statistical techniques in order to analyze an audit data set. However. since they mainly analyze the average behavior of a user's activities, some anomalies can be detected inaccurately. In this paper, a new clustering algorithm for modeling the normal pattern of a user's activities is proposed. Since clustering can identify an arbitrary number of dense ranges in an analysis domain, it can eliminate the inaccuracy caused by statistical analysis. Also, clustering can be used to model common knowledge occurring frequently in a set of transactions. Consequently, the common activities of a user can be found more accurately. The common knowledge is represented by the occurrence frequency of similar data objects by the unit of a transaction as veil as the common repetitive ratio of similar data objects in each transaction. Furthermore, the proposed method also addresses how to maintain identified common knowledge as a concise profile. As a result, the profile can be used to detect any anomalous behavior In an online transaction.

• Journal of Digital Policy
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• v.1 no.1
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• pp.15-20
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• 2022

Recently, the healthcare field is trying to develop a model that can improve service quality by reflecting the requirements of various industrial fields. In this paper, we propose an Internet of Behavior (IoB) environment model that can process users' healthcare information in real time in a 5G environment to improve healthcare services. The purpose of the proposed model is to analyze the user's healthcare information through deep learning and then check the health status in real time. In this case, the biometric information of the user is transmitted through communication equipment attached to the portable medical equipment, and user authentication is performed through information previously input to the attached IoB device. The difference from the existing IoT healthcare service is that it analyzes the user's habits and behavior patterns and converts them into digital data, and it can induce user-specific behaviors to improve the user's healthcare service based on the collected data.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.6
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• pp.777-784
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• 2017

In the architectural space, the user's behavior is the most important factor in evaluating the quality of architecture. Normally, the evaluation of user behavioral performance was carried out after a building was completed. Recently, interest in and efforts at pre-simulation based on information technology are accelerating. However, since existing user simulation technology is concerned mainly with simply escaping from a large space, it is impossible to simulate the behavior of multiple users in an architectural space. The present study strives to develop a human-figured intelligent agent for advanced user simulation based on ontology. The main purpose of the study is to employ the intelligent behaviors of a NPC(Non-player Character) to infer the ontology of both spatial and user information. In this paper, we intend to integrate ontology inference technology into the virtual space. And also, this study suggest the ontology visualization technology which illustrate the ontology-based information and their change in the spatial information.

• Management & Information Systems Review
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• v.34 no.3
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• pp.79-100
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• 2015

This study is to examine the factors that influence purchasing behavior and decision-making when consumers buy goods through mobile shopping, define purchasing decision-making with the steps of problem recognition, information search, alternative assessment, and purchasing behavior to understand mobile consumer behavior, and investigate how the factors of each step play roles and influence consumers' purchasing decision-making through positive analysis to figure out consumer purchasing behavior in mobile shopping. The study results, First, the user characteristics of information search influence flow. Second, in the relations between the user characteristics in the step of information search and perceived risk in alternative assessment, if recognition on gains is higher, perceived risk for time loss gets lower, and when the level of skills is higher, perceived risk gets higher, and it has been partly adopted that innovativeness does not influence risk perception. Third, in the relations between flow experience and purchasing intention, it has been found to be partially significant that remote presence and challenge do not influence purchasing intention but do influence excitement, attention concentration, and control and also do influence perceived risk and purchasing intention. Fourth, according to the results of analyzing the difference of consumer purchasing behavior by the types of involvement, practical involvement and sensual involvement, user characteristics and flow, and perceived risk differ by the types of products in terms of the search process, thereby changing purchasing intention. Lastly, the significance and limitations of this study was discussed.

• Journal of Korea Multimedia Society
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• v.11 no.1
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• pp.61-75
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• 2008

A script language, which represents and controls avatar behaviors in a natural language style, is especially remarkable, because it can provide a fast and easy way to develop an animation scenario script. However, the studies that consider avatar behavior interactions with various virtual objects and intuitive interface techniques to design scenario script have been lack. Therefore, we proposed a context-based avatar-object behavior model and layered script language. The model defines context-based elements to solve ambiguity problems that occur in abstract behavior interface and it provides user interface to control avatar in the object-based approach. Also, the proposed avatar behavior script language consisted of a layered structure that represents domain user interface, motion sequence, and implement environment information at each level. Using the proposed methods, the user can conveniently and quickly design an avatar-object behavior scenario script.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.4
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• pp.154-163
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• 2017

Collaborative filtering, one of the most widely used techniques to build recommender systems, is based on the idea that users with similar preferences can help one another find useful items. Credit card user behavior analytics show that most customers hold three or less credit cards without duplicates. This behavior is one of the most influential factors to data sparsity. The 'cold-start' problem caused by data sparsity prevents recommender system from providing recommendation properly in the personalized credit card recommendation scenario. We propose a personalized credit card recommender system to address the cold-start problem, using multiple user profiles. The proposed system consists of a training process and an application process using five user profiles. In the training process, the five user profiles are transformed to five user networks based on the cosine similarity, and an integrated user network is derived by weighted sum of each user network. The application process selects k-nearest neighbors (users) from the integrated user network derived in the training process, and recommends three of the most frequently used credit card by the k-nearest neighbors. In order to demonstrate the performance of the proposed system, we conducted experiments with real credit card user data and calculated the F1 Values. The F1 value of the proposed system was compared with that of the existing recommendation techniques. The results show that the proposed system provides better recommendation than the existing techniques. This paper not only contributes to solving the cold start problem that may occur in the personalized credit card recommendation scenario, but also is expected for financial companies to improve customer satisfactions and increase corporate profits by providing recommendation properly.

• Journal of Information Processing Systems
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• v.14 no.2
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• pp.523-538
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• 2018

Keystroke dynamics user authentication is a behavior-based authentication method which analyzes patterns in how a user enters passwords and PINs to authenticate the user. Even if a password or PIN is revealed to another user, it analyzes the input pattern to authenticate the user; hence, it can compensate for the drawbacks of knowledge-based (what you know) authentication. However, users' input patterns are not always fixed, and each user's touch method is different. Therefore, there are limitations to extracting the same features for all users to create a user's pattern and perform authentication. In this study, we perform experiments to examine the changes in user authentication performance when using feature vectors customized for each user versus using all features. User customized features show a mean improvement of over 6% in error equal rate, as compared to when all features are used.

• Fashion & Textile Research Journal
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• v.3 no.1
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• pp.61-69
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• 2001

The purpose of this study was to analyze the purchasing behavior related factors of Small Store User in Yhasi street of Dong-Sung Ro, Daegu. Frequency, $X^2$-test MANOVA, ANOVA and Duncan multiple range test were used to analyze the sample. The results of this study were as follows: 1) The largest sample were as follows: un married female, college students of twenties, 101-200 thousand won for salaries. 2) The factors of purchasing behavior were classified into 8 factors, enjoy shopping, store image, unique goods, culture space, salesperson, low price, information seeking, value via price orientation. 3) There were significant differences found between attitude on information source, number of seeking store, music in shop, music sound, size, display, price, street, in their factors of purchasing behavior (unique goods, value via price, low price, store image, enjoy shopping) 4) There were significant differences found between demographic characteristics (personal sales, location, transportation) in their factors of purchasing behavior (salesperson, cultural space, store image).