• 디지털산업정보학회논문지
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• 제8권1호
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• pp.183-190
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• 2012

Mobile web applications are different from desktop web applications because of their small screen size and small user input devices. Therefore user interface designers have spent their effort and time to re-design the user interface of mobile web applications to meet these differences. In this paper, we introduce five user interface patterns for mobile web applications to reduce their effort and time. Two of them are for utilizing small screen size efficiently, and they are space overloading pattern and data filtering pattern. These patterns enable designers to reduce screen usage. The other three patterns - data suggestion pattern, input reuse pattern, and incremental data input pattern - are for helping users' data input on mobile devices. These three patterns enable users to reduce direct data input. Our work will help user interface designers develop mobile web interface to utilize screen space efficiently and get data with less errors and less efforts from users.

• 한국컴퓨터정보학회논문지
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• 제22권9호
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• pp.65-71
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• 2017

This paper proposes a smart phone authentication method based on user's behavior and habit that is an authentication method against shoulder surfing attack and brute force attack. As smart phones evolve not only storage of personal data but also a key means of financial services, the importance of personal information security in smart phones is growing. When user authentication of smart phone, pattern authentication method is simple to use and memorize, but it is prone to leak and vulnerable to attack. Using the features of the smart phone pattern method of the user, the pressure applied when touching the touch pad with the finger, the size of the area touching the finger, and the time of completing the pattern are used as feature vectors and applied to user authentication security. First, a smart phone user models and stores three parameter values as prototypes for each section of the pattern. Then, when a new authentication request is made, the feature vector of the input pattern is obtained and compared with the stored model to decide whether to approve the access to the smart phone. The experimental results confirm that the proposed technique shows a robust authentication security using subjective data of smart phone user based on habits and behaviors.

• 한국컴퓨터정보학회논문지
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• 제16권11호
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• pp.17-24
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• 2011

협업적 여과 시스템은 사용자에 대한 클러스터링을 구축한 후, 구축된 클러스터를 기반으로 사용자에게 아이템을 추천한다. 그러나 사용자 클러스터링 구축에 많은 시간이 소요되고, 사용자가 평가한 아이템이 피드백 되었을 경우 재구축이 쉽지 않다. 본 논문에서는 영화 추천 시스템에서의 사용자 클러스터링의 재구축 시간을 단축시키기 위해서 빈발 패턴 네트워크를 이용하여 사용자가 선호하는 장르 패턴을 추출하고, 추출된 패턴을 통해 사용자 클러스터링을 구축한다. 구축된 사용자 클러스터링을 협업적 여과에 적용하여 사용자에게 영화를 추천한다. 사용자 정보가 피드백 될 때, 전통적 협업적 여과는 사용자 클러스터링을 재구축하기 위해 모든 이웃 사용자를 재탐색하여 클러스터링 한다. 하지만 빈발 패턴 네트워크를 이용하여 장르 패턴 기반의 사용자 클러스터링을 적용한 협업적 여과는 사용자 클러스터링을 재구축시 사용자 탐색 공간을 국한시킴으로써 탐색 시간을 줄일 수 있다. 제안하는 장르 패턴기반의 사용자 클러스터링을 통해 사용자 정보가 피드백 된 후 사용자 클러스터를 재구축시 소요되는 시간을 줄일 수 있고, 전통적인 협업적 여과 시스템과 유사한 성능의 추천이 가능하게 되었다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 컴퓨터소사이어티 추계학술대회논문집
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• pp.211-214
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• 2003

In this paper, we intend to find user's TV program choosing pattern and, recommend programs that he/she wants. So we suggest item dependency map which express relation between chosen program. Using an algorithm that we suggest, we can recommend an program, which a user has not saw yet but maybe is likely to interested in. Item dependency map is used as patterns for association in hopfield network so we can extract users global program choosing pattern only using users partial information. Hopfield network can extract global information from sub-information. Our algorithm can predict user's inclination and recommend an user necessary information.

• 한국컴퓨터정보학회논문지
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• 제24권5호
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• pp.59-66
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• 2019

According to the KT telecommunication statistics, people stayed inside their houses on an average of 11.9 hours a day. As well as, according to NSC statistics in the united states, people regardless of age are injured for a variety of reasons in their houses. For purposes of this research, we have investigated an abnormal event detection algorithm to classify infrequently occurring behaviors as accidents, health emergencies, etc. in their daily lives. We propose a fusion method that combines three classification algorithms with vision pattern, audio pattern, and activity pattern to detect unusual user events. The vision pattern algorithm identifies people and objects based on video data collected through home CCTV. The audio and activity pattern algorithms classify user audio and activity behaviors using the data collected from built-in sensors on their smartphones in their houses. We evaluated the proposed individual pattern algorithm and fusion method based on multiple scenarios.

• Journal of Information Processing Systems
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• 제15권3호
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• pp.645-654
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• 2019

The purpose of this study was to collect and analyze personal bio data and social network services (SNS) data, derive user preference and user life pattern, and propose intuitive and precise user modeling. This study not only tried to conduct eye tracking experiments using various smart devices to be the ground of the recommendation system considering the attribute of smart devices, but also derived classification preference by analyzing eye tracking data of collected bio data and SNS data. In addition, this study intended to combine and analyze preference of the common classification of the two types of data, derive final preference by each smart device, and based on user life pattern extracted from final preference and collected bio data (amount of activity, sleep), draw the similarity between users using Pearson correlation coefficient. Through derivation of preference considering the attribute of smart devices, it could be found that users would be influenced by smart devices. With user modeling using user behavior pattern, eye tracking, and user preference, this study tried to contribute to the research on the recommendation system that should precisely reflect user tendency.

• Journal of Information Processing Systems
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• 제14권2호
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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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2014년도 추계학술대회
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• pp.141-142
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• 2014

최근 개인정보 유출 사건으로 이를 이용한 2차적 피해에 대한 우려가 높아지고 있다. 아이디와 패스워드 같은 정보가 유출되더라도 추가적인 피해를 막기 위한 대책이 필요하다. 본 논문은 똑같은 단어를 타이핑 하더라도 사용자 마다 다른 패턴을 분석, 추출하여 로그인 시스템에 적용하는 기법을 제시한다. 즉 타인이 본인의 패스워드를 알아내 입력하더라도 다른 타이핑 패턴으로 인해 접근을 막는 시스템이다. 이를 위해 개인의 타이핑 패턴을 추출하는 알고리즘을 개발하고 이것을 바탕으로 적정 수준의 일치성을 가지는 패턴을 찾아내는데 이용할 수 있다.

• 한국멀티미디어학회논문지
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• 제18권1호
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• pp.71-80
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• 2015

Many researches have been studied to overcome the weak points in authentication schemes of mobile devices such as pattern-authentication that is vulnerable for smudge-attack. Since random-pattern-lock authenticates users by drawing figure of predefined-shape, it can be a method for robust security. However, the authentication performance of random-pattern-lock is influenced by input noise and individual characteristics sign pattern. We introduce an optimization method of user input direction to increase the authentication accuracy of random-pattern-lock. The method uses the likelihood of each direction given an data which is angles of line drawing by user. We adjusted recognition range for each direction and achieved the authentication rate of 95.60%.

• 공학교육연구
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• 제15권4호
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• pp.48-52
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• 2012

The purpose of this paper is to design and implement the user motion pattern control system for the simulated vehicle. After analyzing the user motion patterns in the system, the patterns are used to control the moving direction of the simulated vehicle such as forward, backward, turn right, turn left etc. The patterns in the system around are sent to the simulated vehicle in real time. In order to execute the suggested user motion pattern control system, the Kinect is used for executing the system. The Kinect recognizes the specified user motion patterns and it transmits the data to the user motion pattern control system. There are nine kinds of the user motion patterns in the system for controlling the simulated vehicle. In addition to this, some sensors are used to detect the condition of the simulated vehicle. GPS is also used to estimate the current location of the simulated vehicle and to obtain the driving information.