• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.805-806
• /
• 2009

오늘날 웹 상에는 수 많은 온라인 커뮤니티들이 존재하고, 그 안에서 유저들이 올린 게시글(이하 포스트)를 효과적으로 검색하는 것은 중요한 이슈가 되고 있다. 만약 검색하는 유저들이 각기 다른 성향을 갖고 있다면 그에 맞는 검색 결과를 제공하는 것이 효과적인 검색의 한 예라 할 수 있겠다. 이 논문에서는 이러한 유저 성향 기반의 효과적인 검색을 위하여 유저의 "전문성"과 "대중성"을 정의하고 그에 기반한 포스트 랭킹을 한다. 또한 서로 다른 유저의 성향은 매우 다른 검색 결과를 나타낸다는 우리의 주장을 실험결과로 뒷받침 한다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.401-404
• /
• 2020

현재 전자상거래에서 사용되는 협업 필터링은 고객이 입력한 평점 정보를 이용하여 추천 시스템을 구축한다. 하지만 기존의 평점 정보는 고객이 직접 입력해야 하므로 데이터 희소생의 문제가 있고 허위정보를 가려내지 못한다는 문제점 또한 존재한다. 본 논문에서는 기존 평점 정보 기반의 협업 필터링 추천 시스템의 문제점을 해결하기 위해, 온라인 고객 행동 정보를 활용한 협업 필터링 알고리즘을 제안하였다. 실험 결과 본 연구에서 제안한 Collaborative Filtering based on Online Behavior Information (BICF) 알고리즘이 기존의 평점 기반 협업 필터링 방식보다 우수한 성능을 보임을 보여주었다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.540-543
• /
• 2020

본 논문에서는 대표적인 이미지 분류 모델인 CNN(Convolutional Neural Network)과 시간에 따른 이미지의 변화를 학습할 수 있는 LSTM(Long Short-Term Memory) 기반의 온라인 서명인식 모델을 제안한다. 실제로는 위조서명을 미리 구하기 어렵다는 사실을 고려해 서명검증 대상자가 아닌 타인의 진서명과 대상자의 일반 필기 데이터를 음의 데이터로서 학습에 사용하였다. 실험 결과, 전체 이미지 중 서명 부분의 비율에 따라 좋은 성능을 보이는 검증 모델이 다르며 Accuracy 성능지표를 통해 이 비율이 높거나 낮을 경우 CNN-LSTM 이, 중간일 경우 CNN 이 적합하다는 것을 확인하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.11a
• /
• pp.171-174
• /
• 2007

최근에 인간과 컴퓨터의 상호작용을 위한 입력장치 중 테이블-탑 디스플레이라는 멀티터치 입력장치가 활발히 연구되고 있다. 본 논문에서는 테이블-탑 디스플레이라는 환경에서 인간에게 가장 직관적인 도구인 손을 사용하여 입력된 온라인 필기 숫자를 전처리하는 방법을 제안한다. 테이블-탑 디스플레이 환경에 적합한 전처리 기법으로 대표점 추출을 위한 거리 필터링과 획 구분 및 잡음제거 등을 사용하였고, 데이터를 16방향 체인코드로 변환하였다. 이는 실제 필기운동 시의 궤적을 크게 왜곡 시키지 않으면서 테이블-탑 디스플레이가 갖는 환경에 기인한 잡음을 없애고, 데이터양을 줄일 수 있는 장점이 있다. 총450개의 필기 숫자 데이터를 사용하여 실험한 결과, 잡음이 제거되고 데이터양이 줄어들었으며 인식에 용이한 체인코드를 형성해 내었다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.559-572
• /
• 2020

As internet technology advances, users can share content online, and many sharing services exist. According to a recently published digital forensic study, when playing an online streaming service, you can restore the played video by reconstructing the Chrome cache file left on local device such as a PC. This can be seen as evidence that the user watched illegal video content. From a different point of view, copyright infringement occurs when a malicious user restores video stream and share it to another site. In this paper, we selected 23 online streaming services that are widely used both at home and abroad. After streaming videos, we tested whether we can recover original video using cache files stored on the PC or not. As a result, the paper found that in most sites we can restore the original video by reconstructing cache files. Furthermore, this study also discussed methodologies for preventing copyright infringement in online streaming service.

• The Journal of Society for e-Business Studies
• /
• v.14 no.4
• /
• pp.19-33
• /
• 2009

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through the reviews. Product Reviews are results expressing customer's sentiments and thus are divided into positive reviews and negative ones. However, as the number of reviews in on-line shopping increases, it is inefficient or sometimes impossible for users to read all the relevant review documents. In this paper, we present a sentiment analysis algorithm for automatically classifying subjective opinions of customer's reviews using opinion mining technology. The proposed algorithm is to focus on product reviews of on-line shopping, and provides summarized results from large product review data by determining whether they are positive or negative. Additionally, this paper introduces an automatic review analysis system implemented based on the proposed algorithm, and also present the experiment results for verifying the efficiency of the algorithm.

• Journal of Korea Multimedia Society
• /
• v.11 no.8
• /
• pp.1129-1137
• /
• 2008

E-learning as a new education trend is being applied not only to cyber school but also various education fields such as employee training for companies or interactive learning for consumers. Users of the E-learning can take online tests individually anywhere, to evaluate their achievement level. Because users who are taking the online tests may show their own IDs or passwords to others, the possibility of cheating is very high. Therefore, it is very important to authenticate the users. In this paper, we propose an adaptive-keyboard authentication method which depends on user behavior patterns through the use of IDs and passwords. This method does not need any additional devices or special effort. An adaptive method to update patterns in which IDs and passwords are entered was previously suggested and this new method has proved to be better than previous methods through simulations and implementation.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.12 no.1
• /
• pp.157-164
• /
• 2012

This paper introduces an implementation of SVC player which runs on Android platform and can play SVC video stream on line from SVC video server. SVC(Scalable Video Coding) is a scalable video encoding technique which supports three scalability such as temporal scalability, spatial scalability, and quality scalability. To implement the SVC player on Android, we implemented a SVC decoder using JSVM open source written in C/C++ as a native part on Android and developed Android UI in Java. Also we built an SVC encoding system off line and an SVC streaming server to conduct on-line SVC streaming experiments. Finally, after we installed the SVC player developed in this paper on Motoroi mobile phone, we evaluated and analyzed on-line streaming performance of the SVC player. The result showed that the player worked well and it had no jitter in streaming with the size of QCIF and 10fps from a fully encoded SVC video source.

• Design Convergence Study
• /
• v.16 no.3
• /
• pp.69-84
• /
• 2017

Service blueprint is used to show the interaction between each service element at a glance and to understand the flow of the whole service centering on the customer at the stage of proposing a new service system. It was proposed in the 1980s before online business was developed. However, current services are changing in a way that provides various forms and channels, and the service blueprint seems to be not enough. To reflect this problem consciousness, we selected PSS among diversified service business models and propose a service blueprint type optimized for each business. After collecting 137 PSS cases to be used in the research, we made a business matrix and classified the cases and selected two representative cases to conduct two experiments. As a result, six types of service blueprint corresponding to the matrix could be derived: online service type, online remote support type, self rental type, online order type, traditional type, and offline support type. The validity of the proposed types of service blueprint was verified to confirm the suitability of those types.

• Journal of Korea Multimedia Society
• /
• v.12 no.11
• /
• pp.1652-1660
• /
• 2009

The main objective of this research is to build a behavior prediction model of gameplay for MMO (Massively Multiplayer Online) game using the GOMS analysis method. GOMS analysis is an observational approach to HCI(Human Computer Interaction) to model and predict behaviors of a human operator in a highly interactive task. In this research, a pilot experiment was previously conducted with three skilled gamers. The gamers were provided with the goals and operators through the user's guide book, and they found methods and selection rules while being observed. Based on the results obtained from the pilot study, this research was expanded and the model was further tested with 30 subjects (game experts). The new outcomes revealed that the relevance of GOMS analysis for predicting selection rules is 96.25% according to the degree of abstraction and 77.35% based on the degree of complexity. This research will provide game designers with a new testing mechanism in the early development stages, in order to improve the quality of the game product.