• Journal of Digital Convergence
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• v.16 no.11
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• pp.281-288
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• 2018

The current study examined the moderating effect of visual cues in eWOM on the relationship between perceived risk and purchase intention. Specifically, the study tested the different directions of the moderating effect in positive and negative eWOM. Two studies from a 2 (perceived risk: high vs. low) by 2 (visual cue: presence vs. absence) experimental design were used with online subjects. Findings from study 1 (n=123) supported that visual cues in positive eWOM help to reduce the negative effect of perceived risk on purchase intention. However, study 2 (n=122) showed that visual cues in negative eWOM intensify the negative effect of perceived risk on purchase intention. The findings demonstrated that visual cues in eWOM influence consumers' decision under high risk conditions. We discussed findings of this study how visual cues in positive and negative eWOM can be strategically managed for new online sellers.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.341-348
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• 2021

This study identified learners' immersion, focusing on online video education platforms, Zoom and Webex, used in colleges after the 'Covid-19', and suggested design improvement measures to improve immersion. Through prior research and literature research, the components of immersion and screen components of the online distance education platform were identified, and measures to improve immersion were suggested through questionnaire surveys and in-depth interviews. The research method was conducted for 5 days from April 7 to 12, 2021 for 50 college students and graduate students in their 20s and 30s who are receiving online education through Zoom and Webex, and 6 people were interviewed in-depth. As a result of the experiment, the communication between learners and lecturers was deduced as the biggest factor, so a design plan to facilitate communication between learners and lecturers was proposed based on Gutenberg's diagram. As online video education is predicted to continue even after the Covid-19, continuous online video education immersion research is needed, and we hope that it can contribute to the direction of the research.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.473-478
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• 2022

Evaluation learning based on code testing is becoming a popular solution in programming education via Online judge(OJ). In the recent past, many papers have been published on how to detect plagiarism through source code similarity analysis to support OJ. However, deep learning-based research to support automated tutoring is insufficient. In this paper, we propose Input & Output side FiLM models to predict whether the input code will pass or fail. By applying Feature-wise Linear Modulation(FiLM) technique to GRU, our model can learn combined information of Java byte codes and problem information that it tries to solve. On experimental design, a balanced sampling technique was applied to evenly distribute the data due to the occurrence of asymmetry in data collected by OJ. Among the proposed models, the Input Side FiLM model showed the highest performance of 73.63%. Based on result, it has been shown that students can check whether their codes will pass or fail before receiving the OJ evaluation which could provide basic feedback for improvements.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.6
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• pp.267-272
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• 2022

Online exams are not limited by time and space. It has the advantage that it does not require a separate exam site for examinees, and there is no time and cost required to move to the exam site. However, the online exam has the disadvantage that various cheating is possible because the exam is conducted in an individual environment. In addition, there is a difficulty in detecting cheating due to the lack of exam supervision methods. In addition, since the exam process and result data exist only as digital data, it is inconvenient to check directly on the server where the exam result is stored in order to check whether the exam result is forged or not. If the data related to the exam is maliciously changed, the authenticity cannot be verified. In this study, we tried to increase the reliability of the online exam by developing a blockchain-based online exam cheating detection system that stores exam progress-related data in the blockchain to detect cheating. Through the experiment, it was confirmed that forgery and falsification are detected as a result of the exam.

• Journal of Digital Convergence
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• v.15 no.12
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• pp.169-185
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• 2017

Given that nowadays things get very fast due to the pervasive use of the Internet and mobile devices, decision-making time can be an important variable in the online economic decisions. Although in experimental and behavioral economics, measures like scores or earnings are usually preferred, this study argues that the time variable can be dealt with as a new decision outcome. Thus, by selecting some psychological factors presumably impactful in the online context (i.e., incidental emotions, psychological distances, and individual's impulsivity), this study tested their effect on decision time in the online centipede game. As a result, the mean decision time in the game was longer (1) in the happiness condition than in the anger condition and (2) in the friend condition than in the stranger condition. The people with attention difficulties spent a short time in the decision and the people who dislike complex problems spent a short time in explaining their decision. This study can contribute to the field as it used the decision time as the dependent variable and it tested the effect of psychological factors in the context of online decision-making. Future studies can be conducted in other online decision situations or by considering other psychological variables.

• The KIPS Transactions:PartB
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• v.13B no.5 s.108
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• pp.515-524
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• 2006

Background extraction is an important technique to find the moving objects in video surveillance system. This paper proposes a new on-line background extraction method for color video using vector order statistics. In the proposed method, using the fact that background occurs more frequently than objects, the vector median of color pixels in consecutive frames Is treated as background at the position. Also, the objects of current frame are consisted of the set of pixels whose distance from background pixel is larger than threshold. In the paper, the proposed method is compared with the on-line multiple background extraction based on Gaussian mixture model(GMM) in order to evaluate the performance. As the result, its performance is similar or superior to the method based on GMM.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.102-106
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• 2013

레스토랑 리뷰 분석을 위해서는 음식메뉴 개체명 인식이 매우 중요하다. 그러나 현재의 개체명 사전을 이용하여 리뷰 분석을 할 경우 구체적이고 복잡한 음식메뉴명을 표현하는데 충분하지 않으며 지속적인 업데이트가 힘들어 새로운 트렌드의 음식 메뉴명 등이 반영되지 않는 문제가 있다. 본 논문에서는 레스토랑 전문 사이트와 레시피 제공 사이트에서 각 레스토랑의 메뉴 정보와 음식명 등을 래퍼기반 웹 크롤러로 수집하였다. 그런 다음 빈도수가 낮은 음식메뉴와 레스토랑 온라인 리뷰에서 쓰이지 않는 음식메뉴를 제거하여 레스토랑 음식 메뉴 사전을 자동으로 구축하였다. 그리고 레스토랑 온라인 리뷰 문서를 이용해 음식 메뉴 사전의 엔티티들이 어느 유형의 레스토랑 리뷰에서 발견되는지를 찾아 빈도수를 구하고 분류 정보에 따른 비율을 사전에 추가하였다. 이 정보를 이용해 여러 분류 유형에 해당되는 음식메뉴를 구분할 수 있다. 실험 결과 한국관광공사 외국어 용례사전의 음식 메뉴명은 1,104개의 메뉴가 실제 레스토랑 리뷰에서 쓰인데 비해 본 논문에서 구축한 사전은 1,602개의 메뉴가 실제 레스토랑 리뷰에서 쓰여 498개의 어휘가 더 구성되어 있는 것을 확인 할 수 있었다. 이와 아울러, 자동으로 수집한 메뉴의 정확도와 재현율을 분석한다. 실험 결과 정확률은 96.2였고 재현율은 78.4, F-Score는 86.4였다.

• Journal of Broadcast Engineering
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• v.23 no.2
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• pp.186-196
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• 2018

In this paper, we propose an on-line tracking method using convolutional neural network (CNN) for tracking object. It is well known that a large number of training samples are needed to train the model offline. To solve this problem, we use an untrained model and update the model by collecting training samples online directly from the test sequences. While conventional methods have been used to learn models by training samples offline, we demonstrate that a small group of samples are sufficient for online object tracking. In addition, we define a loss function containing color information, and prevent the model from being trained by wrong training samples. Experiments validate that tracking performance is equivalent to four comparative methods or outperforms them.

• Journal of Internet Computing and Services
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• v.10 no.6
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• pp.123-143
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• 2009

This study examined factors of presence using the experimental method. The design of this study was to analyze presence through the dimensions of character agents(text, voice, 2D, 3D, reality character dimension and gender) for e-learning platforms that used new technology-based content. There were 232 experimental participants in the study. This study was designed to measure their cognitive awareness of presence by agent dimensions in the first level to measure the presence level in the types of users. The results showed that there were significant correlation between types of users and presence. However there were no statistically significant results on dimensions. In addition, there were significant differences on character gender, voice or non-voice, text or non-text and character dimensions.

• Journal of the Korean Society for Library and Information Science
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• v.31 no.3
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• pp.165-208
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• 1997

The purpose of this study is to evaluate the characteristics of user interface that affect the searching behavior of OPAC users. and then to propose how to design user-friendly interfaces of OPACS. An experiment was conducted on two systems with different interfaces to grasp the effect of user interface to search process and search outcome. A $2\times2$ cross-over design was used for the experiment. Sixty five searchers participated in the experiment. Several statistical techniques such as carry-over effect and system effect of a $2\times2$ cross-over design, $\chi^2$ test, t- test, McNemar test, test of marginal homogeneity through maximum likelihood method, factor analysis, regression analysis, and analysis of variance were applied according to the hypotheses tested and the data analyzed.