• Journal of KIISE:Computer Systems and Theory
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• v.33 no.12
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• pp.885-893
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• 2006

The Bayesian network(BN) has emerged in recent years as a powerful technique for handling uncertainty iii complex domains. Parameter learning of BN to find the most proper network from given data set has been investigated to decrease the time and effort for designing BN. Off-line learning needs much time and effort to gather the enough data and since there are uncertainties in real world, it is hard to get the complete data. In this paper, we propose an online learning method of Bayesian network parameters from incomplete data. It provides higher flexibility through learning from incomplete data and higher adaptability on environments through online learning. The results of comparison with Voting EM algorithm proposed by Cohen at el. confirm that the proposed method has the same performance in complete data set and higher performance in incomplete data set, comparing with Voting EM algorithm.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.185-214
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• 2009

The purpose of this study is aimed to clarify the usefulness of meta search system using Open API of library online catalog by constructing OPAC-based search system using Open API of library online catalog and meta search system using Open API of library online catalog, and comparing the usability of the two experimental search systems. As for usability, on the whole, it was higher in meta search system using Open API of library online catalog than OPAC-based search system using Open API of library online catalog, and there was statistically significant difference. Therefore, if libraries share and use enriched content which is provided through Open API for book search, which is opened by Internet bookstores, search engines and Web portals, it is expected that it will be helpful in enhancing bibliographic data, expanding subject access point, empowering subject search ability, extending meta search service, improving book availability, and reducing catalog cost.

• Journal of Internet Computing and Services
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• v.19 no.5
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• pp.89-96
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• 2018

In online communities, a large number of participants can exchange their opinion using replies without time and space restrictions. While the online space provides quick and free communication, it also easily triggers unnecessary quarrels and conflicts. The network established on the discussion participants is an important cue to analyze the confrontation and predict serious disputes. In this paper, we present a quantitative measure for polarity observed on the discussion network built from reply exchanges in online communities. The proposed method uses the comment exchange information to establish the user interaction network graph, computes its maximum spanning tree, and then performs vertex coloring to assign two colors to each node in order to divide the discussion participants into two subsets. Using the proportion of the comment exchanges across the partitioned user subsets, we compute the polarity measure, and quantify how discussion participants are bipolarized. Using experimental results, we demonstrate the effectiveness of our method for detecting polarization and show participants of a specific discussion subject tend to be divided into two camps when they debate.

• The Journal of the Korea Contents Association
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• v.20 no.1
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• pp.117-126
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• 2020

This study was conducted to explore the effect of the type of dashboard on online group discussion activities based on learning analysis. The experimental research was conducted among 51 learners from a university by dividing them into 2 groups. Group A provided participation and interaction dashboard, and group B provided the discussion topics and message type dashboard. First, pre-tests were conducted on attitudes toward computer writing and the level of motivation that could affect online discussion activities. Then the students participated three different topics of online group discussions. The participation and interaction data were automatically collected through the dashboard, and learning outcome data were collected through post-tests. The results showed level of participation in Group B (M=47.56, SD=2.37) that provided discussion topics and message type dashboard was significantly higher than the level of participation in Group A (M=38.13, SD=2.21) that provided participation and interaction dashboard. On the other hand, there were no differences in the level of interaction and learning outcomes. In future studies, we suggest that the dashboard effects based on the learners' characteristics should be carried out because the learners' characteristics may affect the use of the dashboard.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.189-196
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• 2022

Skyline query is a scheme for exploring objects that are suitable for user preferences based on multiple attributes of objects. Existing skyline queries return search results as batch processing, but the need for real-time search results has increased with the advent of interactive apps or mobile environments. Online algorithm for Skyline improves the return speed of objects to explore preferred objects in real time. However, the object navigation process requires unnecessary navigation time due to repeated comparative operations. This paper proposes a Pre-processing Online Algorithm for Skyline Query (POA) to eliminate unnecessary search time in Online Algorithm exploration techniques and provide the results of skyline queries in real time. Proposed techniques use the concept of range-limiting to existing Online Algorithm to perform pretreatment and then eliminate repetitive rediscovering regions first. POAs showed improvement in standard distributions, bias distributions, positive correlations, and negative correlations of discrete data sets compared to Online Algorithm. The POAs used in this paper improve navigation performance by minimizing comparison targets for Online Algorithm, which will be a new criterion for rapid service to users in the face of increasing use of mobile devices.

• The Journal of the Convergence on Culture Technology
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• v.8 no.6
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• pp.463-470
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• 2022

The purpose of this study was to identify the effects of online practicum based on action learning on self-directed learning ability, academic self-efficacy, and cooperation ability of nursing students. Similar experimental study of single group pre-post test design was used to investigate the effects of this online class. A total of 47 nursing students who were junior grade from one college participated in this study and self-administered questionnaire was used for data collection. Data were analyzed by frequencies, paired t test using the SPSS/Window 21.0 program. The result of this study showed that the improvement after online practicum based on action learning was significant in self-directed learning ability(t=3.832, p<.001), academic self-efficacy(t=4.258, p<.001), and cooperation ability(t=3.853, p<.001). These findings imply the value of online practicum based on action learning to enhance competency of nursing students. In the future, more studies should be conducted in the same group including control group and blended learning method using action learning to validate the effectiveness.

• Information Systems Review
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• v.23 no.3
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• pp.127-158
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• 2021

Many online travel agencies (OTAs) provide average ratings and time-relevant information or the most recently posted reviews regarding hotels to satisfy customers. To identify these two factors' relative influence on behavioral decision-making processes, we conducted two studies: (1) an experimental research design to explore the relative influence of the two on online review consumption and (2) an empirical approach to examine their relative impact on online review generation. The results show that when review posters observe an inconsistency between average ratings and recent reviews, they tend to deviate from the recent reviews regardless of the overall direction (reactance behavior). Meanwhile, review consumers tend to conform to the opinions presented in recent reviews (herding behavior). Additionally, in both cases, the effects are amplified in case of a negative aberration. Based on the findings, this study provides theoretical and practical implications regarding the relative influences of average rating and recently posted reviews and their different impacts on online review consumption and generation.

• Information Systems Review
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• v.25 no.1
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• pp.27-46
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• 2023

Research on restaurant recommender systems has been proposed due to the development of the food service industry and the increasing demand for restaurants. Existing restaurant recommendation studies extracted consumer preference information through quantitative information or online review sensitivity analysis, but there is a limitation that it cannot reflect consumer semantic preference information. In addition, there is a lack of recommendation research that reflects the detailed attributes of restaurants. To solve this problem, this study proposed a model that can learn the interaction between consumer preferences and restaurant attributes by applying deep learning techniques. First, the convolutional neural network was applied to online reviews to extract semantic preference information from consumers, and embedded techniques were applied to restaurant information to extract detailed attributes of restaurants. Finally, the interaction between consumer preference and restaurant attributes was learned through the element-wise products to predict the consumer preference rating. Experiments using an online review of Yelp.com to evaluate the performance of the proposed model in this study confirmed that the proposed model in this study showed excellent recommendation performance. By proposing a customized restaurant recommendation system using big data from the restaurant industry, this study expects to provide various academic and practical implications.

• Journal of Engineering Education Research
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• v.6 no.2
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• pp.5-14
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• 2003

In this paper, we developed an efficient virtual experiment kit with creative and interactive multimedia contents, which can be used to enhance the quality of education in the area of digital logic circuits. Since our virtual experiment kit is implemented to describe the on-campus laboratory, the learners can obtain similar experimental data through it. Also, our web-based virtual experiment kit is designed to enhance the efficiency of both the learners and the educators. The learners will be able to achieve high learning standard and the educators save time and labor. The virtual experiment is performed according to the following procedure: (1) Circuit Composition on the Bread Board (2) Applying Input Voltage (3) Output Measurements (4) Checkout of Experiment Results. Furthermore, the circuit composition on the bread board and its corresponding online schematic diagram are displayed together on the virtual experiment kit for the learner's convenience. Finally, we have obtained several affirmative effects such as reducing the total experimental hours and the damage rate for experimental equipments and increasing learning efficiencies as well as faculty productivity.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.75-88
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• 2020

Given the development of information technology, various methods for efficient information delivery have been constructed as the method of delivering product information moves from offline and 2D to online and 3D. These attempts not only are about delivering product information in an online space where no real product exists but also play a crucial role in diversifying and revitalizing online shopping by providing virtual experiences to consumers. 360 product image is a photorealistic VR that allows a subject to be rotated and photographed to view objects in three dimensions. 360 product image has also attracted considerable attention considering that it can deliver richer information about an object compared with the existing still image photography. 360 product image is influenced by divergent production factors, and accordingly, a difference emerges in the responses of users. However, as the history of technology is short, related research is also insufficient. Therefore, this study aimed to grasp the responses of users, which vary depending on the type of products and the number of source images in the 360 product image process. To this end, a representative product among the product groups that can be frequently found in online shopping malls was selected to produce a 360 product image and experiment with 75 users. The emotional responses to the 360 product image were analyzed through an experimental questionnaire to which the semantic classification method was applied. The results of this study could be used as basic data to understand and grasp the sensitivity of consumers to 360 product image.