• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.39-57
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• 2012

Recently, due to the introduction of high-tech equipment in interactive exhibits, many people's attention has been concentrated on Interactive exhibits that can double the exhibition effect through the interaction with the audience. In addition, it is also possible to measure a variety of audience reaction in the interactive exhibition. Among various audience reactions, this research uses the change of the facial features that can be collected in an interactive exhibition space. This research develops an artificial neural network-based prediction model to predict the response of the audience by measuring the change of the facial features when the audience is given stimulation from the non-excited state. To present the emotion state of the audience, this research uses a Valence-Arousal model. So, this research suggests an overall framework composed of the following six steps. The first step is a step of collecting data for modeling. The data was collected from people participated in the 2012 Seoul DMC Culture Open, and the collected data was used for the experiments. The second step extracts 64 facial features from the collected data and compensates the facial feature values. The third step generates independent and dependent variables of an artificial neural network model. The fourth step extracts the independent variable that affects the dependent variable using the statistical technique. The fifth step builds an artificial neural network model and performs a learning process using train set and test set. Finally the last sixth step is to validate the prediction performance of artificial neural network model using the validation data set. The proposed model is compared with statistical predictive model to see whether it had better performance or not. As a result, although the data set in this experiment had much noise, the proposed model showed better results when the model was compared with multiple regression analysis model. If the prediction model of audience reaction was used in the real exhibition, it will be able to provide countermeasures and services appropriate to the audience's reaction viewing the exhibits. Specifically, if the arousal of audience about Exhibits is low, Action to increase arousal of the audience will be taken. For instance, we recommend the audience another preferred contents or using a light or sound to focus on these exhibits. In other words, when planning future exhibitions, planning the exhibition to satisfy various audience preferences would be possible. And it is expected to foster a personalized environment to concentrate on the exhibits. But, the proposed model in this research still shows the low prediction accuracy. The cause is in some parts as follows : First, the data covers diverse visitors of real exhibitions, so it was difficult to control the optimized experimental environment. So, the collected data has much noise, and it would results a lower accuracy. In further research, the data collection will be conducted in a more optimized experimental environment. The further research to increase the accuracy of the predictions of the model will be conducted. Second, using changes of facial expression only is thought to be not enough to extract audience emotions. If facial expression is combined with other responses, such as the sound, audience behavior, it would result a better result.

• The Journal of Society for e-Business Studies
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• v.21 no.1
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• pp.147-163
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• 2016

Gender information of a smart device user is essential to provide personalized services, and multi-modal data obtained from the device is useful for predicting the gender of the user. However, the method for utilizing each of the multi-modal data for gender prediction differs according to the characteristics of the data. Therefore, in this study, an ensemble method for predicting the gender of a smart device user by using three classifiers that have text, application, and acceleration data as inputs, respectively, is proposed. To alleviate privacy issues that occur when text data generated in a smart device are sent outside, a classification method which scans smart device text data only on the device and classifies the gender of the user by matching text data with predefined sets of word. An application based classifier assigns gender labels to executed applications and predicts gender of the user by comparing the label ratio. Acceleration data is used with Support Vector Machine to classify user gender. The proposed method was evaluated by using the actual smart device log data collected from an Android application. The experimental results showed that the proposed method outperformed the compared methods.

• Management & Information Systems Review
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• v.29 no.4
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• pp.305-332
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• 2010

This study was searching for elements affecting satisfaction of customers by comparing health management service businesses such as wellbeing care, yoga, and fitness. The discovered elements were analyzed and verified to find which elements are affecting what businesses through case studies. Multidirectional analysis was implemented for each service type using program, physical environment, and provided service drawn from the previous researches with SERVQUAL criteria and measured values on customer satisfactions. According to the analysis, physical environment in forest wellbeing care, program in yoga, and provided service in fitness were the most affecting elements. Thus, each health management service business must consider the lifestyle and trend of customers, and the specialized service corresponding to its uniqueness must be provided to customers. Surely, modernized exercise equipment, personalized program, and comfortable-luxurious settings are must have in order to be competitive. In addition, the business owners have to realize that customers are moving to quality from quantity. This means exercise must be brought up to the level of social value for relationship and links rather than left at the level of simple physical and mental trainings. To achieve these, other programs to support relationship among customers and circulating system with friendly environment must be considered at the same time.

• The Journal of the Korea Contents Association
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• v.16 no.10
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• pp.72-80
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• 2016

In this paper, we did not observe only the actual condition of accounting education, but also proposed the improvements for better education in commercial high schools in Jeju. We expect that those enable the high school students to be interested in accounting. These days, crisis of the accounting education is not only caused by poor educational system, but also is caused in large part by lack of efforts for improving the accounting education. In order to improve the quality of the accounting education and enable the students to be interested in accounting, it is necessary to understand why the students feel difficult about accounting. Learning the reasons enables teachers to educate by much more personalized curriculum, so that those will provide the opportunities to enable the students to pay more attention to accounting. we analyzed the realities in educating accounting by conducting a survey for the students. We also proposed the improvements based on the results of analyzing the realities of the accounting educations. The improvements are followings. First, the accounting education should be educated through various ways. Second, curriculum fitted to the high school students level should be developed. Third, subjects related with accounting should be improved by considering efficiency.

• The Journal of Bigdata
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• v.5 no.1
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• pp.29-39
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• 2020

Purpose The atmosphere of respect for individual tastes that have spread throughout society has changed the consumption trend. As a result, the travel industry is also seeing customized travel as a new trend that reflects consumers' personal tastes. In particular, there is a growing interest in 'film-induced tourism', one of the areas of travel industry. We hope to satisfy the individual's motivation for traveling while watching movies with customized travel proposals, which we expect to be a catalyst for the continued development of the 'film-induced tourism industry'. Design/methodology/approach In this study, we implemented a methodology through 'OCR' of extracting and suggesting film location information that viewers want to visit. First, we extract a scene from a movie selected by a user by using 'OpenCV', a real-time image processing library. In addition, we detected the location of characters in the scene image by using 'EAST model', a deep learning-based text area detection model. The detected images are preprocessed by using 'OpenCV built-in function' to increase recognition accuracy. Finally, after converting characters in images into recognizable text using 'Tesseract', an optical character recognition engine, the 'Google Map API' returns actual location information. Significance This research is significant in that it provides personalized tourism content using fourth industrial technology, in addition to existing film tourism. This could be used in the development of film-induced tourism packages with travel agencies in the future. It also implies the possibility of being used for inflow from abroad as well as to abroad.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.1-14
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• 2014

Both researchers and practitioners are showing an increased interested in interactive exhibition services. Interactive exhibition services are designed to directly respond to visitor responses in real time, so as to fully engage visitors' interest and enhance their satisfaction. In order to install an effective interactive exhibition service, it is essential to adopt intelligent technologies that enable accurate estimation of a visitor's emotional state from responses to exhibited stimulus. Studies undertaken so far have attempted to estimate the human emotional state, most of them doing so by gauging either facial expressions or audio responses. However, the most recent research suggests that, a multimodal approach that uses people's multiple responses simultaneously may lead to better estimation. Given this context, we propose a new multimodal emotional state estimation model that uses various responses including facial expressions, gestures, and movements measured by the Microsoft Kinect Sensor. In order to effectively handle a large amount of sensory data, we propose to use stratified sampling-based MRA (multiple regression analysis) as our estimation method. To validate the usefulness of the proposed model, we collected 602,599 responses and emotional state data with 274 variables from 15 people. When we applied our model to the data set, we found that our model estimated the levels of valence and arousal in the 10~15% error range. Since our proposed model is simple and stable, we expect that it will be applied not only in intelligent exhibition services, but also in other areas such as e-learning and personalized advertising.

• Journal of The Korean Association of Information Education
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• v.23 no.5
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• pp.395-413
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• 2019

The purpose of this study is to explore non-major students' perception and experiences in college software education. For this, we analyzed the reflection journals of 36 non-major students in D University based on the Consensual Qualitative Research(CQR). As a result, there was not general core concept to all students nor a typical core concept that appeared to more than 50% students. However, various variable core concepts could be derived. Overall, 57 variable concepts were derived from experience in SW education and 7 variable concepts for perception of SW education. Based on this result, we found many of non-major students feel difficulty from unfamiliarity to SW education. Also, many students have satisfaction in their perception to SW education about personalized learning that their professor provided in the class. Lastly, we conclude that a methodology for SW education needs to have a careful operation strategy and interactive design. Although this study has not been able to elucidate general core concepts that appear to all learners, it has significant implication in terms of providing various implicit core concepts and suggestions for effective software education for non-major students.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.357-380
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• 2019

The purpose of this study is to analyze the Elementary and Secondary Education Act(ESEA) of the U.S. reauthorized by the Every Student Succeeds Act in 2015 from the viewpoint of the library and information services, and to derive implications for improving the library and information services. For the first time, ESEA includes effective school library programs and school librarians, and links school library programs and school librarians with literacy, digital literacy, books, resources, up-to-date materials, technology, library services and educational services. It provides a financial and institutional base for library and information services in elementary and secondary schools of the US to be more conducted. In addition, school librarians are required to participate in personalized learning experiences, evidence-based assessments, and professional development in the law, so school librarians must provide library and information services to students, staff, and parents in order to improve student achievement and digital literacy. Based on these analyses, this study discussed strengthening access to the school library, specifying the work of the teacher-librarian's library and information services, strengthening collaboration with school members, educational activities based on evidence base, sharing educational effects and developing of library and information curriculum.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.2
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• pp.207-216
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• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.