• Korean Journal of School Psychology
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• v.8 no.3
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• pp.379-403
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• 2011

This study examined coping strategies and perceived changes of the Chinese international students in South Korea. A total of 56 Chinese students participated and data was analyzed using the concept mapping method. The results showed that Chinese international students adopted developing language proficiency and using social networks as important coping strategies. They also perceived a wide range of changes including changes in the cultural and personal self. However, the perception of these coping strategies and changes differed by the length of stay. Chinese international students who stayed longer reported using specific coping strategies for mood regulation, which was distinctive from the coping strategies used by students who stayed for a shorter period of time. Students who stayed longer perceived negative changes as well as positive changes, while students who stayed for a shorter period reported predominantly positive changes. The findings indicate that university staff needs to understand and sensitively respond to the distinctive experiences of Chinese international students which vary across time.

• Journal of Information Technology Services
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• v.22 no.5
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• pp.99-108
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• 2023

As the computerization of hospitals becomes more advanced, security issues regarding data generated from various medical devices within hospitals are gradually increasing. For example, because hospital data contains a variety of personal information, attempts to attack it have been continuously made. In order to safely protect data from external attacks, each hospital has formed an internal team to continuously monitor whether the computer network is safely protected. However, there are limits to how humans can monitor attacks that occur on networks within hospitals in real time. Recently, artificial intelligence models have shown excellent performance in detecting outliers. In this paper, an experiment was conducted to verify how well an artificial intelligence model classifies normal and abnormal data in network traffic data generated from medical devices. There are several models used for outlier detection, but among them, Random Forest and Tabnet were used. Tabnet is a deep learning algorithm related to receive and classify structured data. Two algorithms were trained using open traffic network data, and the classification accuracy of the model was measured using test data. As a result, the random forest algorithm showed a classification accuracy of 93%, and Tapnet showed a classification accuracy of 99%. Therefore, it is expected that most outliers that may occur in a hospital network can be detected using an excellent algorithm such as Tabnet.

• Korean Journal of Community Nutrition
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• v.25 no.5
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• pp.416-429
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• 2020

Objectives: This study examined the personal, interpersonal and community factors related to food sufficiency and variety among Korean adults using data from the 2017 Community Health Survey. Methods: A total of 228,310 adults aged ≥ 19 years were classified into three groups: food sufficiency with variety, food sufficiency without variety and food insufficiency. Personal factors included sociodemographic characteristics, health behavior and health status. Interpersonal factors included social networking and social activities, and community factors included safety, natural environment, living environment, availability of public transportation and health care services. The association of food sufficiency and variety with interpersonal and community factors was assessed using multivariable logistic regression analyses. Results: Of the total sample, the food-sufficiency-without-variety group and food insufficiency group accounted for 31.5% and 3.2%, respectively. The sociodemographic factors associated with food insufficiency and non-variety were women, ≥ 65 years of age, with low education level, low household income, unemployed, single, and living in areas of small population sizes. There were significant differences in health behavior and health status, interpersonal and community factors among the three groups. Multivariable logistic regression analyses conducted after adjusting for confounding factors showed that lack of social networking and social activities and lower satisfaction derived from community environments were associated with the risk of food insufficiency and non-variety. Conclusions: Our results showed that interpersonal and community factors as well as personal factors were related to food sufficiency and variety. Therefore, public policies to help build social networks and participation in social activities, and improve community environment are needed together with food assistance to overcome the problems of food insufficiency and non-variety.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.163-177
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• 2019

As smartphones are getting widely used, human activity recognition (HAR) tasks for recognizing personal activities of smartphone users with multimodal data have been actively studied recently. The research area is expanding from the recognition of the simple body movement of an individual user to the recognition of low-level behavior and high-level behavior. However, HAR tasks for recognizing interaction behavior with other people, such as whether the user is accompanying or communicating with someone else, have gotten less attention so far. And previous research for recognizing interaction behavior has usually depended on audio, Bluetooth, and Wi-Fi sensors, which are vulnerable to privacy issues and require much time to collect enough data. Whereas physical sensors including accelerometer, magnetic field and gyroscope sensors are less vulnerable to privacy issues and can collect a large amount of data within a short time. In this paper, a method for detecting accompanying status based on deep learning model by only using multimodal physical sensor data, such as an accelerometer, magnetic field and gyroscope, was proposed. The accompanying status was defined as a redefinition of a part of the user interaction behavior, including whether the user is accompanying with an acquaintance at a close distance and the user is actively communicating with the acquaintance. A framework based on convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks for classifying accompanying and conversation was proposed. First, a data preprocessing method which consists of time synchronization of multimodal data from different physical sensors, data normalization and sequence data generation was introduced. We applied the nearest interpolation to synchronize the time of collected data from different sensors. Normalization was performed for each x, y, z axis value of the sensor data, and the sequence data was generated according to the sliding window method. Then, the sequence data became the input for CNN, where feature maps representing local dependencies of the original sequence are extracted. The CNN consisted of 3 convolutional layers and did not have a pooling layer to maintain the temporal information of the sequence data. Next, LSTM recurrent networks received the feature maps, learned long-term dependencies from them and extracted features. The LSTM recurrent networks consisted of two layers, each with 128 cells. Finally, the extracted features were used for classification by softmax classifier. The loss function of the model was cross entropy function and the weights of the model were randomly initialized on a normal distribution with an average of 0 and a standard deviation of 0.1. The model was trained using adaptive moment estimation (ADAM) optimization algorithm and the mini batch size was set to 128. We applied dropout to input values of the LSTM recurrent networks to prevent overfitting. The initial learning rate was set to 0.001, and it decreased exponentially by 0.99 at the end of each epoch training. An Android smartphone application was developed and released to collect data. We collected smartphone data for a total of 18 subjects. Using the data, the model classified accompanying and conversation by 98.74% and 98.83% accuracy each. Both the F1 score and accuracy of the model were higher than the F1 score and accuracy of the majority vote classifier, support vector machine, and deep recurrent neural network. In the future research, we will focus on more rigorous multimodal sensor data synchronization methods that minimize the time stamp differences. In addition, we will further study transfer learning method that enables transfer of trained models tailored to the training data to the evaluation data that follows a different distribution. It is expected that a model capable of exhibiting robust recognition performance against changes in data that is not considered in the model learning stage will be obtained.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.499-508
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• 2018

This research aimed to determine what male nursing students experienced in the course of serving in a medical care unit in the military and how this experience affected the nursing curriculum after they returned to school. This study was intended to provide basic information on nursing education for male nursing students. This qualitative research conducted a focus group interview to gather comprehensive data that are common among the subjects, in which 15 male nursing college students who experienced serving in a medical unit were divided into three focus groups. The results of this study identified five themes: 'recognition of the various role of a medic', 'caring ability and management of rare diseases', 'adaptation to different works in each military ranks', 'recognition of health care networks', and 'prerequisite learning of school subjects'. It was concluded that experience as a medic during military service helped to develop various capabilities, and these competencies were shown to guide students to demonstrate their abilities in a school environment or help accomplish their given tasks with confidence and establish interpersonal relationships as nurses in the future. The results of this study provide a theoretical basis for demonstrating that male nursing student's experience as a medic during military service aids to reinforce not only personal and academic capabilities but also the skills needed as a nurse.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.1
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• pp.181-195
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• 2015

Financial industries have operated internal and external network with an unified system for continual business process of customers and other organizations in the past. The financial supervising authority requires more technical and managerial protecting policy to financial industries related to the exposure as danger of external attacks or information leakage. Financial industries performed network separation into internal business and external internet networks for protecting IT assets from malware infection accessing internet or hacking attacks and prohibiting leakage of customers' personal and financial information following financial supervising authority and redefine security policy to fit on network separated-condition. In this study, effectiveness for network separation policy was examined on malware inflow and verified that malware inflow in all routes can be blocked by the policy with analyzing operration data of a financial company, estimating network separation. Result of this study proves that malware infection route by portable storages was not completely blocked even on adapting network-separated condition. As a solution for this, efficient security policy would be suggested in this paper as controlling portable storages for maximizing effectiveness of network separation.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.11 no.4
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• pp.71-78
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• 2016

Religion can affect on every day lives of individuals in society. Also, religion can affect on personal networks and the formation of a social culture that may lead to influence individual decision makers. Religion can influence values and ethics of people in society. However, according to the secularization hypothesis, as an economy becomes more efficient and the members of a society becomes more educated, the influence and control of religion over people tend to become diminished more. How is religion related with entrepreneurship? There are not so many empirical studies that examine relationship between religion and entrepreneurship. The purpose of this article is that we empirically examine how religious commitment, in addition to individual traits such as need for achievement, perceived creativity, problem solving ability and entrepreneurial parents or friends. Our study is based on survey sample of 229 college students in Korea. The results we found are as follows. First, religious commitment does not have effect on entrepreneurial intention. Also, religion commitment has no relationship with need for achievement, perceived creativity, problem solving ability, and entrepreneurial parents or friends. Second, consistent with previous studies, need for achievement and entrepreneurial parents or friends have meaningful effect on entrepreneurial intention. Third, perceived creativity is a full mediation variable between need for achievement and entrepreneurial intention.

• Journal of Distribution Science
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• v.11 no.6
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• pp.41-50
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• 2013

Purpose - Inducing consumers' behavioral intent to use an outlet shopping center is a critical issue for managers since it can be used as a guide for developing marketing strategies. Low prices could lead to a growth in retail purchases, but there might also be a positive relationship between prices and customer perceptions of product quality. The extent to which consumers use price as a predictor of quality may differ according to the availability of important alternative cues such as brand, store name, and identity salience triggered by the store. Consumers can obtain non-economic benefits from marketing exchanges that go beyond basic economic achievement. We argue that identity salience can play a crucial mediating role when consumers, acting as exchange partners, seek to obtain social benefits. This study shows that identity salience could mediate the relationship between identity salience-inducing factors such as multi-finality, prestige and role performance, and consumers' behavioral intent to use an outlet shopping center. Research design, data and methodology - The survey was conducted on college students enrolled in marketing classes. A total of 200 questionnaires were distributed, of which only 194 were returned. After five incomplete questionnaires were excluded, a final sample of 189 was used for empirical analysis. Using a covariance structural analysis in Amos17, we confirmed the fit of the research model and estimated its parameters by using the maximum likelihood method. Results - The results of the hypotheses testing are as follows. First, both identity salience and economic benefits have positive effects on the behavioral intent to use an outlet shopping center. Second, role performance, prestige, and multi-finality have positive effects on identity salience. Finally, the additive analysis of the direct effects of identity salience-inducing factors shows that the role performance, prestige, and multi-finality factors have no direct effects on the behavioral intent to use an outlet shopping center, suggesting that identity salience plays a positive mediating role. Conclusions - This study informs marketers that not only price but shoppers' identity salience directly affects their intent to visit an outlet shopping center. To strengthen shoppers' identity salience, marketers should find ways to help shoppers fulfill their multiple social roles, realize their multiple goals, and achieve prestige. In other words, outlet shopping centers must improve their personal service environment in order to enhance their employees' service quality and assist the execution of multi-finality by minimizing the perceived costs (e.g., travel time, effort) associated with shopping trips, thus making it easier for consumers to combine visits to multiple stores in outlet shopping centers and buy the items required for their consumption goals. Outlet shopping centers must also offer assortments with both breadth and depth in order to help consumers play the social roles their social networks have given them.

• Cartoon and Animation Studies
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• s.40
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• pp.423-442
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• 2015

In a digital environment that is keep in change content from smart phones to tablet PC are a social network service is holding deep place in our lives. Social networking applications has building a network and communication with others than other application, it means that Social networking applications are sharing not only personal purpose in that trend of variety and competition of these social networks can be expected to trend and be developed thru analysis of user certification. this study of social network service application is proposed to developing of application thru analyze the two-effective application which is high ranked in google store. the theoretical foundation was set based on the seven elements of the social network service of the information structures designed by Jean Smith. This study proceeded analysis is for the functional elements of Facebook and Instagram, and the advantages and disadvantages through survey research. As a result of the empirical analysis to user of Instagram and face book of communication, identity, satisfaction for the group are equally. Instagram is about the presence, reputation, and Facebook has had a high level of satisfaction for each sharing and relationship. Facebook got high satisfaction from sharing features, but user feel of discomfort in the randomly showing advertising content. Instagram is not showing off advertise on common page of content, it is good point to be complementary to facebook. And, Instagram hashtag is good for convenience, but satisfaction is high with Facebook. in order to increase the satisfaction of Instagram, it is necessary to consider the main advantage of the communication and the functional aspects of the share from facebook.