• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.222-224
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• 2007

Social participation of the elderly and the disabled continuously becomes more active due to the improvement of social systems and technological development. Various systems such as intelligence robots and intelligence home systems have been developed to support their social participation, and those systems obviously contribute to the independent lives of the elderly and the disabled. Those systems, however, usually require special hardware, which make them very expensive. Considering the economic difficulties of the users, the problem should be tackled with software-oriented approaches using existing hardware such as laptops. The software should be adapted to users with limited capabilities by enabling them to utilize the system without much knowledge related to computers and also without keyboards and mice. This paper suggests software-oriented approaches to solve those problems with the description of the unified user interface for a special work chair, and introduces an actual development procedure of the program together with real application of related theories.

• Journal of Digital Convergence
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• v.9 no.4
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• pp.191-200
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• 2011

Smart work is a new paradigm of the way of working for knowledge workers. We propose a model which determines the levels of smart work. As the determinants, we identify freedom from both space and time. As an extension of the model by adding a new determinant of the mobilization of social intelligence, we show that a new smart work model, smart work 2.0, can be derived. For successful implementation of smart work 2.0, we also provide strategic suggestions, along with the implication of the study and future research direction.

• Korean Journal of Artificial Intelligence
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• v.10 no.2
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• pp.1-6
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• 2022

In this paper, the research and development aim to strengthen the digital accessibility of the elderly by developing a kiosk incorporating AI voice recognition technology that can replace the promotional signage currently being installed and spread in the elderly and social welfare centers most frequently used by the digital underprivileged. It was intended to develop a converged system for the use of bulletin board functions, educational functions, and welfare center facilities, and to seek ways to increase the user's digital device experience through direct experience and education. Through interviews and surveys of senior citizens and social welfare centers, it was intended to collect problems and pain Points that the elderly currently experience in the process of using kiosks and apply them to the development process, and improve problems through pilot services. Through this study, it was confirmed that voice recognition technology is 2 to 6 times faster than keyboard input, so it is helpful for the elderly who are not familiar with device operation. However, it is necessary to improve the problem that there is a difference in the accuracy of the recognition rate according to the surrounding environment with noise. Through small efforts such as this study, we hope that the elderly will be a little free from digital alienation.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.677-687
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• 2022

We propose a detection algorithm based on tree-structured convolutional neural networks (TsCNNs) that finds pornography, propaganda, or other inappropriate content on a social media network. The algorithm sequentially applies the typical convolutional neural network (CNN) algorithm in a tree-like structure to minimize classification errors in similar classes, and thus improves accuracy. We implemented the detection system and conducted experiments on a data set comprised of 6 ordinary classes and 11 inappropriate classes collected from the Korean military social network. Each model of the proposed algorithm was trained, and the performance was then evaluated according to the images and videos identified. Experimental results with 20,005 new images showed that the overall accuracy in image identification achieved a high-performance level of 99.51%, and the effectiveness of the algorithm reduced identification errors by the typical CNN algorithm by 64.87 %. By reducing false alarms in video identification from the domain, the TsCNNs achieved optimal performance of 98.11% when using 10 minutes frame-sampling intervals. This indicates that classification through proper sampling contributes to the reduction of computational burden and false alarms.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.522-527
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• 2008

Socially Intelligent agent is the agent not only having the ability to recognize and to process human affect through learning and adaptation, but also having human-like social intelligence. By making human feel familiar with the computer, the agent is expected to enhance human-computer interaction (HCI) by providing users with the personalized services and interfaces. This paper proposes the framework for socially intelligent agents behaving socially according to the emotions recognized by ID3 algorithm and psychological OCC model. Also, the agent could process with the emotion to make socially intelligent response through three layered affect functioning model. Finally, the proposed agent can be applied for the development and application of socially intelligent agent in wide areas as the agent framework having similar affect and cognitive structure with human being.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• Journal of Hospice and Palliative Care
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• v.18 no.2
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• pp.112-119
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• 2015

Purpose: The purpose of this study was to explore the level of occupational stress and emotional intelligence in hospice volunteers. Methods: Participants were 158 hospice volunteers at hospitals in B city. Occupational stress, emotional intelligence, and general characteristics of hospice volunteers were measured. The data were analyzed with descriptive statistics, t-tests, ANOVA, and Pearson's correlation coefficients. Results: The mean score for occupational stress was 2.16 points out of 4 and 4.65 out of 7 for emotional intelligence. The scores for occupational stress and emotional intelligence were significantly different by educational level and monthly income in hospice volunteers. Occupational stress was negatively correlated with emotional intelligence (r=-0.196, P=0.013). Conclusion: A continuous educational program is needed to offer volunteers with new hospice-related trends. To ensure quality care for patients and their families, it should be helpful to encourage hospice volunteers to build a social network to enhance their emotional intelligence.

• Journal of Digital Convergence
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• v.15 no.7
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• pp.307-317
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• 2017

This study was to identify the factors influencing college life adjustment and sub-scales of nursing students. Self- report questionnaire surveys were conducted toward 282 freshman nursing students to measure college life adjustment, psychological well-being, emotional intelligence, and self-efficacy. Data were collected from September 22 through October 7, 2016. This study was analyzed using SPSS Win 18.0. The average mean of college life adjustment was 3.36 and academic activity was the highest, followed by individual psychology, social experience, Interpersonal relationship, and career preparation. A correlation of psychological well-being, emotional intelligence, self-efficacy, college life adjustment and sub-scales showed positive correlation. The strongest predictor of college life adjustment was a self-efficacy. And sub-scales, the strongest predictor of academic activity was academic achievement, career preparation was self-efficacy, individual psychology and social experience was emotional intelligence, and Interpersonal relationship was psychological well-being. An intervention program which includes these significant variables of subjects is essential to improve of college life adjustment.

• The Journal of Society for e-Business Studies
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• v.23 no.4
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• pp.19-39
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• 2018

Recently, fake news has attracted worldwide attentions regardless of the fields. The Hyundai Research Institute estimated that the amount of fake news damage reached about 30.9 trillion won per year. The government is making efforts to develop artificial intelligence source technology to detect fake news such as holding "artificial intelligence R&D challenge" competition on the title of "searching for fake news." Fact checking services are also being provided in various private sector fields. Nevertheless, in academic fields, there are also many attempts have been conducted in detecting the fake news. Typically, there are different attempts in detecting fake news such as expert-based, collective intelligence-based, artificial intelligence-based, and semantic-based. However, the more accurate the fake news manipulation is, the more difficult it is to identify the authenticity of the news by analyzing the news itself. Furthermore, the accuracy of most fake news detection models tends to be overestimated. Therefore, in this study, we first propose a method to secure the fairness of false news detection model accuracy. Secondly, we propose a method to identify the authenticity of the news using the social data broadly generated by the reaction to the news as well as the contents of the news.

• 한국정보교육학회:학술대회논문집
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• 2011.01a
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• pp.233-240
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• 2011

Social Media is the open online tool and media platform for sharing and participation of users opinion, experience, viewpoiont, so general situation that is one-side flowing from production to consume doesn't act, and while use of two-way, user create contents use of sharing and participation. This social media include Blog, Social Network Service(SNS), Wiki, User Create Contents(UCC), Micro Blog, 5 types. In broad terms, Social Learning is self-learning that user sharing with coperation and collective intelligence through Social Media, and in few wards Social Learning is learning for Social Media. In this research, we define Social Media and Social Learning, and research of method of use of Elementary Education.