• Journal of The Korean Association of Information Education
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• v.25 no.6
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• pp.947-960
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• 2021

In this study, we examine the AI ethics perception of university students to explore the direction of AI ethics education. For this, 83 students wrote their thoughts about 5 discussion topics on online bulletin board. We analyzed it using language networks, one of the text mining techniques. As a result, 62.5% of students spoke the future of the AI society positively. Second, if there is a self-driving car accident, 39.2% of students thought it is the vehicle owner's responsibility at the current level of autonomous driving. Third, invasion of privacy, abuse of technology, and unbalanced information acquisition were cited as dysfunctions of the development of AI. It was mentioned that ethical education for both AI users and developers is required as a way to minimize malfunctions, and institutional preparations should be carried out in parallel. Fourth, only 19.2% of students showed a positive opinion about a society where face recognition technology is universal. Finally, there was a common opinion that when collecting data including personal information, only the part with the consent should be used. Regarding the use of AI without moral standards, they emphasized the ethical literacy of both users and developers. This study is meaningful in that it provides information necessary to design the contents of artificial intelligence ethics education in liberal arts education.

• Archives of design research
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• v.18 no.3 s.61
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• pp.63-74
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• 2005

Modern society is diversified society and is under complicated situation as the boundary of each area has been disappeared. To understand and accept such complicated situation as widely as possible, it is required to understand interaction. of receiver with intertextual environmental arts as the structure of open text. This study examined interaction of environmental arts in terms of intertextual feature based on experience of receiver on combined element of different space and time, combination of genres. This is the concept of meaning personal experience or situation as receiver participates the process of completing art works, and set the fantasy, post-place and speciality of location and temporal-spatial expression method, as characteristics of intertextuality. Features of such experience elements are used as methodology of analyzing characteristics of each work. Feature of fantasy uses strategy of inducing spatial experience of receiver with dematerialization for post-place and expands the place where events occur with intervention of contingency and event situation. It suggests the spatial-temporal expression method as the features focusing on process and reflecting changes in spatial-temporal continuum and speciality of place emphasizing context of place. In conclusion, environmental arts needs to be deep rooted on complicated existence aspect of receiver beyond metaphysical dimension depending on presence and to accomplish conversion of awareness of supplying bisection of life from that place. By doing so, environmental arts can live textual life as it gets together with all other texts in terms of text dimension and creativity can be reborn as practical creativity in intertextuality rather than uniqueness. Such combination with other areas and acceptance of various aspects of receivers who see and experience this will result to creation of open works which can be create newly over and over again in multi-dimensional aspects.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.227-248
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• 2017

This paper introduces an integrated model for systematically constructing a linguistic resource database that can be used by machine learning-based biomedical information extraction systems. The proposed method suggests an orderly process of collecting and constructing dictionaries and training sets for both named-entity recognition and relation extraction. Multiple heterogeneous structures for the resources which are collected from diverse sources are analyzed to derive essential items and fields for constructing the integrated database. All the collected resources are converted and refined to build an integrated linguistic resource storage. In this paper, we constructed entity dictionaries of gene, protein, disease and drug, which are considered core linguistic elements or core named entities in the biomedical domains and conducted verification tests to measure their acceptability.

• Journal of Digital Contents Society
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• v.17 no.4
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• pp.219-226
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• 2016

In our general life, what we recognize information with our human eyes and use it is diverse and massive. But even the current technologies improved by artificial intelligence are exorbitantly deficient comparing to human visual processing ability. Nevertheless, many researchers are trying to get information in everyday life, especially concentrate effort on recognizing information consisted of text. In the fields of recognizing text, to extract the text from the general document is used in some information processing fields, but to extract and recognize the text from real image is deficient too much yet. It is because the real images have many properties like color, size, orientation and something in common. In this paper, we applies an adaptive edge enhanced MSER(Maximally Stable Extremal Regions) to extract the text area in those diverse environments and the scene text, and show that the proposed method is a comparatively nice method with experiments.

• Journal of Korean Society of Archives and Records Management
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• v.17 no.4
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• pp.225-250
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• 2017

The Fourth Industrial Revolution has become a focus of attention. Artificial intelligence (AI) is the key technology that will lead us to the industrial revolution. AI is also used to facilitate efficient workflow in records and archives management area, particularly abroad. In this study, we introduced the concept of AI and examined the background on how it rose. Then we reviewed the various applications of AI with prominent examples. We have also examined how AI is used in various areas such as text analysis, and image and speech recognition. In each of these areas, we have reviewed the application of AI from the viewpoint of records and archives management and suggested further utilization of the methods, including module and interface for intelligent records and archives information services.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.4
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• pp.285-307
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• 2019

In 2011, the Korean Medical Association revised the name of schizophrenia to remove the social stigma for the sick. Although it has been about nine years since the revision of the terminology, no studies have quantitatively analyzed how much social awareness has changed. Thus, this study investigates the changes in social awareness of schizophrenia caused by the revision of the disease name by analyzing Naver news articles related to the disease. For text analysis, LDA topic modeling, TF-IDF, word co-occurrence, and sentiment analysis techniques were used. The results showed that social awareness of the disease was more negative after the revision of the terminology. In addition, social awareness of the former term among two terms used after the revision was more negative. In other words, the revision of the disease did not resolve the stigma.

• Journal of the Korea Society of Computer and Information
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• v.26 no.9
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• pp.115-123
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• 2021

This study aims to analyze the changes in public perception of abandoned dogs through big data analysis. Data from January 2017 to July 2020 were collected to analyze how the quantitative change in social issues with abandoned dogs as a keyword had an effect on public perception of abandoned dogs, and factors that influence positive/negative perceptions. As a result of the study, it was confirmed that the number of stray dogs and the number of documents related to stray dogs had a positive correlation, and specific time series changes were found through various analysis techniques such as text mining, network analysis, and sentiment analysis. This study will have significance as basic data that can be used for policy establishment or other research on abandoned dogs. we hope it will help to solve problems so as to improve awareness of abandoned dogs and develop a sense of responsibility.

• Journal of The Korean Association of Information Education
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• v.26 no.6
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• pp.553-566
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• 2022

This study collects social data and academic research data from portal sites and RISS, and analyzes TF-IDF, N-Gram, semantic network analysis, and CONCOR analysis to analyze the social awareness and current aspects of 'AI Literacy' and 'AI Convergence Education'. Through this, we tried to understand the social awareness aspect and the current situation, and to suggest implications and directions. In the social data, the collection of 'AI Convergence Education' was more than twice that of 'AI Literacy', indicating that awareness of 'AI Literacy' was relatively low. In 'AI Literacy', the keyword 'human' in social data showed no cluster to which it belonged, indicating a lack of philosophical interest in and awareness of humanities and AI. In addition, the keyword 'Ministry of Education' showed high frequency, importance, and centrality of connection only in the social data of 'AI convergence education', confirming that 'AI convergence education' is closely related to government policy.

• The Transactions of the Korea Information Processing Society
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• v.13 no.4
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• pp.174-180
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• 2024

In this paper, we explore an emotion classification method through multimodal learning utilizing wav2vec 2.0 and KcELECTRA models. It is known that multimodal learning, which leverages both speech and text data, can significantly enhance emotion classification performance compared to methods that solely rely on speech data. Our study conducts a comparative analysis of BERT and its derivative models, known for their superior performance in the field of natural language processing, to select the optimal model for effective feature extraction from text data for use as the text processing model. The results confirm that the KcELECTRA model exhibits outstanding performance in emotion classification tasks. Furthermore, experiments using datasets made available by AI-Hub demonstrate that the inclusion of text data enables achieving superior performance with less data than when using speech data alone. The experiments show that the use of the KcELECTRA model achieved the highest accuracy of 96.57%. This indicates that multimodal learning can offer meaningful performance improvements in complex natural language processing tasks such as emotion classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.1694-1697
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• 2012

음성 인식은 인간이 발성하는 음성을 컴퓨터 프로그램을 이용하여 문자 정보로 변환하는 기술이다. 음성은 사람마다 각기 다르기 때문에 인식률도 각각 차이가 나게 되어 범용 인터페이스로 사용되기에는 적합하지 않다. 하지만 최근 구글, 다음 등 대형 포털을 중심으로 서버 기반의 음성 인식 서비스가 제공되면서 사용자 인터페이스로 음성을 이용하는 것이 주요 이슈로 부각되고 있다. 카카오톡과 같은 메신저 프로그램은 네트워크를 이용하여 그룹 내의 사용자들 간에 메시지를 주고받는다. 여기에 사용되는 터치 자판은 간격이 좁아서 오타가 많이 발생하고, 긴 문장을 입력할 때 시간이 많이 걸리며, 운전 중에 사용할 경우 사고 위험이 높아지는 단점이 있다. 이러한 문제들을 해결하기 위해서는 음성 인식 인터페이스를 접목하는 것이 이상적이다. 본 논문에서는 음성 인식 기반의 스마트폰용 모바일 메신저 프로그램을 설계 및 구현하였다. 외부의 음성 인식 서버를 이용하여 음성을 인식하고, 인식된 음성을 텍스트로 변환하며, 채팅 서버를 통해 메시지를 전달한다.