• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.197-209
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• 2024

This study aimed to analyze recent research trends in Artificial Intelligence (AI) education within South Korea with the overarching objective of exploring the future direction of AI education. For this purpose, an analysis of 697 papers related to AI education published in Research Information Sharing Service (RISS) from 2016 to November 2023 were analyzed using word cloud and Latent Dirichlet Allocation (LDA) topic modeling technique. As a result of the analysis, six major topics were identified: generative AI utilization education, AI ethics education, AI convergence education, teacher perceptions and roles in AI utilization, AI literacy development in university education, and AI-based education and research directions. Based on these findings, I proposed several suggestions, (1) including expanding the use of generative AI in various subjects, (2) establishing ethical guidelines for AI use, (3) evaluating the long-term impact of AI education, (4) enhancing teachers' ability to use AI in higher education, (5) diversifying the curriculum of AI education in universities, (6) analyzing the trend of AI research, and developing an educational platform.

• Information Systems Review
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• v.25 no.3
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• pp.27-41
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• 2023

Unlike the previous works focusing on the state-of-the-art methodologies to improve the performance of machine learning models, this study improves the 'quality' of training data used in machine learning. We propose a method to enhance the quality of training data through the processing of 'local grammar,' frequently used in corpus analysis. We collected a vast amount of unstructured corporate review text data posted by employees working in the top 100 companies in Korea. After improving the data quality using the local grammar process, we confirmed that the classification model with local grammar outperformed the model without it in terms of classification performance. We defined five factors of work engagement as classification categories, and analyzed how the pattern of reviews changed before and after the COVID-19 pandemic. Through this study, we provide evidence that shows the value of the local grammar-based automatic identification and classification of employee experiences, and offer some clues for significant organizational cultural phenomena.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.71-89
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• 2016

Sentiment analysis is used for identifying emotions or sentiments embedded in the user generated data such as customer reviews from blogs, social network services, and so on. Various research fields such as computer science and business management can take advantage of this feature to analyze customer-generated opinions. In previous studies, the star rating of a review is regarded as the same as sentiment embedded in the text. However, it does not always correspond to the sentiment polarity. Due to this supposition, previous studies have some limitations in their accuracy. To solve this issue, the present study uses a supervised sentiment classification model to measure a more accurate sentiment polarity. This study aims to propose an advanced sentiment classifier and to discover the correlation between movie reviews and box-office success. The advanced sentiment classifier is based on two supervised machine learning techniques, the Support Vector Machines (SVM) and Feedforward Neural Network (FNN). The sentiment scores of the movie reviews are measured by the sentiment classifier and are analyzed by statistical correlations between movie reviews and box-office success. Movie reviews are collected along with a star-rate. The dataset used in this study consists of 1,258,538 reviews from 175 films gathered from Naver Movie website (movie.naver.com). The results show that the proposed sentiment classifier outperforms Naive Bayes (NB) classifier as its accuracy is about 6% higher than NB. Furthermore, the results indicate that there are positive correlations between the star-rate and the number of audiences, which can be regarded as the box-office success of a movie. The study also shows that there is the mild, positive correlation between the sentiment scores estimated by the classifier and the number of audiences. To verify the applicability of the sentiment scores, an independent sample t-test was conducted. For this, the movies were divided into two groups using the average of sentiment scores. The two groups are significantly different in terms of the star-rated scores.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.235-242
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• 2014

Internet telephony is an Internet service which supports voice telephone using VoIP technology on the IP-based Internet. It has some advantages in that voice telephone services can be accompanied with multimedia services such as video communication and messaging services. In this paper we suggested an Asterisk-based Internet telephony system which can be easily scalable. Most current systems use text files to manage their configuration: SIP users, dialplans, IVR service and etc. But we designed the management system which introduces database tables for efficiency and scalability. It also supports web-based functions developed by using Asterisk, Apache, MySQL, jQuery, PHP and open source softwares.

• Journal of Korean Library and Information Science Society
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• v.44 no.1
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• pp.5-26
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• 2013

Although organizing standardized metadata is important for effective management of cultural heritage information, current metadata are represented differently according to the properties of the resources or objectives of the organizations in which they are accumulated. This research compared 6 different metadata formats and created 18 data elements for constructing databases. A religious architecture heritage information database was constructed based on 72 historic religious architectures, each composing of three parts. An ontology based on religious architecture heritage information was designed using a revised CIDOC-CRM, and was developed with a semi-automated corpus program.

• Journal of Digital Convergence
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• v.10 no.5
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• pp.267-273
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• 2012

The semantic relationship structures hierarchically the huge amount of document objects which is usually not formatted. However, it is very difficult to structure relevant data from various distributed application domains. This paper proposed a new object management method to service the distributed domain objects by using semantic reference relationship. The proposed mechanism utilized the profile structure in order to extract the semantic similarity from application domain objects and utilized the joint matrix to decide the semantic relationship of the extracted objects. This paper performed the simulation to show the performance of the proposed method, and simulation results show that the proposed method has better retrieval performance than the existing text mining method and information extraction method.

• Journal of Digital Contents Society
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• v.17 no.6
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• pp.545-553
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• 2016

In this study, we propose an information providing and learning system using augmented reality in the exhibition (museum, performance) environment. In a typical exhibition space, a description of a picture or a photograph is provided in a printed matter or a space in the form of an information space (explanatory space), or an auxiliary explanation using an 'audio guide' or a 'docent' program. Augmented reality technology is applied to the exhibition space in the form of a fusion of these methods, and the description of the exhibition is provided to the user in various forms such as text, picture, audio and image, thereby providing stereoscopic information and learning. We apply the augmented reality technology in a specific exhibition space and utilize it as a tool of providing information and learning using image description of pictures.

• Journal of the Korean association of regional geographers
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• v.22 no.3
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• pp.730-744
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• 2016

This study aims to seek for solution to reinforce affective domain in World Geography instruction using travelog. The result can be summarized as follows. First, The World Geography textbooks are given too much emphasis on cognitive domain. This overevaluation is due to the fact that official World Geography curriculum is concentrated in cognitive domain. Second, Travelogs can be effectively used for reinforcing affective domain in World Geography education. They can reinforce the various attitudes and values that students need. I hope that this study could activate discussion on affective domain and graphic skills in geography education.

• Journal of Information Technology Services
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• v.14 no.2
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• pp.159-176
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• 2015

As a public medium and one of the big data sources that is accumulated informally and real time, online news comments or replies are considered a significant resource to understand mentalities of article readers. The comments are also being regarded as an important medium of WOM (Word of Mouse) about products, services or the enterprises. If the diffusing effect of the comments is referred to as the degrees of agreement and disagreement from an angle of WOM, figuring out which characteristics of the comments would influence the agreements or the disagreements to the comments in very early stage would be very worthwhile to establish a comment-based eWOM (electronic WOM) strategy. However, investigating the effects of the characteristics of the comments on eWOM effect has been rarely studied. According to this angle, this study aims to conduct an empirical analysis which understands the characteristics of comments that affect the numbers of agreement and disagreement, as eWOM performance, to particular news articles which address a specific product, service or enterprise per se. While extant literature has focused on the quantitative attributes of the comments which are collected by manually, this paper used text mining techniques to acquire the qualitative attributes of the comments in an automatic and cost effective manner.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.111-115
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• 2017

목적 지향적 대화 시스템(Goal-oriented dialogue system) 은 텍스트나 음성을 통해 특정한 목적을 수행 할 수 있는 시스템이다. 최근 RNN(recurrent neural networks)을 기반으로 대화 데이터를 end-to-end learning 방식으로 학습하여 대화 시스템을 구축하는데에 활용한 연구가 있다. End-to-end 방식의 학습은 도메인에 대한 지식 없이 학습 데이터 자체만으로 대화 시스템 구축을 위한 학습이 가능하다는 장점이 있지만 도메인 지식을 학습하기 위해서는 많은 양의 데이터가 필요하다는 단점이 존재한다. 이에 본 논문에서는 도메인 특정 지식을 결합하여 end-to-end learning 방식의 학습이 가능한 Hybrid Code Network 구조를 기반으로 한국어로 구성된 식당 예약에 관련한 대화 데이터셋을 이용하여 식당 예약을 목적으로하는 대화 시스템을 구축하는 방법을 제안한다. 실험 결과 본 시스템은 응답 별 정확도 95%와 대화 별 정확도 63%의 성능을 나타냈다.