• The Journal of the Korea Contents Association
• /
• v.21 no.11
• /
• pp.64-76
• /
• 2021

Culture technology (CT) becomes important in the recent environment where digital technology drives content-based innovations. However, technological trends of CT have not been systematically discussed. Especially, the trends of CT should be analyzed from the national perspective, because CT has grown with the help of government-driven innovation. Therefore, this paper aims to analyze CT trends focusing on national research projects. We collected data on CT from the national science and technology information service (NTIS) database, analyzed the keyword co-occurrence network, and identified the patterns of technological innovation using a clustering analysis. As a result, we found that CT has contributed to the digital content and cultural media, and has been actively developed with the help of machine learning technique. Especially, due to the rise of Covid-19, the non-face-to-face online content is rapidly increasing. This study provides important clues for understanding, analyzing CT trends.

• Journal of the Korea Convergence Society
• /
• v.13 no.2
• /
• pp.21-29
• /
• 2022

Based on the importance of dialect research, preservation, and communication, this paper conducted a study on machine translation of Korean dialects for dialect users who may be marginalized. For the dialect data used, AIHUB dialect data distributed based on the highest administrative district was used. We propose a many-to-one dialect machine translation that promotes the efficiency of model distribution and modeling research to improve the performance of the dialect machine translation by applying Copy mechanism. This paper evaluates the performance of the one-to-one model and the many-to-one model as a BLEU score, and analyzes the performance of the many-to-one model in the Korean dialect from a linguistic perspective. The performance improvement of the one-to-one machine translation by applying the methodology proposed in this paper and the significant high performance of the many-to-one machine translation were derived.

• Journal of Digital Convergence
• /
• v.20 no.2
• /
• pp.269-275
• /
• 2022

As artificial intelligence services targeting humans increase, social demands are increasing that artificial intelligence should also be made on an ethical basis. Following this trend, the government and businesses are preparing policies and norms related to artificial intelligence ethics. In order to establish reasonable policies and norms, the first step is to understand the public's perceptions. In this paper, social data and news comments were collected and analyzed to understand the public's perception related to artificial intelligence and ethics. Interest analysis, emotional analysis, and discourse analysis were performed and visualized on the collected datasets. As a result of the analysis, interest in "artificial intelligence ethics" and "artificial intelligence" favorability showed an inversely proportional correlation. As a result of discourse analysis, the biggest issue was "personal information leakage," and it also showed a discourse on contamination and deflection of learning data and whether computer-made artificial intelligence should be given a legal personality. This study can be used as data to grasp the public's perception when preparing artificial intelligence ethical norms and policies.

• Journal of Korean Society for Quality Management
• /
• v.50 no.4
• /
• pp.751-776
• /
• 2022

Purpose: The purpose of this paper is to present implications by analyzing research trends on CSR, CSV and ESG by text analysis and visual analysis(Comprehensive/ Fields / Years-based) which are big data analyses, by collecting data based on previous studies on CSR, CSV and ESG. Methods: For the collection of analysis data, deep learning was used in the integrated search on the Academic Research Information Service (www.riss.kr) to search for "CSR", "CSV" and "ESG" as search terms, and the Korean abstracts and keyword were scrapped out of the extracted paper and they are organize into EXCEL. For the final step, CSR 2,847 papers, CSV 395 papers, ESG 555 papers derived were analyzed using the Rx64 4.0.2 program and Rstudio using text mining, one of the big data analysis techniques, and Word Cloud for visualization. Results: The results of this study are as follows; CSR, CSV, and ESG studies showed that research slowed down somewhat before 2010, but research increased rapidly until recently in 2019. Research have been found to be heavily researched in the fields of social science, art and physical education, and engineering. As a result of the study, there were many keyword of 'corporate', 'social', and 'responsibility', which were similar in the word cloud analysis. Looking at the frequent keyword and word cloud analysis by field and year, overall keyword were derived similar to all keyword by year. However, some differences appeared in each field. Conclusion: Government support and expert support for CSR, CSV and ESG should be activated, and researches on technology-based strategies are needed. In the future, it is necessary to take various approaches to them. If researches are conducted in consideration of the environment or energy, it is judged that bigger implications can be presented.

• Journal of Korea Port Economic Association
• /
• v.37 no.3
• /
• pp.1-17
• /
• 2021

Forecasting the daily volume of container is important in many aspects of port operation. In this article, we utilized a machine-learning algorithm based on decision tree to predict future container throughput of Busan port. Accurate volume forecasting improves operational efficiency and service levels by reducing costs and shipowner latency. We showed that our method is capable of accurately and reliably predicting container throughput in short-term(days). Forecasting accuracy was improved by more than 22% over time series methods(ARIMA). We also demonstrated that the current method is assumption-free and not prone to human bias. We expect that such method could be useful in a broad range of fields.

• Journal of Industrial Convergence
• /
• v.20 no.2
• /
• pp.29-36
• /
• 2022

Computational thinking in the AI and Big Data era for digital society means a series of problem-solving methods that involve expressing problems and their solutions in ways that computers can execute. Computational thinking is an approach to solving problems, designing systems, and understanding human behavior by deriving basic concepts in computer science, and solving difficult problems and elusive puzzles for students. We recently studied 93 pre-service teachers who are currently a freshman at ◯◯ university. The results of the first semester class, the participants created a satisfactory algorithm of the video level. Also, the proposed model was found to contribute greatly to the understanding of the computational thinking of the students participating in the class.

• Journal of Internet Computing and Services
• /
• v.23 no.6
• /
• pp.87-96
• /
• 2022

This paper is an empirical analysis as a reference model that can predict up to the maximum number of elementary school student care needs in local governments across the country. This study analyzed and predicted the characteristics of the region based on machine learning to predict the demand for elementary care in a new apartment complex. For this purpose, a total of 292 variables were used, including data related to apartment structure, such as number of parking spaces per household, and building-to-land ratio, environmental data around apartments such as distance to elementary schools, and population data of administrative districts. The use of various variables is of great significance, and it is meaningful in complex analysis. It is also an empirical case study that increased the reliability of the model through comparison with the actual value of the basic local government.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.81-94
• /
• 2022

Real estate and artworks were considered challenging investment targets for individual investors because of their relatively high average transaction price despite their long investment history. Recently, the so-called fractional investment, generally known as investing in a share of the ownership right for real-life assets, etc., and most investors perceive that they actually own a piece (fraction) of the ownership right through their investments, is gaining popularity. Founded in 2016, Musicow started the first service that allows users to invest in copyright fees related to music distribution. Using the LSTM algorithm, one of the deep learning algorithms, this research predict the price of right to participate in copyright fees traded in Musicow. In addition to variables related to claims such as transfer price, transaction volume of claims, and copyright fees, comprehensive indicators indicating the market conditions for music copyright fees participation, exchange rates reflecting economic conditions, KTB interest rates, and Korea Composite Stock Index were also used as variables. As a result, it was confirmed that the LSTM algorithm accurately predicts the transaction price even in the case of fractional investment which has a relatively low transaction volume.

• Journal of The Korean Association of Information Education
• /
• v.25 no.6
• /
• pp.927-934
• /
• 2021

AI(Artificial intelligence) is gradually taking up a large part of our lives, and the pace of AI development is accelerating. It is called ACT that develop students' computational thinking in the way artificial intelligence learns. Among ACTs, pattern recognition is an essential factor in efficiently solving problems. Pattern analysis is part of the pattern recognition process. In fact, Netflix's personalized movie recommendation service and what it named Covid-19 after repeated symptoms are all the results of pattern analysis. While the importance of ACT, including pattern recognition, is highlighted, software education for kindergarten and elementary school lower grades is much insufficient compared to foreign countries. Therefore, this study aims to design and develop textbooks for the development of artificial intelligence-based computational thinking through pattern analysis for kindergarten students.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.5
• /
• pp.817-826
• /
• 2022

AI technology is being successfully introduced in many fields, and models deployed as a service are deployed with black box environment that does not expose the model's information to protect intellectual property rights and data. In a black box environment, attackers try to steal data or parameters used during training by using model output. This paper proposes a method of inferring the type of model to directly find out the composition of layer of the target model, based on the fact that there is no attack to infer the information about the type of model from the deep learning model. With ResNet, VGGNet, AlexNet, and simple convolutional neural network models trained with MNIST datasets, we show that the types of models can be inferred using the output values in the gray box and black box environments of the each model. In addition, we inferred the type of model with approximately 83% accuracy in the black box environment if we train the big and small relationship feature that proposed in this paper together, the results show that the model type can be infrerred even in situations where only partial information is given to attackers, not raw probability vectors.