• Journal of Intelligence and Information Systems
• /
• v.29 no.2
• /
• pp.323-338
• /
• 2023

TV advertising with deep analysis of watching pattern of audiences is important to set-top box audience targeting. Applying session-based recommendation model(SBR) to internet commercial, or recommendation based on searching history of user showed its effectiveness in previous studies, but applying SBR to the TV advertising was difficult in South Korea due to data unavailabilities. Also, traditional SBR has limitations for dealing with user preferences, especially in data with user identification information. To tackle with these problems, we first obtain set-top box data from three major broadcasting companies in South Korea(SKB, KT, LGU+) through collaboration with Korea Broadcast Advertising Corporation(KOBACO), and this data contains of watching sequence of 4,847 anonymized users for 6 month respectively. Second, we develop personalized session-based recommendation model to deal with hierarchical data of user-session-item. Experiments conducted on set-top box audience dataset and two other public dataset for validation. In result, our proposed model outperformed baseline model in some criteria.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.953-964
• /
• 2023

Climate change is undeniably the most urgent challenge that humanity faces today. Despite this, the level of public awareness and understanding of climate change remains insufficient, indicating a need for more proactive education and the development of supportive content. In particular, it is crucial to intensify climate change education during elementary and secondary schooling when values and ethical consciousness begin to form. However, there is a significant lack of age-appropriate, experiential educational content. To address this, our study has developed an innovative 3D simulator, enabling learners to indirectly experience the effects of climate change, specifically sea-level rise. This simulator considers not only sea-level rise caused by climate change but also storm surges, which is a design based on the analysis of long-term wave observation big data. To make the simulator accessible and engaging for students, we utilized the 'Unity' game engine. We further propose using this simulator as a part of a comprehensive educational program on climate change.

• Journal of Cadastre & Land InformatiX
• /
• v.53 no.2
• /
• pp.53-66
• /
• 2023

Recently, there has been a significant interest in the development of autonomous driving simulation environment based on digital twin. In the development of such digital twin-based simulation environment, many researches has been conducted not only performance and functionality validation of autonomous driving, but also generation of virtual training data for deep learning. However, such digital twin-based autonomous driving simulation system has the problem of requiring a significant amount of time and cost for the system development and the data construction. Therefore, in this research, we aim to propose a method for rapidly designing and implementing a digital twin-based autonomous driving simulation system, using only the existing 3D models and high-definition map. Specifically, we propose a method for integrating 3D model of FBX and NGII HD Map for the Busan EDC area into CARLA, and a method for adding and modifying CARLA functions. The results of this research show that it is possible to rapidly design and implement the simulation system at a low cost by using the existing 3D models and NGII HD map. Also, the results show that our system can support various functions such as simulation scenario configuration, user-defined driving, and real-time simulation of traffic light states. We expect that usability of the system will be significantly improved when it is applied to broader geographical area in the future.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.2
• /
• pp.207-216
• /
• 2024

As big data was built due to the 4th Industrial Revolution, personalized services increased rapidly. As a result, the amount of personal information collected from online services has increased, and concerns about users' personal information leakage and privacy infringement have increased. Online service providers provide privacy policies to address concerns about privacy infringement of users, but privacy policies are often misused due to the long and complex problem that it is difficult for users to directly identify risk items. Therefore, there is a need for a method that can automatically check whether the privacy policy is safe. However, the safety verification technique of the conventional blacklist and machine learning-based privacy policy has a problem that is difficult to expand or has low accessibility. In this paper, to solve the problem, we propose a safety verification technique for the privacy policy using the GPT-3.5 API, which is a generative artificial intelligence. Classification work can be performed evenin a new environment, and it shows the possibility that the general public without expertise can easily inspect the privacy policy. In the experiment, how accurately the blacklist-based privacy policy and the GPT-based privacy policy classify safe and unsafe sentences and the time spent on classification was measured. According to the experimental results, the proposed technique showed 10.34% higher accuracy on average than the conventional blacklist-based sentence safety verification technique.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.2
• /
• pp.17-26
• /
• 2024

The purpose of this study is to explore the direction of the role of lifelong vocational education as a field of lifelong education based on the content analysis of the innovation plan for lifelong vocational education and training in 2018. The conclusion is as follows. First, in terms of policy orientation, the direction of lifelong vocational education is possible to carry out life-integrated lifelong vocational education in terms of lifelong education. Second, in the education and training process, lifelong education can be carried out in specific practical competency areas in terms of lifelong vocational education. Third, in the case of the education and training system, in terms of lifelong vocational education, the philosophy of lifelong education is to establish an education system that spans the entire life. Fourth, in the case of education and training programs, various programs can be operated by type of lifelong education institution in terms of lifelong vocational education. Fifth, in the case of education and training, lifelong education can be distributed evenly across life stages from the perspective of lifelong vocational education. Sixth, in terms of the relationship with the industrial sector, lifelong education can strengthen the connection with industrial demand in terms of lifelong vocational education, and it can also strengthen the connection with government agencies. Seventh, in the case of support for the underprivileged, lifelong education is viewed from the perspective of lifelong vocational education. Free education and customized support for the underprivileged are possible at provincial and provincial lifelong education institutions and city, county, and district lifelong learning centers.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.519-531
• /
• 2024

The purpose of this study is to analyze the aspects and characteristics of educational innovation planned and implemented at the university site targeting universities in Busan, Ulsan, and Gyeongnam, and to explore their limitations and tasks. For this purpose, we analyzed the contents of innovation strategy programs among the plans of 17 universities in the national innovation support projects in Busan, Ulsan, and Gyeongnam area. First, the university innovation strategy was divided into input, process, infrastructure, and other factors, and among them, the process factor was divided into education, research, and industry-university cooperation to examine the aspects and characteristics of innovation. As a result of the study, the aspects of university innovation at universities in Busan, Ulsan, and Gyeongnam were analyzed in the areas of education, research, and industry-academia cooperation. Characteristics of innovation were emphasis on convergence education, competency development, smart system foundation, introduction of innovative teaching and learning techniques, consumer-centeredness, and regional linkage. The limitations and tasks of university innovation revealed through the research are as follows. First, a specialized university innovation business structure should be prepared in consideration of the context of local universities. Second, established strategies with high innovativeness must be implemented and sustained, and consensus among members is required for this. Third, the innovation of universities should not mean the centralization of academics, and the role and efforts of universities as a research institutions should be improved. Fourth, it should not be overlooked that more important than the visible innovation strategy of university innovation is the education innovation that occurs directly to students as a result of the education effect.

• Knowledge Management Research
• /
• v.25 no.3
• /
• pp.121-143
• /
• 2024

The COVID-19 pandemic has affected various aspects of life, particularly affecting the learning, health, psychology, emotions, and daily routines of children and adolescents. The increasing number of counseling sessions related to depression and stress among this demographic highlights the urgent need for effective mental health management. This study investigates current mental care management services and proposes improvements by analyzing the mental health needs of Korean children and adolescents. From an evidence-based administrative perspective, the study examines the current status of emotional management, focusing on data from public institutions such as the Ministry of Education, the Ministry of Health and Welfare, and the Ministry of the Interior and Safety. We propose a three-phase service model: 1) data-driven emotional management services, 2) specialized services for vulnerable children and adolescents, and 3) mydata services for customized emotional management. Additionally, the study reviews relevant legal frameworks and issues, proposing directions for improvement to realize the proposed services. These services and legal considerations are expected to contribute to the effective implementation of emotional management policies for children and adolescents in the future.

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.221-241
• /
• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of Korean Home Economics Education Association
• /
• v.30 no.1
• /
• pp.15-28
• /
• 2018

Teachers are not completed by appointment, but gradually made through self-development and training for a long time. In order to improve a sense of responsibility of home economics teachers, and also to suggest the purpose and direction of program through job training, the needs of training subjects should be preferentially understood. Thus, this study aims to provide basic data for establishing the developmental operation measures of training for home economics teachers, by researching the needs for training performed after the qualification training for first-grade teachers, targeting the teachers participating in the qualification training program for first-grade teachers of home economics in 2017. About the half of the research subjects received the home economics training one time or less for last three years. Through the training for first-grade teachers, the technical improvement of lesson instruction was demanded the most. As professional qualifications that should be cultivated through training, the ability to develop teaching methods and teaching/learning materials was the highest. Regarding the theme of training, the development of teaching/learning materials for home economics was desired the most. They wanted the training method including direct participation with high utilization for lesson, sublation of competition-centered evaluation, preference of instructors with field experience, continuous opportunity of home economics training, and communicative training. Regarding the needs for the 2015 revised curriculum, the demand for the training of 'human development and family' area was the highest. Therefore, in order to improve the professionalism of teachers through home economics training, it would be necessary to improve the educational environment such as temporal room for training and administrative support, and also to provide diverse types of training like group training, remote training, and smartphone app training suitable for changes in the generation of teachers. Also, on top of forming communities of home economics teachers, and sharing great contents of training, there should be individually-customized training for practice and sharing lesson cases.

• Journal of Intelligence and Information Systems
• /
• v.29 no.3
• /
• pp.167-183
• /
• 2023

In the recent field of recommendation systems, various studies have been conducted to model sparse data effectively. Among these, GLocal-K(Global and Local Kernels for Recommender Systems) is a research endeavor combining global and local kernels to provide personalized recommendations by considering global data patterns and individual user characteristics. However, due to its utilization of kernel tricks, GLocal-K exhibits diminished performance on highly sparse data and struggles to offer recommendations for new users or items due to the absence of side information. In this paper, to address these limitations of GLocal-K, we propose the GEase-K (Global and EASE kernels for Recommender Systems) model, incorporating the EASE(Embarrassingly Shallow Autoencoders for Sparse Data) model and leveraging side information. Initially, we substitute EASE for the local kernel in GLocal-K to enhance recommendation performance on highly sparse data. EASE, functioning as a simple linear operational structure, is an autoencoder that performs highly on extremely sparse data through regularization and learning item similarity. Additionally, we utilize side information to alleviate the cold-start problem. We enhance the understanding of user-item similarities by employing a conditional autoencoder structure during the training process to incorporate side information. In conclusion, GEase-K demonstrates resilience in highly sparse data and cold-start situations by combining linear and nonlinear structures and utilizing side information. Experimental results show that GEase-K outperforms GLocal-K based on the RMSE and MAE metrics on the highly sparse GoodReads and ModCloth datasets. Furthermore, in cold-start experiments divided into four groups using the GoodReads and ModCloth datasets, GEase-K denotes superior performance compared to GLocal-K.