• Journal of Korean Library and Information Science Society
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• v.51 no.4
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• pp.185-210
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• 2020

The library faced an unexpected crisis of COVID-19, and as a countermeasure strategy, non-face-to-face online service has been reinforced. Therefore, this study attempted to present a plan to overcome the challenges arising from rapidly changing external environment and current crisis. To this end, data search, electronic library, library service, cultural event and open space management status of 288 public libraries serviced as an integrated site were investigated. Based on this, the meaning of online services in the post-COVID-19 era and the implication of it were examined. As a result, first, the increase in the use rate of online data search services with the spread of non-face-to-face culture, second, the expansion of the services of the electronic library, third, the diversification of non-face-to-face, online services, fourth, expansion of online cultural event services, fifth, the diversification of open space services were proposed, sixth, Introduced an artificial intelligence system for unattended loan return based on access and the Seventh, expansion of experiential cultural support services and educational contents through VR, AR and MR. It is deemed necessary for the research on the future direction of the library's non-face-to-face services to be conducted by investigating the current status of online services in various types of libraries and the types and case studies of library services in the era of COVID-19.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.525-531
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• 2020

In today's IT environment where various pieces of information are distributed in large volumes, recommendation systems are in the spotlight capable of figuring out users' needs fast and helping them with their decisions. The current recommendation systems, however, have a couple of problems including that user preference may not be reflected on the systems right away according to their changing tastes or interests and that items with no relations to users' preference may be recommended, being induced by advertising. In an effort to solve these problems, this study set out to propose a Fuzzy-AHP-based movie recommendation system by applying the BRNN(Bidirectional Recurrent Neural Network) language model. Applied to this system was Fuzzy-AHP to reflect users' tastes or interests in clear and objective ways. In addition, the BRNN language model was adopted to analyze movie-related data collected in real time and predict movies preferred by users. The system was assessed for its performance with grid searches to examine the fitness of the learning model for the entire size of word sets. The results show that the learning model of the system recorded a mean cross-validation index of 97.9% according to the entire size of word sets, thus proving its fitness. The model recorded a RMSE of 0.66 and 0.805 against the movie ratings on Naver and LSTM model language model, respectively, demonstrating the system's superior performance in predicting movie ratings.

• Smart Media Journal
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• v.9 no.4
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• pp.66-72
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• 2020

As the online education market expands, educational contents with various presentation methods are being developed and released. In addition, it is imperative to develop content that reflects the usability and user environment of users who use this educational content. However, for qualitative growth of contents that will support quantitative expansion of markets, existing model analysis methods are urgently needed at a time when development direction of newly developed contents is secured. In this process of content development, a typical model for setting development goals is needed, as the rules of the prototype affect the entire development process and the final development outcome. It can also provide a positive benefit that screens the issue of performance dualization between processes due to the absence of communication between a single entity or between a number of entities. In the case of AR-based educational content which is effective to secure data necessary for development by securing samples of similar categories because there are not enough ready-made samples released. Therefore, a big data statistical analysis service is needed that can easily collect data and make decisions using big data. In this paper, we would like to design analysis services that enable the selection and detection of intuitive multidimensional factors and attributes, and propose big data-based statistical analysis services that can assist cooperative activities within an organization or among many companies.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• Journal of Korean Library and Information Science Society
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• v.52 no.1
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• pp.155-178
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• 2021

In the era of the 4th industrial revolution, public libraries need a strategy for promoting intelligent library services in order to actively respond to changes in the external environment such as artificial intelligence. Therefore, in this study, based on the concept of artificial intelligence and analysis of domestic and foreign artificial intelligence related trends, policies, and cases, we proposed the future direction of introduction and development of artificial intelligence services in the library. Currently, the library operates a reference information service that automatically provides answers through the introduction of artificial intelligence technologies such as deep learning and natural language processing, and develops a big data-based AI book recommendation and automatic book inspection system to increase business utilization and provide customized services for users. Has been provided. In the field of companies and industries, regardless of domestic and overseas, we are developing and servicing technologies based on autonomous driving using artificial intelligence, personal customization, etc., and providing optimal results by self-learning information using deep learning. It is developed in the form of an equation. Accordingly, in the future, libraries will utilize artificial intelligence to recommend personalized books based on the user's usage records, recommend reading and culture programs, and introduce real-time delivery services through transport methods such as autonomous drones and cars in the case of book delivery service. Service development should be promoted.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.337-355
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• 2021

Demographic changes such as an aging and low fertility, as well as changes in industrial structure and residential environment, revealed the limitations of urban development policies. Accordingly, the government is making efforts to ensure a prosperous life for the people by including the plan to expand the living SOC in the national urban regeneration policy. The main priority tasks of the Living SOC include the establishment of welfare infrastructure for children and the vulnerable. This means that interest in welfare is increasing recently. In this study, we analyzed the supply status of welfare facilities for the senior, child and the disabled in 17 cities and provinces nationwide using LQ (Location Quotient). After analyzing the causes of the imbalance in the supply of welfare facilities by region, the improvement plan was suggested. Each welfare facility was highly localized by region, especially the accessibility gap between cities and provinces is very large. Welfare finances were similar in most cities and provinces, with the exception of some cities and provinces. In the case of cities with very high living standards, sufficient facilities were not provided. Improvement methods are as follows; Combination of welfare facilities that can maximize space efficiency, Securing appropriate welfare finance in consideration of living standards by city and province, Differentiation of supply method considering demand and user types for welfare facilities.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.181-199
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• 2021

Recently, financial companies are promoting chatbot services in line with the reduction of branches and the expansion of non-face-to-face services. However, it is difficult to expand the chatbot services at once in the presence of technical limitations and constraints of internal and external environment. Therefore, it is necessary to analyze the various situations of chatbot service to preemptively identify problems that can occur in stages and seek solutions. This study conducted interviews with 12 field practitioners and researchers to examine the intentions and behaviors of financial chatbot service users and interpreted them using TPB. The study revealed the characteristics of 'feelings and attitudes' such as convenience or inconvenience from the chatbot experience, 'subjective norms' such as herd behavior or the yearning for empathy of others, and 'behavioral control' according to the recognition of difficulty or convenience of chatbot use process. This study shows that this characteristic can affect the intention and actual behavior of users to use chatbot service continuously. In the future research, it is necessary to empirically study specific intentions and influence factors for actual users.

• Informatization Policy
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• v.28 no.4
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• pp.19-35
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• 2021

The purpose of this research is to investigate the attributes of the online world and to analyze their influence on democracy. The research focuses on the mayoral by-elections that were held in Seoul and Busan, South Korea, on April 4, 2021. The study demonstrates the characteristics of online spaces and the polarization of the online public through news articles and user comments from the Internet. The research includes topic modeling to measure the diversity of media reports, sentiment analysis to measure online public opinion, and interrupted time series analysis to understand how a particular event influences online attitudes. A combination of these methods is used to attempt to estimate the strength of political polarity in the online environment. The study shows diverse media reports by election region and candidate, where the online public repeatedly reveals high negative and low positive attitudes towards each candidate. Moreover, political polarity can differ based on the level of interest in an election. Although voters pay less attention to a by-election than a presidential election, there is a solid political polarity in the online world. Hence, the research recommends preparing measures to alleviate the polarization as politics requires significant online participation.

• The Journal of the Korea Contents Association
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• v.22 no.4
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• pp.1-12
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• 2022

Since the early 20th century, along with the interest and development of alternative psychotherapy, the field of art therapy has also been developed and expanded. In particular, the recent development of technology and the untact era brought about by the COVID-19 Pandemic is accelerating the development of new digital art therapy contents. Among them, the hot interest in virtual reality is raising expectations and questions about the effectiveness of psychotherapy given by new media beyond traditional art therapy. In this study, the characteristics of VRAT (Virtual Reality Art Therapy) content therapeutic factors were investigated through qualitative literature analysis based on the conceptual framework and therapeutic components of ETC (Expressive Therapies Continumm), an integrated art therapy theory. As a result of the study, VRAT contents showed mostly therapeutic factors in the left hemisphere that triggered the user's dynamic, perceptual, and cognitive factors, and the therapeutic factors in the right hemisphere, which focused on sensory, emotional, and symbolic factors, were relatively few. The reason seems to be due to the nature of the experimental stage, the absence of active intervention by the therapist and long-term session composition, and the fear, clumsiness, and unfamiliarity of users about VRAT in addition to the characteristics and technical limitations of the VRAT medium. The limitations of the study include the small number of documents to be analyzed and the insufficient form of current VRAT to be called art therapy. It is expected that the characteristics of the therapeutic factors of VRAT content media and environment derived as a result of this study will be usefully used for the appropriate development of VRAT content in the future.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.69-78
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• 2022

As the use of Internet of Things (IoT) devices has expanded, digital forensics coverage of the National Police Agency has expanded to smart home areas. Accordingly, most of the existing studies conducted to acquire smart home platform data were mainly conducted to analyze local data of mobile devices and analyze network perspectives. However, meaningful data for evidence analysis is mainly stored on cloud storage on smart home platforms. Therefore, in this paper, we study how to acquire stored in the cloud in a Hey Home Air environment by extracting accessToken of user accounts through a cookie database of browsers such as Microsoft Edge, Google Chrome, Mozilia Firefox, and Opera, which are recorded on a PC when users use the Hey Home app-based "Hey Home Square" service. In this paper, the it was configured with smart temperature and humidity sensors, smart door sensors, and smart motion sensors, and artifacts such as temperature and humidity data by date and place, device list used, and motion detection records were collected. Information such as temperature and humidity at the time of the incident can be seen from the results of the artifact analysis and can be used in the forensic investigation process. In addition, the cloud data acquisition method using OpenAPI proposed in this paper excludes the possibility of modulation during the data collection process and uses the API method, so it follows the principle of integrity and reproducibility, which are the principles of digital forensics.