• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.34 no.1
• /
• pp.209-238
• /
• 2023

This study aims to propose the main learning contents, functions and activation plans for building an e-learning platform for libraries through a literature review, case analysis and expert survey. Through the literature review, it was found that libraries must play a role in providing high-quality online education for users in the e-learning ecosystem. Based on the previous studies, a learning function analysis tool was developed for the analysis of the library's e-learning platform. Based on this, the learning contents, learning functions and characteristics of library e-learning platforms were analyzed, and expert surveys and interviews were conducted. As a results, the construction of a platform for effectively applying learning processes and technology is essential for the library's sustainable e-learning services. The contents that should be provided for characteristics of library education, reading guidance, information literacy instruction, library usage instruction, and the latest IT technologies. And The main learning functions include the ability to conduct video lectures and real-time classes among learning types, and learning activity support functions, a cloud platform support function and a personalized environment support function. Additionally, suggested re-education for library staff to improve their technical skills and the formation of an e-learning team.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.5
• /
• pp.1019-1034
• /
• 2022

Malware attacks become more prevalent in the hyper-connected society of the 4th industrial revolution. To respond to such malware, automation of malware detection using artificial intelligence technology is attracting attention as a new alternative. However, using artificial intelligence without collateral for its reliability poses greater risks and side effects. The EU and the United States are seeking ways to secure the reliability of artificial intelligence, and the government announced a reliable strategy for realizing artificial intelligence in 2021. The government's AI reliability has five attributes: Safety, Explainability, Transparency, Robustness and Fairness. We develop four elements of safety, explainable, transparent, and fairness, excluding robustness in the malware detection model. In particular, we demonstrated stable generalization performance, which is model accuracy, through the verification of external agencies, and developed focusing on explainability including transparency. The artificial intelligence model, of which learning is determined by changing data, requires life cycle management. As a result, demand for the MLops framework is increasing, which integrates data, model development, and service operations. EXE-executable malware and documented malware response services become data collector as well as service operation at the same time, and connect with data pipelines which obtain information for labeling and purification through external APIs. We have facilitated other security service associations or infrastructure scaling using cloud SaaS and standard APIs.

• KIPS Transactions on Computer and Communication Systems
• /
• v.11 no.3
• /
• pp.73-82
• /
• 2022

Auto-scaling is one of the most important functions for cloud computing technology. Even if the number of users or service requests is explosively increased or decreased, system resources and service instances can be appropriately expanded or reduced to provide services suitable for the situation and it can improves stability and cost-effectiveness. However, since the policy is performed based on a single metric data at the time of monitoring a specific system resource, there is a problem that the service is already affected or the service instance that is actually needed cannot be managed in detail. To solve this problem, in this paper, we propose a method to predict system resource and service response time using a multivariate time series analysis model and establish an auto-scaling policy based on this. To verify this, implement it as a custom scheduler in the Kubernetes environment and compare it with the Kubernetes default auto-scaling method through experiments. The proposed method utilizes predictive data based on the impact between system resources and response time to preemptively execute auto-scaling for expected situations, thereby securing system stability and providing as much as necessary within the scope of not degrading service quality. It shows results that allow you to manage instances in detail.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.29-43
• /
• 2022

With the advent of the Internet and the development of mobile digital devices such as smartphones and tablet PCs, the communication service paradigm began to shift from existing voice services to data services. Recently, as social network services (SNS) are activated and 4th industrial revolution technologies centered on ICT (Information and Communication Technologies) such as Big Data, Blockchain, Cloud, and 5G/6G are rapidly developed, the amount of shared data type and the amount of data are increasing rapidly. As the transition to a digital society begins actively, the importance of using data information, as well as the economic and social values of personal information are becoming increasingly important. As a result, they are actively discussing policies to revitalize the data information industry around the world and ways to efficiently obtain, analyze, and utilize increasingly diverse and vast data, as well as to protect/guarantee the rights of information subjects (providers) in various fields such as society, culture, economy, and politics.. In this paper, in order to improve the self-determination right of personal information on data produced by information subjects, and further expand the use of safe data and the data economy, a differentiated data trusts system was considered and suggested. In addition, the components and data trusts procedures necessary to efficiently operate the data trusts system in Korea were considered, and the non-profit data trusts system and the for-profit data trusts system were considered as a way to flexibly operate the data trusts system. Furthermore, the legal items necessary for the implementation of the data trusts system were investigated and considered. In this paper, in order to propose a domestic data trusts system, cases related to existing data trusts systems such as the United States, Japan, and Korea were reviewed and analyzed. In addition, in order to prepare legislation necessary for the data trusts system, data-related laws in major countries and domestic legal and policy trends were reviewed to study the rights that conflict or overlap with existing laws, and differences were investigated and considered. The Data trusts system proposed in this paper is a reasonable system that is expected to recognize the asset value of data in the capitalist market economy system, to provide legitimate compensation for data produced by data subjects, and further to contribute greatly to the use of safe data and creation of a new service market.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
• /
• v.8 no.2
• /
• pp.1-11
• /
• 2018

Recently ICT, new technologies such as IoT, Cloud, and Artificial Intelligence are changing the information society explosively. But personal information leakage incidents of consignee's company are increasing more and more because of the expansion of consignment business and the latest threats such as Ransomware and APT. Therefore, in order to strengthen the security of consignee's company, this study derived the checklists through the analysis of the status such as the feature of consignment and the security standard management system and precedent research. It also analyzed laws related to consignment. Finally we found out the relative importance of checklists after it was applied to proposed AHP(Analytic Hierarchy Process) Model. Relative importance was ranked as establishment of an internal administration plan, privacy cryptography, life cycle, access authority management and so on. The purpose of this study is to reduce the risk of leakage of customer information and improve the level of personal information protection management of the consignee by deriving the check items required in handling personal information of consignee and demonstrating the model. If the inspection activities are performed considering the relative importance of the checklist items, the effectiveness of the input time and cost will be enhanced.

• Convergence Security Journal
• /
• v.22 no.4
• /
• pp.11-16
• /
• 2022

Development of science technology make integrated CPS(Cyber-Physical System) appear. In CPS era, cyberspace and physical-space are hard to separate anymore, that is developing toward integrated CPS. The reality is not stopping, that is consistently changing and the concept of space is developing too. But several articles are considering for cyberspace and physical-space separately, and they are developing tailed alternative each case. The theorical approaching that is not considering reality is dwelled on past, and is dangerous from dropping down to floating cloud that is not considering progressed reality. This article is suggested to consider rimland theory to control measures in cyberspace. That is dedicated to integrated approaching from physical-space to cyberspace. And that is developing concreted controling measures in cyberspace. Especially, this article is suggested to policy alternative by analyzing north korea cyber warfare from rimland theory including human sources. Simplicity is the ultimate sophistication. This article make integrated approaching effects about cyberspace and physical-space to preparing in the CPS era.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.6B
• /
• pp.551-560
• /
• 2009

This paper describes a newly developed pollutant transport model named ARPTM which was designed to simulate the transport and characteristics of pollutant materials after an accidental spill in upstream of river system up to a given position in the downstream. In particular, the ARPTM incorporated ADCP data to compute longitudinal dispersion coefficient and advection velocity which are necessary to apply one-dimensional advection-dispersion equation. ARPTM was built on top of the geographic information system platforms to take advantage of the technology's capabilities to track geo-referenced processes and visualize the simulated results in conjunction with associated geographic layers such as digital maps. The ARPTM computes travel distance, time, and concentration of the pollutant cloud in the given flow path from the river network, after quickly finding path between the spill of the pollutant material and any concerned points in the downstream. ARPTM is closely connected with a recently developed GIS-based Arc River database that stores inputs and outputs of ARPTM. ARPTM thereby assembles measurements, modeling, and cyberinfrastructure components to create a useful cyber-tool for determining and visualizing the dynamics of the clouds of pollutants while dispersing in space and time. ARPTM is expected to be potentially used for building warning system for the transport of pollutant materials in a large basin.

• Korean Journal of Remote Sensing
• /
• v.39 no.5_2
• /
• pp.827-835
• /
• 2023

Drone light detection and ranging (LiDAR) is a state-of-the-art surveying technology that enables close investigation of the top of the mountain slope or the inaccessible slope, and is being used for field surveys in mountainous terrain. To build topographic information using Drone LiDAR, a preprocessing process is required to effectively separate ground and non-ground points from the acquired point cloud. Therefore, in this study, the point group data of the mountain topography was acquired using an aerial LiDAR mounted on a commercial drone, and the application and accuracy of the cloth simulation filtering algorithm, one of the ground separation techniques, was verified. As a result of applying the algorithm, the separation accuracy of the ground and the non-ground was 84.3%, and the kappa coefficient was 0.71, and drone LiDAR data could be effectively used for landslide field surveys in mountainous terrain.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.12
• /
• pp.147-153
• /
• 2023

As it spreads to all industries of artificial intelligence technology, AIaaS equipped with artificial intelligence services is emerging. In particular, non-IT companies are suffering from the absence of software experts, difficulties in training big data models, and difficulties in collecting and analyzing various types of data. AIaaS makes it easier and more economical for users to build a system by providing various IT resources necessary for artificial intelligence software development as well as functions necessary for artificial intelligence software in the form of a service. Therefore, the supply and demand for such cloud-based AIaaS services will increase rapidly. However, the quality of services provided by AIaaS becomes an important factor in what is required as the supply and demand for AIaaS increases. However, research on a comprehensive and practical quality evaluation metric to measure this is currently insufficient. Therefore, in this paper, we develop and propose a usability, replacement, scalability, and publicity metric, which are the four metrics necessary for measuring reusability, based on implementation, convenience, efficiency, and accessibility, which are characteristics of AIaaS, for reusability evaluation among the service quality measurement factors of AIaaS. The proposed metrics can be used as a tool to predict how much services provided by AIaaS can be reused for potential users in the future.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.29 no.6
• /
• pp.536-542
• /
• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.