• Journal of Korea Multimedia Society
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• v.4 no.4
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• pp.287-296
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• 2001

This paper proposes an efficient algorithm to select the proper retrieval results from various information sources in Meta search. The algorithm collects and evaluates the related documents to the given query Then, it determines the appropriate retrieval results based on the relevance between the query and the collected documents. This algorithm depends on the Meta information such as the size N of population, top-ranked information of related documents and the precision in order to choose the most appropriate retrieval result.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.1
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• pp.65-65
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• 1992

Recently many organizations have attempted to build hypermedia systems to expand their working areas to internet-based virtual work places. It is thus important to manage corporate hypermedia documents effectively. Metadata play a critical role for managing these documents. This paper identifies metadata roles and components to build its schema. Furthermore a meta-information system HyDoMiS(Hyperdocument Meta-information System) is proposed by the use of this metadata schema. HyDoMiS performs three functions metadata management search and reporting The metadata management function is concerned with workflow document and database. The system is more likely to help implement and maintsain hypermedia information system effectively.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.639-640
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• 2009

유비쿼터스 사회는 인터넷망을 이용하여 생활에 적용되는 서비스들이 무수히 많아지고, 이를 안전하고 정확하게 서비스하기 사용자 인증이 중요한 요소로 강조되고 있다. 인터넷망 환경에서는 비대면 및 비서면의 사용자 인증이 이루어지고 있으며, 다양한 인증 요소를 통한 보안성 강화를 이루고 있다. 그 중 생체정보를 이용한 사용자 인증은 생체정보의 고유성, 불변성 등과 같은 특성으로 인해 강력한 인증 수단으로서 주목받고 있다. 그러나 이러한 강력한 인증 수단을 제공하는 고유성으로 인해 사용자 인증 수단의 변경 불가능이라는 문제점도 가지고 있다. 본 논문에서는 이와 같은 생체정보의 변경 불가 문제점을 해결하기 위하여 개인의 생체정보를 활용한 Meta-Biometric 정보를 생성하여, 사용자의 생체정보를 변경 및 재사용 할 수 있는 방안을 제안한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.4
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• pp.848-854
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• 2014

A Meta-analysis refers to a statistical literature synthesis method from the quantitative results of many known empirical studies. We conducted a meta-analysis and review of external factors based on the technology acceptance model for Smartphone-related researches. This study surveyed 106 research papers that established causal relationships in the technology acceptance model published in Korean academic journals during 2008 and 2013. The result of the meta-analysis might be summarized that the playfulness has the highest effect size in the path from external factors to the perceived usefulness, with the effect size(0.536). Also the self efficacy showed the highest effect size(0.626) in the path from external factors to the perceived ease of use. Based on these findings, several theoretical and practical implications were suggested and discussed with the difference from previous researches.

• Journal of Information Management
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• v.42 no.3
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• pp.147-165
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• 2011

This paper presents a core meta-tag of OpenURL written in Key/Encoded-Value format in the field of observation research, to distribute the scientific data, produced in many experimentations and observations, on the OpenURL service architecture. So far, the OpenURL hasn't supplied a meta-tag represented scientific data because it has focused on circulation of scholarly and technological information extracted from thesis, proceedings, journals, literatures, etc. The DataCite consortium metadata were analyzed and compared with the Dublin Core metadata, OECD metadata, and Directory Interchange Format metadata to develop a core meta-tag in observation research.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1711-1718
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• 2015

A Meta-analysis refers to a statistical literature synthesis method from the quantitative results of many known empirical studies. Recently, the convergence of knowledge-based society and, information telecommunication technologies has a rapid impact on politics, economics and various fields. A meta-analysis was conducted to identify the relationships among satisfaction, trust, and loyalty in e-commerce studies. A total of 57 research papers published in Korean academic journals during 2002 and 2013 were reviewed and the causal relationships among satisfaction, trust, and loyalty in e-commerce were established. The result of the meta-analysis might be summarized that the highest effect size (r =. 591) was in the path from the satisfaction to the trust. The second biggest effect size (r =. 554) was found in the path between the satisfaction to the loyalty. The third biggest effect size (r =. 552) was found in the path between the trust to the loyalty. By the way, the smallest effect size (r =. 484) was found in the path between the trust to the satisfaction.

• Journal of Information Science Theory and Practice
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• v.10 no.4
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• pp.53-65
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• 2022

Altmetrics or alternative metrics gauge the digital attention received by scientific outputs from the web, which is treated as a supplement to traditional citation metrics. In this study, we performed a meta-analysis of correlations between classic citation metrics and altmetrics indicators of library and information science (LIS) articles. We followed the systematic review method to select the articles and Erasmus Rotterdam Institute of Management Guidelines for reporting the meta-analysis results. To select the articles, keyword searches were conducted on Google Scholar, Scopus, and ResearchGate during the last week of November 2021. Eleven articles were assessed, and eight were subjected to meta-analysis following the inclusion and exclusion criteria. The findings reported negative and positive associations between citations and altmetric indicators among the selected articles, with varying correlation coefficient values from -.189 to 0.93. The result of the meta-analysis reported a pooled correlation coefficient of 0.47 (95% confidence interval, 0.339 to 0.586) for the articles. Sub-group analysis based on the citation source revealed that articles indexed on the Web of Science showed a higher pooled correlation coefficient (0.41) than articles indexed in Google Scholar (0.30). The study concluded that the pooled correlation between citation metrics with altmetric indicators was positive, ranging from low to moderate. The result of the study gives more insights to the scientometrics community to propose and use altmetric indicators as a proxy for traditional citation indicators for quick research impact evaluation of LIS articles.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.305-312
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• 2023

In introducing memory methods in various educational fields, programs in a mobile environment can be used for the purpose of increasing accessibility and enhancing the effectiveness of education. It is much easier to remember words with meaning than to remember numerical information such as years. From the standpoint of increasing the educational effect, the part that needs to be supplemented with the help of the application can be said to be numerical information. Most studies related to conventional numerical memory have focused on the form that helps memory by imaging numbers. In the paper on memory-based meta-record algorithms in the mobile environment, the application developed in the previous study attempts to supplement this by discovering and simply modifying the user's mistakes in the entered numerical information. In this study, we aim to increase the memory rate by constructing metadata based on personalized log information and correcting mistakes. To do this, applications suitable for the mobile environment are developed, a structure of meta-record data is proposed, and meta-record application algorithms are implemented and evaluated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.765-777
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• 2022

The SABER algorithm, a PKE/KEM algorithm presented in NIST PQC Standardization Round 3, is an algorithm based on the Module-LWR problem among lattice-based problems and has a Meta-PKE structure. At this time, the secret information used in the encryption process is called a ephemeral key, and in this paper, the ephemeral key reuse attack using the Meta-PKE structure is described. For each parameter satisfying the security strengths required by NIST, we present a detailed analysis of the previous studies attacked using 4, 6, and 6 queries, and improve them, using only 3, 4, and 4 queries. In addition, we introduce how to reduce the computational complexity of recovering ephemeral keys with a single query from the brute-force complexity on the n-dimension lattice, 2^7.91×n, 2^10.51×n, 2^12.22×n to 2^4.91×n, 2^6.5×n, 2^6.22×n, for each parameter, and present the results and limitations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.