• Journal of the Korean BIBLIA Society for library and Information Science
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• v.28 no.4
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• pp.35-55
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• 2017

The Master Data is constructed for the wide use within the institution, and it is mainly used in the enterprise. In this research, we have conducted research for the purpose of building master data on infrastructure that can be used by public institutions in the country. To do this, we analyzed individual attributes of the standard data set provided by the public data portal. Among these, we extracted standard elements that match the characteristics of the Master Data. Finally, the standardized elements are verified through the standardization system that is utilized in the country.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.2
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• pp.419-434
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• 2020

The purpose of the study is to design and propose metadata standard for building AI training dataset of landmark images. In order to achieve the purpose, we first examined and analyzed the state of art of the types of image retrieval systems and their indexing methods, comprehensively. We then investigated open training dataset and machine learning tools for image object recognition. Sequentially, we selected metadata elements optimized for the AI training dataset of landmark images and defined the input data for each element. We then concluded the study with implications and suggestions for the development of application services using the results of the study.

• The Korean Journal of Archival Studies
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• no.70
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• pp.41-83
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• 2021

After the revision of the Enforcement Decree of the Public Records Act, the archives created a management standard table for data set records management and performed management and control. Therefore, in this study, the data set record identification procedure and evaluation index were developed for systematic data set record management of archives. By applying this, a management standard table was prepared after identifying the records of 8 datasets in kr's electronic procurement system, and the evaluation was carried out according to the evaluation index, and the retention period, transfer, and collection were determined. It is hoped that this case study will be of practical use to the archives at a time when concrete examples of procedures for the management of dataset records are lacking.

• Journal of the Korean Society for Library and Information Science
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• v.53 no.2
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• pp.247-266
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• 2019

The purpose of this study is to propose a W3C standard, DCAT, to manage and service dataset that is becoming increasingly important as new knowledge information resources. To do this, we first analyzed the class and properties of the four core classes of DCAT. In addition, I modeled and presented a system that can manage and service various data sets based on DCAT in digital library. The system is divided into source data, data set management, linked data connection, and user service. Especially, the DCAT mapping function is suggested in dataset management. This feature can ensure interoperability of various datasets.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.2
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• pp.398-406
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• 2011

In this paper, we introduce a new architecture of PSO-based Polynomial Neural Networks (PNN) and discuss its comprehensive design methodology. The conventional PNN is based on a extended Group Method of Data Handling (GMDH) method, and utilized the polynomial order (viz. linear, quadratic, and modified quadratic) as well as the number of node inputs fixed (selected in advance by designer) at Polynomial Neurons located in each layer through a growth process of the network. Moreover it does not guarantee that the conventional PNN generated through learning results in the optimal network architecture. The PSO-based PNN results in a structurally optimized structure and comes with a higher level of flexibility that the one encountered in the conventional PNN. The PSO-based design procedure being applied at each layer of PNN leads to the selection of preferred PNs with specific local characteristics (such as the number of input variables, input variables, and the order of the polynomial) available within the PNN. In the sequel, two general optimization mechanisms of the PSO-based PNN are explored: the structural optimization is realized via PSO whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the PSO-based PNN, the model is experimented with using Gas furnace process data, and pH neutralization process data. For the characteristic analysis of the given entire data with non-linearity and the construction of efficient model, the given entire system data is partitioned into two type such as Division I(Training dataset and Testing dataset) and Division II(Training dataset, Validation dataset, and Testing dataset). A comparative analysis shows that the proposed PSO-based PNN is model with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of Korean Society of Archives and Records Management
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• v.21 no.1
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• pp.75-95
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• 2021

In this study, we focused on creating plans to manage the administrative information dataset of public records in closed universities. In particular, according to various reference materials and internal materials of the institution, we studied the theoretical discussion about the dataset and figured out the management status of the closed university's dataset. Therefore, as a measure for the data management of the Comprehensive Information Management System, recording targets are selected, retention periods are determined, administrative information dataset management standards are prepared, administrative information dataset evaluation and deletion are implemented, and comprehensive management systems of closed universities are established.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.229-238
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• 2015

Web shells are programs that are written for a specific purpose in Web scripting languages, such as PHP, ASP, ASP.NET, JSP, PERL-CGI, etc. Web shells provide a means to communicate with the server's operating system via the interpreter of the web scripting languages. Hence, web shells can execute OS specific commands over HTTP. Usually, web attacks by malicious users are made by uploading one of these web shells to compromise the target web servers. Though there have been several approaches to detect such malicious web shells, no standard dataset has been built to compare various web shell detection techniques. In this paper, we present a collection of web shell files, WebSHArk 1.0, as a standard dataset for current and future studies in malicious web shell detection. To provide baseline results for future studies and for the improvement of current tools, we also present some benchmark results by scanning the WebSHArk dataset directory with three web shell scanning tools that are publicly available on the Internet. The WebSHArk 1.0 dataset is only available upon request via email to one of the authors, due to security and legal issues.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.31 no.2
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• pp.139-160
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• 2020

Since the Public Records Management Act was enacted in 2007, the administrative information system has already been included in the electronic records production system, and dataset has been subject to record management as a type of electronic records. With the recent revision of the enforcement decree, dataset records management has been enacted. This study analyzes business units related to administrative information systems of public institutions and examines the current status of retention periods estimation. For this purpose, we collected 36 records classification systems from 49 public institutions among the direct management agencies of the National Archives and disaster management agencies. And we discriminated 824 business units related to administrative information system and divided into large and small groups according to types. We also compared the retention period estimation of records. The problems and improvement plans of this study are expected to be used as basic data in preparing the standard of administrative dataset management in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3658-3679
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• 2022

Classification of persons wearing and not wearing face masks in images has emerged as a new computer vision problem during the COVID-19 pandemic. In order to address this problem and scale up the research in this domain, in this paper a hybrid technique by employing ResNet-101 and multi-layer perceptron (MLP) classifier has been proposed. The proposed technique is tested and validated on a self-created face masks classification dataset and a standard dataset. On self-created dataset, the proposed technique achieved a classification accuracy of 97.3%. To embrace the proposed technique, six other state-of-the-art CNN feature extractors with six other classical machine learning classifiers have been tested and compared with the proposed technique. The proposed technique achieved better classification accuracy and 1-6% higher precision, recall, and F1 score as compared to other tested deep feature extractors and machine learning classifiers.