• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.4
• /
• pp.487-494
• /
• 2022

With the recent demand for analysis using data, the size of the knowledge graph, which is the data to be analyzed, gradually increased, reaching about 82 billion edges when extracted from the web as a knowledge graph. A lot of knowledge graphs are represented in the form of Resource Description Framework (RDF), which is a standard of W3C for representing metadata for web resources. Because of the characteristics of RDF, existing RDF storages have the limitations of processing time overhead when converting and storing large amounts of RDF data. To resolve these limitations, in this paper, we propose a method of compressing and converting large amounts of RDF data into integer IDs using MapReduce, and vertically partitioning and storing them. Our proposed method demonstrated a high performance improvement of up to 25.2 times compared to RDF-3X and up to 3.7 times compared to H2RDF+.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.6
• /
• pp.1800-1817
• /
• 2022

With the development of the economy and the improvement of living standards, the hot issues in the subject area have become the main research direction, and the mining of the hot issues in the subject currently has problems such as a large amount of data and a complex algorithm structure. Therefore, in response to this problem, this study proposes a method for extracting hot keywords in scientific journals based on the improved BERT model.It can also provide reference for researchers,and the research method improves the overall similarity measure of the ensemble,introducing compound keyword word density, combining word segmentation, word sense set distance, and density clustering to construct an improved BERT framework, establish a composite keyword heat analysis model based on I-BERT framework.Taking the 14420 articles published in 21 kinds of social science management periodicals collected by CNKI(China National Knowledge Infrastructure) in 2017-2019 as the experimental data, the superiority of the proposed method is verified by the data of word spacing, class spacing, extraction accuracy and recall of hot keywords. In the experimental process of this research, it can be found that the method proposed in this paper has a higher accuracy than other methods in extracting hot keywords, which can ensure the timeliness and accuracy of scientific journals in capturing hot topics in the discipline, and finally pass Use information technology to master popular key words.

• International Journal of Computer Science & Network Security
• /
• v.22 no.6
• /
• pp.390-399
• /
• 2022

Cyber security and resilience are phrases that describe safeguards of ICTs (information and communication technologies) from cyber-attacks or mitigations of cyber event impacts. The sole purpose of Risk models are detections, analyses, and handling by considering all relevant perceptions of risks. The current research effort has resulted in the development of a new paradigm for safeguarding services offered online which can be utilized by both service providers and users. customers. However, rather of relying on detailed studies, this approach emphasizes task selection and execution that leads to successful risk treatment outcomes. Modelling intelligent CSGs (Cyber Security Games) using MLTs (machine learning techniques) was the focus of this research. By limiting mission risk, CSGs maximize ability of systems to operate unhindered in cyber environments. The suggested framework's main components are the Threat and Risk models. These models are tailored to meet the special characteristics of online services as well as the cyberspace environment. A risk management procedure is included in the framework. Risk scores are computed by combining probabilities of successful attacks with findings of impact models that predict cyber catastrophe consequences. To assess successful attacks, models emulating defense against threats can be used in topologies. CSGs consider widespread interconnectivity of cyber systems which forces defending all multi-step attack paths. In contrast, attackers just need one of the paths to succeed. CSGs are game-theoretic methods for identifying defense measures and reducing risks for systems and probe for maximum cyber risks using game formulations (MiniMax). To detect the impacts, the attacker player creates an attack tree for each state of the game using a modified Extreme Gradient Boosting Decision Tree (that sees numerous compromises ahead). Based on the findings, the proposed model has a high level of security for the web sources used in the experiment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.11
• /
• pp.3950-3969
• /
• 2021

Radio Frequency (RF)-based indoor localization technologies play significant roles in various Internet of Things (IoT) services (e.g., location-based service). Most such technologies require that all the devices comply with a specified technology (e.g., WiFi, ZigBee, and Bluetooth). However, this requirement limits its application scenarios in today's IoT context where multiple devices complied with different standards coexist in a shared environment. To bridge the gap, in this paper, we propose a cross-technology localization approach, which is able to localize target nodes using a different type of devices. Specifically, the proposed framework reuses the existing WiFi infrastructure without introducing additional cost to localize Non-WiFi device (i.e., ZigBee). The key idea is to leverage the interference between devices that share the same operating frequency (e.g., 2.4GHz). Such interference exhibits unique patterns that depend on the target device's location, thus it can be leveraged for cross-technology localization. The proposed framework uses Principal Components Analysis (PCA) to extract salient features of the received WiFi signals, and leverages Dynamic Time Warping (DTW), Gradient Boosting Regression Tree (GBRT) to improve the robustness of our system. We conduct experiments in real scenario and investigate the impact of different factors. Experimental results show that the average localization accuracy of our prototype can reach 1.54m, which demonstrates a promising direction of building cross-technology technologies to fulfill the needs of modern IoT context.

• Journal of Internet Computing and Services
• /
• v.22 no.6
• /
• pp.83-89
• /
• 2021

The RMF (Cyber Security Risk Management Framework) is a more strengthened U.S. defense cybersecurity framework that is currently used throughout the U.S. federal government beyond the defense sector. In the past decade, the proportion of cyber warfare in non-regular warfare encountered by the United States, especially cyberattacks caused by China and North Korea, has been increasing. In the end, the U.S. is newly establishing an RMF system to prepare a more strengthened cybersecurity policy at the pan-government level, and the U.S. Department of Defense aims to expand the U.S. defense RMF evaluation policy beyond the federal government level. The South Korean military has already applied RMF at the request of the U.S. that notified the policy to apply RMF when obtaining F-35A. The application of RMF by the Korean military is no longer inevitable. Now is the time for the Korean military to seriously think about what to prepare for the early establishment of a successful Korean RMF system.

• Journal of Preventive Medicine and Public Health
• /
• v.47 no.1
• /
• pp.64-71
• /
• 2014

Objectives: Physical activity behavior begins to decline during adolescence and continues to decrease throughout young adulthood. This study aims to explain factors that influence physical activity behavior in a sample of female adolescents using a health promotion model framework. Methods: This cross-sectional survey was used to explore physical activity behavior among a sample of female adolescents. Participants completed measures of physical activity, perceived self-efficacy, self-esteem, social support, perceived barriers, and perceived affect. Interactions among the variables were examined using path analysis within a covariance modeling framework. Results: The final model accounted for an $R^2$ value of 0.52 for physical activity and offered a good model-data fit. The results indicated that physical activity was predicted by self-esteem (${\beta}$=0.46, p<0.001), perceived self-efficacy (${\beta}$=0.40, p<0.001), social support (${\beta}$=0.24, p<0.001), perceived barriers (${\beta}$=-0.19, p<0.001), and perceived affect (${\beta}$=0.17, p<0.001). Conclusions: The findings of this study showed that the health promotion model was useful to predict physical activity behavior among the Iranian female adolescents. Information related to the predictors of physical activity behavior will help researchers plan more tailored culturally relevant health promotion interventions for this population.

• Communications for Statistical Applications and Methods
• /
• v.17 no.5
• /
• pp.745-753
• /
• 2010

Compared to the U.S. Government Accountability Office(GAO) and the U.K. National Audit Office(NAO), the Board of Audit and Inspection of Korea(BAI) has not laid a rather solid system for effective assessment and judgment on the reliability of computer-processed data used as audit evidence in its public auditing activities. Accordingly, based on the experiences of GAO and NAO, this study suggests criteria and methods as the key elements of the methodological framework for assessing the reliability of information system data. Then, the usefulness and effectiveness of the criteria and techniques for assessing data reliability were tested and proved by applying to the analysis of allotment for mandatory disabled employment data that have been computer-processed and managed by the Korea Employment Agency for the Disabled(KEAD).

• Journal of Information Technology Services
• /
• v.19 no.3
• /
• pp.151-163
• /
• 2020

This paper describes a study on the FRACAS(Failure Reporting Analysis and Corrective Action System)-based dependability control platform for domestic urban railway trains. There are more demands for the verification of the dependability of trains as it becomes a regulation for train manufacturers to verify the dependability recently. Train manufacturers as well as railway operators need a effective FRACAS solution to perform the verification of the dependability. Yet current FRACAS solutions have limitations to support the verification processes effectively. This paper addresses the issues of current FRACAS solutions and suggests a FRACAS framework designed for the domestic urban railway trains. Service failure scenarios are standardized using the proposed availability model to implement a more user-friendly and reliable platform. A new FRACAS-based platform for the dependability control (SCARF^®) has been developed to implement the suggested framework. The detail interfaces and functions of the platform are explained. The SCARF^® platform is expected to engage the increasing demands for the dependability control successfully enhancing the reliability, maintainability, availability and safety of domestic urban railway trains eventually.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.14 no.2
• /
• pp.252-261
• /
• 2014

A Bayesian network (BN) based fault diagnosis framework for semiconductor etching equipment is presented. Suggested framework contains data preprocessing, data synchronization, time series modeling, and BN inference, and the established BNs show the cause and effect relationship in the equipment module level. Statistically significant state variable identification (SVID) data of etch equipment are preselected using principal component analysis (PCA) and derivative dynamic time warping (DDTW) is employed for data synchronization. Elman's recurrent neural networks (ERNNs) for individual SVID parameters are constructed, and the predicted errors of ERNNs are then used for assigning prior conditional probability in BN inference of the fault diagnosis. For the demonstration of the proposed methodology, 300 mm etch equipment model is reconstructed in subsystem levels, and several fault diagnosis scenarios are considered. BNs for the equipment fault diagnosis consists of three layers of nodes, such as root cause (RC), module (M), and data parameter (DP), and the constructed BN illustrates how the observed fault is related with possible root causes. Four out of five different types of fault scenarios are successfully diagnosed with the proposed inference methodology.

• Computers and Concrete
• /
• v.1 no.3
• /
• pp.303-324
• /
• 2004

The Component-Based Software Development (CBSD) has established itself as a sound paradigm in the software engineering discipline and has gained wide spread acceptance in the industry. The CBSD relies on the availability of standard software components for encapsulation of specific functionality. This paper presents the framework for the development of a software component for the design of general member cross-sections. The proposed component can be used in component-based structural engineering software or as a stand-alone program developed around the component. This paper describes the use-case scenarios for the component, its design patterns, object models, class hierarchy, the integrated and unified handling of cross-section behavior and implementation issue. It is expected that a component developed using the proposed patterns and model can be used in analysis, design and detailing packages to handle reinforced concrete, partially prestressed concrete, steel-concrete composite and steel sections. The component can provide the entire response parameters of the cross section including determination of geometric properties, elastic stresses, flexural capacity, moment-curvature, and ductility ratios. The component can also be used as the main computational engine for stand-alone section design software. The component can be further extended to handle the retrofitting and strengthening of cross-sections, shear and torsional response, determination of fire-damage parameters, etc.