• Journal of Information Processing Systems
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• v.13 no.4
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• pp.914-927
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• 2017

Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.

• The Journal of the Korea Contents Association
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• v.14 no.9
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• pp.1-13
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• 2014

Apart from the physical realism, the implementation of various physical actions of an agent to respond to dynamically changing situations is essential for the design of an agent in a cyber world. To achieve a maximum diversity in actions, we develop a mechanism that allows composite actions to be constructed by reusing a set of primitive motions and enables an agent to instantly react to changes in the ambient states. Specifically we model an agent's body in terms of joints, and a primitive or composite motion is performed in a real time. To implement this mechanism, we produce an animation for basic joint movements and develop a method to construct overall motions out of the primitive motions. These motions can be assembled into a plan by which an agent can achieve a goal. In this manner, diverse actions can be implemented without excessive efforts. This approach has conspicuous advantages when constructing a parallel action, e.g., eating while walking, that is, two or more parallel actions can be naturally merged into a parallel action according to their priority. We implement several composite and parallel actions to demonstrate the viability of our approach.

• Korean Journal of Social Welfare Studies
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• v.49 no.1
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• pp.297-334
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• 2018

This study focused on accomplishment of successful life development after discharge a child-care facility even though they had a variety of diversity in hard environment on their own past. Furthermore the research analyzed expression progress and detail information of resilience effect on twenty healthy members of society, who lived theirselves for more than 10 years at out of chid-care centers, based on grounded theory methodology participants. As written in the result section of the study, using the paradigm model analysis showed that it was caused by 'Practical planning for the future', 'Organization of positive meaning' and 'Understanding in another's shoe'. The contextual condition appeared as 'Self examination, 'Seeking anchor as ontology' and 'Natural intimacy among family members'. The centralization phenomenon came in 'Living philosophy of internal stabilities'. The intervention condition appeared as 'Rise up through the world' and 'faith all the world'. The action-interaction showed up as 'Strict self-discipline' and 'Growing up own family'. The consequence appeared as 'Making social family' and 'Being a someone's social mentor'. This research shows discussion in depth based on above analyzed result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.06a
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• pp.231-234
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• 2007

This study proposes a Web GUI system using MGIS-based three-dimensional data models and hydrodynamic model. The study of Web GUI (Graphic User Information) system based on the MGIS (Marine Geographic Information System) is mainly performed on effective methodologies which transform real world data to computing world data. Finally, we design a Seomjin River Estuary Web GUI system integrating above data models. It must adds more ecological information and the various service item for approach more easily in order to user.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.3
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• pp.79-90
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• 2021

This study aims to investigate the relationship between urbanization, economic growth, energy consumption, and CO2 emissions in Indonesia. The data used in the study are time-series data for the period 1985-2017; the data utilized are sourced from World Development Indicators obtained on the World Bank database. The method uses a quantitative approach that applies the vector error correction model based on the Granger causality test. The empirical results reveal that, in the short run, there is evidence that urbanization and energy consumption can causes CO2 emissions, and they also prove that urbanization can cause energy consumption. Also, other findings prove the existence of long-run relationships flowing from energy consumption, economic growth, and CO2 emissions toward urbanization, as well as the existence of the relationship flowing from urbanization, economic growth, and CO2 emissions towards energy consumption. The results of testing the relationship between economic growth and CO2 emissions reveal that the environmental Kuznets curve hypothesis is proven in Indonesia. Thus, policies are needed to limit the impact of urbanization through high awareness-raising to maintain environmental quality and greater use of energy. Also, energy conservation policies are needed in all sectors, especially the electricity, industry, and transportation sectors.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.149-158
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• 2022

The pandemic of Covid-19 (Coronavirus Disease 19) has devastated the world, affected millions of people, and disrupted the world economy. The cause of the Covid19 epidemic has been identified as a new variant known as Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV2). It motives irritation of a small air sac referred to as the alveoli. The alveoli make up most of the tissue in the lungs and fill the sac with mucus. Most human beings with Covid19 usually do no longer improve pneumonia. However, chest x-rays of seriously unwell sufferers can be a useful device for medical doctors in diagnosing Covid19-both CT and X-ray exhibit usual patterns of frosted glass (GGO) and consolidation. The introduction of deep getting to know and brand new imaging helps radiologists and medical practitioners discover these unnatural patterns and pick out Covid19-infected chest x-rays. This venture makes use of a new deep studying structure proposed to diagnose Covid19 by the use of chest X-rays. The suggested model in this work aims to predict and forecast the patients at risk and identify the primary COVID-19 risk variables

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.285-294
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• 2022

Using the ARDL approach, this study examined the impact of macro factors on Vietnam's stock market in the short and long run from 2010 to 2021. The State Bank of Vietnam and the International Monetary Fund provided time series data for this study. Research results show that in the long run, money supply and exchange rate respectively affect the stock market. The money supply had a positive effect on the VN-Index, while the exchange rate showed the opposite effect. However, the study did not find a relationship between world oil price and interest rates on VN-Index in the long run. On the other hand, in the short term, there are relationships between variables; specifically, interest rates and exchange rates have a negative impact on the VN-Index, while the world oil price and the fluctuation of money supply M2 of the previous one and two months showed an impact in the same direction on this index. The differences in the regression results on the impact of exchange rate and oil price on the VN-Index compared to previous studies come from the characteristics of Vietnam's stock market, with the large capitalization of companies in the oil and gas sector, and the structure of Vietnam's economy with export heavily depends on FDI sector.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.40-52
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• 2024

Cyber-Physical Systems (CPS) are introduced as complex, interconnected systems that combine physical components with computational elements and networking capabilities. They bridge the gap between the physical world and the digital world, enabling the monitoring and control of physical processes through embedded computing systems and networked communication. These systems introduce several security challenges. These challenges, if not addressed, can lead to vulnerabilities that may result in substantial losses. Therefore, it is crucial to thoroughly examine and address the security concerns associated with CPS to guarantee the safe and reliable operation of these systems. To handle these security concerns, different existing security requirements methods are considered but they were unable to produce required results because they were originally developed for software systems not for CPS and they are obsolete methods for CPS. In this paper, a Security Requirements Engineering Methodology for CPS (CPS-SREM) is proposed. A comparison of state-of-the-art methods (UMLSec, CLASP, SQUARE, SREP) and the proposed method is done and it has demonstrated that the proposed method performs better than existing SRE methods and enabling experts to uncover a broader spectrum of security requirements specific to CPS. Conclusion: The proposed method is also validated using a case study of the healthcare system and the results are promising. The proposed model will provide substantial advantages to both practitioners and researcher, assisting them in identifying the security requirements for CPS in Industry 4.0.

• Journal of Information Technology Services
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• v.23 no.2
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• pp.63-81
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• 2024

With the advancement of Information and Communication Technologies (ICT) and Artificial Intelligence (AI), the metaverse has emerged as a transformative model across various sectors, offering a three-dimensional virtual world where activities mirroring the real world occur. This study delves into the significant factors influencing fashion brand companies' investments in the metaverse, an evolved concept from Virtual Reality (VR) that extends beyond gaming to include real-life activities through avatars. This study highlights the surge in virtual fashion engagements, as evidenced by increased avatar updates and purchases of digital fashion items on platforms like Roblox. Luxury brands are steadily entering the metaverse indicating a new revenue stream within the fashion industry. This study employs a mixed-methods approach, integrating text mining and interviews to identify key factors for fashion companies considering metaverse investments. By proposing strategies based on these findings, this study not only enriches academic discourse in fashion, e-commerce, and information systems but also serves as a guideline for fashion companies aiming to navigate the burgeoning digital market, contributing to the generation of new revenue streams in the fashion sector.

• Journal of Information Technology Services
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• v.11 no.4
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• pp.233-250
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• 2012

In this paper, credit card delinquency means the possibility of occurring bad debt within the certain near future from the normal accounts that have no debt and the problem is to predict, on the monthly basis, the occurrence of delinquency 3 months in advance. This prediction is typical binary classification problem but suffers from the issue of data imbalance that means the instances of target class is very few. For the effective prediction of bad debt occurrence, Support Vector Machine (SVM) with kernel trick is adopted using credit card usage and payment patterns as its inputs. SVM is widely accepted in the data mining society because of its prediction accuracy and no fear of overfitting. However, it is known that SVM has the limitation in its ability to processing the large-scale data. To resolve the difficulties in applying SVM to bad debt occurrence prediction, two stage clustering is suggested as an effective data reduction method and ensembles of SVM models are also adopted to mitigate the difficulty due to data imbalance intrinsic to the target problem of this paper. In the experiments with the real world data from one of the major domestic credit card companies, the suggested approach reveals the superior prediction accuracy to the traditional data mining approaches that use neural networks, decision trees or logistics regressions. SVM ensemble model learned from T2 training set shows the best prediction results among the alternatives considered and it is noteworthy that the performance of neural networks with T2 is better than that of SVM with T1. These results prove that the suggested approach is very effective for both SVM training and the classification problem of data imbalance.