• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2003.07a
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• pp.37-42
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• 2003

Threshold signature and blind signature are playing important roles in cryptography as well as practical applications such as e-cash and e-voting systems. In this paper, we present a new threshold blind digital signature based on pairings without a trusted third party. Our scheme operates on Gap Diffie-Hellman group, where Computational Diffie-Hellman problems are hard but Decision Diffie-Hellman problems are easy. For example, we use pairings that could be built from Weil pairing or Tate pairing. To the best of our knowledge, we claim that our scheme is the first threshold blind signature using pairings with provable security in the random oracle model.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.101-128
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• 2018

Thanks to its potential in many applications, Blockchain has recently been nominated as one of the technologies exciting intense attention. Blockchain has solved the problem of changing the original low-trust centralized ledger held by a single third-party, to a high-trust decentralized form held by different entities, or in other words, verifying nodes. The key contribution of the work of Blockchain is the consensus algorithm, which decides how agreement is made to append a new block between all nodes in the verifying network. Blockchain algorithms can be categorized into two main groups. The first group is proof-based consensus, which requires the nodes joining the verifying network to show that they are more qualified than the others to do the appending work. The second group is voting-based consensus, which requires nodes in the network to exchange their results of verifying a new block or transaction, before making the final decision. In this paper, we present a review of the Blockchain consensus algorithms that have been researched and that are being applied in some well-known applications at this time.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1203-1212
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• 2017

Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.

• Korean journal of communication and information
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• v.32
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• pp.113-146
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• 2006

This study explored how media and interpersonal communication affected voters in Busan mayoral by-election, focusing on the mutual relations among media use and attentive use of political campaign, interpersonal communication, and voting participation. Also, comparative analysis between image factor and the factor of political party influencing the decision of a candidate were examined. Additionally, it was analysed differential media effects on candidates' image. According to the results, the local media use and attentive use of political campaign had the influence on the increase of interpersonal communication about the election. Voters who had much interpersonal discussion with others participated more than voters who had less interpersonal discussion. Media use did not directly affect the participation of voting, but indirectly contributed to participation of voting through interpersonal discussion. The assumption of differential media effects on candidates image was partly proved. There were statistically significant differences in the factor of competence of candidates' image among three experimental groups (attentive use of TV discussion program, Internet web sites of two candidates, and printing materials of political advertisement). Furthermore, with three main vote variables, issues, candidates image, party identification, the results of comparative analysis between image factor and the factor of political party influencing the choice of a candidate suggested that a sense of oneness with a party was highly related to the choice of the candidates of the party, however, candidates' image was not related to the decision of a candidate. Political party had more impact on for whom to vote than candidates' image in this study.

• Journal of the Korea Society of Computer and Information
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• v.21 no.6
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• pp.9-19
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• 2016

In finance literature, stock liquidity showing how stocks can be cashed out in the market has received rich attentions from both academicians and practitioners. The reasons are plenty. First, it is known that stock liquidity affects significantly asset pricing. Second, macroeconomic announcements influence liquidity in the stock market. Therefore, stock liquidity itself affects investors' decision and managers' decision as well. Though there exist a great deal of literature about stock liquidity in finance literature, it is quite clear that there are no studies attempting to investigate the stock liquidity issue as one of decision making problems. In finance literature, most of stock liquidity studies had dealt with limited views such as how much it influences stock price, which variables are associated with describing the stock liquidity significantly, etc. However, this paper posits that stock liquidity issue may become a serious decision-making problem, and then be handled by using data mining techniques to estimate its future extent with statistical validity. In this sense, we collected financial data set from a number of manufacturing companies listed in KRX (Korea Exchange) during the period of 2010 to 2013. The reason why we selected dataset from 2010 was to avoid the after-shocks of financial crisis that occurred in 2008. We used Fn-GuidPro system to gather total 5,700 financial data set. Stock liquidity measure was computed by the procedures proposed by Amihud (2002) which is known to show best metrics for showing relationship with daily return. We applied five data mining techniques (or classifiers) such as Bayesian network, support vector machine (SVM), decision tree, neural network, and ensemble method. Bayesian networks include GBN (General Bayesian Network), NBN (Naive BN), TAN (Tree Augmented NBN). Decision tree uses CART and C4.5. Regression result was used as a benchmarking performance. Ensemble method uses two types-integration of two classifiers, and three classifiers. Ensemble method is based on voting for the sake of integrating classifiers. Among the single classifiers, CART showed best performance with 48.2%, compared with 37.18% by regression. Among the ensemble methods, the result from integrating TAN, CART, and SVM was best with 49.25%. Through the additional analysis in individual industries, those relatively stabilized industries like electronic appliances, wholesale & retailing, woods, leather-bags-shoes showed better performance over 50%.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.29-39
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• 2017

Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.

• Journal of the Korean Society of Costume
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• v.64 no.4
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• pp.21-36
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• 2014

The purpose of this study is to suggest an effective fashion design process using social network services(SNS) as a method to develop designs. Fashion design process was systemized through literature study. The characteristics of social network, and element and method of network formation were investigated, and then design processes using SNS were suggested through survey study. This was done by applying formation of network and its method in SNS with contents of fashion to stage of process to develop fashion design. The study results are as follows. First, Fashion design process using SNS is composed of 5 stages. Second, SNS types with contents of fashion were classified to five types: blog, community, connection of fashion web service and SNS, fashion SNS, and fashion SNS game. Among them, types where development of fashion design and product distribution was done by formation of network are connected type of fashion web service and SNS, fashion SNS type. Fashion design development can be done by compiling, having contests, and cooperative work. A method that can be used for making assessments and decision is voting and predicting the market. Third, Fashion design process using SNS is composed of the stages such as planning, compiling, analysis, decision, implementation, and formation of network. It was analyzed that by connecting stages of collection and evaluation of information through participation of users, new contents were produced and there was a structure that was cycled continuously.

• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.30-39
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• 2007

The existing content-based spam mail filtering algorithms have difficulties in filtering spam mails when e-mails contain images but little text. In this thesis we propose an efficient spam mail classification algorithm that utilizes the link structure of e-mails. We compute the number of hyperlinks in an e-mail and the in-link frequencies of the web pages hyperlinked in the e-mail. Using these two features we classify spam mails and legitimate mails based on the decision tree trained for spam mail classification. We also suggest a hybrid system combining three different algorithms by majority voting: the link structure analysis algorithm, a modified link structure analysis algorithm, in which only the host part of the hyperlinked pages of an e-mail is used for link structure analysis, and the content-based method using SVM (support vector machines). The experimental results show that the link structure analysis algorithm slightly outperforms the existing content-based method with the accuracy of 94.8%. Moreover, the hybrid system achieves the accuracy of 97.0%, which is a significant performance improvement over the existing method.

• Journal of Labour Economics
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• v.41 no.4
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• pp.107-131
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• 2018

This paper presents a model for the Minimum Wage Commission's decision process and analyzes the strategic actions of the participants in the process. The Minimum Wage Commission has used two ways of setting the minimum wage. The commission has voted either on the labor's against the management' final proposals or has voted on the public interest commissioners' proposal. According to the model, the minimum wage is determined at a level that is very close to or at a level preferred by the median voter among the public interest commissioners. But the probability of adopting labor or management proposal is ex-ante the same. Empirical evidence from the minimum wage decision process is consistent with the predictions of the model. The probability of adopting the labor's proposal in the minimum wage commission voting is not statistically significantly different from 50%. The model also suggests that the preference of the median voter among public interest commissioners determines the minimum wage level. Since the government appoints public interest commissioners and thus, in fact, the median voters, the government can decide the minimum wage level. This proposition is also consistent with data. The annual growth rate of the minimum wage under the progressive governments is higher than under conservative governments.