• Journal of Information Processing Systems
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• v.14 no.4
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• pp.837-850
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• 2018

Bitcoin is a decentralized crypto-currency, which is based on the peer-to-peer network, and was introduced by Satoshi Nakamoto in 2008. Bitcoin transactions are written by using a scripting language. The hash value of a transaction's script is used to identify the transaction over the network. In February 2014, a Bitcoin exchange company, Mt. Gox, claimed that they had lost hundreds of millions US dollars worth of Bitcoins in an attack known as transaction malleability. Although known about since 2011, this was the first known attack that resulted in a company loosing multi-millions of US dollars in Bitcoins. Our reason for writing this paper is to understand Bitcoin transaction malleability and to propose an efficient solution. Our solution is a softfork (i.e., it can be gradually implemented). Towards the end of the paper we present a detailed analysis of our scheme with respect to various transaction malleability-based attack scenarios to show that our simple solution can prevent future incidents involving transaction malleability from occurring. We compare our scheme with existing approaches and present an analysis regarding the computational cost and storage requirements of our proposed solution, which shows the feasibility of our proposed scheme.

• The Transactions of the Korea Information Processing Society
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• v.6 no.9
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• pp.2311-2319
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• 1999

We present two heuristic algorithms for the task allocation problem (NP-complete problem) in parallel computing. The problem is to find an optimal mapping of multiple communicating tasks of a parallel program onto the multiple processing nodes of a distributed-memory multicomputer. The purpose of mapping these tasks into the nodes of the target architecture is the minimization of parallel execution time without sacrificing solution quality. Many heuristic approaches have been employed to obtain satisfactory mapping. Our heuristics are based on genetic algorithms and simulated annealing. We formulate an objective function as a total computational cost for a mapping configuration, and evaluate the performance of our heuristic algorithms. We compare the quality of solutions and times derived by the random, greedy, genetic, and annealing algorithms. Our experimental findings from a simulation study of the allocation algorithms are presented.

• The KIPS Transactions:PartB
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• v.12B no.2 s.98
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• pp.209-214
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• 2005

The use of protein interaction data is highly reliable for predicting functions to proteins without function in proteomics study. The computational studies on protein function prediction are mostly based on the concept of guilt-by-association and utilize large-scale interaction map from revealed protein-protein interaction data. This study compares graph-based approaches such as neighbor-counting and $\chi^2-statistics$ methods using protein-protein interaction data and proposes an approach that is effective in analyzing large-scale protein interaction data. The proposed approach is also based protein interaction map but sequence similarity and heuristic knowledge to make prediction results more reliable. The test result of the proposed approach is given for KDD Cup 2001 competition data along with those of neighbor-counting and $\chi^2-statistics$ methods.

• IE interfaces
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• v.20 no.2
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• pp.186-194
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• 2007

Recent deregulation of Korean electricity industry has made each power generation company pay more attention to maximizing its own profit instead of minimizing the overall system operation cost while guaranteeing system security. Electricity power generation problem is typically defined as the problem of determining both the on and off status and the power generation level of each generator under the given fuel constraints, which has been known as Profit-Based Unit Commitment (PBUC) problem. To solve the PBUC problem, the previous research mostly focused on devising Lagrangian Relaxation (LR) based heuristic algorithms due to the complexity of the problem and the nonlinearity of constraints and objectives. However, these heuristic approaches have been reported as less practical in real world applications since the computational run time is usually quite high and it may take a while to implement the devised heuristic algorithms as software applications. Especially when considering long-term planning problem which spans at least one year, the complexity becomes higher. Therefore, this paper proposes an explicit column generation algorithm using power generation patterns and the proposed algorithm is successfully applied to a Korean power generation company. The proposed scheme has a robust structure so that it is expected to extend general PBUC problems.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.123-136
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• 2015

Science has developed with great achievements after Galileo's discovery of the law depicting a relationship between observable variables. However, many natural phenomena have been better explained by models including unobservable random effects. A mixed effect model was the first statistical model that included unobservable random effects. The importance of the mixed effect models is growing along with the advancement of computational technologies to infer complicated phenomena; subsequently mixed effect models have extended to various statistical models such as hierarchical generalized linear models. Hierarchical likelihood has been suggested to estimate unobservable random effects. Our special issue about mixed effect models shows how they can be used in statistical problems as well as discusses important needs for future developments. Frequentist and Bayesian approaches are also investigated.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.59-74
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• 2007

Computer simulation models are in general quite complex and time-consuming to run, and therefore, a simpler meta-model is usually constructed for further analysis. In this paper, JANUS, a war-game simulator, is used to describe a certain tank combat situation. Then, second-order response surface and artificial neural network meta-models are developed using the data from eight different experimental designs. Relative performances of the developed meta-models are compared in terms of the mean squared error of prediction. Computational results indicate that, for the given problem, the second-order response surface meta-model generally performs better than the neural network, and the orthogonal array-based Latin hypercube design(LHD) or LHD using maximin distance criterion may be recommended.

• Journal of Life Science
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• v.17 no.5 s.85
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• pp.714-718
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• 2007

The high-throughput sequencing of a lot of genomes has resulted in the relatively rapid accumulation of an enormous amount of genomic sequence data. In this context, the problem posed by the detection of promoters in genomic DNA sequences via computational methods has attracted considerable attention in recent years since exact promoter prediction can give a clue to the elucidation of overall genetic networks. In this study, applications of support vector machine(SVM) to promoter prediction are explored to show a right approaches to discriminate between promoter and non-promoter regions by means of SVM. The results of various experiments show that encoding method, encoding region and learning data constitution can play an important role in the performance of SVM.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.4204-4222
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• 2015

Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.3
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• pp.30-37
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• 2000

Management science/operations research (MS/OR) educational software packages are widely used at the present time. Those software packages are expected to help students understand MS/OR techniques better. However, MS/OR educational software packages are often used as computational tools to obtain model solutions efficiently rather than as the tutoring software packages. Several possible reasons for the lack of effective tutoring capacity in MS/OR educational software packages are identified in this paper. The authors believe that the deficiency of tutoring capacity in those software is mainly due to technological limitations (computers and artificial intelligence) and the MS/OR professionals' perception about those software packages. Given technological limitations, feasible design and development approaches are provided to improve the tutoring effectiveness of MS/OR educational software packages.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.332-339
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• 2003

To solve the large scale optimization problem approximately, various approaches have been introduced. Recently the Particle Swarm Optimization has been introduced. The Particle Swarm Optimization simulates the process of birds flocking or fish schooling for food, as with the information of each agent is skated by other agents. The Particle Swarm Optimization technique has been applied to various optimization problems whose variables are continuous. However, there are seldom trials for visualization of searching process. This paper proposes a new visualization tool for searching process of Particle Swarm Optimization algorithm. The proposed tool is effective for understanding the searching process of Particle Swarm Optimization method and educational for students. The computational results can be shown tiny and very helpful for education.