• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.381-394
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• 2016

Graphical model represents conditional dependencies between variables as a graph with nodes and edges. It is widely used in various fields including physics, economics, and biology to describe complex association. Conditional dependencies can be estimated from a inverse covariance matrix, where zero off-diagonal elements denote conditional independence of corresponding variables. This paper proposes a efficient BCDR (block coordinate descent with random permutation) algorithm using graphics processing units and random permutation for the CONCORD (convex correlation selection method) based on the BCD (block coordinate descent) algorithm, which estimates a inverse covariance matrix based on pseudo-likelihood. We conduct numerical studies for two network structures to demonstrate the efficiency of the proposed algorithm for the CONCORD in terms of computation times.

• KIISE Transactions on Computing Practices
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• v.23 no.10
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• pp.579-587
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• 2017

Cyber Mission Impact Assessment is one of the essential tasks which many militaries and industrial major companies should perform to effectively achieve their mission. The unexpected damage to an organization's assets results in damage to the whole system's performance of the organizations. In order to minimize the damage, it is necessary to quantify the available capacity of the mission, which can be achieved only with the remaining assets, and to immediately prepare a new second best plan in a moment. We therefore need to estimate the exact cyber attack's impact to the mission when the unwanted damage occurs by modeling the relationship between the assets and the missions. In this paper, we propose a new model which deals with the dependencies between assets and missions for obtaining the exact impact of a cyber attack. The proposed model distinguishes task management from asset management for an efficient process, and it is implemented to be optimized using a vectorized operation for parallel processing and using a buffer to reduce the computation time.

• The Transactions of the Korea Information Processing Society
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• v.4 no.6
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• pp.1636-1651
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• 1997

In task parallel language like Fortran M, programmer writes a task parallel program using parallel constructs which is provided. When some data dependencies exist between called procedures in various applications, it is difficult for programmer to write program according to their dependencies. Therefore, it is desirous that compiler can detect some implicit parallelisms and transform a program to parallelized form by using the task parallel constructs like PROCESSES block or PROCESSDO loop of Fortran M. But current task parallel language compilers can't provide these works. In this paper, we analyze the cases according to dependence relations and detect the implicit parallelism which can be transformed to task parallel constructs like PROCESSES block and PROCESSDO loop of Fortran M. Also, For the case which program can be paralleized both PROCESSES block and PROCESSDO loop, we analyze that which construct is more effective for various conditions.

• Journal of Environmental Health Sciences
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• v.34 no.2
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• pp.124-130
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• 2008

This study investigated the age dependencies in ambient air pollution-associated asthma hospitalization from 2003 to 2005 in Seoul. For all ages and the age groups of 0-14, 15-64, and 65+years, the Generalized Additive Model (GAM) was used to estimate the relative risks of daily asthma hospitalization associated with changes in particulate matter and ozone. The time-trends, seasonal variances, day effects, temperature, humidity, and pressure at sea level were controlled in the models. Significant associations were observed between asthma hospitalization and the levels of $PM_{10}$ and $O_3$. The relative risks (RRs) of asthma hospitalization for every 10 unit increases in $PM_{10}({\mu}g/m^3)$ and $O_3$(ppb) were 1.008 (95% CI 1.005-1.012), and 1.012 (95% CI 1.003-1.020), respectively. Evaluated over $10\;{\mu}g/m^3$ increase in $PM_{10}$, we found the relative risks of asthma hospitalization to be 1.009 (95% CI 1.004-1.014) in 0-14 age group, and 1.015 (95% CI 1.008-1.022) in 65+ age group. Considering 10 ppb increase in $O_3$, those were 1.014 (95% CI 1.003-1.024) in 0-14 age group, and 1.025 (95% CI 1.009-1.041) in 65+ age group. It was concluded that current levels of ambient air pollution in Seoul make a significant contribution to the variation in daily asthma hospitalization. Further reduction in air pollution is necessary to protect the health of the community, especially that of the higher risky groups including children and elderly population.

• Journal of The Korean Association of Information Education
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• v.21 no.2
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• pp.161-169
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• 2017

We often use spellcheckers in order to correct the syntactic errors in our documents. However, these computer programs are not enough for elementary school students, because their sentences are not smooth even after correcting the syntactic errors in many cases. In this paper, we introduce an automated method for evaluating the smoothness of two synonymous sentences. This method uses a recurrent neural network to solve the problem of long-term dependencies and exploits subwords to cope with the rare word problem. We trained the recurrent neural network language model based on a monolingual corpus of about two million English sentences. In our experiments, the trained model successfully selected the more smooth sentences for all of nine types of test set. We expect that our approach will help in elementary school writing after being implemented as an application for smart devices.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.763-782
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• 2019

This paper proposes a probabilistic document ranking model incorporating term dependencies. Document ranking is a fundamental information retrieval task. The task is to sort documents in a collection according to the relevance to the user query (Qin et al., Information Retrieval Journal, 13, 346-374, 2010). A probabilistic model is a model for computing the conditional probability of the relevance of each document given query. Most of the widely used models assume the term independence because it is challenging to compute the joint probabilities of multiple terms. Words in natural language texts are obviously highly correlated. In this paper, we assume a multinomial distribution model to calculate the relevance probability of a document by considering the dependency structure of words, and propose an information retrieval model to rank a document by estimating the probability with the maximum entropy method. The results of the ranking simulation experiment in various multinomial situations show better retrieval results than a model that assumes the independence of words. The results of document ranking experiments using real-world datasets LETOR OHSUMED also show better retrieval results.

• JSTS:Journal of Semiconductor Technology and Science
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• v.15 no.6
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• pp.685-694
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• 2015

In this paper, a new dynamic reference scheme is proposed to improve the read voltage margin better than the previous static reference scheme. The proposed dynamic reference scheme can be helpful in compensating not only the background pattern dependence but also the cell position dependence. The proposed dynamic reference is verified by simulating the CMOS-memristor hybrid circuit using the practical CMOS SPICE and memristor Verilog-A models. In the simulation, the percentage read voltage margin is compared between the previous static reference scheme and the new dynamic reference scheme. Assuming that the critical percentage of read voltage margin is 5%, the memristor array size with the dynamic scheme can be larger by 60%, compared to the array size with the static one. In addition, for the array size of $64{\times}64$, the interconnect resistance in the array with the dynamic scheme can be increased by 30% than the static reference one. For the array size of $128{\times}128$, the interconnect resistance with the proposed scheme can be improved by 38% than the previous static one, allowing more margin on the variation of interconnect resistance.

• The Transactions of the Korea Information Processing Society
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• v.5 no.5
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• pp.1172-1184
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• 1998

ILP(Instruction Level Parallelism) processors use code reordering algorithms to expose parallelism in a given sequential program. When applied to a loop, this algorithm produces a software-pipelined loop. In a software-pipelined loop, each iteration contains a sequence of parallel instructions that are composed of data-independent instructions collected across from several iterations. For vector loops, however the software pipelining technique can not expose the maximum parallelism because it schedules the program based only on data-dependencies. This paper proposes to schedule differently for vector loops. We develop an algorithm to detect vector loops at object code level and suggest a new vector scheduling algorithm for them. Our vector scheduling improves the performance because it can schedule not only based on data-dependencies but on loop structure or iteration conditions at the object code level. We compare the resulting schedules with those by software-pipelining techniques in the aspect of performance.

• Journal of the Korea Society of Computer and Information
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• v.15 no.12
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• pp.189-196
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• 2010

Domestic software test maturity level is the most Level 2 below based on TMMi. The first condition of enhancement of test maturity is that know exactly what the current maturity. There is no Level 1 definition in TMMi, but even the same level of maturity level of an organization, there are clear differences. In this study, grasp a maturity level of Level 1 organization, while less effort to improve in Level organization, the ways to achieve Level 2 is presented. Sub practices with dependencies in Level 2 is grouped with the corresponding process areas and define a Level 1 newly at 3 phases. If sub practices with dependencies are tied and improve a process, it will produce the effect which attains the practices all at once. Verify the adequacy of three phases, as a result, an organization clearly identify Test Maturity Level 1, we know that the organization can set specific improvement goals and direction.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.29-41
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• 2021

Recently, there are increasing attempts to utilize deep learning methodology in the fashion industry. Accordingly, research dealing with various fashion-related problems have been proposed, and superior performances have been achieved. However, the studies for fashion style classification have not reflected the characteristics of the fashion style that one outfit can include multiple styles simultaneously. Therefore, we aim to solve the multi-label classification problem by utilizing the dependencies between the styles. A multi-label recognition model based on a graph convolution network is applied to detect and explore fashion styles' dependencies. Furthermore, we accelerate model training and improve the model's performance through transfer learning. The proposed model was verified by a dataset collected from social network services and outperformed baselines.