• Title/Summary/Keyword: random data analysis

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Safety Comparison Analysis Against Known/Chosen Plaintext Attack of RBF (Random Block Feedback) Mode to Other Block Cipher Modes of Operation (블록 암호 연산 모드 RBF(Random Block Feedback)의 알려진/선택 평문 공격에 대한 안전성 비교 분석)

  • Kim, Yoonjeong;Yi, Kang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.39B no.5
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    • pp.317-322
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    • 2014
  • Data security and integrity is a critical issue in data transmission over wired/wireless links. A large amount of data is encrypted before transmission, by block cipher using mode of operation. RBF mode is a block cipher mode of operation which uses random characteristics. In this paper, we analyze the safety against known plaintext attack and chosen plaintext attack of RBF mode compared to the traditional modes. According to the analysis, RBF mode is known to be secure while the traditional modes are not secure against them.

Asymptotic Test for Dimensionality in Probabilistic Principal Component Analysis with Missing Values

  • Park, Chong-sun
    • Communications for Statistical Applications and Methods
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    • v.11 no.1
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    • pp.49-58
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    • 2004
  • In this talk we proposed an asymptotic test for dimensionality in the latent variable model for probabilistic principal component analysis with missing values at random. Proposed algorithm is a sequential likelihood ratio test for an appropriate Normal latent variable model for the principal component analysis. Modified EM-algorithm is used to find MLE for the model parameters. Results from simulations and real data sets give us promising evidences that the proposed method is useful in finding necessary number of components in the principal component analysis with missing values at random.

Negative binomial loglinear mixed models with general random effects covariance matrix

  • Sung, Youkyung;Lee, Keunbaik
    • Communications for Statistical Applications and Methods
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    • v.25 no.1
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    • pp.61-70
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    • 2018
  • Modeling of the random effects covariance matrix in generalized linear mixed models (GLMMs) is an issue in analysis of longitudinal categorical data because the covariance matrix can be high-dimensional and its estimate must satisfy positive-definiteness. To satisfy these constraints, we consider the autoregressive and moving average Cholesky decomposition (ARMACD) to model the covariance matrix. The ARMACD creates a more flexible decomposition of the covariance matrix that provides generalized autoregressive parameters, generalized moving average parameters, and innovation variances. In this paper, we analyze longitudinal count data with overdispersion using GLMMs. We propose negative binomial loglinear mixed models to analyze longitudinal count data and we also present modeling of the random effects covariance matrix using the ARMACD. Epilepsy data are analyzed using our proposed model.

A New Approach for Information Security using an Improved Steganography Technique

  • Juneja, Mamta;Sandhu, Parvinder Singh
    • Journal of Information Processing Systems
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    • v.9 no.3
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    • pp.405-424
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    • 2013
  • This research paper proposes a secured, robust approach of information security using steganography. It presents two component based LSB (Least Significant Bit) steganography methods for embedding secret data in the least significant bits of blue components and partial green components of random pixel locations in the edges of images. An adaptive LSB based steganography is proposed for embedding data based on the data available in MSB's (Most Significant Bits) of red, green, and blue components of randomly selected pixels across smooth areas. A hybrid feature detection filter is also proposed that performs better to predict edge areas even in noisy conditions. AES (Advanced Encryption Standard) and random pixel embedding is incorporated to provide two-tier security. The experimental results of the proposed approach are better in terms of PSNR and capacity. The comparison analysis of output results with other existing techniques is giving the proposed approach an edge over others. It has been thoroughly tested for various steganalysis attacks like visual analysis, histogram analysis, chi-square, and RS analysis and could sustain all these attacks very well.

Taxation Analysis Using Machine Learning (머신러닝을 이용한 세금 계정과목 분류)

  • Choi, Dong-Bin;Jo, In-su;Park, Yong B.
    • Journal of the Semiconductor & Display Technology
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    • v.18 no.2
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    • pp.73-77
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    • 2019
  • Data mining techniques can also be used to increase the efficiency of production in the tax sector, which requires professional skills. As tax-related computerization was carried out, large amounts of data were accumulated, creating a good environment for data mining. In this paper, we have developed a system that can help tax accountant who have existing professional abilities by using data mining techniques on accumulated tax related data. The data mining technique used is random forest and improved by using f1-score. Using the implemented system, data accumulated over two years was learned, showing high accuracy at prediction.

A simple and efficient data loss recovery technique for SHM applications

  • Thadikemalla, Venkata Sainath Gupta;Gandhi, Abhay S.
    • Smart Structures and Systems
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    • v.20 no.1
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    • pp.35-42
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    • 2017
  • Recently, compressive sensing based data loss recovery techniques have become popular for Structural Health Monitoring (SHM) applications. These techniques involve an encoding process which is onerous to sensor node because of random sensing matrices used in compressive sensing. In this paper, we are presenting a model where the sampled raw acceleration data is directly transmitted to base station/receiver without performing any type of encoding at transmitter. The received incomplete acceleration data after data losses can be reconstructed faithfully using compressive sensing based reconstruction techniques. An in-depth simulated analysis is presented on how random losses and continuous losses affects the reconstruction of acceleration signals (obtained from a real bridge). Along with performance analysis for different simulated data losses (from 10 to 50%), advantages of performing interleaving before transmission are also presented.

Performance Analysis of Turbo-Code with Random (and s-random) Interleaver based on 3-Dimension Algorithm (3차원 알고리듬을 이용한 랜덤(or s-랜덤) 인터리버를 적용한 터보코드의 성능분석)

  • Kong, Hyung-Yun;Choi, Ji-Woong
    • The KIPS Transactions:PartA
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    • v.9A no.3
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    • pp.295-300
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    • 2002
  • In this paper, we apply the 3-dimension algorithm to the random interleaver and s-random interleaver and analyze the performance of the turbo code system with random interleaver (or s-random interleaver). In general, the performance of interleaver is determined by minimum distance between neighbor data, thus we could improve the performance of interleaver by increasing the distance of the nearest data. The interleaver using 3-dimension algorithm has longer minimum distance and average distance compared to existing random-interleaver (s-random interleaver) because the output data is generated randomly from 3-dimension storage. To verify and compare the performance of our proposed system, the computer simulations have been performed in turbo code system under gaussian noise environment.

Effects of Call-back Rules and Random Selection of Respondents: Statistical Re-analysis of R&R’s Ulsan Survey Data. (전화조사에서 재통화 규칙준수와 응답자 임의선택의 영향 - R&R 울산 사례의 통계적 재분석 -)

  • 허명회;임여주;노규형
    • The Korean Journal of Applied Statistics
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    • v.16 no.2
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    • pp.247-259
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    • 2003
  • In Korea, quota sampling is mainly adopted in telephone surveys, instead of random sampling which requires call-back procedure and random selection of respondent within households. The contact mode based on the se $x^{*}$age quotas is economically more advantageous and less time-consuming. However, it lacks theoretical ground for valid statistical inference, so that it is hardly accepted in academic circles despite of widely spread practice. Subsequently, survey theoreticians argued that random sampling-based telephone surveys should be tried. In response, Research & Research (R&R), a private research company in Seoul, executed atelephone survey by random sampling mode for the prediction of 2002 Ulsan City Mayor Election. The aim of this case study is to find out various effects of the call-back rule with random selection of respondents by statistically re-analyzing R&R’s Ulsan Survey Data.s by statistically re-analyzing R&R’s Ulsan Survey Data.

Multiple Comparisons With the Best in the Analysis of Covariance

  • Lee, Young-Hoon
    • Journal of the Korean Statistical Society
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    • v.23 no.1
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    • pp.53-62
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    • 1994
  • When a comparison is made with respect to the unknown best treatment, Hsu (1984, 1985) proposed the so called multiple comparisons procedures with the best in the analysis of variance model. Applying Hsu's results to the analysis of covariance model, simultaneous confidence intervals for multiple comparisons with the best in a balanced one-way layout with a random covariate are developed and are applied to a real data example.

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An Analysis of Panel Count Data from Multiple random processes

  • Park, You-Sung;Kim, Hee-Young
    • Proceedings of the Korean Statistical Society Conference
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    • 2002.11a
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    • pp.265-272
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    • 2002
  • An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

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