Securing color image transmission using compression-encryption model with dynamic key generator and efficient symmetric key distribution

There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve these problems.This model consists of three processes.The first process is the dynamic symmetric key generation.The second one is the compression process,which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map.The last process is the symmetric key distribution.The symmetric key is encrypted twice using Rivest-Shamir-Adleman(RSA)to provide both authentication and confidentiality.Then,it is inserted into the cipher image using the End of File(EoF)method.The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key.Hence,the image data is safe from ciphertext-only attacks.This model is fast and able to withstand entropy attacks,statistical attacks,differential attacks,and brute-force attacks.

Distribution of the Permian Monodiexodina in Karakorum and Kunlun and its Geological Significance

The Permian fusulinoidean genus Monodiexodina is widely distributed in east Tethys. The genus might be an important indicator for the northern margin of Gondwana in northwestern China, but this is disputed. Monodiexodina-bearing areas can be restored as in either northern or southern middle latitudes with a symmetrical distribution between a high latitudinal, cool/cold water climatic realm and a paleotropical, warm water realm. Permian strata bearing Monodiexodina in Karakorum, Muztag Pear, and Buka Daban Pear of the east Kunlun Mountains can be correlated with each other. Faunal analyses and the stratigraphical position of Monodiexodina-bearing strata indicate that both Karakorum, east Kunlun, and the Pamirs were formed in a cool temperate sea area of the northern hemisphere in middle latitudes during the Permian, rather than at the Gondwana margin.

Supervised Learning Algorithm on Unstructured Documents for the Classification of Job Offers: Case of Cameroun

Nowadays, in data science, supervised learning algorithms are frequently used to perform text classification. However, African textual data, in general, have been studied very little using these methods. This article notes the particularity of the data and measures the level of precision of predictions of naive Bayes algorithms, decision tree, and SVM (Support Vector Machine) on a corpus of computer jobs taken on the internet. This is due to the data imbalance problem in machine learning. However, this problem essentially focuses on the distribution of the number of documents in each class or subclass. Here, we delve deeper into the problem to the word count distribution in a set of documents. The results are compared with those obtained on a set of French IT offers. It appears that the precision of the classification varies between 88% and 90% for French offers against 67%, at most, for Cameroonian offers. The contribution of this study is twofold. Indeed, it clearly shows that, in a similar job category, job offers on the internet in Cameroon are more unstructured compared to those available in France, for example. Moreover, it makes it possible to emit a strong hypothesis according to which sets of texts having a symmetrical distribution of the number of words obtain better results with supervised learning algorithms.

Variable bit rate video traffic modeling by multiplicative multifractal model

Multiplicative multifractal process could well modal video traffic. The multiplier distributions in the multiplicatire multifractal model for video traffic are investigated and it is found that Gaussian is not suitable for describing the multipliers on the small time scales. A new statistical distribution-symmetric Pareto distribution is introduced. It is applied instead of Gaussian for the multipliers on those scales. Based on that, the algorithm is updated so that symmetric pareto distribution and Gaussian distribution are used to model video traffic but on different time scales. The simulation results demonstrate that the algorithm could model video traffic more accurately.

Double Moving Extremes Ranked Set Sampling Design

The traditional simple random sampling(SRS)design method is inefficient in many cases.Statisticians proposed some new designs to increase efficiency.In this paper,as a variation of moving extremes ranked set sampling(MERSS),double MERSS(DMERSS)is proposed and its properties for estimating the population mean are considered.It turns out that,when the underlying distribution is symmetric,DMERSS gives unbiased estimators of the population mean.Also,it is found that DMERSS is more efficient than the SRS and MERSS methods for usual symmetric distributions(normal and uniform).For asymmetric distributions considered in this study,the DMERSS has a small bias and it is more efficient than SRS for usual asymmetric distribution(exponential)for small sample sizes.

TESTING INEQUALITY CONSTRAINTS IN A LINEAR REGRESSION MODEL WITH SPHERICALLY SYMMETRIC DISTURBANCES

This paper develops a Wald statistic for testing the validity of multivariate inequality constraints in linear regression models with spherically symmetric disturbances,and derive the distributions of the test statistic under null and nonnull hypotheses.The power of the test is then discussed.Numerical evaluations are also carried out to examine the power performances of the test for the case in which errors follow a multivariate student-t(Mt) distribution.

Distribution Estimation with Smoothed Auxiliary Information

Distribution estimation is very important in order to make statistical inference for parameters or its functions based on this distribution. In this work we propose an estimator of the distribution of some variable with non-smooth auxiliary information, for example, a symmetric distribution of this variable, A smoothing technique is employed to handle the non-differentiable function. Hence, a distribution can be estimated based on smoothed auxiliary information. Asymptotic properties of the distribution estimator are derived and analyzed. The distribution estimators based on our method are found to be significantly efficient than the corresponding estimators without these auxiliary information. Some simulation studies are conducted to illustrate the finite sample performance of the proposed estimators.

Design and measurement of a piezoresistive triaxial accelerometer based on MEMS technology

With the springing up of the MEMS industry,research on accelerometers is focused on miniaturization, integration,high reliability,and high resolution,and shares extensive application prospects in military and civil fields.Comparing with the traditional single cantilever beam structure or ＂cantilever-mass＂ structure,the proposed structure of ＂8-beams/mass＂ with its varistor completely symmetric distribution in micro-silicon piezoresistive triaxial accelerometer in this paper has a higher axial sensitivity and smaller cross-axis sensitivity.Adopting ANSYS, the process of structural analysis and the manufacturing flow of sensing unit are showed.In dynamic testing conditions, it can be concluded that the axial sensitivity of x,y,and z are Sx-48μV/g,Sy = 54μV/g and Sz = 217μV/g respectively,and the nonlinearities are 0.4%,0.6%and 0.4%.

High-dimensional proportionality test of two covariance matrices and its application to gene expression data

With the development of modern science and technology, more and more high-dimensionaldata appear in the application fields. Since the high dimension can potentially increase the com-plexity of the covariance structure, comparing the covariance matrices among populations isstrongly motivated in high-dimensional data analysis. In this article, we consider the proportion-ality test of two high-dimensional covariance matrices, where the data dimension is potentiallymuch larger than the sample sizes, or even larger than the squares of the sample sizes. We devisea novel high-dimensional spatial rank test that has much-improved power than many exist-ing popular tests, especially for the data generated from some heavy-tailed distributions. Theasymptotic normality of the proposed test statistics is established under the family of ellipticallysymmetric distributions, which is a more general distribution family than the normal distribu-tion family, including numerous commonly used heavy-tailed distributions. Extensive numericalexperiments demonstrate the superiority of the proposed test in terms of both empirical sizeand power. Then, a real data analysis demonstrates the practicability of the proposed test forhigh-dimensional gene expression data.