Search

In this paper, an encryption scheme, based on a double deterministic phase mask and the Yang-Gu algorithm, is proposed. It consists of double random phase encoding and the Yang-Gu algorithm in Fresnel and Fourier transform, respectively. The random phase masks are replaced with deterministic phase masks that are constructed using chaotic logistic and cubic maps. The encrypted image obtained by the scheme in one-way binary modulation is real and more convenient for storage and transmission. The validation of the proposed cryptosystem is carried out using numerical simulations on greyscale images of Man, DICOM and CUH binary images. The proposed scheme is investigated for its robustness through statistical analysis using histograms and correlation plots. The scheme is also tested for its resistance against occlusion, noise and cryptographic attacks. The proposed cryptosystem is found to be highly sensitive to its private key and Fresnel parameters and therefore secure. [ABSTRACT FROM AUTHOR]

The circular cartogram, also known as the Dorling map, is a widely used tool for visualizing statistical data. It represents regions as circles with their areas in proportion to the statistical values and requires circle displacement to avoid overlap and maintain spatial relationships. In this paper, we propose a new approach for circular cartogram production that utilizes the elastic beam displacement algorithm in cartographic generalization. First, the initial circles are generated with their areas in proportion to the statistical values. Second, an elastic beam structure is built as a proximity graph based on the spatial relations between the circles. Third, the circles violating the quality requirements are considered to have a force on the nodes of a beam. Fourth, the elastic beam algorithm is applied to assign forces for each node to determine the new positions of the circles. Steps two through four are repeated until a circular cartogram that meets the defined quality requirements is obtained. The experiments indicate that the proposed approach can successfully generate circular cartograms without overlaps while maintaining topology and relative relationships with higher quality than existing approaches. Project website: [ABSTRACT FROM AUTHOR]

Data preprocessing is an essential task to prepare suitable target datasets to apply statistical and data mining algorithms. It has become one of the complex segments in Web usage mining due to the massive and unstructured nature of Web server log. The data preprocessing segment in Web usage mining is divided into several phases such as data fusion, data cleaning, user identification, session identification, path completion, and data formatting. This paper focuses on the initial phases of the Web usage mining process, such as data cleaning, user identification, and session identification. Due to the growing size of log data at terabyte and petabyte scale, traditional data preprocessing algorithms fail at scalability points and possess Big Data issues. During the previous few years, the MapReduce framework has evolved as one of the most used parallel programming frameworks for processing Big Data on a cluster of nodes. In this paper, a MapReduce-based data preprocessing algorithm is developed. This algorithm comprises data preprocessing subphases such as data cleaning, user identification, and session identification. Various efficient heuristics are incorporated into existing MapReduce-based data preprocessing algorithm to detect ethical and unethical robots. Further several experiments are performed on a cluster of nodes and found that the proposed MapReduce-based data preprocessing algorithm is efficient and scalable for larger datasets. Moreover, we have also analyzed the impact of robots' requests on sessions generated in the session identification phase to measure the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable. Such incomplete observation of the response variable is called censoring. Censorship can occur for a variety of reasons, including limitations of measurement equipment, design of the experiment, and non-occurrence of the event of interest until the end of the study. In the presence of censoring, the dependence of the response variable on the explanatory variables can be explored through regression analysis. In this paper, we propose to examine the censorship problem in context of the class of asymmetric, i.e., we have proposed a linear regression model with censored responses based on skew scale mixtures of normal distributions. We develop a Monte Carlo EM (MCEM) algorithm to perform maximum likelihood inference of the parameters in the proposed linear censored regression models with skew scale mixtures of normal distributions. The MCEM algorithm has been discussed with an emphasis on the skew-normal, skew Student-t-normal, skew-slash and skew-contaminated normal distributions. To examine the performance of the proposed method, we present some simulation studies and analyze a real dataset. [ABSTRACT FROM AUTHOR]

The paper proposes a multi-scale uncertainty quantification framework of composite laminated plates. The micro and macro uncertainty parameters are represented by random and interval variables according to available uncertainty information. The data-driven Polynomial Chaos Expansion model is constructed, where polynomial coefficients of random variables are determined based on their high-dimensional statistical information, and coefficients of interval variables are calculated using sampling analysis method. The proposed framework is applied for multi-scale buckling analysis, natural frequency calculation and reliability estimation. The comparison results with Monte Carlo Sampling method demonstrate the computational superiority of the proposed algorithm to achieve high accuracy in multiple conditions. [ABSTRACT FROM AUTHOR]