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Universal steganography detector based on an artificial immune system for JPEG images 15

Universal steganography detector based on an artificial immu...

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Joint 15th IEEE International conference on Trust, Security and Privacy in Computing and Communications, 10th IEEE International conference on Big Data Science and Engineering and 14th IEEE International Symposium on parallel and distributed processing with Applications, IEEE TrustCom/BigDataSE/ISPA 2016

作者： Pérez, José De Jesús Serrano Rosales, Moises Salinas Cruz-Cortés, Nareli Cybersecurity Lab Centro de Investigación en Computación Instituto Politécnico Nacional Mexico City Mexico

ISBN: (纸本)9781509032051

Steganography is a hiding information technique heavily used nowadays. Though initially it was used to establish hidden communication channels, modern steganography has been found useful to hide code inside multimedia objects, mostly images. Its goal is to infiltrate malware into organizations or personal devices. This kind of malware is called stegomalware. As countermeasure, modern steganalysis methods employing different Computational Intelligence techniques such as Support Vector Machine, Machine Learning, Fisher Linear Discriminant, and others have been utilized. In this work we present a new stegananalysis method based on an Artificial Immune System (AIS), to detect JPEG images modified with three well known steganographic tools: F5, Outguess, or Steghide. It is also proposed the usage of Haar Wavelets to extract a feature vector that best describes the analyzed image, this is due the Haar Wavelets fast calculation and information synthesis. Our experimentation results are competitive against techniques representative of the state of the art. © 2016 IEEE.

关键词： image analysis

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Reversible Data Hiding Using Non-local Means Prediction 16th

Reversible Data Hiding Using Non-local Means Prediction

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16th International conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Fang, Yingying Ou, Bo Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9783319499567;9783319499550

In this paper, we propose a prediction-error expansion based reversible data hiding scheme by incorporating non-local means (NLM) prediction. The traditional local predictors reported in literatures rely on the local correlation and behave badly in predicting textural pixels. To remedy this, we propose to use NLM to achieve better prediction in texture regions and globally utilize the potential self-similarity contained in the image itself. More specifically, the textural pixels distinguished by its local complexity are predicted by NLM while the smooth pixels having high local correlation are predicted by a local predictor. The incorporation of NLM makes the proposed method possible to achieve accurate predictions in both smooth and texture regions. Optimal parameters in the method are obtained by minimizing the prediction-error entropy. Experimental results show that the proposed method can yield an improvement compared with some state-of-the-art methods.

关键词： Reversible data hiding Non-local means image similarity

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High Performance parallel Summed-Area Table Kernels for Multi-core and Many-core Systems 22nd

High Performance Parallel Summed-Area Table Kernels for Mult...

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22nd International conference on parallel and distributed Computing (Euro-Par)

作者： Papatriantafyllou, Angelos Sacharidis, Dimitris TU Wien Fac Informat Inst Informat Syst Res Grp Parallel Comp Vienna Austria TU Wien Fac Informat Inst Software Technol & Interact Syst E Commerce Grp Vienna Austria

ISBN: (纸本)9783319436593;9783319436586

The summed-area table ( SAT), also known as integral image, is a data structure extensively used in computer graphics and vision for fast image filtering. The parallelization of its construction has been thoroughly investigated and many algorithms have been proposed for GPUs. Generally speaking, state-of-the-art methods cannot efficiently solve this problem in multi-core and many-core (Xeon Phi) systems due to cache misses, strided and/or remote memory accesses. This work proposes three novel cache-aware parallel SAT algorithms, which generalize parallel block-based prefix-sums algorithms. In addition, we discuss 2D matrix partitioning policies which play an important role in the efficient operation of the cache subsystem. The combination of a SAT algorithm and a partition is manually tuned according to the matrix layout and the number of threads. Experimental evaluation of our algorithms on two NUMA systems and Intel's Xeon Phi, and for three datatypes (int, float, double) by utilizing all system cores, shows, in all experimental settings, better performance compared to the best known CPU and GPU approaches (up to 4.55x on NUMA and 2.8x on Xeon Phi).

关键词： Program processors

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SinglA image super-resolution via classified sparse Representation 13

SinglA image super-resolution via classified sparse Represen...

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13th International conference on Embedded Software and System, ICESS 2016

作者： Lai, Chao Li, Fangzhao Li, Bao Jin, Shiyao National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781509037261

Single image super-resolution reconstruction is a challenging ill-posed inverse problem currently. In this paper, we propose a method based on image classification and sparse representation for single image super-resolution reconstruction. Various images belong to different image category, and each category contains different contents and structures respectively, especially the high-frequency feature. Therefore, we extract the features of the input low-resolution image, and classify it into the corresponding category. Then the high-resolution image is reconstructed by sparse representation with the dictionary trained from the corresponding database, which consists of high-resolution and low-resolution image patch pairs. The experimental results demonstrate that our method achieves better performance in visual effect and qualitative analysis, by comparison with some well-known methods. © 2016 IEEE.

关键词： image classification

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Enabling Factor Analysis on Thousand-Subject Neuroimaging Datasets 4

Enabling Factor Analysis on Thousand-Subject Neuroimaging Da...

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4th IEEE International conference on Big Data (Big Data)

作者： Anderson, Michael J. Capota, Mihai Turek, Javier S. Zhu, Xia Willke, Theodore L. Wang, Yida Chen, Po-Hsuan Manning, Jeremy R. Ramadge, Peter J. Norman, Kenneth A. Intel Corp Parallel Comp Lab Hillsboro OR 97124 USA Princeton Univ Princeton NJ 08544 USA Dartmouth Coll Hanover NH 03755 USA

ISBN: (纸本)9781467390057

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and the Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99x and 2062x speedups on the two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5x respectively with 20 nodes on real datasets. We demonstrate weak scaling on a synthetic dataset with 1024 subjects, equivalent in size to the biggest fMRI dataset collected until now, on up to 1024 nodes and 32,768 cores.

关键词： functional Magnetic Resonance Imaging Multi-subject Analysis Scaling Factor Analysis

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A parallel microsimulation package for modelling cancer screening policies 12

A parallel microsimulation package for modelling cancer scre...

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12th IEEE International conference on e-Science (e-Science)

作者： Karlsson, Andreas Olofsson, Niten Laure, Erwin Clements, Mark Karolinska Inst Dept Med Epidemiol & Biostat Stockholm Sweden SeRC Stockholm Sweden Karolinska Inst Dept Neurosci Stockholm Sweden Combitech AB Stockholm Sweden Royal Inst Technol Ctr High Performance Comp Stockholm Sweden

ISBN: (纸本)9781509042739

Microsimulation with stochastic life histories is an important tool in the development of public policies. In this article, we use microsimulation to evaluate policies for prostate cancer testing. We implemented the microsimulations as an R package, with pre- and post-processing in R and with the simulations written in C++. Calibrating a microsimulation model with a large population can be computationally expensive. To address this issue, we investigated four forms of parallelism: (i) shared memory parallelism using R;(ii) shared memory parallelism using OpenMP at the C++ level;(iii) distributed memory parallelism using R;and (iv) a hybrid shared/distributed memory parallelism using OpenMP at the C++ level and MPI at the R level. The close coupling between R and C++ offered advantages for ease of software dissemination and the use of high-level R parallelisation methods. However, this combination brought challenges when trying to use shared memory parallelism at the C++ level: the performance gained by hybrid OpenMP/MPI came at the cost of significant re-factoring of the existing code. As a case study, we implemented a prostate cancer model in the microsimulation package. We used this model to investigate whether prostate cancer testing with specific re-testing protocols would reduce harms and maintain any mortality benefit from prostate-specific antigen testing. We showed that four-yearly testing would have a comparable effectiveness and a marked decrease in costs compared with two-yearly testing and current testing. In summary, we developed a microsimulation package in R and assessed the cost-effectiveness of prostate cancer testing. We were able to scale up the microsimulations using a combination of R and C++, however care was required when using shared memory parallelism at the C++ level.

关键词： C++ languages Prostate cancer parallel processing Libraries Testing Computational modeling

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Bilateral histogram equalization with pre-processing for contrast enhancement

Bilateral histogram equalization with pre-processing for con...

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ACIS International conference on Software Engineering, Artificial Intelligence, Networking, and parallel/distributed Computing (SNPD)

作者： Feroz Mahmud Amil Md. Mostafijur Rahman Shanto Rahman Emon Kumar Dey Mohammad Shoyaib Institute of Information Technology University of Dhaka Institute of Information Technology University of Dhaka Bangladesh

Various methods have been proposed for enhancing the images. Some of those perform well in some specific application areas but most of the techniques suffer from artifacts due to over enhancement. To overcome this problem, we have introduced a new image enhancement technique namely Bilateral Histogram Equalization with Pre-processing (BHEP) which uses Harmonic mean to divide the histogram of the image. We have performed both qualitative and quantitative measurements for experiments and the results show that BHEP creates less artifacts in several standard images than the existing state-of-the-art image enhancement techniques.

关键词： Histograms Mathematical model image enhancement Harmonic analysis image color analysis Adaptive equalizers image edge detection

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2016 3rd International conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016

2016 3rd International Conference on Informative and Cyberne...

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3rd International conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016

ISBN: (纸本)9781509033676

The proceedings contain 75 papers. The topics discussed include: a kernel logistic neural network based on restricted Boltzmann machine;random feature based multiple kernel clustering;exponential distributed leader-follower consensus formation control of a networked fourth-order multi-quadrotor system;adaptive fuzzy fault tolerant control of unmeasured states nonlinear systems based on fault alarm;UKF for nonlinear systems with event-triggered data transmission and packet dropout;approximation learning methods of harmonic mappings in relation to hardy spaces;comparison between genetic algorithm and differential evolution algorithm applied to one dimensional bin-packing problem;three-dimensional cement image registration based on multi-layer PSO and mutual information;parallel rational world based privacy preservation mechanism for group privacy;S-KNN: an efficient approach for processing k-NN queries over moving objects;clothes classification based on deep belief network;and the prediction of cement compressive strength based on gray level images and neural network.

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image Enhancement Based on Bi-Histogram Equalization with Non-Parametric Modified Technology

Image Enhancement Based on Bi-Histogram Equalization with No...

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International conference on parallel and distributed Systems (ICPADS)

作者： Zhijun Yao Quan Zhou Zhongyuan Lai Zhiming Ren Liming Liu The 723 Institute of China Shipbuilding Industry Corporation Yangzhou China Key Lab of Ministry of Education for Broad Band Communication & Sensor Network Technology Nanjing University of Posts & Telecommunication Nanjing China Institute for Interdisciplinary Research Jianghan University Wuhan China

ISBN: (纸本)9781509053827

This paper presents a new image enhancement method using histogram equalization called Bi-Histogram Equalization with Non-parametric Modified Technology (BHENMT). Our proposed method consists of three steps: (i) The input original histogram is divided into two parts using the Otsu method. (ii) Then the histogram modification technique is used to control over enhancement and maximize entropy. (iii) Two sub images are enhanced by the traditional histogram equalization method using the corresponding modified histogram respectively and finally are merged into one output enhanced image. The experimental results show that BHENMT is better than other contrast enhancement methods according to subjective evaluation and various image objective evaluation measures, i.e. Entropy, AMBE and PSNR.

关键词： Histograms image segmentation image enhancement Entropy Brightness Mathematical model Optimization

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RPL: A Domain-Specific Language for Designing and Implementing parallel C++ Applications

RPL: A Domain-Specific Language for Designing and Implementi...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： V. Janjic C. Brown K. Mackenzie K. Hammond M. Danelutto M. Aldinucci J. Daniel Garcia Uni. of St Andrews UK Uni. of Pisa Italy Uni. of Torino Italy Uni. Carlos III Madrid Spain

parallelising sequential applications is usually a very hard job, due to many different ways in which an application can be parallelised and a large number of programming models (each with its own advantages and disadvantages) that can be used. In this paper, we describe a method to semi-automatically generate and evaluate different parallelisations of the same application, allowing programmers to find the best parallelisation without significant manual reengineering of the code. We describe a novel, high-level domain-specific language, Refactoring Pattern Language (RPL), that is used to represent the parallel structure of an application and to capture its extra-functional properties (such as service time). We then describe a set of RPL rewrite rules that can be used to generate alternative, but semantically equivalent, parallel structures (parallelisations) of the same application. We also describe the RPL Shell that can be used to evaluate these parallelisations, in terms of the desired extra-functional properties. Finally, we describe a set of C++ refactorings, targeting OpenMP, Intel TBB and FastFlow parallel programming models, that semi-automatically apply the desired parallelisation to the application's source code, therefore giving a parallel version of the code. We demonstrate how the RPL and the refactoring rules can be used to derive efficient parallelisations of two realistic C++ use cases (image Convolution and Ant Colony Optimisation).

关键词： Skeleton Libraries parallel processing Pipelines parallel programming Writing

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