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On-Demand processing for Remote Sensing Big Data Analysis 15

On-Demand Processing for Remote Sensing Big Data Analysis

引用

15th IEEE International Symposium on parallel and distributed processing with Applications (ISPA) / 16th IEEE International conference on Ubiquitous Computing and Communications (IUCC)

作者： Huang, Zhenchun Zhong, Anrun Li, Guoqing Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China

ISBN: (纸本)9781538637906

In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as "big geo data" because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, "on-demand processing", which tries to upload data analysis models and execute them "near" where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture;XML/JSON based runtime environment description;and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 similar to 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world's most powerful super computer.

关键词： remote sensing data analysis on-demand processing big geo data

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On Extending a Fixed Size, Persistent and Lock-Free Hash Map Design to Store Sorted Keys

On Extending a Fixed Size, Persistent and Lock-Free Hash Map...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Miguel Areias Ricardo Rocha CRACS & INESC TEC University of Porto Porto Portugal

ISBN: (纸本)9781728111421;9781728111414

Searching is a crucial time-consuming part of many programs, and using a good search method instead of a bad one often leads to a substantial increase in performance. Hash tries are a trie-based data structure with nearly ideal characteristics for the implementation of hash maps. In this paper, we present a novel, simple and concurrent hash map design that fully supports the concurrent search, insert and remove operations on hash tries designed to store sorted keys. To the best of our knowledge, our design is the first concurrent hash map design that puts together the following characteristics: (i) use fixed size data structures; (ii) use persistent memory references; (iii) be lock-free; and (iv) store sorted keys. Experimental results show that our design is quite competitive when compared against other state-of-the-art designs implemented in Java.

关键词： Sorting Indexes Arrays Search methods Java Synchronization

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Study about the Edge Detection Algorithm and its Applications 18

Study about the Edge Detection Algorithm and its Application...

引用

18th International Carpathian Control conference (ICCC)

作者： Popa, Bogdan Univ Craiova Dept Automat & Appl Informat Craiova Romania

ISBN: (纸本)9781509048625

Nowadays one of the most used algorithm for the image processing systems is the edge detection algorithm, starting from the identification systems in different domains to the complex systems direct from industry. With more different approaches this algorithm is today necessary in priority areas and his performance of time processing, quality differ in each case. In the backside of the algorithm there are a lot of mathematical calculus that influence the speed and the accuracy of the results. This article highlights some methods that compose this algorithm and propose some ideas and some tests over the possibility of distributed calculus that influences his performance. In this study it is proposed a set of analysis over the results over more tests and case scenarios that show them main problems in the calculus part. Also a parallel implementation that can offer a better perspective of this algorithm is proposed. Starting with this point over the edge detection of the algorithm, in this paper it is revealed also a good utilization in another different domain for this algorithm.

关键词： image processing edge detection algorithm performance case study

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A Novel Approach for Identifying Lateral Movement Attacks Based on Network Embedding

A Novel Approach for Identifying Lateral Movement Attacks Ba...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Mingyi Chen Yepeng Yao Junrong Liu Bo Jiang Liya Su Zhigang Lu School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781728111421;9781728111414

The growing targeted incidents constitute a permanent menace to internal security with more frequent data breach and service interruption events nowadays. Attackers move laterally and reside in the internal systems for accessing valuable information continuously. This paper proposed a novel approach based on network embedding synthesizing the information on hosts, traffics and correlations to forestall further loss under a deliberate attack. The approach begins with constructing a host communication graph with features extracted from the original data recorded in the internal network and the topology structures of the constructed network. Inspired by previous works, the approach uses a features aggregation learning method, that is composing the features on vertices and edges with neighbors' features to aggregate new composite features. Then the features are selected and learned iteratively. The ultimately selected features were reduced to lower dimension for training and used for the malicious host classification task. Besides, the approach can reemployment the classification results to optimize the dimensionality reduction. Compared with the state-of-the-art models and methods, the proposed approach is (i) flexible with multiple types of data and exchangeable methods, (ii) accurate in feature learning, selecting and extracting with the remarkably average accuracy of 99.9% and the average precision of 91.3%.

关键词： Feature extraction Topology Training Biological system modeling image edge detection Security Data mining

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image Annotation by Deep Neural Networks with Attention Shaping 9

Image Annotation by Deep Neural Networks with Attention Shap...

引用

9th International conference on Digital image processing (ICDIP)

作者： Zheng, Kexin Lv, Shaohe Ma, Fang Chen, Fei Jin, Chi Dou, Yong Natl Univ Def Technol Natl Lab Parallel & Distributed Proc 109 Deya Rd Changsha 410073 Hunan Peoples R China Univ South China Sch Comp Sci & Technol Changsha 410012 Hunan Peoples R China

ISBN: (纸本)9781510613058;9781510613041

image annotation is a task of assigning semantic labels to an image. Recently, deep neural networks with visual attention have been utilized successfully in many computer vision tasks. In this paper, we show that conventional attention mechanism is easily misled by the salient class, i.e., the attended region always contains part of the image area describing the content of salient class at different attention iterations. To this end, we propose a novel attention shaping mechanism, which aims to maximize the non-overlapping area between consecutive attention processes by taking into account the history of previous attention vectors. Several weighting polices are studied to utilize the history information in different manners. In two benchmark datasets, i.e., PASCAL VOC2012 and MIRFlickr-25k, the average precision is improved by up to 10% in comparison with the state-of-the-art annotation methods.

关键词： computer vision image annotation DNN attention mechanism

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parallel and Efficient Sensitivity Analysis of Microscopy image Segmentation Workflows in Hybrid Systems

Parallel and Efficient Sensitivity Analysis of Microscopy Im...

引用

IEEE International conference on Cluster Computing (CLUSTER)

作者： Barreiros, Willian, Jr. Teodoro, George Kurc, Tahsin Kong, Jun Melo, Alba C. M. A. Saltz, Joel Univ Brasilia Dept Comp Sci Brasilia DF Brazil SUNY Stony Brook Dept Biomed Informat Stony Brook NY 11794 USA Oak Ridge Natl Lab Sci Data Grp Oak Ridge TN USA Emory Univ Biomed Informat Dept Atlanta GA 30322 USA

ISBN: (纸本)9781538623268

We investigate efficient sensitivity analysis (SA) of algorithms that segment and classify image features in a large dataset of high-resolution images. Algorithm SA is the process of evaluating variations of methods and parameter values to quantify differences in the output. A SA can be very compute demanding because it requires re-processing the input dataset several times with different parameters to assess variations in output. In this work, we introduce strategies to efficiently speed up SA via runtime optimizations targeting distributed hybrid systems and reuse of computations from runs with different parameters. We evaluate our approach using a cancer image analysis workflow on a hybrid cluster with 256 nodes, each with an Intel Phi and a dual socket CPU. The SA attained a parallel efficiency of over 90% on 256 nodes. The cooperative execution using the CPUs and the Phi available in each node with smart task assignment strategies resulted in an additional speedup of about 2x. Finally, multi-level computation reuse lead to an additional speedup of up to 2.46x on the parallel version. The level of performance attained with the proposed optimizations will allow the use of SA in large-scale studies.

关键词： image segmentation Sensitivity analysis Optimization Algorithm design and analysis Runtime image analysis

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AdaLearner: An Adaptive distributed Mobile Learning System for Neural Networks 17

AdaLearner: An Adaptive Distributed Mobile Learning System f...

引用

IEEE/ACM 36th International conference on Computer-Aided Design (ICCAD)

作者： Mao, Jiachen Qin, Zhuwei Xu, Zirui Nixon, Kent W. Chen, Xiang Li, Hai Chen, Yiran Duke Univ Durham NC 27706 USA George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9781538630938

Neural networks hold a critical domain in machine learning algorithms because of their self-adaptiveness and state-of-the-art performance. Before the testing (inference) phases in practical use, sophisticated training (learning) phases are required, calling for efficient training methods with higher accuracy and shorter converging time. Many existing studies focus on the training optimization on high-performance servers or computing clusters, e.g. GPU clusters. However, training neural networks on resource-constrained devices, e.g. mobile platforms, is an important research topic barely touched. In this paper, we implement AdaLearneran-adaptive distributed mobile learning system for neural networks that trains a single network with heterogenous mobile resources under the same local network in parallel. To exploit the potential of our system, we adapt neural networks training phase to mobile device-wise resources and fiercely decrease the transmission overhead for better system scalability. On three representative neural network structures trained from two image classification datasets, AdaLearner boosts the training phase significantly. For example, on LeNet, 1.75-3.37x speedup is achieved when increasing the worker nodes from 2 to 8, thanks to the achieved high execution parallelism and excellent scalability.

关键词： Training Computational modeling Data models Neural networks Load modeling parallel processing Mobile communication

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Optical Projection Tomography via Phase Retrieval Algorithms for Hidden Three Dimensional Imaging 3

Optical Projection Tomography via Phase Retrieval Algorithms...

引用

conference on Quantitative Phase Imaging iii

作者： Ancora, Daniele Di Battista, Diego Giasafaki, Georgia Psycharakis, Stylianos Liapis, Evangelos Zacharopoulos, Athanasios Zacharakis, Giannis Fdn Res & Technol Hellas Inst Elect Struct & Laser Iraklion Greece Univ Crete Dept Mat Sci Iraklion Greece Univ Crete Dept Med Iraklion Greece

ISBN: (数字)9781510605909

ISBN: (纸本)9781510605893;9781510605909

Optical tomography in biomedical imaging is a highly dynamic field in which non-invasive optical and computational techniques are combined to obtain a three dimensional representation of the specimen we are interested to image. Although at optical wavelengths scattering is the main obstacle to reach diffraction limited resolution, recently several studies have shown the possibility to image even objects fully hidden behind a turbid layer exploiting the information contained in the speckle autocorrelation via an iterative phase retrieval algorithm. In this work we explore the possibility of blind three dimensional reconstruction approach based on the Optical Projection Tomography principles, a widely used tool to image almost transparent model organism such as C. Elegans and D. Rerio. By using autocorrelation information rather than projections at each angle we prove, both numerically and experimentally, the possibility to perform exact three dimensional reconstructions via a specifically designed phase retrieval algorithm, extending the capability of the projection-based tomographic methods to image behind scattering curtains. The reconstruction scheme we propose is simple to implement, does not require post-processing data alignment and moreover can be trivially implemented in parallel to fully exploit the computing power offered by modern GPUs, further reducing the need for costly computational resources.

关键词： Phase Retrieval Scattering Optical Projection Tomography Three Dimensional Imaging Biomedical Imaging image Reconstruction Turbid Lens Memory Effect

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On-Demand processing for Remote Sensing Big Data Analysis

On-Demand Processing for Remote Sensing Big Data Analysis

引用

IEEE International Symposium on parallel and distributed processing with Applications and IEEE International conference on Ubiquitous Computing and Communications (ISPA/IUCC)

作者： Zhenchun Huang Anrun Zhong Guoqing Li Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781538637913

In the recent decades, remote sensing data are rapidly growing in size and variety, and considered as “big geo data” because of their huge data volume, significant heterogeneity and challenge of fast analysis. In the traditional remote sensing analysis workflows, the data transfer for downloading raw image files to local workstations often costs a lot of time and slows down the data analysis workflows. Because results of remote sensing data analysis models are usually much smaller than raw data to be processed, “on-demand processing”, which tries to upload data analysis models and execute them “near” where data stores, can significantly accelerate the execution of remote sensing analysis workflows. In this paper, a framework for on-demand remote sensing data analysis is proposed based on three-layered architecture; XML/JSON based runtime environment description; and on-demand model deployment methods. The evaluation on a prototype system shows that on-demand processing framework accelerates the execution of analysis models in 2.8 ~ 12.7 times by reducing data transfers, especially for those analysis workflows which transfer data through low bandwidth Internet. By on-demand processing, classical remote sensing data service systems can evolve into remote sensing data processing infrastructures, which provide IaaS (Infrastructure-as-a-Service) and PaaS (Platform-as-a-Service) services, and make it possible to exchange knowledge among scientists by sharing models. Furthermore, a remote sensing data analysis platform for carbon satellites is designed based on the on-demand processing proposed by this paper and will soon be implemented under the support of SunWay-TaihuLight, the world's most powerful super computer.

关键词： Tools XML distributed processing conferences Ubiquitous computing Sensors Data analysis

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Simulation of parallel processing for distributed Multi-Target Tracking in Smart Camera Networks 16

Simulation of Parallel Processing for Distributed Multi-Targ...

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10th International conference on distributed Smart Cameras (ICDSC)

作者： Jafarizadeh, Mehdi YadollahPour, Naeeme Shahid Beheshti Univ Comp Engn Dept Tehran Iran Univ Tehran Dept Management & Accounting Tehran Iran

ISBN: (纸本)9781450347860

In order to reduce the real-time processing gap of distributed algorithms on smart camera networks, we have evaluated a parallel processing method which exploits the processing capabilities of free cameras as auxiliary for busy ones. The communication infrastructure or camera processing capabilities are not included in the traditional evaluation methods like datasets or even virtual reality tools, so they are not suitable to analyze the complexity of distributed algorithms. For this purpose, we have developed a modular framework based on OMNET++ simulation environment named CAM-DIST, in which the velocity model of soccer players are emulated. Simulation results show that parallel processing improves overall efficiency, but could have side effects on individual camera estimations;so choosing optimal sharing value and destinations will enhance the performance.

关键词： Smart camera networks OMNET plus complexity analysis of distributed algorithms

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