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International Conference on computer Sciences and Convergence Information Technology (ICCIT)

作者： Yuan Yuan Shengyun Liu Yuxing Peng National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Network coding brings a new solution for IP congestion control, since more than one buffered packets can be encoded together and removed as a coded packet. This may significantly decrease the packet loss during the congestion, but at the cost of building redundant paths. However, how to minimize the overhead of redundant paths turns out to be a NP-hard problem. In this paper, we propose a novel approximation algorithm called FlowGrouping, which transforms the redundant paths building problem into a limited clique partition problem by increasing edge weights, and can find a good approximate solution within O(n 3 ) computation time.

关键词： Encoding Partitioning algorithms Buildings Merging Approximation methods Network coding IP networks

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AUModel:A Conceptual model for Adaptive Software

AUModel:A Conceptual model for Adaptive Software

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2014 IEEE 5th International Conference on Software Engineering and Service Science

作者： Hui Liu Bo Ding Dianxi Shi Huaimin Wang National Key Laboratory for Parallel and Distributed Processing College of Computer ScienceNational University of Defense Technology

Pervasive software should be able to adapt itself to the changing environments and user ***,it will bring great challenges to the software engineering *** paper proposes AUModel,a conceptual model for adaptive software,which takes adaptability as an inherent feature and can act as the foundation of the engineering *** introducing AUModel,the reuse of software adaptation infrastructure as well as the separation of adaptation concerns are enabled,which can facilitate both the development and maintenance of adaptive *** paper also presents our initial attempts to realize this model,including a middleware prototype to support this model and an application to validate its effectiveness.

关键词： pervasive computing adaptive software middleware

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A load management system for running HLA-based distributed simulations over the grid

A load management system for running HLA-based distributed s...

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International Workshop/Symposium on distributed Simulation and Real-Time Applications (DS-RT)

作者： W. Cai S.J. Turner H. Zhao Parallel and Distributed Processing Laboratory School of Computer Engineering Nanyang Technological University Singapore

Running a large-scale distributed simulation may need a large amount of computing resources at geographically different locations. These resources may be from different organizations. The simulation may run for a long period of time and the availability and amount of computing resources available may change during the course of simulation execution. Therefore, coordinating and managing resources for distributed simulation to complete the simulation efficiently and effectively is a critical issue. This paper describes a load management system for HLA-based distributed simulation. The system is constructed on top of a grid computing environment supported by Globus. The overall structure of the system is presented in the paper and how the system saves and restores a federate is also discussed in detail.

关键词： Load management Computational modeling Resource management distributed computing computer simulation Grid computing Large-scale systems Atherosclerosis Least squares approximation Concurrent computing

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HDSVM: A High Efficiency distributed SVM Framework over Data Stream

HDSVM: A High Efficiency Distributed SVM Framework over Data...

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

作者： Yan Hou Yijie Wang Xingkong Ma Li Cheng College of Computer National University of Defense Technology Changsha National Laboratory for Parallel and Distributed Processing China

ISBN: (纸本)9781538637913

The application of Support Vector Machine (SVM) over data stream is growing with the increasing real-time processing requirements in classification field, like anomaly detection and real-time image processing. However, the dynamic live data with high volume and fast arrival rate in data streams make it challenging to apply SVM in data stream processing. Existing SVM implementations are mostly designed for batch processing and hardly satisfy the efficiency requirement of stream processing for its inherent complexity. To address the challenges, we propose a high efficiency distributed SVM framework over data stream (HDSVM), which consists of two main algorithms, incremental learning algorithm and distributed algorithm. Firstly, we propose a partial support vectors reserving incremental learning algorithm (PSVIL). By selecting a subset of support vectors based on their distances to classification hyperplane instead of the universal set to update SVM, the algorithm achieves lower time overhead while ensuring accuracy. Secondly, we propose a distribution remaining partition and fast aggregation distributed algorithm (DRPFA) for SVM. The real-time data is partitioned based on the original distribution with clustering instead of random partition, and historical support vectors are partitioned based on their distances to the classification hyperplane. The global hyperplane can be obtained by averaging the parameters of local hyperplanes due to the above partition strategy. Extensive experiments on Apache Storm show that the proposed HDSVM achieve lower time overhead and similar accuracy compared with the state-of-art. Speed-up ratio is increased by 2-8 times within 1% accuracy deviation.

关键词： Support vector machines Training distributed databases distributed algorithms Real-time systems Partitioning algorithms Streaming media

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Software Ranking based on Mining Market Requirements

Software Ranking based on Mining Market Requirements

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2015 6th IEEE International Conference on Software Engineering and Service Science(ICSESS 2015)

作者： Bingxun Liu Gang Yin Tao Wang Yiang Gan Yun Zhan Parallel and Distributed Laboratory School of Computer National University of Defense Technology

Because of the rapid growth of open source software, how to choose software from many alternatives becomes a great challenge. Traditional ranking approaches mainly focus on the characteristics of the software themselves. In this paper we investigate the market demands for software engineers, and propose a novel approach for ranking software by analyzing the market requirements for special software. As industries always need to balance several different factors for selecting software, the market demands can be a good indicator for ranking software. This paper provides quite a different perspective, and it can be a valuable supplement for traditional ranking methods.

关键词： software ranking data extraction matching Internet job advertisements

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A Virtual Machine Server Aggregation Algorithm Based on Hierarchical Clustering

A Virtual Machine Server Aggregation Algorithm Based on Hier...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Feng Huang Dongsheng Li National Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Combining virtual machine technology and network computing technology will be able to effectively aggregate the widely distributed heterogeneous and autonomous resources in the Internet. This paper proposes a virtual machine server aggregation algorithm, called DVSA, based on hierarchical clustering method for virtual computing environment. According to network latencies, the algorithm clusters virtual machine servers into groups. If servers in the same group are scheduled, the latencies between virtual machines which host on these servers will be small, and the performance and stability of the distributed execution environment could be improved.

关键词： Virtual machining Clustering algorithms Network servers Web server Delay computers Aggregates IP networks Clustering methods Processor scheduling

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Image Annotation by Object Hypotheses-oriented Deep Neural Networks

Image Annotation by Object Hypotheses-oriented Deep Neural N...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Fang MA Shao-he LV Ke-xin ZHENG Chi JIN Fei CHEN Ke YANG and Yong DOU National Laboratory for Parallel and Distributed Processing National University of Defense Technology University of South China School of Computer Science and Technology

Image annotation generates a set of semantic labels that describe the contents of an input *** deep learning techniques have achieved significant success in many areas of image *** this paper,we present a multi-label image annotation method that combines unsupervised object hypotheses generation and deep neural *** an image,object hypotheses are generated in an unsupervised *** we extract the image features for each hypothesis with a deep neural network *** combining the features of all hypotheses,we get the features of the entire ***,we calculate for each label the probability of that the label is correlated with the given *** can be trained in an end-to-end way using the standard backward propagation *** results on multiple benchmark datasets show that our method is better than the state-of-the-art ones.

关键词： Deep learning Multi-label annotation Object hypotheses

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Efficient Virtual Machine Deployment in Large Scale Resource Environment

Efficient Virtual Machine Deployment in Large Scale Resource...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Feng Huang Dongsheng Li National Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Combining virtual machine technology, virtual computing is able to effectively aggregate the widely distributed resources to provide users services. We view the federation of multiple data centers and voluntary resources on the Internet as a very large scale resource pool. Based on the tree structure of the pool, this paper proposes a virtual machine deployment algorithm, called iVDA, considers users' requests and the capabilities of the physical resources as well as the dynamic load, implements an adaptive mechanism to scheduling servers to host virtual machines forming virtual execution environments for various applications, and supports on-demand computing.

关键词： Virtual machining Servers Heuristic algorithms Internet Dynamic scheduling Cloning Processor scheduling

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Exploring frame segmentation networks for temporal action localization

arXiv

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arXiv 2019年

作者： Yang, Ke Shen, Xiaolong Qiao, Peng Li, Shijie Li, Dongsheng Dou, Yong National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Temporal action localization is an important task of computer vision. Though many methods have been proposed, it still remains an open question how to predict the temporal location of action segments precisely. Most state-of-the-art works train action classifiers on video segments pre-determined by action proposal. However, recent work found that a desirable model should move beyond segment-level and make dense predictions at a fine granularity in time to determine precise temporal boundaries. In this paper, we propose a Frame Segmentation Network (FSN) that places a temporal CNN on top of the 2D spatial CNNs. Spatial CNNs are responsible for abstracting semantics in spatial dimension while temporal CNN is responsible for introducing temporal context information and performing dense predictions. The proposed FSN can make dense predictions at frame-level for a video clip using both spatial and temporal context information. FSN is trained in an end-to-end manner, so the model can be optimized in spatial and temporal domain jointly. We also adapt FSN to use it in weakly supervised scenario (WFSN), where only video level labels are provided when training. Experiment results on public dataset show that FSN achieves superior performance in both frame-level action localization and temporal action localization. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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TEMPORAL PYRAMID RELATION NETWORK FOR VIDEO-BASED GESTURE RECOGNITION

TEMPORAL PYRAMID RELATION NETWORK FOR VIDEO-BASED GESTURE RE...

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IEEE International Conference on Image processing

作者： Ke Yang Rongchun Li Peng Qiao Qiang Wang Dongsheng Li Yong Dou National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha China

Gesture recognition in video is an important application of computer vision. However, there are few works talked about the temporal order or relation of the frames in video, which is important for model gestures. In this paper, we propose Temporal Pyramid Relation Network (TPRN) which can model the temporal relation of video frames effectively and efficiently. First, we use Temporal Pyramid Pooling (TPP) layer to get temporal feature sequences of multiple scale pyramids. Then, a Temporal Relation Network (TRN) is stacked on the feature sequence of each scale respectively to model the temporal relations of video frames at multiple scales. At last, representations of all scales are aggregated to get the final prediction. TPRN can take video clips of various length as input and is scalable for video length. We evaluate TPRN on a recently released very large video-based gesture recognition dataset - 20BN-Jester dataset v1, and TPRN achieves competitive performance.

关键词： Feature extraction Gesture recognition Solid modeling Hidden Markov models Three-dimensional displays Computational modeling Training

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