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检索条件"机构=Parallel and Distributed Processing Laboratory School of Computer Science and Technology"

共 529 条记录，以下是61-70 订阅

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Collaborative search in large-scale unstructured peer-to-peer networks

Collaborative search in large-scale unstructured peer-to-pee...

引用

36th International Conference on parallel processing in Xi'an, ICPP

作者： Zhang, Yiming Li, Dongsheng Chen, Lei Lu, Xicheng National Laboratory for Parallel and Distributed Processing NUDT Department of Computer Science Hong Kong University of Science and Technology

ISBN: (纸本)076952933X

Searching in large-scale unstructured peer-to-peer networks is challenging due to the lack of effective hint information to guide queries. In this paper, we propose POP, a parallel, cOllaborative and Probabilistic search mechanism, in which query messages are viewed as search units to collaborate with each other and aggregate the distributed hints during the search process. A scheme called distributed Bloom Filter (DBF) is presented to propagate the hints with a bandwidth-aware manner, in which a node divides the received Bloom filter vector into subvectors and disseminates the fragments to its neighbors according to their bandwidth capacity. The effectiveness of POP is demonstrated through theoretical analysis and extensive simulations. © 2007 IEEE.

关键词： Bandwidth

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An integration framework of cloud computing with wireless sensor networks

An integration framework of cloud computing with wireless se...

引用

7th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2012

作者： You, Pengfei Li, Huiba Peng, Yuxing Li, Ziyang National Key Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology 410073 Changsha China

ISBN: (纸本)9789400758568

Wireless sensor networks (WSN) is a key technology extensively applied in many fields, such as transportation, health-care and environment monitoring. Despite rapid development, the exponentially increasing data emanating from WSN is not efficiently stored and used. Besides, the data from multiple different types and locations of WSN needs to be well analyzed, fused and supplied to various types of clients, such as PC, workstation and smart phone. The emerging cloud computing technology provides scalable data process and storage power and some types of connectable services, which can helpfully utilize sensor data from WSN. In this paper, we propose an integration framework of cloud computing with WSN, in which sensor data is transmitted from WSN to cloud, and processed and stored in cloud, then mined and analyzed so as to be supplied to various clients. By applying virtualization and cloud storage technology, and Infrastructure as a Service (IaaS) and Software as a Service (SaaS) of cloud service model, the framework can fully process and store mass sensor data from multiple types of WSN. Besides, it efficiently mines and analyzes sensor data, based on which the data applications are well supplied to various types of clients in form of services. © 2013 Springer science+Business Media.

关键词： Virtualization

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Word Embedding-based Context-sensitive Network Flow Payload Anomaly Detection 3

Word Embedding-based Context-sensitive Network Flow Payload ...

引用

3rd International Conference on Applied Machine Learning, ICAML 2021

作者： Li, Yizhou Wang, Yijie Cheng, Li Xu, Hongzuo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421256

Payload anomaly detection can discover malicious beliaviors tiidden in network packets. It is liard to liandle payload due to its various possible characters and complex semantic context, and tlius identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which appUes the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method. © 2021 IEEE

关键词： Embeddings

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A Novel Deep Neural Network Model for Credit Risk Prediction of Chinese Farmers 6

A Novel Deep Neural Network Model for Credit Risk Prediction...

引用

6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Xie, Yalong Li, Aiping Liu, Ziniu Chen, Kai Tu, Hongkui College Of Computer National University Of Defense Technology Science And Technology On Parallel And Distributed Processing Laboratory Changsha China

ISBN: (纸本)9781665418157

China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic development and the improvement of people's standard of living that how to reasonably use funds to subsidize the agricultural population and reduce the risk of rural loans. At present, credit risk prediction of farmers mainly depends on the experience of experts in the business field, and there is little published research on using artificial intelligence methods to solve this problem. This paper presents a complete set of methods, including data collection, feature selection, etc. We propose a novel deep neural network model named DNN-CRP for credit risk prediction of Chinese framers. Experiments on an actual credit loan dataset of Chinese farmers are presented, and experimental results show that the comprehensive performance of the DNN-CRP model is better than current state-of-the-art models. It is believed that the DNN-CRP model proposed in this paper can help banks improve the efficiency of the credit loan business of farmers and reduce credit risks. © 2021 IEEE.

关键词： Neural network models

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Data storage optimization of application-level checkpointing on heterogeneous systems

Data storage optimization of application-level checkpointing...

引用

2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Jia, Jia Song, Wei National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781479916603

General purpose GPU's (GPGPU) appearance made it possible that heterogeneous computing can be used by human beings. And it's also produce a reform for GPU's general purpose computing and parallel computing. Heterogeneous Systems has been adopted by large-scale of high-performance computers. Nowadays, fault tolerance technique is necessary among these large-scale kinds of scientific computing, but in a few years of GPGPU and heterogeneous system appearance, there is not an effective fault tolerance method come out, therefore, towards this situation, this paper will apply the traditional fault tolerance technique - application-level checkpointing to heterogeneous system. Cause the main solution of reducing overhead of the application-level checkpointing is reducing checkpoint data size, so after analyzing the heterogeneous system and GPGPU program, we propose a method to optimize the data storage of application-level checkpointing technique and validate its optimization by experiments. © 2013 IEEE.

关键词： Fault tolerance

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Labelwalking nonnegative matrix factorization 40

Labelwalking nonnegative matrix factorization

引用

40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Lan, Long Guan, Naiyang Zhang, Xiang Huang, Xuhui Luo, Zhigang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781467369978

Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along high density areas defined by unlabeled examples, LP assumes nearby examples should share the same label, thus, it unavoidably pushes the labels to the wrong examples, especially when different la- beled examples are not strictly separated. Seed K-means uses labeled examples to initialize class centers, and avoid getting stuck in poor local optima comparing to traditional K-means, however the hard constraint of each example's membership makes Seed K-means failed in many real world applications. This paper proposes a novel label walking nonnegative matrix factorization method (LWNMF) to handle labeled examples in SSL based on the framework of NMF. LWNMF decomposes the whole dataset into the product of a basis matrix and a coefficient matrix, and to travel labels to unlabeled examples, LWNMF regards the class indicators of labeled examples as their coefficients and iteratively updates both basis matrix and coefficients of unlabeled examples. Since LWNMF learns comprehensive class centroids, labels iteratively walk to unlabeled examples through these significant centroids. © 2015 IEEE.

关键词： K-means Label propagation Nonnegative matrix factorization Semisupervised learning

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Evaluating the performance and accuracy impact of trace generation to the bigsim emulator

Evaluating the performance and accuracy impact of trace gene...

引用

10th IEEE International Conference on computer and Information technology, CIT-2010, 7th IEEE International Conference on Embedded Software and Systems, ICESS-2010, 10th IEEE Int. Conf. Scalable Computing and Communications, ScalCom-2010

作者： Che, Yonggang Xu, Chuanfu Lu, Pingjing National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9780769541082

This paper quantitatively studies the trace effects to the performance and accuracy of the BigSim Emulator, a scalable parallel emulator for large-scale computers. To assess the accuracy effect we modify the emulator code to collect the predicted computation time. Four MPI programs with different computation to communication ratios are used as benchmarks. The emulation time and the predicted computation time, both when trace generation are enabled and disabled, are collected on two parallel host machines. The results show that although the BigSim Emulator only traces communication events and dependencies, trace generation still evidently degrades the emulation performance for programs with high communication to computation ratios. Trace generation also significantly affects the accuracy of the predicted computation time for communication intensive programs, which is an issue that can not be overlooked. © 2010 IEEE.

关键词： BigSim emulator MPI programs digital simulation emulator code large scale computers message passing parallel host machines parallel programming performance evaluation scalable parallel emulator trace generation accuracy impact

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Feature preserving consolidation for unorganized point clouds

Feature preserving consolidation for unorganized point cloud...

引用

IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Li, Bao Jiang, Wei Cheng, Zhiquan Dang, Gang Jin, Shiyao National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781424467860

We introduce a novel method for the consolidation of unorganized point clouds with noise, outliers, non-uniformities as well as sharp features. This method is feature preserving, in the sense that given an initial estimation of normal, it is able to recover the sharp features contained in the original geometric data which are usually contaminated during the acquisition. The key ingredient of our approach is a weighting term from normal space as an effective complement to the recently proposed consolidation techniques. Moreover, a normal mollification step is employed during the consolidation to get normal information respecting sharp features besides the position of each point. Experiments on both synthetic and real-world scanned models validate the ability of our approach in producing denoised, evenly distributed and feature preserving point clouds, which are preferred by most surface reconstruction methods. ©2010 IEEE.

关键词： Consolidation Feature preserving Point clouds Sharp features

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection 48

Smoothing Point Adjustment-Based Evaluation of Time Series A...

引用

48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Mingyu Wang, Yijie Xu, Hongzuo Zhou, Xiaohui Li, Bin Wang, Yongjun National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Changsha China

ISBN: (纸本)9781728163277

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation. © 2023 IEEE.

关键词： Anomaly Detection Evaluation Protocol Point Adjustment Time Series

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A two-phase mapping method for parallel applications simulation

A two-phase mapping method for parallel applications simulat...

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2009 International Conference on Computational Intelligence and Software Engineering, CiSE 2009

作者： Fang, Jianbin Xu, Chuanfu Che, Yonggang Weng, Yufen Wang, Zhenghua National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781424445073

Efficient mapping of logical processes to physical processes is one of key technologies to accelerate parallel performance simulation. Aiming at minimizing the communications between SMP nodes and between host physical processes, this paper presents a novel method named TPsmp-LP3M. It automatically extracts communication pattern of logical processes from trace and then generates a two-phase mapping from logical processes to SMP clusters. Experimental results show that TPsmp-LP3M can outperform the regular mapping method by up to 20.2%. ©2009 IEEE.

关键词： Mapping

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