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arXiv 2019年

作者： Li, Bo Xu, Kele Feng, Dawei Mi, Haibo Wang, Huaimin Zhu, Jian Beijing University of Posts and Telecommunications Automation Dept. Beijing100876 China National Key Lab of Parallel and Distributed Processing Changsha China National University of Defense Technology Changsha China Department of Linguistics University of Michigan Ann Arbor United States

B-mode ultrasound tongue imaging is widely used in the speech production field. However, efficient interpretation is in a great need for the tongue image sequences. Inspired by the recent success of unsupervised deep learning approach, we explore unsupervised convolutional network architecture for the feature extraction in the ultrasound tongue image, which can be helpful for the clinical linguist and phonetics. By quantitative comparison between different unsupervised feature extraction approaches, the denoising convolutional autoencoder (DCAE)-based method outperforms the other feature extraction methods on the reconstruction task and the 2010 silent speech interface challenge. A Word Error Rate of 6.17% is obtained with DCAE, compared to the state-of-the-art value of 6.45% using Discrete cosine transform as the feature extractor. Our codes are available at https://***/DeePBluE666/Source-code1. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Optimistic data consistency maintenance method based on data dependence

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第5期31卷 741-753页

作者： Zhou, Jing Wang, Yi-Jie Li, Si-Kun National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

This paper proposes an optimistic data consistency method according to the question about data dependence in data consistency. In the method, data object is partitioned into data blocks by fixed size as the basic unit of data management. Updates are compressed by Bloom filter technique and propagated in double-path. Negotiation algorithms detect and reconcile update conflicts, and dynamic data management algorithms accommodate dynamic data processing. The results of the performance evaluation show that it is an efficient method to achieve consistency, good dynamic property, and strong robustness when choosing the size of data block appropriately. At the same time, a feasible way is put forward on how to choose appropriate data block size.

关键词： Data processing

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NR-MPI: A Non-stop and Fault Resilient MPI

NR-MPI: A Non-stop and Fault Resilient MPI

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International Conference on parallel and distributed Systems (ICPADS)

作者： Guang Suo Yutong Lu Xiangke Liao Min Xie Hongjia Cao State Key Laboratory of High Performance Computing National University of Defense Technology Changsha Hunan Province China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha Hunan Province China

Fault resilience has became a major issue for HPC systems, in particular in the perspective of future E-scale systems, which will consist of millions of CPU cores and other components. Fault tolerant MPI was proposed to offer support of software level fault tolerance approaches. However, the widely used MPI implementations, such as MPICH and Mvapich2, provide limited support for fault tolerance. This paper proposes NR-MPI, a Non-stop and Fault Resilient MPI. NR-MPI implements the semantics of FT-MPI based on MPICH. Specifically, this paper focuses on failure detection in MPI library, online failure recovery of communicators for multiple failures, friendly programming interface extending for NR-MPI. Furthermore, to support failure recovery of applications, NR-MPI implements data backup and restore interfaces based on double in-memory checkpoint/restart. We conduct experiments with NPB benchmarks on TH-1A supercomputer. Experimental results show that NR-MPI based fault tolerant programs can recover from failures online without restarting, and the overhead is small even for applications with tens of thousands of cores.

关键词： Fault tolerance Fault tolerant systems Libraries Resource management Context Programming Semantics

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Optimistic data consistency maintenance method based on key-attributes

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Ruan Jian Xue Bao/Journal of Software 2008年第8期19卷 2114-2126页

作者： Zhou, Jing Wang, Yi-Jie Li, Si-Kun National Key Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology Changsha 410073 China

The features of simple description, small updates item and weak dependence are the main characteristics of updates of key-attributes in P2P systems. Accordingly, an optimistic data consistency maintenance method based on key-attributes is proposed. In the method, the update of key-attributes is separated from user update requests. key-Updates are propagated by latency-overlay update propagation model, that is, updates are always propagated to the nodes having maximum or minimum latency, and assured and uncertain propagation paths of updates are all taken into account. Based on classifying key-update conflicts, a double-level reconciling mechanism including buffer preprocessing and update-log processing is applied to detect and reconcile conflicts, and then conflicts are solved by policies as last-writer-win and divide-and-rule. Lastly, update-log management method and maintenance method brought by node failure and network partitioning are discussed for the above is deployed based on the information storied in update-log. Delaying key-attributes updates cannot occur by the optimistic disposal method, and then it cannot depress efficiency of resource location based on key-attributes, which adapts well to P2P systems for Internet. The simulation results show that it is an effective optimistic data consistency maintenance method, achieving good consistency overhead, resource location and resource access overhead, and having strong robustness.

关键词： Maintenance

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General audio tagging with ensembling convolutional neural networks and statistical features

arXiv

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arXiv 2018年

作者： Xu, Kele Zhu, Boqing Kong, Qiuqiang Mi, Haibo Ding, Bo Wang, Dezhi Wang, Huaimin National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China University of Surrey United Kingdom College of Meteorology and Oceanography National Univ. of Defense Tech Changsha China

Audio tagging aims to infer descriptive labels from audio clips. Audio tagging is challenging due to the limited size of data and noisy labels. In this paper, we describe our solution for the DCASE 2018 Task 2 general audio tagging challenge. The contributions of our solution include: We investigated a variety of convolutional neural network architectures to solve the audio tagging task. Statistical features are applied to capture statistical patterns of audio features to improve the classification performance. Ensemble learning is applied to ensemble the outputs from the deep classifiers to utilize complementary information. a sample reweight strategy is employed for ensemble training to address the noisy label problem. Our system achieves a mean average precision (mAP@3) of 0.958, outperforming the baseline system of 0.704. Our system ranked the 1st and 4th out of 558 submissions in the public and private leaderboard of DCASE 2018 Task 2 challenge. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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An effective control scheme to ensure real-time causal order in large-scale distributed interactive simulations

An effective control scheme to ensure real-time causal order...

引用

International Conference on Communication Software and Networks, ICCSN

作者： Hangjun Zhou Sha Fu Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China Department of Information Management Hunan University of Finance and Economics Changsha Hunan China

When a large-scale distributed interactive simulation system is running on WAN, the sites usually disperse over a wide area in geography, which results in the simulation clock of each site is hardly to be accurately synchronized with that of other sites. The asynchronous clocks and large transmission latency on WAN bring on a problem for the large-scale simulations to preserve the real-time causal order delivery of received events at each site. In this article, we analyze the indirect way to compare the values of asynchronous simulation clocks at first, and then propose a novel scheme which can select the reconstructible causal control information for each message so as to ensure the causal ordering of events in real time. Experiments demonstrate that the scheme can weaken the effect of network latency, reduce the overhead of the transmission amount of control information and improve the causal order consistency in asynchronous distributed simulations.

关键词： Clocks Educational institutions

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Intention oriented itinerary recommendation by bridging physical trajectories and online social networks

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Ruan Jian Xue Bao/Journal of Software 2012年第SUPPL.期23卷 159-168页

作者： Meng, Xiang-Xu Wang, Xiao-Dong Zhou, Xing-Ming National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha 410073 China

Human itineraries are often initiated by some general intentions and will be optimized after considering all kinds of constraints and available information. This paper proposes a category-based itinerary recommendation framework to help the user transfer from intentions to itinerary planning, which join physical trajectories and information of location based social networks. The main contributions are: (1) Build the category based activity scheduling model;(2) Design and implement the category tree based POI (point or interest) query strategy and algorithm;(3) Propose the Voronoi graph based GPS trajectory analysis method to build traffic information networks;(4) Combine social networks with traffic information networks to implement category based recommendation by ant colony algorithm. The study conducts experiments on datasets from FourSquare and GeoLife project. A test on satisfaction of recommended items is also performed. Results show that the satisfaction reaches 80% in average.

关键词： Trajectories

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High-performance Network Traffic Classification Based on Graph Neural Network

High-performance Network Traffic Classification Based on Gra...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Bo Pang Yongquan Fu Siyuan Ren Yan Jia College of Computer Science and Technology Harbin Institute of Technology Shenzhen China National Key Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changesha China Peng Cheng Laboratory Shen Zhen China

Network traffic classification is crucial for network security and network management and is one of the most important network tasks. Current state-of-the-art traffic classifiers are based on deep learning models to automatically extract features from packet streams. Unfortunately, current approaches fail to effectively combine the structural information of traffic packets with the content features of the packets, resulting in limited classification accuracy. In this paper, we propose a graph neural network model for network traffic classification, which can well perceive the interaction feature of packets in traffic. Firstly, we design a graph structure for packets’ flows to hold the interaction information between packets, which embeds both packet contents and sequence relationships into a unified graph. Secondly, we propose a graph neural network framework for graph classification to automatically learn the structural features of the packets’ flows together with the packets’ features. Extensive evaluation results on real-world traffic data show that the proposed model improves the prediction accuracy of improves the prediction accuracy by 2% to 37% for malicious traffic classification.

关键词： Deep learning Automation Telecommunication traffic Predictive models Network security Feature extraction Graph neural networks

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An improved algorithm for reconstructing singular connection in the multi-block cfd applications

arXiv

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arXiv 2017年

作者： Yong-Xian, Wang Li-Lun, Zhang Yong-Gang, Che Chuan-Fu, Xu Wei, Liu Hua-Yong, Liu Zheng-Hua, Wang College of Computer National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China State Key Laboratory of Aerodynamics Mianyang621000 China

In this paper, an improved algorithm is proposed for the reconstruction of singularity connectivity from the available pairwise connections during preprocessing phase. To evaluate the performance of our algorithm, an in-house CFD code, in which high-order finite-difference method for spatial discretization, running on the Tianhe-1A supercomputer is employed. Test cases with a varied amount of mesh points are chosen, and the test results indicate that the improved singular connection reconstruction algorithm can achieve a 2000× speedup at least compared with the naive search method adopt in the former version of our code. Moreover, the parallel efficiency can be benefited from the strategy of local communication based on the new algorithm. Copyright © 2017, The Authors. All rights reserved.

关键词： Finite difference method

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A consistency algorithm for causal and totally ordered event delivery in DVE

A consistency algorithm for causal and totally ordered event...

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International Conference on Computer Science and Information Technology (CSIT)

作者： Hangjun Zhou Wei Zhang Yuxing Peng Sikun Li Department of Information Management Hunan College of Finance and Economics Changsha Hunan China Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha Hunan China

How to preserve causal and totally ordered event delivery is an important issue in real-time serverless DVE(distributed Virtual Environment). However, most of the related works are designed to maintain causal order merely or time stamped order with intensive computation and bandwidth overhead. In this paper, we proposed a novel distributed algorithm to maintain the before-and-after relationship between events, both causal and concurrent, of DVE at each individual node. Several simulation experiments are carried out to evaluate the performance of our algorithm and the results demonstrate that the algorithm is effective in preserving causal and totally ordered event delivery and more efficient than the previous algorithms.

关键词： Artificial neural networks Silicon Educational institutions

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