检索结果-内蒙古大学图书馆

2017 5th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2017)

作者： Xiaocheng Luo Jingfei Jiang Junjie Zhu Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Audio feature extraction is a very important technique in the field of sound processing. It extremely impacts the effectiveness and correctness of sound recognition, sound verification, etc. It is a computation intensive stage in the whole sound recognition process, which is a challenging for acceleration. In this paper, a coarse-grained parallel feature extraction algorithm for high throughput of audio slices is proposed to improve the efficiency of audio feature extraction. Three typical audio feature extraction algorithms, Mel Frequency Cepstrum Coefficients(MFCC), Spectrogram image features(SIF), Octave-Based Spectral Contrast, are chosen to parallelize. Experiments results on different platforms show that the speedup of accelerated audio feature extraction is up to 17.23 on the platform with 16 cores 32 threads.

关键词： Feature Extraction Sound Recognition parallel Algorithm

来源：评论

学校读者我要写书评

暂无评论

Zero Pronoun Identification in Chinese Language with Deep Neural Networks

Zero Pronoun Identification in Chinese Language with Deep Ne...

引用

2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Tao Chang Shaohe Lv Xiaodong Wang Dong Wang National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (纸本)9781510845541

Zero pronoun resolution is very important in natural language processing. Identification of zero pronoun is the fundamental task of its resolution. Existing feature engineering based identification approaches are unsuitable for big data applications due to labor-intensive work. Furthermore, as extracted from parse trees which are not unique for a certain sentence, features may be improper for zero pronoun identification. In this paper, we constructed a two-layer stacked bidirectional LSTM model to tackle identification of zero pronoun. To extract semantic knowledge, the first layer obtains the structure information of the sentence, and the second layer combines the part-of-speech information with obtained structure information. The results in two different kinds of experimental environment show that, our approach significantly outperforms the state-of-the-art method with an absolute improvement of 4.3% and 20.3% F-score in Onto Notes 5.0 corpus respectively.

关键词： zero pronoun Identification LSTM

来源：评论

学校读者我要写书评

暂无评论

A Research on Minisat Using Coarse-grained parallel Technique Based on Multi-core and Multi-platform

A Research on Minisat Using Coarse-grained Parallel Techniqu...

引用

2017 5th International Conference on Machinery,Materials and Computing Technology(ICMMCT 2017)

作者： Junjie Zhu Jingfei Jiang Xiaocheng Luo Yong Dou National Laboratory of Parallel and Distributed Processing National University of Defence Technology

In this paper, we make a research on a widely-used SAT solver, Minisat, aiming to improve its performance using coarse-grained parallel method on multi-core and multi-platform. Firstly, we parallel the Minisat by mean of Open MP and test its performance with different threads by running a test set consisting of 2000 SAT problems on an X86 computer. Besides, a scheduling strategy with time sequence is added to the process and achieves a better speed-up ratio. Then, we move the algorithm to an ARM computer and repeat the same process, finding that the performance of Minisat on X86 is better than that on ARM, but ARM platform has a better scale effect than X86 platform when running at full load and is able to perform better than X86 when they have the same hardware configuration.

关键词： Minisat solver coarse-grained multi-core multi-platform

来源：评论

学校读者我要写书评

暂无评论

General audio tagging with ensembling convolutional neural networks and statistical features

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

CloudDPI: Cloud-based privacy-preserving deep packet inspection via reversible sketch 1

引用

9th International Symposium on Cyberspace Safety and Security, CSS 2017

作者： Li, Jie Su, Jinshu Wang, Xiaofeng Sun, Hao Chen, Shuhui School of Computer National University of Defense Technology Changsha Hunan410073 China National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan410073 China

ISBN: (数字)9783319694719

ISBN: (纸本)9783319694702

Hardware-based middleboxes are ubiquitous in computer networks, which usually incur high deployment and management expenses. A recently arsing trend aims to address those problems by outsourcing the functions of traditional hardware-based middleboxes to high volume servers in a cloud. This technology is promising but still faces a few challenges. First, the widely adopted data encryption techniques contradict with payload inspection needs of some middleboxes such as DPI and IDS devices. Second, the inspection rules of middleboxes may be commercial properties, thus the middlebox providers want to keep their rules confidential under third-party cloud environments, and this creates hindrances for the cloud to perform outsourced middlebox functions. Third, performance of the outsourced middlebox is an inevitable issue that needs deliberate consideration. In this paper, we propose a cloud-based DPI middlebox implementation which performs payload inspection over encrypted traffic while preserving the privacy of both communication data and inspection rules. Our design employs a modified reversible sketch structure which is used for efficient error-free membership testing, and we utilize unkeyed one-way hash functions instead of complex cryptographic protocols to achieve the privacy preservation requirements. CloudDPI supports a wide range of real-world inspection rules, we conduct evaluations on ClamAV rule set and the experiment results demonstrate the effectiveness of our proposal. © 2017, Springer International Publishing AG.

关键词： Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Single event upset induced by single event double transient and its well-structure dependency in 65-nm bulk CMOS technology

引用

Science China(Information Sciences) 2016年第4期59卷 152-159页

作者： Pengcheng HUANG Shuming CHEN Jianjun CHEN College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Single event upset (SEU) is one of the most important origins of soft errors in aerospace *** technology scales down persistently, charge sharing is playing a more and more significant effect on SEU of flip-flop. Charge sharing can often bring about multi-node charge collection in storage nodes and non-storage nodes in a flip-flop. In this paper, multi-node charge collection in flip-flop data input and flip-flop clock signal is investigated by 3D TCAD mixed-mode simulations, and the simulate results indicate that single event double transient (SEDT) in flip-flop data input and flip-flop clock signal can also cause a SEU in flip-flop. This novel mechanism is called the SEDT-induced SEU, and it is also verified by heavy-ion experiment in 65 nm twin-well process. The simulation results also indicate that this mechanism is closely related with the well-structure,and the triple-well structure is more effective to increase the SEU threshold of this mechanism than twin-well structure.

关键词： single event upset (SEU) single event double transient (SEDT) SEDT-induced SEU parasitic bipolar effect (PBE) charge sharing

来源：评论

学校读者我要写书评

暂无评论

A Review of Indoor-Outdoor Scene Classification

A Review of Indoor-Outdoor Scene Classification

引用

2017 2nd International Conference on Control, Automation and Artificial Intelligence(CAAI2017)

作者： Zhehang Tong Dianxi Shi Bingzheng Yan Jing Wei National Laboratory for Parallel and Distributed Processing School of Computer National University of Defense Technology

ISBN: (纸本)9781510845541

Indoor-Outdoor scene classification problem have been proposed for almost 20 years and widely applied to general scene classification, image retrieval, image processing and robot application. But there is no consensus on one particular scene classification technique that can solve the Indoor-Outdoor scene classification problem perfectly. As larger image dataset has been developed and machine learning technology especially deep learning based methods achieve remarkable performance in computer vision, we aim to provide guidance and direction for researchers to tackle the Indoor-Outdoor scene classification problem with more powerful and robust solution through concluding the Indoor-Outdoor scene classification approaches which have been proposed in last 20 years. In this paper, we review the Indoor-Outdoor scene classification including feature extraction, classifier and related dataset. Their advantages and disadvantages are discussed. At last we conclude some challenging problems remain unsolved and propose some potential solutions.

关键词： indoor-outdoor scene classification computer vision

来源：评论

学校读者我要写书评

暂无评论

Exploring temporal preservation networks for precise temporal action localization

arXiv

引用

arXiv 2017年

作者： Yang, Ke Qiao, Peng Li, Dongsheng Lv, Shaohe Dou, Yong National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

Temporal action localization is an important task of computer vision. Though a variety of methods have been proposed, it still remains an open question how to predict the temporal boundaries of action segments precisely. Most works use segment-level classifiers to select video segments predetermined by action proposal or dense sliding windows. However, in order to achieve more precise action boundaries, a temporal localization system should make dense predictions at a fine granularity. A newly proposed work exploits Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the predictions of 3D ConvNets, making it possible to perform per-frame action predictions and achieving promising performance in terms of temporal action localization. However, CDC network loses temporal information partially due to the temporal downsampling operation. In this paper, we propose an elegant and powerful Temporal Preservation Convolutional (TPC) Network that equips 3D ConvNets with TPC filters. TPC network can fully preserve temporal resolution and downsample the spatial resolution simultaneously, enabling frame-level granularity action localization with minimal loss of time information. TPC network can be trained in an end-to-end manner. Experiment results on public datasets show that TPC network achieves significant improvement on per-frame action prediction and competing results on segment-level temporal action localization. Copyright © 2017, The Authors. All rights reserved.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Collaborative deep learning across multiple data centers

arXiv

引用

arXiv 2018年

作者： Xu, Kele Mi, Haibo Feng, Dawei Wang, Huaimin Chen, Chuan Zheng, Zibin Lan, Xu National Key Laboratory of Parallel and Distributed Processing Changsha China College of Computer National University of Defense Technology Changsha China School of Data and Computer Science Sun Yat-Sen University Guangzhou China Queen Mary University of London London United Kingdom

Valuable training data is often owned by independent organizations and located in multiple data centers. Most deep learning approaches require to centralize the multi-datacenter data for performance purpose. In practice, however, it is often infeasible to transfer all data to a centralized data center due to not only bandwidth limitation but also the constraints of privacy regulations. Model averaging is a conventional choice for data parallelized training, but its ineffectiveness is claimed by previous studies as deep neural networks are often non-convex. In this paper, we argue that model averaging can be effective in the decentralized environment by using two strategies, namely, the cyclical learning rate and the increased number of epochs for local model training. With the two strategies, we show that model averaging can provide competitive performance in the decentralized mode compared to the data-centralized one. In a practical environment with multiple data centers, we conduct extensive experiments using state-of-the-art deep network architectures on different types of data. Results demonstrate the effectiveness and robustness of the proposed method. Copyright © 2018, The Authors. All rights reserved.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “A distributed Relation Detection Approach in the Internet of Things”

引用

Mobile Information Systems 2019年第1期2019卷

作者： Weiping Zhu Hongliang Lu Xiaohui Cui Jiannong Cao International School of Software Wuhan University Wuhan *** Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha *** Department of Computing Hong Kong Polytechnic University Kowloon Hong Kongpolyu.edu.hk

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：