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arXiv 2020年

作者： Gao, Liang Xu, Kele Wang, Huaimin Peng, Yuxing National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha410073 China

As an important component of multimedia analysis tasks, audio classification aims to discriminate between different audio signal types and has received intensive attention due to its wide applications. Generally speaking, the raw signal can be transformed into various representations (such as Short Time Fourier Transform and Mel Frequency Cepstral Coefficients), and information implied in different representations can be complementary. Ensembling the models trained on different representations can greatly boost the classification performance, however, making inference using a large number of models is cumbersome and computationally expensive. In this paper, we propose a novel end-to-end collaborative learning framework for the audio classification task. The framework takes multiple representations as the input to train the models in parallel. The complementary information provided by different representations is shared by knowledge distillation. Consequently, the performance of each model can be significantly promoted without increasing the computational overhead in the inference stage. Extensive experimental results demonstrate that the proposed approach can improve the classification performance and achieve state-of-the-art results on both acoustic scene classification tasks and general audio tagging tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Outlier detection ensemble with embedded feature delection

arXiv

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arXiv 2020年

作者： Cheng, Li Wang, Yijie Liu, Xinwang Li, Bin Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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A distributed class-incremental learning method based on neural network parameter fusion 21

A distributed class-incremental learning method based on neu...

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21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Guan, Hongtao Wang, Yijie Ma, Xingkong Li, Yongmou National Laboratory for Parallel and Distributed Processing College of Computer Science and Technology National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728120584

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating. © 2019 IEEE.

关键词： Iterative methods

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A Heterogeneous Processor Design for CNN-Based AI Applications on IoT Devices 8

A Heterogeneous Processor Design for CNN-Based AI Applicatio...

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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Liu, Zhiqiang Jiang, Jingfei Lei, Guoqing Chen, Kai Qin, Buyue Zhao, Xiaoqiang Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China College of Computer National University of Defense Technology Changsha China College of System Engineering National University of Defense Technology Changsha China

This paper proposes a heterogeneous processor design for CNN-based AI applications on IoT devices. The heterogeneous processor contains an embedded RISC-V CPU that works as a general processor and an efficient CNN-accelerator that supports a variety of CNN models with a list of macro instructions. For demonstration, we implement a prototype on an FPGA platform with the RISC-V CPU working under 20 MHz and the CNN accelerator working under 100 MHz. As a case study, we run a CNN-based face detection and recognition application on this prototype. The prototype can process one image in 0.72 seconds and an ASIC implementation working under 400 MHz can process one image in less than 0.15 seconds by estimation, which can satisfy the needs for many IoT scenarios such as access control systems and check-in systems. © 2020 The Authors. Published by Elsevier B.V.

关键词： Internet of things

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DFTracker: detecting double-fetch bugs by multi-taint parallel tracking

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Frontiers of computer Science 2019年第2期13卷 247-263页

作者： Pengfei WANG Kai LU Gen LI Xu ZHOU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha 410073 China College of Computer National University of Defense Technology Changsha 410073 China Collaborative Innovation Center of High Performance Computing National University of Defense Technology Changsha 410073 China

A race condition is a common trigger for concurrency bugs. As a special case, a race condition can also occur across the kernel and user space causing a doublefetch bug, which is a field that has received little research attention. In our work, we first analyzed real-world doublefetch bug cases and extracted two specific patterns for doublefetch bugs. Based on these patter ns, we proposed an approach of multi-taint parallel tracking to detect double-fetch bugs. We also implemented a prototype called DFTracker (doublefetch bug tracker), and we evaluated it with our test suite. Our experiments demonstrated that it could effectively find all the double-fetch bugs in the test suite including eight realworld cases with no false negatives and minor false positives. In addition, we tested it on Linux kernel and found a new double-fetch bug. The execution overhead is approximately 2x for single-file cases and approximately 9x for the whole kernel test, which is acceptable. To the best of the authors1 knowledge, this work is the first to introduce multi-taint parallel tracking to double-fetch bug detection—an innovative method that is specific to double-fetch bug features—and has better path coverage as well as lower runtime overhead than the widely used dynamic approaches.

关键词： multi-taint parallel tracking double fetch race condition between kernel and user time of check to time of use real-world case analysis Clang Static Analyzer

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App-Net: A Hybrid Neural Network for Encrypted Mobile Traffic Classification

App-Net: A Hybrid Neural Network for Encrypted Mobile Traffi...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Xin Wang Shuhui Chen Jinshu Su College of Computer Science and Technology National University of Defense Technology National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

With the exponential growth of mobile traffic data, mobile traffic classification is in a great need. It is an essential step to improve the performance of network services such as QoS and security monitoring. However, the widespread use of encrypted protocols, especially the TLS protocol, has posed great challenges to traditional traffic classification techniques. As the rule-based deep packet inspection approaches are ineffective for encrypted traffic classification, various machine learning methods have been studied and used. Recently, deep learning solutions which enable automatic feature extraction are also proposed to classify encrypted traffic. In this paper we propose App-Net, an end-to-end hybrid neural network, to learn effective features from raw TLS flows for mobile app identification. App-Net is designed by combining RNN and CNN in a parallel way. So that it can learn a joint flow-app embedding to characterize both flow sequence patterns and unique app signatures. We evaluate App-Net on a real-world dataset that covering 80 apps. The results show that our method can achieve an excellent performance and outperform the state-of-the-art methods.

关键词： Cryptography Feature extraction Payloads Recurrent neural networks Machine learning Convolutional neural networks

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Research on Fine-Grained Sentiment Classification 8th

Research on Fine-Grained Sentiment Classification

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8th CCF International Conference on Natural Language processing and Chinese Computing, NLPCC 2019

作者： Wang, Zhihui Wang, Xiaodong Chang, Tao Lv, Shaohe Guo, Xiaoting National Key Laboratory of Parallel and Distributed Processing College of Computer Science and Technology National University of Defence Technology Changsha410073 China

ISBN: (纸本)9783030322359

Aiming at the fine-grained sentiment classification that distinguishes the emotional intensity, the commonly used dataset SST-1 is analyzed in depth. Through the analysis, it is found that the dataset has serious problems such as data imbalance and small overall scale, which seriously restricts the classification effect. In order to solve the related problems, data augmentation method is adopted to realize the optimization of the dataset. The IMDB and other data which are relatively homologous to the original dataset are annotated, and the focus is to expand the categories with fewer numbers. By this way, the problem of data imbalance is effectively alleviated and the original data scale is expanded. Then, based on the Bidirectional Encoder Representations from Transformers (BERT) model, which has good overall performance on natural language processing, the benchmark classification model is built. Through multiple comparison experiments on the original dataset and the enhanced data, the influence of the deficiency of the original dataset on the classification effect is verified. And, it is fully demonstrated that the enhanced data can effectively improve the test results and solve the problem of large differences in performance between different categories well. © 2019, Springer Nature Switzerland AG.

关键词： Classification (of information)

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Optimizing One by One Direct Convolution on ARMv8 Multi-core CPUs

Optimizing One by One Direct Convolution on ARMv8 Multi-core...

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IEEE International Conference on Joint Cloud Computing (JCC)

作者： Qinglin Wang Dongsheng Li Songzhu Mei Siqi Shen Xiandong Huang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha China School of Computer Science National University of Defense Technology Changsha China

ISBN: (数字)9781728169811

ISBN: (纸本)9781728169828

Convolutional layers are ubiquitous in a variety of deep neural networks. Due to the lower computation complexity and the smaller number of parameters, convolutions with small filter sizes are often used, such as one by one convolution. Nevertheless, these small convolution operations are still time-consuming. A common approach to implementing convolutions is to transform them into matrix multiplications, known as GEMM-based convolutions. The approach maybe incurs additional memory overhead and calls matrix multiplication routines, which are not optimized for matrices generated by convolutions. In this paper, we present a new parallel one by one direct convolution implementation on ARMv8 multi-core CPUs, which doesn't incur any additional memory space requirement. Our implementation is verified on two ARMv8 CPUs, Phytium FT-1500A and FT-2000plus. In terms of performance and scalability, our implementation is better than GEMM-based implementations in all the tests on Phytium FT-1500A. On Phytium FT-2000plus, our approach gives much better performance and scalability than GEMM-based approaches in most cases.

关键词： Convolution Registers Layout Neural networks parallel processing computer architecture Tensile stress

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Correlation-based software search by leveraging software term database

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Frontiers of computer Science 2018年第5期12卷 923-938页

作者： Zhixing LI Gang YIN Tao WANG Yang ZHANG Yue YU Huaimin WANG National Laboratory for Parallel and Distributed Processing College of Computer National University of Defense Technology Changsha 410073 China

Internet-scale open source software （OSS） pro- duction in various communities generates abundant reusable resources for software developers. However, finding the de- sired and mature software with keyword queries from a considerable number of candidates, especially for the fresher, is a significant challenge because current search services often fail to understand the semantics of user queries. In this paper, we construct a software term database （STDB） by analyzing tagging data in Stack Overflow and propose a correlationbased software search （CBSS） approach that performs correlation retrieval based on the term relevance obtained from STDB. In addition, we design a novel ranking method to optimize the initial retrieval result. We explore four research questions in four experiments, respectively, to evaluate the effectiveness of the STDB and investigate the performance of the CBSS. The experiment results show that the proposed CBSS can effectively respond to keyword-based software searches and significantly outperforms other existing search services at finding mature software.

关键词： software retrieval software term database open source software

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Analysis of single-event transient sensitivity in fully depleted silicon-on-insulator MOSFETs

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Nuclear Science and Techniques 2018年第4期29卷 108-113页

作者： Jing-Yan Xu Shu-Ming Chen Rui-Qiang Song Zhen-Yu Wu Jian-Jun Chen College of Computer National University of Defense Technology National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Based on 3 D-TCAD simulations, single-event transient(SET) effects and charge collection mechanisms in fully depleted silicon-on-insulator(FDSOI) transistors are investigated. This work presents a comparison between28-nm technology and 0.2-lm technology to analyze the impact of strike location on SET sensitivity in FDSOI devices. Simulation results show that the most SET-sensitive region in FDSOI transistors is the drain region near the gate. An in-depth analysis shows that the bipolar amplification effect in FDSOI devices is dependent on the strike locations. In addition, when the drain contact is moved toward the drain direction, the most sensitive region drifts toward the drain and collects more charge. This provides theoretical guidance for SET hardening.

关键词： Single-event transient Charge collection Bipolar amplification Fully depleted silicon-on-insulator

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