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Why do they ask? An exploratory study of crowd discussions about Android application programming interface in stack overflow

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Journal of Central South University 2019年第9期26卷 2432-2446页

作者： FAN Qiang WANG Tao YANG Cheng YIN Gang YU Yue WANG Huai-min National Laboratory for Parallel and Distributed Processing College of Computer ChangshaChangsha 410073China

Nowadays,more and more Android developers prefer to seek help from Q&A website like Stack Overflow,despite the rich official *** researches have studied the limitations of the official application programming interface(API)documentations and proposed approaches to improve ***,few of them digged into the requirements of the third-party developers to study *** this work,we gain insight into this question from multidimensional perspectives of API developers and API users by a kind of *** propose a hybrid approach,which combines manual inspection on artifacts and online survey on corresponding developers,to explore the different focus between these two types of *** our work,we manually inspect 1000 posts and receive 319 questionnaires in *** the mutual verification of the inspection and survey process,we found that the users are more concerned with the usage of API,while the official documentation mainly provides functional ***,we identified 9 flaws of the official documentation and summarized 12 aspects(from the content to the representation)for promotion to improve the official API documentations.

关键词： API documentation Android online survey Stack Overflow

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Quantization improvements for LU Decomposition to Solve Linear Equations *

Quantization improvements for LU Decomposition to Solve Line...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Lin Chen Kang He Hao Jiang Roberto Barrio Jie Liu Tiejun Li Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China University of Zaragoza Zaragoza Spain

Quantization is an effective method for compressing Deep Neural Networks. Now, it is considered to accelerate the traditional HPC applications. In this article, we present a quantization method (q8gemm) using integer 8-bits(INT8) for matrix multiplication, which is implemented by block techniques to overcome the overflow problem. We choose different scales for one matrix’s each row and those for the other matrix’s each column to keep the numbers in range. With the intel advanced matrix extension cblas_gemm_s8u8s32, our q8gemm can accelerate panel-matrix multiplication in LU factorization. Furthermore, we propose a new mixed-precision solver (q8gesv 1 ) for the linear equations. Using q8gemm in INT8, q8gesv modifies LU factorization in FP32 and then utilizes GMRES iterative refinement in FP64 to bring the solution back to 64 bits accuracy. Numerical tests show that on two Intel CPU platforms with avx2 and avx512, q8gemm achieves a speedup of 1.313-1.621 over cblas_sgemm of MKL. The execution time of cblas_gemm_s8u8s32 makes up 86.78%-90.85% of the q8gemm. By means of q8gemm, our q8gesv can achieve a speedup of 1.176-1.493 over the optimized Higham’s mixed-precision algorithm utilized in the HPL-AI benchmark and a speedup of 1.1-1.459 over the LAPACKE_dsgesv function from MKL LAPACK. Meanwhile, q8gesv runs up to 2.859X faster on Intel(R) Core(TM) i5-10600KF and to 2.224X faster on Intel(R) Xeon(R) Gold 6130 than the LAPACKE_dgesv function from MKL LAPACK.

关键词： Deep learning Gold Quantization (signal) Benchmark testing

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Multi-representation knowledge distillation for audio classification

arXiv

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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

引用

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 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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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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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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NUMA-aware FFT-based convolution on ARMv8 many-core CPUs

arXiv

引用

arXiv 2021年

作者： Huang, Xiandong Wang, Qinglin Lu, Shuyu Hao, Ruochen Mei, Songzhu Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China School of Computer Science National University of Defense Technology Changsha410073 China Department of Biomedical Informatics University of Pittsburgh Pittsburgh United States

Convolutional Neural Networks (CNNs), one of the most representative algorithms of deep learning, are widely used in various artificial intelligence applications. Convolution operations often take most of the computational overhead of CNNs. The FFT-based algorithm can improve the efficiency of convolution by reducing its algorithm complexity, there are a lot of works about the high-performance implementation of FFT-based convolution on many-core CPUs. However, there is no optimization for the non-uniform memory access (NUMA) characteristics in manycore CPUs. In this paper, we present a NUMA-aware FFT-based convolution implementation on ARMv8 many-core CPUs with NUMA architectures. The implementation can reduce a number of remote memory access through the data reordering of FFT transformations and the three-level parallelization of the complex matrix multiplication. The experiment results on a ARMv8 many-core CPU with NUMA architectures demonstrate that our NUMA-aware implementation has much better performance than the state-of-the-art work in most cases. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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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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