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23rd International Conference on parallel and distributed Computing, Applications, and Technologies, PDCAT 2022

作者： Hao, Ruochen Wang, Qinglin Yin, Shangfei Zhou, Tianyang Zhang, Qingyang Mei, Songzhu Shen, Siqi Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China College of Computer National University of Defense Technology Changsha410073 China Xiamen University Xiamen China

ISBN: (纸本)9783031299261

Depthwise convolutions are widely used in lightweight convolutional neural networks (CNNs). The performance of depthwise convolutions is mainly bounded by the memory access rather than the arithmetic operations for classic convolutions so that direct algorithms are often more efficient than indirect ones (matrix multiplication-, Winograd-, and FFT-based convolutions) with additional memory accesses. However, the existing direct implementations of depthwise convolutions on ARMv8 architectures feature a bad trade-off between register-level reuse of different tensors, which usually leads to sub-optimal performance. In this paper, we propose a new direct implementation of depthwise convolutions by means of implicit padding, register tiling, etc. Compared to the existing ones, our new implementations can incur much less communication overhead between registers and cache. Experimental results on two ARMv8 CPUs show that our implementation can averagely deliver 4.88 × performance improvement over the existing direct ones in open-source libraries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Adversarial Attack and Defense for Transductive Support Vector Machine

Adversarial Attack and Defense for Transductive Support Vect...

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International Joint Conference on Neural Networks (IJCNN)

作者： Li Liu Haiyan Chen Changchun Yin Liming Fang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

As a classic semi-supervised approach, the Transductive Support Vector Machine (TSVM) has exhibited remarkable accuracy by utilizing unlabeled data. However, the robustness of TSVM against adversarial attacks remains a subject of investigation, prompting concerns about its reliability in security-critical applications. To unveil the vulnerability of TSVM, we introduce a finite-attack model specifically tailored to its characteristics, effectively manipulating its outputs. Additionally, we present Adversarial Defense-based TSVM (AD-TSVM), the first dedicated defense scheme designed for TSVM. AD-TSVM incorporates adversarial information into the optimization process, enhancing robustness by rebuilding a customized loss function and decision margin to counteract attacks. Rigorous experiments conducted on benchmark datasets demonstrate the effectiveness of AD-TSVM in significantly improving both the accuracy and stability of TSVM when confronted with adversarial attacks. This pioneering research assesses the weaknesses of TSVM and, more importantly, offers valuable insights and solutions for developing secure and trustworthy TSVM systems in the face of emerging threats.

关键词： Training Support vector machines Accuracy Machine learning algorithms Neural networks Robustness Vectors

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DPSS: Dynamic Parameter Selection for Outlier Detection on Data Streams

DPSS: Dynamic Parameter Selection for Outlier Detection on D...

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International Conference on parallel and distributed Systems (ICPADS)

作者： Ruyi Zhang Yijie Wang Haifang Zhou Bin Li Hongzuo Xu Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology

Outlier detection on data streams identifies unusual states to sense and alarm potential risks and faults of the target systems in both the cyber and physical world. As different parameter settings of machine learning algorithms can result in dramatically different performance, automatic parameter selection is also of great importance in deploying outlier detection algorithms in data streams. However, current canonical parameter selection methods suffer from two key challenges: (i) Data streams generally evolve over time, but these existing methods use a fixed training set, which fails to handle this evolving environment and often results in suboptimal parameter recommendations; (ii) The stream is infinite, and thus any parameter selection method taking the entire stream as input is infeasible. In light of these limitations, this paper introduces a Dynamic Parameter Selection method for outlier detection on data Streams (DPSS for short). DPSS uses Gaussian process regression to model the relationship between parameters and detecting performance and uses Bayesian optimization to explore the optimal parameter setting. For each new subsequence, DPSS updates the recommended parameter setting to suit the evolving characteristics. Besides, DPSS only uses historical calculations to guide the parameter setting sampling and adjust the Gaussian process regression results. DPSS can be employed as an auxiliary plug-in tool to improve the detection performance of outlier detection methods. Extensive experiments show that our method can significantly improve the F-score of outlier detectors in data streams compared to its counterparts and obtains more superior parameter selection performance than other state-of the-art parameter selection approaches. DPSS also achieves better time and memory efficiency compared to competitors.

关键词： Training Fault diagnosis Machine learning algorithms Memory management Gaussian processes Detectors Bayes methods

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Evaluating matrix multiplication-based convolution algorithm on multi-core digital signal processors

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Guofang Keji Daxue Xuebao/Journal of National University of Defense technology 2023年第1期45卷 86-94页

作者： Wang, Qinglin Pei, Xiangdong Liao, Linyu Wang, Haoxu Li, Rongchun Mei, Songzhu Li, Dongsheng College of Computer Science and Technology National University of Defense Technology Changsha410073 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

The matrix multiplication-based convolutional algorithm, which can efficiently implement convolutions with different parameters, is the first choice of convolution performance optimization for a given chip. Based on the architecture of Phytium heterogeneous multi-core DSPs(digital signal processors) developed by National University of Defense technology and the characteristic of the matrix multiplication-based convolutional algorithm, a parallel implementation of the matrix multiplication-based convolutional algorithm (called ftmEConv) for different convolutions on multi-core DSPs was proposed. The ftmEConv consists of four parallelized parts(input feature maps transformation, filter transformation, matrix multiplication, and output feature maps transformation), all of which were optimized for multi-core DSPs, and the performance of each part was improved by effectively exploiting the potential of all functional units in DSP cores. The experimental results demonstrate that ftmEConv achieves computational efficiency of up to 42.90%. Compared with other implementations of the matrix multiplication-based convolutional algorithm on heterogeneous chips, ftmEConv gets a speedup of up to 7.79 times. © 2023 National University of Defense technology. All rights reserved.

关键词： Convolution

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A survey of script learning

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Frontiers of Information technology & Electronic Engineering 2021年第3期22卷 341-373页

作者： Yi HAN Linbo QIAO Jianming ZHENG Hefeng WU Dongsheng LI Xiangke LIAO Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410000China Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410000China School of Data and Computer Science Sun Yat-sen UniversityGuangzhou 510006China

Script is the structured knowledge representation of prototypical real-life event *** the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible *** learning is an interesting and promising research direction,in which a trained script learning system can process narrative texts to capture script knowledge and draw ***,there are currently no survey articles on script learning,so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on script *** research field contains three main topics:event representations,script learning models,and evaluation *** each topic,we systematically summarize and categorize the existing script learning systems,and carefully analyze and compare the advantages and disadvantages of the representative *** also discuss the current state of the research and possible future directions.

关键词： Script learning Natural language processing Commonsense knowledge modeling Event reasoning

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Jammer Localization for Wireless Sensor Networks

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Chinese Journal of Electronics 2023年第4期20卷 735-738页

作者： SUN Yanqiang WANG Xiaodong ZHOU Xingming National Key Laboratory for Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China

Jamming attack can severely affect the performance of Wireless sensor networks (WSNs) due to the broadcast nature of wireless medium. In order to localize the source of the attacker, we in this paper propose a jammer localization algorithm named as Minimum-circle-covering based localization (MCCL). Comparing with the existing solutions that rely on the wireless propagation parameters, MCCL only depends on the location information of sensor nodes at the border of the jammed region. MCCL uses the plane geometry knowledge, especially the minimum circle covering technique, to form an approximate jammed region, and hence the center of the jammed region is treated as the estimated position of the jammer. Simulation results showed that MCCL is able to achieve higher accuracy than other existing solutions in terms of jammer's transmission range and sensitivity to nodes' density.

关键词： Location awareness Wireless communication Geometry Wireless sensor networks Sensitivity Simulation Jamming

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Quality evaluation of airfoil hybrid mesh based on graph neural network 19th

Quality evaluation of airfoil hybrid mesh based on graph neu...

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19th Chinese Intelligent Systems Conference, CISC 2023

作者： Wang, Huaiqing Pang, Yufei Xiao, Sumei Wang, Zhichao School of Manufacturing Science and Engineering Southwest University of Science and Technology Mianyang621010 China Computational Aerodynamics Istitute China Aerodynamics Research and Development Center Mianyang621010 China Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819968817

In airfoil numerical simulation, the mesh quality has an important influence on the accuracy and error of numerical simulation. The existing mesh quality evaluation requires a lot of manual interaction, which greatly reduces the efficiency of mesh generation and necessitates the implementation of intelligent mesh evaluation methods. Graph neural networks can extract features from graph data, possess self-adaptability and generalization ability, and have been successfully applied in many industries. In this paper, we propose a deep graph neural network, SDeepNet, to evaluate mesh quality and construct a large-scale mixed mesh dataset, MixSet, for training and validating the model. We test and compare the performance of the mesh quality evaluation models GridNet, GMeshNet, and SDeepNet on the mesh dataset MixSet. The experimental results show that the SDeepNet model can achieve high accuracy and recall in the mixed mesh quality evaluation task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Mesh generation

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Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection

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

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

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

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.

关键词： Protocols Smoothing methods Time series analysis Detectors Signal processing Distance measurement Acoustics

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