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6th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2023

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

ISBN: (纸本)9781665460033

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. © 2023 IEEE.

关键词： Graph neural networks

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Fast flow field prediction of pollutant leakage diffusion based on deep learning

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Environmental Science and Pollution Research 2024年第36期31卷 49393-49412页

作者： YunBo, Wan Zhong, Zhao Jie, Liu KuiJun, Zuo Yong, Zhang Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Computational Aerodynamics Institute China Aerodynamics Research and Development Center Mianyang621000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China School of Aeronautics Northwestern Polytechnical University Xi’an710072 China

Abstract: Predicting pollutant leakage and diffusion processes is crucial for ensuring people’s safety. While the deep learning method offers high simulation efficiency and superior generalization, there is currently a lack of research on predicting pollutant leakage and diffusion flow field using deep learning. Therefore, it is necessary to conduct further studies in this area. This paper introduces a two-level network method to model the flow characteristics of pollutant diffusion. The proposed method in this study demonstrates a significant enhancement in flow field prediction accuracy compared to traditional deep learning methods. Moreover, it improves computational efficiency by over 800 times compared to traditional computational fluid dynamics (CFD) methods. Unlike conventional CFD methods that require grid expansion to calculate all operation conditions, the deep learning method is not confined by grid limitations. While deep learning methods may not entirely replace CFD methods, they can serve as a valuable supplementary tool, expanding the versatility of CFD methods. The findings of this research establish a robust foundation for incorporating deep learning methods in addressing pollutant leakage and diffusion challenges. Graphical abstract: A deep learning method for simulating pollutant leakage diffusion. (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diffusion

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Optimizing Depthwise Convolutions on ARMv8 Architecture 23rd

Optimizing Depthwise Convolutions on ARMv8 Architecture

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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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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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CHIME: A Cache-Efficient and High-Performance Hybrid Index on Disaggregated Memory 24

CHIME: A Cache-Efficient and High-Performance Hybrid Index o...

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30th ACM Symposium on Operating Systems Principles, SOSP 2024

作者： Luo, Xuchuan Shen, Jiacheng Zuo, Pengfei Wang, Xin Lyu, Michael R. Zhou, Yangfan National Key Laboratory of Parallel and Distributed Computing Changsha China School of Computer Science Fudan University Shanghai China Duke Kunshan University Kunshan China Huawei Cloud Shenzhen China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400712517

Disaggregated memory (DM) is a widely discussed datacenter architecture in academia and industry. It decouples computing and memory resources from monolithic servers into two network-connected resource pools. Range indexes are widely adopted by storage systems on DM to efficiently locate and query remote data. However, existing range indexes on DM suffer from either high computing-side cache consumption or high memory-side read amplifications. In this paper, we propose CHIME, a hybrid index combining B+ trees with hopscotch hashing, to achieve low cache consumption and low read amplifications simultaneously. There are three challenges in constructing CHIME on DM, i.e., the complicated optimistic synchronization, the extra metadata access, and the read amplifications introduced by hopscotch hashing. CHIME leverages 1) a three-level optimistic synchronization scheme to synchronize read and write operations with various granularities, 2) an access-aggregated metadata management technique to eliminate extra metadata accesses by piggybacking and replicating metadata, and 3) an effective hotness-aware speculative read mechanism to mitigate the read amplifications of hopscotch hashing. Experimental results show that CHIME outperforms the state-of-the-art range indexes on DM by up to 5.1× with the same cache size and achieves similar performance with up to 8.7× lower cache consumption. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Memory architecture

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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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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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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Prophet: Fine-grained Load Balancing for parallel Training of Large-scale MoE Models

Prophet: Fine-grained Load Balancing for Parallel Training o...

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IEEE International Conference on Cluster Computing

作者： Wei Wang Zhiquan Lai Shengwei Li Weijie Liu Keshi Ge Yujie Liu Ao Shen Dongsheng Li National Laboratory for Parallel and Distributed Processing(PDL) College Of Computer National University Of Defense Technology Changsha China

Mixture of Expert (MoE) has received increasing attention for scaling DNN models to extra-large size with negligible increases in computation. The MoE model has achieved the highest accuracy in several domains. However, a significant load imbalance occurs in the device during the training of a MoE model, resulting in significantly reduced throughput. Previous works on load balancing either harm model convergence or suffer from high execution overhead. To address these issues, we present Prophet: a fine-grained load balancing method for parallel training of large-scale MoE models, which consists of a planner and a scheduler. Prophet planner first employs a fine-grained resource allocation method to determine the possible scenarios for the expert placement in a fine-grained manner, and then efficiently searches for a well-balanced expert placement to balance the load without introducing additional overhead. Prophet scheduler exploits the locality of the token distribution to schedule the resource allocation operations using a layer-wise fine-grained schedule strategy to hide their overhead. We conduct extensive experiments in four clusters and five representative models. The results indicate that Prophet gains up to 2.3x speedup compared to the state-of-the-art MoE frameworks including Deepspeed-MoE and FasterMoE. Additionally, Prophet achieves a load balancing enhancement of up to 12.06x when compared to FasterMoE.

关键词：

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Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional Neural Network 21

Prediction of the Cyanobacteria Coverage in Time-series Imag...

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4th International Conference on Control and computer Vision, ICCCV 2021

作者： Ye, Xiangyu Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology China

ISBN: (纸本)9781450390477

In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria. © 2021 ACM.

关键词： Lakes

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