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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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High-performance Network Traffic Classification Based on Graph Neural Network 6

High-performance Network Traffic Classification Based on Gra...

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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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Transformer in reinforcement learning for decision-making:a survey

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Frontiers of Information Technology & Electronic Engineering 2024年第6期25卷 763-790页

作者： Weilin YUAN Jiaxing CHEN Shaofei CHEN Dawei FENG Zhenzhen HU Peng LI Weiwei ZHAO College of Information and Communication National University of Defense TechnologyWuhan 430014China College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410072China Laboratory for Parallel and Distributed Processing National University of Defense TechnologyChangsha 410072China

Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world ***,deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making *** by current major success of Transformer in natural language processing and computer vision,numerous bottlenecks have been overcome by combining Transformer with RL for *** paper presents a multiangle systematic survey of various Transformer-based RL(TransRL)models applied in decision-making tasks,including basic models,advanced algorithms,representative implementation instances,typical applications,and known *** work aims to provide insights into problems that inherently arise with the current RL approaches,and examines how we can address them with better TransRL *** our knowledge,we are the first to present a comprehensive review of the recent Transformer research developments in RL for *** hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future *** keep track of the rapid TransRL developments in the decision-making domains,we summarize the latest papers and their open-source implementations at https://***/williamyuanv0/Transformer-in-Reinforcement-Learning-for-Decision-Making-A-Survey.

关键词： Transformer Reinforcement learning(RL) Decision-making(DM) Deep neural network(DNN) Multi-agent reinforcement learning(MARL) Meta-reinforcement learning(Meta-RL)

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Adaptive Self-Supervised Continual Learning 26

Adaptive Self-Supervised Continual Learning

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Lilei Wang, Zhen Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Tsinghua University Beijing100084 China

ISBN: (纸本)9781643684369

Continual Learning (CL) studies the problem of developing a robust model that can learn new tasks while retaining previously learned knowledge. However, the current CL methods exclusively focus on data with annotations, disregarding that unlabelled data is the mainstream in real-world applications. To close this research gap, this study concentrates on continual self-supervised learning, which is plagued by challenges of memory over-fitting and class imbalance. Besides, these challenges are exacerbated throughout incremental training. Aimed at addressing these challenges from both loss and data perspectives, we introduce a framework, Adaptive Self-supervised Continual Learning (ASCL). Specifically, we devise an Adaptive Sharpness-Aware Minimization (ASAM) module responsible for identifying flatter local minima in the loss landscape with a smaller memory over-fitting risk. Additionally, we design an Adaptive Memory Enhancement (AME) module responsible for rebalancing self-supervised loss with new and old tasks from a data perspective. Finally, the adaptive mechanisms in AME and ASAM modules dynamically adjust the loss landscape sharpness and memory enhancement strength with the feedback of intermediate training results. The results of our extensive experiments demonstrate the state-of-the-art performance of our methods in continual self-supervised learning scenarios across multiple datasets. © 2023 The Authors.

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Mixed Weighted Classification Loss Function and TFcaNet Attention Mechanism for Remote Sensing Images Object Detection 6

Mixed Weighted Classification Loss Function and TFcaNet Atte...

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2021 6th International Conference on Information Science, Computer Technology and Transportation, ISCTT 2021

作者： Wang, Junhua Jiang, Jingfei Xu, Jinwei Gu, Qinyi National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Department of Image Technology No.95806 Troop Beijing China

ISBN: (纸本)9783800757282

With the development of intelligent technology, the intelligent processing of remote sensing images has become a research hotspot and the object detection for remote sensing images is becoming more and more extensive. The characteristics of sample imbalance in categories, numerous small objects, and blurry object edges that remote sensing images contains bring huge challenges to object detection. Aiming at the characteristics of sample imbalance in categories of remote sensing images, we designed a mixed weighted classification loss function. At the same time, the TFcaNet attention mechanism is proposed to improve the capabilities of small objects detection and localization of the deep learning model. We create a model named YOLOv5-FRD based on YOLOv5. Through training and testing, the mAP on DOTA dataset reached 73.53%. Compared with the current mainstream one-stage models, YOLOv5-FRD achieves the second place in mAP and the highest AP in the most categories. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Object detection

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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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A neuro-genetic approach for inferring gene regulatory networks from gene expression data 22

A neuro-genetic approach for inferring gene regulatory netwo...

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Proceedings of the 9th International Conference on Bioinformatics Research and Applications

作者： Guo Mao Zhengbin Pang Jie Liu Ke Zuo Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology China

ISBN: (纸本)9781450396868

Accurate prediction of gene regulation rules is important for understanding complex life processes. Existing computational algorithms designed for bulk transcriptome datasets typically require a large number of time points to infer gene regulatory networks (GRNs), are suitable for a small number of genes, and cannot efficiently detect potential regulatory relationships. We propose an approach based on a deep learning framework to reconstruct GRNs from bulk transcriptome datasets, assuming that the expression levels of transcription factors involved in gene regulation are strong predictors of the expression of their target genes. The algorithm uses multilayer perceptrons to infer the regulatory relationship between multiple transcription factors and a gene, and uses genetic algorithms to search for the best regulatory gene combination. The results show that our approach is more accurate than other methods for reconstructing gene regulatory networks on real-world and simulated bulk transcriptome gene expression datasets.

关键词： gene regulatory networks datasets multilayer perceptrons genetic algorithms

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DDPER: DECENTRALIZED distributed PRIORITIZED EXPERIENCE REPLAY

DDPER: DECENTRALIZED DISTRIBUTED PRIORITIZED EXPERIENCE REPL...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Liu, Sidun Qiao, Peng Dou, Yong Li, Rongchun National Laboratory for Parallel and Distributed Processing National University of Defense Technology Hunan China

ISBN: (纸本)9781665438643

In off-policy reinforcement learning, prioritized experience replay plays an important role. However, the centralized prioritized experience replay becomes the bottleneck for efficient training. We propose to approximate the centralized prioritized experience replay in a distributed and decentralized way under certain mild assumptions. To be specific, each actor stores samples in its local replay in the same way as prioritized experience replay, the learner fetches a batch of samples from these replays following a certain strategy. We implement a Deep Q-Learning off-policy algorithm upon the proposed framework. The comparison experiments are performed on a commonly used subset of the Atari-57 learning environment. The experimental results show that the proposed framework speeds up training as the number of actors increases. With the same algorithm and hyper-parameter settings, the proposed framework with 16 actors achieves superior performance that Ape-X with 32 and even more actors does. © 2021 IEEE

关键词： Reinforcement learning

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MeshONet: A Generalizable and Efficient Operator Learning Method for Structured Mesh Generation

arXiv

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arXiv 2025年

作者： Xiao, Jing Chen, Xinhai Peng, Jiaming Wang, Qingling Liu, Jie Laboratory of Digitizing Software for Frontier Equipment Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China

Mesh generation plays a crucial role in scientific computing. Traditional mesh generation methods, such as TFI and PDE-based methods, often struggle to achieve a balance between efficiency and mesh quality. To address this challenge, physics-informed intelligent learning methods have recently emerged, significantly improving generation efficiency while maintaining high mesh quality. However, physics-informed methods fail to generalize when applied to previously unseen geometries, as even small changes in the boundary shape necessitate burdensome retraining to adapt to new geometric variations. In this paper, we introduce MeshONet, the first generalizable intelligent learning method for structured mesh generation. The method transforms the mesh generation task into an operator learning problem with multiple input and solution functions. To effectively overcome the multivariable mapping restriction of operator learning methods, we propose a dual-branch, shared-trunk architecture to approximate the mapping between function spaces based on input-output pairs. Experimental results show that MeshONet achieves a speedup of up to four orders of magnitude in generation efficiency over traditional methods. It also enables generalization to different geometries without retraining, greatly enhancing the practicality of intelligent methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Mesh generation

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RiverMapper: Step-wisely Mapping the Surface Rivers on Optical Remote Sensing Images

RiverMapper: Step-wisely Mapping the Surface Rivers on Optic...

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2022 International Conference on Neural Networks, Information, and Communication Engineering, NNICE 2022

作者： Zhang, Peng Pan, Hengyue Yang, Ke Dou, Yong Niu, Xin National Laboratory for Parallel and Distributed Processing National University of Defense Technology 410073 China Artificial Intelligence Research Center National Innovation Institute of Defense Technology BeiJing100850 China

ISBN: (纸本)9781510655171

Accurately mapping the surface rivers is important in ecological environment monitoring and disaster prevention. The development of remote sensing technology and computer vision greatly improves the efficiency of this task. However, there are few methods that map the rivers from an image directly. The existing automatic river mapping methods usually had two successive stages: waterbody extraction and flow-path extraction, where the latter methods were very dependent on the waterbody masks generated by the former methods. Errors in waterbody masks caused breaks and redundancies in the extracted graphs. This paper proposed RiverMapper, which mapped the rivers step-wisely without dividing into two stages. Following the directions and actions predicted by the convolution neural network, RiverMapper walked along the rivers step by step and cropped the fixed-size image patches at each step for segmentation. Final river graphs were constructed by the waterbody mask patches and those tracks generated by RiverMapper. We applied RiverMapper on optical remote sensing images containing the Changjiang River and the Huanghe River. Without the degradation of the performance on waterbody extraction, RiverMapper outperformed other methods in terms of the local topological and geometrical similarity between the predicted and the ground-truth river graphs. © 2022 SPIE.

关键词： Mapping

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