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FAT: Field-Aware Transformer for Point Cloud Segmentation With Adaptive Attention Fields

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第9期20卷 10825-10835页

作者： Zhou, Junjie Liu, Baolin Xiong, Yongping Chiu, Chinwai Liu, Fangyu Gong, Xiangyang Beijing Univ Posts & Telecommun Sch Comp Sci State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Point cloud segmentation is crucial for various industrial applications, such as autonomous driving and robotics. Recent developments underscore the significant potential of transformer models in this field. However, existing attention mechanisms apply the same feature learning paradigm for all points equally, ignoring the considerable size differences among objects in a scene. To rectify this, we introduce the field-aware transformer (FAT), engineered to tailor effective receptive fields to objects of varying sizes. Our FAT achieves field-aware learning through two primary components: the multigranularity attention (MGA) scheme and the reattention module. The MGA scheme is proficient in aggregating tokens from distant areas while preserving multiscale features within each attention layer. The reattention module dynamically adjusts the attention scores to the fine- and coarse-grained features output by MGA for each point. Extensive experimental results underscore the effectiveness and efficiency of our FAT, which delivers state-of-the-art performance on both the stanford 3D indoor scene dataset (S3DIS) and ScanNetV2 datasets.

关键词： 3-D scene understanding multigranularity attention (MGA) point cloud semantic segmentation transformer model

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Network Load Balancing with no Out-of-order Packet for RoCE

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IEEE Network 2024年第2期39卷 194-200页

作者： Luo, Huimin Zhang, Jiao Yu, Mingxuan Pan, Yongchen Pan, Tian Huang, Tao the State Key Laboratory of Networking and Switching Technology BUPT Beijing100876 China the Purple Mountain Laboratories Nanjing211111 China

—Remote Direct Memory Access (RDMA) is becoming a popular high-speed network technology. It uses kernel bypass and zero copy to allow the host to exchange data directly in memory, providing high throughput and low latency while reducing CPU overhead. However, RDMA is based on single-connection transmission and does not make good use of the end-to-end path diversity of the data center. At the same time, existing load balancing algorithms cannot be well applied to RDMA. In this paper, we focus on the flow characteristics of RDMA and load balancing algorithm for RDMA. We first observe the flow characteristics of RDMA and the transmission mode in the lossless environment through experiments, and analyze the reasons for this phenomenon. Then we propose Ursine, a load balancing algorithm for RDMA. It achieves fine-grained RDMA load balancing while avoiding packet out-of-order, and effectively utilizes the end-to-end path diversity in the data center. We implemented the prototype based on DPDK and verified through small-scale experiments that Ursine provides better performance than existing algorithms. Finally, we analyze and look forward to the future and development trends of RDMA load balancing. © 2024 IEEE.

关键词： HIgh speed networks

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Quantum private query protocol based on counterfactual quantum key distribution with noiseless attack

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QUANTUM INFORMATION PROCESSING 2024年第10期23卷 1-15页

作者： Liu, Dongmei Li, Jian Chen, Xiubo Ye, Chongqiang Wang, Zhuo Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Most of the previous quantum private query protocols are based on the BB84 key distribution type and generally use a method that wastes most quantum resources to complete security detection. In order to save resources, this paper proposes a noiseless counterfactual quantum private query protocol with high efficiency. This protocol improves the counterfactual quantum key distribution protocol proposed by Rao and Srikanth (Phys Rev A 104:022424, 2021. https://***/10.1103/PhysRevA.104.022424). The communicating parties randomly select quantum bits with probability f to perform flipping to complete the quantum private query. The detector at the sending end receives the counterfactual bit, which we use for security detection;the detector at the receiving end is called a non-counterfactual bit, which is combined with random bit flipping to realize key transmission, followed by traditional post-processing and private query. Finally, when f=0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f=0.5$$\end{document}, the response probability (keys rate) of the non-counterfactual detector at the receiving end can reach a minimum value of 0.5.

关键词： Counterfactual quantum key distribution Quantum private query High efficiency Noiseless

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MVC-RSN: A Malware Classification Method With Variant Identification Ability

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IEEE INTERNET OF THINGS JOURNAL 2024年第21期11卷 35654-35668页

作者： Wu, Wei Peng, Haipeng Zhu, Haotian Li, Lixiang Beijing Univ Posts & Telecommun Informat Secur Ctr State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

With the rapid development of the Internet of Things (IoT), a substantial number of mobile devices may need to be connected to and communicate with the other devices. IoT smart nodes, which play a key role in these processes, provide not only connectivity but also data security during data collection and transmission. While existing research has considered security issues concerning data collection and transmission to some extent, the inherent vulnerabilities of smart nodes themselves have rarely received adequate attention. Actually, smart nodes can be susceptible to malware attacks, and the fundamental task to protect them is the timely and accurate identification of malware. However, the stealthy nature of malware, the imbalance in its types, and the sharp increase in malware variants make this task challenging. Existing static malware classification methods, which require known malware features, may struggle with a whole new set of malware variants. Dynamic methods plausibly have the potential to identify malware variants but necessitate running malware beforehand. In this article, we propose a deep-learning-based malware classification method designed to help most smart IoT nodes running mainstream operating systems preventing attacks from the malware and its variants. The proposed method incorporates an automatically adjusting threshold mechanism to extract trivial features of malware, refining the classification process even if original features are modified. It also performs well with insufficient malware samples and is compatible with most mainstream convolutional neural network (CNN) models allowing flexible implements. Experiments concerning byte-level variants, operation-code-level variants, and noise-caused variants are conducted. The results show that for the malware files and their variants, the proposed method can accurately accomplish the malware classification tasks. In addition, the proposed method offers a robust solution for the malware classifi

关键词： deep learning malware classification Convolutional neural network (CNN) variant variant malware classification variant

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Plume:Lightweight and Generalized Congestion Control with Deep Reinforcement Learning

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China Communications 2022年第12期19卷 101-117页

作者： Dehui Wei Jiao Zhang Xuan Zhang Chengyuan Huang State Key Laboratory of Networking and Switching Technology BUPT 100876China

Congestion control(CC)is always an important issue in the field of networking,and the enthusiasm for its research has never diminished in both academia and *** current years,due to the rapid development of machine learning(ML),the combination of reinforcement learning(RL)and CC has a striking ***,These complicated schemes lack generalization and are too heavyweight in storage and computing to be directly implemented in mobile *** order to address these problems,we propose Plume,a high-performance,lightweight and generalized RL-CC *** proposes a lightweight framework to reduce the overheads while preserving the original ***,Plume innovatively modifies the framework parameters of the reward function during the retraining process,so that the algorithm can be applied to a variety of *** results show that Plume can retain almost all the performance of the original model but the size and decision latency can be reduced by more than 50%and 20%,***,Plume has better performances in some special scenes.

关键词： congestion control deep reinforcement learning lightweight generalization

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FPCA: Parasitic Coding Authentication for UAVs by FM Signals

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IEEE-ACM TRANSACTIONS ON networking 2024年第6期32卷 4570-4584页

作者： Zhu, Shaopeng Zheng, Xiaolong Liu, Liang Ma, Huadong Beijing Univ Posts & Telecommun Sch Comp Sci State Key Lab Networking & Switching Technol Beijing 100088 Peoples R China

De-authentication attack is one of the major threats to Unmanned Aerial Vehicle (UAV) communication, in which the attacker continuously sends de-authentication frames to disconnect the UAV communication link. Existing defense methods are based on authentication by digital passwords or physical channel features. But they suffer from replay attacks or cannot adapt to the UAV mobility. In this paper, instead of enhancing the in-channel authentication, we leverage the ambient broadcasting signal to establish a low-cost additional channel for authentication. Different from methods using another dedicated secure communication channel to perform an independent authentication, we use the ambient FM radio broadcasting channel and couple the two channels by encoding parasitic bits on the host signals of the broadcasting channel, which is called parasitic coding. To further enhance the security, we propose the FM-based Parasitic Coding Authentication (FPCA) that leverages elaborate host signal processing and vector coding to ensure that the attacker cannot decode our authentication even knowing the FM receiving frequency. We implement FPCA on the embedded UAV platform. The extensive experiments show that FPCA can resist replay attacks and brute force searching, achieving reliable continuous authentication for UAVs.

关键词： Authentication Autonomous aerial vehicles Encoding Frequency modulation Reliability Receivers Security Broadcasting Wireless communication Vectors UAV de-authentication attack authentication FM signal vector coding

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Subarray-Based Hybrid-Field Channel Estimation for Terahertz Wideband UM-MIMO Systems Without Prior Location Knowledge

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IEEE TRANSACTIONS ON VEHICULAR technology 2024年第5期73卷 7363-7367页

作者： Sun, Yanran Yang, Chuang Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol SKL N Beijing 100876 Peoples R China

Terahertz (THz) wideband ultra-massive multiple-input multiple-output (UM-MIMO) systems are critical for the upcoming 6G. The far- and near-field regions always coexist as the hybrid-field regions in the THz wideband UM-MIMO systems, but the existing hybrid-field estimation schemes can not perform well for the wideband channel without prior knowledge of user location information. In this work, we propose an efficient subarray-based hybrid-field channel estimation algorithm for the THz wideband UM-MIMO systems without prior knowledge of user location. The estimation problem is decomposed into a series of sub-problems, each of which considers only one subarray for channel estimation in combination with the wideband sparse detection. Then, the hybrid-field channel is recovered by combining all the subarray channels. Simulation results show that the proposed algorithm is able to recover the hybrid-field wideband channel accurately at a low pilot overhead and the bandwidth has little effect on the performance of the proposed.

关键词： Terahertz (THz) channel estimation hybrid-field wideband

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Joint mmWave Beamforming and Resource Allocation in NOMA-MEC Network for Internet of Things

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IEEE TRANSACTIONS ON VEHICULAR technology 2023年第4期72卷 4969-4980页

作者： Qi, Xiaolei Peng, Mugen Zhang, Hongming Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Mobile edge computing (MEC) technology can reduce the computational burden of Internet of Things (IoT) devices by offloading computationally intensive tasks to physically proximate edge servers. The combination of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technique has draw significant attention for its high spectral efficiency. This paper merges the computation offloading and mmWave massive MIMO-NOMA communication to seek energy efficient performance for IoT devices in MEC network. First, we propose an IoT device clustering algorithm to schedule all IoT devices into limited beams. Following this, we minimize the total energy consumption of computing and offloading in the NOMA-MEC network by jointly optimizing hybrid beamforming, offloading task assignment (OTA) ratio and power allocation. Then, the non-convex joint optimization problem is solved by proposing a suboptimal solution with two stages. In the first stage, a cyclic block-coordinate update algorithm is proposed for analog beamforming design, and then the digital beamforming vectors are obtained by zero-forcing method. In the second stage, an alternating minimization algorithm is proposed to optimize the OTA ratio and power allocation. Finally, simulation results are provided to validate the convergence of the proposed algorithms and the superiority of our proposed schemes over other benchmark schemes, which demonstrate the outstanding performance leveraging the massive MIMO-NOMA technique during computational offloading.

关键词： Internet of Things NOMA Task analysis Millimeter wave communication Array signal processing Resource management Minimization Hybrid beamforming massive MIMO mobile edge computing resource allocation

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An efficient quantum proactive incremental learning algorithm

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Science China(Physics,Mechanics & Astronomy) 2025年第1期68卷 45-53页

作者： Lingxiao Li Jing Li Yanqi Song Sujuan Qin Qiaoyan Wen Fei Gao State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China

In scenarios where a large amount of data needs to be learned,incremental learning can make full use of old knowledge,signif-icantly reduce the computational cost of the overall learning process,and maintain high *** this paper,taking the MaxCut problem as our example,we introduce the idea of incremental learning into quantum computing,and propose a Quantum Proactive Incremental Learning algorithm(QPIL).Instead of a one-off training of quantum circuit,QPIL contains a multi-phase training on gradually-increased subgraphs of all vertices,proactively reducing large-scale problems to smaller ones to solve in steps,providing an efficient solution for ***,some vertices and corresponding edges are randomly selected for training to obtain optimized parameters of the quantum circuit at ***,in each incremental phase,the remaining vertices and corresponding edges are gradually added and the parameters obtained from the previous phase are reused in the parameter initialization of the current *** perform experiments on 120 different small-scale graphs,and it shows that QPIL performs superior to prevalent quantum and classical baselines in terms of approximation ratio(AR),time cost,anti-forgetting,and solv-ing *** particular,QPIL’s AR surpasses 20%of mainstream quantum baselines,and the time cost is less than 1/5 of *** idea of QPIL is expected to inspire efficient and high-quality solutions in large-scale MaxCut and other combinatorial optimization problems.

关键词： variational quantum algorithm incremental learning multi-phase training MaxCut quantum computing

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An Improved Deep Neural Network for Small-Ship Detection in SAR Imagery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 2596-2609页

作者： Hu, Boyi Miao, Hongxia Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Ship detection by using remote-sensing images based on a synthetic aperture radar (SAR) plays an important role in managing water transportation and marine safety. However, complex background, a small-ship size, and low focus on small ships results in difficulties in feature extraction and low detection accuracy. This study proposes a new small SAR ship-detection network. First, a transformer-based dynamic sparse attention module is used to improve the focus and extraction of small-ship features. Second, the feature maps are fused with deep layers, and small target-friendly detection heads are used to improve the processing of global information in the network. Third, a more suitable fused loss function is used for small ships to ensure the multiscale detection capability. Experimental results on publicly available datasets, LS-SSDD_v1.0 and AIR-SARShip-1.0, show that the proposed method effectively improves the detection accuracy of small ships on SAR images without computational burden boost. Compared with other methods based on the convolutional neural network, the proposed method demonstrates the better multiscale detection performance.

关键词： Convolutional neural network (CNN) ship detection synthetic aperture radar (SAR) transformer

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