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arXiv 2023年

作者： Wan, Wei Hu, Shengshan Li, Minghui Lu, Jianrong Zhang, Longling Zhang, Leo Yu Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Federated learning (FL) is a nascent distributed learning paradigm to train a shared global model without violating users' privacy. FL has been shown to be vulnerable to various Byzantine attacks, where malicious participants could independently or collusively upload well-crafted updates to deteriorate the performance of the global model. However, existing defenses could only mitigate part of Byzantine attacks, without providing an all-sided shield for FL. It is difficult to simply combine them as they rely on totally contradictory assumptions. In this paper, we propose FPD, a four-pronged defense against both non-colluding and colluding Byzantine attacks. Our main idea is to utilize absolute similarity to filter updates rather than relative similarity used in existingI works. To this end, we first propose a reliable client selection strategy to prevent the majority of threats in the bud. Then we design a simple but effective score-based detection method to mitigate colluding attacks. Third, we construct an enhanced spectral-based outlier detector to accurately discard abnormal updates when the training data is not independent and identically distributed (non-IID). Finally, we design update denoising to rectify the direction of the slightly noisy but harmful updates. The four sequentially combined modules can effectively reconcile the contradiction in addressing non-colluding and colluding Byzantine attacks. Extensive experiments over three benchmark image classification datasets against four state-of-the-art Byzantine attacks demonstrate that FPD drastically outperforms existing defenses in IID and non-IID scenarios (with 30% improvement on model accuracy). © 2023, CC BY.

关键词： Classification (of information)

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Simplified Neural Network Based Optical Performance Monitoring Using Amplitude Histogram Metrics and Tap Coefficients

Simplified Neural Network Based Optical Performance Monitori...

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OptoElectronics and Communications Conference, OECC

作者： Zhiquan Wan Zhenming Yu Kun Xu Kun Yin Hui Yu Chuliang Hu Research Center of High Efficiency Computing System Zhejiang Lab Hangzhou China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing China Institute of Innovative Computing Technology Chinese Academy of Sciences Hangzhou China

We demonstrate a simplified neural network to estimate OSNR in nonlinear regime using tap coefficients and metrics extracted from amplitude histogram. The MSE of OSNR estimation improves 1 dB with the help of tap coef... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Measurement Histograms Estimation Optical computing Artificial neural networks Optical fiber networks Optical noise Nonlinear optics Monitoring Signal to noise ratio

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

arXiv

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arXiv 2023年

作者： Zhang, Yechao Hu, Shengshan Zhang, Leo Yu Shi, Junyu Li, Minghui Liu, Xiaogeng Wan, Wei Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Adversarial examples for deep neural networks (DNNs) have been shown to be transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical use. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient similarity. Building on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifest in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other,

关键词： Deep neural networks

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LCW: A Lightweight Recommendation Framework for Non-profit Crowdfunding Projects 21

LCW: A Lightweight Recommendation Framework for Non-profit C...

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4th International Conference on Computer Science and Software Engineering, CSSE 2021

作者： Liu, Jinxin Xiao, Yingyuan Zheng, Wenguang Engineering Research Center of Learning-BasedIntelligent System Ministry of Education Tianjin University of Technology China Tianjin Key Laboratory OfIntelligence Computing and Novel Software Technology Tianjin University of Technology China

ISBN: (纸本)9781450390675

In recent years, crowdfunding platform has gradually emerged as a new economic model, and more and more people choose crowdfunding to obtain the initial funds needed by the project. However, with the increasing number of projects, it is difficult for sponsors to find suitable projects they want to invest in. Therefore, it is very important to build an efficient recommendation system on the crowdfunding platform. Different from previous crowdfunding recommendation systems designed to improve returns, this paper designs a lightweight recommendation framework LCW for non-profit crowdfunding platforms. A large number of experiments on real kiva data sets have proved the effectiveness of this method and it obtains the SOTA results. © 2021 ACM.

关键词： Recommender systems

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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A fully-customized dataflow engine for 3D earthquake simulation with a complex topography

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Science China(Information Sciences) 2022年第5期65卷 65-80页

作者： Bingwei CHEN Haohuan FU Wayne LUK Guangwen YANG Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University National Supercomputer Center in Wuxi Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System ScienceTsinghua University Department of Computing Imperial College London

With HPC(high performance computing) evolving into the exascale era, improvements in computing performance and power efficiency have become increasingly more important. Based on our previous work on enabling earthquake simulations on a large scale on Sunway Taihu Light, we further explore other possibilities to improve the application through a fully-customized hardware design on reconfigurable FPGA(field programmable gate array) devices. We investigate the feasibility and the potential benefits of a complete fixed-point design. We first perform a coarse-resolution-based simulation to analyze the representation range and precision needed to capture both the total energy and the energy distribution of variables over space and time. We then derive a complete fixed-point design that identifies the suitable bitwidth for major categories of variables and dynamically represents the range through a dynamic scaling scheme. Finally, we use the optimized fixed-point design to run a case of the Wenchuan earthquake to demonstrate the potential of supporting large-scale scientific simulations on FPGA devices. The results demonstrate that an 18-bit fixed-point design already provides an almost identical description of the seismic events in the Wenchuan scenario down to a single-precision floating-point version and provides sustainable performance equivalent to 13.1 Intel Xeon Gold 6154 18-core CPUs or 2.10 Sunway 260-core processors, with performance per watt(power efficiency) improved by 15.3 and 3.72 times compared with the Intel Xeon Gold 6154 18-core CPUs and the Sunway 260-core processors, respectively.

关键词： reconfigurable computing earthquake simulation FPGA hardware-software co-design architecture exploration

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Tuning 2D magnetism in Fe3+XGeTe2 films by element doping

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National Science Review 2022年第6期9卷 163-170页

作者： Shanshan Liu Zihan Li Ke Yang Enze Zhang Awadhesh Narayan Xiaoqian Zhang Jiayi Zhu Wenqing Liu Zhiming Liao Masaki Kudo Takaaki Toriyama Yunkun Yang Qiang Li Linfeng Ai Ce Huang Jiabao Sun Xiaojiao Guo Wenzhong Bao Qingsong Deng Yanhui Chen Lifeng Yin Jian Shen Xiaodong Han Syo Matsumura Jin Zou Yongbing Xu Xiaodong Xu Hua Wu Faxian Xiu State Key Laboratory of Surface Physics and Department of Physics Fudan University Institute for Nanoelectronic Devices and Quantum Computing Fudan University College of Science University of Shanghai for Science and Technology Laboratory for Computational Physical Sciences(MOE) Fudan University Solid State and Structural Chemistry Unit Indian Institute of Science School of Electronic Science and Engineering Nanjing University Department of Physics University of Washington Department of Electronic Engineering Royal Holloway University of London Materials Engineering The University of Queensland Beijing Key Laboratory of Microstructure and Property of Advanced Materials Institute of Microstructure and Property of Advanced Materials Beijing University of Technology The Ultramicroscopy Research Center Kyushu University State Key Laboratory of ASIC and System School of Microelectronics Fudan University Collaborative Innovation Center of Advanced Microstructures Department of Applied Quantum Physics and Nuclear Engineering Kyushu University Centre for Microscopy and Microanalysis The University of Queensland Shanghai Research Center for Quantum Sciences

Two-dimensional（2D） ferromagnetic materials have been discovered with tunable magnetism and orbital-driven nodal-line features. Controlling the 2D magnetism in exfoliated nanoflakes via electric/magnetic fields enables a boosted Curie temperature（TC） or phase transitions. One of the challenges, however, is the realization of high TC2D magnets that are tunable, robust and suitable for large scale fabrication. Here, we report molecular-beam epitaxy growth of wafer-scale Fe3+XGeTe2films with TCabove room temperature. By controlling the Fe composition in Fe3+XGeTe2, a continuously modulated TCin a broad range of 185–320 K has been achieved. This widely tunable TCis attributed to the doped interlayer Fe that provides a 40% enhancement around the optimal composition X = 2. We further fabricated magnetic tunneling junction device arrays that exhibit clear tunneling signals. Our results show an effective and reliable approach, i.e. element doping, to producing robust and tunable ferromagnetism beyond room temperature in a large-scale 2D Fe3+XGeTe2fashion.

关键词： 2D ferromagnetic material Fe3+XGeTe2 film element doping above room temperature TC tunability

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On the Outage of Far-Reach Communications via RIS-Empowered Satellites

On the Outage of Far-Reach Communications via RIS-Empowered ...

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IEEE Pacific Rim Conference on Communications, Computers and Signal Processing

作者： Fan Wang Ruihong Jiang Yuan Liu Tingnan Bao Mugen Peng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Zhejiang Laboratory Research Center for Space Computing System Hangzhou China School of Electrical Engineering and Computer Science University of Ottawa Ottawa ON Canada

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

The rising demand for reliable, high-bandwidth satellite communications in far-reach areas drives the integration of low earth orbit (LEO) satellites into future 6G networks. In this paper, we investigate the deployment of reconfigurable intelligent surface (RIS) technology and rate splitting multiple access (RSMA) within LEO satellite systems to improve communication quality and system capacity. We derive closed-form expressions for outage probability (OP) and reliable throughput that validate the theoretical advantages of these technologies under various signal-to-noise ratio conditions. Numerical results demonstrate that, compared with traditional space division multiple access (SDMA) and non-orthogonal multiple access (NOMA) approaches, our RSMA and RIS-integrated framework significantly can reduce OP and enhance reliable throughput with an appropriate power-splitting factor, showcasing its superiority in RIS-enabled satellite communication scenarios.

关键词： NOMA Satellites Closed-form solutions Computer network reliability Low earth orbit satellites Reconfigurable intelligent surfaces Reliability theory Throughput Multiaccess communication Satellite communications

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A Lane Detection Method for Advancing Sustainable Energy Internet

A Lane Detection Method for Advancing Sustainable Energy Int...

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IEEE Conference on Energy Internet and Energy system Integration (EI2)

作者： Xiting Peng Yang Yang Xiaoyu Zhang Xiaoling Zhang School of Information Science and Engineering Shenyang University of Technology Liaoning Liaohe Laboratory Shenyang Key Laboratory of Advanced Computing and Application Innovation Shenyang China School of Information Science and Engineering Shenyang University of Technology Shenyang China School of Artificial Intelligence Shenyang University of Technology Shenyang Key Laboratory of Industrial Intelligent Chip and Network System Innovation Application Shenyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Artificial Intelligence (AI) technology plays a key role in driving the development of a green and smart Energy Internet. As one of the application areas of the Energy Internet, lane detection in intelligent transportation systems can provide accurate real-time traffic data, thus supporting intelligent scheduling and resource optimization, reducing congestion, energy consumption, and emissions, and improving overall energy efficiency. Therefore, deep learning-based lane detection methods can further enhance the intelligence level of the Energy Internet and promote its efficient operation. However, current methods often rely on Convolutional Neural Networks (CNNs), which excel at local feature extraction but struggle to capture global information in complex conditions. In varying light, shadows, and obstacles, CNNs struggle to integrate information, leading to inaccuracies, particularly in dense traffic or complex road structures, where diverse local features result in identification errors and hinder lane continuity. This reduces decision-making efficiency and increases energy consumption. Consequently, we present LFKNet, a lane detection framework designed for the fusion of the Lane Focus Mechanism (LFM) and Kolmogorov-Arnold Network (KAN) to enhance accuracy and robustness. It consists of two key components: the Lane Focus Mechanism , which utilizes the geometrical topology of lanes for improved global fea ture extraction in complex scenarios, and the Kolmogorov-Arnold Network, which employs a learnable nonlinear activation function to optimize performance on complex and nonlinear features. The experiments demonstrate that LFKNet achieves 97.00% accuracy with a 97.76% F1-score on the Tusimple dataset. It also achieves a 79.43% F1-score on the CULane dataset. Overall, LFKNet’s performance validates its effectiveness in lane detection, enabling accurate traffic monitoring and analysis while providing reliable data support for the Energy Internet.

关键词： Energy consumption Accuracy Lane detection Decision making Energy Internet system integration Feature extraction Robustness Topology Artificial intelligence

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