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arXiv 2024年

作者： Zhu, Panyu Lv, Jingjie Han, Yunghsiang S. Song, Linqi Hou, Hanxu Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China School of Electrical Engineering & Intelligentization Dongguan University of Technology China Department of Computer Science City University of Hong Kong Hong Kong

EVENODD+ codes are binary maximum distance separable (MDS) array codes for correcting double disk failures in RAID-6 with asymptotically optimal encoding/decoding/update complexities. However, the number of bits stored in each disk of EVENODD+ codes should be an odd number minus one. In this paper, we present a new construction of EVENODD+ codes that have more flexible parameters. The number of bits stored in each disk of our codes is an odd minus one times any positive integer. Moreover, our codes not only have asymptotically optimal encoding/decoding/update complexities but also have lower encoding/decoding/update complexities than the existing EVENODD+ codes. Copyright © 2024, The Authors. All rights reserved.

关键词： Encoding (symbols)

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YOLOv7-ship: An Efficient Method for Marine Ship Detection Based on Wave Glider 12

YOLOv7-ship: An Efficient Method for Marine Ship Detection B...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Sun, Xiujun Zhang, Xudong Wang, Yong Sang, Hongqiang Zhou, Ying Physical Oceangraphy Laboratory Ocean University of China Qingdao China Department of Computer Science and Technology Ocean University of China Qingdao China School of Mechanical Engineering Tianjin Key Laboratory of Advanced Mechatronic Equipment Technology Tiangong University Tianjin China Institute for Advanced Ocean Study Ocean University of China Qingdao China

ISBN: (纸本)9798400707988

Ship detection based on wave gliders plays an important role in tasks such as maritime patrols, target tracking, and maritime traffic control. However, the complex and diverse sea environment, as well as the large differences in the appearance and scale of ships on the sea, have led to traditional detection algorithms experiencing issues such as false detection, missed detection of small targets, and incomplete detection of ship mast areas in ship detection tasks. To alleviate this dilemma, we designed a ship detector named YOLOv7-ship based on YOLOv7-tiny. Firstly, we introduce a parameter-free attention module (SimAM) in the backbone network to search for attention regions in the scene, thereby helping to solve the problem of false detection under complex marine environment interference. Secondly, we designed a mixed upsampling feature fusion method and specifically added a small target detection layer to enhance the detection of small targets. Finally, we introduce Wise-IOU into the loss function, and carry out different gradient gain allocation strategies in stages to solve the problem of incomplete detection of ship mast. The experimental results show that the YOLOv7-ship detector we designed achieves mAP@0.5 99.4% and 81.1% on the public dataset SeaShips and our wave glider dataset WG-Ships, respectively, while maintaining real-time inference speed and outperforming most mainstream models in performance. © 2023 ACM.

关键词： Ships

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Video Streaming Caching and Transcoding for Heterogeneous Mobile Users

Video Streaming Caching and Transcoding for Heterogeneous Mo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jinbo Cai Mingjun Xiao He Sun Junjie Shao Yu Zhao Tongxiao Zhang School of Computer Science and Technology & Suzhou Institute for Advanced Study University of Science and Technology of China Hefei P. R. China School of Computer and Communication Engineering Northeastern University at Qinhuangdao P.R.China

With the increasing prevalence of video streaming media, users have higher requirements for the quality of video streams, which can be quantified as Quality of Experience (QoE). Caching and transcoding video chunks on edge servers are effective methods to improve users’ QoE and reduce the cost of retrieving video chunks. In this paper, we investigate how to cache and transcode video chunks for mobile users to maximize their QoE, while taking into consideration the cooperation between edge servers, users’ mobility, and heterogeneous preferences, simultaneously. We employ a Multi-Agent Reinforcement Learning (MARL) framework and propose an MARLbased Cache replacement and Transcoding (MACT) mechanism. More specifically, we formulate the problem as a multi-agent game and prove this game is a state-based potential game. Then, we decompose this multi-agent game into individual agent decision problems to be solved. Through extensive simulations, we demonstrate that the proposed MACT mechanism achieves about 19.3% performance improvement, compared to the state-of-the-art works.

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Sampling from the Mean-field Stationary Distribution

arXiv

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arXiv 2024年

作者： Kook, Yunbum Zhang, Matthew S. Chewi, Sinho Erdogdu, Murat A. Li, Mufan School of Computer Science Georgia Institute of Technology United States Department of Computer Science University of Toronto Vector Institute Canada School of Mathematics Institute for Advanced Study United States Department of Operations Research and Financial Engineering Princeton University United States

We study the complexity of sampling from the stationary distribution of a mean-field SDE, or equiva- lently, the complexity of minimizing a functional over the space of probability measures which includes an interaction term. Our main insight is to decouple the two key aspects of this problem: (1) approximation of the mean-field SDE via a finite-particle system, via uniform-in-time propagation of chaos, and (2) sam- pling from the finite-particle stationary distribution, via standard log-concave samplers. Our approach is conceptually simpler and its flexibility allows for incorporating the state-of-the-art for both algorithms and theory. This leads to improved guarantees in numerous settings, including better guarantees for optimizing certain two-layer neural networks in the mean-field regime. A key technical contribution is to establish a new uniform-in-N log-Sobolev inequality for the stationary distribution of the mean-field Langevin dynamics. © 2024, CC BY.

关键词： Multilayer neural networks

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Continual Trajectory Prediction with Uncertainty-Aware Generative Memory Replay

Continual Trajectory Prediction with Uncertainty-Aware Gener...

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IEEE International Conference on Data Mining (ICDM)

作者： Xiushi Feng Shuncheng Liu Haitian Chen Kai Zheng University of Electronic Science and Technology of China China Yangtze Delta Region Institute (Quzhou) School of Computer Science and Engineering and Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China

A reliable autonomous driving system should take safe and efficient actions in constantly changing traffic. This requires the trajectory prediction model to continuously learn from incoming data and adapt to new scenarios. In the context of rapidly growing data volume, existing trajectory prediction models must retrain on all datasets to avoid forgetting previously learned knowledge when facing additional data from new environments. In contrast, the paradigm of continual learning solely necessitates training on new data, saving a significant amount of training overhead. Therefore, it is crucial to equip the trajectory prediction model with the ability of continual learning. In this paper, inspired by rehearsal and pseudo-rehearsal methods in continual learning, we propose a continual trajectory prediction framework with uncertainty-aware generative memory replay, CTP-UGR. Our framework effectively avoids excessive memory space requirements while generating trajectory data that is authentic, representative and discriminative for continual learning. Extensive experiments on two real-world datasets demonstrate our proposed CTP-UGR significantly outperforms other baselines in terms of both accuracy and catastrophic forgetting. Besides, our framework can be combined with other state-of-the-art trajectory prediction models to achieve better performance.

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Enabling Robust Android Malicious Packet Capturing and Detection via Android Kernel

Enabling Robust Android Malicious Packet Capturing and Detec...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Mingyang Li Weina Niu Xinglong Chen Jiacheng Gong Kegang Hao Xiaosong Zhang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China Southwest China Research Institute of Electronic Equipment Chengdu China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The prevalence of Android malware presents significant challenges to the security of the Android operating system. Malicious packet detection is a critical technique in combating Android malware. Android malicious packet detection often includes packet capturing and detecting the packets through machine learning or deep learning models. Nonetheless, evolving malware with anti-packet capturing measures, disrupts packet capturing and diminishes the performance of malicious packet detection models. To address these challenges, we propose ePacket, a novel framework for capturing Android malicious packet. By leveraging eBPF technology, ePacket captures application level packets from apps at the Android kernel, effectively circumventing anti-packet capturing strategies employed by malware. Additionally, we have compiled a summary of anti-packet capturing strategies observed in real Android malware. We evaluate ePacket using over 3,000 apps. The results demonstrate that ePacket effectively circumvents anti-packet capturing strategies and captures a significantly higher volume of malicious packet (up to 30% more) compared to other three state-of-the-art tools.

关键词： Deep learning Privacy Operating systems Malware Security Kernel

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Ponzi Scheme Detection in Smart Contracts Using Heterogeneous Semantic Graph 6th

Ponzi Scheme Detection in Smart Contracts Using Heterogeneo...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Chen, Wei Jiang, Xinjun Lan, Tian Liu, Leyuan Li, Chengyu Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu610000 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610000 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China

ISBN: (纸本)9789819614103

Behind the booming decentralized finance (DeFi) ecosystem driven by blockchain technology, various financial risks lurking, including money laundering, gambling, Ponzi schemes, and phishing. Due to the decentralization and anonymity of Ethereum, Ponzi schemes can be easily deployed, causing huge economic losses to investors. Existing detection methods based on transaction data are difficult to provide early risk warnings, while detection methods based on smart contract source code and opcodes have insufficient feature fusion at multiple levels. We propose an approach for constructing a multi-level Heterogeneous Semantic Graph (HSG) of smart contracts, and improve the HAN model to detect Ponzi scheme smart contracts based on the Heterogeneous Semantic Graph. The experimental results demonstrate the effectiveness of our approach, achieving an accuracy of 97.21%, a precision of 94.29%, and an F1 score of 90.41%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decentralized finance

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Deep Learning and Medical Imaging for COVID-19 Diagnosis: A Comprehensive Survey

arXiv

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arXiv 2023年

作者： Wu, Song Ren, Yazhou Yang, Aodi Chen, Xinyue Pu, Xiaorong He, Jing Nie, Liqiang Yu, Philip S. School of Computer Science and Engineering Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China China Nuffield Department of Clinical Neurosciences University of Oxford United Kingdom School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science University of Illinois Chicago United States

COVID-19 (Coronavirus disease 2019) has been quickly spreading since its outbreak, impacting financial markets and healthcare systems globally. Countries all around the world have adopted a number of extraordinary steps to restrict the spreading virus, where early COVID-19 diagnosis is essential. Medical images such as X-ray images and Computed Tomography scans are becoming one of the main diagnostic tools to combat COVID-19 with the aid of deep learning-based systems. In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis. We also provide a summary of the X-ray and CT image datasets used in various studies for COVID-19 detection. The key difficulties and potential applications of deep learning in fighting against COVID-19 are finally discussed. This work summarizes the latest methods of deep learning using medical images to diagnose COVID-19, highlighting the challenges and inspiring more studies to keep utilizing the advantages of deep learning to combat COVID-19. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Unsupervised 3D SAR Imaging Network Based on Generative Adversary Learning

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IEEE Transactions on Antennas and Propagation 2025年

作者： Wang, Mou Hu, Yifei Wei, Shunjun Li, Jiahui Shen, Rong Shi, Jun Cui, Guolong Kong, Lingjiang Guo, Yongxin University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China UESTC Shenzhen Institute for Advanced Study Shenzhen518000 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong National University of Singapore Department of Electrical and Computer Engineering 117576 Singapore

Reconstructing three-dimensional synthetic aperture radar (3D SAR) images from the sparsely sampled echo measurements holds significant importance in simplifying the system complexity and sensing costs. The conventional compressed sensing-based imaging algorithms address the problem by suspecting the sparsity of the imaging space, showing good reconstruction performance but failing to tackle the imaging tasks in weakly sparse scenes. Besides, traditional iterative imaging algorithms also suffer from high computational complexity, cumbersome parameter tuning, and poor adaptability. To address these issues, a novel unsupervised 3D SAR image reconstruction network is proposed for estimating 3D SAR images from the corresponding incomplete echo measurements. The proposed scheme contains two stages. Wherein, the first stage aims to estimate the missing echo elements from the incomplete observations by designing a generative adversary network based on partial convolution (PCGAN). The second phase focuses on reconstructing the target SAR image from the estimated echoes by constructing an unsupervised adaptive fast iterative shrinkage-thresholding algorithm (FISTA)-inspired deep unfolding network (AdaFIST-Net). Finally, simulations and real-measured experiments are carried out. Experimental results show that the proposed imaging network outperforms the current state-of-the-art algorithms in reconstructing 3D images from sparsely sampled echoes in various sampling conditions and SNR cases. © 1963-2012 IEEE.

关键词： Radar imaging

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相位分布管理的准二维钙钛矿薄膜在纳秒脉冲激光泵浦下实现高效放大自发发射

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science China Materials 2024年第3期67卷 921-930页

作者：韦晓燕刘征征杨洁唐孝生王艳平黄业雄姚亿洪徐新鑫皮明雨杜鹃张丁可 School of Physics and Electronic Engineering Chongqing Normal UniversityChongqing 401331China State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine MechanicsChinese Academy of SciencesShanghai 201800China College of Optoelectronic Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Chongqing Research Institute Changchun University of Science and TechnologyChongqing 401135China School of Physics and Optoelectronic Engineering Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

准二维钙钛矿表现出独特的光物理性质,这使它们成为激光技术潜在进步的优秀候选材料.高性能纳秒脉冲激光和放大自发发射(ASE)是实现连续波抽运光电泵浦激光器的关键工艺.然而,在纳秒脉冲激光泵浦下发展准二维钙钛矿ASE或激光仍然存在一... 详细信息

准二维钙钛矿表现出独特的光物理性质,这使它们成为激光技术潜在进步的优秀候选材料.高性能纳秒脉冲激光和放大自发发射(ASE)是实现连续波抽运光电泵浦激光器的关键工艺.然而,在纳秒脉冲激光泵浦下发展准二维钙钛矿ASE或激光仍然存在一些困难,包括表面缺陷浓度高、俄歇复合和能量传递效率低.本文通过无添加剂的Cs^(+)阳离子掺杂策略,可以很好地控制准二维钙钛矿膜的形貌和相分布,从而解决了低n相导致的能量传递途径不足的问题.本文通过快速的能量传递过程揭示了增益性质和载流子复合.基于此,掺杂适当比例Cs^(+)阳离子的PEA_(2)(FA_(0.7)Cs_(0.3))_(2)Pb_(3)Br_(10)钙钛矿膜的净增益系数比原始钙钛矿薄膜提高了近3倍,从而将纳秒激光激发下的放大自发发射阈值降低到1/3.研究结果表明,在准二维钙钛矿中掺杂Cs^(+)可为实现高性能激光器件的相位分布设计提供见解.

关键词： quasi-2D perovskite cesium cation amplified spontaneous emission energy transfer phase distribution

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