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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Weidong Feng Chunjie Zhang Xiaolong Zheng Beijing Key Laboratory of Advanced Information Science and Network Technology School of Computer Science and Technology Beijing Jiaotong University Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

In the field of text recognition models, existing adversarial attack algorithms mainly target white-box scenarios, which are usually limited to single-color backgrounds and short text length. However, with the complex color textures of ID card images and long text length, these methods are not satisfactory when performing adversarial attacks on ID card text data. To overcome these challenges, this paper introduces a novel black-box attack method tailored for ID card text recognition models. The proposed approach accounts for the complexities of intricate image backgrounds, extended text data, and the limitations inherent in black-box conditions. The proposed algorithm introduces an adaptive step-size search method based on line search, aiming to overcome the limitations of traditional methods that rely on fixed step sizes and projection techniques for perturbation generation. This refinement aims to enhance the success rate of black-box attacks while maintaining efficacy in white-box scenarios. Spatial transformations and random noise are incorporated to fortify the robustness of adversarial samples. Experimental results, conducted under various norms, demonstrate that the fusion of the traditional method with the adaptive step-size search method yields superior attack performance.

关键词： Adaptation models Text recognition Perturbation methods Noise Closed box Search problems Robustness Kernel Optimization Glass box

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HCF-Net: Hierarchical Context Fusion network for Infrared Small Object Detection

arXiv

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arXiv 2024年

作者： Xu, Shibiao Zheng, ShuChen Xu, Wenhao Xu, Rongtao Wang, Changwei Zhang, Jiguang Teng, Xiaoqiang Li, Ao Guo, Li Artificial Intelligence Beijing University of Posts and Telecommunications China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China School of Computer Science and Technology Harbin University of Science and Technology China

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep learning method, HCF-Net, that significantly improves infrared small object detection performance through multiple practical modules. Specifically, it includes the parallelized patch-aware attention (PPA) module, dimension-aware selective integration (DASI) module, and multi-dilated channel refiner (MDCR) module. The PPA module uses a multi-branch feature extraction strategy to capture feature information at different scales and levels. The DASI module enables adaptive channel selection and fusion. The MDCR module captures spatial features of different receptive field ranges through multiple depth-separable convolutional layers. Extensive experimental results on the SIRST infrared single-frame image dataset show that the proposed HCF-Net performs well, surpassing other traditional and deep learning models. Code is available at https://***/zhengshuchen/HCFNet. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Joint Image and Feature Enhancement for Object Detection under Adverse Weather Conditions

Joint Image and Feature Enhancement for Object Detection und...

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International Joint Conference on Neural networks (IJCNN)

作者： Mengyu Yin Mingyang Ling Kan Chang Zijian Yuan Qingpao Qin Boning Chen School of Computer and Electronic Information Guangxi University Nanning China School of Electrical Engineering Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Computing and Information Systems The University of Melbourne Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Object detection under adverse weather conditions remains a challenging problem to date. To address this problem, a joint image and feature enhancement method called JE-YOLO is proposed. Firstly, a lightweight image enhancement network is used to enhance the low-quality image captured under adverse weather conditions. Secondly, to provide rich information for detection, two detection backbones are applied in parallel to extract features from both the low-quality image and its enhanced result. Afterwards, the extracted features are further enhanced by a foreground-guided feature refinement module (FFRM), which introduces a task-driven attention mechanism and explores inter-layer correlation. Finally, the enhanced features from different branches are fused by the adaptive multi-branch weighting (AMW) strategy, and then fed to the neck and head of detector. Experiments are carried out on both the low-light and foggy conditions, and the results demonstrate that compared with state-of-the-art (SOTA) methods, the proposed JE-YOLO is able to achieve the highest accuracy of detection in all cases. Code will be available at https://***/Murray-Yin/JE-YOLO.

关键词： Head Neural networks Object detection Detectors Feature extraction Neck Reliability

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The Super-large Ensemble Experiments of CAS FGOALS-g3

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Advances in Atmospheric Sciences 2022年第10期39卷 1746-1765,I0021-I0025页

作者： Pengfei LIN Bowen ZHAO Jilin WEI Hailong LIU Wenxia ZHANG Xiaolong CHEN Jie JIANG Mengrong DING Wenmin MAN Jinrong JIANG Xu ZHANG Yuewen DING Wenrong BAI Chenyang JIN Zipeng YU Yiwen LI Weipeng ZHENG Tianjun ZHOU State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Computer Network Information Center Chinese Academy of SciencesBeijing 100190China Group of Alpine Paleoecology and Human Adaptation(ALPHA) State Key Laboratory of Tibetan Plateau Earth SystemResources and Environment(TPESRE)Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100864China Key Laboratory of Western China’s Environmental Systems(Ministry of Education) College of Earth and Environmental ScienceLanzhou UniversityLanzhou 730000China

A super-large ensemble simulation dataset with 110 members has been produced by the fully coupled model FGOALS-g3 developed by researchers at the Institute of Atmospheric Physics,Chinese Academy of *** is the first dataset of large ensemble simulations with a climate system model developed by a Chinese modeling *** simulation has the largest realizations up to now worldwide in terms of single-model initial-condition large *** member includes a historical experiment(1850-2014)and an experiment(2015-99)under the very high greenhouse gas emissions Shared Socioeconomic Pathway scenario(SSP5-8.5).The dataset includes monthly and daily temperature,precipitation,and other variables,requiring storage of 275 ***,the surface air temperature(SAT)and land precipitation simulated by the FGOALS-g3 super-large ensemble have been validated and *** ensemble can capture the response of SAT and land precipitation to external forcings well,and the internal variabilities can be *** availability of more than 100 realizations will help researchers to study rare events and improve the understanding of the impact of internal variability on forced climate changes.

关键词： large ensemble forced response internal variability climate change

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Performance Optimization for Vehicular Cooperative Sensing: A Graph Attention Based Reinforcement Learning Approach

Performance Optimization for Vehicular Cooperative Sensing: ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xuefei Li Mingzhe Chen Danpu Liu Zhilong Zhang Tony Q. S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

关键词： Base stations Correlation network topology Simulation Collaboration Reinforcement learning Vectors Sensors Global communication Optimization

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50-km Weakly-Coupled Transmission with L-Band 4-LP-Mode FM-EDFA

50-km Weakly-Coupled Transmission with L-Band 4-LP-Mode FM-E...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shuailuo Huang Baolong Zhu Gang Qiao Yuyang Gao Mingqing Zuo Shikui Shen Zelin Wang Yongqi He Zhangyuan Chen Juhao Li China Unicom Research Institute Beijing China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Fundamental Network Technology China Mobile Research Institute Beijing China Pengcheng National Laboratory Shenzhen China

50-km weakly-coupled transmission based on a L-band 4-LP-mode FM-EDFA is experimentally demonstrated only adopting $\mathbf{2}\times \mathbf{2}$ or $\mathbf{4}\times \mathbf{4}$ MIMO-DSP.

ISBN: (数字)9798350379266

ISBN: (纸本)9798350379273

50-km weakly-coupled transmission based on a L-band 4-LP-mode FM-EDFA is experimentally demonstrated only adopting $\mathbf{2}\times \mathbf{2}$ or $\mathbf{4}\times \mathbf{4}$ MIMO-DSP.

关键词： Asia Gain measurement Optical fiber communication MIMO L-band Photonics

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Graph Representation Learning for Microarchitecture Design Space Exploration

Graph Representation Learning for Microarchitecture Design S...

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Design Automation Conference

作者： Xiaoling Yi Jialin Lu Xiankui Xiong Dong Xu Li Shang Fan Yang State Key Laboratory of Integrated Chips and Systems Fudan University Shanghai China School of Microelectronics Fudan University Shanghai China ZTE Corporation China State Key Laboratory of Mobile Network and Mobile Multimedia Technology China China and Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China

Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic microarchitecture search framework based on graph embeddings. GRL-DSE uses graph representation learning to build a compact and continuous embedding space. Multi-objective Bayesian optimization using an ensemble surrogate model conducts microarchitecture design space exploration in the graph embedding space to efficiently and holistically optimize performance-power-area (PPA) objectives. Experimental studies on RISC-V BOOM show that GRLDSE outperforms previous techniques by 74.59% on Pareto front quality and outperforms manual designs in terms of PPA.

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AutoToolVCC: Variable consistency check tool for automotive software projects

AutoToolVCC: Variable consistency check tool for automotive ...

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作者： Xie, Pingfu Wang, Yunlong He, Bo Xie, Guoqi College of Computer Science and Electronic Engineering Hunan University Changsha China Key Laboratory for Embedded and Network Computing of Hunan Province Changsha China United Automotive Electronic Systems Co. Ltd. Shanghai China

Automotive software systems have become increasingly complex and huge, leading to long development cycle and increased development difficulty. When a complete automotive software project has reached a scale of several gigabytes, the complicated development process is prone to produce the following two irregular problems: (1) variable inconsistency problem and (2) code coexistence problem. These irregular problems may eventually lead to serious consequences, such as car crashes. In response to the above irregular problems, we develop a variable consistency check tool called AutoToolVCC for automotive software projects. AutoToolVCC divides the variable consistency check process into two stages: (1) extracting all variable information from the automotive software project and (2) checking the consistency of the variables through the variable preprocessing technique and the variable attribute check technique. After an actual check of multiple automotive software projects (approximately 500–1000 MB in size) provided by an automotive manufacturer, the accuracy of variable consistency check is as high as 100%, and the check time only takes about 10–30 s, whereas the pervious plain method needs 100–600 s. © 2020 John Wiley & Sons, Ltd.

关键词： Accidents

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Enabling Efficient and Secure Health Data Sharing for Healthcare Iot systems

SSRN

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SSRN 2023年

作者： Zhu, Liehuang Xie, Yumeng Zhou, Yuao Fan, Qing Zhang, Chuan Liu, Ximeng School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Security of Network Systems Fuzhou350108 China

The Healthcare Internet of Things (HIoT) system enables remote monitor patient health state and feedback on medical treatments timely. The success of HIoT relies on how to share health data securely and efficiently. Existing solutions either fail to ensure the security or incur significant overheads. In this paper, we put forward an efficient and secure health data sharing scheme for HIoT systems, named ESDS, to solve this dilemma. In ESDS, we first signcrypt the health data collected by IoT devices to protect patient privacy and ensure data authenticity. Next, we aggregate N signatures for N pieces of different health data into a short signature, which significantly reduces storage space and transmission size, i.e., from O(N) to O(1). Then, we apply local verifiability to verify one piece of health data under the aggregated signature. With this property, a doctor only needs one auxiliary message generated by the patient to check the target message without obtaining whole health data, which improves system verification efficiency. Extensive security analysis and performance evaluation show that ESDS is secure and efficient. © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Animal Sound Recognition Based on Double Feature of Spectrogram

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Chinese Journal of Electronics 2019年第4期28卷 667-673页

作者： LI Ying HUANG Hongkeng WU Zhibin College of Mathematics and Computer Science Fuzhou University Key Laboratory of Information Security of Network Systems (Fuzhou University)

Due to existence of different environments and noises, the existing method is difficult to ensure the recognition accuracy of animal sound in low Signal-to-noise(SNR) conditions. To address these problems, we propose a double feature, which consists of projection feature and Local binary pattern variance(LBPV) feature, combined with Random forest(RF) for animal sound *** feature extraction, an operation of projecting is made on spectrogram to generate the projection ***, LBPV feature is generated by means of accumulating the corresponding variances of all pixels for every Uniform local binary pattern(ULBP) in the spectrogram. Short-time spectral estimation algorithm is used to enhance sound signals in severe mismatched noise conditions. In the experiments, we classify 40 kinds of common animal sounds under different SNRs with rain noise, traffic noise, and wind noise. As the experimental results show, the proposed framework consisting of shorttime spectrum estimation, double feature, and RF, can recognize a wide range of animal sounds and still remains a recognition rate over 80% even under 0dB SNR.

关键词： Animal sound recognition Local binary pattern variance(LBPV) Projection feature Random forest(RF) Sound enhancement

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