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Towards Reliable Configuration Management in Clouds: A Lightweight Consistency Validation Mechanism for Virtual Private Clouds

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Chinese Journal of Electronics 2025年第1期34卷 49-63页

作者： Yuhang Qiu Gongming Zhao Hongli Xu Long Li He Huang Liusheng Huang School of Computer Science and Technology University of Science and Technology of China Suzhou Institute for Advanced Research University of Science and Technology of China School of Computer Science and Technology Soochow University

The virtual private cloud service currently lacks a real-time end-to-end consistency validation mechanism, which prevents tenants from receiving immediate feedback on their requests. Existing solutions consume excessive communication and computational resources in such large-scale cloud environments, and suffer from poor timeliness. To address these issues, we propose a lightweight consistency validation mechanism that includes real-time incremental validation and periodic full-scale validation. The former leverages message layer aggregation to enable tenants to swiftly determine the success of their requests on hosts with minimal communication overhead. The latter utilizes lightweight validation checksums to compare the expected and actual states of hosts locally, while efficiently managing the checksums of various host entries using inverted indexing. This approach enables us to efficiently validate the complete local configurations within the limited memory of hosts. In summary, our proposed mechanism achieves closed-loop implementation for new requests and ensures their long-term effectiveness.

关键词： Cloud computing Memory management Configuration management Reliability engineering Real-time systems Indexing

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LucIE: Language-guided local image editing for fashion images

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Computational Visual Media 2025年第1期11卷 179-194页

作者： Huanglu Wen Shaodi You Ying Fu School of Computer Science and Technology Beijing Institute of TechnologyBeijingChina Computer Vision Research Group in the Institute of Informatics University of AmsterdamAmsterdamthe Netherlands

Language-guided fashion image editing is challenging,as fashion image editing is local and requires high precision,while natural language cannot provide precise visual information for *** this paper,we propose LucIE,a novel unsupervised language-guided local image editing method for fashion *** adopts and modifies recent text-to-image synthesis network,DF-GAN,as its ***,the synthesis backbone often changes the global structure of the input image,making local image editing *** increase structural consistency between input and edited images,we propose Content-Preserving Fusion Module(CPFM).Different from existing fusion modules,CPFM prevents iterative refinement on visual feature maps and accumulates additive modifications on RGB *** achieves local image editing explicitly with language-guided image segmentation and maskguided image blending while only using image and text *** on the DeepFashion dataset shows that LucIE achieves state-of-the-art *** with previous methods,images generated by LucIE also exhibit fewer *** provide visualizations and perform ablation studies to validate LucIE and the *** also demonstrate and analyze limitations of LucIE,to provide a better understanding of LucIE.

关键词： deep learning language-guided image editing local image editing content preservation fashion images

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Refinement modeling and verification of secure operating systems for communication in digital twins

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Digital Communications and Networks 2024年第2期10卷 304-314页

作者： Zhenjiang Qian Gaofei Sun Xiaoshuang Xing Gaurav Dhiman School of Computer Science and Engineering Changshu Institute of TechnologySuzhou215500China University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityMohali140413India Department of Computer Science and Engineering Graphic Era Deemed to be UniversityDehradun248002India

In traditional digital twin communication system testing,we can apply test cases as completely as possible in order to ensure the correctness of the system implementation,and even then,there is no guarantee that the digital twin communication system implementation is completely *** verification is currently recognized as a method to ensure the correctness of software system for communication in digital twins because it uses rigorous mathematical methods to verify the correctness of systems for communication in digital twins and can effectively help system designers determine whether the system is designed and implemented *** this paper,we use the interactive theorem proving tool Isabelle/HOL to construct the formal model of the X86 architecture,and to model the related assembly *** verification result shows that the system states obtained after the operations of relevant assembly instructions is consistent with the expected states,indicating that the system meets the design expectations.

关键词： Theorem proving Isabelle/HOL Formal verification System modeling Correctness verification

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Existence and uniqueness of mean field equilibrium in continuous bandit game

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science China(Information sciences) 2025年第3期68卷 395-396页

作者： Xiong WANG Yuqing LI Riheng JIA School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan University Wuhan University Shenzhen Research Institute School of Computer Science and Technology Zhejiang Normal University

Multiarmed bandit(MAB) models are widely used for sequential decision-making in uncertain environments, such as resource allocation in computer communication systems.A critical challenge in interactive multiagent systems with bandit feedback is to explore and understand the equilibrium state to ensure stable and tractable system performance.

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Low-noise and high-rate front-end ASIC for APD detectors in STCF ECAL

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Nuclear science and Techniques 2025年第4期36卷 127-139页

作者： Chao Liu Ran Zheng Jia Wang Xiao-Min Wei Fei-Fei Xue Rui-Guang Zhao Yann Hu School of Computer Science and Technology Northwestern Polytechnical UniversityXi’an 710072China Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Electronics and Information Northwestern Polytechnical UniversityXi’an 710072China

This study presents a low-noise,high-rate front-end readout application-specific integrated circuit(ASIC)designed for the electromagnetic calorimeter(ECAL)of the Super Tau-Charm Facility(STCF).To address the high background-count rate in the STCF ECAL,the temporal features of signals are analyzed node-by-node along the chain of the analog front-end ***,the system is optimized to mitigate the pile-up effects and elevate the count rate to megahertz ***,a charge-sensitive amplifier(CSA)with a fast reset path is developed,enabling quick resetting when the output reaches the maximum *** prevents the CSA from entering a pulse-dead zone owing to amplifier saturation caused by the ***,a high-order shaper with baseline holder circuits is improved to enhance the anti-pile-up capability while maintaining an effective noise-filtering ***,a high-speed peak-detection and hold circuit with an asynchronous first-input-first-output buffer function is proposed to hold and read the piled-up signals of the *** ASIC is designed and manufactured using a standard commercial 1P6M 0.18μm mixed-signal CMOS process with a chip area of 2.4 mm×1.6 *** measurement results demonstrate a dynamic range of 4–500 fC with a nonlinearity error below 1.5%.For periodically distributed input signals,a count rate of 1.5 MHz/Ch is achieved with a peak time of 360 ns,resulting in an equivalent noise charge(ENC)of 2500 e^(-)-.The maximum count rate is 4 MHz/Ch at a peak time of 120 *** a peak time of 1.68μs with a 270 pF external capacitance,the minimum ENC is 1966 e^(-)-,and the noise slope is 3.08 e^(-)-∕*** timing resolution is better than 125 ps at an input charge of 200 *** power consumption is 35 mW/Ch.

关键词： Readout electronics APD Charge measurement High count rate STCF

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PointSmile: point self-supervised learning via curriculum mutual information

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science China(Information sciences) 2024年第11期67卷 121-135页

作者： Xin LI Mingqiang WEI Songcan CHEN School of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics

Self-supervised learning is attracting significant attention from researchers in the point cloud processing field. However, due to the natural sparsity and irregularity of point clouds, effectively extracting discriminative and transferable features for efficient training on downstream tasks remains an unsolved challenge. Consequently, we propose PointSmile, a reconstruction-free self-supervised learning paradigm by maximizing curriculum mutual information(CMI) across the replicas of point cloud objects. From the perspective of how-and-what-to-learn, PointSmile is designed to imitate human curriculum learning, i.e.,starting with easier topics in a curriculum and gradually progressing to learning more complex topics in the curriculum. To solve “how-to-learn”, we introduce curriculum data augmentation(CDA) of point *** encourages PointSmile to follow a learning path that starts from learning easy data samples and progresses to learning hard data samples, such that the latent space can be dynamically affected to create better embeddings. To solve “what-to-learn”, we propose maximizing both feature-and class-wise CMI to better extract discriminative features of point clouds. Unlike most existing methods, PointSmile does not require a pretext task or cross-modal data to yield rich latent representations; additionally, it can be easily transferred to various backbones. We demonstrate the effectiveness and robustness of PointSmile in downstream tasks such as object classification and segmentation. The study results show that PointSmile outperforms existing self-supervised methods and compares favorably with popular fully supervised methods on various standard architectures. The code is available at https://***/theaalee/PointSmile.

关键词： PointSmile self-supervised learning curriculum mutual information point cloud representation learning

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Numerical study on lightning strike protection method for composite rotor blade based on air breakdown and insulating adhesive layer

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Chinese Journal of Aeronautics 2025年第2期38卷 185-199页

作者： Kenan LI Xiaoquan CHENG Ziqian AN Wenjun HUANG School of Aeronautic Science and Engineering Beihang UniversityBeijing 100191China AVIC China Helicopter Research and Development Institute Jingdezhen 333001China

The protection effectiveness of traditional Lightning Strike Protection(LSP)for composite rotor blade of helicopter can be diminished due to the explosion risk in overlapping attachment under lightning strike,so a new protection method based on Air Breakdown and insulating adhesive layer(AB-LSP method)was designed to avoid *** this study,a numerical method was developed to simulate the electrical breakdown,and verified by experiment *** on this method,a Finite Element Model(FEM)was established to investigate the effect of two factors(breakdown strength and initial ablation temperature of adhesive layer)on the LSP *** results show that the breakdown strength impacts more to the ablation damage in composite than that of high-temperature ***,another FEM was established to predict the ablation damage by lightning strike in the AB-LSP method protected composite rotor *** mechanisms and potential key parameters(magnitude of lightning current,discharge channel location,adhesive layer thickness,and air gap width)that could affect the protection effectiveness were *** introduction of air breakdown changes the current conduction path and reduces explosion *** rational design,this method can offer effective lightning protection for composite helicopter rotor blade and other composite structures.

关键词： Helicopter rotors Composite structures Air breakdown Insulating adhesive Lightning protection Finite element method

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Dimensional-noise-aware battery lifetime prediction via an EM-TLS framework

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Progress in Natural science:Materials International 2025年第1期35卷 146-155页

作者： Chenlong Yu Ting Lu Guohua Liu Xiaoang Zhai Wuyan Deng Jiayu Wan Yang Liu Xin Li School of Computer Science and Technology Donghua University Future Battery Research Center Global Institute of Future Technology Shanghai Jiaotong University Data Science Research Center Duke Kunshan University

Machine learning has been massively utilized to construct data-driven solutions for predicting the lifetime of rechargeable batteries in recent years, which project the physical measurements obtained during the early charging/discharging cycles to the remaining useful lifetime. While most existing techniques train the prediction model through minimizing the prediction error only, the errors associated with the physical measurements can also induce negative impact to the prediction accuracy. Although total-least-squares(TLS) regression has been applied to address this issue, it relies on the unrealistic assumption that the distributions of measurement errors on all input variables are equivalent, and cannot appropriately capture the practical characteristics of battery degradation. In order to tackle this challenge, this work intends to model the variations along different input dimensions, thereby improving the accuracy and robustness of battery lifetime prediction. In specific, we propose an innovative EM-TLS framework that enhances the TLS-based prediction to accommodate dimension-variate errors, while simultaneously investigating the distributions of them using expectation-maximization(EM). Experiments have been conducted to validate the proposed method based on the data of commercial Lithium-Ion batteries, where it reduces the prediction error by up to 29.9 % compared with conventional TLS. This demonstrates the immense potential of the proposed method for advancing the R&D of rechargeable batteries.

关键词： Battery lifetime prediction Dimension-variate measurement error Energy storage Expectation-maximization Total-least-squares regression

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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关键词： autonomous vehicle transportation

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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