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Detection accuracy of target accelerations based on vortex electromagnetic wave in keyhole space

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Chinese Physics B 2024年第2期33卷 283-290页

作者：郭凯雷爽雷艺周红平郭忠义 school of {1. and information Hefei University of TechnologyHefei 230009China

The influence of the longitudinal acceleration and the angular acceleration of detecting target based on vortex electromagnetic waves in keyhole space are *** spectrum spreads of different orbital angular momentum(OAM)modes in different non-line-of-sight situations are *** errors of target accelerations in detection are calculated and compared based on the OAM spectra spreading by using two combinations of composite OAM modes in the keyhole *** to the research,the effects about spectrum spreads of higher OAM modes are more *** error in detection is mainly affected by OAM spectrum spreading,which can be reduced by reasonably using different combinations of OAM modes in different practical *** above results provide a reference idea for investigating keyhole effect when vortex electromagnetic wave is used to detect accelerations.

关键词： vortex electromagnetic waves detect accelerations keyhole space spectrum spread

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CToMP: a cycle-task-oriented memory protection scheme for unmanned systems

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Science China(information Sciences) 2024年第6期67卷 399-417页

作者： Chengyan MA Ning XI Di LU Yebo FENG Jianfeng MA school of Cyber Engineering Xidian University school of {1. Science and Technology Xidian University Department of computer and {1. Science University of Oregon

Memory corruption attacks(MCAs) refer to malicious behaviors of system intruders that modify the contents of a memory location to disrupt the normal operation of computing systems, causing leakage of sensitive data or perturbations to ongoing processes. Unlike general-purpose systems, unmanned systems cannot deploy complete security protection schemes, due to their limitations in size, cost and *** in unmanned systems are particularly difficult to defend against. Furthermore, MCAs have diverse and unpredictable attack interfaces in unmanned systems, severely impacting digital and physical sectors. In this paper, we first generalize, model and taxonomize MCAs found in unmanned systems currently, laying the foundation for designing a portable and general defense approach. According to different attack mechanisms,we found that MCAs are mainly categorized into two types — return2libc and return2shellcode. To tackle return2libc attacks, we model the erratic operation of unmanned systems with cycles and then propose a cycle-task-oriented memory protection(CToMP) approach to protect control flows from tampering. To defend against return2shellcode attacks, we introduce a secure process stack with a randomized memory address by leveraging the memory pool to prevent Shellcode from being executed. Moreover, we discuss the mechanism by which CTo MP resists the return-oriented programming(ROP) attack, a novel variant of return2libc attacks. Finally, we implement CTo MP on CUAV V5+ with Ardupilot and Crazyflie. The evaluation and security analysis results demonstrate that the proposed approach CTo MP is resilient to various MCAs in unmanned systems with low footprints and system overhead.

关键词： unmanned system memory corruption attack memory protection system security randomized memory address

A Deep Deterministic Policy Gradient-Based Method for Enforcing Service Fault-Tolerance in MEC

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Chinese Journal of Electronics 2024年第4期33卷 899-909页

作者： Tingyan LONG Peng CHEN Yunni XIA Yong MA Xiaoning SUN Jiale ZHAO Yifei LYU College of computer Science Chongqing University school of {1. and Software Engineering Xihua University school of {1. and information Engineering Jiangxi Normal University school of {1. and information Science Chongqing Normal University

Mobile edge computing(MEC) provides edge services to users in a distributed and on-demand *** to the heterogeneity of edge applications, deploying latency and resource-intensive applications on resourceconstrained devices is a key challenge for service providers. This is especially true when underlying edge infrastructures are fault and error-prone. In this paper, we propose a fault tolerance approach named DFGP, for enforcing mobile service fault-tolerance in MEC. It synthesizes a generative optimization network(GON) model for predicting resource failure and a deep deterministic policy gradient(DDPG) model for yielding preemptive migration *** show through extensive simulation experiments that DFGP is more effective in fault detection and guaranteeing quality of service, in terms of fault detection accuracy, migration efficiency, task migration time, task scheduling time,and energy consumption than other existing methods.

关键词： Fault tolerance Multi-access edge computing Processor scheduling Fault detection Image edge detection Fault tolerant systems Quality of service

BioKG-CMI: a multi-source feature fusion model based on biological knowledge graph for predicting circRNA-miRNA interactions

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Science China(information Sciences) 2024年第8期67卷 343-344页

作者： Mengmeng WEI Lei WANG Yang LI Zhengwei LI Bowei ZHAO Xiaorui SU Yu WEI Zhuhong YOU school of {1. Science and Technology China University of Mining and Technology school of {1. Science and Engineering Zaozhuang University school of {1. Science and information Engineering Hefei University of Technology Xinjiang Technical Institute of Physics & Chemistry Chinese Academy of Sciences school of {1. Engineering Xijing University school of {1. Science Northwestern Polytechnical University

Increasing experimental evidence has shown that circRNA has the potential to serve as a biomarker for disease diagnosis and prognosis, especially in cancer [1.. circ RNA actively participates in numerous pathological processes by serving as an miRNA sponge. Consequently, the precise prediction of circRNA-miRNA interactions(CMIs) is crucial for narrowing down the scope of biological experiments and expediting the research and development of disease treatments.

关键词： Knowledge graph

Federated Meta Reinforcement Learning for Personalized Tasks

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Tsinghua Science and Technology 2024年第3期29卷 911-926页

作者： Wentao Liu Xiaolong Xu Jintao Wu Jielin Jiang school of {1. Science Nanjing University of Information Science and TechnologyNanjing 210044China school of Software Nanjing University of Information Science and TechnologyNanjing 210044China

As an emerging privacy-preservation machine learning framework,Federated Learning(FL)facilitates different clients to train a shared model collaboratively through exchanging and aggregating model parameters while raw data are kept local and *** this learning framework is applied to Deep Reinforcement Learning(DRL),the resultant Federated Reinforcement Learning(FRL)can circumvent the heavy data sampling required in conventional DRL and benefit from diversified training data,besides privacy preservation offered by *** FRL implementations presuppose that clients have compatible tasks which a single global model can *** practice,however,clients usually have incompatible(different but still similar)personalized tasks,which we called task *** may severely hinder the implementation of FRL for practical *** this paper,we propose a Federated Meta Reinforcement Learning(FMRL)framework by integrating Model-Agnostic Meta-Learning(MAML)and ***,we innovatively utilize Proximal Policy Optimization(PPO)to fulfil multi-step local training with a single round of ***,considering the sensitivity of learning rate selection in FRL,we reconstruct the aggregation optimizer with the Federated version of Adam(Fed-Adam)on the server *** experiments demonstrate that,in different environments,FMRL outperforms other FL methods with high training efficiency brought by Fed-Adam.

关键词： federated learning reinforcement learning meta-learning personalization

Robust video question answering via contrastive cross-modality representation learning

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Science China(information Sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG school of {1. Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center school of {1. Science and information Engineering Hefei University of Technology Institutes of Physical Science and information Technology Anhui University school of {1. Science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

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computers, Materials & Continua 2024年第8期80卷 2049-2063页

作者： Shujing Li Zhangfei Li Wenhui Cheng Chenyang Qi Linguo Li school of {1. and information Engineering Fuyang Normal UniversityFuyang236041China

To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy ***,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial ***,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local ***,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),*** experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

关键词： Image segmentation image thresholding chimp optimization algorithm chaos initialization Cauchy mutation

Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

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computers, Materials & Continua 2024年第4期79卷 607-622页

作者： Kai Wei Song Yu Qingxian Pan school of {1. and Control Engineering Yantai UniversityYantai264005China school of {1. and information Science Southwest UniversityChongqing400715China

Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource *** traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and *** address these issues,this study proposes a novel two-part invalid detection task allocation *** the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous *** to the baseline method,the model achieves an approximately 4%increase in the F1.value on the public *** the second step of the framework,task allocation modeling is performed using a twopart graph matching *** phase introduces a P-queue KM algorithm that implements a more efficient optimization *** allocation efficiency is improved by approximately 23.83%compared to the baseline *** results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.

关键词： Mobile crowdsourcing task allocation anomaly detection GAN attention mechanisms

A deep learning model enabled multi-event recognition for distributed optical fiber sensing

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Science China(information Sciences) 2024年第3期67卷 291-307页

作者： Yujiao LI Xiaomin CAO Wenhao NI Kuanglu YU Institute of information Science School of Computer and Information TechnologyBeijing Jiaotong University Beijing Key Laboratory of Advanced information Science and Network Technology Beijing Jiaotong University

Fiber optic sensors that utilize backscattered light offer distributed real-time measurements and have been seen tremendous improvements in sensing distance and spatial resolution over the last ***, these improvements in sensor capabilities lead to a significant increase in the amount of data that needs to be processed. Traditional processing schemes are no longer adequate, so the development of novel signal processing methods is critical. Phase-sensitive optical time domain reflectometry(Φ-OTDR) is now applied in various applications for multi-event recognition, and it would usually be difficult, sometimes even unrealistic to label all the acquired samples due to its real-time and seamless monitoring nature. To fully take advantage of the information contained within the large number of unlabeled samples, which were formerly not utilized and hence wasted, we propose a semi-supervised model to boost the event classification performance of Φ-OTDR. The model extracts respectively the temporal features and the spatial bidirectional features together with a dual attention mechanism. Its classification accuracy has been improved up to 96.9%with only 1.30 labeled samples. In addition, our model shows significant advantages when the number of labeled samples is reduced. Importantly, our method improves the accuracy of multi-event classification without any modification to the optical setup.

关键词： Φ-OTDR event recognition semi-supervised learning mean teacher MT-ACNN-SA-BiLSTM