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TransTM:A device-free method based on time-streaming multiscale transformer for human activity recognition

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Defence Technology（防务技术） 2024年第2期32卷 619-628页

作者： Yi Liu Weiqing Huang Shang Jiang Bobai Zhao Shuai Wang Siye Wang Yanfang Zhang Institute of information Engineering Chinese Academy of sciences School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina School of information Management Beijing Information Science and Technology UniversityBeijingChina

RFID-based human activity recognition(HAR)attracts attention due to its convenience,noninvasiveness,and privacy *** RFID-based HAR methods use modeling,CNN,or LSTM to extract features ***,they have shortcomings:1)requiring complex hand-crafted data cleaning processes and 2)only addressing single-person activity recognition based on specific RF *** solve these problems,this paper proposes a novel device-free method based on Time-streaming Multiscale Transformer called *** model leverages the Transformer's powerful data fitting capabilities to take raw RFID RSSI data as input without ***,we propose a multiscale convolutional hybrid Transformer to capture behavioral features that recognizes singlehuman activities and human-to-human *** with existing CNN-and LSTM-based methods,the Transformer-based method has more data fitting power,generalization,and ***,using RF signals,our method achieves an excellent classification effect on human behaviorbased classification *** results on the actual RFID datasets show that this model achieves a high average recognition accuracy(99.1%).The dataset we collected for detecting RFID-based indoor human activities will be published.

关键词： Human activity recognition RFID Transformer

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Analysis of phase preservation and interferometric offset test in sparse SAR imaging

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Science China(information sciences) 2024年第2期67卷 219-231页

作者： Zhongqiu XU Bingchen ZHANG Guangzuo LI Xueli ZHAN Yanfei BAO Yirong WU Key Laboratory of Spatial information Processing and Application System Technology Chinese Academy of Sciences Aerospace information Research Institute Chinese Academy of Sciences School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences PLA Academy of Military sciences

Sparse synthetic aperture radar(SAR) imaging has emerged as a reliable microwave imaging scheme in the recent decade and excels in down-sampling reconstruction and full-sampling performance improvements such as noise, sidelobe, speckle, and ambiguity suppression. To utilize complex image products of sparse reconstruction for improvement in polarimetric, interferometric, and tomographic SAR imaging, it is necessary to evaluate the phase preservation of sparse SAR imaging. In this study, we first introduce the general alternating direction method of multipliers(ADMM) as the universal framework for sparse reconstruction algorithms and adopt chirp scaling algorithm(CSA)-based azimuth-range decouple operators to avoid expensive data storage and processing. Further, we theoretically analyze the phase preservation of the sparse reconstruction algorithm through a comparison with the reconstruction results of CSA. Finally,we conduct the interferometric offset test on the sparse reconstruction results of simulated and real Gaofen-3(GF-3) SAR data, demonstrating the phase-preserving ability of sparse methods.

关键词： sparse SAR imaging phase preservation interferometric offset test general ADMM azimuth-range decouple

A Review on the Recent Trends of Image Steganography for VANET Applications

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Computers, Materials & Continua 2024年第3期78卷 2865-2892页

作者： Arshiya S.Ansari Department of information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Image steganography is a technique of concealing confidential information within an image without dramatically changing its outside *** vehicular ad hoc networks(VANETs),which enable vehicles to communicate with one another and with roadside infrastructure to enhance safety and traffic flow provide a range of value-added services,as they are an essential component of modern smart transportation *** steganography has been suggested by many authors for secure,reliable message transfer between terminal/hope to terminal/hope and also to secure it from attack for privacy *** paper aims to determine whether using steganography is possible to improve data security and secrecy in VANET applications and to analyze effective steganography techniques for incorporating data into images while minimizing visual quality *** to simulations in literature and real-world studies,Image steganography proved to be an effectivemethod for secure communication on VANETs,even in difficult network *** this research,we also explore a variety of steganography approaches for vehicular ad-hoc network transportation systems like vector embedding,statistics,spatial domain(SD),transform domain(TD),distortion,masking,and *** study possibly shall help researchers to improve vehicle networks’ability to communicate securely and lay the door for innovative steganography methods.

关键词： Steganography image steganography image steganography techniques information exchange data embedding and extracting vehicular ad hoc network(VANET) transportation system

Real-Time Prediction of Urban Traffic Problems Based on Artificial Intelligence-Enhanced Mobile Ad Hoc Networks(MANETS)

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Computers, Materials & Continua 2024年第5期79卷 1903-1923页

作者： Ahmed Alhussen Arshiya S.Ansari Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of information Technology College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

Traffic in today’s cities is a serious problem that increases travel times,negatively affects the environment,and drains financial *** study presents an Artificial Intelligence(AI)augmentedMobile Ad Hoc Networks(MANETs)based real-time prediction paradigm for urban traffic *** are wireless networks that are based on mobile devices and may *** distributed nature of MANETs and the power of AI approaches are leveraged in this framework to provide reliable and timely traffic congestion *** study suggests a unique Chaotic Spatial Fuzzy Polynomial Neural Network(CSFPNN)technique to assess real-time data acquired from various sources within *** framework uses the proposed approach to learn from the data and create predictionmodels to detect possible traffic problems and their severity in real ***-time traffic prediction allows for proactive actions like resource allocation,dynamic route advice,and traffic signal optimization to reduce *** framework supports effective decision-making,decreases travel time,lowers fuel use,and enhances overall urban mobility by giving timely information to pedestrians,drivers,and urban *** simulations and real-world datasets are used to test the proposed framework’s prediction accuracy,responsiveness,and *** results show that the suggested framework successfully anticipates urban traffic issues in real-time,enables proactive traffic management,and aids in creating smarter,more sustainable cities.

关键词： Mobile AdHocNetworks(MANET) urban traffic prediction artificial intelligence(AI) traffic congestion chaotic spatial fuzzy polynomial neural network(CSFPNN)

DLP:towards active defense against backdoor attacks with decoupled learning process

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Cybersecurity 2024年第1期7卷 122-134页

作者： Zonghao Ying Bin Wu State Key Laboratory of information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

Deep learning models are well known to be susceptible to backdoor attack,where the attacker only needs to provide a tampered dataset on which the triggers are *** trained on the dataset will passively implant the backdoor,and triggers on the input can mislead the models during *** study shows that the model shows different learning behaviors in clean and poisoned subsets during *** on this observation,we propose a general training pipeline to defend against backdoor attacks *** models can be trained from the unreli-able dataset by decoupling the learning process into three stages,i.e.,supervised learning,active unlearning,and active semi-supervised *** effectiveness of our approach has been shown in numerous experiments across various backdoor attacks and datasets.

关键词： Deep learning Backdoor attack Active defense

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

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Computers, Materials & Continua 2024年第1期78卷 1189-1205页

作者： K.Akilandeswari Nithya Rekha Sivakumar Hend Khalid Alkahtani Shakila Basheer Sara Abdelwahab Ghorashi Department of Computer sciences Government Arts College(Autonomous)Salem636007India Department of Computer sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and *** exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and *** many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during ***,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare *** first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction ***,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable *** attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced *** that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and *** evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several *** quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.

关键词： Internet of Things smart health care monitoring human activity recognition intelligent agent learning statistical partial regression support vector

UNIFORM SUPERCONVERGENCE ANALYSIS OF A TWO-GRID MIXED FINITE ELEMENT METHOD FOR THE TIME-DEPENDENT BI-WAVE PROBLEM MODELING D-WAVE SUPERCONDUCTORS

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Journal of Computational Mathematics 2024年第2期42卷 415-431页

作者： Yanmi Wu Dongyang Shi PLA information Engineering University Zhengzhou 450001China School of Mathematics and information sciences Yantai UniversityYantai 264005China

In this paper,a two-grid mixed finite element method(MFEM)of implicit Backward Euler(BE)formula is presented for the fourth order time-dependent singularly perturbed Bi-wave problem for d-wave superconductors by the nonconforming EQ_(1)^(rot) *** this approach,the original nonlinear system is solved on the coarse mesh through the Newton iteration method,and then the linear system is computed on the fine mesh with Taylor’s *** on the high accuracy results of the chosen element,the uniform superclose and superconvergent estimates in the broken H^(1)-norm are derived,which are independent of the negative powers of the perturbation parameter appeared in the considered *** results illustrate that the computing cost of the proposed two-grid method is much less than that of the conventional Galerkin MFEM without loss of accuracy.

关键词： Time-dependent Bi-wave problem Two-grid mixed finite element method Uniform superclose and superconvergent estimates

TULAM:trajectory-user linking via attention mechanism

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Science China(information sciences) 2024年第1期67卷 51-68页

作者： Hao LI Shuyu CAO Yaqing CHEN Min ZHANG Dengguo FENG Trusted Computing and information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences University of Chinese Academy of sciences

Recently,the application of location-based services(LBS) has become a prevalent means to provide convenience in customers’ everyday ***,because massive volumes of location information are collected by LBS applications,users may suffer from serious privacy *** studies have shown that the identities of users can be inferred from historical anonymous trajectories,which is formulated as the trajectory-user linking(TUL) *** some recurrent neural network(RNN)-based models have been proposed to capture implicit movement patterns among trajectories to improve TUL performance,they cannot learn the sequential and contextual semantics within any individual trajectory completely,leaving the advantages of RNNs *** therefore propose an RNN model with an attention mechanism called TULAM to improve the accuracy of the TUL *** learns sequential relationships within individual trajectories via RNN and captures contextual semantics from trajectories via a multi-head attention ***,we propose a novel location encoding method called approximate one-hot to solve the corpus shortage problem of trajectory *** were conducted on real datasets from the Gowalla and Foursquare LBS *** experimental results indicate that TULAM is a practical solution that achieves significant improvements over existing methods with satisfactory model complexity and convergence.

关键词： information security data security and privacy trajectory-user linking deep learning recurrent neural network attention mechanism

MMCo:using multimodal deep learning to detect malicious traffic with noisy labels

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Frontiers of Computer Science 2024年第1期18卷 239-241页

作者： Qingjun YUAN Gaopeng GOU Yuefei ZHU Yongjuan WANG Strategic Support Force information Engineering University Zhengzhou 450001China Institute of information Engineering Chinese Academy of SciencesBeijing 100093China

1 Introduction The success of a deep learning-based network intrusion detection systems(NIDS)relies on large-scale,labeled,realistic traffic[1,2].However,automated labeling of realistic traffic,such as by sandbox and rule-based approaches,is prone to errors[3],which in turn affects deep learning-based *** effective schemes for learning with noisy labels(LNL)have been proposed,among which the use of parallel networks for sample selection during training has been shown to be *** is argued that parallel networks can discriminate samples from different perspectives,thus adding additional information to the training *** centerpiece is maintaining disagreement of networks.

关键词： network networks modal