This study summarises current advances in sign language recognition systems, emphasising trends, problems, and prospects. Twenty key research publications are analysed, spanning a wide range of sign language recogniti...
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This article presents a highly integrated novel silicon micromachined single-pole-single-throw waveguide switch based on two microelectromechanically reconfigurable switching surfaces (MEMS-RSs), which allows optimizi...
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Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary *** it remains an unso...
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Prevailing linguistic steganalysis approaches focus on learning sensitive features to distinguish a particular category of steganographic texts from non-steganographic texts,by performing binary *** it remains an unsolved problem and poses a significant threat to the security of cyberspace when various categories of non-steganographic or steganographic texts *** this paper,we propose a general linguistic steganalysis framework named LS-MTL,which introduces the idea of multi-task learning to deal with the classification of various categories of steganographic and non-steganographic ***-MTL captures sensitive linguistic features from multiple related linguistic steganalysis tasks and can concurrently handle diverse tasks with a constructed *** the proposed framework,convolutional neural networks(CNNs)are utilized as private base models to extract sensitive features for each steganalysis ***,a shared CNN is built to capture potential interaction information and share linguistic features among all ***,LS-MTL incorporates the private and shared sensitive features to identify the detected text as steganographic or *** results demonstrate that the proposed framework LS-MTL outperforms the baseline in the multi-category linguistic steganalysis task,while average Acc,Pre,and Rec are increased by 0.5%,1.4%,and 0.4%,*** ablation experimental results show that LS-MTL with the shared module has robust generalization capability and achieves good detection performance even in the case of spare data.
作者:
Du, AnJia, JieChen, JianWang, XingweiHuang, MingNortheastern University
School of Computer Science and Engineering Engineering Research Center of Security Technology of Complex Network System Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Northeastern University
School of Computer Science and Engineering Shenyang110819 China
Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm of...
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Facial Expression Recognition (FER) has created widespread interest due to its potential uses in personalized technology and mental health, notably in systems that recommend music based on emotion. These systems can i...
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In an ever-changing environment,Software as a Service(SaaS)can rarely protect users'*** able to manage and control the privacy is therefore an important goal for *** the participant of composite service is substit...
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In an ever-changing environment,Software as a Service(SaaS)can rarely protect users'*** able to manage and control the privacy is therefore an important goal for *** the participant of composite service is substituted,it is unclear whether the composite service satisfy user privacy requirement or *** this paper,we propose a privacy policies automatic update method to enhance user privacy when a service participant change in the composite ***,we model the privacy policies and service variation ***,according to the service variation rules,the privacy policies are automatically generated through the negotiation between user and service ***,we prove the feasibility and applicability of our method with the *** the service quantity is 50,ratio that the services variations are successfully checked by monitor is 81%.Moreover,ratio that the privacy policies are correctly updated is 93.6%.
The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the ...
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The rapid evolution of artificial intelligence(AI)technologies has significantly propelled the advancement of the Internet of Vehicles(IoV).With AI support,represented by machine learning technology,vehicles gain the capability to make intelligent *** a distributed learning paradigm,federated learning(FL)has emerged as a preferred solution in *** to traditional centralized machine learning,FL reduces communication overhead and improves privacy *** these benefits,FL still faces some security and privacy concerns,such as poisoning attacks and inference attacks,prompting exploration into blockchain integration to enhance its security *** paper introduces a novel blockchain-enabled federated learning(BCFL)scheme with differential privacy(DP)tailored for *** order to meet the performance demanding IoV environment,the proposed methodology integrates a consortium blockchain with Practical Byzantine Fault Tolerance(PBFT)consensus,which offers superior efficiency over the conventional public *** addition,the proposed approach utilizes the Differentially Private Stochastic Gradient Descent(DP-SGD)algorithm in the local training process of FL for enhanced privacy *** results indicate that the integration of blockchain elevates the security level of FL in that the proposed approach effectively safeguards FL against poisoning *** the other hand,the additional overhead associated with blockchain integration is also limited to a moderate level to meet the efficiency criteria of ***,by incorporating DP,the proposed approach is shown to have the(ε-δ)privacy guarantee while maintaining an acceptable level of model *** enhancement effectively mitigates the threat of inference attacks on private information.
Covert communication is considered a promising technology for hiding transmission processes and activities from malicious eavesdropping. With the development of detection technology, the traditional point-to-point cov...
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Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detect...
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Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this ***,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage *** limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile *** solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature ***,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of *** addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context ***,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction *** evaluate our method on three public crack datasets:DeepCrack,CFD,and *** results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight cr
Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from ineff...
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Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen
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