The development of deep learning has led to increasing demands for computation and memory, making multi-chiplet accelerators a powerful solution. Multi-chiplet accelerators require more precise consideration of hardwa...
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Federated learning is widely used to solve the problem of data decentralization and can provide privacy protectionfor data owners. However, since multiple participants are required in federated learning, this allows a...
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Federated learning is widely used to solve the problem of data decentralization and can provide privacy protectionfor data owners. However, since multiple participants are required in federated learning, this allows attackers tocompromise. Byzantine attacks pose great threats to federated learning. Byzantine attackers upload maliciouslycreated local models to the server to affect the prediction performance and training speed of the global model. Todefend against Byzantine attacks, we propose a Byzantine robust federated learning scheme based on backdoortriggers. In our scheme, backdoor triggers are embedded into benign data samples, and then malicious localmodels can be identified by the server according to its validation dataset. Furthermore, we calculate the adjustmentfactors of local models according to the parameters of their final layers, which are used to defend against datapoisoning-based Byzantine attacks. To further enhance the robustness of our scheme, each localmodel is weightedand aggregated according to the number of times it is identified as malicious. Relevant experimental data showthat our scheme is effective against Byzantine attacks in both independent identically distributed (IID) and nonindependentidentically distributed (non-IID) scenarios.
Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learnin...
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Palmprint recognition is an emerging biometrics technology that has attracted increasing attention in recent years. Many palmprint recognition methods have been proposed, including traditional methods and deep learning-based methods. Among the traditional methods, the methods based on directional features are mainstream because they have high recognition rates and are robust to illumination changes and small noises. However, to date, in these methods, the stability of the palmprint directional response has not been deeply studied. In this paper, we analyse the problem of directional response instability in palmprint recognition methods based on directional feature. We then propose a novel palmprint directional response stability measurement (DRSM) to judge the stability of the directional feature of each pixel. After filtering the palmprint image with the filter bank, we design DRSM according to the relationship between the maximum response value and other response values for each pixel. Using DRSM, we can judge those pixels with unstable directional response and use a specially designed encoding mode related to a specific method. We insert the DRSM mechanism into seven classical methods based on directional feature, and conduct many experiments on six public palmprint databases. The experimental results show that the DRSM mechanism can effectively improve the performance of these methods. In the field of palmprint recognition, this work is the first in-depth study on the stability of the palmprint directional response, so this paper has strong reference value for research on palmprint recognition methods based on directional features.
The growing dependence on deep learning models for medical diagnosis underscores the critical need for robust interpretability and transparency to instill trust and ensure responsible usage. This study investigates th...
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In this article, write-once-read-many-times (WORM) memory behavior of HfZrO (HZO) ferroelectric material is demonstrated. A stoichiometric Hf0.5Zr0.5O2 thin film prepared using a sol-gel process is used as a resistive...
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Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence perio...
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Time series data plays a crucial role in intelligent transportation *** flow forecasting represents a precise estimation of future traffic flow within a specific region and time *** approaches,including sequence periodic,regression,and deep learning models,have shown promising results in short-term series ***,forecasting scenarios specifically focused on holiday traffic flow present unique challenges,such as distinct traffic patterns during vacations and the increased demand for long-term ***,the effectiveness of existing methods diminishes in such ***,we propose a novel longterm forecasting model based on scene matching and embedding fusion representation to forecast long-term holiday traffic *** model comprises three components:the similar scene matching module,responsible for extracting Similar Scene Features;the long-short term representation fusion module,which integrates scenario embeddings;and a simple fully connected layer at the head for making the final *** results on real datasets demonstrate that our model outperforms other methods,particularly in medium and long-term forecasting scenarios.
Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,***,the existing research on sentiment analysis is relatively *** does not eff...
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Sentiment analysis in Chinese classical poetry has become a prominent topic in historical and cultural tracing,ancient literature research,***,the existing research on sentiment analysis is relatively *** does not effectively solve the problems such as the weak feature extraction ability of poetry text,which leads to the low performance of the model on sentiment analysis for Chinese classical *** this research,we offer the SA-Model,a poetic sentiment analysis ***-Model firstly extracts text vector information and fuses it through Bidirectional encoder representation from transformers-Whole word masking-extension(BERT-wwmext)and Enhanced representation through knowledge integration(ERNIE)to enrich text vector information;Secondly,it incorporates numerous encoders to remove text features at multiple levels,thereby increasing text feature information,improving text semantics accuracy,and enhancing the model’s learning and generalization capabilities;finally,multi-feature fusion poetry sentiment analysis model is *** feasibility and accuracy of the model are validated through the ancient poetry sentiment *** with other baseline models,the experimental findings indicate that SA-Model may increase the accuracy of text semantics and hence improve the capability of poetry sentiment analysis.
To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentat...
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To enhance the efficiency and accuracy of environmental perception for autonomous vehicles,we propose GDMNet,a unified multi-task perception network for autonomous driving,capable of performing drivable area segmentation,lane detection,and traffic object ***,in the encoding stage,features are extracted,and Generalized Efficient Layer Aggregation Network(GELAN)is utilized to enhance feature extraction and gradient ***,in the decoding stage,specialized detection heads are designed;the drivable area segmentation head employs DySample to expand feature maps,the lane detection head merges early-stage features and processes the output through the Focal Modulation Network(FMN).Lastly,the Minimum Point Distance IoU(MPDIoU)loss function is employed to compute the matching degree between traffic object detection boxes and predicted boxes,facilitating model training *** results on the BDD100K dataset demonstrate that the proposed network achieves a drivable area segmentation mean intersection over union(mIoU)of 92.2%,lane detection accuracy and intersection over union(IoU)of 75.3%and 26.4%,respectively,and traffic object detection recall and mAP of 89.7%and 78.2%,*** detection performance surpasses that of other single-task or multi-task algorithm models.
Unmanned Aerial Vehicle (UAV) crowdsensing, as a complement to Mobile Crowdsensing (MCS), can provide ubiquitous sensing in extreme environments and has gathered significant attention in recent years. In this paper, w...
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Computing-aware routing is the core technology of Computing Power Network. Through independently determining the forwarding target in each router, computingaware routing aims at scheduling the computing task to an opt...
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