With the widespread application of federated learning across various domains, backdoor attacks pose a serious threat to the security of models. In this paper, we proposes a text watermarking-based federated learning b...
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In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of tech...
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Dynamic resource discovery in a network of dispersed computing resources is an open problem. The establishment and maintenance of resource pool information are critical, which involves both the polymorphic migration o...
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Dynamic resource discovery in a network of dispersed computing resources is an open problem. The establishment and maintenance of resource pool information are critical, which involves both the polymorphic migration of the network and the time and energy costs resulting from node selection and frequent interactions of information between nodes. The resource discovery problem for dispersed computing can be considered a dynamic multi-level decision problem. A bi-level programming model of dispersed computing resource discovery is developed, which is driven by time cost, energy consumption and accuracy of information acquisition. The upper-level model is to design a reasonable network structure of resource discovery, and the lower-level model is to explore an effective discovery mode. Complex network topology features are used for the first time to analyze the polymorphic migration characteristics of resource discovery networks. We propose an integrated calibration method for energy consumption parameters based on two discovery modes(i.e., agent mode and self-directed mode). A symmetric trust region based heuristic algorithm is proposed for solving the system model. The numerical simulation is performed in a dispersed computing network with multiple modes and topological states, which proves the feasibility of the model and the effectiveness of the algorithm.
Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.
People’s demand for vehicles has been increasing day by day over the last few decades. A survey tells us that over 50,000 vehicles run on the roads per day. Such a large number of vehicles causes traffic. A survey te...
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'Memory hot pages' usually refer to memory pages that are frequently accessed within a period of time. Using faster memory pages for these hot pages can optimize application performance. Using 'fast memory...
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Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis ...
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Facial beauty analysis is an important topic in human *** may be used as a guidance for face beautification applications such as cosmetic *** neural networks(DNNs)have recently been adopted for facial beauty analysis and have achieved remarkable ***,most existing DNN-based models regard facial beauty analysis as a normal classification *** ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty *** be specific,landmarks of the whole face and facial organs are introduced to extract geometric features to make the *** by this,we introduce a novel dual-branch network for facial beauty analysis:one branch takes the Swin Transformer as the backbone to model the full face and global patterns,and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial ***,the designed multi-scale feature fusion module can further facilitate our network to learn complementary semantic information between the two *** model optimisation,we propose a hybrid loss function,where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric *** performed on the SCUT-FBP5500 dataset and the SCUT-FBP dataset demonstrate that our model outperforms the state-of-the-art convolutional neural networks models,which proves the effectiveness of the proposed geometric regularisation and dual-branch structure with the hybrid *** the best of our knowledge,this is the first study to introduce a Vision Transformer into the facial beauty analysis task.
In this study,the effect of presentation rates on pupil dilation is investigated for target recognition in the Rapid Serial Visual Presentation(RSVP)*** this experiment,the RSVP paradigm with five different presentati...
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In this study,the effect of presentation rates on pupil dilation is investigated for target recognition in the Rapid Serial Visual Presentation(RSVP)*** this experiment,the RSVP paradigm with five different presentation rates,including 50,80,100,150,and 200 ms,is *** pupillometry data of 15 subjects are collected and *** pupillometry results reveal that the peak and average amplitudes for pupil size and velocity at the 80-ms presentation rate are considerably higher than those at other presentation *** average amplitude of pupil acceleration at the 80-ms presentation rate is significantly higher than those at the other presentation *** latencies under 50-and 80-ms presentation rates are considerably lower than those of 100-,150-,and 200-ms presentation ***,no considerable differences are observed in the peak,average amplitude,and latency of pupil size,pupil velocity,and acceleration under 100-,150-,and 200-ms presentation *** results reveal that with the increase in the presentation rate,pupil dilation first increases,then decreases,and later reaches *** 80-ms presentation rate results in the largest point of pupil *** correlation is observed between pupil dilation and recognition accuracy under the five presentation rates.
The need for vigorous security methods becomes a great concern as container-based virtualization continues to gain close attention in modern edge computing environments. This study proposed a novel hybrid security alg...
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Object detection has made a significant leap forward in recent ***,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susceptible to ...
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Object detection has made a significant leap forward in recent ***,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susceptible to missed detection due to background ***,small object information is affected due to the downsampling *** learning-based detection methods have been utilized to address the challenge posed by small *** this work,we propose a novel method,the Multi-Convolutional Block Attention Network(MCBAN),to increase the detection accuracy of minute objects aiming to overcome the challenge of information loss during the downsampling *** multi-convolutional attention block(MCAB);channel attention and spatial attention module(SAM)that make up MCAB,have been crafted to accomplish small object detection with higher *** have carried out the experiments on the Karlsruhe Institute of technology and Toyota Technological Institute(KITTI)and Pattern Analysis,Statical Modeling and Computational Learning(PASCAL)Visual Object Classes(VOC)datasets and have followed a step-wise process to analyze the *** experiment results demonstrate that significant gains in performance are achieved,such as 97.75%for KITTI and 88.97%for PASCAL *** findings of this study assert quite unequivocally the fact that MCBAN is much more efficient in the small object detection domain as compared to other existing approaches.
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