In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,a...
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In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation *** this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised *** this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the ***,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd *** addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density *** experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.
Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilizat...
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Container-based virtualization technology has been more widely used in edge computing environments recently due to its advantages of lighter resource occupation, faster startup capability, and better resource utilization efficiency. To meet the diverse needs of tasks, it usually needs to instantiate multiple network functions in the form of containers interconnect various generated containers to build a Container Cluster(CC). Then CCs will be deployed on edge service nodes with relatively limited resources. However, the increasingly complex and timevarying nature of tasks brings great challenges to optimal placement of CC. This paper regards the charges for various resources occupied by providing services as revenue, the service efficiency and energy consumption as cost, thus formulates a Mixed Integer Programming(MIP) model to describe the optimal placement of CC on edge service nodes. Furthermore, an Actor-Critic based Deep Reinforcement Learning(DRL) incorporating Graph Convolutional Networks(GCN) framework named as RL-GCN is proposed to solve the optimization problem. The framework obtains an optimal placement strategy through self-learning according to the requirements and objectives of the placement of CC. Particularly, through the introduction of GCN, the features of the association relationship between multiple containers in CCs can be effectively extracted to improve the quality of *** experiment results show that under different scales of service nodes and task requests, the proposed method can obtain the improved system performance in terms of placement error ratio, time efficiency of solution output and cumulative system revenue compared with other representative baseline methods.
Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,in...
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Thyroid nodules,a common disorder in the endocrine system,require accurate segmentation in ultrasound images for effective diagnosis and ***,achieving precise segmentation remains a challenge due to various factors,including scattering noise,low contrast,and limited resolution in ultrasound *** existing segmentation models have made progress,they still suffer from several limitations,such as high error rates,low generalizability,overfitting,limited feature learning capability,*** address these challenges,this paper proposes a Multi-level Relation Transformer-based U-Net(MLRT-UNet)to improve thyroid nodule *** MLRTUNet leverages a novel Relation Transformer,which processes images at multiple scales,overcoming the limitations of traditional encoding *** transformer integrates both local and global features effectively through selfattention and cross-attention units,capturing intricate relationships within the *** approach also introduces a Co-operative Transformer Fusion(CTF)module to combine multi-scale features from different encoding layers,enhancing the model’s ability to capture complex patterns in the ***,the Relation Transformer block enhances long-distance dependencies during the decoding process,improving segmentation *** results showthat the MLRT-UNet achieves high segmentation accuracy,reaching 98.2% on the Digital Database Thyroid Image(DDT)dataset,97.8% on the Thyroid Nodule 3493(TG3K)dataset,and 98.2% on the Thyroid Nodule3K(TN3K)*** findings demonstrate that the proposed method significantly enhances the accuracy of thyroid nodule segmentation,addressing the limitations of existing models.
The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited di...
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The use of generative adversarial network(GAN)-based models for the conditional generation of image semantic segmentation has shown promising results in recent ***,there are still some limitations,including limited diversity of image style,distortion of detailed texture,unbalanced color tone,and lengthy training *** address these issues,we propose an asymmetric pre-training and fine-tuning(APF)-GAN model.
With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of t...
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With the popularity of the Internet of Vehicles(IoV), a large amount of data is being generated every day. How to securely share data between the IoV operator and various value-added service providers becomes one of the critical issues. Due to its flexible and efficient fine-grained access control feature, Ciphertext-Policy Attribute-Based Encryption(CP-ABE) is suitable for data sharing in IoV. However, there are many flaws in most existing CP-ABE schemes, such as attribute privacy leakage and key misuse. This paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP). A partially hidden access structure is adopted to hide sensitive user attribute values, and attribute categories are sent along with the ciphertext to effectively avoid privacy exposure. In addition, key tracking and malicious user revocation are introduced with broadcast encryption to prevent key misuse. Since the main computation task is outsourced to the cloud, the burden of the user side is relatively low. Analysis of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.
Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distri...
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Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.
The effectiveness of modeling contextual information has been empirically shown in numerous computer vision tasks. In this paper, we propose a simple yet efficient augmented fully convolutional network(AugFCN) by aggr...
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The effectiveness of modeling contextual information has been empirically shown in numerous computer vision tasks. In this paper, we propose a simple yet efficient augmented fully convolutional network(AugFCN) by aggregating content-and position-based object contexts for semantic ***, motivated because each deep feature map is a global, class-wise representation of the input,we first propose an augmented nonlocal interaction(AugNI) to aggregate the global content-based contexts through all feature map interactions. Compared to classical position-wise approaches, AugNI is more efficient. Moreover, to eliminate permutation equivariance and maintain translation equivariance, a learnable,relative position embedding branch is then supportably installed in AugNI to capture the global positionbased contexts. AugFCN is built on a fully convolutional network as the backbone by deploying AugNI before the segmentation head network. Experimental results on two challenging benchmarks verify that AugFCN can achieve a competitive 45.38% mIoU(standard mean intersection over union) and 81.9% mIoU on the ADE20K val set and Cityscapes test set, respectively, with little computational overhead. Additionally, the results of the joint implementation of AugNI and existing context modeling schemes show that AugFCN leads to continuous segmentation improvements in state-of-the-art context modeling. We finally achieve a top performance of 45.43% mIoU on the ADE20K val set and 83.0% mIoU on the Cityscapes test set.
To mitigate the challenges posed by data uncertainty in Full-Self Driving (FSD) systems. This paper proposes a novel feature extraction learning model called Adaptive Region of Interest Optimized Pyramid Network (ARO)...
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As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of *** connection between industrial control networks and the external internet is becoming increa...
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As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of *** connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security *** paper proposes a model for the industrial control *** includes a malware containment strategy that integrates intrusion detection,quarantine,and ***,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment *** addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction ***,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is *** otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control *** earlier the immunization of key nodes,the *** the time exceeds the threshold,immunizing key nodes is almost *** analysis provides a better way to contain the malware in the industrial control network.
Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like ...
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Semantic segmentation of driving scene images is crucial for autonomous *** deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small *** address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image *** adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different *** strengthens overlooked image details,extending the IAEN module’s *** the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation *** entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network *** lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image *** experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.
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