Semantic Communication(SemCom)can significantly reduce the transmitted data volume and keep robustness.Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly,rather than achiev...Semantic Communication(SemCom)can significantly reduce the transmitted data volume and keep robustness.Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly,rather than achieving precise bit-by-bit reconstruction.Existing image SemCom systems directly perform semantic encoding and decoding on the entire image,which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise.To this end,we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks(GANs).Specifically,the accurate semantics of the image are extracted by the semantic encoder,and divided into two parts for different downstream tasks:Regions of Interest(ROI)and Regions of Non-Interest(RONI).By reducing the quantization accuracy of RONI,the amount of transmitted data volume is reduced significantly.During the transmission process of semantics,a Signal-to-Noise Ratio(SNR)is randomly initialized,enabling the model to learn the average noise distribution.The experimental results demonstrate that by reducing the quantization level of RONI,transmitted data volume is reduced up to 60.53%compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks.Moreover,our model exhibits increased robustness with variable SNRs.展开更多
Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encoun...Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.展开更多
Text-to-image generation is a vital task in different fields,such as combating crime and terrorism and quickly arresting lawbreakers.For several years,due to a lack of deep learning and machine learning resources,poli...Text-to-image generation is a vital task in different fields,such as combating crime and terrorism and quickly arresting lawbreakers.For several years,due to a lack of deep learning and machine learning resources,police officials required artists to draw the face of a criminal.Traditional methods of identifying criminals are inefficient and time-consuming.This paper presented a new proposed hybrid model for converting the text into the nearest images,then ranking the produced images according to the available data.The framework contains two main steps:generation of the image using an Identity Generative Adversarial Network(IGAN)and ranking of the images according to the available data using multi-criteria decision-making based on neutrosophic theory.The IGAN has the same architecture as the classical Generative Adversarial Networks(GANs),but with different modifications,such as adding a non-linear identity block,smoothing the standard GAN loss function by using a modified loss function and label smoothing,and using mini-batch training.The model achieves efficient results in Inception Distance(FID)and inception score(IS)when compared with other architectures of GANs for generating images from text.The IGAN achieves 42.16 as FID and 14.96 as IS.When it comes to ranking the generated images using Neutrosophic,the framework also performs well in the case of missing information and missing data.展开更多
This study developed a hybrid model combining a Convolutional Neural Network(CNN)and a Generative Adversarial Network(GAN)for the task of single-image super-resolution reconstruction.The CNN is responsible for hierarc...This study developed a hybrid model combining a Convolutional Neural Network(CNN)and a Generative Adversarial Network(GAN)for the task of single-image super-resolution reconstruction.The CNN is responsible for hierarchical image feature extraction and maintaining structural integrity,while the GAN synthesizes realistic texture details through an adver sarial training m echanism to enhance visual realism.The generator is constructed using densely connected convolutional blocks and is combined with an image block-based discriminator to evaluate the authenticity of local regions.The composite loss function is designed to integrate the root mean square error,perceptual loss,and adversarial loss of the pre-trained GTS network,balancing pixel-level accuracy and visual perceptual effect.Tests on benchmark datasets such as DIV2K and Set14 show that this model outperforms tr aditional interpolation algorithms and deep learning models in objective indicators such as PSNR and SSIM,as well as in the perception evaluation of LPIPS.Especially in complex texture restoration tasks,the model demonstrates excellent d etail restoratio n capabilities.Experimental data confirm that the adversarial training mechanism effectively solves the common problem of excessive smoothing in traditional super-resolution methods,making the reconstructed image closer to the actual optical imaging effe ct.This technology provides new ideas for scenarios that require high-fidelity reconstruction,such as medical image analysis and satellite map optimization.展开更多
In this paper,we undertake further investigation to alleviate the issue of limit cycling behavior in training generative adversarial networks(GANs)through the proposed predictive centripetal acceleration algorithm(PCA...In this paper,we undertake further investigation to alleviate the issue of limit cycling behavior in training generative adversarial networks(GANs)through the proposed predictive centripetal acceleration algorithm(PCAA).Specifically,we first derive the upper and lower complexity bounds of PCAA for a general bilinear game,with the last-iterate convergence rate notably improving upon previous results.Then,we combine PCAA with the adaptive moment estimation algorithm(Adam)to propose PCAA-Adam,for practical training of GANs to enhance their generalization capability.Finally,we validate the effectiveness of the proposed algorithm through experiments conducted on bilinear games,multivariate Gaussian distributions,and the CelebA dataset,respectively.展开更多
基金supported by the National Science Fund for Excellent Young Scholars(No.62422112).
文摘Semantic Communication(SemCom)can significantly reduce the transmitted data volume and keep robustness.Task-oriented SemCom of images aims to convey the implicit meaning of source messages correctly,rather than achieving precise bit-by-bit reconstruction.Existing image SemCom systems directly perform semantic encoding and decoding on the entire image,which has not considered the correlation between image content and downstream tasks or the adaptability to channel noise.To this end,we propose a content-aware robust SemCom framework for image transmission based on Generative Adversarial Networks(GANs).Specifically,the accurate semantics of the image are extracted by the semantic encoder,and divided into two parts for different downstream tasks:Regions of Interest(ROI)and Regions of Non-Interest(RONI).By reducing the quantization accuracy of RONI,the amount of transmitted data volume is reduced significantly.During the transmission process of semantics,a Signal-to-Noise Ratio(SNR)is randomly initialized,enabling the model to learn the average noise distribution.The experimental results demonstrate that by reducing the quantization level of RONI,transmitted data volume is reduced up to 60.53%compared to using globally consistent quantization while maintaining comparable performance to existing methods in downstream semantic segmentation tasks.Moreover,our model exhibits increased robustness with variable SNRs.
基金National Natural Science Foundation of China(62072392).
文摘Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation.While traditional methods for task allocation can help reduce costs and improve efficiency,they may encounter challenges when dealing with abnormal data flow nodes,leading to decreased allocation accuracy and efficiency.To address these issues,this study proposes a novel two-part invalid detection task allocation framework.In the first step,an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data.Compared to the baseline method,the model achieves an approximately 4%increase in the F1 value on the public dataset.In the second step of the framework,task allocation modeling is performed using a twopart graph matching method.This phase introduces a P-queue KM algorithm that implements a more efficient optimization strategy.The allocation efficiency is improved by approximately 23.83%compared to the baseline method.Empirical results confirm the effectiveness of the proposed framework in detecting abnormal data nodes,enhancing allocation precision,and achieving efficient allocation.
文摘Text-to-image generation is a vital task in different fields,such as combating crime and terrorism and quickly arresting lawbreakers.For several years,due to a lack of deep learning and machine learning resources,police officials required artists to draw the face of a criminal.Traditional methods of identifying criminals are inefficient and time-consuming.This paper presented a new proposed hybrid model for converting the text into the nearest images,then ranking the produced images according to the available data.The framework contains two main steps:generation of the image using an Identity Generative Adversarial Network(IGAN)and ranking of the images according to the available data using multi-criteria decision-making based on neutrosophic theory.The IGAN has the same architecture as the classical Generative Adversarial Networks(GANs),but with different modifications,such as adding a non-linear identity block,smoothing the standard GAN loss function by using a modified loss function and label smoothing,and using mini-batch training.The model achieves efficient results in Inception Distance(FID)and inception score(IS)when compared with other architectures of GANs for generating images from text.The IGAN achieves 42.16 as FID and 14.96 as IS.When it comes to ranking the generated images using Neutrosophic,the framework also performs well in the case of missing information and missing data.
文摘This study developed a hybrid model combining a Convolutional Neural Network(CNN)and a Generative Adversarial Network(GAN)for the task of single-image super-resolution reconstruction.The CNN is responsible for hierarchical image feature extraction and maintaining structural integrity,while the GAN synthesizes realistic texture details through an adver sarial training m echanism to enhance visual realism.The generator is constructed using densely connected convolutional blocks and is combined with an image block-based discriminator to evaluate the authenticity of local regions.The composite loss function is designed to integrate the root mean square error,perceptual loss,and adversarial loss of the pre-trained GTS network,balancing pixel-level accuracy and visual perceptual effect.Tests on benchmark datasets such as DIV2K and Set14 show that this model outperforms tr aditional interpolation algorithms and deep learning models in objective indicators such as PSNR and SSIM,as well as in the perception evaluation of LPIPS.Especially in complex texture restoration tasks,the model demonstrates excellent d etail restoratio n capabilities.Experimental data confirm that the adversarial training mechanism effectively solves the common problem of excessive smoothing in traditional super-resolution methods,making the reconstructed image closer to the actual optical imaging effe ct.This technology provides new ideas for scenarios that require high-fidelity reconstruction,such as medical image analysis and satellite map optimization.
基金supported by the Major Program of National Natural Science Foundation of China(Grant Nos.11991020 and 11991024)the Team Project of Innovation Leading Talent in Chongqing(Grant No.CQYC20210309536)+1 种基金NSFC-RGC(Hong Kong)Joint Research Program(Grant No.12261160365)the Scientific and Technological Research Program of Chongqing Municipal Education Commission(Grant No.KJQN202300528)。
文摘In this paper,we undertake further investigation to alleviate the issue of limit cycling behavior in training generative adversarial networks(GANs)through the proposed predictive centripetal acceleration algorithm(PCAA).Specifically,we first derive the upper and lower complexity bounds of PCAA for a general bilinear game,with the last-iterate convergence rate notably improving upon previous results.Then,we combine PCAA with the adaptive moment estimation algorithm(Adam)to propose PCAA-Adam,for practical training of GANs to enhance their generalization capability.Finally,we validate the effectiveness of the proposed algorithm through experiments conducted on bilinear games,multivariate Gaussian distributions,and the CelebA dataset,respectively.
