Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently...Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.展开更多
Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and ...Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods.Due to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant progress.However,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred images.To this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated differentially.In particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right views.Based on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image regions.Moreover,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch.In this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions.Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)methods.For the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.展开更多
High-resolution line scan cameras with wide-angle lenses are highly accurate and efficient for tunnel detection.However,due to the curvature of the tunnel,there are variations in object distance that exceed the depth ...High-resolution line scan cameras with wide-angle lenses are highly accurate and efficient for tunnel detection.However,due to the curvature of the tunnel,there are variations in object distance that exceed the depth of field of the lens,resulting in uneven defocus blur in the captured images.This can significantly affect the accuracy of defect recognition.While existing deblurring algorithms can improve image quality,they often prioritize results over inference time,which is not ideal for high-speed tunnel image acquisition.To address this issue,we developed a lightweight tunnel structure defect deblurring network(TSDDNet)for curved-tunnel line scanning with wide-angle lenses.Our method employs an innovative progressive structure that balances network depth and feature breadth to simultaneously achieve good performance and short inference time.The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network.Additionally,the proposed feature refinement block captures the structurally similar features to enhance the image details,increasing the peak signal-to-noise ratio(PSNR).A raw dataset containing tunnel blur images was created using a high-resolution line scan camera and used to train and test our model.TSDDNet achieved a PSNR of 26.82 dB and a structural similarity index measure of 0.888,while using one-third of the parameters of comparable alternatives.Moreover,our method exhibited a higher computational speed than that of conventional methods,with inference times of 8.82 ms for a single 512×512 pixel image patch and 227.22 ms for completely processing a 2048×2560 pixel image.The test results indicated that the structural scalability of the network allows it to accommodate large inputs,making it effective for high-resolution images.展开更多
Images are frequently affected because of blurring,and data loss occurred by sampling and noise occurrence.The images are getting blurred because of object movement in the scenario,atmospheric misrepresentations,and o...Images are frequently affected because of blurring,and data loss occurred by sampling and noise occurrence.The images are getting blurred because of object movement in the scenario,atmospheric misrepresentations,and optical aberrations.The main objective of image restoration is to evaluate the original image from the corrupted data.To overcome this issue,the multiobjective reptile search algorithm is proposed for performing an effective image deblurring and restoration(MORSA-IDR).The proposed MORSA is used in two different processes such as threshold and kernel parameter calculation.In that,threshold values are used for detecting and replacing the noisy pixel removal using deep residual network,and estimation of kernel is performed for deblurring the images.The main objective of the proposed MORSA-IDR is to enhance the process of deblurring for recovering low-level contextual information.The MORSA-IDR is evaluated using peak signal noise ratio(PSNR)and structural similarity index.The existing researches such as enhanced local maximum intensity(ELMI)prior and deep unrolling for blind deblurring(DUBLID)are used to evaluate the MORSA-IDR.The PSNR of MORSA-IDR for image 6 is 30.98 dB,which is high when compared with the ELMI and DUBLID.展开更多
In the internet protocol（IP） over multi-granular optical switch network （IP/MG-OXC）, the network node is a typical multilayer switch comprising several layers, the IP packet switching （PXC） layer, wavelength swi...In the internet protocol（IP） over multi-granular optical switch network （IP/MG-OXC）, the network node is a typical multilayer switch comprising several layers, the IP packet switching （PXC） layer, wavelength switching （WXC） layer and fiber switching （FXC） layer. This network is capable of both IP layer grooming and wavelength grooming in a hierarchical manner. Resource provisioning in the multi-granular network paradigm is called hierarchical grooming problem. An integer linear programming （ILP） model is proposed to formulate the problem. An iterative heuristic approach is developed for solving the problem in large networks. Case study shows that IP/MG-OXC network is much more extendible and can significantly save the overall network cost as compared with IP over wavelength division multiplexing network.展开更多
The research on named entity recognition for label-few domain is becoming increasingly important.In this paper,a novel algorithm,positive unlabeled named entity recognition(PUNER)with multi-granularity language inform...The research on named entity recognition for label-few domain is becoming increasingly important.In this paper,a novel algorithm,positive unlabeled named entity recognition(PUNER)with multi-granularity language information,is proposed,which combines positive unlabeled(PU)learning and deep learning to obtain the multi-granularity language information from a few labeled in-stances and many unlabeled instances to recognize named entities.First,PUNER selects reliable negative instances from unlabeled datasets,uses positive instances and a corresponding number of negative instances to train the PU learning classifier,and iterates continuously to label all unlabeled instances.Second,a neural network-based architecture to implement the PU learning classifier is used,and comprehensive text semantics through multi-granular language information are obtained,which helps the classifier correctly recognize named entities.Performance tests of the PUNER are carried out on three multilingual NER datasets,which are CoNLL2003,CoNLL 2002 and SIGHAN Bakeoff 2006.Experimental results demonstrate the effectiveness of the proposed PUNER.展开更多
基金National Natural Science Foundation of China(11971211,12171388).
文摘Complex network models are frequently employed for simulating and studyingdiverse real-world complex systems.Among these models,scale-free networks typically exhibit greater fragility to malicious attacks.Consequently,enhancing the robustness of scale-free networks has become a pressing issue.To address this problem,this paper proposes a Multi-Granularity Integration Algorithm(MGIA),which aims to improve the robustness of scale-free networks while keeping the initial degree of each node unchanged,ensuring network connectivity and avoiding the generation of multiple edges.The algorithm generates a multi-granularity structure from the initial network to be optimized,then uses different optimization strategies to optimize the networks at various granular layers in this structure,and finally realizes the information exchange between different granular layers,thereby further enhancing the optimization effect.We propose new network refresh,crossover,and mutation operators to ensure that the optimized network satisfies the given constraints.Meanwhile,we propose new network similarity and network dissimilarity evaluation metrics to improve the effectiveness of the optimization operators in the algorithm.In the experiments,the MGIA enhances the robustness of the scale-free network by 67.6%.This improvement is approximately 17.2%higher than the optimization effects achieved by eight currently existing complex network robustness optimization algorithms.
基金supported by the National Natural Science Foundation of China (61971165, 61922027, 61773295)in part by the Fundamental Research Funds for the Central Universities (FRFCU5710050119)+1 种基金the Natural Science Foundation of Heilongjiang Province(YQ2020F004)the Chinese Association for Artificial Intelligence(CAAI)-Huawei Mind Spore Open Fund
文摘Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods.Due to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant progress.However,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred images.To this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated differentially.In particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right views.Based on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image regions.Moreover,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch.In this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions.Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)methods.For the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.
基金supported by the National Natural Science Foundation of China(Grant No.51978582).
文摘High-resolution line scan cameras with wide-angle lenses are highly accurate and efficient for tunnel detection.However,due to the curvature of the tunnel,there are variations in object distance that exceed the depth of field of the lens,resulting in uneven defocus blur in the captured images.This can significantly affect the accuracy of defect recognition.While existing deblurring algorithms can improve image quality,they often prioritize results over inference time,which is not ideal for high-speed tunnel image acquisition.To address this issue,we developed a lightweight tunnel structure defect deblurring network(TSDDNet)for curved-tunnel line scanning with wide-angle lenses.Our method employs an innovative progressive structure that balances network depth and feature breadth to simultaneously achieve good performance and short inference time.The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network.Additionally,the proposed feature refinement block captures the structurally similar features to enhance the image details,increasing the peak signal-to-noise ratio(PSNR).A raw dataset containing tunnel blur images was created using a high-resolution line scan camera and used to train and test our model.TSDDNet achieved a PSNR of 26.82 dB and a structural similarity index measure of 0.888,while using one-third of the parameters of comparable alternatives.Moreover,our method exhibited a higher computational speed than that of conventional methods,with inference times of 8.82 ms for a single 512×512 pixel image patch and 227.22 ms for completely processing a 2048×2560 pixel image.The test results indicated that the structural scalability of the network allows it to accommodate large inputs,making it effective for high-resolution images.
文摘Images are frequently affected because of blurring,and data loss occurred by sampling and noise occurrence.The images are getting blurred because of object movement in the scenario,atmospheric misrepresentations,and optical aberrations.The main objective of image restoration is to evaluate the original image from the corrupted data.To overcome this issue,the multiobjective reptile search algorithm is proposed for performing an effective image deblurring and restoration(MORSA-IDR).The proposed MORSA is used in two different processes such as threshold and kernel parameter calculation.In that,threshold values are used for detecting and replacing the noisy pixel removal using deep residual network,and estimation of kernel is performed for deblurring the images.The main objective of the proposed MORSA-IDR is to enhance the process of deblurring for recovering low-level contextual information.The MORSA-IDR is evaluated using peak signal noise ratio(PSNR)and structural similarity index.The existing researches such as enhanced local maximum intensity(ELMI)prior and deep unrolling for blind deblurring(DUBLID)are used to evaluate the MORSA-IDR.The PSNR of MORSA-IDR for image 6 is 30.98 dB,which is high when compared with the ELMI and DUBLID.
基金Sponsored by Agency for Singapore Technology and Advance Research(RGM01/16)
文摘In the internet protocol（IP） over multi-granular optical switch network （IP/MG-OXC）, the network node is a typical multilayer switch comprising several layers, the IP packet switching （PXC） layer, wavelength switching （WXC） layer and fiber switching （FXC） layer. This network is capable of both IP layer grooming and wavelength grooming in a hierarchical manner. Resource provisioning in the multi-granular network paradigm is called hierarchical grooming problem. An integer linear programming （ILP） model is proposed to formulate the problem. An iterative heuristic approach is developed for solving the problem in large networks. Case study shows that IP/MG-OXC network is much more extendible and can significantly save the overall network cost as compared with IP over wavelength division multiplexing network.
基金the National Natural Science Foundation of China(No.61876144)the Strategy Priority Research Program of Chinese Acade-my of Sciences(No.XDC02070600).
文摘The research on named entity recognition for label-few domain is becoming increasingly important.In this paper,a novel algorithm,positive unlabeled named entity recognition(PUNER)with multi-granularity language information,is proposed,which combines positive unlabeled(PU)learning and deep learning to obtain the multi-granularity language information from a few labeled in-stances and many unlabeled instances to recognize named entities.First,PUNER selects reliable negative instances from unlabeled datasets,uses positive instances and a corresponding number of negative instances to train the PU learning classifier,and iterates continuously to label all unlabeled instances.Second,a neural network-based architecture to implement the PU learning classifier is used,and comprehensive text semantics through multi-granular language information are obtained,which helps the classifier correctly recognize named entities.Performance tests of the PUNER are carried out on three multilingual NER datasets,which are CoNLL2003,CoNLL 2002 and SIGHAN Bakeoff 2006.Experimental results demonstrate the effectiveness of the proposed PUNER.
