Cloud manufacturing is one of the three key technologies that enable intelligent manufacturing.This paper presents a novel attribute-based encryption(ABE)approach for computer-aided design(CAD)assembly models to effec...Cloud manufacturing is one of the three key technologies that enable intelligent manufacturing.This paper presents a novel attribute-based encryption(ABE)approach for computer-aided design(CAD)assembly models to effectively support hierarchical access control,integrity verification,and deformation protection for co-design scenarios in cloud manufacturing.An assembly hierarchy access tree(AHAT)is designed as the hierarchical access structure.Attribute-related ciphertext elements,which are contained in an assembly ciphertext(ACT)file,are adapted for content keys decryption instead of CAD component files.We modify the original Merkle tree(MT)and reconstruct an assembly MT.The proposed ABE framework has the ability to combine the deformation protection method with a content privacy of CAD models.The proposed encryption scheme is demonstrated to be secure under the standard assumption.Experimental simulation on typical CAD assembly models demonstrates that the proposed approach is feasible in applications.展开更多
Bayesian Matting has four limitations.Firstly,Bayesian matting makes strong assumption that the texture distribution of nature image satisfies Gaussian distribution with fixed variance.This assumption will fail for co...Bayesian Matting has four limitations.Firstly,Bayesian matting makes strong assumption that the texture distribution of nature image satisfies Gaussian distribution with fixed variance.This assumption will fail for complex texture distribution.In order to extract the nature images with complex texture distribution,we design an information entropy approach to estimate the scalable variance.Secondly,when the opacity is near the boundary of the value range,Bayesian matting method may be failure because of the error computation of opacity.Therefore,a rectification approach is proposed to adjust the computation model and keep the opacity within the valid value range.Thirdly,Bayesian matting is a local sample method which may miss some valid samples of matting.We propose a selection function to integrate valid global sample matting result into above matting framework as a supplement to the local sample matting result.The proposed function is compose of three criteria,that is,the similarity of results,the overlapping degree of samples,and the similarity of neighborhood.Fourthly,in order to obtain a smooth and vivid matte,the result is further refined by considering correlation between neighbouring pixels.Finally,We use online benchmark for image matting to evaluate the proposed method with both qualitative observation and quantitative analysis.The experiments show that our method achieves a competitive advantages over other methods.展开更多
This paper presents a one-way data transmission method in order to ensure the safety of data transmission from mobile storage to secure PC.First,an optocoupler is used to achieve the one-way transmission of physical c...This paper presents a one-way data transmission method in order to ensure the safety of data transmission from mobile storage to secure PC.First,an optocoupler is used to achieve the one-way transmission of physical channel,so that data can only be transmitted from mobile storage to secure PC,while the opposite direction is no physical channel.Then,a safe and reliable software system is designed which contains one-way communication protocol,fast CRC check method and packet retransmission algorithm together to ensure the safety of data transmission.After that,to obtain the maximum transmission rate,the frequency of data bus(slwr)and the packet size(num)which effect on transmission rate are detailed analyzed.Experimental results show the proposed method is high-efficiency and safe.展开更多
This paper presents a novel tracking algorithm which integrates two complementary trackers. Firstly, an improved Bayesian tracker(B-tracker) with adaptive learning rate is presented. The classification score of B-trac...This paper presents a novel tracking algorithm which integrates two complementary trackers. Firstly, an improved Bayesian tracker(B-tracker) with adaptive learning rate is presented. The classification score of B-tracker reflects tracking reliability, and a low score usually results from large appearance change. Therefore, if the score is low, we decrease the learning rate to update the classifier fast so that B-tracker can adapt to the variation and vice versa. In this way, B-tracker is more suitable than its traditional version to solve appearance change problem. Secondly, we present an improved incremental subspace learning method tracker(Stracker). We propose to calculate projected coordinates using maximum posterior probability, which results in a more accurate reconstruction error than traditional subspace learning tracker. Instead of updating at every time, we present a stopstrategy to deal with occlusion problem. Finally, we present an integrated framework(BAST), in which the pair of trackers run in parallel and return two candidate target states separately. For each candidate state, we define a tracking reliability metrics to measure whether the candidate state is reliable or not, and the reliable candidate state will be chosen as the target state at the end of each frame. Experimental results on challenging sequences show that the proposed approach is very robust and effective in comparison to the state-of-the-art trackers.展开更多
The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazi...The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing methods.However,the data set trained in deep learning does not match well this model for three reasons.Firstly,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing real-scene.Secondly,it is difficult to get the depth of outdoor scenes for ASM.Thirdly,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this phenomenon.In this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ASM.Analytically,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement item.The interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed image.Based on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training.Specifically,we propose a new way to construct a haze patch on the flight of network training.The patch is randomly selected from the input images and the thickness of haze is also randomly set.Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.展开更多
Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the ...Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT frame- work, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm（CFS） based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly, It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.展开更多
Hardware/software partitioning is an essential step in hardware/software co-design.For large size problems,it is difficult to consider both solution quality and time.This paper presents an efficient GPU-based parallel...Hardware/software partitioning is an essential step in hardware/software co-design.For large size problems,it is difficult to consider both solution quality and time.This paper presents an efficient GPU-based parallel tabu search algorithm(GPTS)for HW/SW partitioning.A single GPU kernel of compacting neighborhood is proposed to reduce the amount of GPU global memory accesses theoretically.A kernel fusion strategy is further proposed to reduce the amount of GPU global memory accesses of GPTS.To further minimize the transfer overhead of GPTS between CPU and GPU,an optimized transfer strategy for GPU-based tabu evaluation is proposed,which considers that all the candidates do not satisfy the given constraint.Experiments show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW partitioning.The proposed parallelization is significant when considering the ordinary GPU platform.展开更多
In recent years,there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance.In most cases,it requires a larger number of data to train a robust...In recent years,there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance.In most cases,it requires a larger number of data to train a robust deep learning model,which contains a lot of parameters to fit training data.However,both data of user ratings and social networks are facing critical sparse problem,which makes it not easy to train a robust deep neural network model.Towards this problem,we propose a novel correlative denoising autoencoder(CoDAE)method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation.We develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater,truster and trustee,respectively.Especially,on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user,we propose to utilize shared parameters to learn common information of the units that corresponding to same users.Moreover,we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model.We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task.The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.展开更多
Multi-user collaborative editors are useful computer-aided tools to support human-to-human collaboration.For multi-user collaborative editors,selective undo is an essential utility enabling users to undo any editing o...Multi-user collaborative editors are useful computer-aided tools to support human-to-human collaboration.For multi-user collaborative editors,selective undo is an essential utility enabling users to undo any editing operations at any time.Collaborative editors usually adopt operational transformation(OT)to address concurrency and consistency issues.However,it is still a great challenge to design an efficient and correct OT algorithm capable of handling both normal do operations and user-initiated undo operations because these two kinds of operations can interfere with each other in various forms.In this paper,we propose a semi-transparent selective undo algorithm that handles both do and undo in a unified framework,which separates the processing part of do operations from the processing part of undo operations.Formal proofs are provided to prove the proposed algorithm under the well-established criteria.Theoretical analysis and experimental evaluation are conducted to show that the proposed algorithm outperforms the prior OT-based selective undo algorithms.展开更多
基金supported by the National Natural Science Foundation of China(62072348)the Science and Technology Major Project of Hubei Province(Next-Generation AI Technologies,2019AEA170).
文摘Cloud manufacturing is one of the three key technologies that enable intelligent manufacturing.This paper presents a novel attribute-based encryption(ABE)approach for computer-aided design(CAD)assembly models to effectively support hierarchical access control,integrity verification,and deformation protection for co-design scenarios in cloud manufacturing.An assembly hierarchy access tree(AHAT)is designed as the hierarchical access structure.Attribute-related ciphertext elements,which are contained in an assembly ciphertext(ACT)file,are adapted for content keys decryption instead of CAD component files.We modify the original Merkle tree(MT)and reconstruct an assembly MT.The proposed ABE framework has the ability to combine the deformation protection method with a content privacy of CAD models.The proposed encryption scheme is demonstrated to be secure under the standard assumption.Experimental simulation on typical CAD assembly models demonstrates that the proposed approach is feasible in applications.
文摘Bayesian Matting has four limitations.Firstly,Bayesian matting makes strong assumption that the texture distribution of nature image satisfies Gaussian distribution with fixed variance.This assumption will fail for complex texture distribution.In order to extract the nature images with complex texture distribution,we design an information entropy approach to estimate the scalable variance.Secondly,when the opacity is near the boundary of the value range,Bayesian matting method may be failure because of the error computation of opacity.Therefore,a rectification approach is proposed to adjust the computation model and keep the opacity within the valid value range.Thirdly,Bayesian matting is a local sample method which may miss some valid samples of matting.We propose a selection function to integrate valid global sample matting result into above matting framework as a supplement to the local sample matting result.The proposed function is compose of three criteria,that is,the similarity of results,the overlapping degree of samples,and the similarity of neighborhood.Fourthly,in order to obtain a smooth and vivid matte,the result is further refined by considering correlation between neighbouring pixels.Finally,We use online benchmark for image matting to evaluate the proposed method with both qualitative observation and quantitative analysis.The experiments show that our method achieves a competitive advantages over other methods.
文摘This paper presents a one-way data transmission method in order to ensure the safety of data transmission from mobile storage to secure PC.First,an optocoupler is used to achieve the one-way transmission of physical channel,so that data can only be transmitted from mobile storage to secure PC,while the opposite direction is no physical channel.Then,a safe and reliable software system is designed which contains one-way communication protocol,fast CRC check method and packet retransmission algorithm together to ensure the safety of data transmission.After that,to obtain the maximum transmission rate,the frequency of data bus(slwr)and the packet size(num)which effect on transmission rate are detailed analyzed.Experimental results show the proposed method is high-efficiency and safe.
基金This paper was supported by the National Natural Science Foundation of China (Grant No. 61472289)the National Key Research and Development Project of China (2016YFC0106305).
文摘This paper presents a novel tracking algorithm which integrates two complementary trackers. Firstly, an improved Bayesian tracker(B-tracker) with adaptive learning rate is presented. The classification score of B-tracker reflects tracking reliability, and a low score usually results from large appearance change. Therefore, if the score is low, we decrease the learning rate to update the classifier fast so that B-tracker can adapt to the variation and vice versa. In this way, B-tracker is more suitable than its traditional version to solve appearance change problem. Secondly, we present an improved incremental subspace learning method tracker(Stracker). We propose to calculate projected coordinates using maximum posterior probability, which results in a more accurate reconstruction error than traditional subspace learning tracker. Instead of updating at every time, we present a stopstrategy to deal with occlusion problem. Finally, we present an integrated framework(BAST), in which the pair of trackers run in parallel and return two candidate target states separately. For each candidate state, we define a tracking reliability metrics to measure whether the candidate state is reliable or not, and the reliable candidate state will be chosen as the target state at the end of each frame. Experimental results on challenging sequences show that the proposed approach is very robust and effective in comparison to the state-of-the-art trackers.
基金supported by the National Natural Science Foundation of China(Grant No.62072348)the National Key RD Program of China under(2019YFC1509604)the Science and Technology Major Project of Hubei Province China(Next-Generation AI Technologies)(2019AEA170)。
文摘The haze phenomenon seriously interferes the image acquisition and reduces image quality.Due to many uncertain factors,dehazing is typically a challenge in image processing.The most existing deep learning-based dehazing approaches apply the atmospheric scattering model(ASM)or a similar physical model,which originally comes from traditional dehazing methods.However,the data set trained in deep learning does not match well this model for three reasons.Firstly,the atmospheric illumination in ASM is obtained from prior experience,which is not accurate for dehazing real-scene.Secondly,it is difficult to get the depth of outdoor scenes for ASM.Thirdly,the haze is a complex natural phenomenon,and it is difficult to find an accurate physical model and related parameters to describe this phenomenon.In this paper,we propose a black box method,in which the haze is considered an image quality problem without using any physical model such as ASM.Analytically,we propose a novel dehazing equation to combine two mechanisms:interference item and detail enhancement item.The interference item estimates the haze information for dehazing the image,and then the detail enhancement item can repair and enhance the details of the dehazed image.Based on the new equation,we design an antiinterference and detail enhancement dehazing network(AIDEDNet),which is dramatically different from existing dehazing networks in that our network is fed into the haze-free images for training.Specifically,we propose a new way to construct a haze patch on the flight of network training.The patch is randomly selected from the input images and the thickness of haze is also randomly set.Numerous experiment results show that AIDEDNet outperforms the state-of-the-art methods on both synthetic haze scenes and real-world haze scenes.
文摘Recently, compressive tracking （CT） has been widely proposed for its efficiency, accuracy and robustness on many challenging sequences. Its appearance model employs non-adaptive random projections that preserve the structure of the image feature space. A very sparse measurement matrix is used to extract features by multiplying it with the feature vector of the image patch. An adaptive Bayes classifier is trained using both positive samples and negative samples to separate the target from background. On the CT frame- work, however, some features used for classification have weak discriminative abilities, which reduces the accuracy of the strong classifier. In this paper, we present an online compressive feature selection algorithm（CFS） based on the CT framework. It selects the features which have the largest margin when using them to classify positive samples and negative samples. For features that are not selected, we define a random learning rate to update them slowly, It makes those weak classifiers preserve more target information, which relieves the drift when the appearance of the target changes heavily. Therefore, the classifier trained with those discriminative features couples its score in many challenging sequences, which leads to a more robust tracker. Numerous experiments show that our tracker could achieve superior result beyond many state-of-the-art trackers.
基金This paper was supported by the National Natural Science Foundation of China(Grant No.61472289)National Key Research and Development Project(2016YFC0106305).We also would like to thank the anonymous reviewers for their valuable and constructive comments.
文摘Hardware/software partitioning is an essential step in hardware/software co-design.For large size problems,it is difficult to consider both solution quality and time.This paper presents an efficient GPU-based parallel tabu search algorithm(GPTS)for HW/SW partitioning.A single GPU kernel of compacting neighborhood is proposed to reduce the amount of GPU global memory accesses theoretically.A kernel fusion strategy is further proposed to reduce the amount of GPU global memory accesses of GPTS.To further minimize the transfer overhead of GPTS between CPU and GPU,an optimized transfer strategy for GPU-based tabu evaluation is proposed,which considers that all the candidates do not satisfy the given constraint.Experiments show that GPTS outperforms state-of-the-art work of tabu search and is competitive with other methods for HW/SW partitioning.The proposed parallelization is significant when considering the ordinary GPU platform.
基金supported by the National Natural Science Foundation of China(Grant No.61472289)the National Key Research and Development Project(2016YFC0106305).
文摘In recent years,there are numerous works been proposed to leverage the techniques of deep learning to improve social-aware recommendation performance.In most cases,it requires a larger number of data to train a robust deep learning model,which contains a lot of parameters to fit training data.However,both data of user ratings and social networks are facing critical sparse problem,which makes it not easy to train a robust deep neural network model.Towards this problem,we propose a novel correlative denoising autoencoder(CoDAE)method by taking correlations between users with multiple roles into account to learn robust representations from sparse inputs of ratings and social networks for recommendation.We develop the CoDAE model by utilizing three separated autoencoders to learn user features with roles of rater,truster and trustee,respectively.Especially,on account of that each input unit of user vectors with roles of truster and trustee is corresponding to a particular user,we propose to utilize shared parameters to learn common information of the units that corresponding to same users.Moreover,we propose a related regularization term to learn correlations between user features that learnt by the three subnetworks of CoDAE model.We further conduct a series of experiments to evaluate the proposed method on two public datasets for Top-N recommendation task.The experimental results demonstrate that the proposed model outperforms state-of-the-art algorithms on rank-sensitive metrics of MAP and NDCG.
基金National Key R&D Program of China(2017YFB0503004)the National Natural Science Foundation of China(Grant No.62072348)+1 种基金China Postdoctoral Science Foundation(2019M662709)Natural Science Foundation of Hubei Province(2016FC0106305 and 2019CFB627).
文摘Multi-user collaborative editors are useful computer-aided tools to support human-to-human collaboration.For multi-user collaborative editors,selective undo is an essential utility enabling users to undo any editing operations at any time.Collaborative editors usually adopt operational transformation(OT)to address concurrency and consistency issues.However,it is still a great challenge to design an efficient and correct OT algorithm capable of handling both normal do operations and user-initiated undo operations because these two kinds of operations can interfere with each other in various forms.In this paper,we propose a semi-transparent selective undo algorithm that handles both do and undo in a unified framework,which separates the processing part of do operations from the processing part of undo operations.Formal proofs are provided to prove the proposed algorithm under the well-established criteria.Theoretical analysis and experimental evaluation are conducted to show that the proposed algorithm outperforms the prior OT-based selective undo algorithms.
