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  • Wiley  (10)
  • Mathematics  (10)
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  • Wiley  (10)
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  • Mathematics  (10)
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  • 1
    Online Resource
    Online Resource
    Wiley ; 2022
    In:  Software: Practice and Experience Vol. 52, No. 1 ( 2022-01), p. 115-129
    In: Software: Practice and Experience, Wiley, Vol. 52, No. 1 ( 2022-01), p. 115-129
    Abstract: The risk of sharing data in cloud computing has gathered increasing attention. After the owner of some confidential data outsources the data to cloud storage services and shares it with others, the data owner lost the control to the data to a large extent. To achieve data sharing while keeping data confidentiality, attribute‐based encryption (ABE) can be employed by cloud storage services. However, ABE can only guarantee that outsourced data on the cloud is decrypted by attribute‐satisfying users but cannot restrict data from being accessed by dishonest users whose attributes also satisfy the access‐control policy. It is impossible for the data owner to control the shared data after it has been decrypted by dishonest users, especially when a set of attribute‐satisfying dishonest users may collude. To address this concern, we propose a traceable data sharing scheme called TraceChain. In TraceChain, data is encrypted over a new CP‐ABE scheme called E‐CP‐ABE. Furthermore, the system parameters for generating the private key in E‐CP‐ABE are uploaded to the private blockchain and transactions are performed on the chain. The data owner can obtain the identity of users by monitoring system parameters simultaneously and control data sharing on the blockchain. To prove the security of our scheme, the security analysis is given in this paper. Meanwhile, experimental results also show that our system is viable and efficient.
    Type of Medium: Online Resource
    ISSN: 0038-0644 , 1097-024X
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 120252-2
    detail.hit.zdb_id: 1500326-7
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  • 2
    Online Resource
    Online Resource
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience
    In: Concurrency and Computation: Practice and Experience, Wiley
    Abstract: In hierarchical classification learning, the feature space of data has high dimensionality and is unknown with emergent features. To solve the above problems, we propose an online hierarchical feature selection algorithm based on adaptive ReliefF. Firstly, ReliefF is adaptively improved via using the density information of instances around the target sample, making it unnecessary to prespecify parameters. Secondly, the hierarchical relationship between classes is used, and a new method for calculating the feature weight of hierarchical data is defined. Then, an online correlation analysis method based on feature interaction is designed. Finally, the adaptive ReliefF algorithm is improved based on feature redundancy, and the feature weight is scaled by the correlation between features in order to achieve the dynamic updating of feature redundancy. A large number of experiments verify the effectiveness of the proposed algorithm.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 3
    Online Resource
    Online Resource
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 4 ( 2023-02-15)
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 4 ( 2023-02-15)
    Abstract: The construction of health indicators of rolling bearing is significant for the monitoring of its health condition and residual life prediction. The main work is based on the frequency band sparse optimization algorithm in the rolling bearing health index. On the one hand, the index construction method based on machine learning model has poor interpretability; on the other hand, there are many interference components in the original vibration signal, extracting statistical features directly based on the original signal as a poor health indicator. For the above problems, this paper proposes a rolling bearing health index construction method based on sparse frequency band characterization. First, the signal including each independent sub‐band has been obtained by wavelet packet decomposition. Second, a sparse model is constructed by combining the sub‐band amplitudes and matrices decomposed by the wavelet packet in the fault state and the normal state, so as to select the frequency band sensitive to the fault information. Thereafter, the spectral amplitude sum of sensitive frequency band (SASSFB) is calculated as a health indicator that reflects the degradation information during the complete life operation of the rolling bearing. Finally, the effectiveness of the method is verified by utilizing the published bearing accelerated life experimental data set.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 4
    In: Concurrency and Computation: Practice and Experience, Wiley
    Abstract: Multi‐organ segmentation is a critical prerequisite for many clinical applications. Deep learning‐based approaches have recently achieved promising results on this task. However, they heavily rely on massive data with multi‐organ annotated, which is labor‐ and expert‐intensive and thus difficult to obtain. In contrast, single‐organ datasets are easier to acquire, and many well‐annotated ones are publicly available. It leads to the partially labeled issue: How to learn a unified multi‐organ segmentation model from several single‐organ datasets? Pseudo‐label‐based methods and conditional information‐based methods make up the majority of existing solutions, where the former largely depends on the accuracy of pseudo‐labels, and the latter has a limited capacity for task‐related features. In this paper, we propose the Conditional Dynamic Attention Network (CDANet). Our approach is designed with two key components: (1) multisource parameter generator, fusing the conditional and multiscale information to better distinguish among different tasks, and (2) dynamic attention module, promoting more attention to task‐related features. We have conducted extensive experiments on seven partially labeled challenging datasets. The results show that our method achieved competitive results compared with the advanced approaches, with an average Dice score of 75.08%. Additionally, the Hausdorff Distance is 26.31, which is a competitive result.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 5
    Online Resource
    Online Resource
    Wiley ; 2011
    In:  Communications on Pure and Applied Mathematics Vol. 64, No. 4 ( 2011-04), p. 556-590
    In: Communications on Pure and Applied Mathematics, Wiley, Vol. 64, No. 4 ( 2011-04), p. 556-590
    Type of Medium: Online Resource
    ISSN: 0010-3640
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 1468142-0
    detail.hit.zdb_id: 1568-4
    detail.hit.zdb_id: 220318-2
    SSG: 17,1
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  • 6
    Online Resource
    Online Resource
    Wiley ; 2011
    In:  Communications on Pure and Applied Mathematics Vol. 64, No. 9 ( 2011-09), p. 1297-1304
    In: Communications on Pure and Applied Mathematics, Wiley, Vol. 64, No. 9 ( 2011-09), p. 1297-1304
    Type of Medium: Online Resource
    ISSN: 0010-3640
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2011
    detail.hit.zdb_id: 1468142-0
    detail.hit.zdb_id: 1568-4
    detail.hit.zdb_id: 220318-2
    SSG: 17,1
    Location Call Number Limitation Availability
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  • 7
    Online Resource
    Online Resource
    Wiley ; 2017
    In:  Communications on Pure and Applied Mathematics Vol. 70, No. 6 ( 2017-06), p. 1115-1145
    In: Communications on Pure and Applied Mathematics, Wiley, Vol. 70, No. 6 ( 2017-06), p. 1115-1145
    Abstract: We consider the evolution of two incompressible, immiscible fluids with different densities in porous media, known as the Muskat problem [21], which in two dimensions is analogous to the Hele‐Shaw cell [24] . We establish, for a class of large and monotone initial data, the global existence of weak solutions. The proof is based on a local well‐posedness result for the initial data with certain specific asymptotics at spatial infinity and a new maximum principle for the first derivative of the graph function.© 2016 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0010-3640 , 1097-0312
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2017
    detail.hit.zdb_id: 1468142-0
    detail.hit.zdb_id: 1568-4
    detail.hit.zdb_id: 220318-2
    SSG: 17,1
    Location Call Number Limitation Availability
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  • 8
    Online Resource
    Online Resource
    Wiley ; 2019
    In:  Communications on Pure and Applied Mathematics Vol. 72, No. 10 ( 2019-10), p. 2063-2120
    In: Communications on Pure and Applied Mathematics, Wiley, Vol. 72, No. 10 ( 2019-10), p. 2063-2120
    Abstract: This paper studies the inviscid limit of the two‐dimensional incompressible viscoelasticity, which is a system coupling a Navier‐Stokes equation with a transport equation for the deformation tensor. The existence of global smooth solutions near the equilibrium with a fixed positive viscosity was known since the work of [35]. The inviscid case was solved recently by the second author [28] . While the latter was solely based on the techniques from the studies of hyperbolic equations, and hence the two‐dimensional problem is in general more challenging than that in higher dimensions, the former was relied crucially upon a dissipative mechanism. Indeed, after a symmetrization and a linearization around the equilibrium, the system of the incompressible viscoelasticity reduces to an incompressible system of damped wave equations for both the fluid velocity and the deformation tensor. These two approaches are not compatible. In this paper, we prove global existence of solutions, uniformly in both time t  ∈ [0, +∞) and viscosity μ  ≥ 0 . This allows us to justify in particular the vanishing viscosity limit for all time. In order to overcome difficulties coming from the incompatibility between the purely hyperbolic limiting system and the systems with additional parabolic viscous perturbations, we introduce in this paper a rather robust method that may apply to a wide class of physical systems of similar nature. Roughly speaking, the method works in the two‐dimensional case whenever the hyperbolic system satisfies intrinsically a “strong null condition.” For dimensions not less than three, the usual null condition is sufficient for this method to work. © 2019 Wiley Periodicals, Inc.
    Type of Medium: Online Resource
    ISSN: 0010-3640 , 1097-0312
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2019
    detail.hit.zdb_id: 1468142-0
    detail.hit.zdb_id: 1568-4
    detail.hit.zdb_id: 220318-2
    SSG: 17,1
    Location Call Number Limitation Availability
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  • 9
    Online Resource
    Online Resource
    Wiley ; 2023
    In:  Concurrency and Computation: Practice and Experience Vol. 35, No. 21 ( 2023-09-25)
    In: Concurrency and Computation: Practice and Experience, Wiley, Vol. 35, No. 21 ( 2023-09-25)
    Abstract: Label distribution learning (LDL) is an emerging learning paradigm, which can be used to solve the label ambiguity problem. In spite of the recent great progress in LDL algorithms considering label correlations, the majority of existing methods only measure pairwise label correlations through the commonly used similarity metric, which is incapable of accurately reflecting the complex relationship between labels. To solve this problem, a novel label distribution learning method—based on high‐order label correlations (LDL‐HLC) is proposed. By virtue of the ‐regularization sparse reconstruction of the label space, the high‐order label correlations matrix is firstly obtained. Then, a new regular term can be constructed to fit the final prediction label distribution via the correction matrix. Furthermore, efficient classification performance and complete feature selection are guaranteed by common features learning via ‐regularization. Finally, the performance and effectiveness of the proposed algorithm are well illustrated through extensive experiments on 14 label distribution datasets and comparisons with some existing algorithms.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    URL: Issue
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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  • 10
    In: Concurrency and Computation: Practice and Experience, Wiley
    Abstract: Multi‐label feature selection eliminates irrelevant and redundant features, and then improves the performance of multi‐label classification models. Most multi‐label feature selection algorithms assume that the training set contains logical labels, which means that labels are equally important for instances. However, in practical applications, there are different importances with respect to labels. To solve the problem, a multi‐label feature selection method based on relative entropy and fuzzy neighborhood mutual discriminant index is proposed. Firstly, logical labels are converted to label distribution through label enhancement. Secondly, the neighborhood and relative entropy are introduced into the label distribution, the label neighborhood similarity matrix is constructed to describe the similarity of samples under label space. Finally, the fuzzy neighborhood mutual discrimination index is used to combine the candidate features with the label neighborhood similarity matrix, which is used to judge the distinguishing ability of the candidate features. Comprehensive experiment of eight multi‐label datasets shows that the proposed algorithm has better classification performance than other compared algorithms.
    Type of Medium: Online Resource
    ISSN: 1532-0626 , 1532-0634
    RVK:
    Language: English
    Publisher: Wiley
    Publication Date: 2023
    detail.hit.zdb_id: 2052606-4
    SSG: 11
    Location Call Number Limitation Availability
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