GLORIA

GEOMAR Library Ocean Research Information Access

X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Oxford University Press (OUP)  (6)
  • Mathematics  (6)
Material
Publisher
  • Oxford University Press (OUP)  (6)
Person/Organisation
Language
Years
Subjects(RVK)
RVK
  • 1
    Online Resource
    Online Resource
    An Attention Enhanced Cross-Modal Image–Sound Mutual Generation Model for Birds
    Oxford University Press (OUP) ; 2022
    In:  The Computer Journal Vol. 65, No. 2 ( 2022-02-14), p. 410-422
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 65, No. 2 ( 2022-02-14), p. 410-422
    Abstract: Cross-modal bird image–audio mutual generation has appealing potential benefits for bird classification. To achieve promising cross-modal bird visual–audio mutual generation, we propose an attention enhanced cross-modal cycle adversarial generation network. Specifically, the attention module endows our model with long-term intra-modality dependency and inter-modality dependency capabilities, which can provide more information during the generation process and further improve the generation performance. Moreover, because there was no dataset concerning bird visual–audio mutual generation, the authors established a novel bird cross-modal generation dataset, called Bird_Crossmodal_Generation (BCG). Based on BCG, our model obtains promising performance and achieves significant improvement under both inception score and Frechet inception distance criteria. The experimental results validate the feasibility of the proposed task and the superiority of our model. Additionally, this investigation provides a basis for more researchers to develop cross-modality methods for bird visual–audio generation.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxaa188
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1477172-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 2
    Online Resource
    Online Resource
    Visual Exploration of 3D Geospatial Networks in a Virtual Reality Environment
    Oxford University Press (OUP) ; 2018
    In:  The Computer Journal Vol. 61, No. 3 ( 2018-03-01), p. 447-458
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 61, No. 3 ( 2018-03-01), p. 447-458
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxx117
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 1477172-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 3
    Online Resource
    Online Resource
    Threshold Homomorphic Encryption From Provably Secure NTRU
    Oxford University Press (OUP) ; 2022
    In:  The Computer Journal ( 2022-09-11)
    Details
    In: The Computer Journal, Oxford University Press (OUP), ( 2022-09-11)
    Abstract: Homomorphic Encryption (HE) supports computation on encrypted data without the need to decrypt, enabling secure outsourcing of computing to an untrusted cloud. Motivated by application scenarios where private information is offered by different data owners, Multi-Key Homomorphic Encryption (MKHE) and Threshold Homomorphic Encryption (ThHE) were proposed. Unlike MKHE, ThHE schemes do not require expensive ciphertext extension procedures and are therefore as efficient as their underlying single-key HE schemes. In this work, we propose a novel NTRU-type ThHE scheme which caters to the computation scenarios with pre-defined participants. In addition to inheriting the simplicity of NTRU scheme, our construction has no expensive relinearization and correspondingly no costly evaluation keys. Controlling noise to make it increase linearly and then using a wide key distribution, our scheme is immune to the subfield lattice attacks and its security follows from the hardness of the standard R-LWE problem. Finally, based on the {0,1}-linear secret sharing and noise flooding techniques, we design a single round distributed threshold decryption protocol, where the decryption is able to be completed even when only given a subset (say $t$-out-of-$k$) of partial decryptions. To the best of our knowledge, our construction is the first NTRU-type ThHE scheme.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxac126
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1477172-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 4
    Online Resource
    Online Resource
    A Multiple QoS Metrics-Aware Virtual Network Embedding Algorithm
    Oxford University Press (OUP) ; 2023
    In:  The Computer Journal ( 2023-05-02)
    Details
    In: The Computer Journal, Oxford University Press (OUP), ( 2023-05-02)
    Abstract: As a key issue of network virtualisation, virtual network embedding (VNE) aims to embed multiple virtual network requests (VNRs) from different applications onto the substrate network effectively. In real networks, about 90% of traffic is generated by different quality of service (QoS) sensitive applications. However, most existing VNE algorithms do not account for the difference. Although several VNE algorithms considered the delay metric of applications, they usually provide strict delay guarantees for all VNRs, leading to a low VNR acceptance ratio. In this paper, we focus on the VNE problem involving multiple QoS metrics and propose a multiple QoS metrics-aware VNE algorithm based on reinforcement learning (RLQ-VNE). We first classify VNRs according to their different requirements for multiple QoS metrics including delay, jitter and packet loss rate, and then introduce reinforcement learning to implement differentiated VNE. Specifically, RLQ-VNE provides strict QoS guarantees for the VNRs with high-level QoS requirements and provides lower QoS guarantees for the VNRs with low-level QoS requirements, thus balancing the QoS guarantee and request acceptance ratio. Simulation results from multiple experimental scenarios show that RLQ-VNE improves the request acceptance ratio and network resource utilisation by sacrificing less QoS.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxad050
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1477172-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 5
    Online Resource
    Online Resource
    Practical Blockchain-Based Steganographic Communication Via Adversarial AI: A Case Study In Bitcoin
    Oxford University Press (OUP) ; 2022
    In:  The Computer Journal Vol. 65, No. 11 ( 2022-11-11), p. 2926-2938
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 65, No. 11 ( 2022-11-11), p. 2926-2938
    Abstract: With the development of 5G, the wireless Internet of Things (IoT) has become possible; how to provide privacy protections for the communication of IoT devices in a more vulnerable wireless transmission environment is a huge challenge. Thus, steganography is introduced as a safe and effective technology. Blockchain systems have been widely used in the area of steganography. Several works attempted to embed covert data into transactions in public blockchain systems such as Bitcoin, Ethereum and Monero. However, most of them merely focus on putting covert data into certain fields in transactions based on cryptographic algorithms. In this paper, a Covert Transaction Recognition (CTR) model is proposed by the Text Convolutional Neural Networks and Back Propagation Neural Networks. When utilizing the covert data-embedded field for recognizing, our CTR model can attain 0.79 precision and 0.83 recall on average for seven covert transaction construction schemes. The precision and recall can increase by at most 43 and 47%, respectively, if other unembedded fields were additionally exploited for recognition. We further propose a Practical Covert Transaction Construction (PCTC) model. This model fixes the contents in the embedded fields of the constructed transactions, and generates the contents in other fields using Generative Adversarial Networks. Experimental results demonstrated that the precision and recall are greatly decreased when identifying the covert transactions generated by our PCTC model. The data underlying this article are available in ‘covert-transaction-model’, at https://github.com/1997mint/covert-transaction-model.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxac090
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1477172-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
  • 6
    Online Resource
    Online Resource
    A Virtual Network Embedding Algorithm Based On Double-Layer Reinforcement Learning
    Oxford University Press (OUP) ; 2021
    In:  The Computer Journal Vol. 64, No. 6 ( 2021-06-19), p. 973-989
    Details
    In: The Computer Journal, Oxford University Press (OUP), Vol. 64, No. 6 ( 2021-06-19), p. 973-989
    Abstract: Virtual network embedding (VNE) algorithms dominate the effectiveness of resource sharing in network virtualization. Heuristic embedding algorithms generally make embedding decisions by artificially specified strategies, in which the node importance is measured by simply summing or multiplying several node attributes. However, the contributions of different attributes may be combined through complex functional relationships. The reinforcement learning-based VNE algorithms can optimize node embedding. However, the existing algorithms only consider the local node attributes, and only simple shortest path-based embedding policy is adopted for link embedding, resulting limited embedding effects. To overcome the above defects, we propose a double-layer reinforcement learning-based VNE algorithm (DRL-VNE). In DRL-VNE, both the global and local node attributes are extracted to represent the status of network nodes, then a policy network is constructed to optimize node embedding, and the other policy network is designed to optimize link embedding. The performance of DRL-VNE is evaluated under different network scenarios and is compared with that of heuristic and machine learning-based VNE algorithms. Simulation results show that in hierarchical network scenario, the request acceptance ratio and the resource utilization of DRL-VNE are respectively improved by 14% and by 27% compared with the best performance comparison algorithm.
    Type of Medium: Online Resource
    ISSN: 0010-4620 , 1460-2067
    URL: Article
    DOI: 10.1093/comjnl/bxab040
    RVK:
    SQ 1100
    RVK:
    SA 3740
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1477172-X
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...