Information sharing has become a vital part in our day-to-day life due to the pervasiveness of Internet technology. In any given collaboration, information needs to flow from one participant to another. While particip...
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ISBN:
(纸本)9781841023755
Information sharing has become a vital part in our day-to-day life due to the pervasiveness of Internet technology. In any given collaboration, information needs to flow from one participant to another. While participants may be interested in sharing information with one another, it is often necessary for them to establish the impact of sharing certain kinds of information. This is because certain information could have detrimental effects when it ends up in wrong hands. For this reason, any would-be participant in a given collaboration may need to establish the guarantees that the collaboration provides, in terms of protecting sensitive information, before joining the collaboration as well as evaluating the impact of sharing a given piece of information with a given set of entities. In order to address this issue, earlier work introduced a trust domains taxonomy that aims at managing trust-related issues in information sharing. This paper attempts to empirically investigate the proposed taxonomy through a possible scenario (e.g. the ConfiChair system). The study results determined that Role, Policy, Action, Control, Evidence and Asset elements should be incorporated into the taxonomy for securely sharing information among others. Additionally, the study results showed that the ConfiChair, a novel cloud-based conference management system, offers strong privacy and confidentiality guarantees.
The aim of this work is to compare dynamic and static methods of measurement for the determination of Young's modulus (E) and the internal stress (/spl sigma/) caused by fabrication of micromachined structures. Th...
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The aim of this work is to compare dynamic and static methods of measurement for the determination of Young's modulus (E) and the internal stress (/spl sigma/) caused by fabrication of micromachined structures. These data are essential far accurate modelling of resonant microstructures. Quoted values of E for boron doped silicon are between 122 and 220 Gpa for the [110] direction, 131 Gpa for the [100] direction and between 90 and 190 Gpa for polysilicon. For other materials, such as silicon dioxide and silicon nitride, there is also uncertainty. For these films we have used both resonant and indent type tests to attempt to evaluate E. We wish to ultimately measure both E for all these materials and also the pre-stress /spl sigma/ caused by fabrication of clamped structures. Comparison is made between the results of dynamic tests, in which the resonant frequencies were measured accurately for transverse modes up to about 200 kHz, and static tests in which the point compliance was determined. Measurements were made on two bridges. Both were 200 /spl mu/m wide and about 3 /spl mu/m thick. One had a length of 2.2 mm and the other was 3.6 mm long.
With the increase in number of vehicles, the requirement of intelligent parking management is indispensable in smart cities. One of the major requirements in smart parking system is handling parking violations efficie...
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In this paper the results of a study of the dynamic characteristics of thin-walled composite curved viscoelastic pipes under the influence of internal pulsating pressures is presented. The relationship between stress ...
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Although Graph Convolutional Networks (GCNs) have made significant progress in healthcare fraud detection, they still face challenges such as severe data imbalance and noise intentionally introduced by fraudsters. To ...
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ISBN:
(数字)9798331506582
ISBN:
(纸本)9798331506599
Although Graph Convolutional Networks (GCNs) have made significant progress in healthcare fraud detection, they still face challenges such as severe data imbalance and noise intentionally introduced by fraudsters. To address these issues, this study aims to solve the data imbalance problem by maxi-mizing the Area Under the ROC Curve (AUC). To mitigate the impact of noise deliberately introduced by fraudsters on the topological structure, this research enhances the performance of GCNs by incorporating a pruning algorithm. A Deep Q-Network (DQN) is employed to search for an optimal pruning strategy, reducing the influence of noisy edges on the AUC and improving the GCN's performance in node embedding and classification tasks. Experimental results demonstrate that the EP-GCN (Edge-Pruning GCN) achieves strong performance in terms of AUC and other evaluation metrics across multiple datasets.
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