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Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods. 相似文献
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针对云系统服务器之间共享策略文件所面临的完整性安全威胁,提出一种新的基于固件的策略安全机制。该机制可在云系统服务器平台之间安全且快速地进行共享策略文件交互,并可对共享策略文件进行完整性校验,进而提高协同工作的服务器所共享策略文件的安全性。实验结果表明,该机制有效实现了云系统环境下共享策略文件的完整性安全,同时提高了云系统管理员的管理效率。 相似文献
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A wireless sensor network (WSN) commonly whilst a body sensor network (BSN) must be secured with requires lower level security for public information gathering, strong authenticity to protect personal health information. In this paper, some practical problems with the message authentication codes (MACs), which were proposed in the popular security architectures for WSNs, are reconsidered. The analysis shows that the recommended MACs for WSNs, e.g., CBC- MAC (TinySec), OCB-MAC (MiniSec), and XCBC-MAC (SenSee), might not be exactly suitable for BSNs. Particularly an existential forgery attack is elaborated on XCBC-MAC. Considering the hardware limitations of BSNs, we propose a new family of tunable lightweight MAC based on the PRESENT block cipher. The first scheme, which is named TukP, is a new lightweight MAC with 64-bit output range. The second scheme, which is named TuLP-128, is a 128-bit variant which provides a higher resistance against internal collisions. Compared with the existing schemes, our lightweight MACs are both time and resource efficient on hardware-constrained devices. 相似文献
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