It is well known that each Public Key Infrastructure (PKI) system forms a closed security domain and only recognizes certificates in its own domain (such as medical systems, financial systems, and 5G networks). When users need to access services in other domains, their identities often cannot be recognized or PKI systems require extremely complex operations to authenticate the users' identities. This is the cross-domain authentication problem. The distributed consensus feature of blockchain provides a technical approach to solve this problem. However, there are some unresolved problems in existing blockchain-based schemes. On one hand, due to the low throughput of blockchain systems, the response speed may be insufferable when the number of cross-domain authentication requirements becomes enormous. On the other hand, these schemes insufficiently consider the privacy risk in the cross-domain scenario. In this article, we propose an efficient privacy-preserving cross-domain authentication scheme called XAuth that is integrated naturally with the existing PKI and Certificate Transparency (CT) systems. Specifically, we design a lightweight correctness verification protocol based on Multiple Merkle Hash Tree for rapid response. To protect users' privacy, we present an anonymous authentication protocol for cross-domain authentication. The security analysis and experimental results demonstrate that XAuth is secure and efficient.
We propose a new method for finding similarity in 3-D protein structure comparison. Different from the other existing methods, our method is grounded in the theory of fractal geometry. The proposed feature vectors of protein structures are simple to implement. The method is very fast because it requires neither alignment of the chains nor any chain-chain comparison. We calculate the fractal features of a set of 200 protein structures selected from PDB(Protein Data Bank). The experimental result shows that our method is very effective in classification of 3-D protein structures.
The performance of modern computer system is greatly limited by the bandwidth of DRAM-based memory. Altering the sequence of main memory accesses can reduce observed access latency, therefore improve bus utilization. While previous reordering mechanisms consider factors related to memory access separately, this paper groups several factors together to build a priority expression for bank arbitration based on burst scheduling. The expression considers three factors: wait time of a burst, burst length, and priority of read or write accesses. To make the expression suitable for both read and write accesses, write queue in a bank is designed to buffer bursts, which are defined to be clusters of row hits, other than single write accesses. Experiment results from a modified M5 simulator running selected SPEC CPU2000 and Stream benchmarks show that the priority-expression-based burst scheduling improves the bus utilization about 74% and reduces the execution time 41% over the conventional in-order memory scheduling. It also outperforms burst scheduling 9% and 5% in bus utilization and execution time reduction respectively. The priority-expression-based burst scheduling is proved to be feasible.
Conventional detection networks usually need abundant labeled training samples, while humans can learn new concepts incrementally with just a few examples. This paper focuses on a more challenging but realistic class-incremental few-shot object detection problem (iFSD). It aims to incrementally transfer the model for novel objects from only a few annotated samples without catastrophically forgetting the previously learned ones. To tackle this problem, we propose a novel method LEAST, which can transfer with Less forgetting, fEwer training resources, And Stronger Transfer capability. Specifically, we first present the transfer strategy to reduce unnecessary weight adaptation and improve the transfer capability for iFSD. On this basis, we then integrate the knowledge distillation technique using a less resource-consuming approach to alleviate forgetting and propose a novel clustering-based exemplar selection process to preserve more discriminative features previously learned. Being a generic and effective method, LEAST can largely improve the iFSD performance on various benchmarks.
Zero-shot detection (ZSD) aims to locate and recognize unseen objects in pictures or videos to address the shortage of labeled training data. While most existing ZSD methods lay their emphasis on object classification, challenges lie in both object proposal and category prediction for detectors to get over domain shift. In this paper, we first design an experiment to verify the impact of transfer ability of the object proposal step on detection recall and further introduce a transferable mechanism to relate the co-occurrence among categories. We use a confidence distribution over all the classes for object confidence prediction. Experimental results show our method outperforms other zero-shot detectors on PASCAL VOC and MSCOCO datasets, even with a simple linear approach for classification.
General zero-shot learning (ZSL) approaches exploit transfer learning via semantic knowledge space. In this paper, we reveal a novel relational knowledge transfer (RKT) mechanism for ZSL, which is simple, generic and effective. RKT resolves the inherent semantic shift problem existing in ZSL through restoring the missing manifold structure of unseen categories via optimizing semantic mapping. It extracts the relational knowledge from data manifold structure in semantic knowledge space based on sparse coding theory. The extracted knowledge is then transferred backwards to generate virtual data for unseen categories in the feature space. On the one hand, the generalizing ability of the semantic mapping function can be enhanced with the added data. On the other hand, the mapping function for unseen categories can be learned directly from only these generated data, achieving inspiring performance. Incorporated with RKT, even simple baseline methods can achieve good results. Extensive experiments on three challenging datasets show prominent performance obtained by RKT, and we obtain 82.43% accuracy on the Animals with Attributes dataset.
Zero-shot recognition aims to accurately recognize objects of unseen classes by using a shared visual-semantic mapping between the image feature space and the semantic embedding space. This mapping is learned on training data of seen classes and is expected to have transfer ability to unseen classes. In this paper, we tackle this problem by exploiting the intrinsic relationship between the semantic space manifold and the transfer ability of visual-semantic mapping. We formalize their connection and cast zero-shot recognition as a joint optimization problem. Motivated by this, we propose a novel framework for zero-shot recognition, which contains dual visual-semantic mapping paths. Our analysis shows this framework can not only apply prior semantic knowledge to infer underlying semantic manifold in the image feature space, but also generate optimized semantic embedding space, which can enhance the transfer ability of the visual-semantic mapping to unseen classes. The proposed method is evaluated for zero-shot recognition on four benchmark datasets, achieving outstanding results.
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