This paper presents an efficient subverted symmetric encryption scheme, which outputs a random initialization vector (IV). Compared with the available scheme of the same kind in the literature, our attack provides a saboteur (big brother) with much faster recovery of a key used in a victim's symmetric encryption scheme. Our result implies that care must be taken when a symmetric encryption scheme with a random IV such as randomized CBC is deployed.

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In our previous work [12], [13], we introduced generalized feed-forward shift registers (GF²SR, for short) to apply them to secure and testable scan design. In this paper, we introduce another class of generalized shift registers called generalized feedback shift registers (GFSR, for short), and consider the properties of GFSR that are useful for secure scan design. We present how to control/observe GFSR to guarantee scan-in and scan-out operations that can be overlapped in the same way as the conventional scan testing. Testability and security of scan design using GFSR are considered. The cardinality of each class is clarified. We also present how to design strongly secure GFSR as well as GF²SR considered in [13].

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Nonnegative component representation (NCR) is a mid-level representation based on nonnegative matrix factorization (NMF). Recently, it has attached much attention and achieved encouraging result for action recognition. In this paper, we propose a novel hierarchical dictionary learning strategy (HDLS) for NMF to improve the performance of NCR. Considering the variability of action classes, HDLS clusters the similar classes into groups and forms a two-layer hierarchical class model. The groups in the first layer are disjoint, while in the second layer, the classes in each group are correlated. HDLS takes account of the differences between two layers and proposes to use different dictionary learning methods for this two layers, including the discriminant class-specific NMF for the first layer and the discriminant joint dictionary NMF for the second layer. The proposed approach is extensively tested on three public datasets and the experimental results demonstrate the effectiveness and superiority of NCR with HDLS for large-scale action recognition.

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This paper proposes a novel object tracking method via online boosting. The on-line boosting technique is combined with local features to treat tracking as a keypoint matching problem. First, We improve matching reliability by exploiting the statistical repeatability of local features. In addition, we propose 2D scale-rotation invariant quasi-keypoint matching to further improve matching efficiency. Benefiting from SURF feature's statistical repeatability and the complementary quasi-keypoint matching technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experimental results show that the proposed method achieves better performance compared with previously reported trackers.

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This work presents an object tracking framework which is based on integration of Deformable Part based Models (DPMs) and Dynamic Conditional Random Fields (DCRF). In this framework, we propose a DCRF based novel way to track an object and its details on multiple resolutions simultaneously. Meanwhile, we tackle drastic variations in target appearance such as pose, view, scale and illumination changes with DPMs. To embed DPMs into DCRF, we design specific temporal potential functions between vertices by explicitly formulating deformation and partial occlusion respectively. Furthermore, temporal transition functions between mixture models bring higher robustness to perspective and pose changes. To evaluate the efficacy of our proposed method, quantitative tests on six challenging video sequences are conducted and the results are analyzed. Experimental results indicate that the method effectively addresses serious problems in object tracking and performs favorably against state-of-the-art trackers.

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Recently, a high dimensional classification framework has been proposed to introduce spatial and anatomical priors in classical single kernel support vector machine optimization scheme, wherein the sequential minimal optimization (SMO) training algorithm is adopted, for brain image analysis. However, to satisfy the optimization conditions required in the single kernel case, it is unreasonably assumed that the spatial regularization parameter is equal to the anatomical one. In this letter, this approach is improved by combining SMO algorithm with multiple kernel learning to avoid that assumption and optimally estimate two parameters. The improvement is comparably demonstrated by experimental results on classification of Alzheimer patients and elderly controls.

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