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.

Distributed compressed video sensing (DCVS), combining advantages of compressed sensing and distributed video coding, is developed as a novel and powerful system to get an encoder with low complexity. Nevertheless, it is still unclear how to explore the method to achieve an effective video recovery through utilizing realistic signal characteristics as much as possible. Based on this, we present a novel spatiotemporal dictionary learning (DL) based reconstruction method for DCVS, where both the DL model and the l1-analysis based recovery with correlation constraints are included in the minimization problem to achieve the joint optimization of sparse representation and signal reconstruction. Besides, an alternating direction method with multipliers (ADMM) based numerical algorithm is outlined for solving the underlying optimization problem. Simulation results demonstrate that the proposed method outperforms other methods, with 0.03-4.14 dB increases in PSNR and a 0.13-15.31 dB gain for non-key frames.

Using traditional single-layer dictionary learning methods, it is difficult to reveal the complex structures hidden in the hyperspectral images. Motivated by deep learning technique, a deep dictionary learning approach is proposed for hyperspectral image denoising, which consists of hierarchical dictionary learning, feature denoising and fine-tuning. Hierarchical dictionary learning is helpful for uncovering the hidden factors in the spectral dimension, and fine-tuning is beneficial for preserving the spectral structure. Experiments demonstrate the effectiveness of the proposed approach.

Robust crater recognition is a research focus on deep space exploration mission, and sparse representation methods can achieve desirable robustness and accuracy. Due to destruction and noise incurred by complex topography and varied illumination in planetary images, a robust crater recognition approach is proposed based on dictionary learning with a low-rank error correction model in a sparse representation framework. In this approach, all the training images are learned as a compact and discriminative dictionary. A low-rank error correction term is introduced into the dictionary learning to deal with gross error and corruption. Experimental results on crater images show that the proposed method achieves competitive performance in both recognition accuracy and efficiency.

In a patch-based super-resolution algorithm, a low-resolution patch is influenced by surrounding patches due to blurring. We propose to remove this boundary effect by subtracting the blur from the surrounding high-resolution patches, which enables more accurate sparse representation. We demonstrate improved performance through experimentation. The proposed algorithm can be applied to most of patch-based super-resolution algorithms to achieve additional improvement.

A dictionary attack against SSH is a common security threat. Many methods rely on network traffic to detect SSH dictionary attacks because the connections of remote login, file transfer, and TCP/IP forwarding are visibly distinct from those of attacks. However, these methods incorrectly judge the connections of automated operation tasks as those of attacks due to their mutual similarities. In this paper, we propose a new approach to identify user authentication methods on SSH connections and to remove connections that employ non-keystroke based authentication. This approach is based on two perspectives: (1) an SSH dictionary attack targets a host that provides keystroke based authentication; and (2) automated tasks through SSH need to support non-keystroke based authentication. Keystroke based authentication relies on a character string that is input by a human; in contrast, non-keystroke based authentication relies on information other than a character string. We evaluated the effectiveness of our approach through experiments on real network traffic at the edges in four campus networks, and the experimental results showed that our approach provides high identification accuracy with only a few errors.

The concept of sparse representation is gaining momentum in image processing applications, especially in image compression, from last one decade. Sparse coding algorithms represent signals as a sparse linear combination of atoms of an overcomplete dictionary. Earlier works shows that sparse coding of images using learned dictionaries outperforms the JPEG standard for image compression. The conventional method of image compression based on sparse coding, though successful, does not adapting the compression rate based on the image local block characteristics. Here, we have proposed a new framework in which the image is classified into three classes by measuring the block activities followed by sparse coding each of the classes using dictionaries learned specific to each class. K-SVD algorithm has been used for dictionary learning. The sparse coefficients for each class are Huffman encoded and combined to form a single bit stream. The model imparts some rate-distortion attributes to compression as there is provision for setting a different constraint for each class depending on its characteristics. We analyse and compare this model with the conventional model. The outcomes are encouraging and the model makes way for an efficient sparse representation based image compression.

Hidden Credential Retrieval (HCR) protocols are designed for access credentials management where users who remember short passwords can retrieve his/her various credentials (access keys and tokens) with the help of a remote storage server over insecure networks (e.g., the Internet). In this paper, we revisit two HCR protocols, both of which are based on blind signature schemes: one (we call it B-HCR) was proposed in ASIACCS 2009 and the other (we call it MRS-HCR) was in WISA 2010. In particular, we show that the B-HCR protocol is insecure against an outside attacker who impersonates server S. Specifically, the attacker can find out the user's password pw with off-line dictionary attacks by eavesdropping the communications between the user and a third-party online service provider. Also, we show that the MRS-HCR protocol does not work correctly itself. In other words, user U can not retrieve the plaintext Msg (i.e., credentials) even if he/she has a knowledge of the password.

Although password-only authenticated key exchange (PAKE) in the three-party setting has been widely studied in recent years, it remains a challenging area of research. A key challenge in designing three-party PAKE protocols is to prevent insider dictionary attacks, as evidenced by the flaws discovered in many published protocols. In this letter, we revisit Abdalla and Pointcheval's three-party PAKE protocol from FC 2005 and demonstrate that this protocol, named 3PAKE, is vulnerable to a previously unpublished insider offline dictionary attack. Our attack is dependant on the composition of 3PAKE and the higher-level protocol that uses the established session key.

In this paper, we propose a representation method based on local spatial strokes for scene character recognition. High-level semantic information, namely co-occurrence of several strokes is incorporated by learning a sparse dictionary, which can further restrain noise brought by single stroke detectors. The encouraging results outperform state-of-the-art algorithms.

This paper tries to model spatial layout beyond the traditional spatial pyramid (SP) in the coding/pooling scheme for scene text character recognition. Specifically, we propose a novel method to build a dictionary called spatiality embedded dictionary (SED) in which each codeword represents a particular character stroke and is associated with a local response region. The promising results outperform other state-of-the-art algorithms.

In this letter, we exploit a new framework for detecting the non-specific object via combing the top-down and bottom-up cues. Specifically, a novel supervised discriminative dictionaries learning method is proposed to learn the coupled dictionaries for the object and non-object feature spaces in terms of the top-down cue. Different from previous dictionary learning methods, the new data reconstruction residual terms of coupled feature spaces, the sparsity penalty measures on the representations and an inconsistent regularizer for the learned dictionaries are all incorporated in a unitized objective function. Then we derive an iterative algorithm to alternatively optimize all the variables efficiently. Considering the bottom-up cue, the proposed discriminative dictionaries learning is then integrated with an unsupervised dictionary learning to capture the objectness windows in an image. Experimental results show that the non-specific object detection problem can be effectively solved by the proposed dictionary leaning framework and outperforms some established methods.

Cross-view action recognition is a challenging research field for human motion analysis. Appearance-based features are not credible if the viewpoint changes. In this paper, a new framework is proposed for cross-view action recognition by topic based knowledge transfer. First, Spatio-temporal descriptors are extracted from the action videos and each video is modeled by a bag of visual words (BoVW) based on the codebook constructed by the k-means cluster algorithm. Second, Latent Dirichlet Allocation (LDA) is employed to assign topics for the BoVW representation. The topic distribution of visual words (ToVW) is normalized and taken to be the feature vector. Third, in order to bridge different views, we transform ToVW into bilingual ToVW by constructing bilingual dictionaries, which guarantee that the same action has the same representation from different views. We demonstrate the effectiveness of the proposed algorithm on the IXMAS multi-view dataset.

An augmented PAKE (Password-Authenticated Key Exchange) protocol provides password-only authentication in the presence of an attacker, establishment of session keys between the involving parties, and extra protection for server compromise (i.e., exposure of password verification data). Among many augmented PAKE protocols, AMP variants (AMP2 [16] and AMP+ [15]) have been standardized in IEEE 1363.2 [9] and ISO/IEC 11770-4 [10]. In this paper, we thoroughly investigate APKAS-AMP (based on AMP2 [16]) and KAM3 (based on AMP+ [15]) which require several validity checks on the values, received and computed by the parties, when using a secure prime. After showing some attacks on APKAS-AMP and KAM3, we suggest new sanity checks that are clear and sufficient to prevent an attacker from doing these attacks.

Pieces of personal information, such as personal names and relationships, are crucial in text mining applications. Obituaries are good sources for this kind of information. This study proposes an effective method for extracting various facts about people from obituary Web pages. Experiments show that the proposed method achieves high performance in terms of recall and precision.

A shift of finite type (SFT) is a set of all bi-infinite sequences over some alphabet which is characterized by a finite set of forbidden words. It is a typical example of sofic shifts and has been used in media storage area, such as CD's or DVD's. The study of sofic shifts is based on graph theory, and the irreducibility of shifts is an important property to be considered for the study. In this paper, we will provide some sufficient conditions for an SFT to be irreducible from the perspective of the antidictionary of a word and the number of forbidden words. We also present a necessary and sufficient condition for an SFT to be irreducible when the number of forbidden words is one less than the alphabet size.

This paper presents a real-time and memory-efficient arrhythmia detection system with binary classification that uses antidictionary coding for the analysis and classification of electrocardiograms (ECGs). The measured ECG signals are encoded using a lossless antidictionary encoder, and the system subsequently uses the compression rate to distinguish between normal beats and arrhythmia. An automated training data procedure is used to construct the automatons, which are probabilistic models used to compress the ECG signals, and to determine the threshold value for detecting the arrhythmia. Real-time computer simulations with samples from the MIT-BIH arrhythmia database show that the averages of sensitivity and specificity of the proposed system are 97.8% and 96.4% for premature ventricular contraction detection, respectively. The automatons are constructed using training data and comprise only 11 kilobytes on average. The low complexity and low memory requirements make the system particularly suitable for implementation in portable ECG monitors.

A directional lapped orthogonal transform (DirLOT) is an orthonormal transform of which basis is allowed to be anisotropic with the symmetric, real-valued and compact-support property. Due to its directional property, DirLOT is superior to the existing separable transforms such as DCT and DWT in expressing diagonal edges and textures. The goal of this paper is to enhance the ability of DirLOT further. To achieve this goal, we propose a novel image restoration technique using multiple DirLOTs. This paper generalizes an image denoising technique in [1], and expands the application of multiple DirLOTs by introducing linear degradation operator P. The idea is to use multiple DirLOTs to construct a redundant dictionary. More precisely, the redundant dictionary is constructed as a union of symmetric orthonormal discrete wavelet transforms generated by DirLOTs. To select atoms fitting a target image from the dictionary, we formulate an image restoration problem as an l1-regularized least square problem, which can efficiently be solved by the iterative-shrinkage/thresholding algorithm (ISTA). The proposed technique is beneficial in expressing multiple directions of edges/textures. Simulation results show that the proposed technique significantly outperforms the non-subsampled Haar wavelet transform for deblurring, super-resolution, and inpainting.

In this paper, we present new important privacy goals for on-line matchmaking protocols, which are resistance to off-line dictionary attacks and forward privacy of users' identities and matching wishes. We enhance traditional privacy requirements (e.g., user anonymity, matching-wish authenticity) with our new privacy goals and define the notion of privacy-enhanced matchmaking. We show that previous solutions for on-line matchmaking do not satisfy the new privacy goals and argue that privacy-enhanced matchmaking cannot be provided by solutions to seemingly related problems such as secret handshakes, set intersection, and trust negotiation. We define an adversary model, which captures the key security properties of privacy-enhanced matchmaking, and show that a simple, practical protocol derived by a two-step transformation of a password-based authenticated key exchange counters adversary attacks in a provable manner (in the standard model of cryptographic security).

This paper presents a method for learning an overcomplete, nonnegative dictionary and for obtaining the corresponding coefficients so that a group of nonnegative signals can be sparsely represented by them. This is accomplished by posing the learning as a problem of nonnegative matrix factorization (NMF) with maximization of the incoherence of the dictionary and of the sparsity of coefficients. By incorporating a dictionary-incoherence penalty and a sparsity penalty in the NMF formulation and then adopting a hierarchically alternating optimization strategy, we show that the problem can be cast as two sequential optimal problems of quadratic functions. Each optimal problem can be solved explicitly so that the whole problem can be efficiently solved, which leads to the proposed algorithm, i.e., sparse hierarchical alternating least squares (SHALS). The SHALS algorithm is structured by iteratively solving the two optimal problems, corresponding to the learning process of the dictionary and to the estimating process of the coefficients for reconstructing the signals. Numerical experiments demonstrate that the new algorithm performs better than the nonnegative K-SVD (NN-KSVD) algorithm and several other famous algorithms, and its computational cost is remarkably lower than the compared algorithms.