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Linear representation based methods have been extensively applied in image recognition, especially for those with noise, illumination changes, and occlusions. However, most existing methods assume a specific distribution for image noise estimation, which is intractable to handle complex variations. Besides, they usually use convex norm to describe the noise sparse and low-rank property, and it is a biased approximation. To address these problems, we propose a novel nonconvex regularized robust mixed error coding (NRRM) method, which uses mixed norms from both 1D and 2D perspectives to model the complex image noise without convex relaxation. In specific, we use weighted ℓ 2 -norm based robust coding to characterize the sparse noise in images, and weighted matrix nuclear norm to characterize the low-rank noise. Compared with traditional regression approaches, our method can more fine-grained and accurate to capture noise and alleviate its negative influence for robust recognition. Besides, we constrain the representation component in a group-wise manner to weigh the roles of different classes. The NRRM model is solved efficiently by adopting an alternating direction method of multipliers (ADMM) algorithm. Comprehensive experiments on some benchmark face image databases validate the superiority of NRRM over several state-of-the-art linear representation based methods. [ABSTRACT FROM AUTHOR]

Modern industrial computer systems should be reliable, noise-immune, high-speed, and error-free which is difficult due to noise and industrial interferences. In the case of single additive errors, bit insertion/dropping can occur in the outputs of demodulators which implies long error packets which are hard to correct. To provide highly reliable data transmission, parameters of bit errors exemplified by the length of insertion/dropping and position of bits have to be evaluated but widely used methods for detecting and evaluating bit errors do not yield such parameters. In this paper, two new methods are proposed as a solution to this problem for the estimation of bit error parameters under single additive errors in which the difference between two positions of clock elements in the reference sequence and the received pseudorandom sequence is calculated. The new method, in addition to algorithmic effectiveness and efficiency, makes it also possible to reduce the hardware complexity of devices to be implemented for detecting and estimating the bit error parameters. Specifically, when implemented on a specialized programmable signal processor the new approach yields the computational complexity as ∼200 + 27 T clock cycles. [ABSTRACT FROM AUTHOR]

A text written using symbols from a given alphabet can be compressed using the Huffman code, which minimizes the length of the encoded text. It is necessary, however, to employ a text-specific codebook, i.e. the symbol-codeword dictionary, to decode the original text. Thus, the compression performance should be evaluated by the full code length, i.e. the length of the encoded text plus the length of the codebook. We studied several alphabets for compressing texts – letters, n -grams of letters, syllables, words, and phrases. If only sufficiently short texts are retained, an alphabet of letters or two-grams of letters is optimal. For the majority of Project Gutenberg texts, the best alphabet (the one that minimizes the full code length) is given by syllables or words, depending on the representation of the codebook. Letter 3 and 4-grams, having on average comparable length to syllables/words, perform noticeably worse than syllables or words. Word 2-grams also are never the best alphabet, on the account of having a very large codebook. We also show that the codebook representation is important – switching from a naive representation to a compact one significantly improves the matters for alphabets with large number of symbols, most notably the words. Thus, meaning-expressing elements of the language (syllables or words) provide the best compression alphabet. [ABSTRACT FROM AUTHOR]

Code mapping (CM) is an efficient technique for reversible data hiding (RDH) in JPEG images, which embeds data by constructing a mapping relationship between the used and unused codes in the JPEG bitstream. This study presents a new framework for designing a CM-based RDH method. First, a new code mapping strategy is proposed to suppress file size expansion and improve applicability. Based on our proposed strategy, the mapped codes are redefined by creating a new Huffman table rather than selecting them from the unused codes in the original Huffman table. The critical issue of designing the CM-based RDH method, that is, constructing code mapping, is converted into a combinatorial optimization problem. This study proposes a novel CM-based RDH method that utilizes a genetic algorithm (GA). The experimental results demonstrate that the proposed method achieves a high embedding capacity with no signal distortion while suppressing file size expansion. [ABSTRACT FROM AUTHOR]

Interval-valued data describe the random phenomenon that abounds in the real world, a pivotal research orientation in uncertainty processing. With the rapid development of big data, we may gather information from multiple information sources. To effectively acquire knowledge from multiple information sources, information fusion is commonly used to get a unified representation. However, sometimes data gathered from multiple sources may be lost; it is meaningful and necessary to study the fusion of multi-source incomplete interval-valued data. We propose a novel information fusion method based on information entropy for multi-source incomplete interval-valued data and four incremental fusion mechanisms characterized by the change in information sources and attributes. The corresponding static and dynamic fusion algorithms are designed, and their time complexities are analyzed. Experimental results show that the proposed method outperforms the mean, max, and min fusion methods. Furthermore, the four incremental fusion mechanisms reduced the runtime compared with the static fusion mechanism. [ABSTRACT FROM AUTHOR]

Researchers at Xi'an Jiaotong University have conducted a study on optimizing the DEFLATE algorithm for compressing 3D image models. The DEFLATE algorithm, which combines the LZ77 algorithm and Huffman coding, is commonly used for compressing multimedia data and 3D models. The study found that the DEFLATE algorithm performed exceptionally well in compressing small and medium-sized 3D models, achieving higher compression ratios and shorter compression times compared to other algorithms. While the compression ratio for large 3D models was comparable to other algorithms, the DEFLATE algorithm significantly reduced the compression time. The research provides valuable insights for enhancing and optimizing the DEFLATE algorithm and serves as a reference for future research on 3D image model compression. [Extracted from the article]

Contemporary cloud storage and social media networks have matured and; as a result, the privacy protection of image content in these applications have attracted more attention. R eversible d ata h iding in e ncrypted i mages (RDHEI) is an efficient technique to embed additional data such as timestamps, watermarks, and copyright information into encrypted images to protect the image content. This paper proposes a reversible data hiding scheme in encrypted images based on a b lock-based a daptive M SB e ncoding technique (BAME-RDHEI). The content owner first uses a block-based image encryption method, including block permutation, pixel permutation in each block, and the block-based bitwise exclusive-or operation, to encrypt an image. This specific image encryption method preserves the relevance of the MSB bit planes in each block, and the block-based adaptive MSB encoding technique can be applied to classify and encode eight block types according to the number of MSB bit planes where all the values are all '1' or '0'. After embedding the indicators that are generated by Huffman coding into the first MSB bit plane of each block, the remaining values of these MSB bit planes can be vacated for data embedding. The receiver can extract additional data, decrypted the image or recover the original image according to the different keys. The experimental results demonstrate that our proposed scheme provides enhanced security, significantly improves the embedding rate, and obtains higher visual quality for the decrypted image compared to other state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach of supervised feature selection is proposed based on the principle of dense subgraph discovery. To exploit dense subgraph discovery for the purpose of feature selection, the dataset is initially mapped to an equivalent weighted graph notation by considering the set of all features as its vertex set and the mutual dependency between each pair of features as the weight of the corresponding edge. The proposed feature selection algorithm proceeds in a two-phase manner. In the first stage, a dense sub-graph is first discovered so that the features within it become maximally non-redundant among each other and the averaged class relevance as well as averaged standard deviation of all these features are obtained as maximal as possible. In this regard, a novel induced degree is also defined for each feature by incorporating the aforesaid three important objectives of feature selection. In this phase, a modified version of an existing approximation algorithm is also used to find dense subgraph module. Finally, in the second stage, a floating forward–backward search is performed on the dense subgraph so obtained to reveal a better feature subset. In both stages, an existing version of the normalized mutual information score is employed to compute both the class relevance and redundancy. The main contribution of this paper is proposing a feature selection strategy by which the reduced features have the characteristics like maximal average class relevance, minimal average pairwise redundancy, and good discriminating power. The experimental results demonstrate that the proposed approach is competent with several conventional as well as state-of-art algorithms of supervised feature selection. [ABSTRACT FROM AUTHOR]

April 7, 2020 In the mass tort arena, removing a case to federal court can make all the difference, especially when a defense like federal preemption is at issue. Removal Basics Federal courts are courts of limited jurisdiction and, as such, are empowered to hear only cases they have been authorized to hear by Congress. State court trial order: an order-but not a tentative ruling-in the state court after the first remand that may establish jurisdiction. [Extracted from the article]

Combining traditional diversity and re-balancing techniques serves to design effective ensembles for solving imbalanced classification problems. Therefore, to explore the performance of new diversification procedures and new re-balancing methods is an attractive research subject which can provide even better performances. In this contribution, we propose to create ensembles of the recently introduced binary Bayesian classifiers, that show intrinsic re-balancing capacities, by means of a diversification mechanism which is based on applying different cost policies to each ensemble learner as well as appropriate aggregation schemes. Experiments with an extensive number of representative imbalanced datasets and their comparison with those of several selected high-performance classifiers show that the proposed approach provides the best overal results. [ABSTRACT FROM AUTHOR]

The computation of moments of probabilistic finite-state automata (PFA) is researched in this article. First, the computation of moments of the length of the paths is introduced for general PFA, and then, the computation of moments of the number of times that a symbol appears in the strings generated by the PFA is described. These computations require a matrix inversion. Acyclic PFA, such as word graphs, are quite common in many practical applications. Algorithms for the efficient computation of the moments for acyclic PFA are also presented in this paper. [ABSTRACT FROM AUTHOR]

• A binary pattern based descriptor is proposed to LLNA. • LLNA is a network automata for network pattern recognition. • The binary pattern descriptor enhance the LLNA performance. We propose a descriptor based on binary patterns extracted from network-automata time-evolution patterns (TEP) aiming to characterize networks. More, in particular, we explore TEPs descriptors from the Life-Like Network Automata (LLNA), a cellular automaton inspired by the rules of the "Life-Like" family that uses a network as tessellation, and based on its dynamics to extract features for network characterization. In recent work, the LLNA has been introduced as a pattern recognition tool that uses a descriptor based on the histograms of complexity measures such as the entropy, word length, and Lempel-Ziv complexity. However, these descriptors correspond to continuous values, and consequently, their histograms lack of an optimal number of bins, which therefore turns out to be a parametric issue. To overcome this disadvantage, we propose a new descriptor that computes feature vectors formed by discrete binary patterns histograms with different lengths D. Furthermore, we show a statistical improvement of the proposed method compared to earlier approaches such as the original LLNA and classical network structural measurements. Our experimental results show the performance improvement of the proposed method in six synthetic network databases and eight real network databases. [ABSTRACT FROM AUTHOR]

A system for the compression of data files, viewed as strings of characters, is presented. The method is general, and applies equally well to English, to PL/I, or to digital data. The system consists of an encoder, an analysis program, and a decoder. Two algorithms for encoding a string differ slightly from earlier proposals. The analysis program attempts to find an optimal set of codes for representing substrings of the file. Four new algorithms for this operation are described and compared. Various parameters in the algorithms are optimized to obtain a high degree of compression for sample texts. [ABSTRACT FROM AUTHOR]

In the era of explosive data growth, data sources and volumes are rapidly increasing. A multi-source data refers to information from multi-sources. However, not every source of information is equally important; some sources are more important and some are essentially worthless. Therefore, it is very meaningful to study how to select the most valuable sources and to efficiently fuse information. Multi-source incomplete interval-valued data (MSIIV-data) is an important kind of multi-source data. This paper proposes a novel method to information fusion in MSIIV-data via conditional information entropy (CIE) and considers its application to attribute reduction based on mutual information entropy. First, the distance between two information values for each incomplete interval-valued data is defined, the neighborhood classes with a tunable parameter are obtained, and the neighborhood granularity structure is established. Then, a source selection method is given via CIE, which is used to fuse MSIIV-data into single-source incomplete interval-valued data (SSIIV-data). Based on the minimization of CIE, this method allows worthy and reliable information sources to be chosen. Moreover, an attribute reduction algorithm (denoted as MMQPSO) for the fused SSIIV-data is proposed by means of combining mutual information entropy and QPSO-algorithm. Finally, experiments are done to validate the effectiveness of the proposed algorithms. The results of experiment and statistical test on 12 datasets show that the proposed algorithms have certain feasibility and advancement than 6 other advanced algorithms. [ABSTRACT FROM AUTHOR]

Deep neural networks typically require a large number of accurately labeled images for training with cross-entropy loss, and often overfit noisy labels. Contrastive learning has proven impressive in noisy label learning because it can learn discrimination representations. However, the weak correlation between the samples and their semantic class, which ignores the correlation between the instances and labels, as well as the instance semantic divergence with the same label, may inevitably lead to class collisions, hampering the label correction. To address these problems, this study proposes a noisy label learning framework that performs label correction and constructs a contrastive prototypical classifier cooperatively. In particular, the prototypical classifier maximizes the distance between the instances and class prototypes to improve the intraclass compactness using contrastive prototypical loss. Furthermore, we provide a theoretical guarantee that the contrastive prototypical loss has a smaller Lipschitz constant and boosts the robustness. Motivated by the theoretical analysis, this framework performs label correction using the prediction of a contrastive prototypical classifier. Extensive experiments demonstrate that the proposed framework achieves superior classification accuracy on synthetic datasets with various noise patterns and levels. [ABSTRACT FROM AUTHOR]

To improve the performance of reversible data hiding on JPEG images, a multi-level sorting prediction enhancement-based two-dimensional reversible data hiding algorithm for JPEG images is proposed in this paper. Firstly, the smoothness of each DCT block is evaluated by counting the location of the last non-zero coefficient in each DCT block. Then, the gradient of the object block is calculated with the DC coefficients of its surrounding blocks, and the lowest gradient direction blocks are employed to achieve the prediction of object block AC coefficients. Secondly, these gradient of adjacent blocks of the object block are compared to found the lowest gradient direction. The AC coefficient of the object block is accurately predicted by coefficients at the same position of its lowest gradient-direction adjacent blocks. Thirdly, the obtained DCT coefficient prediction errors are paired with a similar prediction-error pairing strategy to construct a two-dimensional prediction-error plane, and the secret data is imperceptibly embedded into the AC coefficients by using the two-dimensional histogram shifting algorithm. The performance of the proposed algorithm is evaluated on different datasets, and six representative images are involved in demonstrating its superiority. Extensive experimental results show that, compared to other state-of-the-art reversible data hiding algorithms, the proposed algorithm can achieve higher visual quality of the data-embedded image while maintaining low JPEG image file size increment. [ABSTRACT FROM AUTHOR]

This paper presents a lossy compression technique for encrypted images using Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD) and Huffman coding. The core idea of the proposed technique lies in the selection of significant and less significant coefficients in the wavelet domain. Significant and less significant coefficients are encrypted using pseudo-random number sequence and coefficient permutation respectively. Furthermore, encrypted significant data is compressed by quantization and entropy coding while, less significant encrypted data is efficiently compressed by discarding irrelevant information using SVD and Huffman coding techniques. At receiver side, a reliable decompression and decryption technique is used to reconstruct the original image content with the help of compressed bit streams and secret keys. The performance of proposed technique is evaluated using parameters such as Compression Ratio (CR) and Peak-Signal-to-Noise Ratio (PSNR). Experimental results demonstrate the effectiveness of proposed work over prior work on compression of encrypted images and obtain the compression performance comparable to state of art work on compression of unencrypted images i.e. JPEG standard. [ABSTRACT FROM AUTHOR]

We refine a uniform algebraic approach for deriving upper bounds on reset thresholds of synchronizing automata. We express the condition that an automaton is synchronizing in terms of linear algebra, and obtain new upper bounds for automata with a short word of small rank. The results are applied to make several improvements in the area. In particular, we improve the upper bound for reset thresholds of finite prefix codes (Huffman codes): we show that an n -state synchronizing decoder has a reset word of length at most O ( n log 3 n ). In addition to that, we prove that the expected reset threshold of a uniformly random synchronizing binary n -state decoder is at most O ( n log  n ). We prove the Černý conjecture for n -state automata with a letter of rank ≤ 6 n − 6 3 . In another corollary, we show that the probability that the Černý conjecture does not hold for a random synchronizing binary automaton is exponentially small in terms of the number of states, and that the expected value of the reset threshold is at most n 3 / 2 + o ( 1 ) . Moreover, all of our bounds are constructible. We present suitable polynomial algorithms for the task of finding a reset word of length within our bounds. [ABSTRACT FROM AUTHOR]

With the development of cloud computing, reversible data hiding in encrypted images (RDHEI) has attracted increasing attention. In this paper, a high-performance RDHEI algorithm with Adaptive Huffman Code (RDHEI-AHC) is proposed for cloud computing. First, multi-MSB (Most Significant Bit) of each pixel is predicted by geometric correction, which takes full advantage of spatial correlation. Next, the image is encrypted by stream cipher. Adaptive Huffman code is then used to insert the auxiliary information and secret message into the encrypted image. Finally, to resist ciphertext-only attacks, the marked encrypted image must be scrambled. In comparison to most state-of-the-art RDHEI approaches, experimental results show that the RDHEI-AHC method delivers a greater embedding rate and improved security. [ABSTRACT FROM AUTHOR]

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The article presents author's views on the research article "Storage-Efficient Representation of Decimal Data" by computer professionals Tien Chi Chen and Irving T. Ho related to compression of numbers stored in Binary Coded Decimal form. The research article indicates that data compression involves coding in two or three digits at a time in seven bit or ten bit fields and requires only permutations, deletions and insertions for coding and decoding. The article author says that variable length codes were rejected by Chen and Ho in their research analysis because they think it was difficult to decode, highly sensitive to errors, and have an uncertain storage requirement while variable length codes supports coding and decoding infinite number of memory. The author says that Huffman's coding system has two advantages. The first advantage is that it allows very rapid coding and decoding of the digits by using a algorithm table and the second advantage that it supersedes Chen and Ho's coding system in which a table of 6 entries rather than 128 entries for decoding and encoding.