Linear Hash Functions Research Articles

A new lattice-based partially blind signature with more complete proof

Partially blind signatures are introduced on the basis of blind signatures, which not only retain the advantages of blind signatures, but also solve the contradiction between anonymity and controllability in blind signatures. With the development of quantum computing technology, it becomes more urgent to construct secure partially blind signature schemes in quantum environments. In this paper, we present a new partially blind signature scheme and prove the security under the Ring-SIS assumption in the random oracle model. To avoid the restart problem of signature schemes caused by rejection sampling, a large number of random numbers are sampled in advance, so that they only need to be re-selected at the current stage without terminating the whole signature process when the conditions are not met. In addition, the hash tree technology is used to reduce communication costs and improve interactive performance. In order to avoid the errors in the security proof of the previous scheme, our proof builds upon and extends the modular framework for blind signatures of Hauck et al. and the correctness, partial blindness, and one-more unforgeability of the scheme are proved in detail according to the properties of the linear hash function.

Linear hash functions and their applications to error detection and correction

We describe and explore so-called linear hash functions and show how they can be used to build error detection and correction codes. The method can be applied for different types of errors (for example, burst errors). When the method is applied to a model where the number of distorted letters is limited, the obtained estimate of its performance is slightly better than the known Varshamov–Gilbert bound. We also describe random code whose performance is close to the same boundary, but its construction is much simpler. The proposed error correction codes are close to those obtained in the theory of linear codes, but there are examples when the proposed algorithms are more efficient.

Design of College English Online Teaching Platform Based on Dual-Server PEKS Framework

To improve the English learning efficiency of college students and improve the performance of network teaching, the research on the college English network teaching platform is carried out under the dual-server PEKS framework. After systematic testing and debugging, deployment and implementation, course migration, personnel training, and other links, the system has successfully completed deployment and implementation, which strongly support the research on the college English network teaching platform. The proposed dual-server PEKS search framework makes the teaching platform more secure by designing a linear homomorphic smooth projection HASH function and solves the security loopholes of traditional PEKS. Relevant research groups have conducted questionnaire surveys and information feedback on some teachers and students and generally have a good evaluation of the online teaching platform, indicating that the system has better realized the predetermined functions and effectively supported the research on the college English online teaching platform.

Cluster-wise unsupervised hashing for cross-modal similarity search

Cross-modal hashing similarity retrieval plays dual roles across various applications including search engines and autopilot systems. More generally, these methods also known to reduce the computation and memory storage in a training scheme. The key limitation of current methods are that: (i) they relax the discrete constrains to solve the optimization problem which may defeat the model purpose, (ii) projecting heterogenous data into a latent space may encourage to loss the diverse representations in such data, (iii) transforming real-valued data point to the binary codes always resulting in a loss of information and producing the suboptimal continuous latent space. In this paper, we propose a novel framework to project the original data points from different modalities into its own low-dimensional latent space and finds the cluster centroid points in its a low-dimensional space, using Cluster-wise Unsupervised Hashing (CUH). In particular, the proposed clustering scheme aims to jointly learns the compact hash codes and the corresponding linear hash functions. A discrete optimization framework is developed to learn the unified binary codes across modalities under of the guidance cluster-wise code-prototypes. Extensive experiments over multiple datasets demonstrate the effectiveness of our proposed model in comparison with the state-of-the-art in unsupervised cross-modal hashing tasks.

Unsupervised hashing based on the recovery of subspace structures

Unsupervised semantic hashing should in principle keep the semantics among samples consistent with the intrinsic geometric structures of the dataset. In this paper, we propose a novel multiple stage unsupervised hashing method, named “Unsupervised Hashing based on the Recovery of Subspace Structures” (RSSH) for image retrieval. Specifically, we firstly adapt the Low-rank Representation (LRR) model into a new variant which treats the real-world data as samples drawn from a union of several low-rank subspaces. Then, the pairwise similarities are represented in a space-and-time saving manner based on the learned low-rank correlation matrix of the modified LRR. Next, the challenging discrete graph hashing is employed for binary hashing codes. Notably, we convert the original graph hashing model into an optimization-friendly formalization, which is addressed with efficient closed-form solutions for its subproblems. Finally, the devised linear hash functions are fast achieved for out-of-samples. Retrieval experiments on four image datasets testify the superiority of RSSH to several state-of-the-art hashing models. Besides, it’s worth mentioning that RSSH, a shallow model, significantly outperforms two recently proposed unsupervised deep hashing methods, which further confirms its effectiveness.

Model Optimization Boosting Framework for Linear Model Hash Learning.

Efficient hashing techniques have attracted extensive research interests in both storage and retrieval of highdimensional data, such as images and videos. In existing hashing methods, a linear model is commonly utilized owing to its efficiency. To obtain better accuracy, linear-based hashing methods focus on designing a generalized linear objective function with different constraints or penalty terms that consider the inherent characteristics and neighborhood information of samples. Differing from existing hashing methods, in this study, we propose a self-improvement framework called Model Boost (MoBoost) to improve model parameter optimization for linear-based hashing methods without adding new constraints or penalty terms. In the proposed MoBoost, for a linear-based hashing method, we first repeatedly execute the hashing method to obtain several hash codes to training samples. Then, utilizing two novel fusion strategies, these codes are fused into a single set. We also propose two new criteria to evaluate the goodness of hash bits during the fusion process. Based on the fused set of hash codes, we learn new parameters for the linear hash function that can significantly improve the accuracy. In general, the proposed MoBoost can be adopted by existing linear-based hashing methods, achieving more precise and stable performance compared to the original methods, and adopting the proposed MoBoost will incur negligible time and space costs. To evaluate the proposed MoBoost, we performed extensive experiments on four benchmark datasets, and the results demonstrate superior performance.

О ПОСТРОЕНИИ (n, k)-СХЕМЫ ВИЗУАЛЬНОЙ КРИПТОГРАФИИ С ПРИМЕНЕНИЕМ КЛАССА ЛИНЕЙНЫХ ХЭШ-ФУНКЦИЙ НАД БИНАРНЫМ ПОЛЕМ

О ПОСТРОЕНИИ (n, k)-СХЕМЫ ВИЗУАЛЬНОЙ КРИПТОГРАФИИ С ПРИМЕНЕНИЕМ КЛАССА ЛИНЕЙНЫХ ХЭШ-ФУНКЦИЙ НАД БИНАРНЫМ ПОЛЕМ

Построение (4, 8)-схемы визуальной криптографии на основе класса линейных хэш-функций

Построение (4, 8)-схемы визуальной криптографии на основе класса линейных хэш-функций

Semi-Supervised Nonlinear Hashing Using Bootstrap Sequential Projection Learning

In this paper, we study the effective semi-supervised hashing method under the framework of regularized learning-based hashing. A nonlinear hash function is introduced to capture the underlying relationship among data points. Thus, the dimensionality of the matrix for computation is not only independent from the dimensionality of the original data space but also much smaller than the one using linear hash function. To effectively deal with the error accumulated during converting the real-value embeddings into the binary code after relaxation, we propose a semi-supervised nonlinear hashing algorithm using bootstrap sequential projection learning which effectively corrects the errors by taking into account of all the previous learned bits holistically without incurring the extra computational overhead. Experimental results on the six benchmark data sets demonstrate that the presented method outperforms the state-of-the-art hashing algorithms at a large margin.

Linear normalised hash function for clustering gene sequences and identifying reference sequences from multiple sequence alignments

BackgroundComparative genomics has put additional demands on the assessment of similarity between sequences and their clustering as means for classification. However, defining the optimal number of clusters, cluster density and boundaries for sets of potentially related sequences of genes with variable degrees of polymorphism remains a significant challenge. The aim of this study was to develop a method that would identify the cluster centroids and the optimal number of clusters for a given sensitivity level and could work equally well for the different sequence datasets.ResultsA novel method that combines the linear mapping hash function and multiple sequence alignment (MSA) was developed. This method takes advantage of the already sorted by similarity sequences from the MSA output, and identifies the optimal number of clusters, clusters cut-offs, and clusters centroids that can represent reference gene vouchers for the different species. The linear mapping hash function can map an already ordered by similarity distance matrix to indices to reveal gaps in the values around which the optimal cut-offs of the different clusters can be identified. The method was evaluated using sets of closely related (16S rRNA gene sequences of Nocardia species) and highly variable (VP1 genomic region of Enterovirus 71) sequences and outperformed existing unsupervised machine learning clustering methods and dimensionality reduction methods. This method does not require prior knowledge of the number of clusters or the distance between clusters, handles clusters of different sizes and shapes, and scales linearly with the dataset.ConclusionsThe combination of MSA with the linear mapping hash function is a computationally efficient way of gene sequence clustering and can be a valuable tool for the assessment of similarity, clustering of different microbial genomes, identifying reference sequences, and for the study of evolution of bacteria and viruses.