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Binary code similarity measurement is a popular research area in binary analysis with the recent development of deep learning-based models. Current state-of-the-art methods often use the pre-trained language model (PTLM) to embed instructions into basic blocks as representations of nodes within a control flow graph (CFG). These methods will then use the graph neural network (GNN) to embed the whole CFG and measure the binary similarities between these code embeddings. However, these methods almost directly treat the assembly code as a natural language text and ignore its code-specific features when training PTLM. Moreover, They barely consider the direction of edges in the CFG or consider it less efficient. The weaknesses of the above approaches may limit the performances of previous methods. In this paper, we propose a novel method called function similarity using code-specific PPTs and order-sensitive GNN (FUSION). Since the similarity of binary codes is a symmetric/asymmetric problem, we were guided by the ideas of symmetry and asymmetry in our research. They measure the binary function similarity with two code-specific PTLM training strategies and an order-sensitive GNN, which, respectively, alleviate the aforementioned weaknesses. FUSION outperforms the state-of-the-art binary similarity methods by up to 5.4% in accuracy, and performs significantly better. [ABSTRACT FROM AUTHOR]

It is essential to estimate the sleep quality and diagnose the clinical stages in time and at home, because they are closely related to and important causes of chronic diseases and daily life dysfunctions. However, the existing "gold-standard" sensing machine for diagnosis (Polysomnography (PSG) with Electroencephalogram (EEG) measurements) is almost infeasible to deploy at home in a "ubiquitous" manner. In addition, it is costly to train clinicians for the diagnosis of sleep conditions. In this paper, we proposed a novel technical and systematic attempt to tackle the previous barriers: first, we proposed to monitor and sense the sleep conditions using the infrared (IR) camera videos synchronized with the EEG signal; second, we proposed a novel cross-modal retrieval system termed as Cross-modal Contrastive Hashing Retrieval (CCHR) to build the relationship between EEG and IR videos, retrieving the most relevant EEG signal given an infrared video. Specifically, the CCHR is novel in the following two perspectives. Firstly, to eliminate the large cross-modal semantic gap between EEG and IR data, we designed a novel joint cross-modal representation learning strategy using a memory-enhanced hard-negative mining design under the framework of contrastive learning. Secondly, as the sleep monitoring data are large-scale (8 h long for each subject), a novel contrastive hashing module is proposed to transform the joint cross-modal features to the discriminative binary hash codes, enabling the efficient storage and inference. Extensive experiments on our collected cross-modal sleep condition dataset validated that the proposed CCHR achieves superior performances compared with existing cross-modal hashing methods. [ABSTRACT FROM AUTHOR]

$\mathbb {Z}_{2^{s}}$ -additive codes are subgroups of $\mathbb {Z}^{n}_{2^{s}}$ , and can be seen as a generalization of linear codes over $\mathbb {Z}_{2}$ and $\mathbb {Z}_{4}$. A $\mathbb {Z}_{2^{s}}$ -linear code is a binary code (not necessarily linear) which is the Gray map image of a $\mathbb {Z}_{2^{s}}$ -additive code. We consider $\mathbb {Z}_{2^{s}}$ -additive simplex codes of type $\alpha $ and $\beta $ , which are a generalization over $\mathbb {Z}_{2^{s}}$ of the binary simplex codes. These codes are related to the $\mathbb {Z}_{2^{s}}$ -additive Hadamard codes. In this paper, we use this relationship to find a linear subcode of the corresponding $\mathbb {Z}_{2^{s}}$ -linear codes, called kernel, and a representation of these codes as cosets of this kernel. In particular, this also gives the linearity of these codes. Similarly, $\mathbb {Z}_{2^{s}}$ -additive MacDonald codes are defined for $s>2$ , and equivalent results are obtained. [ABSTRACT FROM AUTHOR]

Let F q be a finite field with q = p e elements, where p is a prime and e is a positive integer. In 2017, Fan and Zhang introduced ℓ -Galois inner products on the n-dimensional vector space F q n for 0 ≤ ℓ < e , which generalized the Euclidean inner product and Hermitian inner product. ℓ -Galois self-orthogonal codes are generalizations of Euclidean self-orthogonal codes and Hermitian self-orthogonal codes, and can be used to construct entanglement-assisted quantum error-correcting codes. In this paper, we study ℓ -Galois self-orthogonal constacyclic codes of length n over the finite field F q . Sufficient and necessary conditions for constacyclic codes of length n over F q being ℓ -Galois self-orthogonal and ℓ -Galois self-dual are characterized. A sufficient and necessary condition for the existence of nonzero ℓ -Galois self-orthogonal constacyclic codes of length n over F q is obtained. Formulae to enumerate the number of ℓ -Galois self-orthogonal and ℓ -Galois self-dual constacyclic codes of length n over F q are found. In particular, formulae to enumerate the number of Hermitian self-orthogonal and Hermitian self-dual constacyclic codes of length n over F q are obtained. Weight distributions of two classes of ℓ -Galois self-orthogonal constacyclic codes are calculated. A family of MDS ℓ -Galois self-orthogonal constacyclic codes over F q is constructed. [ABSTRACT FROM AUTHOR]

This paper gives new methods of constructing symmetric self-dual codes over a finite field GF(q) where q is a power of an odd prime. These methods are motivated by the well-known Pless symmetry codes and quadratic double circulant codes. Using these methods, we construct an amount of symmetric self-dual codes over GF(11), GF(19), and GF(23) of every length less than 42. We also find 153 new self-dual codes up to equivalence: they are [32, 16, 12], [36, 18, 13], and [40, 20, 14] codes over GF(11), [36, 18, 14] and [40, 20, 15] codes over GF(19), and [32, 16, 12], [36, 18, 14], and [40, 20, 15] codes over GF(23). They all have new parameters with respect to self-dual codes. Consequently, we improve bounds on the highest minimum distance of self-dual codes, which have not been significantly updated for almost two decades. [ABSTRACT FROM AUTHOR]

In this paper, we first generalize the polycyclic codes over finite fields to polycyclic codes over the mixed alphabet Z 2 Z 4 , and we show that these codes can be identified as Z 4 [ x ] -submodules of R α , β with R α , β = Z 2 [ x ] / ⟨ t 1 (x) ⟩ × Z 4 [ x ] / ⟨ t 2 (x) ⟩ , where t 1 (x) and t 2 (x) are monic polynomials over Z 2 and Z 4 , respectively. Then we provide the generator polynomials and minimal generating sets for this family of codes based on the strong Gröbner basis. In particular, under the proper defined inner product, we study the dual of Z 2 Z 4 -additive polycyclic codes. Finally, we focus on the characterization of the Z 2 Z 4 -MDSS and MDSR codes, and as examples, we also present some (almost) optimal binary codes derived from the Z 2 Z 4 -additive polycyclic codes. [ABSTRACT FROM AUTHOR]

This paper defines a new design, simulation and analysis of all-optical analog-to-digital converters (ADC) based on silicon waveguide based all-optical synchronous up counter. The proposed structure consists of a nonlinear magnitude comparator, synchronous up counter and an optical digital to analog converter (DAC). Every proposed counter type ADC module is modelled using silicon optical micro-ring resonator (OMRR) as its core element. The magnitude comparator compares the analog signal to be digitized with the DAC output to generate 4-bit binary codes using a synchronous up counter depending on the evaluation states of the comparator. The digital optical output is detected at the output port of the synchronous up counter circuit numerical simulations authorizing the results with the theoretical results. In addition, the possible configurations of OMRR radius and detuning are specified by analyzing the system performance in terms of the amplitude deviation measure. Furthermore, several performance parameters, including extinction ratio (ER) and on–off ratio, are computed from the simulation outputs attained on MATLAB to evaluate the circuits' accurateness and practicability. [ABSTRACT FROM AUTHOR]

In Attribute-Based Access Control（ABAC）, how to respond quickly to the retrieved access control request is very important, and it is undoubtedly time-consuming to traverse all attribute values in each rule of the policy set until finding the appropriate rule. Therefore, the paper proposes an ABAC retrieval method based on binary sequence, and uses binary identification and binary coding to represent attribute based access control policies and access control requests. Through the logical operation of binary identification, select the appropriate group. In the group, find the appropriate rules by matching the binary code of access control request with the binary code of all rules, reduce the process of matching the attributes of rules in the policy set with the attributes of access control request, and improve the efficiency of policy retrieval. In the experiment, this paper compares the efficiency of similar retrieval methods from three aspects: strategy preprocessing, strategy evaluation time and total strategy retrieval time. The results show that the strategy retrieval method proposed in this paper has higher retrieval efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we determine all dihedral subgroups in $PGL_{2}(\mathbb {F}_{q})$ , where $q=2^{l}$ and $l$ is a positive integer; and construct binary expurgated and extended Goppa codes with dihedral automorphism groups. Moreover, it is easy to obtain binary quasi-cyclic expurgated or extended Goppa codes. [ABSTRACT FROM AUTHOR]

Blaum–Roth Codes are binary maximum distance separable (MDS) array codes over the binary quotient ring F 2 [ x ] / (M p (x)) , where M p (x) = 1 + x + ⋯ + x p − 1 , and p is a prime number. Two existing all-erasure decoding methods for Blaum–Roth codes are the syndrome-based decoding method and the interpolation-based decoding method. In this paper, we propose a modified syndrome-based decoding method and a modified interpolation-based decoding method that have lower decoding complexity than the syndrome-based decoding method and the interpolation-based decoding method, respectively. Moreover, we present a fast decoding method for Blaum–Roth codes based on the LU decomposition of the Vandermonde matrix that has a lower decoding complexity than the two modified decoding methods for most of the parameters. [ABSTRACT FROM AUTHOR]

Due to the evolution in the digital domain limitless multimedia is generated daily. It creates a necessity of potential and appealing image resuscitation system. In this paper, a shape and texture-based image retrieval system is proposed that estimates the resemblances of each query image with the images stored in the repository in the form of shape and textural facets and retrieves the images within an expected range of resemblance. The proposed approach employs a statistical approach for image retrieval. The proposed approach takes into account discriminative features of the input image for generating the shape and texture descriptors that produce outstanding results for image databases of restricted variety, which merely includes homogeneous patterns, this approach yielded satisfactory results. For texture images it uses the spatial gray level dependency matrix (SGLDM) and proposes an algorithm to compute the the inverse difference moment (IDM) as the optimal image representative feature. It further employs K-Nearest Neighbour (KNN) classifier for the classification and retrieval tasks. The proposed system outperforms the various other ultra-modern content-based image retrieval (CBIR) systems in many respects. [ABSTRACT FROM AUTHOR]

A Z 2 s -additive code C of length n is a subgroup of Z 2 s n . Carlet (IEEE Trans Inf Theory 44:1543–1547, 1998) introduced a Gray map Φ (Carlet's Gray map) on Z 2 s and Tapia-Recillas and Vega (SIAM J Discret Math 17:103–113, 2003) have been shown that Φ (C) is a binary linear code if and only if for every u , v ∈ C , 2 (u ⊙ v) ∈ C , which their number is ∣ C ∣ 2 - ∣ C ∣ 2 . Let C be a linear code over Z 8 of type { δ 0 , δ 1 , δ 2 } . In this paper, by using the rows of the generator matrix (in standard form) of C , we introduce a set S with ∣ S ∣ ≤ δ 0 (δ 0 + 3) 2 + δ 1 and show that Φ (C) is linear if and only if for every u , v ∈ S , 2 (u ⊙ v) ∈ C . The results show that for a linear code C over Z 8 calculations to check the linearity of Φ (C) are less than Tapia-Recillas and Vega's method. In addition, for a cyclic code of odd length over Z 8 , a condition on the generator polynomial of this code is given which its Carlet's Gray image is linear. Also, as a result, we show that the Carlet's Gray image of a quadratic residue code over Z 8 is not linear. [ABSTRACT FROM AUTHOR]

With the improvement of software copyright protection awareness, code obfuscation technology plays a crucial role in protecting key code segments. As the obfuscation technology becomes more and more complex and diverse, it has spawned a large number of malware variants, which make it easy to evade the detection of anti-virus software. Malicious code detection mainly depends on binary code similarity analysis. However, the existing software analysis technologies are difficult to deal with the growing complex obfuscation technologies. To solve this problem, this paper proposes a new obfuscation-resilient program analysis method, which is based on the data flow transformation relationship of the intermediate representation and the graph network model. In our approach, we first construct the data transformation graph based on LLVM IR. Then, we design a novel intermediate language representation model based on graph networks, named DFSGraph, to learn the data flow semantics from DTG. DFSGraph can detect the similarity of obfuscated code by extracting the semantic information of program data flow without deobfuscation. Extensive experiments prove that our approach is more accurate than existing deobfuscation tools when searching for similar functions from obfuscated code. [ABSTRACT FROM AUTHOR]

Multivariate time series usually have entangled temporal patterns and various anomaly types. Meanwhile, they often contain both continuous and discrete features. Many existing methods directly model correlations in complex multivariate time series to conduct anomaly detection. Decomposing time series into different components, such as the overall trend and fluctuations, can contribute to better extracting semantic information and detecting anomalies. Existing decomposition-based anomaly detection methods still have several limitations. First, they directly decompose all features without considering that discrete features are unsuitable for decomposition because they do not have trends or fluctuations. Second, they adopt the same networks for different components with different characteristics, limiting their ability to extract semantic information. Moreover, due to the nature of Transformers, existing reconstruction-based methods using Transformers rarely form information bottlenecks, reducing the differentiation between the reconstruction errors of normal data and anomalies. This paper proposes a multivariate time series anomaly detection method with separation, decomposition, and dual Transformer-based autoencoder (SDDformer). Different from existing methods, SDDformer separates continuous and discrete features and only decomposes continuous features into trend and residual components. Considering the different characteristics of different components, SDDformer adopts Crossformer and the vanilla Transformer as the backbone of two different autoencoders to reconstruct the trend and residual components. Information bottlenecks are better formed using an extra token as the latent variable between the encoder and the decoder. SDDformer regards reconstructing a discrete feature as a classification task and calculates Cross-Entropy as its reconstruction error. Three different metrics are adopted in this paper to compare SDDformer with a variety of typical anomaly detection methods on public data sets, and the experimental results prove that SDDformer can achieve state-of-the-art performance. • A two-stream multivariate time series anomaly detection method is proposed. • Continuous and discrete features are separated before decomposition. • Only continuous features are decomposed into the trend and residual components. • Two different autoencoders are proposed to reconstruct different components. • The reconstruction of a discrete feature is regarded as a classification task. [ABSTRACT FROM AUTHOR]

In this paper, we focus on decoding nonbinary low-density parity-check (LDPC) codes in Galois fields of characteristic two via the proximal alternating direction method of multipliers (proximal-ADMM). By exploiting Flanagan/Constant-Weighting embedding techniques and the decomposition technique based on three-variables parity-check equations, two efficient proximal-ADMM decoders for nonbinary LDPC codes are proposed. We show that both of them are theoretically guaranteed convergent to some stationary point of the decoding model and either of their computational complexities in each proximal-ADMM iteration scales linearly with LDPC code’s length and the size of the considered Galois field. Moreover, the decoder based on the Constant-Weight embedding technique satisfies the favorable property of codeword symmetry. Simulation results demonstrate their effectiveness in comparison with state-of-the-art LDPC decoders. [ABSTRACT FROM AUTHOR]

Recent deep convolutional neural networks have achieved great reconstruction accuracy for single image super-resolution (SISR). Most of them, however, need to train a specific set of parameters for a single scaling factor or a particular group of scaling factors. This means, multiple sets of model parameters have to be used for different scaling factors, each of which can be already very large. In this paper, we study a new problem of fine-grained scale space learning of SISR, which uses one set of parameters while achieving varying scales. Specifically, we aim to use an arbitrary base SISR × 2 model to realize high-quality SISR for a continuous-integer spectrum of scaling factors, e.g., 2 ∼ 8 . To this end, first for the base scaling factor 2, we propose low-resolution reconstruction, blind kernel estimation and recursive error compensation, to generate three loss functions, helping to boost the training quality of the base model. Then, we cascade the boosted SISR × 2 model and extend the low-resolution reconstruction to incorporate multiple LR loss functions covering { 3 , 4 , ... , 2 n } scales. By this way, the SISR × 2 model can be effectively tuned to work well for continuous-integer scaling factors, with exactly the same set of parameters. Extensive experiments verify the capability of our approach to enable state-of-the-art methods to realize fine-grained scale space learning of SISR, with higher accuracy and much less parameters. [ABSTRACT FROM AUTHOR]

In many contemporary applications, large amounts of unlabelled data are readily available while labelled examples are limited. There has been substantial interest in semi‐supervised learning (SSL) which aims to leverage unlabelled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labelled data are selected uniformly at random from the population of interest. Stratified sampling, while posing additional analytical challenges, is highly applicable to many real‐world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model when the labelled data are not selected uniformly at random. In this paper, we propose a two‐step SSL procedure for evaluating a prediction rule derived from a working binary regression model based on the Brier score and overall misclassification rate under stratified sampling. In step I, we impute the missing labels via weighted regression with nonlinear basis functions to account for stratified sampling and to improve efficiency. In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model. The final estimator is then obtained with the augmented imputations. We provide asymptotic theory and numerical studies illustrating that our proposals outperform their supervised counterparts in terms of efficiency gain. Our methods are motivated by electronic health record (EHR) research and validated with a real data analysis of an EHR‐based study of diabetic neuropathy. [ABSTRACT FROM AUTHOR]

Recently, the construction of Narain CFT from a certain class of quantum error correcting codes has been discovered. In particular, the spectral gap of Narain CFT corresponds to the binary distance of the code, not the genuine Hamming distance. In this paper, we show that the binary distance is identical to the so-called EPC distance of the boolean function uniquely associated with the quantum code. Therefore, seeking Narain CFT with large spectral gap can be addressed by getting a boolean function with high EPC distance. Furthermore, this problem can be undertaken by finding lower Peak-to-Average Power ratio (PAR) with respect to the binary truth table of the boolean function. Though this is neither sufficient nor necessary condition for high EPC distance, we construct some examples of relatively high EPC distances referring to the constructions for lower PAR. We also see that codes with high distance are related to induced graphs with low independence numbers. [ABSTRACT FROM AUTHOR]

This paper presents a low‐power self‐contained overvoltage detector. Compared with conventional detectors that consist of stacked Zener diodes, the proposed structure can trim the threshold with binary codes to accommodate system‐level changes. The proposed structure enables trimming by adding a decoder, a biasing circuit, and switches in parallel with Zener diodes. The newly configured structure forms a standalone detector that can operate independently and be powered from the supply under detection with negligible current addition. The proposed structure is applicable to any high‐voltage process that contains N‐type isolation layer and needs no high‐voltage device. The proposed circuit with three bits adjustment circuit was fabricated in a 0.18 μm Bipolar‐CMOS‐DMOS (BCD) process, the overvoltage threshold can be adjusted from 8 to 38 V with post trim variation of ±2.5 V. The measured quiescent current was around 0 μA at 10 V and 28 μA at 38 V, which was comparable to the current consumption in a conventional structure with 38 V overvoltage threshold. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Let F q be a finite field of order q = p m , where p is an odd prime. This paper presents the study of self-dual and LCD double circulant codes over a class of finite commutative non-chain rings R q = F q + u F q + u 2 F q + ⋯ + u q - 1 F q where u q = u . Here, the whole contribution is two-folded. First, we enumerate self-dual and LCD double circulant codes of length 2n over R q , where n is an odd integer. Then by considering a dual-preserving Gray map ϕ , we show that Gray images of such codes are asymptotically good. Second, we investigate the algebraic structure of 1-generator quasi-cyclic (QC) codes over R q for q = 3 . In that context, we present their generator polynomials along with their minimal generating sets and minimum distance bounds. Here, it is proved that ϕ (C) is an sq-QC code of length nq over F q if C is an s-QC code of length n over R q . [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new texture image retrieval method based on discrete non-separable shearlet transform (DNST) domain local neighborhood intensity pattern (LNIP), which can fully characterize the discriminative information among particular coefficient and its adjacent neighbors within a local window. Firstly, the original image is decomposed into different subbands of frequency and orientation responses by using discrete non-separable shearlet transform (DNST). Secondly, based on center symmetric-local binary pattern theory, the sign and magnitude patterns of DNST domain LNIP descriptor are constructed using novel computation rule. Meanwhile, a new binary bit-string encoding scheme is developed, which can overcome the information redundant and inaccurate problem of conventional encoding strategy in LNIP. Thirdly, the mean, standard deviation and entropy values of the DNST domain LNIP at different scales are calculated and fused into a vector as the image texture feature values, which effectively reduce the feature dimensions. And finally, image similarity measurement is accomplished by using closed-form solutions for the Kullback–Leibler divergences between the DNST domain LNIP based image texture features. Experimental results on some test images and comparison with well-known existing methods demonstrate the efficacy and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Non-overlapping codes are a set of codewords in $\bigcup _{n \ge 2} \mathbb {Z}_{q}^{n}$ , where $\mathbb {Z}_{q} = \{0,1, {\dots },q-1\}$ , such that the prefix of each codeword is not a suffix of any codeword in the set, including itself; and for variable-length codes, a codeword does not contain any other codeword as a subword. In this paper, we investigate a generic method to generalize binary codes to $q$ -ary ones for $q > 2$ , and analyze this generalization on the two constructions given by Levenshtein (also by Gilbert; Chee, Kiah, Purkayastha, and Wang) and Bilotta, respectively. The generalization on the former construction gives large non-expandable fixed-length non-overlapping codes whose size can be explicitly determined; the generalization on the latter construction is the first attempt to generate $q$ -ary variable-length non-overlapping codes. More importantly, this generic method allows us to utilize the generating function approach to analyze the cardinality of the underlying $q$ -ary non-overlapping codes. The generating function approach not only enables us to derive new results, e.g., recurrence relations on their cardinalities, new combinatorial interpretations for the constructions, and the superior limit of their cardinalities for some special cases, but also greatly simplifies the arguments for these results. Furthermore, we give an exact formula for the number of fixed-length words that do not contain the codewords in a variable-length non-overlapping code as subwords. This thereby solves an open problem by Bilotta and induces a recursive upper bound on the maximum size of variable-length non-overlapping codes. [ABSTRACT FROM AUTHOR]

In video super-resolution, exploiting spatial information of reference frame and temporal information from neighbouring frames is significant but challenging. Since existing image super-resolution (SR) methods have achieved remarkable reconstruction results, in this paper, we propose a generic frame-wise dynamic fusion module (DFM) to fully aggregate temporal information into reference frame. Specifically, we employ dynamic convolution to flexibly fuse element-wise temporal information frame by frame. Before that, to handle large motion across frames, we propose a self-calibrated deformable (SCD) alignment module, in which motion offsets are predicted via self-calibrated convolution that explicitly expand receptive field of each convolutional layer through internal communications in a multi-resolution manner. The aligned features of each neighbouring frame are then fed to the DFM to make a temporal information fusion. Finally, the reference features containing spatial and temporal information are sent into SR reconstruction module for the high-resolution frame. Experimental results on several datasets demonstrate superior performance to state-of-the-art published methods on video super-resolution. [ABSTRACT FROM AUTHOR]

In an asymmetric-key cryptosystem, the secure storage of private keys is a challenging task. This paper proposes a novel approach for generating the same public and private key pair on a need basis. Hence, the need for secure storage of the private key is done away with. The proposed approach for generating the key pair is based on two factors: fingerprint biometrics and password. A stable binary string is generated from the distances among pairs of minutiae points in a fingerprint using a gray code-based method. Experiments show that gray code representation significantly reduces the number of inconsistencies between the generated bit strings from two instances of the same fingerprint as against the binary code representation. Hence, the Reed–Solomon error correction code successfully corrects errors due to variations in multiple instances of the same fingerprint to induce stability in the generated string. Hash of the stable string generated from the fingerprint and the string generated from hashed password are XORed to derive a stable seed value. The proposed approach uses this seed value to generate two large prime numbers. These prime numbers are used to generate the public and private key pair using the RSA key generation method. This seed value ensures the generation of the same key pair every time. The experimental results show that the proposed approach can ensure a stable generation of the key. It is not required to store either the fingerprint template or the password. Moreover, the generated private key is also not stored. It can be regenerated on a need basis. [ABSTRACT FROM AUTHOR]

Group Theory-based Optimization Algorithm (GTOA) is a novel population-based global optimization algorithm, which is used to solve combinatorial optimization problems. This paper studies the applicability of GTOA in numerical optimization and proposes two versions of GTOA based on binary coding (GTOA-b) and 0 ∼ 9 integer coding (GTOA-d). Firstly, the coding transformation methods for representing the feasible solutions are introduced, which make GTOA suitable for continuous optimization. On this basis, the original evolution operators are modified to reduce the deviation and speed up global convergence. The experiment using the CEC2017 test suit is carried out to validate the performance of the algorithms. The influence of parameter values and the differences between the calculated results are analyzed by nonparametric tests. Computation results showed that the convergence rate of GTOA-d is faster than that of GTOA-b, and it achieved better results on the benchmark functions with higher dimensions. The comparison against twelve state-of-the-art and recently introduced meta-heuristic algorithms showed that GTOA-d has the superiority on convergence stability, it obtained significantly better performance than seven of its competitors. Finally, all the algorithms are applied to an Amplitude Variation with Offset inversion case study. The simulation showed that the proposed GTOA-d achieved satisfactory inversion results, and it has better performance in terms of convergence rate and average accuracy. The results demonstrate that the proposed GTOA-d is an effective algorithm for numerical optimization. [ABSTRACT FROM AUTHOR]

Community detection is a fundamental task in network data mining. Various algorithms have been proposed to detect the communities of a network. However, the output of these algorithms are meaningful only if community structure exists in the network. It is necessary to statistically test the presence of community structure before applying any community detection algorithms. The existing algorithms or testing procedures mainly focus on unweighted graph, that is, the edge presence or absence is coded as a binary variable. However, most real-world networks have weights. Recently, several algorithms have been devised to detect communities in weighted networks. In this paper, we consider the fundamental problem whether community structure exists in a weighted network. Specifically, we propose a test statistic based on the number of weighted triangles and edges, derive its limiting distribution under the null hypothesis and analyze its power. The simulation results and real data application show that the proposed test can achieve high power. [ABSTRACT FROM AUTHOR]

Automatic identification of function clones on cross‐platform aims at determining whether two functions are identical or not without access to the source code, which is a fundamental challenge in vulnerability search, code plagiarism detection, and malware classification. With the rapid development of deep neural network in program analysis, the state‐of‐the‐art neural network‐based function clone identification methods propose to represent functions as embeddings by graph neural network (GNN). However, such a novel representation of functions brings in two challenges. (1) The feature engineering that accurately maps the raw data of binary code to machine learning features is complicated. (2) A highly accurate embedding of functions requires a customized GNN to focus on the most critical features to identify binary code. To the best of our knowledge, currently, a comprehensive work that can overcome the above challenges is still missing. In this paper, we propose a novel prototype named as Focus, which is designed to accurately and efficiently identify similar functions. Specifically, inspired by natural language processing techniques which effectively learns text semantic across natural languages, Focus can learn representative semantic features of functions by a customized learning model. To address the second challenge, a multi‐head attention mechanism can be employed to capture the critical features of a function. Through extensive experiments, we demonstrate that Focus achieves high accuracy of function clone identification on a broad range of eight architectures. In particular, the identification performance (AUC value) of Focus is 97% and 99% for cross‐platform and single‐platform, respectively. Furthermore, the evaluation in real world applications shows that our Focus identifies 24 vulnerable functions among the top‐30 candidates, which is one time higher than the baseline approaches. [ABSTRACT FROM AUTHOR]

The paper describes an algorithm for optimal symbol-by-symbol reception of signal structures based on signals with multiphase keying and block correcting codes in non-binary Galois fields. It is shown that the basis of the developed algorithm for symbol-by-symbol reception is the spectral transform in the Walsh–Hadamard basis and the resulting complexity of the reception algorithm is determined by the dimension of the dual code, which makes it promising for block correction codes with a high code rate. The results of simulation of the reception algorithm are given in order to analyze the noise immunity of several signal structures based on phase-shift keyed signals and on parity-check codes in non-binary Galois fields. [ABSTRACT FROM AUTHOR]

Let A be a ring with identity, σ a ring endomorphism of A that maps the identity to itself, δ a σ -derivation of A , and consider the skew-polynomial ring A X ; σ , δ . When A is a finite field, a Galois ring, or a general ring, some fairly recent literature used A X ; σ , δ to construct new interesting codes (e.g., skew-cyclic and skew-constacyclic codes) that generalize their classical counterparts over finite fields (e.g., cyclic and constacyclic linear codes). This paper presents results concerning monic principal skew codes, called herein monic principal f , σ , δ -codes, where f ∈ A X ; σ , δ is monic. We provide recursive formulas that compute the entries of both a generator matrix and a control matrix of such a code C. When A is a finite commutative ring and σ is a ring automorphism of A , we also give recursive formulas for the entries of a parity-check matrix of C. Also, in this case, with δ = 0 , we present a characterization of monic principal σ -codes whose dual codes are also monic principal σ -codes, and we deduce a characterization of self-dual monic principal σ -codes. Some corollaries concerning monic principal σ -constacyclic codes are also given, and a good number of highlighting examples is provided. [ABSTRACT FROM AUTHOR]

In entropy coding, universal coding of integers (UCI) is a binary universal prefix code, such that the ratio of the expected codeword length to $\max \{1, H(P)\}$ is less than or equal to a constant expansion factor $K_{\mathcal {C}}$ for any probability distribution $P$ , where $H(P)$ is the Shannon entropy of $P$. $K_{\mathcal {C}}^{*}$ is the infimum of the set of expansion factors. The optimal UCI is defined as a class of UCI possessing the smallest $K_{\mathcal {C}}^{*}$. Based on prior research, the range of $K_{\mathcal {C}}^{*}$ for the optimal UCI is $2\leq K_{\mathcal {C}}^{*}\leq 2.75$. Currently, the code constructions achieve $K_{\mathcal {C}}=2.75$ for UCI and $K_{\mathcal {C}}=3.5$ for asymptotically optimal UCI. In this paper, we construct a class of UCI, termed $\iota $ code, to achieve $K_{\mathcal {C}}=2.5$. This further narrows the range of $K_{\mathcal {C}}^{*}$ to $2\leq K_{\mathcal {C}}^{*}\leq 2.5$. Next, a family of asymptotically optimal UCIs is presented, where their expansion factor infinitely approaches 2.5. Then, tighter upper bounds of the expansion factors are derived for several classic UCIs. Finally, we prove that the length of the first codeword of optimal UCI is one. [ABSTRACT FROM AUTHOR]

Linear codes over $\mathbb {Z}_{p^{s}}$ of length $n$ are subgroups of $\mathbb {Z}_{p^{s}}^{n}$. These codes are also called $\mathbb {Z}_{p^{s}}$ -additive codes and can be seen as a generalization of linear codes over $\mathbb {Z}_{2}$ and $\mathbb {Z}_{4}$. A $\mathbb {Z}_{p^{s}}$ -linear code is a code over $\mathbb {Z}_{p}$ , not necessarily linear, which is the generalized Gray map image of a $\mathbb {Z}_{p^{s}}$ -additive code. In 2015, a systematic encoding was found for $\mathbb {Z}_{4}$ -linear codes. Moreover, an alternative permutation decoding method, which is suitable for any binary code (not necessarily linear) with a systematic encoding, was established. In this paper, we generalize these results by presenting a systematic encoding for any $\mathbb {Z}_{p^{s}}$ -linear code with $s\geq 2$ and $p$ prime. We also describe a permutation decoding method for any systematic code over $\mathbb {Z}_{p}$ , not necessarily linear, and show some examples of how to use this systematic encoding in this decoding method. [ABSTRACT FROM AUTHOR]

Recommendation efficiency and data sparsity problems have been regarded as two challenges of improving performance for online recommendation. Most of the previous related work focus on improving recommendation accuracy instead of efficiency. In this paper, we propose a Deep Pairwise Hashing (DPH) to map users and items to binary vectors in Hamming space, where a user's preference for an item can be efficiently calculated by Hamming distance, which significantly improves the efficiency of online recommendation. To alleviate data sparsity and cold-start problems, the user-item interactive information and item content information are unified to learn effective representations of items and users. Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation. Finally, we adopt the alternating optimization method to optimize the proposed model with discrete constraints. Extensive experiments on three different datasets show that DPH can significantly advance the state-of-the-art frameworks regarding data sparsity and item cold-start recommendation. [ABSTRACT FROM AUTHOR]

The probability Jaccard similarity was recently proposed as a natural generalization of the Jaccard similarity to measure the proximity of sets whose elements are associated with relative frequencies or probabilities. In combination with a hash algorithm that maps those weighted sets to compact signatures which allow fast estimation of pairwise similarities, it constitutes a valuable method for big data applications such as near-duplicate detection, nearest neighbor search, or clustering. This paper introduces a class of one-pass locality-sensitive hash algorithms that are orders of magnitude faster than the original approach. The performance gain is achieved by calculating signature components not independently, but collectively. Four different algorithms are proposed based on this idea. Two of them are statistically equivalent to the original approach and can be used as drop-in replacements. The other two may even improve the estimation error by introducing statistical dependence between signature components. Moreover, the presented techniques can be specialized for the conventional Jaccard similarity, resulting in highly efficient algorithms that outperform traditional minwise hashing and that are able to compete with the state of the art. [ABSTRACT FROM AUTHOR]

In the last several years, deep learning has been introduced to recover a hyperspectral image (HSI) from a single RGB image and demonstrated good performance. In particular, attention mechanisms have further strengthened discriminative features, but most of them are learned by convolutions with limited receptive fields or require much computational cost, which hinders the function of attention modules. Furthermore, the performance of these deep learning methods is hampered by tackling multi-level features equally. To this end, in this paper, based on multiple lightweight densely residual modules, we propose a densely residual network with dual attention (DRN-DA), which utilizes advanced attention and adaptive fusion strategy for more efficient feature correlation learning and more powerful feature extraction. Specifically, an SE layer is applied to learn channel-wise dependencies, and dual downsampling spatial attention (DDSA) is developed to capture long-range spatial contextual information. All the intermediate-layer feature maps are adaptively fused. Experimental results on four data sets from the NTIRE 2018 and NTIRE 2020 Spectral Reconstruction Challenges demonstrate the superiority of the proposed DRN-DA over state-of-the-art methods (at least −6.19% and −1.43% on NTIRE 2018 "Clean" track and "Real World" track, −6.85% and −5.30% on NTIRE 2020 "Clean" track and "Real World" track) in terms of mean relative absolute error. [ABSTRACT FROM AUTHOR]

Due to its high anti-counterfeiting and universality, the use of finger-vein pattern for identity authentication has recently attracted extensive attention in academia and industry. Despite recent advances in finger-vein recognition, most of the hand-crafted descriptors require strong prior knowledge, which may be ineffective in expressing its distinctiveness. In this paper, we present a novel compact multi-representation feature descriptor (CMrFD) with visual and semantic consistency, for finger-vein feature representation. Given the finger-vein images, we first form two-view representations to describe the informative vein features in local patches. Then, we jointly learn a feature transformation to map the two-view representations into discriminative binary codes. For the projection function, we linearly combine multi-view information and minimize the quantization error between the projected binary features and the original real-valued features. In terms of visual consistency, we minimize the Euclidean distance of each representation from the same class, at the same time, maximize the Euclidean distance from different classes in the projected space. Semantic consistency is used to ensure that similar images have compact multi-representation combined projection features. Lastly, we calculate the block-wise histograms as the final extracted features for finger-vein recognition. Experimental results on four widely used finger-vein databases demonstrate that the proposed method outperforms the state-of-the-art finger-vein recognition methods. [ABSTRACT FROM AUTHOR]

With the dramatic increase in the amount of multimedia data, cross-modal similarity retrieval has become one of the most popular yet challenging problems. Hashing offers a promising solution for large-scale cross-modal data searching by embedding the high-dimensional data into the low-dimensional similarity preserving Hamming space. However, most existing cross-modal hashing usually seeks a semantic representation shared by multiple modalities, which cannot fully preserve and fuse the discriminative modal-specific features and heterogeneous similarity for cross-modal similarity searching. In this paper, we propose a joint specifics and consistency hash learning method for cross-modal retrieval. Specifically, we introduce an asymmetric learning framework to fully exploit the label information for discriminative hash code learning, where 1) each individual modality can be better converted into a meaningful subspace with specific information, 2) multiple subspaces are semantically connected to capture consistent information, and 3) the integration complexity of different subspaces is overcome so that the learned collaborative binary codes can merge the specifics with consistency. Then, we introduce an alternatively iterative optimization to tackle the specifics and consistency hashing learning problem, making it scalable for large-scale cross-modal retrieval. Extensive experiments on five widely used benchmark databases clearly demonstrate the effectiveness and efficiency of our proposed method on both one-cross-one and one-cross-two retrieval tasks. [ABSTRACT FROM AUTHOR]

Fine-grained hashing is a new topic in the field of hashing-based retrieval and has not been well explored up to now. In this paper, we raise three key issues that fine-grained hashing should address simultaneously, i.e., fine-grained feature extraction, feature refinement as well as a well-designed loss function. In order to address these issues, we propose a novel Fine-graIned haSHing method with a double-filtering mechanism and a proxy-based loss function, FISH for short. Specifically, the double-filtering mechanism consists of two modules, i.e., Space Filtering module and Feature Filtering module, which address the fine-grained feature extraction and feature refinement issues, respectively. Thereinto, the Space Filtering module is designed to highlight the critical regions in images and help the model to capture more subtle and discriminative details; the Feature Filtering module is the key of FISH and aims to further refine extracted features by supervised re- weighting and enhancing. Moreover, the proxy-based loss is adopted to train the model by preserving similarity relationships between data instances and proxy-vectors of each class rather than other data instances, further making FISH much efficient and effective. Experimental results demonstrate that FISH achieves much better retrieval performance compared with state-of-the-art fine-grained hashing methods, and converges very fast. The source code is publicly available: https://github.com/chenzhenduo/FISH. [ABSTRACT FROM AUTHOR]

The rth generalized Hamming weight of a linear [n, k] code C is the cardinality of the smallest support of any r-dimensional subcode of C. In this paper, we obtain the generalized Hamming weights of the binary linear codes whose parity check matrices are the incidence matrices of the Cartesian product of graphs. We also determine the rth generalized Hamming weights of their dual codes. [ABSTRACT FROM AUTHOR]

In this paper, the design of all-optical gray code converter for binary and BCD inputs using two-dimensional photonic crystal is proposed. The proposed design contains square lattice which includes silicon rods in air in the form of point defect, line defect and nano resonators. The plane wave expansion and finite difference time domain method is used in the simulation to calculate the band gap and system performance. All-optical OR and XOR logic outputs are achieved by introducing suitable phase difference between two input data to interfere appropriately. The proposed design exhibits acceptable performance with time delay of 0.154 ps, contrast ratio of 12.01 dB, foot print of 658 µm2 for the considered BCD and binary inputs. [ABSTRACT FROM AUTHOR]

In this paper we show that the largest possible size of a subset of F q n avoiding right angles, that is, distinct vectors x , y , z such that x − z and y − z are perpendicular to each other is at most O (n q − 2). This improves on the previously best known bound due to Naslund [13] and refutes a conjecture of Ge and Shangguan [11]. A lower bound of n q / 3 is also presented. It is also shown that a subset of F q n avoiding triangles with all right angles can have size at most O (n 2 q − 2). Furthermore, asymptotically tight bounds are given for the largest possible size of a subset A ⊆ F q n for which x − y is not self-orthogonal for any distinct x , y ∈ A. The exact answer is determined for q = 3 and n ≡ 2 (mod 3). Our methods can also be used to bound the maximum possible size of a binary code where no two codewords have Hamming distance divisible by a fixed prime q. Our lower- and upper bounds are asymptotically tight and both are sharp in infinitely many cases. [ABSTRACT FROM AUTHOR]

Univariate data has been the focus of optimal design for generalized linear models. It is common practice to conduct experiments with multiple dependent responses that are modelled using regression techniques. To design the experiment for all these responses, one needs a multivariate distribution supporting a previously chosen data model. The T‐optimal criterion is used to discriminate between two competing models. The designs that are optimal for discrimination between two models may be unsuitable for describe the quality of predictions for test data. G‐optimal criterion overcomes this restriction by minimizing the maximum variance of the predicted values. The design of experiments with dependent bivariate binary data is a topic we discuss in this paper. For dependent bivariate binary data, a technique for deriving TG‐optimal experimental designs using Copula is shown. The new compound TG‐criterion satisfy the dual aim to selecting the true model and minimizing the maximum prediction variance. The equivalence theorem is demonstrated and proved for the new compound criterion. A numerical example is presented to show the properties of the new compound criterion for bivariate binary setting and its value in meeting the dual aims. [ABSTRACT FROM AUTHOR]

In this paper, we prove the conjectured order lower bound for the k-th moment of central values of quadratic twisted self-dual GL(3) L-functions for all k ≥ 1, based on our recent work on the twisted first moment of central values in this family of L-functions. [ABSTRACT FROM AUTHOR]

In a category with a proper ()-factorization system, we study the notions of strict, co-strict, initial and final morphisms with respect to a topogenous order. Besides showing that they allow simultaneous study of four classes of morphisms obtained separately with respect to closure, interior and neighbourhood operators, the initial and final morphisms lead us to the study of topogenous orders induced by pointed and co-pointed endofunctors. We also lift the topogenous orders along an -fibration. This permits one to obtain the lifting of interior and neighbourhood operators along an -fibration and includes the lifting of closure operators found in the literature. A number of examples presented at the end of the paper demonstrates our results. [ABSTRACT FROM AUTHOR]

In this paper, a simple feature fusion scheme is proposed for content-based image retrieval (CBIR) using color, texture and shape feature moments. The low dimensional feature descriptor is constructed by fusing color, texture and shape moments of an image effectively. The color moments are extracted from the image using probability histogram model while the Gray Level Co-occurrence Matrix (GLCM) based texture moments are computed in a very new fashion by selecting salient components in Discrete Cosine Transform (DCT) domain after determining the inter-relationship between the DCT blocks. Alone, color or texture information is not adequate to produce the desire results in image retrieval system. So, suggested work has also considered the multi-resolution based shape information, since the single resolution level of image does not provide an adequate image information and it may be possible that the fine details may be visualized on other resolution levels of image. Therefore, shape feature descriptor is determined by calculating the invariant moments of multi-resolution based sub-images at the various levels. Finally, fused single feature descriptor is computed by proficient fusion of color, texture and shape feature moments. The modified distance is also suggested for image retrieval task. The proposed feature fusion scheme is implemented on Corel-1K, OT-8 and GHIM-10K image databases to evaluate the retrieval performance and experimental results show the effectiveness of our scheme over the other existing CBIR schemes. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of correcting multiple criss-cross insertions and deletions in arrays. More precisely, we study the unique recovery of $n \times n$ arrays affected by ${t}$ -criss-cross deletions defined as any combination of ${t_{\mathrm {r}}}$ row and ${t_{\mathrm {c}}}$ column deletions such that ${t_{\mathrm {r}}}+ {t_{\mathrm {c}}}= {t}$ for a given $t$. We show an equivalence between correcting ${t}$ -criss-cross deletions and ${t}$ -criss-cross insertions and show that a code correcting ${t}$ -criss-cross insertions/deletions has redundancy at least ${t} n + {t}\log n - \log ({t}!)$. Then, we present an existential construction of a ${t}$ -criss-cross insertion/deletion correcting code with redundancy bounded from above by ${t} n + \mathcal {O}({t}^{2} \log ^{2} n)$. The main ingredients of the presented code construction are systematic binary ${t}$ -deletion correcting codes and Gabidulin codes. The first ingredient helps locating the indices of the inserted/deleted rows and columns, thus transforming the insertion/deletion-correction problem into a row/column erasure-correction problem which is then solved using the second ingredient. [ABSTRACT FROM AUTHOR]

Many Error-Correcting Output Codes (ECOC) algorithms had been proposed based on the hard coding (HC) schemes: binary coding {1, 0} or ternary coding {+ 1, -1, 0}. This paper introduces two novel strategies to recode the original code matrices with the mean values and the intervals of learners' outputs, which are named Mean Value Recoding (MVR) and Interval Recoding (IR) strategies. Both strategies are designed to reduce the distance between the outputs of base learners and the target codewords, aiming to produce more accurate results compared with the HC schemes. It is the first time that two concepts, soft recoding, and learner dependent, are injected into the ECOC framework to the best of our knowledge. To verify the effectiveness of our strategies, four data-independent ECOC algorithms and two data-dependent ECOC algorithms are deployed in the experiments based on UCI data sets. The experiments are carried out using the original HC strategies and our soft recoding strategies, and results verify that our strategies outperform the HC-based algorithms in most cases by producing balanced results among classes. In short, our strategies can improve the performance of different ECOC algorithms. Our python code and the corresponding data sets are available for non-commercial or research use at: https://github.com/MLDMXM2017/softcoding-ECOC. [ABSTRACT FROM AUTHOR]

We previously proposed the first nontrivial examples of a code having support t-designs for all weights obtained from the Assmus–Mattson theorem and having support t ′ -designs for some weights with some t ′ > t . This suggests the possibility of generalizing the Assmus–Mattson theorem, which is very important in design and coding theory. In the present paper, we generalize this example as a strengthening of the Assmus–Mattson theorem along this direction. As a corollary, we provide a new characterization of the extended Golay code G 24 . [ABSTRACT FROM AUTHOR]

In this paper, we study the algebraic structure of a new family of linear codes over the mixed alphabet Z 2 R , where R = Z 2 + u Z 2 + u 2 Z 2 + u 3 Z 2 , u 4 = 0 . We present generator and parity-check matrices of Z 2 R -linear codes in standard form. We define a Z 2 R -cyclic code of length (r, s) as a R [ x ] -submodule of Z 2 [ x ] ⟨ x r - 1 ⟩ × R [ x ] ⟨ x s - 1 ⟩ and determine its generator polynomial. Also, we determine the size of a Z 2 R -cyclic code by giving a minimal spanning set. Furthermore, we present the generator polynomial of dual code of a Z 2 R -cyclic code of length (r, s) for odd s, while r is set arbitrary. Finally, optimal binary codes are constructed from Gray images of Z 2 R -cyclic codes. [ABSTRACT FROM AUTHOR]