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The purpose of this paper is to introduce a novel notion of hypergraph-based codes, L.C-hypergraphs and codeable hypergraphs with respect to binary (linear) codes. In order to realize the article's goals, we define the concepts of code-based graphs, consider them as complex networks and construct them from binary (linear) codes via an equivalence relation on (L.C-hypergraphs) codeable hypergraphs. The paper includes implications for the development of fuzzy graph and for modeling the security problems by codeable hypergraphs and applications in complex hypernetworks. The new conception of codeable hypergraphs was given for the first time in this paper. Finally (L.C-hypergraphs), codeable hypergraphs, as complex hypernetworks, are applied as a tool in wireless sensor hypernetworks, code-based graphs, in wireless sensor networks and the bilateral relations between them. [ABSTRACT FROM AUTHOR]

Linear codes generated by component functions of perfect nonlinear (PN for short) and almost perfect nonlinear (APN for short) functions and the first-order Reed-Muller codes have been an object of intensive study in coding theory. The objective of this paper is to investigate some binary shortened codes of two families of linear codes from APN functions and some $p$ -ary shortened codes associated with PN functions. The weight distributions of these shortened codes and the parameters of their duals are determined. The parameters of these binary codes and $p$ -ary codes are flexible. Many of the codes presented in this paper are optimal or almost optimal. The results of this paper show that the shortening technique is very promising for constructing good codes. [ABSTRACT FROM AUTHOR]

This paper explores the algebraic matching approach for detection of vulnerabilities in binary codes. The algebraic programming system is used for implementing this method. It is anticipated that models of vulnerabilities and programs to be verified are presented as behavior algebra and action language specifications. The methods of algebraic matching are based on rewriting rules and techniques with usage of conditional rewriting. This process is combined with symbolic modeling that gives a possibility to provide accurate detection of vulnerabilities. The paper provides examples of formalization of vulnerability models and translation of binary codes to behavior algebra expressions. [ABSTRACT FROM AUTHOR]

This paper generalizes the method proposed by Poulliat et al. for the determination of the optimal Galois field coefficients of a non-binary LDPC parity check constraint based on the binary image of the code. Optimal, or almost-optimal, parity check coefficients are given for check degree varying from 4 to 20 and Galois field varying from GF (64) up to GF (1024). For all given sets of coefficients, no codeword of Hamming weight two exists. A reduced complexity algorithm to compute the binary Hamming weight 3 of a parity check is proposed. When the number of sets of coefficients is too high for an exhaustive search and evaluation, a local greedy search is performed. Explicit tables of coefficients are given. The proposed sets of coefficients can effectively replace the random selection of coefficients often used in NB-LDPC construction. [ABSTRACT FROM AUTHOR]

Consider a channel with a given input alphabet size and input distribution. Our aim is to degrade or upgrade it to a channel with at most L output letters. Channel Q is degraded with respect to a channel W if Q can be obtained from W by processing the output of W. Upgrading is the inverse relation. This paper contains four main results. The first result, from which the paper title is derived, deals with the so called “greedy–merge” algorithm. We derive an upper bound on the reduction in mutual information between input and output, as a function of L. This upper bound is within a constant factor of an algorithm-independent lower bound. Thus, we establish that greedy–merge is optimal in the power-law sense (i.e. the power of L). The other main results deal with upgrading. The second result shows that a certain sequence of channels, which was previously shown to be “hard” for degrading, displays the same hardness in the context of upgrading. That is, suppose we are given such a channel and a corresponding input distribution. If we upgrade (degrade) to a new channel with L output letters, we incur an increase (decrease) in mutual information between input and output. We show that a previously derived bound on the decrease in mutual information for the degrading case is also a lower bound on the increase for the upgrading case. The third result is an efficient algorithm for optimal upgrading, in the binary-input case. That is, we are given a channel and an input distribution. We must find an upgraded channel with L output letters, for which the increase in mutual information is minimal. We give a simple characterization of such a channel, which implies an efficient algorithm. The fourth result is an analog of the first result for the upgrading case when the input is binary. That is, we first present a sub-optimal algorithm for the setting considered in the third result. The main advantage of the sub-optimal algorithm is that it is amenable to analysis. We carry out the analysis and show that the increase incurred in mutual information is within a constant factor of the lower bound derived in the second result. [ABSTRACT FROM AUTHOR]

Linear codes with certain special properties have received renewed attention in recent years due to their practical applications. Among them, binary linear complementary dual (LCD) codes play an important role in implementations against side-channel attacks and fault injection attacks. Self-orthogonal codes can be used to construct quantum codes. In this paper, four classes of binary linear codes are constructed via a generic construction which has been intensively investigated in the past decade. Simple characterizations of these linear codes to be LCD or self-orthogonal are presented. Resultantly, infinite families of binary LCD codes and self-orthogonal codes are obtained. Infinite families of binary LCD codes from the duals of these four classes of linear codes are produced. Many LCD codes and self-orthogonal codes obtained in this paper are optimal or almost optimal in the sense that they meet certain bounds on general linear codes. In addition, the weight distributions of two sub-families of the proposed linear codes are established in terms of Krawtchouk polynomials. [ABSTRACT FROM AUTHOR]

This paper deals with Hadamard \mathbb Z2 \mathbb Z4 {Q}8 -codes, which are binary codes after a Gray map from a subgroup of direct products of \mathbb Z2 , \mathbb Z4 , and Q8 , where Q8 is the quaternionic group. In a previous work, these codes were classified in five shapes. In this paper, we analyze the allowable range of values for the rank and dimension of the kernel, which depends on the particular shape of the code. We show that all these codes can be represented in a standard form, from a set of generators, which can help in understanding the characteristics of each shape. The main results we present are the characterization of Hadamard \mathbb Z2 \mathbb Z4 {Q}8 -codes as a quotient of a semidirect product of \mathbb Z2 \mathbb Z4 -linear codes and the construction of Hadamard \mathbb Z2 \mathbb Z4 {Q}8 -codes with each allowable pair of values for the rank and dimension of the kernel. [ABSTRACT FROM AUTHOR]

We show an explicit (that is, efficient and deterministic) capacity approaching interactive coding scheme that simulates any interactive protocol under random errors with nearly optimal communication rate. Specifically, over the binary symmetric channel with crossover probability $\epsilon $ , our coding scheme achieves a communication rate of $1 - O(\sqrt {H({\epsilon })})$ , together with negligible $\exp (-\Omega (\epsilon ^{4}\,\,n/\log n))$ failure probability (over the randomness of the channel). A rate of $1 - \tilde \Theta (\sqrt {H({\epsilon })})$ is likely asymptotically optimal as a result of Kol and Raz (2013) suggests. Prior to this paper, such a communication rate was achievable only using randomized coding schemes [Kol and Raz (2013); Hauepler (2014)]. [ABSTRACT FROM AUTHOR]

In this paper, a class of binary three-weight linear codes is constructed. Their dual codes are also studied. The dual codes are either optimal or almost optimal. These codes have applications in authentication codes, secret sharing schemes, and association schemes, in addition to their applications in consumer electronics and communication and data storage systems. [ABSTRACT FROM PUBLISHER]

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems, and communication systems as they have efficient encoding and decoding algorithms. In this paper, two classes of cyclic codes whose duals have two zeros are presented. The weight distributions of these cyclic codes are settled with the help of Gaussian periods. The duals of one class of cyclic codes are also studied. Some of the cyclic codes presented in this paper are optimal in the sense that they meet some bounds of linear codes. [ABSTRACT FROM AUTHOR]

In the age of the artificial intelligence of things (AIoT), wearable devices have been extensively developed for smart healthcare. This paper proposes a self‐powered and self‐sensing lower‐limb system (SS‐LS) with negative energy harvesting and motion capture for smart healthcare. The SS‐LS achieves self‐sustainability via a half‐wave electromagnetic generator (HW‐EMG) that recovers negative work from walking with a low cost of harvesting. Additionally, the motion capture function of the system is achieved by the three‐channel triboelectric nanogenerator (TC‐TENG) based on binary code, which can accurately detect the angle and direction of the knee joint rotation. The bench test experiment indicates that the HW‐EMG has an average output power of 11.2 mW, sufficient to power a wireless sensor. The three‐channel voltage signal of TC‐TENG fits well with the binary signal, which can precisely detect the angle and direction of rotation. Furthermore, the SS‐LS demonstrates an identification accuracy of 99.68% and a motion detection accuracy of 99.96% based on an LSTM deep learning model. Demonstrations of Parkinson's disease and fall detection and monitoring of three training modes (sit‐and‐stand, balance, and walking training) are also performed, which exhibit outstanding sensing capabilities. The SS‐LS is highly promising in sports rehabilitation medicine and can contribute to the development of smart healthcare. [ABSTRACT FROM AUTHOR]

In this paper we prove that the class of Goppa codes whose Goppa polynomial is of the form g (x) = Tr F q m \ F q where Tr F q m \ F q is a trace polynomial from a field extension of degree m ≥ 3 has a better minimum distance than what the Goppa bound d ≥ 2 deg (g (x)) + 1 implies. This result is a significant improvement compared to the minimum distance of Trace Goppa codes over quadratic field extensions (the case m = 2 ). We present two different techniques to improve the minimum distance bound. For general p we prove that the Goppa code C (L , Tr F q m \ F q ) is equivalent to another Goppa code C(M, h) where deg (h) > deg (Tr F q m \ F q ) . For p = 2 we use the fact that the values of Tr F q m \ F q are fixed under q–powers to find several new parity check equations which increase the known distance bounds. [ABSTRACT FROM AUTHOR]

Nowadays, theatre aims to become once again an act of healing the individual and the community, to express the tragic dimension of human existence through the actors' bodies. It is in this context that the present paper puts into discussion and briefly analyses a few key notions such as violence, crime, limit, sight/blindness, non-recognition, misunderstanding and incompleteness from Albert Camus's perspective on the absurd existence of the human being. Starting from the idea that violence, as a language in binary code, manifests itself within a relationship between two entities in a conflict situation, we remark that, today, due to excessive repetitiveness and mediatization, the acts of violence, which can lead to tragic situations, have come not only to be accepted but also to be treated as normal acts. In this context, the role of theatre practitioners to give birth to a new theatre of cruelty, a sacred theatre with powers of healing, capable of generating changes in the spectator's consciousness, has become essential. [ABSTRACT FROM AUTHOR]

Let C be a four-weight binary code, which has all one vector. Furthermore, we assume that C supports t-designs for all weights obtained from the Assmus–Mattson theorem. We previously showed that t ≤ 5 . In the present paper, we show an analogue of this result in the cases of five and six-weight codes. [ABSTRACT FROM AUTHOR]

The hull of a linear code over finite fields is the intersection of the code and its dual code, which has been widely studied due to its wide applications. In this paper, we develop a general method for constructing linear codes with small hulls using the eigenvalues of the generator matrices. Using this method, we construct many optimal Euclidean and Hermitian LCD codes, which improve the previously known lower bound on the largest minimum distance. We also obtain many (near) MDS LCD codes and (near) MDS codes with one-dimensional hull. Furthermore, we give three tables about formally self-dual LCD codes. [ABSTRACT FROM AUTHOR]

In this paper, we prove strong subconvexity bounds for self-dual |$\textrm {GL}(3)\ L$| -functions in the |$t$| -aspect and for |$\textrm {GL}(3)\times \textrm {GL}(2)$| |$L$| -functions in the |$\textrm {GL}(2)$| -spectral aspect. The bounds are strong in the sense that they are the natural limit of the moment method pioneered by Xiaoqing Li, modulo current knowledge on estimate for the second moment of |$\textrm {GL}(3)$| |$L$| -functions on the critical line. [ABSTRACT FROM AUTHOR]

Many metrics exist for the evaluation of binary classifiers, all with their particular advantages and shortcomings. Recently, an "Efficiency Index" (EI) for the evaluation of classifiers has been proposed, based on the consistency (or matching) and contradiction (or mismatching) of outcomes. This metric and its confidence intervals are easy to calculate from the base data in a 2 × 2 contingency table, and their values can be qualitatively and semi-quantitatively categorised. For medical tests, in which context the Efficiency Index was originally proposed, it facilitates the communication of risk (of the correct diagnosis versus misdiagnosis) to both clinicians and patients. Variants of the Efficiency Index (balanced, unbiased) which take into account disease prevalence and test cut-offs have also been described. The objectives of the current paper were firstly to extend the EI construct to other formulations (balanced level, quality), and secondly to explore the utility of the EI and all four of its variants when applied to the dataset of a large prospective test accuracy study of a cognitive screening instrument. This showed that the balanced level, quality, and unbiased formulations of the EI are more stringent measures. [ABSTRACT FROM AUTHOR]

The outputs of deep hash network (DHN) are binary codes, so DHN has high retrieval efficiency in matching phase and can be used for high-speed palmprint recognition, which is a promising biometric modality. In this paper, the templates and network parameters are both quantized for fast and light-weight palmprint recognition. The parameters of DHN are binarized to compress the network weight and accelerate the speed. To avoid accuracy degradation caused by quantization, mutual information is leveraged to optimize the ambiguity in Hamming space to obtain a tri-valued hash code as a palmprint template. Kleene Logic's tri-valued Hamming distance measures the dissimilarity between palmprint templates. The ablation experiments are tested on the binarization of the network parameter, and the normalization and trivialization of the deep hash output value. The sufficient experiments conducted on several contact and contactless palmprint datasets confirm the multiple advantages of our method. [ABSTRACT FROM AUTHOR]

Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is time-consuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75 percent storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets. [ABSTRACT FROM AUTHOR]

One of the most important functions used in a quantum key distribution (QKD) network is universal hash functions, specially, (almost) strongly universal hash functions which are used in at least three steps of QKD, in particular, in error correction, privacy amplification, and authentication. Also, they have been recently used in several other quantum communication protocols like quantum secret sharing (QSS). These hash functions have also many other important applications from information security to data structures and parallel computing. Recently, Bibak et al. [Quantum Inf. Comput., 2021] introduced quadratic hash which gives much better collision bound than the well-known polynomial hash. In this paper, we define three new universal hash function families which strongly generalize all the previous families and have several advantages over them. Then, using highly influential and pioneering results of Schmidt and of Weil, we show that these new families are (almost) Δ -universal which can then be easily converted to (almost) strongly universal families. This makes them useful for applications in QKD and many other areas. [ABSTRACT FROM AUTHOR]

This paper presents a 19 ps precision and 170 M samples/s time-to-digital converter (TDC) in FPGA. Through the direct count method and tapped delay line method, the coarse count and fine count can be extracted, respectively. The direct count is realized by the 350 M clock and the tapped delay line is constructed by the CARRY4 block. The ones-counter encoder is used to convert the thermometer code with bubble errors into binary code, which is applicable to all the FPGA chips. This work not only explains the schematic of the ones-counter encoder, but also shows how to configure it. Owing to the inconsistency of delay elements caused by process, bin-by-bin calibration is utilized to improve the differential nonlinearities (DNL) and integral nonlinearities (INL) of the TDC. A novel method was developed to compensate the influence of voltage and temperature. As the delay elements vary with voltage and temperature, a frequency counter is used to extrapolate and compensate its effect on the delay line. All of the above strategies use online calibration and improve the precision and sampling rate of TDC. The experimental results show the least significant bit (LSB) achieves 17.4 ps, the DNL is within [−0.90, 1.67] LSB, and the INL is in the range of [−1.90, 3.31] LSB. [ABSTRACT FROM AUTHOR]

With the increasing demand for earth observation in various fields, remote satellites play an important role in ground information assurance. Apparently, the effective scheduling and utilization of multi-satellite resources determine the quality and efficiency of information acquisition. In this paper, focusing on the problem of centralized multi-satellite scheduling, we establish a mathematical model of satellite scheduling with complex constraints of load and platform operation. We also propose a real-coding Population Incremental Based Learning (PBIL) algorithm to solve the multi-satellite scheduling problem. The real-coding format can greatly shorten the coding length compared to the traditional PBIL algorithm with binary coding so that the computational efficiency is improved. Additionally, we design a value probability matrix, correction coefficient and mutation operator to guide better evolution and avoid early convergence. Finally, we take some numerical examples to verify the real-coding PBIL algorithm for multi-satellite scheduling. The performance of the algorithm is analyzed by comparing it with binary-coding PBIL and the Genetic Algorithm (GA). Additionally, the influence of key parameters on algorithm performance, such as probability correction coefficient, is also analyzed. [ABSTRACT FROM AUTHOR]

Classified retrieval is one of the important factors of library management. It can help readers and administrators quickly find the required materials and better enjoy the convenience of modern network. Through data mapping, hash algorithm can directly convert high-dimensional data into low-dimensional data and store it in Hamming space, which greatly reduces storage consumption and increases computational convenience. For the multimedia retrieval and classification in the era of big data, hash algorithm is right to the point. This paper is aimed at studying the classification and retrieval method of multimedia book cloud resources, extracting different classification categories, determining classification items; cloud resources contain a large amount of image text information, and according to the characteristics of the image text, the features can be well extracted for classification and retrieval, find the optimal book retrieval and classification method, and realize the classification and retrieval of multimedia book cloud resources with the help of hash algorithm. The experimental results show that the hash algorithm can help the classification and retrieval of multimedia books, optimize the classification of multimedia book cloud resources, and improve the efficiency by 20% and user satisfaction by 30%. [ABSTRACT FROM AUTHOR]

The paper deals with state-of-the-art trends in the field of automated hardware development, specifically development of digital systems using programmable logic integrated circuits, as exemplified by field-programmable logic arrays. The paper suggests a modeling method of development using an algebraic model of design specifications, requirements and binary codes in order to apply formal methods of verification, model-based testing, as well as methods of algebraic matching. The specifications of the algebraic hardware model are behavior algebra determined on a set of actions and behaviors. [ABSTRACT FROM AUTHOR]

Hashing has recently received substantial attention in large-scale multimedia search for its extremely low-cost storage cost and high retrieval efficiency. However, most existing hashing techniques focus on learning hash codes for single-view or cross-view retrieval. It is still an unsolved problem that how to efficiently learn discriminative binary codes for multi-view data that is common in real world multimedia search. In this paper, we propose an efficient Supervised Discrete Multi-view Hashing (SDMH) to solve the problem. SDMH first properly detects the shared binary hash codes, with an integrated multi-view feature mapping and latent hash coding, by exploiting the complementarity of different view-specific features and removing the involved inter-view redundancy. To further enhance the discriminative capability of hash codes, SDMH directly represses the explicit semantic labels of data samples with their corresponding binary codes. Different from most existing multi-view hashing methods that adopt “relaxing+rounding” hash optimization strategy or the discrete optimization method based on discrete cyclic coordinate descent, an efficient augmented Lagrangian multiplier (ALM) based discrete hash optimization method is developed in this paper to optimize the hash codes within a single step. Experimental results on four benchmark datasets demonstrate the superior performance of the proposed approach over state-of-the-art hashing techniques, in terms of both learning efficiency and retrieval accuracy. [ABSTRACT FROM AUTHOR]

The goal of this paper is twofold. Firstly, a Siamese-twin random projection neural network (ST-RPNN) is proposed for unsupervised binary hashing of images and compared with state-of-the-art techniques. Secondly, a comparison between Hamming-distance-based retrieval and a proposed bagging trees retrieval (BT-retrieval) algorithm operating directly on the PCA features is made with respect to performance, storage and search time. The ST-RPNN is made of two identical random projection neural networks and is trained to produce similar binary codes for similar input image pairs and different binary codes otherwise. The learning process is divided into two steps: a fast sparse neurons selection algorithm followed by an unsupervised bagging trees algorithm to extract the compact required-length code. Moreover, a BT-retrieval algorithm is proposed in this paper as a fast retrieval tool that ranks the database with respect to a query without distance calculations. Furthermore, (BT-PCA) is a novel extension where the BT-retrieval is applied directly on the PCA features with a significantly lower time search than Hamming-distance-based approach. The proposed technique is compared with ten unsupervised image binary hashing techniques on the COREL1K dataset and the CIFAR10 dataset. The proposed technique obtained better precision–recall results than all compared techniques on the COREL1K dataset, and better than eight of them on the CIFAR10 dataset. [ABSTRACT FROM AUTHOR]

Entropy coding is a fundamental technology in video coding that removes statistical redundancy among syntax elements. In high efficiency video coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the primary entropy coding method. The CABAC consists of three steps: binarization, context modeling, and binary arithmetic coding. As the binarization processes and context models are both manually designed in CABAC, the probability of the syntax elements may not be estimated accurately, which restricts the coding efficiency of CABAC. To address the problem, we propose a convolutional neural network-based arithmetic coding (CNNAC) method and apply it to compress the syntax elements of the intra-predicted residues in HEVC. Instead of manually designing the binarization processes and context models, we propose directly estimating the probability distribution of the syntax elements with a convolutional neural network (CNN), as CNNs can adaptively build complex relationships between inputs and outputs by training with a lot of data. Then, the values of the syntax elements, together with their estimated probability distributions, are fed into a multi-level arithmetic codec to perform entropy coding. In this paper, we have utilized the CNNAC to code the syntax elements of the DC coefficient; the lowest frequency AC coefficient; the second, third, fourth, and fifth lowest frequency AC coefficients; and the position of the last non-zero coefficient in the HEVC intra-predicted residues. The experimental results show that our proposed method achieves up to 6.7% BD-rate reduction and an average of 4.7% BD-rate reduction compared to the HEVC anchor under all intra (AI) configuration. [ABSTRACT FROM AUTHOR]

In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N = 50 and N = 100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking. [ABSTRACT FROM AUTHOR]

Nowadays content-based image retrieval (CBIR) framework is drawing in consideration of numerous analysts because of far-reaching applications found in numerous territories. In this paper, a new CBIR methodology is proposed and adequacy of any CBIR framework relies upon the features extracted from a color picture. In this work, firstly find the region of interest of the image using Sobel and Canny method and later on output is applied on HSV color space, it is clear to human vision eye. For classification, neural network is used and categorized the data with class labels. The similarity distance is estimated between the query image and stored image with different similarity metrics like Manhattan distance, Euclidean distance, Chebyshev, Hamming distance and Jaccard distance. The experimental result is estimated on accuracy, precision. The experiment performed on two well-known databases i.e.: Corel-1k and Corel-5k dataset and new methodology proves the better accuracy results up to 87.33% and 68.93% respectively and improves the precision results also up to 86.36% and 68.47% respectively. In this paper, results are also extended up to 80%. [ABSTRACT FROM AUTHOR]

The quad-phase codes of a given length may be organized into equivalence classes relative to a set of operations preserving autocorrelation peak sidelobe level (PSL). Knowledge about these equivalence classes can be exploited for faster searches or for efficient listing of low- $\rm PSL$ codes. This paper is part of an effort to enumerate autocorrelation $\rm PSL$ equivalence classes for quad-phase codes of any length. An earlier paper achieved a formula for the number of $\rm PSL$ equivalence classes for even lengths $N \geq 2$ , as well as a simple algorithm for generating equivalence class representatives. This paper completes the enumeration task by extending the same formula to the odd lengths $N \geq 3$. The results make use of work by Yurramendi in enumerating equivalence classes for binary rectangular grids relative to horizontal reflection and vertical reflection. In addition, a method is described for generating a minimal set of $\rm PSL$ equivalence class representatives covering the space of quad-phase codes of any odd length $N > 3$. [ABSTRACT FROM AUTHOR]

The basic idea of Visual Cryptography (VC) is to divide a secret image into several partitions which are called VC shares. With various categories of VC schemes having been developed to enhance the maturity of VC since its emergence, one of obsessions in current investigations of VC is that each VC share lacks authentication. In this paper, we analyze VC authentication methods using 2D barcodes and embed binary codes into VC shares for authentication purpose. The objective of this paper is to present a method of improving the authentication of traditional VC scheme. Our contribution of this paper is to propose a scheme of embedding 2D barcodes into given VC shares to prevent cheating, we search the best region of a given share where the 2D barcode could be embedded into so as to keep the visual quality of the revealed secret. [ABSTRACT FROM AUTHOR]

At present, there are many methods to realize the matching of specified images with features, and the basic components include image feature point detection, feature description, and image matching. Based on this background, this article has done different research and exploration around these three aspects. The image feature point detection method is firstly studied, which commonly include image edge information-based feature detection method, corner information-based detection method, and various interest operators. However, all of the traditional detection methods are involved in problems of large computation burden and time consumption. In order to solve this problem, a feature detection method based on image grayscale information-FAST operator is used in this paper, which is combined with decision tree theory to effectively improve the speed of extracting image feature points. Then, the feature point description method BRIEF operator is studied, which is a local expression of detected image feature points based on descriptors. Since the descriptor does not have rotation invariance, the detection operator is endowed by a direction that is proposed in this paper, and then the local feature description is conducted on the feature descriptor to generate a binary string array containing direction information. Finally, the feature matching machine learning method is analyzed, and the nearest search method is used to find the nearest feature point pair in Euclidean distance, of which the calculation burden is small. The simulation results show that the proposed nearest neighbor search and matching machine learning algorithm has higher matching accuracy and faster calculation speed compared with the classical feature matching algorithm, which has great advantages in processing a large number of array images captured by the light field camera. [ABSTRACT FROM AUTHOR]

In this paper, we study the decoding error probability of linear codes over the erasure channel under the list decoding. The notion of the $q^\ell $ -incorrigible sets of linear codes is introduced to characterize its decoding error probability under the list decoding or the maximum likelihood decoding. By calculating the $q^\ell $ -incorrigible set distributions, the decoding error probability of a linear code over the erasure channel under the list decoding or the maximum likelihood decoding is expressed by its support weight distributions. For the ensemble of all $[n,k]$ linear codes, the average decoding error probability under the maximum likelihood decoding and the average unsuccessful decoding probability under unambiguous decoding are determined. Furthermore, the error exponent of the average unsuccessful decoding probability under the unambiguous decoding is determined for the ensemble of all $[n,nR]$ linear codes. [ABSTRACT FROM AUTHOR]

Due to its high query speed and low storage cost, binary hashing has been widely used in approximate nearest neighbors (ANN) search. However, the binary bits are generally considered to be equal, which causes data points with different codes to share the same Hamming distance to the query sample. To solve the above distance measure ambiguity, bitwise weights methods were proposed. Unfortunately, in most of the existing methods, the bitwise weights and the binary codes are learnt separately in two stages, and their performances cannot be further improved. In this paper, to effectively address the above issues, we propose an adaptive mechanism that jointly generate the bitwise weights and the binary codes by preserving different types of similarity relationship. As a result, the binary codes are utilized to obtain the initial retrieval results, and they are further re-ranked by the weighted Hamming distance. This ANN search mechanism is termed AR-Rank in this paper. First, this joint mechanism allows the bitwise weights and the binary codes to be used as mutual feedback during the training stage, and they are well adapted to one other when the algorithm converges. Furthermore, the bitwise weights are required to preserve the relative similarity which is consistent with the nature of ANN search task. Thus, the data points can be accurately re-sorted based on the weighted Hamming distances. Evaluations on three datasets demonstrate that the proposed AR-Rank retrieval system outperforms nine state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper investigates fundamental properties of nonlinear binary codes by looking at the codebook matrix not row-wise (codewords), but column-wise. The family of weak flip codes is presented and shown to contain many beautiful properties. In particular the subfamily fair weak flip codes, which goes back to Shannon et al. and which was shown to achieve the error exponent with a fixed number of codewords $\mathsf {M}$ , can be seen as a generalization of linear codes to an arbitrary number of codewords. The fair weak flip codes are related to binary nonlinear Hadamard codes. Based on the column-wise approach to the codebook matrix, the $r$ -wise Hamming distance is introduced as a generalization to the well-known and widely used (pairwise) Hamming distance. It is shown that the minimum $r$ -wise Hamming distance satisfies a generalized $r$ -wise Plotkin bound. The $r$ -wise Hamming distance structure of the nonlinear fair weak flip codes is analyzed and shown to be superior to many codes. In particular, it is proven that the fair weak flip codes achieve the $r$ -wise Plotkin bound with equality for all $r$. In the second part of this paper, these insights are applied to a binary erasure channel with an arbitrary erasure probability 0 < $\delta$ < 1. An exact formula for the average error probability of an arbitrary (linear or nonlinear) code using maximum likelihood decoding is derived and shown to be expressible using only the $r$ -wise Hamming distance structure of the code. For a number of codewords $\mathsf {M}$ satisfying $\mathsf {M}\le 4$ and an arbitrary finite blocklength $n$ , the globally optimal codes (in the sense of minimizing the average error probability) are found. For $\mathsf {M}$ = 5 or $\mathsf {M}$ = 6 and an arbitrary finite blocklength $n$ , the optimal codes are conjectured. For larger $\mathsf {M}$ , observations regarding the optimal design are presented, e.g., that good codes have a large $r$ -wise Hamming distance structure for all $r$. Numerical results validate our code design criteria and show the superiority of our best found nonlinear weak flip codes compared with the best linear codes. [ABSTRACT FROM AUTHOR]

In this paper, we show how the presence of locality within a binary cyclic code can be exploited to improve decoding performance and to reduce decoding complexity. We pursue two approaches. Under the first approach, we show how the ordered statistics decoding (OSD) method can be modified by inserting a simple single round belief-propagation step at the start that involves only the local codes. The resultant locality-aware OSD algorithm yields an appreciable signal-to-noise ratio (SNR) gain for a given level of reliability and essentially the same level of decoder complexity. Under the second, trellis decoding approach, we show that the careful introduction of locality results in the creation of a cyclic subcode that possesses lower maximum state complexity. In addition, we present a simple means of deriving an upper bound to the state complexity profile of any cyclic code that is based only on the zeros of the code. Furthermore, we show how the decoding speed of either locality-aware OSD or trellis decoding can be significantly increased in the presence of locality, in the moderate-to-high SNR regime, by making the use of a quick-look decoder that often returns the maximum likelihood code word. [ABSTRACT FROM AUTHOR]

In this paper, an extended complete LBP (ELBP) for texture classification is proposed, in which the local feature vectors are composed of the ratio of the central pixel and its neighborhood pixels to a specific threshold. ECLBP_C represents the gray level of the image, which is obtained by comparing the center pixel with the global threshold. ECLBP_S and ECLBP_M represent the symbol component and the magnitude component of the 3-neighbor region of the center pixel respectively, which are obtained by calculating two binary codes using the original LBP algorithm for the 3-neighbor region of the center pixel. In order to make the proposed algorithm scalable, in addition to the 3-neighbor pixels of the central pixel, the proposed algorithm use the center pixel as the center, r as the radius in the circle with ɑ as the filter's radius to generate extended binary coding, such as ECLBP_ES_r,α ECLBP_EM_ r,α. In order to describe the local region feature vector in detail, specified ECLBP_ES_r,α and ECLBP_EM_r,α can be obtained by defining the number of extensions according to actual needs, and then established and concatenated all ECLBP gray histograms for statistics. In the experimental part, we analyze the performance of the proposed algorithm in detail, and prove that the algorithm has good scalability and robustness. The experimental results show that the classification accuracy of the proposed algorithm is up to 99% after 3 expansions in Table 2. The source codes of the proposed algorithm can be downloaded from https://github.com/zenqiang/ECLBP.git. [ABSTRACT FROM AUTHOR]

In this paper, we formulate the problem to cope with the transmission of extra bits over an existing coded transmission link (referred to as coded payload link) without any cost of extra transmission energy or extra bandwidth. This is possible since a gap to the channel capacity typically exists for a practical code. A new concept, termed as accessible capacity, is introduced to specify the maximum rate at which the superposition transmission of extra bits is reliable and has a negligible effect on the performance of the coded payload link. For a binary-input output-symmetric (BIOS) memoryless channel, the accessible capacity can be characterized as the difference between the channel capacity and the mutual information rate of the coded payload link, which can be numerically evaluated for very short payload codes. For a general payload code, we present a simple lower bound on the accessible capacity, given by the channel capacity minus the coding rate of the payload code. We then focus on the scenarios where low-density parity-check (LDPC) codes are implemented for the payload link. We propose to transmit extra bits by random superposition for encoding, and exhaustive search (with the aid of statistical learning) for decoding. We further propose, by establishing an auxiliary channel (called syndrome channel) induced from “zero-forcing” over the binary field, to transmit extra bits with structured codes such as repetition codes and first-order Reed-Muller (RM) codes. Numerical results show that up to 60 extra bits can be reliably transmitted along with a rate-1/2 LDPC code of length 8064. [ABSTRACT FROM AUTHOR]

Binary code homology analysis refers to detecting whether two pieces of binary code are compiled from the same piece of source code, which is a fundamental technique for many security applications, such as vulnerability search, plagiarism detection, and malware detection. With the increase in critical vulnerabilities in IoT devices, homology analysis is increasingly needed to perform cross-platform vulnerability searches. Existing methods for cross-platform binary code homology detection usually convert binary code to instruction sequences and do semantic embedding of the sequences as if they were natural language. However, the gap between natural language and binary code is large, and the spatial features of the binary code are easily lost by directly comparing the semantics. In this paper, we propose a GRU-based graph embedding method to compare the homology of binary functions. First, the attribute control flow graph (ACFG) is built for the assembly function, then the GRU-based graph embedding neural network is used to generate the embedding vector for the ACFG, and finally the homology of the binary code is determined by calculating the distance between the embedding vectors. The experimental results show that our method greatly improves the detection accuracy of negative samples compared with Gemini, the latest method based on graph embedding binary code similarity detection. [ABSTRACT FROM AUTHOR]

Nowadays, Bayesian analysis has found wide applications in many fields of research and practical areas. This paper presents a new approach in implementing the Bayesian approach on non-parametric estimation functions. It is based on the assumption that the parameters of any regression functions are represented by random variables. And the best estimation for a regression function is subject to those parameters which satisfy minimum value of loss function. Accordingly, it is assumed that the estimated function includes a set of coefficients, from β0 to β19. Therefore, a binary indicator, which varies from 00000H to FFFFFH, is employed to scan the whole space of the coefficients main set. This scanning is to search for the optimal subset of coefficients which provides a best estimation to regression function. To meet the classical Bayesian estimation procedure, the binary code, Gray code, was utilized in work to update the binary indicator by a unique bit change at any new iteration. A certain MATLAB algorithm was designated to implement the proposed method. The work results have shown that Gray code is a powerful tool in executing the Bayesian procedure and provided minimum estimation errors based on the adopted error criterions. Moreover, the obtained results have pointed out the validity of the research topic through the well estimation to the regression model of the samples under test. However, the proposed method requires a complete scanning to whole domain of the binary indicator. This point may represent a drawback to the proposed method because it needs a huge computational programming steps. [ABSTRACT FROM AUTHOR]

A new road map for design is emerging out of interdisciplinary research across biology and design. Whilst in the second part of the twentieth century, the emergence of the digital realm altered and radically challenged conventional design and manufacturing processes, the beginning of the twenty-first century marks a strong shift towards the amalgamation of the binary code (1s and 0s) with biological systems. With advances in synthetic biology, we can now 'biofabricate' like Nature does. By tinkering and altering the DNA code or the environment of growth of living organisms, we can effectively 'design' and grow new biomaterials. The role of design is shifting from working with inanimate matter such as plastic and metals to making with animate living entities such as mycelium, yeast and bacteria. This paradigm shift promises to open up new possibilities for biofabricating future intelligent materials as well as for engaging with new sustainable processes. This paper examines strategies and tools for designing with living systems and proposes a framework for design to engage with our future bio-materiality. From biofabrication experiments to synthetic biology propositions, the paper will investigate a series of design artifacts that explores strategies such as co-designing with natural organisms or actuating a new synthetic nature and develop a critique of how biodesign can help shifting towards the crafting of a future sustainable intelligent bio-materiality. [ABSTRACT FROM AUTHOR]

Graph clustering has been a hot research topic and is widely used in many fields, such as community detection in social networks. Lots of works combining auto-encoder and graph neural networks have been applied to clustering tasks by utilizing node attributes and graph structure. These works usually assumed the inherent parameters (i.e., size and variance) of different clusters in the latent embedding space are homogeneous, and hence the assigned probability is monotonous over the Euclidean distance between node embeddings and centroids. Unfortunately, this assumption usually does not hold since the size and concentration of different clusters can be quite different, which limits the clustering accuracy. In addition, the node embeddings in deep graph clustering methods are usually L2 normalized so that it lies on the surface of a unit hyper-sphere. To solve this problem, we proposed Deep Adaptive Graph Clustering via von Mises-Fisher distributions, namely DAGC. DAGC assumes the node embeddings H can be drawn from a von Mises-Fisher distribution and each cluster k is associated with cluster inherent parameters ρk which includes cluster center μ and cluster cohesion degree κ. Then we adopt an EM-like approach (i.e., (H|ρ) and (ρ|H), respectively) to learn the embedding and cluster inherent parameters alternately. Specifically, with the node embeddings, we proposed to update the cluster centers in an attraction-repulsion manner to make the cluster centers more separable. And given the cluster inherent parameters, a likelihood-based loss is proposed to make node embeddings more concentrated around cluster centers. Thus, DAGC can simultaneously improve the intra-cluster compactness and inter-cluster heterogeneity. Finally, extensive experiments conducted on four benchmark datasets have demonstrated that the proposed DAGC consistently outperforms the state-of-the-art methods, especially on imbalanced datasets. [ABSTRACT FROM AUTHOR]

Binary Code Similarity Detection is a method that involves comparing two or more binary code segments to identify their similarities and differences. This technique plays a crucial role in areas such as software security, vulnerability detection, and software composition analysis. With the extensive use of binary code in software development and system optimization, binary code similarity detection has become an important area of research. Traditional methods of source code similarity detection face challenges when dealing with the unreadable and complex nature of binary code, necessitating specialized techniques and algorithms. This review compares and summarizes various techniques and methods of binary code similarity detection, highlighting their strengths and limitations in handling different characteristics of binary code. Additionally, the article suggests potential future research directions. As research and innovation in this technology continue to advance, binary code similarity detection is expected to play an increasingly significant role in fields like software security. [ABSTRACT FROM AUTHOR]

In quantum key distribution (QKD), two users extract a shared secret key using a quantum communication channel in the presence of an eavesdropper. Among QKD protocols, the ones based on energy-time (ET) entanglement of photons have been studied extensively due to their ability to generate high key rates from the arrival times of entangled photons. For the information reconciliation (IR) stage of ET-QKD protocols (where the users communicate using a classical channel in order to reconcile differences in their data), a scheme called multi-level coding (MLC) was proposed by Zhou et al. in prior work. The MLC scheme splits the raw key symbols into bit layers and utilizes binary low-density parity check (LDPC) codes to encode each layer. Although binary LDPC codes are able to offer low complexity decoding for IR, they have poor error-correcting performance compared to their non-binary counterparts, thus leading to low key rates. Additionally, existing LDPC codes do not fully utilize the properties of the QKD channel to optimize the key rates. In this paper, we mitigate the above issues by proposing a flexible protocol for IR in ET-QKD systems called non-binary multi-level coding NB-MLC(a) which is parameterized by a positive integer a. The NB-MLC(a) protocol is a generalization of the MLC scheme and utilizes NB-LDPC codes from a Galois field of size 2 a . We show that by using a small value of a, the NB-MLC(a) protocol significantly improves the key rate without much increase in complexity. To further improve the key rates of the NB-MLC(a) protocol, we propose (i) a joint rate and degree distribution optimization (JRDO) algorithm to design the NB-LDPC codes for the protocol and (ii) an interleaved decoding and communication (IDC) scheme to decode the different layers of the NB-MLC(a) protocol. The JRDO algorithm is designed to use the QKD channel information, and we show that it results in a higher key rate than codes used in prior work. Additionally, the IDC scheme improves the key rate compared to the decoding and communication methods utilized previously in literature. Overall, the NB-MLC(a) protocol that uses JRDO-LDPC codes and the IDC scheme results in a significant 40–60 % improvement in key rates compared to prior work for ET-QKD systems. [ABSTRACT FROM AUTHOR]

This paper illustrates the enhancement of the performance of short-packet full-duplex (FD) transmission by taking the approach of non-binary low density parity check (NB-LDPC) codes over higher Galois field. For the purpose of reducing the impacts of self-interference (SI), high order of modulation, complexity, and latency decoder, a blind feedback process composed of channels estimation and decoding algorithm is implemented. In particular, this method uses an iterative process to simultaneously suppress SI component of FD transmission, estimate intended channel, and decode messages. The results indicate that the proposed technique provides a better solution than both the NB-LDPC without feedback and the binary LDPC feedback algorithms. Indeed, it can significantly improve the performance of overall system in two important factors, which are bit-error-rate (BER) and mean square error (MSE), especially in high order of modulation. The suggested algorithm also shows a robustness in reliability and power consumption for both short-packet FD transmissions and high order modulation communications. [ABSTRACT FROM AUTHOR]

Hashing methods have been widely studied in the image research community due to their low storage and fast computation. However, generating compact hash codes is still a challenging task. In this paper, we propose a novel Asymmetric Similarity-Preserving Discrete Hashing (ASPDH) method to learn compact binary codes for image retrieval. Specifically, the pairwise similarity matrix is approximated in the asymmetric learning manner with two different real-valued embeddings. In addition, ASPDH constructs two distinct hash functions from the kernel feature and label consistency embeddings. Therefore, similarity preservation and hash code learning can be simultaneously achieved and interactively optimized, which further improves the discriminative capability of the learned binary codes. Then, a well-designed iterative algorithm is developed to efficiently solve the optimization problem, resulting in high-quality binary codes with reduced quantization errors. Extensive experiments on three public datasets show the rationality and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In the current paper, we study skew cyclic codes over the ring S : = F 2 × (F 2 + v F 2) with v 2 = v . We investigate the structural properties of skew cyclic codes over the ring S . Also, we describe the dual codes of skew cyclic codes with respect to the Euclidean inner product and the Hermitian inner product. Furthermore, we give a necessary and sufficient condition for a skew cyclic code over S to contain its dual. Using these results, we show that binary quantum error-correcting codes as well as entanglement-assisted quantum error-correcting codes can be constructed from skew cyclic codes over S . Finally, we give examples of binary entanglement-assisted quantum error-correcting codes with good parameters from skew cyclic codes over S . [ABSTRACT FROM AUTHOR]

Unsupervised multi-modal hashing has received considerable attention in large-scale multimedia retrieval areas since its low storage and high search speed. Existing unsupervised multi-modal hashing methods usually aim to mine the complementary information and the structural information for different modalities and preserve them in low-dimensional discrete space. The main limitations are two folds: (1) The shared semantic properties and the specific-modality information between multi-modal data are not explored simultaneously, which limits the improvement of retrieval accuracy. (2) Most multi-modal hashing methods with rough fusion manners cause greatly the information loss. In this paper, we present an unsupervised Joint and Individual Feature Fusion Hashing (JIFFH) that jointly performs the unified feature learning and individual feature learning. A two-layer fusion architecture with an adaptive weighting scheme is adopted to fuse effectively the common semantic properties and the specific-modality data information. The experimental results on three public multi-modal datasets show that our proposed method is better than state-of-the-art unsupervised multi-modal hashing methods. In conclusion, the proposed JIFFH method is very effective to learn discriminative hash codes and can boost retrieval performance. [ABSTRACT FROM AUTHOR]

In this paper, we provide a recursive method to construct self-orthogonal and self-dual codes of the type { k 1 , k 2 , ... , k e } and length n over the quasi-Galois ring F 2 r [ u ] / < u e > from a self-orthogonal code of the same length n and dimension k 1 + k 2 + ⋯ + k ⌈ e 2 ⌉ over F 2 r and vice versa, where F 2 r is the finite field of order 2 r , n ≥ 1 , e ≥ 2 are integers, ⌈ e 2 ⌉ is the smallest integer greater than or equal to e 2 , and k 1 , k 2 , ... , k e are non-negative integers satisfying k 1 ≤ n - (k 1 + k 2 + ⋯ + k e) and k i = k e - i + 2 for 2 ≤ i ≤ e. We further apply this recursive method to provide explicit enumeration formulae for self-orthogonal and self-dual codes of an arbitrary length over the ring F 2 r [ u ] / < u e > . With the help of these enumeration formulae and by carrying out computations in the Magma Computational Algebra system, we classify all self-orthogonal and self-dual codes of lengths 2, 3, 4, 5 over the ring F 2 [ u ] / < u 3 > and of lengths 2, 3, 4 over the ring F 4 [ u ] / < u 2 > . [ABSTRACT FROM AUTHOR]

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods. [ABSTRACT FROM AUTHOR]