Search

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]

Clustering is a long-standing challenging task in pattern recognition and computer vision. In recent years, with development of multimedia technologies and explosive growth of data, the access to data is various. Clustering data from different feature sources has attracted considerable attentions for the remarkable clustering performance due to exploiting complementary information from data of different features. However, in fact, existing work often suffers from heavy computational load that restricts their capacity for large-scale datasets and most existing methods about fusing multi-view data are not perfect enough, namely, the quality of the joint matrix learned from multiple sources is not high enough. In this paper, we propose an efficient and effective clustering approach which combines sparse subspace learning with a bipartite graph for binary/hash codes produced from data of different sources by a collaborative discrete representation learning model that is an efficient and effective data fusion and binary code learning method. The bipartite graph that owns low time complexity is constructed to extract local geometric structure information for improving clustering performance. Extensive experiments performed on four benchmark datasets validate the efficiency and effectiveness of the proposed approach in comparison with ten state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Template security and privacy is of utmost significance while designing a biometric system. Several biometric template protection systems have been presented in the past, but none of them have succeeded in striking a compromise between matching performance and security. This paper proposes a hybrid template protection technique for a multibiometric system based on deep binarization and secure hashing. The technique is employed at different stages of multibiometric fusion. In particular, the proposed technique of multibiometric fusion for template protection is tested using face and electrocardiogram (ECG) biometrics. The pre-trained deep CNN model utilizes transfer learning to analyze both the biometrics and prepare multimodal templates at different stages of biometric fusion e.g. sensors, features, and matchers. The templates obtained from different states of fusion are mapped to their corresponding classes, which are represented as binary codes that are unique and randomly generated. The binary codes are further encrypted for noninvertibility using a cryptographic hash, and thus the information of fused templates is hidden. Finally, hash codes are used to perform matching. The evaluation of the proposed technique using database for face (Multi-PIE) and ECG (PTB) biometrics reports high accuracy satisfying the requirements of unlinkability, cancelability, and irreversibility for template protection. [ABSTRACT FROM AUTHOR]

In this this paper all binary codes of length 552 which admit the sporadic simple group Co 3 as an imprimitive transitive permutation group are determined. Our aim is to understand the results also by using theoretical arguments and to discuss the combinatorial properties of the codes as well as their relation to some special properties of the Leech lattice group Co 3 . For all codes (with two exceptions) we obtain the weight enumerators and in many interesting cases the classification of codewords under the action of the group of code automorphisms Co 3 . The exceptional codes are both self-dual and have minimum weight 12. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep representation learning. In this paper, we concentrated on harmonizing cross-modal representation learning and the full-cycle modeling of high-level semantic associations between vision and language, diverging from traditional statistical methods. We systematically categorized and summarized the challenges and open issues in implementing current technologies and investigated the pipeline of cross-modal retrieval, including pre-processing, feature engineering, pre-training tasks, encoding, cross-modal interaction, decoding, model optimization, and a unified architecture. Furthermore, we propose benchmark datasets and evaluation metrics to assist researchers in keeping pace with cross-modal retrieval advancements. By incorporating recent innovative works, we offer a perspective on potential advancements in cross-modal retrieval. [ABSTRACT FROM AUTHOR]

This paper studies linear and affine error-correcting codes for correcting synchronization errors such as insertions and deletions. We call such codes linear/affine insdel codes. Linear codes that can correct even a single deletion are limited to having an information rate at most 1/2 (achieved by the trivial two fold repetition code). Previously, it was (erroneously) reported that more generally no nontrivial linear codes correcting k deletions exist, i.e., that the (k + 1)-fold repetition codes and its rate of 1/(k + 1) are basically optimal for any k. We disprove this and show the existence of binary linear codes of length n and rate just below 1/2 capable of correcting \Omega(n) insertions and deletions. This identifies rate 1/2 as a sharp threshold for recovery from deletions for linear codes and reopens the quest for a better understanding of the capabilities of linear codes for correcting insertions/deletions. We prove novel outer bounds and existential inner bounds for the rate vs. (edit) distance trade-off of linear insdel codes. We complement our existential results with an efficient synchronization-string-based transformation that converts any asymptotically good linear code for Hamming errors into an asymptotically good linear code for insdel errors. Last, we show that the 2 -rate limitation does not hold for affine codes by giving an explicit affine code of rate 1 - e epsilon which can efficiently correct a constant fraction of insdel errors. [ABSTRACT FROM AUTHOR]

Linear codes with complementary dual (LCD codes) play an important role in armoring implementations against side-channel attacks and fault injection attacks. Hermitian LCD codes are a special class of LCD codes, which were proved to be asymptotically good. In this paper, several classes of optimal quaternary Hermitian LCD codes are constructed via constacyclic codes. The binary images of these classes of quaternary Hermitian LCD codes are characterized. Several classes of optimal binary LCD codes with minimum distance 6, and optimal binary linear codes with one-dimensional hull and minimum distance 6 are also presented. [ABSTRACT FROM AUTHOR]

Recently, security issues of smart contracts are arising great attention due to the enormous financial loss caused by vulnerability attacks. There is an increasing need to detect similar codes for hunting vulnerability with the increase of critical security issues in smart contracts. Binary similarity detection that quantitatively measures the given code diffing has been widely adopted to facilitate critical security analysis. However, due to the difference between common programs and smart contract, such as diversity of bytecode generation and highly code homogeneity, directly adopting existing graph matching and machine learning based techniques to smart contracts suffers from low accuracy, poor scalability and the limitation of binary similarity on function level. Therefore, this paper investigates graph neural network to detect smart contract binary code similarity at the program level, where we conduct instruction-level normalization to reduce the noise code for smart contract pre-processing and construct contract control flow graphs to represent smart contracts. In particular, two improved Graph Convolutional Network (GCN) and Message Passing Neural Network (MPNN) models are explored to encode the contract graphs into quantitatively vectors, which can capture the semantic information and the program-wide control flow information with temporal orders. Then we can efficiently accomplish the similarity detection by measuring the distance between two targeted contract embeddings. To evaluate the effectiveness and efficient of our proposed method, extensive experiments are performed on two real-world datasets, i.e., smart contracts from Ethereum and Enterprise Operation System (EOS) blockchain-based platforms. The results show that our proposed approach outperforms three state-of-the-art methods by a large margin, achieving a great improvement up to 6.1% and 17.06% in accuracy. [ABSTRACT FROM AUTHOR]

Binary code similarity detection (BCSD) plays a big role in the process of binary application security test. It can be applied in several fields, such as software plagiarism detection, malware analysis, vulnerability detection. Most research is based on recurrent neural networks, which is difficult to get the overall or long-distance semantic information of functions. Besides, exiting works simply extract high-level semantic features, lacking in-depth investigations on the potential mechanisms for fusing low-level and high-level semantic features. In this paper we propose a multi-semantic feature fusion attention network (MFFA-Net) for BCSD. MFFA-Net contains two critical modules: semantic feature fusion (SFF) and attention feature fusion (AFF). The SFF module concatenates multiple semantic features to represent the semantics of the function, which helps to obtain the overall semantic information of the function. The AFF module is designed to find useful information from various features, which assigns an attention matrix to research the relationship between features. In order to evaluate the proposed method, we made extensive experiments on two datasets. MFFA-Net can achieve a high degree of AUC at 99.6% and 98.3% respectively on the two datasets. The experimental results show that MFFA-Net has better performance for BCSD. [ABSTRACT FROM AUTHOR]

In this paper, we discussed binary fuzzy codes over a vector space Fn2 by relating classical codes with the probability of a binary symmetric channel (BSC) for receiving a sent codeword correctly. We used the weight of error patterns between a received word and the possible sent codewords to define fuzzy words over n-dimensional vector space Fn2, and used it to define binary fuzzy codes. We also added some properties of Hamming distance of binary fuzzy codes, and the bounds of a Hamming distance of binary fuzzy codes for p=1/r, where r⩾3, and r∈Z+, are determined. Finding Hamming distance of binary fuzzy codes is used for decoding sent messages on a BSC. [ABSTRACT FROM AUTHOR]

In this paper, we propose a randomized Bayesian optimal phase II (RBOP2) design with a binary endpoint (e.g., response rate). A beta-binomial distribution is used to model the binary endpoint for a two-arm phase II trial. Posterior probabilities of the endpoint of interest are evaluated at each interim look and used in the decision to stop the trial due to futility. Compared with other Bayesian designs, the proposed RBOP2 design has the following merits: (i) strongly controls the type I error rate at a pre-defined level; (ii) optimizes the stopping boundaries, thus maximizing the power to detect treatment effects and minimizing the expected sample size for futile treatment; (iii) does not limit the number of interim looks, thus enabling frequent trial monitoring; and (iv) allows the stopping boundaries to be pre-defined in the protocol and is easy to implement. We conduct simulation studies to compare the proposed design with a group sequential design and other Bayesian randomized designs and evaluate its operating characteristics under different scenarios. [ABSTRACT FROM AUTHOR]

Online hashing is a valid storage and online retrieval scheme, which is meeting the rapid increase in data in the optical-sensor network and the real-time processing needs of users in the era of big data. Existing online-hashing algorithms rely on data tags excessively to construct the hash function, and ignore the mining of the structural features of the data itself, resulting in a serious loss of the image-streaming features and the reduction in retrieval accuracy. In this paper, an online hashing model that fuses global and local dual semantics is proposed. First, to preserve the local features of the streaming data, an anchor hash model, which is based on the idea of manifold learning, is constructed. Second, a global similarity matrix, which is used to constrain hash codes is built by the balanced similarity between the newly arrived data and previous data, which makes hash codes retain global data features as much as possible. Then, under a unified framework, an online hash model that integrates global and local dual semantics is learned, and an effective discrete binary-optimization solution is proposed. A large number of experiments on three datasets, including CIFAR10, MNIST and Places205, show that our proposed algorithm improves the efficiency of image retrieval effectively, compared with several existing advanced online-hashing algorithms. [ABSTRACT FROM AUTHOR]

This paper considers three kinds of quasi-cyclic codes of index two with one generator or two generators and their applications in quantum code construction. In accordance with the algebraic structure of linear codes, we determine the lower bounds of the symplectic weights of these quasi-cyclic codes. Quasi-cyclic codes with the dual-containing property enable the construction of quantum codes. Defining the coefficient symmetric polynomials of the generator polynomials gives a concise condition for the dual-containing of the quasi-cyclic codes. The lower bound results can significantly reduce the scope of the search for a larger minimum distance of quasi-cyclic codes. With these algebraic results and computer supports, we obtain classical quasi-cyclic codes with better parameters and some new quantum codes under the symplectic construction. In particular, two examples of the new quantum codes [ [ 63 , 42 , 6 ] ] 2 , [ [ 51 , 35 , 5 ] ] 2 improve the corresponding codes in Grassl's code table. [ABSTRACT FROM AUTHOR]

Chatbot technologies have made our lives easier. To create a chatbot with high intelligence, a significant amount of knowledge processing is required. However, this can slow down the reaction time; hence, a mechanism to enable a quick response is needed. This paper proposes a cache mechanism to improve the response time of the chatbot service; while the cache in CPU utilizes the locality of references within binary code executions, our cache mechanism for chatbots uses the frequency and relevance information which potentially exists within the set of Q&A pairs. The proposed idea is to enable the broker in a multi-layered structure to analyze and store the keyword-wise relevance of the set of Q&A pairs from chatbots. In addition, the cache mechanism accumulates the frequency of the input questions by monitoring the conversation history. When a cache miss occurs, the broker selects a chatbot according to the frequency and relevance, and then delivers the query to the selected chatbot to obtain a response for answer. This mechanism showed a significant increase in the cache hit ratio as well as an improvement in the average response time. [ABSTRACT FROM AUTHOR]

In this paper, we are tackling the weakly-supervised referring expression grounding task, for the localization of a referent object in an image according to a query sentence, where the mapping between image regions and queries are not available during the training stage. In traditional methods, an object region that best matches the referring expression is picked out, and then the query sentence is reconstructed from the selected region, where the reconstruction difference serves as the loss for back-propagation. The existing methods, however, conduct both the matching and the reconstruction approximately as they ignore the fact that the matching correctness is unknown. To overcome this limitation, a discriminative triad is designed here as the basis to the solution, through which a query can be converted into one or multiple discriminative triads in a very scalable way. Based on the discriminative triad, we further propose the triad-level matching and reconstruction modules which are lightweight yet effective for the weakly-supervised training, making it three times lighter and faster than the previous state-of-the-art methods. One important merit of our work is its superior performance despite the simple and neat design. Specifically, the proposed method achieves a new state-of-the-art accuracy when evaluated on RefCOCO (39.21 percent), RefCOCO+ (39.18 percent) and RefCOCOg (43.24 percent) datasets, that is 4.17, 4.08 and 7.8 percent higher than the previous one, respectively. The code is available at https://github.com/insomnia94/DTWREG. [ABSTRACT FROM AUTHOR]

Supervised cross-modal hashing has attracted much attention. However, there are still some challenges, e.g., how to effectively embed the label information into binary codes, how to avoid using a large similarity matrix and make a model scalable to large-scale datasets, how to efficiently solve the binary optimization problem. To address these challenges, in this paper, we present a novel supervised cross-modal hashing method, i.e., scalaBle Asymmetric discreTe Cross-modal Hashing, BATCH for short. It leverages collective matrix factorization to learn a common latent space for the labels and different modalities, and embeds the labels into binary codes by minimizing a distance-distance difference problem. Furthermore, it builds a connection between the common latent space and the hash codes by an asymmetric strategy. In the light of this, it can perform cross-modal retrieval and embed more similarity information into the binary codes. In addition, it introduces a quantization minimization term and orthogonal constraints into the optimization problem, and generates the binary codes discretely. Therefore, the quantization error and redundancy may be much reduced. Moreover, it is a two-step method, making the optimization simple and scalable to large-scale datasets. Extensive experimental results on three benchmark datasets demonstrate that BATCH outperforms some state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Deletion errors are notoriously difficult to correct. The capacity of the binary deletion channel is not yet fully known, and there is no completely satisfactory solution even to the specific problem of correcting only two deletion errors. In this paper, we show that polarization theory, along with the powerful methods of polar coding and successive-cancellation decoding, can be extended to channels with deletion errors. Our results further generalize to channels corrupted by insertion errors and/or combinations of insertions and deletions. It was recently proposed to correct deletion errors using polar codes designed for the BEC, precoded with a sufficiently powerful CRC code. In this approach, in order to correct ${d}$ deletions in a codeword of length ${n}$ , successive-cancellation decoding is run separately for each of the $\binom {n}{d}$ possible deletion patterns, while treating the deleted symbols as erasures. The resulting complexity of decoding is ${O}\bigl ({n}^{d+1}\log {n}\bigr)$. In contrast, we develop herein a natural generalization of successive-cancellation decoding to channels with deletion errors. Our algorithm is based on the recursive structure of polar codes, processing the channel output in ${m} = \log _{2} {n}$ decoding layers, just like the conventional successive-cancellation decoder. Every node in each decoding layer propagates its uncertainty about the deletion pattern to the nodes in the next layer and, eventually, the correct deletion pattern becomes visible at the decoder output, with high probability. This makes it possible to consider at most $\binom {d+2}{2}$ scenarios at each node, and the overall decoding complexity is only ${O}\bigl ({d}^{3} {n} \log {n}\bigr)$ , which scales polynomially rather than exponentially with the number of deletions. Based on the proposed algorithm, we also investigate the channel polarization phenomenon. We are able to prove weak polarization where the number of deletions ${d}$ is small, i.e. ${d}={o}({n})$. We also conjecture the strong polarization for when ${d}={O}(1)$ and provide some evidence to support this conjecture. The strong polarization, if proven, implies that our coding scheme achieves the capacity of any binary-input symmetric channel which furthermore introduces a constant number of deletions. [ABSTRACT FROM AUTHOR]

This paper presents a novel hard decision decoding algorithm for low-density parity-check (LDPC) codes, in which the stand matching pursuit (MP) is adapted for error pattern recovery from syndrome over GF(2). In this algorithm, the operation of inner product can be converted into XOR and accumulation, which makes the matching pursuit work with a high efficiency. In addition, the maximum iteration is theoretically explored in relation to sparsity and error probability according to the sparse theory. To evaluate the proposed algorithm, two MP-based decoding algorithms are simulated and compared over an AWGN channel, i.e., generic MP (GMP) and syndrome MP (SMP). Simulation results show that the GMP algorithm outperforms the SMP by 0.8 dB at BER = 10 − 5 . [ABSTRACT FROM AUTHOR]

Copyright of Iraqi Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning methods are increasingly applied for medical data analysis to reduce human efforts and improve our understanding of disease propagation. When the data is complicated and unstructured, shallow learning methods may not be suitable or feasible. Deep learning neural networks like multilayer perceptron (MLP) and convolutional neural network (CNN), have been incorporated in medical diagnosis and prognosis for better health care practice. For a binary outcome, these learning methods directly output predicted probabilities for patient's health condition. Investigators still need to consider appropriate decision threshold to split the predicted probabilities into positive and negative regions. We review methods to select the cut-off values, including the relatively automatic methods based on optimization of the ROC curve criteria and also the utility-based methods with a net benefit curve. In particular, decision curve analysis (DCA) is now acknowledged in medical studies as a good complement to the ROC analysis for the purpose of decision making. In this paper, we provide the R code to illustrate how to perform the statistical learning methods, select decision threshold to yield the binary prediction and evaluate the accuracy of the resulting classification. This article will help medical decision makers to understand different classification methods and use them in real world scenario. [ABSTRACT FROM AUTHOR]

In this paper, we focus on learning semantic representations for large-scale highly abstract sketches that were produced by the practical sketch-based application rather than the excessively well dawn sketches obtained by crowd-sourcing. We propose a dual-branch CNN-RNN network architecture to represent sketches, which simultaneously encodes both the static and temporal patterns of sketch strokes. Based on this architecture, we further explore learning the sketch-oriented semantic representations in two practical settings, i.e., hashing retrieval and zero-shot recognition on million-scale highly abstract sketches produced by practical online interactions. Specifically, we use our dual-branch architecture as a universal representation framework to design two sketch-specific deep models: (i) We propose a deep hashing model for sketch retrieval, where a novel hashing loss is specifically designed to further accommodate both the abstract and messy traits of sketches. (ii) We propose a deep embedding model for sketch zero-shot recognition, via collecting a large-scale edge-map dataset and proposing to extract a set of semantic vectors from edge-maps as the semantic knowledge for sketch zero-shot domain alignment. Both deep models are evaluated by comprehensive experiments on million-scale abstract sketches produced by a global online game QuickDraw and outperform state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

This paper evaluates the practically achievable best error performance obtained from a dimmable visible light communication (VLC) system that conveys information using binary modulations, such as on-off keying (OOK). As compared to previous studies that address the ideal performance of dimmable VLC systems only over reliable channels with arbitrarily small error probability, this paper presents the best achievable error probability of various configurations with the dimming targets, signal quality, and data rate. The numerical results establish the ultimate decoding error performance targeted for dimmable VLC systems and are expected to provide a practical guideline for the enhanced VLC system design. [ABSTRACT FROM AUTHOR]

Boolean functions have very nice applications in coding theory and cryptography. In coding theory, Boolean functions have been used to construct linear codes in different ways. The objective of this paper is to construct binary linear codes with few weights using the defining-set approach. The defining sets of the codes presented in this paper are defined by some special Boolean functions and some additional restrictions. First, two families of binary linear codes with at most three or four weights from Boolean functions with at most three Walsh transform values are constructed and the parameters of their duals are also determined. Then several classes of binary linear codes with explicit weight enumerators are produced. Some of the binary linear codes are optimal or almost optimal according to the tables of best codes known maintained at http://www.codetables.de, and the duals of some of them are distance-optimal with respect to the sphere packing bound. [ABSTRACT FROM AUTHOR]

Linear codes with a few weights have nice applications in communication, secret sharing schemes, authentication codes, association schemes, block designs and so on. Projective binary linear codes are one of the most important subclasses of linear codes for practical applications. The objective of this paper is to construct projective binary linear codes with some special Boolean functions. Four families of binary linear codes with three or four weights are derived and the parameters of their duals are also determined. It turns out that the duals of these codes are optimal or almost optimal with respect to the sphere-packing bound. As applications, the codes presented in this paper can be used to construct association schemes and secret sharing schemes with interesting access structures. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to examine the impact of corporate governance index (PAKCGI) on firm financial distress for a sample of 152 non-financial firms listed at Pakistan Stock Exchange (PSX) over the period from 2003 to 2017. Design/methodology/approach: To examine the impact of PAKCGI on financial distress (Altman Z-Score), random effect model is applied. The PAKCGI is a self-constructed index based on the five important factors of corporate governance practices, i.e. board of directors, audit committees, right of shareholders, disclosures and risk management. The binary coding approach is adopted for the construction of PAKCGI. Altman Z-Score model is used as a proxy for financial distress indicator. The absolute value of Altman Z-score has been taken as financial distress indicator. Findings: The outcomes of the study indicate a positive impact of PAKCGI on risk of firms' financial distress. The positive coefficient of PAKCGI implies that the good corporate practices work as catalyst to reduce risk of financial distress in Pakistan. A significant negative impact of block holders on financial distress suggests that the concentrated block ownership take monopolistic decision to protect their interests. It has also been observed that significant positive impact of institutional ownership on financial distress exists in the Pakistani listed firms. Furthermore, this study also reveals that significant negative association between board size, CEO duality and financial distress indicator. Research limitations/implications: The findings may encourage the Pakistani listed companies to follow and implement good corporate governance practices, which would lead to increase the confidence of investors, regulators and stakeholders. Originality/value: The current study extends the corporate governance literature by examining the relationship between the corporate governance attributes and the financial distress status of Pakistani listed companies. From the academic perspective, this paper adds to the knowledge concerning the association between corporate governance practices and risk of financial distress in emerging markets. [ABSTRACT FROM AUTHOR]

Given the fact that many software projects are closed-source, analyzing security-related vulnerabilities at the binary level is quintessential to protect computer systems from attacks of malware. Binary code similarity detection is a potential solution for detecting malware from the binaries generated by the processor. In this paper, we proposed a malware detection mechanism based on the binaries using machine learning techniques. Through utilizing the Recurrent Neural Network (RNN), more specifically Long Short-Term Memory (LSTM) network, we generate the uniformed feature embedding of each binary file and further take advantage of the Siamese Neural Network to compute the similarity measure of the extracted features. Therefore, the security risks of the software projects can be evaluated through the similarity measure of the corresponding binaries with existing trained malware. Our real-world experimental results demonstrate a convincing performance in distinguishing out the outliers, and achieved slightly better performance compared with existing state-of-theart methods. [ABSTRACT FROM AUTHOR]

This paper studies a large random matrix system (LRMS) model involving an arbitrary signal distribution and forward error control (FEC) coding. We establish an area property based on the approximate message passing (AMP) algorithm. Under the assumption that the state evolution for AMP is correct for the coded system, the achievable rate of AMP is analyzed. We prove that AMP achieves the constrained capacity of the LRMS with an arbitrary signal distribution provided that a matching condition is satisfied. As a byproduct, we provide an alternative derivation for the constraint capacity of an LRMS using a proved property of AMP. We discuss realization techniques for the matching principle of binary signaling using irregular low-density parity-check (LDPC) codes and provide related numerical results. We show that the optimized codes demonstrate significantly better performance over un-matched ones under AMP. For quadrature phase shift keying (QPSK) modulation, bit error rate (BER) performance within 1 dB from the constrained capacity limit is observed. [ABSTRACT FROM AUTHOR]

Nowadays, the resistance against algebraic attacks and fast algebraic attacks are considered as an important cryptographic property for Boolean functions used in stream ciphers. Both attacks are very powerful analysis concepts and can be applied to symmetric cryptographic algorithms used in stream ciphers. The notion of algebraic immunity has received wide attention since it is a powerful tool to measure the resistance of a Boolean function to standard algebraic attacks. Nevertheless, an algebraic tool to handle the resistance to fast algebraic attacks is not clearly identified in the literature. In the current paper, we propose a new parameter to measure a Boolean function’s resistance to fast algebraic attack. We also introduce the notion of fast immunity profile and show that it informs both on the resistance to standard and fast algebraic attacks. Further, we evaluate our parameter for two secondary constructions of Boolean functions. Moreover, A coding-theory approach to the characterization of perfect algebraic immune functions is presented. Via this characterization, infinite families of binary linear complementary dual codes (or LCD codes for short) are obtained from perfect algebraic immune functions. Some of the binary LCD codes presented in this paper are optimal. These binary LCD codes have applications in armoring implementations against so-called side-channel attacks (SCA) and fault non-invasive attacks, in addition to their applications in communication and data storage systems. [ABSTRACT FROM AUTHOR]

Copyright of Przeglad Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Let θ be an automorphism on a finite field F q. In this paper, we give a way to construct and enumerate Euclidean self-dual θ -cyclic codes of length n over F q when n is even and gcd (n , | θ |) = 1. The restriction gcd (n , | θ |) = 1 implies that the θ -cyclic codes are in fact cyclic codes and q = 2 m , for some integer m ≥ 1. The construction and enumeration are done by analyzing the orbits of cyclotomic cosets under a multiplier map induced by θ. [ABSTRACT FROM AUTHOR]

Symbol-pair codes were introduced by Cassuto and Blaum in 2010 to protect pair errors in symbol-pair read channels. Recently Yaakobi, Bruck and Siegel (2016) generalized this notion to b-symbol codes in order to consider consecutive b errors for a prescribed integer b ≥ 2, and they gave constructions and decoding algorithms. Cyclic codes were considered by various authors as candidates for symbol-pair codes and they established minimum distance bounds on (certain) cyclic codes. In this paper we use algebraic curves over finite fields in order to obtain tight lower and upper bounds on b-symbol Hamming weights of arbitrary cyclic codes over Fq. Here b ≥ 2 is an arbitrary prescribed positive integer and Fq is an arbitrary finite field. We also present a stability theorem for an arbitrary cyclic code C of dimension k and length n: the b-symbol Hamming weight enumerator of C is the same as the k-symbol Hamming weight enumerator of C if k ≤ b ≤ n−1. Moreover, we give improved tight lower and upper bounds on b-symbol Hamming weights of some cyclic codes related to irreducible cyclic codes. Throughout the paper the length n is coprime to q. [ABSTRACT FROM AUTHOR]

A pair of sequences is called a Z-complementary pair (ZCP) if it has zero aperiodic autocorrelation sums at each of the non-zero time-shifts within a certain region, called the zero correlation zone (ZCZ). ZCPs are categorised into two types: Type-I ZCPs and Type-II ZCPs. Type-I ZCPs have the ZCZ around the in-phase position and Type-II ZCPs have the ZCZ around the end-shift position. Till now only a few constructions of Type-II ZCPs are reported in the literature, and all have lengths of the form 2m ± 1 or N + 1 where N = 2a 10b 26c and a, b, c are non-negative integers. In this paper, we propose a recursive construction of ZCPs based on concatenation of sequences. Inspired by Turyn’s construction of Golay complementary pairs, we also propose a construction of Type-II ZCPs from known ones. The proposed constructions can generate optimal Type-II ZCPs with new flexible parameters and Z-optimal Type-II ZCPs with any odd length. In addition, we give upper bounds for the PMEPR of the proposed ZCPs. It turns out that our constructions lead to ZCPs with low PMEPR. [ABSTRACT FROM AUTHOR]

In this paper we study Butson Hadamard matrices, and codes over finite rings coming from these matrices in logarithmic form, called BH-codes. We introduce a new morphism of Butson Hadamard matrices through a generalized Gray map on the matrices in logarithmic form, which is comparable to the morphism given in a recent note of Ó Catháin and Swartz. That is, we show how, if given a Butson Hadamard matrix over the k th roots of unity, we can construct a larger Butson matrix over the ℓ th roots of unity for any ℓ dividing k, provided that any prime p dividing k also divides ℓ . We prove that a Z p s -additive code with p a prime number is isomorphic as a group to a BH-code over Z p s and the image of this BH-code under the Gray map is a BH-code over Z p (binary Hadamard code for p = 2 ). Further, we investigate the inherent propelinear structure of these codes (and their images) when the Butson matrix is cocyclic. Some structural properties of these codes are studied and examples are provided. [ABSTRACT FROM AUTHOR]

Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time frequency analysis methods due to its phase jumping and abrupt discontinuity features which makes it difficult to extract PN (PN) codes of the BPSK signal. To solve this problem, a two-step PN codes estimation method based on sparse recovery is introduced in this paper. The proposed method first pretreats the BPSK signal by estimating its center frequency and converting it to zero intermediate frequency (ZIF). The pretreatment transforms phase jumps of the BPSK signal into the level jumps of the ZIF signal. By nonconvex sparsity promoting regularization, the level jumps of the ZIF signal are extracted through an iterative algorithm. Its effectiveness is verified by numeric simulations and semiphysical tests. The corresponding results demonstrate that the proposed method is able to estimate PN codes from the BPSK signal in serious electromagnetic environments. [ABSTRACT FROM AUTHOR]

This paper presents a low-area 8-bit flash ADC that consumes low power. The flash ADC includes four main blocks—an analog multiplexer (MUX), a comparator, an encoder, and an SPI (Serial Peripheral Interface) block. The MUX allows the selection between eight analog inputs. The comparator block contains a TIQ (Threshold Inverter Quantization) comparator, a control circuit, and a proposed architecture of a Double-Tail (DT) comparator. The advantage of using the DT comparator is to reduce the number of comparators by half, which helps reduce the design area. The SPI block can provide a simple way for the ADC to interface with microcontrollers. This mixed-signal circuitry is designed and simulated using 180 nm CMOS technology. The 8-bit flash ADC only employs 128 comparators. The applied input clock is 80 MHz, with the input voltage ranging from 0.6 V to 1.8 V. The comparator block outputs 127 bits of thermometer code and sends them to the encoder, which exports the seven least significant bits (LSB) of the binary code. The most significant bit (MSB) is decided by only one DT comparator. The design consumes 2.81 mW of power on average. The total area of the layout is 0.088 mm2. The figure of merit (FOM) is about 877 fJ/step. The research ends up with a fabricated chip with the design inserted into it. [ABSTRACT FROM AUTHOR]

This paper presents a lossless image compression method with a fast decoding time and flexible adjustment of coder parameters affecting its implementation complexity. A comparison of several approaches for computing non-MMSE prediction coefficients with different levels of complexity was made. The data modeling stage of the proposed codec was based on linear (calculated by the non-MMSE method) and non-linear (complemented by a context-dependent constant component removal block) predictions. Prediction error coding uses a two-stage compression: an adaptive Golomb code and a binary arithmetic code. The proposed solution results in 30% shorter decoding times and a lower bit average than competing solutions (by 7.9% relative to the popular JPEG-LS codec). [ABSTRACT FROM AUTHOR]

In this paper we generalize the construction of binary self-orthogonal codes obtained from weakly self-orthogonal designs described in Tonchev (J Combinat Theory Ser A 52:197-205, 1989) in order to obtain self-orthogonal codes over an arbitrary field. We extend construction self-orthogonal codes from orbit matrices of self-orthogonal designs and weakly self-orthogonal 1-designs such that block size is odd and block intersection numbers are even described in Crnković (Adv Math Commun 12:607–628, 2018). Also, we generalize mentioned construction in order to obtain self-orthogonal codes over an arbitrary field. We construct weakly self-orthogonal designs invariant under an action of Mathieu group M 11 and, from them, binary self-orthogonal codes. [ABSTRACT FROM AUTHOR]

Let m = 8 k and α be a primitive element of the finite field G F (2 m) , where k ≥ 2 is an integer. In this paper, a class of binary cyclic codes C (u , v) of length 2 m - 1 with two nonzeros α - u and α - v is studied, where (u , v) = (1 , (2 m - 1) / 17) . It turns out that C (u , v) has parameters [ 2 m - 1 , 2 m - m - 9 , 4 ] and is distance-optimal with respect to the Sphere Packing bound. The weight distribution of the dual of C (u , v) is also completely determined based on some results on Gaussian periods. [ABSTRACT FROM AUTHOR]

There are numerous image classification strategies are developed in deep learning. However, due to the complexity of images, conventional image classification strategies have been incapable to meet real application needs. As the amount of pixel information rises, the classification becomes more difficult. However, CNN is widely used method for object identification in picture due to its simple and accurate, but still, it remains hazy which strategies are most supportive for analysing and distinguishing the objects in pictures. In this paper we introduced a CNN network and clustering-based technique called IBCNN to perform classification based on patch extraction. The proposed method can accomplish their goals in the following four different ways: a) Automatic Kernel selection; b) resilient patch size selection; c) CNN layer; and d) pooling layer modification. In addition, it also modifies the pooling layer with average value and calculate the pixel size. The proposed method was applied on ten different image datasets. Finally, the proposed model is compared to three benchmarking models: such as WCNN, MLP, and ELM-CNN to estimate its performance. The obtained results shows that the proposed method gives competitive results compared to the other models. [ABSTRACT FROM AUTHOR]

Steganography is the science of communicating a secret message by hiding it in a cover object. The well known F5 algorithm is based on binary Hamming codes in Hamming graph. It has been an interesting research problem to construct other steganographic schemes from mathematically structured graphs. In this paper, we construct steganographic schemes explicitly from r-perfect codes on Cayley graphs over Gaussian integers, Eisenstein–Jacobi integers, and Lipschitz integers, respectively. Then we further compute various parameters for the suggested steganographic schemes. [ABSTRACT FROM AUTHOR]

Code similarity analysis has become more popular due to its significant applicantions, including vulnerability detection, malware detection, and patch analysis. Since the source code of the software is difficult to obtain under most circumstances, binary-level code similarity analysis (BCSA) has been paid much attention to. In recent years, many BCSA studies incorporating AI techniques focus on deriving semantic information from binary functions with code representations such as assembly code, intermediate representations, and control flow graphs to measure the similarity. However, due to the impacts of different compilers, architectures, and obfuscations, binaries compiled from the same source code may vary considerably, which becomes the major obstacle for these works to obtain robust features. In this paper, we propose a solution, named UPPC (Unleashing the Power of Pseudo-code), which leverages the pseudo-code of binary function as input, to address the binary code similarity analysis challenge, since pseudo-code has higher abstraction and is platform-independent compared to binary instructions. UPPC selectively inlines the functions to capture the full function semantics across different compiler optimization levels and uses a deep pyramidal convolutional neural network to obtain the semantic embedding of the function. We evaluated UPPC on a data set containing vulnerabilities and a data set including different architectures (X86, ARM), different optimization options (O0-O3), different compilers (GCC, Clang), and four obfuscation strategies. The experimental results show that the accuracy of UPPC in function search is 33.2% higher than that of existing methods. [ABSTRACT FROM AUTHOR]

Hashing techniques have attracted considerable attention owing to their advantages of efficient computation and economical storage. However, it is still a challenging problem to generate more compact binary codes for promising performance. In this paper, we propose a novel contrastive vision transformer hashing method, which seamlessly integrates contrastive learning and vision transformers (ViTs) with hash technology into a well‐designed model to learn informative features and compact binary codes simultaneously. First, we modify the basic contrastive learning framework by designing several hash layers to meet the specific requirement of hash learning. In our hash network, ViTs are applied as backbones for feature learning, which is rarely performed in existing hash learning methods. Then, we design a multiobjective loss function, in which contrastive loss explores discriminative features by maximizing agreement between different augmented views from the same image, similarity preservation loss performs pairwise semantic preservation to enhance the representative capabilities of hash codes, and quantization loss controls the quantitative error. Hence, we can facilitate end‐to‐end joint training to improve the retrieval performance. The encouraging experimental results on three widely used benchmark databases demonstrate the superiority of our algorithm compared with several state‐of‐the‐art hashing algorithms. [ABSTRACT FROM AUTHOR]

Semantic learning is a common method for binary vulnerability detection, which can analyze the assembly structure of the binary code. The existing methods such as control flow graphs and labeled semantic flow graphs perform single semantic analysis to determine whether a known vulnerability is hidden in the binary function. However, challenge arises when they perform vulnerability detection cross‐platforms. In this paper, we propose a multigranularity semantic‐based vulnerability search method to deal with the challenge of cross‐platform vulnerability detection, and implement its prototype Taurus. Given the binary function to be searched and vulnerability function, Taurus first extracts their inter‐basic‐block, inter‐function, and inter‐module semantic features respectively, and then calculates the similarity distance between them at three levels. Finally, three similarity scores are combined to rank in descending order to obtain the vulnerability detection report. The performance comparison indicates that Taurus outperforms the state‐of‐the‐art approaches in terms of accuracy. [ABSTRACT FROM AUTHOR]

In mobile edge computing (MEC) systems, enhancing the learning capabilities of edge nodes through Artificial Intelligence (AI) can improve the efficiency of dynamically allocating resources. For the scenario of edge service migration, various tasks in lightweight IoT devices are offloaded to edge nodes and the services on edge nodes are migrated adaptively to another available node nearer to the users while they move. To speed up network application loading during service migration, this paper proposes an intelligent trace-driven predicting approach (ITPA) that improves the efficiency of I/O scheduling and the hit ratio of caching when migrating services between resource-constrained edge nodes. First, based on the characteristics of sequential access to the binary codes of an application during its startup progress, the request loading list is generated by tracing key I/O requests at that phase. Then, an intelligent algorithm is designed to search and select the key I/O requests in the loading list. Finally, the efficiency of data acquisition is improved by implementing a prefetch strategy for the client side and a three-level caching strategy for the server side. Experimental results show that the ITPA reduces the service startup time during stateless migration. [ABSTRACT FROM AUTHOR]

The binary quadratic-residue codes and the punctured Reed-Muller codes ${\mathcal {R}}_{2}((m-1)/2, m))$ are two families of binary cyclic codes with parameters $[n, (n+1)/2, d \geq \sqrt {n}]$. These two families of binary cyclic codes are interesting partly due to the fact that their minimum distances have a square-root bound. The objective of this paper is to construct two families of binary cyclic codes of length $2^{m}-1$ and dimension near $2^{m-1}$ with good minimum distances. When $m \geq 3$ is odd, the codes become a family of duadic codes with parameters $[2^{m}-1, 2^{m-1}, d]$ , where $d \geq 2^{(m-1)/2}+1$ if $m \equiv 3 \pmod {4}$ and $d \geq 2^{(m-1)/2}+3$ if $m \equiv 1 \pmod {4}$. The two families of binary cyclic codes contain some optimal binary cyclic codes. [ABSTRACT FROM AUTHOR]