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Published scholarly articles have increased exponentially in recent years. This growth has brought challenges for academic researchers in locating the most relevant papers in their fields of interest. The reasons for this vary. There is the fundamental problem of synonymy and polysemy, the query terms might be too short, thus making it difficult to distinguish between papers. Also, a new researcher has limited knowledge and often is not sure about what she is looking for until the results are displayed. These issues obstruct scholarly retrieval systems in locating highly relevant publications for a given search query. Researchers seek to tackle these issues. However, the user's intent cannot be addressed entirely by introducing a direct information retrieval technique. In this paper, a novel approach is proposed, which combines query expansion and citation analysis for supporting the scholarly search. It is a two-stage academic search process. Upon receiving the initial search query, in the first stage, the retrieval system provides a ranked list of results. In the second stage, the highest-scoring Term Frequency–Inverse Document Frequency (TF-IDF) terms are obtained from a few top-ranked papers for query expansion behind the scene. In both stages, citation analysis is used in further refining the quality of the academic search. The originality of the approach lies in the combined exploitation of both query expansion by pseudo relevance feedback and citation networks analysis that may bring the most relevant papers to the top of the search results list. The approach is evaluated on the ACL dataset. The experimental results reveal that the technique is effective and robust for locating relevant papers regarding normalized Discounted Cumulative Gain (nDCG), precision, and recall. [ABSTRACT FROM AUTHOR]

An improved lossless compression method with adaptive band reordering and minimum mean square error prediction was proposed to address the problems of huge data volume of remote sensing images, great pressure on transmission and storage and a low compression ratio. This method may determine the optimal band ordering adaptively, and make full use of the ordering correlation to eliminate the image redundancy according to the minimum mean square error criterion. First, it adaptively grouped hyperspectral image bands, and used the minimum spanning tree algorithm for band ordering within each group to enhance the inter-spectral correlation of adjacent bands. Later, it selected the contexts for inter- and intra-spectral prediction adaptively for the bands within the group to remove the redundancy of hyperspectral images. Finally, it conducted binary arithmetic coding of the predicted residuals to remove the statistical redundancy, and complete the lossless compression of hyperspectral images. The test results based on the hyperspectral images of ZY1-02D show that the method in this paper effectively utilizes the intra- and inter-spectral correlations, improves the prediction performance, and outperforms the commonly used compression methods. [ABSTRACT FROM AUTHOR]

In many digital systems, various sequential blocks are used. This paper is devoted to the case where the model of a Mealy finite state machine (FSM) represents the behaviour of a sequential block. The chip area occupied by an FSM circuit is one of the most important characteristics used in logic synthesis. In this paper, a method is proposed which aims at reducing LUT counts for FPGA-based Mealy FSMs with transformation of state codes into FSM outputs. This is done using the combined state codes. Such an approach allows excluding a block of transformation of binary state codes into extended state codes. The proposed method leads to LUT-based Mealy FSM circuits having exactly three levels of logic blocks. Under certain conditions, each function for any logic level is represented by a circuit including a single LUT. The proposed approach is illustrated with an example of synthesis. The results of experiments conducted using standard benchmarks show that the proposed method produces LUT-based FSM circuits with significantly smaller LUT counts than is the case for circuits produced by other investigated methods (Auto and One-hot of Vivado, JEDI, and transformation of binary codes into extended state codes). The LUT count is decreased by an average of 17.96 to 91.8%. Moreover, if some conditions are met, the decrease in the LUT count is accompanied with a slight improvement in the operating frequency compared with circuits based on extended state codes. The advantages of the proposed method multiply with increasing the numbers of FSM inputs and states. [ABSTRACT FROM AUTHOR]

Our goal of this paper is to find a construction of all ℓ-quasi-cyclic self-dual codes over a finite field $ {\mathbb F}_q $ F q of length $ m\ell $ mℓ for every positive even integer ℓ. In this paper, we study the case where $ x^m-1 $ x m − 1 has an arbitrary number of irreducible factors in $ {\mathbb F}_q [x] $ F q [ x ] ; in the previous studies, only some special cases where $ x^m-1 $ x m − 1 has exactly two or three irreducible factors in $ {\mathbb F}_q [x] $ F q [ x ] , were studied. Firstly, the binary code case is completed: for any even positive integer ℓ, every binary ℓ-quasi-cyclic self-dual code can be obtained by our construction. Secondly, we work on the q-ary code cases for an odd prime power q. We find an explicit method for construction of all ℓ-quasi-cyclic self-dual codes over $ {\mathbb F}_q $ F q of length $ m\ell $ mℓ for any even positive integer ℓ, where we require that $ q \equiv 1 \pmod {4} $ q ≡ 1 (mod 4) if the index $ \ell \ge 6 $ ℓ ≥ 6. By implementation of our method, we obtain a new optimal binary self-dual code $ [172, 86, 24] $ [ 172 , 86 , 24 ] , which is also a quasi-cyclic code of index 4. [ABSTRACT FROM AUTHOR]

In visual indoor positioning systems, the method of constructing a visual map by point-by-point sampling is widely used due to its characteristics of clear static images and simple coordinate calculation. However, too small a sampling interval will cause image redundancy, while too large a sampling interval will lead to the absence of any scene images, which will result in worse positioning efficiency and inferior positioning accuracy. As a result, this paper proposed a visual map construction method based on pre-sampled image features matching, according to the epipolar geometry of adjacent position images, to determine the optimal sampling spacing within the constraints and effectively control the database size while ensuring the integrity of the image information. In addition, in order to realize the rapid retrieval of the visual map and reduce the positioning error caused by the time overhead, an image retrieval method based on deep hashing was also designed in this paper. This method used a convolutional neural network to extract image features to construct the semantic similarity structure to guide the generation of hash code. Based on the log-cosh function, this paper proposed a loss function whose function curve was smooth and not affected by outliers, and then integrated it into the deep network to optimize parameters, for fast and accurate image retrieval. Experiments on the FLICKR25K dataset and the visual map proved that the method proposed in this paper could achieve sub-second image retrieval with guaranteed accuracy, thereby demonstrating its promising performance. [ABSTRACT FROM AUTHOR]

To enhance the reliability of UAV-to-Ground data transmission in SC-FDE, this study proposes a combined MMSE equalization and LDPC-RS concatenated code approach, utilizing source redundancy to mitigate errors in low-elevation G2A communication. The proposed coding scheme combines a binary LDPC code as the outer code and an improved RS code as the inner code, resulting in improved resistance to burst and random errors. To address the coding aspect, this paper designs a coding table using RS coding as the underlying logic, enabling efficient coding. For decoding, a decoding scheme based on the minimum Euclidean distance criterion is devised, enabling high-performance decoding. Simulation results demonstrate that the proposed LDPC-RS concatenated coding scheme significantly enhances the performance of the communication system, thereby offering considerable potential for application in UAV video transmission systems. [ABSTRACT FROM AUTHOR]

This paper aims to revisit a construction found in some Spanish varieties which refers to a set constituted by a singular referent and an annex introduced by the item con 'with': PRO(noun)[PL] with-DP. This construction triggers plural agreement and can be doubled by a plural pronoun, indicating that the annex is included in the set to which verbal agreement and the plural pronoun refer. For example, Nosotros con Juan viajamos ayer (literally, 'We with Juan travelled.1PL yesterday') means 'Juan and I travelled yesterday'. We explore the Spanish PRO[PL] with-DP, taking into account its discursive properties together with the syntactic requirements involved in the agreement patterns. In fact, although the two individuals denoted by this construction are involved as equal participants in the event, they have a different discursive status: one of them introduces new information, while the other refers to the immediate communicative situation. If some notions regarding information structure can be coded by binary features such as [+/−anaphor] and [+/−contrast], it is possible to find plurality triggered by the opposite combination of features within the same syntactic object. PRO[PL] with-DP is a possibility that the lexicons of some languages offer. [ABSTRACT FROM AUTHOR]

The number-theoretic code is a class of codes defined by single or multiple congruences. These codes are mainly used for correcting insertion and deletion errors, and for correcting asymmetric errors. This paper presents a formula for a generalization of the complete weight enumerator for the number-theoretic codes. This formula allows us to derive the weight enumerators and cardinalities for the number-theoretic codes. As a special case, this paper provides the Hamming weight enumerators and cardinalities of the non-binary Tenengolts’ codes, correcting single insertion or deletion. Moreover, we show that the formula deduces the MacWilliams identity for the linear codes over the ring of integers modulo $r$. [ABSTRACT FROM AUTHOR]

We propose a successive cancellation list (SCL) encoding and decoding scheme for the Gelfand Pinsker (GP) problem based on the known nested polar coding scheme. It applies SCL encoding for the source coding part, and SCL decoding with a properly defined CRC for the channel coding part. The scheme shows improved performance compared to the existing method. A known issue with nested polar codes for binary dirty paper is the existence of frozen channel code bits that are not frozen in the source code. These bits need to be retransmitted in a second phase of the scheme, thus reducing the rate and increasing the required blocklength. We provide an improved bound on the size of this set, and on its scaling with respect to the blocklength, when the Bhattacharyya parameter of the test channel used for source coding is sufficiently large, or the Bhattacharyya parameter of the channel seen at the decoder is sufficiently small. The result is formulated for an arbitrary binary-input memoryless GP problem, since unlike the previous results, it does not require degradedness of the two channels mentioned above. Finally, we present simulation results for binary dirty paper and noisy write once memory codes. [ABSTRACT FROM AUTHOR]

Reversible codes have a range of wide applications in cryptography, data storage, and communication systems. In this paper, we investigated reversible codes under the Rosenbloom-Tsfasman metric (RT-metric). First, some properties of reversible codes in the RT-metric were described. An essential condition for a reversible code to be a maximum distance separable code (MDS code, in short) in the RT-metric was established. A necessary condition for a binary self-dual code to be reversible was proven and the same was generalized for q-ary self-dual reversible codes. Several constructions for reversible RT-metric codes were provided in terms of their generator matrices. [ABSTRACT FROM AUTHOR]

In this paper, we consider the challenge of ensuring secure communication in block-fading wiretap channels using encoding techniques. We investigate the coding for block-fading wiretap channels using stacked lattice codes constructed over completely real number fields, which is a well-established technique. In this paper, we consider degree-four complex multiplication field 풦 = ℚ(ς8) over ℚ. We employ binary codes to generate a lattice over ℚ(ς8), which is subsequently used to form an integral lattice. The resulting integral lattice can be effectively applied to enhance the security of communication within block-fading wiretap channels. [ABSTRACT FROM AUTHOR]

With the rapid development of multimedia technology, considerable achievement has been achieved in image retrieval technology. On the one hand, users' demand for cross-modal retrieval is increasing rapidly. On the other hand, deep hashing algorithms as one of the most prominent high-dimensional reduction methods have received extensive attention. Under such background, the cross-modal retrieval method based on deep hashing came into being. Although the cross-modal hashing learning method has moved a long way in recent years, it still has space of promotion. First, existing cross-modal based hashing algorithms can only map the feature vectors into binary codes with a fixed length. The length of hash codes cannot be modified according to the practical situation. Second, the saturation level of the probability function used in existing methods is too high to concentrate relevant samples very well, which results in low retrieval efficiency. To solve the above problem, a novel variable-length hash code based on adaptive weight was proposed in this paper. The length of hash codes could be adjusted according to the importance of each bit. And a novel probability function based on Cauchy distribution was proposed to generate compact binary codes and make hashing retrieval more efficient. The experiment shows that the proposed cross-modal image retrieval algorithm based on deep hashing outperforms existing related algorithms on the accuracy of retrieval. [ABSTRACT FROM AUTHOR]

Motivated by the sequence reconstruction problem initiated by Levenshtein, reconstruction codes were introduced by Cai et al. to combat errors when a fixed number of noisy channels are available. The central problem on this topic is to design codes with sizes as large as possible, such that every codeword can be uniquely reconstructed from any N distinct noisy reads, where N is fixed. In this paper, we study binary reconstruction codes with the constraint that every codeword is balanced, which is a fundamental requirement in the technique of DNA-based storage. For all possible channels with a single edit error and their variants, we design asymptotically optimal balanced reconstruction codes for all N, and show that the number of their redundant symbols decreases from 3 2 log 2 n + O (1) to 1 2 log 2 n + log 2 log 2 n + O (1) , and finally to 1 2 log 2 n + O (1) but with different speeds, where n is the length of the code. Compared with the unbalanced case, our results imply that the balanced property does not reduce the rate of the reconstruction code in the corresponding codebook. [ABSTRACT FROM AUTHOR]

In this paper, we first consider the existence conditions, the construction and the enumeration of Hermitian self-dual θ -cyclic and θ -negacyclic codes over I F p 2 , where p is a prime number and θ is the Frobenius automorphism over I F p 2 . We then give necessary and sufficient conditions for the existence of Hermitian self-dual θ -cyclic and θ -negacyclic codes over I F p e where e is an even integer greater than 2. [ABSTRACT FROM AUTHOR]

In recent years, with the development of the Internet, the attribution classification of APT malware remains an important issue in society. Existing methods have yet to consider the DLL link library and hidden file address during the execution process, and there are shortcomings in capturing the local and global correlation of event behaviors. Compared to the structural features of binary code, opcode features reflect the runtime instructions and do not consider the issue of multiple reuse of local operation behaviors within the same APT organization. Obfuscation techniques more easily influence attribution classification based on single features. To address the above issues, (1) an event behavior graph based on API instructions and related operations is constructed to capture the execution traces on the host using the GNNs model. (2) ImageCNTM captures the local spatial correlation and continuous long-term dependency of opcode images. (3) The word frequency and behavior features are concatenated and fused, proposing a multi-feature, multi-input deep learning model. We collected a publicly available dataset of APT malware to evaluate our method. The attribution classification results of the model based on a single feature reached 89.24% and 91.91%. Finally, compared to single-feature classifiers, the multi-feature fusion model achieves better classification performance. [ABSTRACT FROM AUTHOR]

Binary code similarity detection plays a crucial role in various applications within binary security, including vulnerability detection, malicious software analysis, etc. However, existing methods suffer from limited differentiation in binary embedding representations across different compilation environments, lacking dynamic high-level semantics. Moreover, current approaches often neglect multi-level semantic feature extraction, thereby failing to acquire precise semantic information about the binary code. To address these limitations, this paper introduces a novel detection solution called BinBcla. This method employs an enhanced pre-training model to generate instruction embeddings with dynamic semantics for binary functions. Subsequently, multi-feature fusion technique is utilized to extract local semantic information and long-distance global features from the code, respectively, employing self-attention to comprehend the structure information of the code. Finally, an improved cosine similarity method is employed to learn relationships among all elements of the distance vectors, thereby enhancing the model's robustness to new sample functions. Experiments are conducted across different architectures, compilers, and optimization levels. The results indicate that BinBcla achieves higher accuracy, precision and F1 score compared to existing methods. [ABSTRACT FROM AUTHOR]

Linear codes play a crucial role in various fields of engineering and mathematics, including data storage, communication, cryptography, and combinatorics. Minimal linear codes, a subset of linear codes, are particularly essential for designing effective secret sharing schemes. In this paper, we introduce several classes of minimal binary linear codes by carefully selecting appropriate Boolean functions. These functions belong to a renowned class of Boolean functions, namely, the general Maiorana–McFarland class. We employ a method first proposed by Ding et al. (IEEE Trans Inf Theory 64(10):6536–6545, 2018) to construct minimal codes violating the Ashikhmin–Barg bound (wide minimal codes) by using Krawtchouk polynomials. The lengths, dimensions, and weight distributions of the obtained codes are determined using the Walsh spectrum distribution of the chosen Boolean functions. Our findings demonstrate that a vast majority of the newly constructed codes are wide minimal. Furthermore, our proposed codes exhibit a significantly larger minimum distance, in some cases, compared to some existing similar constructions. Finally, we address this method, based on Krawtchouk polynomials, more generally, and highlight certain generic properties related to it. These general results offer insights into the scope of this approach. [ABSTRACT FROM AUTHOR]

This paper proposes a phase unwrapping method based on ternary Gray code in fringe projection profilometry. This method does not require additional projection images or any prior knowledge. It accurately determines the segmentation thresholds of the ternary Gray code using only the average intensity and intensity modulation calculated from phase-shifting fringes, effectively reducing projection costs and achieving lower codeword errors. Additionally, a segment selection strategy suitable for the ternary Gray code is proposed. By employing the complementary method, three sets of candidate orders with certain codeword shifts can be generated. The wrapped phase is then divided into three segments: upper, middle, and lower segments, and correspondingly matched with the central regions of the three sets of candidate orders, thus enabling the solution of the absolute phase. Through the segment selection strategy, error-prone areas at the edges of orders are effectively avoided, further enhancing the accuracy of measurement results. Compared to the binary Gray code, the ternary complementary Gray code can encode up to 3 m − 1 orders when the number of Gray code patterns is m; when the required number of encoding orders is K, only ⌈ log 3 K ⌉ + 1 Gray code patterns are needed. Its additional value capacity precisely enables it to encode more orders, thereby increasing the frequency of phase-shifting fringes and further enhancing measurement efficiency. A series of experiments validates the feasibility, robustness, and superiority of this method. [ABSTRACT FROM AUTHOR]

Scientific researchers are facing a rapidly growing volume of literatures nowadays. While these publications offer rich and valuable information, the scale of the datasets makes it difficult for the researchers to manage and search for desired information efficiently. Literature Explorer is a new interactive visual analytics suite that facilitates the access to desired scientific literatures through mining and interactive visualisation. We propose a novel topic mining method that is able to uncover "thematic topics" from a scientific corpus. These thematic topics have an explicit semantic association to the research themes that are commonly used by human researchers in scientific fields, and hence are human interpretable. They also contribute to effective document retrieval. The visual analytics suite consists of a set of visual components that are closely coupled with the underlying thematic topic detection to support interactive document retrieval. The visual components are adequately integrated under the design rationale and goals. Evaluation results are given in both objective measurements and subjective terms through expert assessments. Comparisons are also made against the outcomes from the traditional topic modelling methods. [ABSTRACT FROM AUTHOR]

The problem of universal source coding for binary trees is considered. Zhang, Yang, and Kieffer derived upper bounds on the average-case redundancy of codes based on directed acyclic graph (DAG) compression for binary tree sources with certain properties. In this paper, a natural class of binary tree sources is presented such that the demanded properties are fulfilled. Moreover, for both subclasses considered in the paper of Zhang, Yang, and Kieffer, their result is improved by deriving bounds on the maximal pointwise redundancy (or worst-case redundancy) instead of the average-case redundancy. Finally, using context-free tree grammars instead of DAGs, upper bounds on the maximal pointwise redundancy for certain binary tree sources are derived. This yields universal codes for new classes of binary tree sources. [ABSTRACT FROM AUTHOR]

Product reviews provide crucial information for both consumers and businesses, offering insights needed before purchasing a product or service. However, existing sentiment analysis methods, especially for Chinese language, struggle to effectively capture contextual information due to the complex semantics, multiple sentiment polarities, and long-term dependencies between words. In this paper, we propose a sentiment classification method based on the BiLSTM algorithm to address these challenges in natural language processing. Self-Attention-CNN BiLSTM (SAC-BiLSTM) leverages dual channels to extract features from both character-level embeddings and word-level embeddings. It combines BiLSTM and Self-Attention mechanisms for feature extraction and weight allocation, aiming to overcome the limitations in mining contextual information. Experiments were conducted on the onlineshopping10cats dataset, which is a standard corpus of e-commerce shopping reviews available in the ChineseNlpCorpus 2018. The experimental results demonstrate the effectiveness of our proposed algorithm, with Recall, Precision, and F1 scores reaching 0.9409, 0.9369, and 0.9404, respectively. [ABSTRACT FROM AUTHOR]

Viruses are one of the main threats to the security of today's cyberspace. With the continuous development of virus and artificial intelligence technologies in recent years, the intelligentization of virus technology has become a trend. It is of urgent significance to study and combat intelligent viruses. In this paper, we design a new type of confirmatory virus from the attacker's perspective that can intelligently infect software frameworks. We aim for structural software as the target and use BCSD (binary code similarity detection) to identify the framework. By incorporating a software framework functional structure recognition model in the virus, the virus is enabled to intelligently recognize software framework functions in executable files. This paper evaluates the BCSD model that is suitable for a virus to carry and constructs a lightweight BCSD model with a knowledge distillation technique. This research proposes a software framework functional structure recognition algorithm, which effectively reduces the recognition precision's dependence on the BCSD model. Finally, this study discusses the next researching direction of intelligent viruses. This paper aims to provide a reference for the research of detection technology for possible intelligent viruses. Consequently, focused and effective defense strategies could be proposed and the technical system of malware detection could be reinforced. [ABSTRACT FROM AUTHOR]

This paper considers a device-to-device (D2D) fog-radio access network wherein a set of users are required to store/receive a set of files. The D2D devices are connected to a subset of the cloud data centers and thus possess a subset of the data. This paper is interested in reducing the total time of communication, i.e., the completion time, required to disseminate all files among all devices using instantly decodable network coding (IDNC). Unlike previous studies that assume a fully connected communication network, this paper tackles the more realistic scenario of a partially connected network in which devices are not all in the transmission range of one another. The joint optimization of selecting the transmitting device(s) and the file combination(s) is first formulated, and its intractability is exhibited. The completion time is approximated using the celebrated decoding delay approach by deriving the relationship between the quantities in a partially connected network. The paper introduces the cooperation graph and demonstrates that the problem is equivalent to a maximum weight clique problem over the newly designed graph. Extensive simulations reveal that the proposed solution provides noticeable performance enhancement and outperforms previously proposed IDNC-based schemes. [ABSTRACT FROM PUBLISHER]

Infrared small target detection (IRSTD) is crucial for applications in security surveillance, unmanned aerial vehicle identification, military reconnaissance, and other fields. However, small targets often suffer from resolution limitations, background complexity, etc., in infrared images, which poses a great challenge to IRSTD, especially due to the noise interference and the presence of tiny, low-luminance targets. In this paper, we propose a novel dual enhancement network (DENet) to suppress background noise and enhance dim small targets. Specifically, to address the problem of complex backgrounds in infrared images, we have designed the residual sparse enhancement (RSE) module, which sparsely propagates a number of representative pixels between any adjacent feature pyramid layers instead of a simple summation. To handle the problem of infrared targets being extremely dim and small, we have developed a spatial attention enhancement (SAE) module to adaptively enhance and highlight the features of dim small targets. In addition, we evaluated the effectiveness of the modules in the DENet model through ablation experiments. Extensive experiments on three public infrared datasets demonstrated that our approach can greatly enhance dim small targets, where the average values of intersection over union ( I o U ), probability of detection ( P d ), and false alarm rate ( F a ) reached up to 77.33%, 97.30%, and 9.299%, demonstrating a performance superior to the state-of-the-art IRSTD method. [ABSTRACT FROM AUTHOR]

In 2018, J. M. Gómez et al. showed that the problem of finding the packing number ρ(F2(Pn)) of the 2-token graph F2(Pn) of the path Pn of length n ≥ 2 is equivalent to determining the maximum size of a binary code S' of constant weight w = 2 that can correct a single adjacent transposition. By determining the exact value of ρ(F2(Pn)), they proved a conjecture of Rob Pratt. In this paper, we study a related problem, which consists of determining the packing number ρ(F3(Pn)) of the graph F3(Pn). This problem corresponds to the Sloane's problem of finding the maximum size of S' of constant weight w = 3 that can correct a single adjacent transposition. Since the maximum packing set problem is computationally equivalent to the maximum independent set problem, which is an NP-hard problem, then no polynomial time algorithms are expected to be found. Nevertheless, we compute the exact value of ρ(F3(Pn)) for n ≤ 12, and we also present some algorithms that produce a lower bound for ρ(F3(Pn)) with 13 ≤ n ≤ 44. Finally, we establish an upper bound for ρ(F3(Pn)) with n ≥ 13. [ABSTRACT FROM AUTHOR]

In this paper, we introduced double cyclic codes over Rr × Rs, where R = Z2 + uZ2 = {0, 1, u, 1 + u} is the ring with four elements and u² = 0. We first determined the generator polynomials of R-double cyclic codes for odd integers r and s, then gave the generators of duals of free double cyclic codes over Rr × Rs. By defining a linear Gray map, we looked at the binary images of R-double cyclic codes and gave several examples of optimal parameter binary linear codes obtained from R-double cyclic codes. Moreover, we studied self-dual R-double cyclic codes and presented an example of a self-dual R-double cyclic code. [ABSTRACT FROM AUTHOR]

In this paper, δ -dual codes over finite commutative semi-simple rings are defined as a generalization of dual codes over finite commutative semi-simple rings. Some properties of δ -dual codes are given. We present necessary and sufficient conditions for a λ -constacyclic code of length n to be δ -self-dual, δ -self-orthogonal, δ -dual-containing, δ -LCD over finite commutative semi-simple rings. We also study the δ -dual of skew Θ - λ -constacyclic codes over finite commutative semi-simple rings. Among others, we also give necessary and sufficient conditions for a skew Θ - λ -constacyclic code of any length n to be δ -self-dual, δ -self-orthogonal, δ -dual-containing, δ -LCD over finite commutative semi-simple rings. [ABSTRACT FROM AUTHOR]

The exponential growth of data raises an unprecedented challenge in data analysis: how to retrieve interesting information from such large-scale data. Hash learning is a promising solution to address this challenge, because it may bring many potential advantages, such as extremely high efficiency and low storage cost, after projecting high-dimensional data to compact binary codes. However, traditional hash learning algorithms often suffer from the problem of semantic inconsistency, where images with similar semantic features may have different binary codes. In this paper, we propose a novel end-to-end deep hashing method based on the similarities of binary codes, dubbed CSDH (Code Similarity-based Deep Hashing), for image retrieval. Specifically, it extracts deep features from images to capture semantic information using a pre-trained deep convolutional neural network. Additionally, a hidden and fully connected layer is attached at the end of the deep network to derive hash bits by virtue of an activation function. To preserve the semantic consistency of images, a loss function has been introduced. It takes the label similarities, as well as the Hamming embedding distances, into consideration. By doing so, CSDH can learn more compact and powerful hash codes, which not only can preserve semantic similarity but also have small Hamming distances between similar images. To verify the effectiveness of CSDH, we evaluate CSDH on two public benchmark image collections, i.e., CIFAR-10 and NUS-WIDE, with five classic shallow hashing models and six popular deep hashing ones. The experimental results show that CSDH can achieve competitive performance to the popular deep hashing algorithms. [ABSTRACT FROM AUTHOR]

The study of linear codes over local rings, particularly non-chain rings, imposes difficulties that differ from those encountered in codes over chain rings, and this stems from the fact that local non-chain rings are not principal ideal rings. In this paper, we present and successfully establish a new approach for linear codes of any finite length over local rings that are not necessarily chains. The main focus of this study is to produce generating characters, MacWilliams identities and generator matrices for codes over singleton local Frobenius rings of order 32. To do so, we first start by characterizing all singleton local rings of order 32 up to isomorphism. These rings happen to have strong connections to linear binary codes and Z 4 codes, which play a significant role in coding theory. [ABSTRACT FROM AUTHOR]

Global contexts are critical to locating salient objects for salient object detection (SOD). However, the convolution operation in CNNs has a local receptive field, which cannot capture long-distance global information. Recent studies have shown that modernized CNN models with large kernel convolution, such as ConvNeXt, can effectively extend the receptive fields. Based on it, this paper explores the potential of large kernel CNN for SOD task. Inspired by the common information between RGB and depth images in salient objects, we propose a ConvNeXt-based Siamese network with shared weight parameters. This structural design can effectively reduce the number of parameters without sacrificing performance. Furthermore, a depth information preprocessing module is proposed to minimize the impact of low-quality depth images on predicted saliency maps. For cross-modal feature interaction, a dynamic fusion module is designed to enhance cross-modal complementarity dynamically. Extensive experiments and evaluation results on six benchmark datasets demonstrate the outstanding performance of the proposed method against 14 state-of-the-art RGB-D methods. Our code will be released at https://github.com/zyh5119232/CSNet. [ABSTRACT FROM AUTHOR]

...-ispotty-byte error correcting codes over Zq [7] are non-binary codes generalizing the binary irregular-spotty-byte codes [3] suitable for semiconductor memories in which a word is divided into irregular bytes not necessarily of the same length. The distance between distinct words is measured in terms of newly defined ... - metric function [7]. In this paper, we present the code construction methods of ...-ispotty-byte error correcting codes in terms of their parity check matrix. [ABSTRACT FROM AUTHOR]

Many different aspects of software system development and verification rely on precise function identification in binary code. Recognition of the source Assembly functions in embedded systems is one of the fundamental challenges in binary program analysis. While numerous approaches assume that the functions are given a priori, correct identification of the functions in binaries remains a great issue. This contribution addresses the problem of uncertainty in binary code in identification of functions, which were optimized during compilation. This paper investigates the difference between debug and optimized functions via modeling of these functions. To do so, we introduce an extensible model-centred hands-on approach for examining similarities between binary functions. The main idea is to model each function using a set of predetermined, experimentally discovered features, and then find a suitable weight vector that could give impact factor to each such a feature. After finding the weight vector, the introduced models of such desired functions can be identified in binary software packages. It means that we reduce the similarity identification problem of the models to a classical version of optimization problems with one optimization criterion. Using our implementation, we found that the proposed approach works smoothly for functions, which contain at least ten Assembly instructions. Our tool guarantees success at a very high level. [ABSTRACT FROM AUTHOR]

In this paper, we give a classification of extremal Type II Z 4 -codes of length 24. This is equivalent to a classification of 4-frames of the Leech lattice up to the action of the automorphism group. [ABSTRACT FROM AUTHOR]

In this paper we construct distance-regular graphs admitting a vertex transitive action of the five sporadic simple groups discovered by E. Mathieu, the Mathieu groups M 11 , M 12 , M 22 , M 23 and M 24 . From the binary code spanned by an adjacency matrix of the strongly regular graph with parameters (176,70,18,34) we obtain block designs having the full automorphism groups isomorphic to the Higman-Sims finite simple group. Moreover, from that code we obtain eight 2-designs having the full automorphism group isomorphic to M 22 , whose existence cannot be explained neither by the Assmus-Mattson theorem nor by a transitivity argument. Further, we discuss a possibility of permutation decoding of the codes spanned by adjacency matrices of the graphs constructed and find small PD-sets for some of the codes. [ABSTRACT FROM AUTHOR]

In this paper, we establish some criteria to decide when a discrete memoryless channel admits a metric in such a way that the maximum likelihood decoding coincides with the nearest neighbor decoding. In particular, we prove a conjecture presented by M. Firer and J. L. Walker, establishing that every binary asymmetric channel admits a matched metric. [ABSTRACT FROM AUTHOR]

The traditional pointer instrument recognition scheme is implemented in three steps, which is cumbersome and inefficient. So it is difficult to apply to the industrial production of real-time monitoring. Based on the improvement of the CSL coding method and the setting of the pre-cache mechanism, an intelligent reading recognition technology of the YOLOv5 pointer instrument is proposed in this paper, which realizes the rapid positioning and reading recognition of the pointer instrument. The problem of angle interaction in rotating target detection is eliminated, the complexity of image preprocessing is avoided, and the problems of poor adaptability of Hough detection are solved in this strategy. The experimental results show that compared with the traditional algorithm, the algorithm in this paper can effectively identify the angle of the pointer instrument, has high detection efficiency and strong adaptability, and has broad application prospects. [ABSTRACT FROM AUTHOR]

This paper reveals the hidden dangers of reverse data modifications on distributed software with network synchronization, during the era of 5G, which may occur in more important domains, such as telemedicine and automatic driving. We used pseudo-codes to formally elaborate the distributed software architectures and design patterns. It is necessary to deal with three challenges for the modification of binary code and data in the distributed software architectures: (1) the base virtual addresses of software objects are changed frequently for safety; (2) prior knowledge of the reverse is not considered; (3) system memory values of some target objects are changed with extreme speed. For this purpose, a novel reverse modification method for binary code and data is proposed. According to the knowledge-based rules, our method can manipulate physical data, sight data, animation data, etc., while the game synchronization mechanism cannot detect the changes. The implementation details of our method are presented using high-level programming languages (C++) and low-level programming languages (assembly), based on multiple snippets, so that readers can understand both the overall distributed software developments and the corresponding reverse processes. In particular, two network games are used for the demonstrations in this paper. The demonstration results show that our proposed methodology is efficient (as proved by formulas and practices) to manipulate the codes and data of distributed software using a synchronization mechanism. [ABSTRACT FROM AUTHOR]

The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6mcompared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection. [ABSTRACT FROM AUTHOR]

This paper proposes a novel automotive radar waveform involving the theory behind M-ary frequency shift key (MFSK) radar systems. Along with the MFSK theory, coding schemes are studied to provide a solution to mutual interference. The proposed MFSK waveform consists of frequency increments throughout the range of 76 GHz to 81 GHz with a step value of 1 GHz. Instead of stepping with a fixed frequency, a triangular chirp sequence allows for static and moving objects to be detected. Therefore, automotive radars will improve Doppler estimation and simultaneous range of various targets. In this paper, a binary coding scheme and a combined transform coding scheme used for radar waveform correlation are evaluated in order to provide unique signals. AVs have to perform in an environment with a high number of signals being sent through the automotive radar frequency band. Efficient coding methods are required to increase the number of signals that are generated. An evaluation method and experimental data of modulated frequencies as well as a comparison with other frequency method systems are presented. [ABSTRACT FROM AUTHOR]

Subfield codes of linear codes over finite fields have recently received a lot of attention, as some of these codes are optimal and have applications in secrete sharing, authentication codes and association schemes. In this paper, a class of binary subfield codes is constructed from a special family of MDS codes, and their parameters are explicitly determined. The parameters of their dual codes are also studied. Some of the codes presented in this paper are optimal or almost optimal. [ABSTRACT FROM AUTHOR]

High-Efficiency Video Coding (HEVC) has a higher coding efficiency, its encoding performance must be increased to keep up with the expanding number of multimedia applications. Therefore, this paper proposes a novel Rectified Linear Unit-Bidirectional Long Short-Term Memory-based Tree Social Relations Optimization (ReLU-BiLSTM-based TSRO) method to enhance the quality of video transmission. The significant objective of our proposed method aims in enhancing the standards of entropy encoding process in HEVC. Here, context-adaptive binary arithmetic coding (CABAC) framework which is prevalent and an improved form of entropy coding model is utilized in HEVC standards. In addition to this, the performances of the proposed method are determined by evaluating various measures such as mean square error, cumulative distribution factor, compression ratio, peak signal-to-noise ratio (PSNR) and bit error rate. Finally, the proposed method is examined with five different sequences of video from football, tennis, garden, mobile and coastguard. The performances of the proposed method are compared with various approaches, and the result analysis shows that the proposed method attained minimum mean square error (MSE) loss with maximum PSNR rate. [ABSTRACT FROM AUTHOR]

This paper investigates the construction of rank-metric codes with specified Ferrers diagram shapes. These codes play a role in the multilevel construction for subspace codes. A conjecture from 2009 provides an upper bound for the dimension of a rank-metric code with given specified Ferrers diagram shape and rank distance. While the conjecture in its generality is wide open, several cases have been established in the literature. This paper contributes further cases of Ferrers diagrams and ranks for which the conjecture holds true. In addition, the proportion of maximal Ferrers diagram codes within the space of all rank-metric codes with the same shape and dimension is investigated. Special attention is being paid to MRD codes. It is shown that for growing field size the limiting proportion depends highly on the Ferrers diagram. For instance, for $[m\times 2]$ -MRD codes with rank 2 this limiting proportion is close to $1/e$. [ABSTRACT FROM AUTHOR]

This paper analyses the design of a healthy interior environment using big data intelligence. The application of big data intelligence in the design of healthy interior environments is necessary because the traditional interior design approaches consume a lot of energy and other problems. Benefited by its strong ability of computation and analytics, artificial intelligence can well improve a series of problems in the field of interior design. The proposal summarizes the sources, classifications, and expressions of behavioral data in interior spaces, carries out analysis and research on behavioral data from two aspects: display space and supermarket space, summarizes the interior methods based on behavioral data, and analyses the visualization application of behavioral data in different interior scenes, to explore the application value of behavioral data in interior design. In contrast to it is the unconscious behavioral response, the biggest characteristic of which is that it is regulated by the behavioral subject's physiological factors or habits of the behavior issuer. In this paper, we convert the layout recommendation problem of a space into a functional classification problem of segmented segments and household segments on a plane. The scene layout features are extracted by binary coding, the abstraction of the cross features between the vector segments is achieved by using a word embedding algorithm, the feature matrix is reduced in dimensionality, and finally, the segmentation network model and the layout network model are constructed, respectively, by using a bidirectional LSTM. The experiments show that the accuracy of the layout recommendation model in this paper is 98%, which can meet the demand for real-time online layouts. [ABSTRACT FROM AUTHOR]

Biclustering is a two-dimensional data analysis technique, where submatrices of a given data matrix are looked for. Its extension into three-dimensional data is called triclustering. In the paper, a new generalized look into n-dimensional binary data n-clustering is presented. The searching is performed in terms of the Boolean reasoning paradigm, where the original case (the data) is coded into the Boolean formula and its prime implicants are equivalent to the solutions of the original issue. The correctness (finding n-clusters containing only 0s or 1s) and maximality (the n-cluster cannot be expanded in any dimension without the correctness requirement violation) of such an approach have strong mathematical foundations. The paper also shows the application of Boolean reasoning-based n-clustering for small three- and four-dimensional artificial data as well as for some biomedical ones. [ABSTRACT FROM AUTHOR]

Many families of binary nonlinear codes (e.g., Kerdock, Goethals, Delsarte–Goethals, Preparata) can be very simply constructed from linear codes over the Z 4 ring (ring of integers modulo 4), by applying the Gray map to the quaternary symbols. Generalized Kerdock codes represent an extension of classical Kerdock codes to the Z 2 S ring. In this paper, we develop two novel soft-input decoders, designed to exploit the unique structure of these codes. We introduce a novel soft-input ML decoding algorithm and a soft-input soft-output MAP decoding algorithm of generalized Kerdock codes, with a complexity of O (N S log 2 N) , where N is the length of the Z 2 S code, that is, the number of Z 2 S symbols in a codeword. Simulations show that our novel decoders outperform the classical lifting decoder in terms of error rate by some 5 dB. [ABSTRACT FROM AUTHOR]

How to model the effect of reflection is crucial for single image reflection removal (SIRR) task. Modern SIRR methods usually simplify the reflection formulation with the assumption of linear combination of a transmission layer and a reflection layer. However, the large variations in image content and the real-world picture-taking conditions often result in far more complex reflection. In this paper, we introduce a new screen-blur combination based on two important factors, namely the intensity and the blurriness of reflection, to better characterize the reflection formulation in SIRR. Specifically, we present Screen-blur Reflection Networks (SRNet), which executes the screen-blur formulation in its network design and adapts to the complex reflection on real scenes. Technically, SRNet consists of three components: a blended image generator, a reflection estimator and a reflection removal module. The image generator exploits the screen-blur combination to synthesize the training blended images. The reflection estimator learns the reflection layer and a blur degree that measures the level of blurriness for reflection. The reflection removal module further uses the blended image, blur degree and reflection layer to filter out the transmission layer in a cascaded manner. Superior results on three different SIRR methods are reported when generating the training data on the principle of the screen-blur combination. Moreover, extensive experiments on six datasets quantitatively and qualitatively demonstrate the efficacy of SRNet over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]