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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]

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]

In this paper, we construct a self-dual complete resolution of a module defined by a pair of embedded complete intersection ideals in a local ring. Our construction is based on a gluing construction of Herzog and Martsinkovsky and exploits the structure of Koszul homology in the embedded complete intersection case. As a consequence of our construction, we produce an isomorphism between certain stable homology and cohomology modules. [ABSTRACT FROM AUTHOR]

The ever-increasing need to ensure that code is reliably, efficiently and safely constructed has fueled the evolution of popular static binary code analysis tools. In identifying potential coding flaws in binaries, tools such as IDA Pro are used to disassemble the binaries into an opcode/ assembly language format in support of manual static code analysis. Because of the highly manual and resource-intensive nature involved with analyzing large binaries, the probability of overlooking potential coding irregularities and inefficiencies is quite high. In this paper, a light-weight, unsupervised data flow methodology is described which uses highly correlated data flow graph (CDFGs) to identify coding irregularities such that analysis time and required computing resources are minimized. Such analysis accuracy and efficiency gains are achieved by using a combination of graph analysis and unsupervised machine learning techniques which allows an analyst to focus on the most statistically significant flow patterns while performing binary static code analysis. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) cross-modal text–image retrieval has great application value in many fields such as military and civilian. Existing methods utilize the deep network to project the images and texts into a common space and measure the similarity. However, the majority of those methods only utilize the inter-modality information between different modalities, which ignores the rich semantic information within the specific modality. In addition, due to the complexity of the RS images, there exists a lot of interference relation information within the extracted representation from the original features. In this paper, we propose a jointly guided deep network for fine-grained cross-modal RS text–image retrieval. First, we capture the fine-grained semantic information within the specific modality and then guide the learning of another modality of representation, which can make full use of the intra- and inter-modality information. Second, to filter out the interference information within the representation extracted from the two modalities of data, we propose an interference filtration module based on the gated mechanism. According to our experimental results, significant improvements in terms of retrieval tasks can be achieved compared with state-of-the-art algorithms. The source code is available at https://github.com/CQULab/JGDN. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for calculating the Euler characteristic in 2D binary images. The problem is addressed using the Three OrThogonal symbol chain code (3OT code), using only one symbol for the calculation of the Euler characteristic. Using this code, it is possible to introduce new geometric concepts represented by the same symbol of the 3OT alphabet and to simplify the overall equation of the Euler characteristic. This process is supported by the proof of a set of theorems and their numerical validation, using a set of binary images with a variable number of holes. Thus, this research proves that the 3OT code can be used not only for image compression as reported in the literature, but also to simplify the expression of the Euler characteristic as well as for the analysis and simplification of the shape of contours. [ABSTRACT FROM AUTHOR]

Let f be a normalized Hecke eigenform of weight k for the full modular group S L 2 (ℤ). In this paper, we obtain the asymptotic of higher moments of general divisor functions associated to the Fourier coefficients of Rankin–Selberg L-functions R (s , f × f) , supported at the integers represented by primitive integral positive-definite binary quadratic forms (reduced forms) of a fixed discriminant D < 0. We improve previous results in the case when the reduced form is given by (x 1 , x 2) = x 1 2 + x 2 2 . [ABSTRACT FROM AUTHOR]

In this paper, we study the relation between the linear subspace of the pseudo-noise (PN)-sequences generated by a primitive polynomial and the simplex code. This family of sequences can be also seen as an Maximum Distance Separable (MDS) 2 -linear code over 2 r . Furthermore, we see how to compute the family of generalized sequences produced by a primitive polynomial by means of a first-order Reed–Muller code. [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]

Let A (n , d) be the size of the maximum binary code of length n and minimum Hamming distance d. A (n , d , w) is defined similarly for binary code with constant weight w. Obviously, finding the value of A (n , d) is equivalent to finding the maximum independent set of the d − 1 th-power of n -dimensional hypercube. Based on this, this paper obtains that A (3 m , 2 m) = 4 , A (m (m + 1) 2 , 2 m − 2 , m) = m + 1 , A (2 m + 1 − 2 , 2 m − 1) = 2 m + 1 , and explores the structure of the maximum code of length 2 m + 2 and minimum Hamming distance 2 m + 1 , where m is an integer. [ABSTRACT FROM AUTHOR]

Existing super-resolution methods convert high-resolution images into low-resolution images, and use the synthesized images as input to train the model. However, it is difficult for synthetic low-resolution images to reflect the characteristics of real low-resolution images, resulting in poor model performance in practical applications. To address this problem, we propose a recurrent super-resolution framework, which consists of a degradation model and a reconstruction model. The degradation model degenerates the real high-resolution image into a more real low-resolution image, which is used as the input of the super-resolution reconstruction network, and then uses the reconstruction model to reconstruct the low-resolution image, and calculates the error with the original image. The generated high-resolution image is input into the degradation model again for degradation processing, forming a symmetrical and cyclic network structure, so that the super-resolution model has a better effect when reconstructing the real low-scoring image. In addition, the spatial attention mechanism is introduced into the generator network, which expands the receptive field of the convolution kernel, better extracts long-distance image features and improves the texture details of super-resolution images, which is consistent with the global. [ABSTRACT FROM AUTHOR]

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709. [ABSTRACT FROM AUTHOR]

Binary code analysis is vital in source code unavailable cases, such as malware analysis and software vulnerability mining. Its first step could be function identification. Most function identification methods are based on function prologs/epilogs. However, functions may not have standard prologs/epilogs. To identify these functions, we need to use other methods. One approach is to identify return instructions first and then identify the start of a function. Currently, the multi-layer perceptron model is exploited to identify and validate a return instruction at a specific location. On this basis, a new approach is proposed to improve accuracy and provide more details. Specifically, a return instruction is classified into three classes: (1) false return instruction, (2) true return instruction inner a function but not the last instruction, and (3) true return instruction at the end of a function. The evaluation is performed on 5782 real-world binaries. Meanwhile, common classifiers including fully connected neural network, Two-layer Bidirectional Recurrent Neural Network (TBRNN), Two-layer Bidirectional Gate Recurrent Unit (TBGRU), Two-layer Bidirectional Long Short-term Memory Network (TBLSTM), Decision Tree, Random Forest, XGBoost, and Support Vector Machine (SVM) are evaluated on the same data set. The result shows that TBLSTM achieves an accuracy of 99.78%, which is higher than that of other classifiers in the evaluation, including the state-of-the-art tool IDA Pro 7.7. [ABSTRACT FROM AUTHOR]