Reviewerly/exHarmony huggingface dataset:
Hybrid deep learning model for Arabic text classification based on mutual information

AbstractAbstractText categorization refers to the process of grouping text or documents into classes or categories according to their content, which is a significant task in natural language processing. The majority of the present work focused on English text, with a few experiments on Arabic text. The text classification process consists of many steps, from preprocessing documents (removing stop words and stem method), to feature extraction and classification phase. A new improved approach for Arabic text categorization was proposed using mutual information in a hybrid deep learning model for classification. To test the proposed model, two datasets of Arabic documents are employed. The experimental results demonstrate that employing the proposed mutual information exceeds other prior techniques in terms of performance. In Akhbarona corpus, the Multi-Layer Perceptron achieved a minimum accuracy of 96.09%, while the hybrid Convolution-Long Short-Term Memory had a performance level of 99.28%. In Khaleej corpus, the Gated Recurrent Unit had the maximum accuracy of 98.23%, while Multi-Layer Perceptron had the lowest accuracy of 97.23%Subject Classification: Primary 68T01Secondary 68U15Keywords: Arabic text classificationDeep learningMutual informationC-LSTM
Extra parameters for conjugate gradient method for removing impulse noise images

In most applications, denoising a photograph is essential for most image processing processes. In this study, a conjugate gradient method for impulse noise reduction is presented, which is based on a two-phase system. The noise candidates are found with an adaptive (center-weighted) median filter, and then restored with the CG method, which entails the minimization of an edge-preserving regularization functional. The fact that the search direction created at each iteration is descending is one of the most appealing characteristics of the suggested technique. Its global convergence result might be obtained if a strong Wolfe line search is used. The spectral conjugate gradient approach for impulse noise reduction is demonstrated through numerical experiments.
Detecting Audio Adversarial Examples in Automatic Speech Recognition Systems Using Decision Boundary Patterns

Automatic Speech Recognition (ASR) systems are ubiquitous in various commercial applications. These systems typically rely on machine learning techniques for transcribing voice commands into text for further processing. Despite their success in many applications, audio Adversarial Examples (AEs) have emerged as a major security threat to ASR systems. This is because audio AEs are able to fool ASR models into producing incorrect results. While researchers have investigated methods for defending against audio AEs, the intrinsic properties of AEs and benign audio are not well studied. The work in this paper shows that the machine learning decision boundary patterns around audio AEs and benign audio are fundamentally different. Using dimensionality-reduction techniques, this work shows that these different patterns can be visually distinguished in two-dimensional (2D) space. This in turn allows for the detection of audio AEs using anomal- detection methods.
A penalty-based algorithm proposal for engineering optimization problems

This paper presents a population-based evolutionary computation model for solving continuous constrained nonlinear optimization problems. The primary goal is achieving better solutions in a specific problem type, regardless of metaphors and similarities. The proposed algorithm assumes that candidate solutions interact with each other to have better fitness values. The interaction between candidate solutions is limited with the closest neighbors by considering the Euclidean distance. Furthermore, Tabu Search Algorithm and Elitism selection approach inspire the memory usage of the proposed algorithm. Besides, this algorithm is structured on the principle of the multiplicative penalty approach that considers satisfaction rates, the total deviations of constraints, and the objective function value to handle continuous constrained problems very well. The performance of the algorithm is evaluated with real-world engineering design optimization benchmark problems that belong to the most used cases by evolutionary optimization researchers. Experimental results show that the proposed algorithm produces satisfactory results compared to the other algorithms published in the literature. The primary purpose of this study is to provide an algorithm that reaches the best-known solution values rather than duplicating existing algorithms through a new metaphor. We constructed the proposed algorithm with the best combination of features to achieve better solutions. Different from similar algorithms, constrained engineering problems are handled in this study. Thus, it aims to prove that the proposed algorithm gives better results than similar algorithms and other algorithms developed in the literature.
Genuinely Multipartite Entanglement Vias Shallow Quantum Circuits

Abstract Multipartite entanglement is of important resources for quantum communication and quantum computation. The goal of this paper is to characterize general multipartite entangled states according to shallow quantum circuits. It is first proved that any genuinely multipartite entanglement on finite‐dimensional spaces can be generated by using 2‐layer shallow quantum circuit consisting of two biseparable quantum channels, which has the smallest nontrivial circuit depth in the shallow quantum circuit model. Further, a semi‐device‐independent entanglement model depending on the local connection ability in the second layer of quantum circuits is proposed. This implies a complete hierarchy of distinguishing genuinely multipartite entangled states. It shows a completely different multipartite nonlocality from the quantum network entanglement. These results show new insights for the multipartite entanglement, quantum network, and measurement‐based quantum computation.
Single-Channel Blind Signal Separation of the MHD Linear Vibration Sensor Based on Singular Spectrum Analysis and Fast Independent Component Analysis

An MHD vibration sensor, as a new type of sensor used for vibration measurements, meets the technical requirements for the low-noisy measurement of acceleration, velocity, and micro-vibration in spacecraft during their development, launch, and orbit operations. A linear vibration sensor with a runway type based on MHD was independently developed by a laboratory. In a practical test, its output signal was mixed with a large amount of noise, in which the continuous narrowband interference was particularly prominent, resulting in the inability to efficiently carry out the real-time detection of micro-vibration. Considering the high interference of narrowband noise in linear vibration signals, a single-channel blind signal separation method based on SSA and FastICA is proposed in this study, which provides a new strategy for linear vibration signals. Firstly, the singular spectrum of the linear vibration signal with noise was analyzed to suppress the narrowband interference in the collected signal. Then, a FastICA algorithm was used to separate the independent signal source. The experimental results show that the proposed method can effectively separate the useful linear vibration signals from the collected signals with low SNR, which is suitable for the separation of the MHD linear vibration sensor and other vibration measurement sensors. Compared with EEMD, VMD, and wavelet threshold denoising, the SNR of the separated signal is increased by 10 times on average. Through the verification of the actual acquisition of the linear vibration signal, this method has a good denoising effect.
3D full-field fringe projection measurement based on imaging view transformation

To address the loss of measurement information caused by limitation of the field of view in fringe projection profilometry, a 3D full-field measurement based on imaging view transformation with a mirror auxiliary is proposed in this work. In this system, the principle of ideal specular reflection is applied to create a symmetrical structure between the real and virtual images for subsequent imaging view transformation. The phase of images is obtained by a multifrequency and multiple phase-shift sinusoidal fringe projection algorithm, and the real and virtual point clouds of measured objects are obtained by system calibration. The specular reflection matrix is obtained by calculating the relationship between virtual and real corner points. As a result, incomplete real point clouds can be compensated with virtual point clouds for the 3D full-field measurement by imaging view transformation. Compared to the standard ladder, the mean absolute error and the maximum relative error are 0.0706 mm and 0.9834%, respectively.
Application of optimized 2D S-transform in digital speckle interferometry phase pattern denoising

To address the problem of a large amount of noise in the phase pattern in digital speckle pattern interferometry, which seriously affects the phase reconstruction and measurement precision, a filtering method based on optimized 2D S-transform is proposed in this paper. The optimized 2D S-transform provides high signal-to-noise ratio denoising method by adding tilt factors and translation factors to keep the Gaussian window of the appropriate size at different frequency positions. The analysis results of different frequency fringe pattern denoising show that the criteria metrics indicator signal-to-noise ratio (SNR) of the results of the proposed method can reach at least 14.5435 dB and the criteria metrics peak signal-to-noise ratio (PSNR) can reach at least 19.4555 dB, which has better precision compared with the traditional method and solves the incongruity between the smoothness of low-density fringe and the integrity of high-density fringe.
Attention-Based Accurate and Robust Facial Landmark Detector

Recently, most of the existing face landmark de-tection algorithms focus on improving the performance and the efficiency of the model. However, due to the scaling and weighting effects as well as the existence of maximum pooling, the traditional convolutional neural network-based face alignment algorithms usually discard position and direction information implicit in the face image, thus decreasing its ability to capture the spatial position relationship between features, and resulting in significant performance degradation when partial occlusion occurs. To overcome this problem, in this paper, we propose an attention-based accurate, and robust face landmark detector called SCPNet. First, a SEBottleneck is used as the proposed detector's foundation module to explicitly describe channel in-terdependencies by adaptively recalibrating feature responses in terms of channels. The attention maps are then inferred sequentially along two independent dimensions (channel and space) by embedding a convolutional block attention module (CBAM) at the backbone network's tail, and the attention maps are multiplied by the input feature maps for adaptive feature refinement. Experimental results on WFLW, COFW, and 300W datasets show that our SCPNet outperforms current mainstream face alignment algorithms.
Predicting athletic performance from physiological parameters using machine learning: Example of bocce ball

Machine learning (ML) is an emerging topic in Sports Science. Some pioneering studies have applied machine learning to prevent injuries, to predict star players, and to analyze athletic performance. The limited number of studies in the literature focused on predicting athletic performance have adopted the cluster-then-predict classification approach. However, these studies have used the independent variable to represent athletic performance at both the clustering and classification stages. In this study we used only physiological parameters in the classification of bocce athletes. Their performance classes were predicted with high accuracy, thus contributing new findings to the literature. The support vector machines-radial basis function (SVM-RBF) kernel correctly predicted all athletes from the high-performance bocce player (HPBP) cluster and 75% of the athletes in the low-performance bocce player (LPBP) cluster. Using machine learning to predict athletic performance from balance data was found to be a time-saving approach for selecting high-potential bocce athletes.
A New Method of Pedestrian Abnormal Behavior Detection Based on Attention Guidance

In public places, some behavior that violates public order and endangers public safety is defined as abnormal behavior. Moreover, it is a necessary auxiliary means to maintain public order and safety by detecting abnormal behavior in a large number of surveillance videos. However, due to the small proportion of abnormal behavior in video data, the extreme imbalance of data seriously restricts the effectiveness of detection. So, weakly supervised learning has become the most suitable and effective detection method. However, existing weakly supervised methods rarely take the locality and slightness of abnormal behavior into account and ignore the details of extracted features. Based on this, an attention-directed abnormal behavior detection model is proposed. In the two common prediction and reconstruction abnormal behavior detection methods based on weak supervision, suitable attention mechanisms are introduced, respectively, and two corresponding attention-directed networks are proposed. In addition, aiming at the problem of inaccurate thresholds for abnormal behavior division, the loss function of the model is improved and a new abnormal behavior evaluation method is proposed. Experiments were carried out on three classical datasets (the USCD Ped1, USCD Ped2, and CUHK Avenue dataset) for abnormal behavior detection. The best results for the area under the curve (AUC) indicator reached 82.7%, 94.5%, and 87.3%, respectively, which are better than many existing literature results.
Improved teaching-learning-based optimization algorithm based on fusion difference mutation1

Teaching-learning-based optimization algorithm (TLBO) is a swarm intelligence optimization algorithm that simulates classroom teaching phenomenon. In order to solve the problem that TLBO algorithm is easy to fall into local optimum and has poor stability, an improved teaching-learning-based optimization algorithm based on fusion difference mutation (IDMTLBO) is proposed. Firstly, adaptive teaching factors are introduced. Secondly, in the teaching stage, each student studies according to the gap between himself and the teacher, which improves the convergence speed and convergence accuracy of the algorithm. Finally, in the learning stage, students are divided into two levels according to their learning level, and two students are randomly selected to improve the iterative equation in the learning stage with the difference mutation strategy, It improves the disadvantage that the algorithm is easy to fall into local optimum. Numerical experiments show that the convergence speed and convergence accuracy of the algorithm are obviously better than TLBO algorithm, DMTLBO algorithm, DSTLBO algorithm.
A secure pixel level self-recovery scheme for digital images

This paper presents a secure image authentication scheme for tamper localization and recovery at pixel level. The proposed scheme encrypts the watermark comprising tamper localization code and self-recovery code using chaotic sequence to ensure security. This scheme uses pixel to block conversion technique for ensuring lossless recovery of the original image from an untampered watermarked image. For enhancing the localization accuracy, a multilevel tamper localization strategy is used. The experimental results show that the proposed scheme generates watermarked images with minimal information loss and can withstand copy-move, image splicing, content removal, vector quantization, collage and content only attacks. This scheme has better security, better tamper localization accuracy and better recovered image quality under extensive tampering and takes less computation time in comparison to the state-of-the-art schemes.
Cervical cell classification based on strong feature CNN-LSVM network using Adaboost optimization

Cervical cancer is one of the most common causes of death in women in the world, and early screening is an effective means of diagnosis and treatment, which can greatly improve the survival rate. Cervical cell classification model is an effective means to assist screening. However, the existing single model, including CNNs and machine learning methods, still has shortcomings such as unclear feature meaning, low accuracy and insufficient supervision. To solve the shortcomings of a single model, a novel framework based on strong feature Convolutional Neural Networks (CNN)-Lagrangian Support Vector Machine (LSVM) model is proposed for the accurate classification of cervical cells. Strong features extracted by hybrid methods are fused with the abstract ones from hidden layers of LeNet-5, then the fused features are processed with dimension reduction and fed into the LSVM classifier optimized by Adaboost for classification. Proposed model is evaluated using the augmented Herlev and private dataset with the metrics including accuracy (Acc), sensitivity (Sn), and specificity (Sp), which outperformed the baselines and state-of-the-art approaches with the Acc of 99.5% and 94.2% in 2&amp;7-class classification, respectively.
Cost-efficient resource scheduling in cloud for big data processing using metaheuristic search black widow optimization (MS-BWO) algorithm

In a cloud computing system, resources can be accessed at a minimal cost whenever users raise request needs. The primary goal of cloud computing is to provide cost-efficiency of service scheduling to clients fast while using the least number of resources. Cloud Service Provisioning (CSP) can match consumer needs with minimal use of resources. There are several metaheuristic optimization algorithms have been developed in the field of CSP resource minimization and adequate computing resources are required to ensure client satisfaction. However, it performs poorly under a variety of practical constraints, including a vast amount of user data, smart filtering to boost user search, and slow service delivery. In this regard, propose a Black Widow Optimization (BWO) algorithm that reduces cloud service costs while ensuring that all resources are devoted only to end-user needs. It is a nature-inspired metaheuristic algorithm that involved a multi-criterion correlation that is used to identify the relationship between user requirements and available services and thereby, it is defined as an MS-BWO algorithm. Thus finds the most efficient virtual space allocation in a cloud environment. It uses a service provisioning dataset with metrics like energy usage, bandwidth utilization rate, computational cost, and memory consumption. In terms of data performance, the proposed MS-BWO outperforms exceed than other existing state-of-art-algorithms including Work-load aware Autonomic Resource Management Scheme(WARMS), Fuzzy Clustering Load balancer(FCL), Agent-based Automated Service Composition (A2SC) and Load Balancing Resource Clustering (LBRC), and an autonomic approach for resource provisioning (AARP)
Vibration amplitude mapping by stroboscopic structured light projection

In this work a method to evaluate the distribution of vibration amplitudes of objects was demonstrated, combining for the first time oblique structured light projection, stroboscopic illumination and fringe evaluation. The light pattern was formed by straight and parallel fringes produced by a slightly misaligned Twyman–Green interferometer illuminated by a 40-mW, 650-nm diode laser. Stroboscopic illumination was achieved by driving the laser with a PWM signal with the same frequency than that of the vibrating object. By evaluating the fringes with phase stepping and phase unwrapping procedures, the amplitude mapping of a formica bar and circular rubber membranes was performed. By averaging the fringe position over the light pulse duration a correction ratio between the actual phase and the measured phase was obtained as a function of the pulse duty cycle, and the dependence of the fringe visibility on the duty cycle was studied. The experiments showed that relatively large amplitudes in a range from tens of millimeters up to few millimeters can be measured.
Optimized dithering approach for 3D shape measurement using a phase-based chaos genetic algorithm

A binary defocusing method has a significant impact on improving 3D shape measurement quality for digital fringe projector (DFP) techniques. In this paper, an optimization framework using the dithering method is presented. This framework utilizes genetic algorithm and chaos maps to optimize the bidirectional error-diffusion coefficients. It can effectively avoid quantization errors of binary patterns in a specific direction and obtain the fringe patterns with better symmetry and higher quality. In the process of the optimization, chaos initialization algorithms are used to generate a series of bidirectional error-diffusion coefficients as initial individuals. Additionally, mutation factors generated by chaotic maps, compared with the mutation rate, determine whether the individual position will mutate. Both simulations and experiments demonstrate that the proposed algorithm can improve the quality of phase and reconstruction at different defocus levels.
DBPNet: A dual-branch pyramid network for document super-resolution

Convolutional neural networks (CNN), aiming to preserve the structural and texture in- formation lost in the initial low-resolution (LR) images, has been widely used to improve the quality of LR images. Most of the existing super-resolution methods focus on image super-resolution reconstruction in natural scenes, while a few super-resolution methods for document images focus on text data. However, most CNN models focus more on the reconstruction quality of the entire document and ignore the feature difference between the text region and the image region which both exist in the document image. To address this issue, this paper proposes a dual-branch pyramid network (DBPNet) for document super-resolution by taking into consideration about the texture difference between the text region and the image region. DBPNet consists of a region segmentation module (RSM), two parallel pyramid SR branches (PPSRB), and text regions, with a pyramid edge restoration module (ERM) and a region fusion module (RFM) for region stacking. Furthermore, to verify that the model can be adapted to super-resolution reconstruction of document images with more features, we construct a new document super-resolution (SR) dataset namely DSR2021. It contains paired LR and HR images involving English, Chinese, Japanese and Korean languages. Experiments on two document image datasets (DSR2021 and Text330) demonstrate that our method outperforms several state-of-the-art methods quantitatively and qualitatively.