Reviewerly/exHarmony huggingface dataset:
Multi-components System for Automatic Arabic Diacritization

In this paper, we propose an approach to tackle the problem of the automatic restoration of Arabic diacritics that includes three components stacked in a pipeline: a deep learning model which is a multi-layer recurrent neural network with LSTM and Dense layers, a character-level rule-based corrector which applies deterministic operations to prevent some errors, and a word-level statistical corrector which uses the context and the distance information to fix some diacritization issues. This approach is novel in a way that combines methods of different types and adds edit distance based corrections. We used a large public dataset containing raw diacritized Arabic text (Tashkeela) for training and testing our system after cleaning and normalizing it. On a newly-released benchmark test set, our system outperformed all the tested systems by achieving DER of 3.39% and WER of 9.94% when taking all Arabic letters into account, DER of 2.61% and WER of 5.83% when ignoring the diacritization of the last letter of every word.
Attention Guided Region Division for Crowd Counting

Crowd counting has drawn more and more attention in computer vision. There are two mainstream approaches to deal with crowd counting tasks, regression and detection. Regression-based methods usually overestimate the count in sparse areas, while detection-based methods tend to underestimation in dense areas. In this paper, we propose a two-branch network combining regression and detection. We introduce the attention mechanism to make the network adaptively divide dense and sparse areas and employ appropriate methods on them respectively. The regression branch predicts density map in extremely dense areas. An improved detection network is applied to detect multi-scale heads in relatively sparse areas. Our method is able to obtain precise head bounding boxes in sparse areas with ensuring counting accuracy in dense areas. Experimental results show that our method achieves state-of-the-art on challenging public crowd counting datasets.
Hijacking Tracker: A Powerful Adversarial Attack on Visual Tracking

Visual object tracking has made important breakthroughs with the assistance of deep learning models. Unfortunately, recent research has clearly proved that deep learning models are vulnerable to malicious adversarial attacks, which mislead the models making wrong decisions by perturbing the input image. The threat to the models alerts us to pay attention to the model security of deep learning- based tracking algorithms. Therefore, we study the adversarial attacks against advanced trackers based on deep learning to better identify the vulnerability of tracking algorithms. In this paper, we propose to add slight adversarial perturbations to the input image by an inconspicuous but powerful attack strategy-hijacking algorithm. Specifically, the hijacking strategy misleads trackers in two aspects: one is shape hijacking that changes the shape of the model output; the other is position hijacking that gradually pushes the output to any position in the image frame. Besides, we further propose an adaptive optimization approach to integrate two hijacking mechanisms efficiently. Eventually, the hijacking algorithm results in fooling the tracker to track the wrong target gradually. The experimental results demonstrate the powerful attack ability of our method-quickly hijacking state-of-the-art trackers and reducing the accuracy of these models by more than 90% on OTB2015.
An experimental evaluation of mixup regression forests

Over the past few decades, the remarkable prediction capabilities of ensemble methods have been used within a wide range of applications. Maximization of base-model ensemble accuracy and diversity are the keys to the heightened performance of these methods. One way to achieve diversity for training the base models is to generate artificial/synthetic instances for their incorporation with the original instances. Recently, the mixup method was proposed for improving the classification power of deep neural networks (Zhang, Cissé, Dauphin, and Lopez-Paz, 2017). Mixup method generates artificial instances by combining pairs of instances and their labels, these new instances are used for training the neural networks promoting its regularization. In this paper, new regression tree ensembles trained with mixup, which we will refer to as Mixup Regression Forest, are presented and tested. The experimental study with 61 datasets showed that the mixup approach improved the results of both Random Forest and Rotation Forest.
Efficient Post-quantum SNARKs for RSIS and RLWE and Their Applications to Privacy

In this paper we give efficient statistical zero-knowledge proofs (SNARKs) for Module/Ring LWE and Module/Ring SIS relations, providing the remaining ingredient for building efficient cryptographic protocols from lattice-based hardness assumptions. We achieve our results by exploiting the linear-algebraic nature of the statements supported by the Aurora proof system (Ben-Sasson et al.), which allows us to easily and efficiently encode the linear-algebraic statements that arise in lattice schemes and to side-step the issue of "relaxed extractors", meaning extractors that only recover a witness for a larger relation than the one for which completeness is guaranteed. We apply our approach to the example use case of partially dynamic group signatures and obtain a lattice-based group signature that protects users against corrupted issuers, and that produces signatures smaller than the state of the art, with signature sizes of less than 300 KB for the comparably secure version of the scheme. To obtain our argument size estimates for proof of knowledge of RLWE secret, we implemented the NIZK using libiop.
H-Vectors: Utterance-Level Speaker Embedding Using a Hierarchical Attention Model

In this paper, a hierarchical attention network is proposed to generate utterance-level embeddings (H-vectors) for speaker identification and verification. Since different parts of an utterance may have different contributions to speaker identities, the use of hierarchical structure aims to learn speaker related information locally and globally. In the proposed approach, frame-level encoder and attention are applied on segments of an input utterance and generate individual segment vectors. Then, segment level attention is applied on the segment vectors to construct an utterance representation. To evaluate the effectiveness of the proposed approach, the data of the NIST SRE2008 Part1 is used for training, and two datasets, the Switchboard Cellular (Part1) and the CallHome American English Speech, are used to evaluate the quality of extracted utterance embeddings on speaker identification and verification tasks. In comparison with two baselines, X-vectors and X-vectors+Attention, the obtained results show that the use of H-vectors can achieve a significantly better performance. Furthermore, the learned utterance-level embeddings are more discriminative than the two baselines when mapped into a 2D space using t-SNE.
A New Encryption Approach Based on Four-Square and Zigzag Encryption (C4CZ)

With the evolution and the development of data-processing tools and their pervasive scopes of applications, the need for data protection today has become a crucial necessity in order to reduce the number of incidents caused by non-authorized data accesses and to prevent malicious usage. With the purpose of maintaining a significantly high level of security, cryptography englobes the entirety of methods as well as a couple of techniques and tools aimed to assure data confidentiality. Indeed, this work’s main focus is a new approach based on a four-square encryption accompanied by a zigzag transformation. This approach allows us to provide the appropriate settings for the four-square technique and the new conception of a transformation that manages future plans. These last are immune to brute attacks and statistic attacks. Considering all of that, this exhaustive study will emphasize the reliability of this approach.
Super-Resolution of 3D Color Point Clouds Via Fast Graph Total Variation

3D point clouds acquired by low-cost sensors are often in lower spatial resolutions than desired for rendering images on high-resolution displays. In this paper, we propose a fast super-resolution (SR) algorithm for color 3D point clouds. We first populate a target low-res point cloud with added interior points. We refine the newly added 3D coordinates and their RGB values by minimizing a graph total variation (GTV) term of connected points' surface normals and RGB values respectively. Unlike non-local methods that require computation-intensive searches of similar patches in a large defined space, our algorithm is inherently local and performs smoothing of newly inserted points only with respect to neighboring points. Moreover, differing from our previous GTV-based SR algorithm that employs gradient descent procedures with sensitive step size parameters due to GTV's non-smooth l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm, we rewrite the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> objective into a linear proxy, so that together with constraints on surface normals / RGB values, it can be solved efficiently as a parameter-free linear program (LP). Experimental results show that our algorithm outperforms competing non-graph-based point cloud SR schemes, and is significantly faster than our previous graph-based SR method.
K-Autoencoders Deep Clustering

In this study we propose a deep clustering algorithm that extends the k-means algorithm. Each cluster is represented by an autoencoder instead of a single centroid vector. Each data point is associated with the autoencoder which yields the minimal reconstruction error. The optimal clustering is found by learning a set of autoencoders that minimize the global reconstruction mean-square error loss. The network architecture is a simplified version of a previous method that is based on mixture-of-experts. The proposed method is evaluated on standard image corpora and performs on par with state-of-the-art methods which are based on much more complicated network architectures.
Low-Dose CT Image Denoising Using Cycle-Consistent Adversarial Networks

Computed tomography (CT) has been widely used in modern medical diagnosis and treatment. However, ionizing radiation of CT for a large population of patients becomes a concern. Low-dose CT is actively pursued to reduce harmful radiation, but faces challenges of elevated noise in images. To address this problem and improve low-dose CT image quality, we develop an image-domain denoising method based on cycle-consistent adversarial networks (CycleGAN). Different from previous deep learning based denoising methods, CycleGAN can learn data distribution of organ structures from unpaired full-dose and low-dose images, i.e. there is no one-to-one correspondence between full-dose and low-dose images. This is an important development of learning-based methods for low-dose CT since it enables the model growth using previously acquired full-dose images and later acquired low-dose images from different patients. As a proof-of-concept study, we used the NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge data to test our CycleGAN denoising method. The results show that the proposed method not only achieves better peak signal-to-noise ratio (PSNR) for quarter-dose images than non-local mean and dictionary learning denoising methods, but also preserves more details reflected by images and structural similarity index (SSIM). Our investigation also reveals that a larger sample size leads to a better denoising performance for CycleGAN.
Parsing Map Guided Multi-Scale Attention Network For Face Hallucination

Face hallucination that aims to transform a low-resolution (LR) face image to a high-resolution (HR) one is an active domain-specific image super-resolution problem. The performance of existing methods is usually not satisfactory, especially when the upscaling factor is large, such as 8×. In this paper, we propose an effective two- step face hallucination method based on a deep neural network with multi-scale channel and spatial attention mechanism. Specifically, we develop a ParsingNet to extract the prior knowledge of an input LR face, which is then fed into a carefully designed FishSRNet to recover the target HR face. Experimental results demonstrate that our method outperforms the state-of-the-arts in terms of quantitative metrics and visual quality.
Selecting a diverse set of benchmark instances from a tunable model problem for black-box discrete optimization algorithms

As the number of practical applications of discrete black-box metaheuristics is growing faster and faster, the benchmarking of these algorithms is rapidly gaining importance. While new algorithms are often introduced for specific problem domains, researchers are also interested in which general problem characteristics are hard for which type of algorithm. The W-Model is a benchmark function for discrete black-box optimization, which allows for the easy, fast, and reproducible generation of problem instances exhibiting characteristics such as ruggedness, deceptiveness, epistasis, and neutrality in a tunable way. We conduct the first large-scale study with the W-Model in its fixed-length single-objective form, investigating 17 algorithm configurations (including Evolutionary Algorithms and local searches) and 8372 problem instances. We develop and apply a machine learning methodology to automatically discover several clusters of optimization process runtime behaviors as well as their reasons grounded in the algorithm and model parameters. Both a detailed statistical evaluation and our methodology confirm that the different model parameters allow us to generate problem instances of different hardness, but also find that the investigated algorithms struggle with different problem characteristics. With our methodology, we select a set of 19 diverse problem instances with which researchers can conduct a fast but still in-depth analysis of algorithm performance. The best-performing algorithms in our experiment were Evolutionary Algorithms applying Frequency Fitness Assignment, which turned out to be robust over a wide range of problem settings and solved more instances than the other tested algorithms.
Inclusive education for inclusive economic participation: the financial access channel

Purpose The purpose of this study is to assess how inclusive education affects inclusive economic participation through the financial access channel. Design/methodology/approach The focus is on 42 sub-Saharan African countries with data for the period 2004-2014. The empirical evidence is based on the generalised method of moments. Findings The following findings are established. First, inclusive secondary education moderates financial access to exert a positive net effect on female labour force participation. Second, inclusive “primary and secondary school education” and inclusive tertiary education modulate financial access for a negative net effect on female unemployment. Third, inclusive secondary education and inclusive tertiary education both moderate financial access for an overall positive net effect on female employment. To provide more gender macroeconomic management policy options, inclusive education thresholds for complementary policies are provided and discussed. Originality/value Policy implications are discussed in the light of challenges of economic development in the sub-region and sustainable development goals.
Large-Scale Heteroscedastic Regression via Gaussian Process

Heteroscedastic regression considering the varying noises among observations has many applications in the fields, such as machine learning and statistics. Here, we focus on the heteroscedastic Gaussian process (HGP) regression that integrates the latent function and the noise function in a unified nonparametric Bayesian framework. Though showing remarkable performance, HGP suffers from the cubic time complexity, which strictly limits its application to big data. To improve the scalability, we first develop a variational sparse inference algorithm, named VSHGP, to handle large-scale data sets. Furthermore, two variants are developed to improve the scalability and capability of VSHGP. The first is stochastic VSHGP (SVSHGP) that derives a factorized evidence lower bound, thus enhancing efficient stochastic variational inference. The second is distributed VSHGP (DVSHGP) that follows the Bayesian committee machine formalism to distribute computations over multiple local VSHGP experts with many inducing points and adopts hybrid parameters for experts to guard against overfitting and capture local variety. The superiority of DVSHGP and SVSHGP compared to the existing scalable HGP/homoscedastic GP is then extensively verified on various data sets.
Efficient Deep Learning-Based Lossy Image Compression Via Asymmetric Autoencoder and Pruning

Recently, deep learning-based lossy image compression methods have been proposed. However, their efficiency in terms of storage and computational costs has not been addressed adequately. In this paper, we propose efficient lossy image compression methods based on asymmetric autoencoder and decoder pruning. Experimental results demonstrate the effectiveness of our methods.
Stochastic Graph Neural Networks

Graph neural networks (GNNs) model nonlinear representations in graph data with applications in distributed agent coordination, control, and planning among others. However, current GNN implementations assume ideal distributed scenarios and ignore link fluctuations that occur due to environment or human factors. In these situations, the GNN fails to address its distributed task if the topological randomness is not considered accordingly. To overcome this issue, we put forth the stochastic graph neural network (SGNN) model: a GNN where the distributed graph convolutional operator is modified to account for the network changes. Since stochasticity brings in a new paradigm, we develop a novel learning process for the SGNN and introduce the stochastic gradient descent (SGD) algorithm to estimate the parameters. We prove through the SGD that the SGNN learning process converges to a stationary point under mild Lipschitz assumptions. Numerical simulations corroborate the proposed theory and show an improved performance of the SGNN compared with the conventional GNN when operating over random time varying graphs.
A New Color Image Encryption Algorithm Using Random Number Generation and Linear Functions

In this paper, we introduce a new method of color image encryption. The principle of this approach is to encrypt any color image (called the original image), into a sequence of N images whose (N − 1) of them are generated randomly, and the Nth image is calculated from the original image and the (N − 1) generated images using a linear equation. This process will be repeated for each color image channel (red, green, and blue). Those (N − 1) generated images are used as a symmetric key in the decryption phase; therefore, they should be shared with the receiver privately or via a secure channel. Only the Nth image will be sent. To obtain the original image, we need all N images in the decryption phase. Our encryption system can resist brute-force attacks as well as statistical attacks. The results are justified by applying several safety criteria, such as correlation coefficient and histogram.
Voice Conversion with Transformer Network

This paper describes an end-to-end voice conversion system, which involves three main ideas: transformer, context preservation mechanisms, and model adaptation. Self-attention in the transformer architecture directly connects all positions, making it easier to learn long range dependencies and improve training efficiency. Context preservation mechanisms accelerate and stabilize training. Adaptation techniques are conductive to the training of the conversion mapping with limited training data. The results show that the proposed method obtains a higher MOS and the training speed is 2.72 times faster than LSTM based baseline system.
Fixed-Point Optimization of Transformer Neural Network

The Transformer model adopts a self-attention structure and shows very good performance in various natural language processing tasks. However, it is difficult to implement the Transformer in embedded systems because of its very large model size. In this study, we quantize the parameters and hidden signals of the Transformer for complexity reduction. Not only matrices for weights and embedding but the input and the softmax outputs are also quantized to utilize low-precision matrix multiplication. The fixed-point optimization steps consist of quantization sensitivity analysis, hardware conscious word-length assignment, quantization and retraining, and post-training for improved generalization. We achieved 27.51 BLEU score on the WMT English-to-German translation task with 4-bit weights and 6-bit hidden signals.
A zero watermarking approach for content authentication and tampering detection of Arabic text based on fourth level order and word mechanism of Markov model

Text information is a natural language dependent. Therefore, improving security and reliability of text information exchanged via Internet network has become one of the most challenges faced by researchers. Content authentication, integrity verification and tampering detection of digital contents have become a major concern in the communication and information exchange area via the Internet. In this paper, an intelligent text Zero-Watermarking approach called ZWAFWMMM (Zero-Watermarking Approach based on Fourth level order of Word Mechanism of Markov Model) has been proposed for content authentication and tampering detection of Arabic text contents. In ZWAFWMMM, watermark embedding and tampering detection were performed logically in which no need to alter the original text document to embed a watermark. Fourth-level order of word mechanism based on hidden Markov model has been integrated with digital zero-watermarking techniques to improve performance, accuracy, capacity and robustness of the previous literature proposed by researchers. Moreover, Fourth-Level order of Markov model is used as a soft computing technique for analysing Arabic text and extracting the interrelationship features from the context of a text, and then utilizing the extracted features as a watermark information to be validated later with attacked Arabic text to detect any tampering that may occur in the text. ZWAFWMMM has been implemented by using PHP with VS code IDE. Robustness, effectiveness, and applicability of ZWAFWMMM are proved with several scenarios of simulations and experiments using four datasets of different lengths under multiple random locations of insertion, reorder and deletion attacks of experimental datasets. The experiment results show that ZWAFWMMM is more sensitive for all kinds of tampering attacks with a high-level accuracy in tampering detection.