Reviewerly/exHarmony huggingface dataset:
An Approach of Trustworthy Supply Chain Management of Ventilator and PPE in COVID-19 Pandemic Through Blockchain Technology

The rapid outbreak of the pandemic COVID-19 has uncovered how our healthcare systems are confined to handle situation like emergency resilience, mitigation of risks, and supply chain management. One of the major problems during this pandemic is the growing demand of personal protective equipment (PPE) and significant medical supplies resulting in serious shortages for health care and frontline workers. But there has been several fake information being spread during this pandemic creating panic among the public and no system to authenticate the information. The lack of transparency of information along with the difficulty of accurate tracking of goods movements within the supply chain needs a robust traceability system. So, for efficient planning of operational processes and resource arrangements, we need to adhere to innovative technologies like blockchain. It is known that blockchain plays a significant role in healthcare systems. It improves the regulatory approvals and provides a prompt data management in clinical trials. Similarly, in supply chain, it streamlines the communication between different stakeholders, assures safe, transparent, and secure exchange of data among the collaborators. Thus, a blockchain-based tracking system will help the public to receive reliable and authentic information from government agencies. This paper analyzes the different blockchain-based applications and diverse prospects for handling this pandemic situation in a systematic way. It also proposes a blockchain-based supply chain framework for managing ventilators and PPE kits using Ethereum smart contracts.
A New Improved Beetle Swarm Optimization Algorithm

Aiming at the problem that the standard particle swarm optimization algorithm (PSO) is easy to fall into local convergence so quickly, and the convergence speed is slow, a new optimization algorithm combining PSO, Beetle Antennae Search Algorithm and Genetic Algorithm is proposed in this paper First, the improved sigmoid function is introduced to form a nonlinear function relation between the learning factor and the step size of the particle swarm optimization algorithm, which can effectively increase the convergence speed. Second, in order to maintain the diversity of the population, the crossover operation and mutation operation of genetic algorithm are introduced to improve the global search ability of the algorithm. In order to verify the performance of the new optimization algorithm, the average fitness values of the four standard test functions in different dimensions are compared to and the corresponding optimal values. The simulation results show that the new algorithm not only improves the premature convergence of the traditional particle swarm optimization algorithm, but also improves the optimization efficiency.
HiPa: Hierarchical Partitioning for Fast PageRank on NUMA Multicore Systems

PageRank, weighing the importance of vertices in a graph, serves as an fundamental algorithm for graph-structured tasks in a variety of domains. However, the processing capacity of multicore systems is oftentimes poorly utilized for large-scale PageRank due to the irregular memory accesses and poor cache efficiency. In this paper, we present HiPa, a novel hierarchical partitioning methodology to accelerate PageRank by utilizing the memory-cache architecture of the multicore system. For the shared memory, HiPa subdivides the graph based on the NUMA characteristics to reduce remote memory access while ensuring workload balance. For the private cache, HiPa further splits the graph into cache-able partitions to promote in-core computing and cache locality. Based on the partitioning strategy, systematical optimizations are proposed, such as thread management and new data layout. These effectively alleviate thread migration and thread contention, thus enhancing the scalability of HiPa. The integration of NUMA- and cache-aware parallelism allows HiPa to harness the potential of multicore systems. The performance of HiPa is evaluated by comparing with the the start-of-the-art graph frameworks and hand-optimized implementations. Over the best among them, HiPa achieves accelerations from 1.11 × to 1.45 × , and reductions in remote memory accesses from 1.87 × to 3.90 × . Moreover, we investigate the behaviors of HiPa on different processor micro-architectures to push its performance closer to hardware limit.
Voice-Based Detection of Speaker Gender Through Co-Occurrence Matrix-Based Features

Gender detection or identification from speech or voice signals is considered a very significant task as it is identified as the preliminary step of speaker recognition. In today’s era, almost all systems are getting converted into smart mode. There are many voice-controlled devices at present, and most interestingly, the number of such devices is getting increased day by day. Speaker gender detection finds its importance in the domain of security also. To provide security in a voice-controlled system detection, the gender of the speaker is very much important. Hence, speaker gender detection is a very much promising research area in the present-day situation. From the performances of voice-controlled devices, it is observed that identification of speaker gets easy if the system first identifies the gender of the speaker as it reduces logical complexity of the system. The gender of the speaker can be identified either through the voice signal of the speaker or through the face image of the speaker. Speaker gender detection through speech signals is comparatively easier than gender detection from facial images. This exertion has considered all the three kinds of gender—male, female as well as trans-gender. Trans-gender has been considered as it is now legally recognized gender. In previous efforts, trans-gender is mostly ignored. The aim of this work is to propose a simple audio facet set to detect all three genders of the speaker. These three genders mostly differ in the frequency domain as well as in the perceptual domain on the point of view of voice signal. For this reason, MFCC-based facets and skewness-based facets have been considered in this exertion. The experimental result proves that the proposed facet set outperforms other previous work.
MR Image Reconstruction Based on Densely Connected Residual Generative Adversarial Network–DCR-GAN

Magnetic Resonance Image (MRI) reconstruction from undersampled data is an important ill-posed problem for biomedical imaging. For this problem, there is a significant tradeoff between the reconstructed image quality and image acquisition time reduction due to data sampling. Recently a plethora of solutions based on deep learning have been proposed in the literature to reach improved image reconstruction quality compared to traditional analytical reconstruction methods. In this paper, a novel densely connected residual generative adversarial network (DCR-GAN) is being proposed for fast and high-quality reconstruction of MR images. DCR blocks enable the reconstruction network to go deeper by preventing feature loss in the sequential convolutional layers. DCR block concatenates feature maps from multiple steps and gives them as the input to subsequent convolutional layers in a feed-forward manner. In this new model, the DCR block’s potential to train relatively deeper structures is utilized to improve quantitative and qualitative reconstruction results in comparison to the other conventional GAN-based models. We can see from the reconstruction results that the novel DCR-GAN leads to improved reconstruction results without a significant increase in the parameter complexity or run times.
Kalman Filter Based Sensor Fusion for Omnidirectional Mechatronic System

The paper describes the sensor fusion for the newly developed omnidirectional mechatronic system. To that end, the kinematic model of the platform and the chosen configuration of omnidirectional Mecanum wheels is described, as well as the principle of operation of all system sensors. The expressions are given for a discrete linear Kalman filter that fuses measurements of a magnetometer and gyroscope, and for a discrete extended Kalman filter that estimates position and orientation of the platform using additional accelerometer measurements. To be able to express the measurement equation four additional states are added to the system model. The developed sensor fusion algorithm was implemented in MATLAB/Simulink programming environment, and very accurate simulation results are reported for estimation of position and orientation of the system. Finally, the real time experimental results are reported for a prototype of the omnidirectional mobile mechatronic system.
Ground-to-Aerial Image Geo-Localization with Cross-View Image Synthesis

The task of ground-to-aerial image geo-localization can be achieved by matching a ground view query image to aerial images with geographic labels in a reference database. It remains challenging due to the drastic change in viewpoint. In this paper, we propose a new cross-view image synthesis conditional generative adversarial networks (cGAN) called Crossview Sequential Fork (CSF) to generate ground images from aerial images. CSF achieves a more detailed synthesis effect by the generation of segmentation maps and edge detection images. And the synthesis ground images are input to the image matching framework Cross View Synthesis Net (CVS-Net) to assist geo-localization, the distance between the descriptors of source ground image and synthesis ground image is calculated to assist the training of the network. CVS-Net is leveraged on the Siamese architecture to do metric learning for the matching task. Moreover, we introduce SARE loss as part of the training procedure and improve it by our data entry form which greatly improves the convergence rate and image retrieval accuracy compared to traditional triplet loss. Experimental results demonstrate the effectiveness and superiority of our proposed method over the state-of-the-art method on two benchmark datasets.
Residual Shuffle Attention Network for Image Super-Resolution

Abstract In order to improve the accuracy of the super-resolution network and reduce the number of model parameters, this paper improves its RCAB module on the basis of RCAN, and builds a reconstruction network RSAN that can improve the quality and efficiency of image super-resolution reconstruction. By replacing the original channel attention module with a more efficient and lightweight shuffle attention, it is mainly used to reduce the number of parameters, supplemented by improving the accuracy; and replacing part of the ordinary convolution in RCAN with split convolution is mainly used to improve accuracy, supplemented by reducing feature redundancy and parameters. The experimental results show that RSAN in this paper can not only obtain better subjective visual evaluation and objective quantitative evaluation, but also reduce the number of network model parameters and improve the efficiency of the network to a certain extent.
Co-evolution-based parameter learning for remote sensing scene classification

The appropriate setting of hyperparameter is a key factor to determine the performance of the deep learning model. Efficient hyperparametric optimization algorithm can not only improve the efficiency and speed of model hyperparametric optimization, but also reduce the application threshold of deep learning model. Therefore, we propose a parameter learning algorithm-based co-evolutionary for remote sensing scene classification. First, a co-evolution framework is proposed to optimize the optimizer’s hyperparameters and weight parameters of the convolutional neural networks (CNNs) simultaneously. Second, with the strategy of co-evolution with two populations, the hyperparameters can learn within the population and the weights of CNN can be updated by utilizing information between the populations. Finally, the parallel computing mechanism is adapted to speed up the learning process, as the two populations can evolve simultaneously. Extensive experiments on three public datasets demonstrate the effectiveness of the proposed approach.
Developing Real Avatars for the Apparel Industry and Analysing Fabric Draping in the Virtual Domain

Abstract The youth of today are much more technologically adept than previous generations. This has led to many technological innovations, and the development of these innovations has in turn altered our perception of the world. The film, gaming, and clothing industries have been forced to embrace new technology to satisfy the demand of people for a more realistic virtual experience. These industries all rely on the use of 3D avatars to create virtual depictions of the human body. In the gaming and film industry, the accuracy of the avatar is not so critical. The avatars used in computer games and CGI sections of films have soft contours, which look visually appealing, but are not necessarily accurate to the human form. By contrast, the apparel industry needs to focus on creating very accurate avatars, which represent each person's body shape for virtual fitting to achieve realistic, well-fitted garments. This article describes the methods used to build an avatar and compares the draping between the following scenarios: the real avatar with a real garment, the real avatar with a virtual garment, and finally the virtual avatar with a virtual garment. This research will help to understand how the body shape can affect the virtual fit.
Moving Target Detection Based on Patrol UAV

Moving target detection is widely used in intelligent video surveillance, multimedia applications, and other fields. Compared with traditional moving target detection, moving target detection of UAV video images has the same ideas, but there are many interference factors. Aiming at the problem of moving target detection of UAV,a moving target detection algorithm based on accurate motion vector compensation is proposed. In this algorithm, SURF algorithm is used to obtain the matching pair of motion vectors, and RANSAC algorithm is used to delete the matching features to obtain the accurate motion vectors.At the same time, the influence of the target motion vector on the global motion estimation is eliminated by the adaptive background filtering method. By establishing the affine transformation model of the background motion vector, the background motion vector is substituted into the model for parameter estimation.After the motion compensation image is calculated, the moving target detection results are obtained by inter-frame difference.Experimental results show that the algorithm is robust and can accurately detect moving targets in the background of UAV shooting.
An Effective Two-stage Screen Positioning Method Based on Improved Binary SIFT Descriptor

With the popularization of smart screens, screen bonding technology has gradually developed. This paper uses the SIFT algorithm to recognize the LCD screen. Aiming at the problem of long matching time and large memory usage for its 128-dimensional floating-point descriptor, this paper proposes an improved binary descriptor, which can effectively increase the percentage of correct matching between key points and reduce the matching time by 1/3. After recognizing the object, this paper proposes a two-stage positioning method. We use the screen center obtained by the SIFT algorithm as the seed to perform Flood-fill processing, and obtain the four inner corners of the LCD screen through sub-pixel corner detection to accurately calculate the screen center. Finally, it is verified through experiments that the positioning error can be kept within 2.5 pixels, and the angle error can be kept within 0.9 degrees, which can meet the screen fitting accuracy requirements.
Comparison of Turkish Paraphrase Generation Models

Paraphrase generation is an important NLP task of generation a sentence that has the same meaning as a given sentence. But, there are few studies on Turkish in this field. In this study, we present the most comprehensive study on Turkish. We focused on question sentences due to the resources we could access. First of all, since there is not enough large parallel paraphrase dataset for Turkish, we created several datasets with various methods. We compared the success of these datasets using the BERT2BERT architecture. We found the combination of automatically generated and manually generated datasets to be the most successful dataset. Using this dataset, we compared the success of the most popular architectures in the field. We found that MBart is the most successful model according to BLEU/Rouge criteria and BERT2BERT according to human evaluation.
Data-Driven Bandpass Filter Design for Estimating Symbol Rate of Sporadic Signal at Low SNR

Symbol rate is one of the most important parameters in signal demodulation process. In real-time signal processing, traditional symbol rate estimation algorithms for the Multiple Phase Shift Keying (M-PSK) and the Multiple Quadrature Amplitude Modulation (M-QAM) are based on the Fourier transform of signal’s complex envelope. At the low signal-to-noise ratio (SNR), the accuracy of symbol rate estimation can be improved by increasing the number of symbols as much as possible. However, this improvement is infeasible in many applications such as the energy-limited Internet of Things devices and sporadic noncooperative transmissions. In this paper, we propose a data-driven bandpass filter (BPF) design scheme for accurate estimation of symbol rate under low SNR with only a small number of symbols available. The proposed scheme considerably improves the estimation performance by optimizing the BPF design using the equivalent dynamic linearization model with time-varying pseudo-partial derivatives. Specifically, the proposed scheme iteratively optimizes the upper and lower cut-off frequencies of the BPF based on the measured complex envelope spectrum until achieving the optimal BPF. Therefore, the peaks of the complex envelope spectrum are extracted as the estimate of the symbol rate by applying the optimal BPF. Experimental results indicate the promise of the proposed scheme as an efficient symbol rate estimator for sporadic signal at low SNR and with a small number of symbols.
Multi-Level Difference Repair Architecture for Face Hallucination

Facial hallucination (FH) aims to construct super-resolution (SR) face images based on the corresponding low-resolution (LR) thumbnails. The desired results of FH require correct identity and photorealistic image quality, however, developing a method that satisfies both requirements remains challenging. Although the lifelike faces predicted by some existing methods, such as PULSE, are impressive, the loss of original identity is unacceptable. In view of the deficiency of PULSE, in this letter we incorporate a conditional constraint to its unsupervised pixel recovery process. Specifically, we embed a PULSE generator within the Laplacian pyramid framework and install a corresponding pre-trained conditional adversarial net (CGAN) at each pyramid level to predict the difference between the reconstructed SR image obtained by PULSE and the ground truth. The difference is added to the reconstructed SR image at each level to obtain a new SR image that is closer to the ground truth, such a process is repeated across the subsequent levels until a full-resolution version is yielded. Extensive experiments have demonstrated that our architecture can obtain a realistic high-resolution (HR) face with a correct identity, which is not possible obtained by other FH methods before.
Hapi: A Domain-Specific Language for the Declaration of Access Policies

Access policies specify what are the actions that different actors can perform on the available resources. Access policies are a core notion in multiuser environments, such as operating systems and distributed databases. Currently, most of these systems use general data specification languages, such as JSON, XML and YAML to describe access policies. Yet, domain-specific languages are also available for this task. One of such languages is Legalease, from Microsoft. This paper presents a new version of Legalease, called Hapi. Hapi replaces Legalease's notion of a lattice with a partially ordered set (poset). We demonstrate that posets already give all the expressivity of Legalease, while simplifying its specification and the implementation of verification algorithms. Hapi is currently publicly available. The language is distributed with tools for translating programs to YAML and for visualizing access rights. Hapi provides developers with an Intermediary Representation of policies that allows this language to be easily embedded in any project. Said representation generalizes the notion of actors, actions and resources to user-defined entities; hence, being more flexible than typical data-access description languages.
Cryptography Over the Twisted Hessian Curve $$H^{3}_{a,d}$$

In this paper, we will give some properties of the twisted Hessian curve over the ring $$\mathbb {F}_{q}[\epsilon ]$$ denoted by $$H^{3}_{a,d}$$ , with $$\mathbb {F}_{q}$$ is a finite field of order $$q=p^{b}$$ , where p is a prime number $$ \ge 5$$ and $$b \in \mathbb {N}^{*}$$ , and we prove that when p doesn’t divide $$\# (H_{a_{0},d_{0}})$$ , then $$H^{3}_{a,d}$$ is a direct sum of $$ H_{a_{0},d_{0}}$$ and $$\mathbb {F}_{q}^2$$ , where $$ H_{a_{0},d_{0}}$$ is the twisted Hessian curve over $$\mathbb {F}_{q}$$ . Other results are deduced from, we cite the equivalence of the discrete logarithm problem on the twisted Hessian curves $$H^{3}_{a,d}$$ and $$ H_{a_{0},d_{0}}$$ , which is beneficial for cryptography and cryptanalysis as well, and we give an application in cryptography.Grini, Abdelâli Chillali, Abdelhakim Mouanis, Hakima
A method for optimal linear super-resolution image restoration

In this paper, we propose a super-resolution (pixel grid refinement) method for digital images. It is based on the linear filtering of a zero-padded discrete signal. We introduce a continuous-discrete observation model to create a reconstruction system. The proposed observation model is typical of real-world imaging systems - an initially continuous signal first undergoes linear (dynamic) distortions and then is subjected to sampling and the effect of additive noise. The proposed method is optimal in the sense of mean square recovery error minimization. In the theoretical part of the article, a general scheme of the linear super-resolution of the signal is presented and expressions for the impulse and frequency responses of the optimal reconstruction system are derived. An expression for the error of such restoration is also derived. For the sake of brevity, the entire description is presented for one-dimensional signals, but the obtained results can easily be generalized for the case of two-dimensional images. The experimental section of the paper is devoted to the analysis of the super-resolution reconstruction error depending on the parameters of the observation model. The significant superiority of the proposed method in terms of the reconstruction error is demonstrated in comparison with linear interpolation, which is usually used to refine the grid of image pixels.
Approximate $$\mathrm {CVP}_{}$$ in Time $$2^{0.802 n}$$ - Now in Any Norm!

We show that a constant factor approximation of the shortest and closest lattice vector problem in any norm can be computed in time $$2^{0.802\, n}$$ . This contrasts the corresponding $$2^n$$ time, (gap)-SETH based lower bounds for these problems that even apply for small constant approximation. For both problems, $$\mathrm {SVP}_{}$$ and $$\mathrm {CVP}_{}$$ , we reduce to the case of the Euclidean norm. A key technical ingredient in that reduction is a twist of Milman’s construction of an M-ellipsoid which approximates any symmetric convex body K with an ellipsoid $$\mathcal {E}$$ so that $$2^{\varepsilon n}$$ translates of a constant scaling of $$\mathcal {E}$$ can cover K and vice versa.