Reviewerly/exHarmony huggingface dataset:
Amorphous Alloys and Ferroelectric Nanomaterials and the Repair of Athletic Ligament Injuries in Soccer

Amorphous alloy refers to the orderly arrangement of the internal structure of various substances in nature. Amorphous solid materials include amorphous inorganic materials, amorphous polymers, and amorphous alloys. Amorphous alloys, also known as glassy alloys or metallic glasses, are atomically condensed, long-range disordered, and short-range ordered alloy materials that have the distinctive properties of both metals and glasses. Ligament injury is damage caused by different degrees of damage to the ligaments of a certain part of the body. The purpose of this paper is to investigate amorphous alloys and ferroelectric nanomaterials and the repair of athletic ligament injuries in the soccer game, using the excellent properties of amorphous alloys and ferroelectric nanomaterials to solve the problem of athletic injuries. Knee and ankle injuries and muscle strains account for a large percentage of soccer injuries. In this paper, researches and explorations have been conducted on the preparation of new composite magnetic nanomaterials, the construction of immunosensor interfaces, and the development of new signal amplification and renewable immunosensors. The rehabilitation effect of the medial collateral ligament of the athlete’s knee joint is used as the research object, and various methods are used. In-depth investigation and research and statistical analysis of the data obtained one by one were carried out. The experimental results in this article show that from the age of ligament injury, the number of people between 18 and 20 is relatively small, and the number of people aged 20–40 is relatively large. The number of people who have been trained for three years and above is 5, and the proportion of ligament injuries is 0.8%; the number of training for two years is 33, and the proportion of ligament injuries is 13.7%, the number of training for one year is 98, and the proportion of ligament injuries is 40.7%, and the number of training for six months is 105, and the proportion of ligaments is 44.8%.
Modern Distributed Data-Parallel Large-Scale Pre-training Strategies For NLP models

Distributed deep learning is becoming increasingly popular due to the expanding demand for computing resources for deep learning models with a larger amount of parameters. Different from traditional training approaches, data-parallel training allows multiple compute nodes to train large deep learning models simultaneously in order to boost the training efficiency. In this paper, we present and compare six strategies for data-parallel training using PyTorch on the language model GPT-2 with 100M parameters using a qualitative approach. These strategies are Single GPU, Single Parameter Server, Distributed Parameter Server, Horovod, Distributed Parameter Server with Apex mixed-precision strategy, and Horovod with Apex mixed-precision strategy. We also analyze the quantitative experiment results from each strategy. In the end, we draw the conclusion that the Distributed Parameter Server with Apex mixed-precision strategy has the best performance on single node training, while Horovod with Apex is the most robust approach to use when we have single or multiple nodes.
Salient deformable network for abdominal multiorgan registration

Image registration has long been an active research area in the society of medical image computing, which is to perform spatial transformation between a pair of images and establish a point-wise correspondence to achieve spatial consistency.Previous work mainly focused on learning complicated deformation fields by maximizing the global-level (i.e., foreground plus background) image similarity. We argue that taking the background similarity into account may not be a good solution, if we only seek the accurate alignment of target organs/regions in real clinical practice.We, therefore, propose a novel concept of Salient$Salient$Registration$Registration$ and introduce a novel deformable network equipped with a saliency module. Specifically, a multitask learning-based saliency module is proposed to discriminate the salient regions-of-registration in a semisupervised manner. Then, our deformable network analyzes the intensity and anatomical similarity of salient regions, and finally conducts the salient deformable registration.We evaluate the efficacy of the proposed network on challenging abdominal multiorgan CT scans. The experimental results demonstrate that the proposed registration network outperforms other state-of-the-art methods, achieving a mean Dice similarity coefficient (DSC) of 40.2%, Hausdorff distance (95 HD) of 20.8 mm, and average symmetric surface distance (ASSD) of 4.58 mm. Moreover, even by training using one labeled data, our network can still attain satisfactory registration performance, with a mean DSC of 39.2%, 95 HD of 21.2 mm, and ASSD of 4.78 mm.The proposed network provides an accurate solution for multiorgan registration and has the potential to be used for improving other registration applications. The code is publicly available at https://github.com/Rrrfrr/Salient-Deformable-Network.
Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently, with the growth of cyber physical systems (CPS), several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides, the cloud computing (CC) enabled CPS offers huge processing and storage resources for CPS that finds helpful for a range of application areas.At the same time, with the massive development of applications that exist in the CPS environment, the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effectiveness of the CC platform, virtualization technologies have been employed for resource management and the applications are executed via virtual machines (VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS, this paper focuses on the design of chaotic sandpiper optimization based VM scheduling (CSPO-VMS) technique for energy efficient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory, which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm, the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives a fitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique, a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.
Multi-Security System Based on Fingerprint Biometric Sensor and Wi-Fi Camera

Due to the importance of security systems in our practice and daily lives for protecting companies, homes and other public and private facilities. In the last few years, these systems have received great attention from developers and researchers with the advancement of modern technology and the development of smart processing units and microelectronic units. Where an Internet of Things subscription constitutes a breakthrough in the world of communications and modern technology. Due to the correct use of mobile Internet networks like 4G or 5G, or fixed networks with a limited signal like LAN or Wi-Fi. While implementations of a unified security system adequately represent a waste of the resource's memory, network bandwidth, it is characterized by as well as real-time data processing and, to avoid software that takes time and cost to the processing. This specific proposal will adequately provide a multi-protection security system improperly using a fingerprint sensor, a Wi-Fi camera, Arduino micro controller board R3, in addition to the effective use of electric locks to properly enter used people from the principal doors through a unified security system via the Internet of Things. Finally, the proposed system properly administers and detects unauthorized persons and preventing them from authorized entry and detecting them when repeated attempts to enter.
Jira: a Central Kurdish speech recognition system, designing and building speech corpus and pronunciation lexicon

This paper introduces the first large vocabulary speech recognition system (LVSR) for the Central Kurdish language, named Jira. The Kurdish language is an Indo-European language spoken by more than 30 million people in several countries, but due to the lack of speech and text resources, there is no speech recognition system for this language. We introduce the first speech corpus and pronunciation lexicon for the Central Kurdish language to fill this gap. Regarding speech corpus, we designed a sentence collection and the sentences are uttered by 576 speakers in a controlled environment with noise-free microphones [called AsoSoft Speech-Office (A part of this corpus is available for public access at https://github.com/AsoSoft/AsoSoft-Speech-Corpus )] and in Telegram social network environment using mobile phones (denoted as AsoSoft Speech-Crowdsourcing), resulting in 43.68 h of speech. Besides, a test set including 11 different document topics is designed and recorded in two corresponding speech conditions [i.e., Office (The office test set is available online at https://github.com/AsoSoft/AsoSoft-Speech-Testset ) and Crowdsourcing]. Furthermore, a 60 K pronunciation lexicon is prepared in this research in which we faced several challenges and proposed solutions for them. Our methods for script standardization of lexical variations and automatic pronunciation of the lexicon tokens are presented in detail. To set up the recognition engine, we used the Kaldi toolkit. A statistical tri-gram language model is used and several standard recipes including HMM-based models (i.e., mono, tri1, tr2, tri2, tri3), SGMM, and DNN methods are used to generate the acoustic model. These methods are trained with AsoSoft Speech-Office and AsoSoft Speech-Crowdsourcing and a combination of them. The SGMM acoustic model achieved the best performance, which results in 13.9% of the average word error rate (on different document topics) and 4.9% for the general topic.
Composition-Aware Image Steganography Through Adversarial Self-Generated Supervision

Steganography is an important and prevailing information hiding tool to perform secret message transmission in an open environment. Existing steganography methods can mainly fall into two categories: predefined rule-based and data-driven methods. The former is susceptible to the statistical attack, while the latter adopts the deep convolution neural networks to promote security. However, deep learning-based methods suffer from perceptible artificial artifacts or deep steganalysis. In this article, we introduce a novel composition-aware image steganography (CAIS) to guarantee both visual security and resistance to deep steganalysis through the self-generated supervision. The key innovation is an adversarial composition estimation module, which has integrated the rule-based composition method and generative adversarial network to help synthesize steganographic images with more naturalness. We first perform a rule-based image blending method to obtain infinite synthetically data-label pairs. Then, we utilize an adversarial composition estimation branch to recognize the message feature pattern from the composite image based on these self-generated data-label pairs. Through the adversarial training, we force the steganography function to synthesize steganographic images, which can fool the composition estimation network. Thus, the proposed CAIS can achieve better information hiding and higher security to resist deep steganalysis. Furthermore, an effective global-and-part checking is designed to alleviate visual artifacts caused by hiding secret information. We conduct a comprehensive analysis of CAIS from various aspects (e.g., security and robustness) to verify the superior performance of the proposed method. Comprehensive experimental results on three large-scale widely used datasets have demonstrated the superior performance of our CAIS compared with several state-of-the-art approaches.
Sinkhorn Adversarial Attack and Defense

Adversarial attacks have been extensively investigated in the recent past. Quite interestingly, a majority of these attacks primarily work in the lp space. In this work, we propose a novel approach for generating adversarial samples using Wasserstein distance. Unlike previous approaches, we use an unbalanced optimal transport formulation which is naturally suited for images. We first compute an adversarial sample using a gradient step and then project the resultant image into Wasserstein ball with respect to original sample. The attack introduces perturbation in the form of pixel mass distribution which is guided by a cost metric. Elaborate experiments on MNIST, Fashion-MNIST, CIFAR-10 and Tiny ImageNet demonstrate a sharp decrease in the performance of state-of-art classifiers. We also perform experiments with adversarially trained classifiers and show that our system achieves superior performance in terms of adversarial defense against several state-of-art attacks. Our code and pre-trained models are available at https://bit.ly/2SQBR4E.
Discretization of a Continuous Frequency Value in a Model of Socially Significant Behavior

Frequency or behavior rate is one of the main characteristics of behavior and can be defined as the average number of episodes of behavior that occur over a period. Knowledge of behavior rate can be used in many applications to predict behavior and estimate other related properties. Previously, a model based on a Bayesian belief network was presented to estimate behavior rate using data on recent episodes of behavior and the minimum and maximum intervals between episodes. Because Bayesian belief networks involve dealing with discrete values, the model uses discretization of continuous values. In this paper, we examine how different methods of discretization of a continuous variable describing the behavior rate affect the effectiveness of this model’s predictions.
Robust correlation filter tracking based on response map analysis network

Conventional discriminative-correlation-filter-based (DCF-based) visual tracking methods always update model at a fixed frequency and learning rate. Without evaluating the tracking confidence scores, the response map generated by filter is the only evidence for locating. Thus, most of the existing DCF-based methods suffer from the model contamination caused by drastic appearance variations, which leads to tracking drift even failure. And excessively frequent update will increase the computational redundancy and risk of over-fitting. In addition, these methods cannot recover target from heavy occlusion neither. Based on the observation that the shape of response maps reflects the matching degree between filter and target, we design and train a small-scale binary network named as response map analysis network (RAN) to evaluate the confidence scores of filters. Further, we propose to learn multiple filters to exploit different kinds of features, and adaptively adjust the update parameters according to the corresponding confidence scores. Moreover, we build a simple occlusion event model to detect heavy occlusion and recover target. Extensive experimental results validate the effectiveness of RAN and demonstrate that the proposed tracker performs favorably against other state-of-the-art (SOTA) DCF-based trackers in terms of precision, overlap rate and efficiency. • We propose an effective way of evaluating the tracking confidence score of correlation filter utilizing CNN. • We propose to learn multiple filters to exploit different features and adaptively adjust the update parameters according to confidence scores. • We build a simple occlusion event model to detect heavy occlusion and recover target. • The improved DCF tracker performs favorably compared to other SOTA trackers on several large-scale benchmarks.
Reducing Motion Sickness When Reading With Head-Mounted Displays By Using See-Through Background Images

With the development of virtual reality technology, head-mounted displays (HMDs) have been increasingly used to view or read 2D content while in vehicles. When 2D content is displayed in a fixed position on an HMD, the visual appearance does not change even with head movements, and body movements cannot be perceived while watching 2D content. This may cause visual-vestibular conflict and severe motion sickness. This study investigates whether motion sickness when reading 2D content fixed to the HMD coordinate system can be reduced by allowing readers to perceive body movements through video see-through images obtained from the front camera of the HMD in situations where the body vibrates. Twenty participants performed 20-minutes reading tasks in which they read a book fixed to the HMD coordinate while seated in a vibration device. Two background conditions of the book were explored: 1) the white background condition—not allowing participants to perceive their movements visually, and 2) the camera background condition—allowing participants to perceive their movements visually through see-through images. Evaluation of motion sickness using the Misery Scale, which is an 11-point questionnaire, showed that motion sickness after task completion was significantly lower in the camera background condition than in the white background condition. This result suggests that motion sickness can be relieved, provided that the users perceive their motion in the peripheral vision through the camera image, even if they gaze at 2D content fixed in the HMD coordinate system in the central vision. This study helps promote the use of HMDs by alleviating motion sickness.
Reliability analysis of man–machine systems using fuzzy cognitive mapping with genetic tuning

This article offers a method for analyzing the reliability of a man-machine system (MMS) and ranking of influencing factors based on a fuzzy cognitive map (FCM). The ranking of influencing factors is analogous to the ranking of system elements the probabilistic theory of reliability. To approximate the dependence of "influencing factors-reliability," the relationship of variable increments is used, which ensures the sensitivity of the reliability level to variations in the levels of influencing factors. The novelty of the method lies in the fact that the expert values of the weights of the FCM graph edges (arcs) are adjusted based on the results of observations using a genetic algorithm. The algorithm's chromosomes are generated from the intervals of acceptable values of edge weights, and the selection criterion is the sum of squares of deviations of the reliability simulation results from observations. The method is illustrated by the example of a multifactor analysis of the reliability of the "driver-car-road" system. It is shown that the FCM adjustment reduces the discrepancy between the reliability forecast and observations almost in half. Possible applications of the method can be complex systems with vaguely defined structures whose reliability depends very much on interrelated factors measured expertly.
Novel Contiguous Cross Propagation Neural Network Built CAD for Lung Cancer

The present progress of visual-based detection of the diseased area of a malady plays an essential part in the medical field.In that case, the image processing is performed to improve the image data, wherein it inhibits unintended distortion of image features or it enhances further processing in various applications and fields.This helps to show better results especially for diagnosing diseases.Of late the early prediction of cancer is necessary to prevent disease-causing problems.This work is proposed to identify lung cancer using lung computed tomography (CT) scan images.It helps to identify cancer cells' affected areas.In the present work, the original input image from Lung Image Database Consortium (LIDC) typically suffers from noise problems.To overcome this, the Gabor filter used for image processing is highly enhanced.In the next stage, the Spherical Iterative Refinement Clustering (SIRC) algorithm identifies cancer-suspected areas on the CT scan image.This approach can help radiologists and medical experts recognize cancer diseases and syndromes so that serious progress can be avoided in the early stages.These new methods help to remove unwanted portions of the CT image and better utilization the image.The subspace extraction of features approach is beneficial for evaluating lung cancer.This paper introduces a novel approach called Contiguous Cross Propagation Neural Network that tends to locate regions afflicted by lung cancer using CT scan pictures (CCPNN).By using the feature values from the fourth step of the procedure, the proposed CCPNN tends to categorize the lesion in the lung nodular site.The efficiency of the suggested CCPNN approach is evaluated using classification metrics such as recall (%), precision (%), F-measure (percent), and accuracy (%).Finally, the incorrect classification ratios are determined to compare the trained networks' effectiveness, through these parameters of CCPNN, it obtains the outstanding performance of 98.06% and it has provided the lowest false ratio of 1.8%.
Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users' experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-commerce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selection's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant, irrelevant, or noisy data.This work presents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Document Frequency (TF-IDF), feature selection with the utilization of River Formation Dynamics (RFD), and the Particle Swarm Optimization (PSO) algorithm.The systolic tree is used for pattern mining, and based on this, the recommendations are given.The proposed methods were evaluated using the MovieLens dataset, and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborative filtering, the precision of 0.89 was achieved.
GPU-Accelerated Collision Analysis of Vehicles in a Point Cloud Environment

We present a GPU-accelerated collision detection method for the navigation of vehicles in enclosed spaces represented using large point clouds. Our approach takes a CAD model of a vehicle, converts it to a volumetric representation or voxels, and computes the collision of the voxels with a point cloud representing the environment to identify a suitable path for navigation. We perform adaptive and efficient collision of voxels with the point cloud without the need for mesh generation. We have developed a GPU-accelerated voxel Minkowski sum algorithm to perform a clearance analysis of the vehicle. Finally, we provide theoretical bounds for the accuracy of the collision and clearance analysis. Our GPU implementation is linked with Unreal Engine to provide flexibility in performing the analysis.