Reviewerly/exHarmony huggingface dataset:
Ground vibrations recorded by fiber-optic cables reveal traffic response to COVID-19 lockdown measures in Pasadena, California

Abstract The COVID-19 lockdown has unprecedently affected the dynamics of our society. As traffic flow is a good proxy for societal activity, traffic monitoring becomes a useful tool to assess the lockdown’s impacts. Here we turned two strands of unused telecommunication fibers in Pasadena, California into a seismic array of ~5,000 sensors and detected ground vibrations caused by moving vehicles along the streets above the cable. We monitor the number of vehicles and their mean speed between December 2019 and August 2020 in high spatial and temporal resolution, and then analyze the traffic patterns change due to the COVID-19 lockdown. Our results show a city-wide decline in traffic volume and an increase in speed due to the lockdown, although the level of impact varies substantially by streets. This study demonstrates the feasibility of using telecommunication fiber optic cables in traffic monitoring, which has implications for public health, economy, and transportation safety.
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning

Most previous methods for text data augmentation are limited to simple tasks and weak baselines.We explore data augmentation on hard tasks (i.e., few-shot natural language understanding) and strong baselines (i.e., pretrained models with over one billion parameters).Under this setting, we reproduced a large number of previous augmentation methods and found that these methods bring marginal gains at best and sometimes degrade the performance much.To address this challenge, we propose a novel data augmentation method FlipDA that jointly uses a generative model and a classifier to generate label-flipped data.Central to the idea of FlipDA is the discovery that generating labelflipped data is more crucial to the performance than generating label-preserved data.Experiments show that FlipDA achieves a good tradeoff between effectiveness and robustness-it substantially improves many tasks while not negatively affecting the others. 1
Color image encryption algorithm based on DNA code and alternating quantum random walk

In recent years, image encryption technology has attracted much attention. As people pay more attention to communication privacy and network security, the requirements for information encryption technology are more stringent. As one of the information carriers, images are valuable for carrying the effectiveness and vividness of the information. This paper proposes a color image encryption algorithm based on DNA encoding and alternating quantum random walk. Quantum random walk is an excellent cryptographic tool that participates in all parts of the algorithm process, and DNA encoding is used as the core encryption method to complete the algorithm. This article describes the encryption and decryption process in detail, and conducts simulation experiments to verify and analyze the results of the proposed algorithm. In the simulation stage, we design the simulation key parameters, encode the color image encryption and decryption experiments, and carry out related analysis. The experimental results show that the color image encryption algorithm proposed in this paper can perform safe and effective color image encryption. The correlation analysis shows that the image histogram after encryption is stable, the pixel correlation coefficient approaches 0, and the key space is &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}${2^{ 128 }} $\end{document}&lt;/tex-math&gt;&lt;alternatives&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="23-20211255_M2.jpg"/&gt;&lt;graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="23-20211255_M2.png"/&gt;&lt;/alternatives&gt;&lt;/inline-formula&gt;, the three-channel information entropy reaches more than 7.997, which can resist statistical attacks, brute force attacks and other attack methods. In addition, DNA coding has unique biological characteristics in addition to the novel coding and calculation methods, which provide new ideas and directions for cryptographic research.
Adaptive Levenberg–Marquardt Algorithm: A New Optimization Strategy for Levenberg–Marquardt Neural Networks

Engineering data are often highly nonlinear and contain high-frequency noise, so the Levenberg–Marquardt (LM) algorithm may not converge when a neural network optimized by the algorithm is trained with engineering data. In this work, we analyzed the reasons for the LM neural network’s poor convergence commonly associated with the LM algorithm. Specifically, the effects of different activation functions such as Sigmoid, Tanh, Rectified Linear Unit (RELU) and Parametric Rectified Linear Unit (PRLU) were evaluated on the general performance of LM neural networks, and special values of LM neural network parameters were found that could make the LM algorithm converge poorly. We proposed an adaptive LM (AdaLM) algorithm to solve the problem of the LM algorithm. The algorithm coordinates the descent direction and the descent step by the iteration number, which can prevent falling into the local minimum value and avoid the influence of the parameter state of LM neural networks. We compared the AdaLM algorithm with the traditional LM algorithm and its variants in terms of accuracy and speed in the context of testing common datasets and aero-engine data, and the results verified the effectiveness of the AdaLM algorithm.
Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in decision making to control the scheduling and shifting of load to nodes. The aim is to improve the container’s schedule requested digitally from users to enhance the efficiency in scheduling and reduce cost. The constraints associated with the existing container scheduling techniques which often assign a node to every new container based on a personal criterion by relying on individual terms has been greatly improved by the new system presented in this study. The KCSS presented in this study provides multicriteria node selection based on artificial intelligence in terms of decision making systems thereby giving the scheduler a broad picture of the cloud's condition and the user's requirements. AI Scheduler allows users to easily make use of fractional Graphics Processing Units (GPUs), integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes.
Optimization Research on Deep Learning and Temporal Segmentation Algorithm of Video Shot in Basketball Games

The analysis of the video shot in basketball games and the edge detection of the video shot are the most active and rapid development topics in the field of multimedia research in the world. Video shots’ temporal segmentation is based on video image frame extraction. It is the precondition for video application. Studying the temporal segmentation of basketball game video shots has great practical significance and application prospects. In view of the fact that the current algorithm has long segmentation time for the video shot of basketball games, the deep learning model and temporal segmentation algorithm based on the histogram for the video shot of the basketball game are proposed. The video data is converted from the RGB space to the HSV space by the boundary detection of the video shot of the basketball game using deep learning and processing of the image frames, in which the histogram statistics are used to reduce the dimension of the video image, and the three-color components in the video are combined into a one-dimensional feature vector to obtain the quantization level of the video. The one-dimensional vector is used as the variable to perform histogram statistics and analysis on the video shot and to calculate the continuous frame difference, the accumulated frame difference, the window frame difference, the adaptive window’s mean, and the superaverage ratio of the basketball game video. The calculation results are combined with the set dynamic threshold to optimize the temporal segmentation of the video shot in the basketball game. It can be seen from the comparison results that the effectiveness of the proposed algorithm is verified by the test of the missed detection rate of the video shots. According to the test result of the split time, the optimization algorithm for temporal segmentation of the video shot in the basketball game is efficiently implemented.
Image steganography using genetic algorithm for cover image selection and embedding

Data interchange through internet becomes an eminent technique and hence data security has become a big challenge in the field of communication with the increased use of internet. Demand for data authentication and effective means to control data integrity has been steadily increasing. Such a demand is due to the ease with which digital data can be tampered. Thus, cryptography and watermarking can be replaced with steganography for secure data communication and data privacy. In this paper, the carrier image is selected such that the payload/secret image and least significant bits of carrier image are matched with larger degree of compatibility and the hiding process introduces negligible changes in the resulted stego image based on genetic algorithm. In the proposed method we have achieved 30 to 40% improvements in the performance when compared to different existing methods. Selection of a suitable cover image and hiding the secret data to enhance the imperceptibility is a very challenging task. Genetic algorithm is used to ease the work of exploring an impossible task of selection from the trillions and millions of combinations.
Unsupervised deep learning for 3D reconstruction with dual-frequency fringe projection profilometry

The fringe projection profilometry (FPP) technique has been widely applied in three-dimensional (3D) reconstruction in industry for its high speed and high accuracy. Recently, deep learning has been successfully applied in FPP to achieve high-accuracy and robust 3D reconstructions in an efficient way. However, the network training needs to generate and label numerous ground truth 3D data, which can be time-consuming and labor-intensive. In this paper, we propose to design an unsupervised convolutional neural network (CNN) model based on dual-frequency fringe images to fix the problem. The fringe reprojection model is created to transform the output height map to the corresponding fringe image to realize the unsupervised training of the CNN. Our network takes two fringe images with different frequencies and outputs the corresponding height map. Unlike most of the previous works, our proposed network avoids numerous data annotations and can be trained without ground truth 3D data for unsupervised learning. Experimental results verify that our proposed unsupervised model (1) can get competitive-accuracy reconstruction results compared with previous supervised methods, (2) has excellent anti-noise and generalization performance and (3) saves time for dataset generation and labeling (3.2 hours, one-sixth of the supervised method) and computer space for dataset storage (1.27 GB, one-tenth of the supervised method).
CRAN: An Hybrid CNN-RNN Attention-Based Model for Arabic Machine Translation

Machine Translation (MT) is one of the challenging tasks in the field of Natural Language Processing (NLP). The Convolutional Neural Network (CNN)-based approaches and Recurrent Neural Network (RNN)-based techniques have shown different capabilities in representing a piece of text. In this work, an hybrid CNN-RNN attention-based neural network is proposed. During training, Adam optimizer algorithm is used, and then, a popular regularization technique named dropout is applied in order to prevent some learning problems such as overfitting. The experiment results show the impact of our proposed system on the performance of Arabic machine translation.
Blockchain Technology Application: Authentication System in Digital Education

The application of technology in various aspects of life has made it easy for many people. However, there are also shortcomings in the use of technology, one of which is security issues, both transactions and data. Given these issues in this paper, we propose blockchain technology for an authentication system that will protect data rights and interests and be safe from interference to store information in the form of confidential text, especially in the application of technology in education. From this writing, there are 2 benefits, the first is that all data stored in the education system is guaranteed and there will be increased trust from both parents, teachers and other parties due to the decentralized nature of blockchain.
Secret key rate of multi-ring M-APSK continuous variable quantum key distribution

Discrete modulation continuous variable quantum key distribution (DM-CV-QKD) is highly considered in real implementations to avoid the complexity of Gaussian modulation (GM), which is optimum in terms of the key rate. DM-CV-QKD systems usually consider M-symbol phase shift keying (M-PSK) constellations. However, this type of constellation cannot reach transmission distances and key rates as high as GM, limiting the practical implementation of CV-QKD systems. Here, by considering M-symbol amplitude and phase shift keying (M-APSK) constellations, we can approximate GM. Indeed, considering finite-size effects, 4 ring 64-APSK can reach 52.0 km, only 7.2 km less than GM and 282% the maximum achievable transmission distance for 8-PSK.
An improved pix2pix model based on Gabor filter for robust color image rendering

In recent years, with the development of deep learning, image color rendering method has become a research hotspot once again. To overcome the detail problems of color overstepping and boundary blurring in the robust image color rendering method, as well as the problems of unstable training based on generative adversarial networks, we propose an color rendering method using Gabor filter based improved pix2pix for robust image. Firstly, the multi-direction/multi-scale selection characteristic of Gabor filter is used to preprocess the image to be rendered, which can retain the detailed features of the image while preprocessing to avoid the loss of features. Moreover, among the Gabor texture feature maps with 6 scales and 4 directions, the texture map with the scale of 7 and the direction of 0° has the comparable rendering performance. Finally, by improving the loss function of pix2pix model and adding the penalty term, not only the training can be stabilized, but also the ideal color image can be obtained. To reflect image color rendering quality of different models more objectively, PSNR and SSIM indexes are adopted to evaluate the rendered images. The experimental results of the proposed method show that the robust image rendered by this method has better visual performance and reduces the influence of light and noise on the image to a certain extent.
On Greedy Randomized Augmented Kaczmarz Method for Solving Large Sparse Inconsistent Linear Systems

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 13 July 2020Accepted: 27 July 2021Published online: 22 November 2021Keywordssystem of linear equations, inconsistency, augmented linear system, Kaczmarz method, randomized iteration, convergence propertyAMS Subject Headings65F10, 65F20, 65K05, 90C25, 15A06Publication DataISSN (print): 1064-8275ISSN (online): 1095-7197Publisher: Society for Industrial and Applied MathematicsCODEN: sjoce3
Service discovery and selection in IoT: A survey and a taxonomy

Recently, Internet has evolved into a new generation, called Internet of Things, thus enabling the connection between the physical and the digital worlds by creating an ubiquitous and self-organizing network. A huge number of smart objects are becoming now identifiable and addressable while being able to communicate with each other. Moreover, the integration of cloud infrastructures in the design of IoT, has moved this new trademark technology into a new dimension, enabling virtualisation and service provisioning. Billions of cloud services with different performance levels, requirements and functionalities are thus being offered in IoT, raising however the issues of their management, discovery and selection. In the literature, a considerable effort has been invested to address service discovery and selection in the context of IoT, despite the lack of standardization that meets the IoT requirements. In this paper, we propose an exhaustive taxonomy to classify service discovery approaches in the context of IoT, that we subsequently evaluate according to different aspects and criteria. Then, we discuss the gaps and advantages of each class of our taxonomy and locate the context and the requirements under which each can operate. Finally, we identify the challenges and future research directions in this domain.
Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm

Digital videos are now low-cost, easy to capture and easy to share on social media due to the common feature of video recording in smart phones and digital devices. However, with the advancement of video editing tools, videos can be tampered (forged) easily for propaganda or to gain illegal advantages—ultimately, the authenticity of videos shared on social media cannot be taken for granted. Over the years, significant research has been devoted to developing new techniques for detecting different types of video tampering. In this paper, we offer a detailed review of existing passive video tampering detection techniques in a systematic way. The answers to research questions prepared for this study are also elaborated. The state-of-the-art research work is analyzed extensively, highlighting the pros and cons and commonly used datasets. Limitations of existing video forensic algorithms are discussed, and we conclude with research challenges and future directions.
An Authentication and Secure Communication Scheme for In-Vehicle Networks Based on SOME/IP

The rapid development of intelligent networked vehicles (ICVs) has brought many positive effects. Unfortunately, connecting to the outside exposes ICVs to security threats. Using secure protocols is an important approach to protect ICVs from hacker attacks and has become a hot research area for vehicle security. However, most of the previous studies were carried out on V2X networks, while those on in-vehicle networks (IVNs) did not involve Ethernet. To this end, oriented to the new IVNs based on Ethernet, we designed an efficient secure scheme, including an authentication scheme using the Scalable Service-Oriented Middleware over IP (SOME/IP) protocol and a secure communication scheme modifying the payload field of the original SOME/IP data frame. The security analysis shows that the designed authentication scheme can provide mutual identity authentication for communicating parties and ensure the confidentiality of the issued temporary session key; the designed authentication and secure communication scheme can resist the common malicious attacks conjointly. The performance experiments based on embedded devices show that the additional overhead introduced by the secure scheme is very limited. The secure scheme proposed in this article can promote the popularization of the SOME/IP protocol in IVNs and contribute to the secure communication of IVNs.
Autonomous Quadcopter Landing on a Moving Target

Autonomous landing on a moving target is challenging because of external disturbances and localization errors. In this paper, we present a vision-based guidance technique with a log polynomial closing velocity controller to achieve faster and more accurate landing as compared to that of the traditional vertical landing approaches. The vision system uses a combination of color segmentation and AprilTags to detect the landing pad. No prior information about the landing target is needed. The guidance is based on pure pursuit guidance law. The convergence of the closing velocity controller is shown, and we test the efficacy of the proposed approach through simulations and field experiments. The landing target during the field experiments was manually dragged with a maximum speed of 0.6 m/s. In the simulations, the maximum target speed of the ground vehicle was 3 m/s. We conducted a total of 27 field experiment runs for landing on a moving target and achieved a successful landing in 22 cases. The maximum error magnitude for successful landing was recorded to be 35 cm from the landing target center. For the failure cases, the maximum distance of vehicle landing position from target boundary was 60 cm.
Combining chest X-rays and electronic health record (EHR) data using machine learning to diagnose acute respiratory failure

Abstract Objective When patients develop acute respiratory failure (ARF), accurately identifying the underlying etiology is essential for determining the best treatment. However, differentiating between common medical diagnoses can be challenging in clinical practice. Machine learning models could improve medical diagnosis by aiding in the diagnostic evaluation of these patients. Materials and Methods Machine learning models were trained to predict the common causes of ARF (pneumonia, heart failure, and/or chronic obstructive pulmonary disease [COPD]). Models were trained using chest radiographs and clinical data from the electronic health record (EHR) and applied to an internal and external cohort. Results The internal cohort of 1618 patients included 508 (31%) with pneumonia, 363 (22%) with heart failure, and 137 (8%) with COPD based on physician chart review. A model combining chest radiographs and EHR data outperformed models based on each modality alone. Models had similar or better performance compared to a randomly selected physician reviewer. For pneumonia, the combined model area under the receiver operating characteristic curve (AUROC) was 0.79 (0.77–0.79), image model AUROC was 0.74 (0.72–0.75), and EHR model AUROC was 0.74 (0.70–0.76). For heart failure, combined: 0.83 (0.77–0.84), image: 0.80 (0.71–0.81), and EHR: 0.79 (0.75–0.82). For COPD, combined: AUROC = 0.88 (0.83–0.91), image: 0.83 (0.77–0.89), and EHR: 0.80 (0.76–0.84). In the external cohort, performance was consistent for heart failure and increased for COPD, but declined slightly for pneumonia. Conclusions Machine learning models combining chest radiographs and EHR data can accurately differentiate between common causes of ARF. Further work is needed to determine how these models could act as a diagnostic aid to clinicians in clinical settings.