Reviewerly/exHarmony huggingface dataset:
Community-Based Workforce for COVID-19 Contact Tracing and Prevention Activities in New York City, July–December 2020

High rates of hospitalization and death disproportionately affected Black, Latino, and Asian residents of New York City at the beginning of the COVID-19 pandemic. To suppress COVID-19 transmission, New York City implemented a workforce of community engagement specialists (CESs) to conduct home-based contact tracing when telephone numbers were lacking or telephone-based efforts were unsuccessful and to disseminate COVID-19 information and sanitary supplies.We describe the recruitment, training, and deployment of a multilingual CES workforce with diverse sociodemographic backgrounds during July-December 2020 in New York City. We developed standard operating procedures for infection control and safety measures, procured supplies and means of transportation, and developed protocols and algorithms to efficiently distribute workload.From July through December 2020, 519 CESs were trained to conduct in-person contact tracing and activities in community settings, including homes, schools, and businesses, where they disseminated educational materials, face masks, hand sanitizer, and home-based specimen collection kits. During the study period, 94 704 records of people with COVID-19 and 61 246 contacts not reached by telephone-based contact tracers were referred to CESs. CESs attempted home visits or telephone calls with 84 230 people with COVID-19 and 49 303 contacts, reaching approximately 55 592 (66%) and 35 005 (71%), respectively. Other CES activities included monitoring recently arrived travelers under quarantine, eliciting contacts at point-of-care testing sites, and advising schools on school-based COVID-19 mitigation strategies.This diverse CES workforce allowed for safe, in-person implementation of contact tracing and other prevention services for individuals and communities impacted by COVID-19. This approach prioritized equitable delivery of community-based support services and resources.
A one-time pad encryption scheme based on efficient physical-layer secret key generation for intelligent IoT system

The one-time pad (OTP) is an application-layer encryption technique to achieve the information-theoretic security, and the physical-layer secret key generation (SKG) technique is a promising candidate to provide the random keys for OTP. In this paper, we propose a joint SKG and OTP encryption scheme with the aid of a reconfigurable intelligent surface (RIS) to boost secret key rate. To maximize the efficiency of secure communication, we divide the process of secure transmission into two stages: SKG and then encrypted packet transmission. Meanwhile, we design an optimal algorithm for allocating time slots for SKG to maximize SKG efficiency without security risk. Furthermore, we design a key updating protocol based on our SKG scheme for OTP encryption. Simulation results verify that our scheme can generate keys securely and efficiently, and significantly improve the secure communication performance in an intelligent IoT system.
High dynamic range and super-resolution from raw image bursts

Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas. This paper introduces the first approach (to the best of our knowledge) to the reconstruction of highresolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. This method uses a physically-accurate model of image formation to combine an iterative optimization algorithm for solving the corresponding inverse problem with a learned image representation for robust alignment and a learned natural image prior. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration, and features that are learned end to end from synthetic yet realistic data. Extensive experiments demonstrate its excellent performance with super-resolution factors of up to ×4 on real photographs taken in the wild with hand-held cameras, and high robustness to low-light conditions, noise, camera shake, and moderate object motion.
A Meta-Analysis on the Effects of Problem-Posing in Mathematics Education on Performance and Dispositions

The purpose of this meta-analysis is to examine the effect of problem-posing on students' mathematical academic outcomes, including problem-solving skills, problem-posing skills, mathematical dispositions, and mathematics achievement. Twenty-one studies that were published between 1990 and 2019 with problem-posing as the intervention were included in the meta-analysis. A random-effects model was employed with robust variance estimation (RVE) to correct for the intercorrelation between effect sizes when necessary. The estimated average standardized mean difference effect size of problem-posing interventions (g = 0.64) demonstrated that problem-posing had a positive impact on students' academic outcomes. Specifically, across the interventions, students' problem-solving skills and mathematical achievement improved by engaging in problem-posing activities. The moderator analyses revealed that problem-posing interventions were more effective when structured, semi-structured, and free problem-posing tasks were all implemented, and longer-duration intervention was associated with larger improvement in students' mathematical dispositions.
Clothing image classification algorithm based on convolutional neural network and optimized regularized extreme learning machine

This paper proposes a new method that uses Alexnet with ImageNet transfer learning as the feature extractor and optimized and regularized extreme learning as the classifier. We keep the first five convolutional layers and the first two fully connected layers of Alexnet, and then train the network. Then, the mutual information between each dimension of the feature and its category is calculated and sorted, and the feature with the highest ranking is selected for feature dimensionality reduction. The regularization penalty term is introduced to the extreme learning machine to control its algorithm complexity and solve the problem of overfitting. Finally, the Runge Kutta optimization algorithm is employed to ameliorate the hidden layer bias and input weight of the regularized extreme learning machine, and the optimized regularized extreme learning machine is used to classify the dimensionality-reduced clothing image traits. The test outcome illustrates that on some apparel classification with style (ACWS) datasets, the precision, recall, F1-score, and accuracy of the proposed algorithm are 93.06%, 93.17%, 92.82%, and 93.14%, respectively, which are better than those of other clothing image classification algorithms. The results verify that the raised algorithm significantly ameliorates the classification property of clothing image algorithms.
Enhanced hierarchical symbolic sample entropy: Efficient tool for fault diagnosis of rotating machinery

Intelligent fault diagnosis of rotating machinery is a key topic for industrial equipment maintenance and fault prevention. In this study, an intelligent diagnosis approach of rotating machinery via enhanced hierarchical symbolic sample entropy (EHSSE) is proposed. Firstly, a novel indicator termed symbolic sample entropy (SSE) is proposed for complexity measure and representation of fault information. By using symbolic dynamic filtering, the raw continuous time-series will be discretized into symbolic data, and analysis of symbolic data is less sensitive to measurement noise, resulting in superior robustness. Secondly, SSE is combined with enhanced hierarchical analysis to further extract fault characteristics hidden in both low- and high-frequency components. To study the performance of SSE and EHSSE, multiple simulated signals and experimental studies are constructed and three widely used entropy methods are employed to present a comprehensive comparison. The comparison results show that EHSSE performs best in diagnosing various faults of planetary gearbox and rotor system with highest identification accuracy compared with other entropy-based approaches.
Robust Dynamic Hand Gesture Recognition Based on Millimeter Wave Radar Using Atten-TsNN

The millimeter-wave radar can sense the subtle movement of the hand. However, the traditional hand gesture recognition methods do not work well in dynamic interference scenarios. To address this issue, a robust hand gesture recognition method is proposed based on the self-attention time-series neural networks. Firstly, the original radar echo is constructed in terms of frame, sequence, and channel at the input terminal of the network. A one-dimensional time-series neural network is built, and the time-distributed layer is used as the wrapper to extract the feature from each frame sequence independently. Then the self-attention mechanism is employed to assign the adequate weights to the sequence of frames entered in parallel to obtain the inter-frame correlation and suppress the random interference. Finally, the Global AvgPooling layer is used to reduce the number of channels, and the fully connected layer outputs the label of the gesture. The experimental results show that the proposed method can achieve a high recognition rate in the presence of random dynamic interference.
A Comprehensive Framework for IoT-Based Data Protection in Blockchain System

Thousands of IoT devices (IoT) equipment are often linked together to share information or services in the cloud. Nonetheless, IoT security and protection are important issues because of the growing number of IoT devices from which data is obtained. Because of its distinguishing feature, which includes persistence, assessment capability, and scalability, blockchain technology notably set out to tackle security and safety concerns in IoT frameworks. This article demonstrates two unique commitments: ultralight customizable blockchain technology and scalable time-based consensus. The Ultralight extensible enables scalability by establishing an overarching networking in which greater asset devices collaborate to deal with the blockchain technology. Preparing expenses and procrastination is the time-based general agreement. The group leaders adopt a confident method to decrease the processing overhead for confirming additional squares indefinitely. Blockchain technology is increasingly been used to provide a distributed, safe, trustworthy, and autonomous framework for the energy cooperation.
Documents, Digitisation and History

Recent years have seen a very large rise in digitisation projects in various state-administered archives in India, including the National Archives of India and regional archives in different parts of the country. This essay argues that rather than increasing access and transparency, digitisation serves to reproduce and potentially even increase state control over historical sources and memory. In addition, the elision of context in digitised collections in state archives poses problems for historical research, particularly for scholars from less privileged institutions. It is imperative that these efforts go beyond an understanding of digitisation as conservation alone in order to harness the creative and pedagogical possibilities offered by digital technology to influence, and even transform, the historical scholarship of a wide variety of users.
Answering knowledge-based visual questions via the exploration of Question Purpose

Visual question answering has been greatly advanced by deep learning technologies, but still remains an open problem subjected to two aspects of factors. First, previous works estimate the correctness of each candidate answer mainly by its semantic correlations with visual questions, overlooking the fact that some questions and their answers are semantically inconsistent. Second, previous works that require external knowledge mainly uses the knowledge facts retrieved by key words or visual objects. However, the retrieved knowledge facts may only be related to the semantics of the question, but are useless or even misleading for answer prediction. To address these issues, we investigate how to capture the purpose of visual questions and propose a Purpose Guided Visual Question Answering model, called PGVQA. It mainly has two appealing properties: (1) It can estimate the correctness of candidate answers based on the Question Purpose (QP) that reveals which aspects of the concept are examined by visual questions. This is helpful for avoiding the negative effect of the semantic inconsistency between answers and questions. (2) It can incorporate the knowledge facts accordant with the QP into answer prediction, which helps to improve the probability of answering visual questions correctly. Empirical studies on benchmark datasets show that PGVQA achieves state-of-the-art performance.
Open Quantum Random Walks and Quantum Markov chains on Trees I: Phase transitions

In the present paper, we construct QMC (Quantum Markov Chains) associated with Open Quantum Random Walks such that the transition operator of the chain is defined by OQRW and the restriction of QMC to the commutative subalgebra coincides with the distribution $P_\rho$ of OQRW. However, we are going to look at the probability distribution as a Markov field over the Cayley tree. Such kind of consideration allows us to investigated phase transition phenomena associated for OQRW within QMC scheme. Furthermore, we first propose a new construction of QMC on trees, which is an extension of QMC considered in Ref. [10]. Using such a construction, we are able to construct QMCs on tress associated with OQRW. Our investigation leads to the detection of the phase transition phenomena within the proposed scheme. This kind of phenomena appears first time in this direction. Moreover, mean entropies of QMCs are calculated.
M2FRED: Mobile Masked Face REcognition Through Periocular Dynamics Analysis

Recent regulations to block the widespread transmission of COVID-19 disease among people impose the use of facial masks indoor and outdoor. Such restriction becomes critical in all those scenarios where access controls take benefit from biometric recognition systems. The occlusions due to the presence of a facial mask make a significant portion of human faces unavailable for feature extraction and analysis. This work explores the contribution of the solely periocular region of the face to achieve a robust recognition approach suitable for mobile devices. Rather than working on a static analysis of the facial features, like largely done by work on periocular recognition in the literature, the proposed study focuses the attention on the analysis of face dynamics so that the spatio-temporal features make the recogniser frame-independent and tolerant to user movements during the acquisition. To obtain a lightweight processing, which is compliant with limited computing power of mobile devices, the spatio-temporal representation of the periocular region has analysed and classified through Machine Learning approaches. The experimental discussion has been performed on a new dataset, Mobile Masked Face REcognition Database, specifically designed to analyse the periocular region dynamics in presence of facial masks. For a wider comparative analysis, a publicly available dataset called XM2VTS has been considered as well as Deep Learning solutions have been experimented to discuss the challenging aspects of the recognition problem. Moreover, a summary of the state-of-the-art on periocular recognition driven by COVID pandemic has been presented, showing how the research efforts in this field focused on recognition of still images. Experimental results show promising levels of performance as well as limitations of the proposed approach, creating the premises for future directions.
Transportation of Hybrid $${\textbf {MoS}}_2$$–$${\textbf {SiO}}_2$$/EG Nanofluidic System Toward Radially Stretched Surface

Abstract A stochastic computing approach is implemented in the present work to solve the nonlinear nanofluidics system that occurs in the model of atomic physics. The process converts the partial differential nanofluidics system with suitable level of similarities transformation into nonlinear systems of differential equations. For the construction of datasets, finite difference scheme (Lobatto IIIA) is applied through different selection of collocation points for nonlinear nanofluidics system having accuracy of order four. Lobatto IIIA has a strong point to tackle extremely nonlinear systems of ordinary differential equations in smooth way. For different scenarios, datasets are well trained through computing scheme to investigate the heat transfer and thermal performance of nanofluidic transportation system of nanofluids and hybrid nanofluids toward stretching surfaces with variation of Biot number, Nusselt number and skin fraction. Furthermore, the reliability, accuracy and efficiency are endorsed through various statistical analysis and graphical illustrations of proposed computing scheme.
A view of computational models for image segmentation

Abstract Image segmentation is a central topic in image processing and computer vision and a key issue in many applications, e.g., in medical imaging, microscopy, document analysis and remote sensing. According to the human perception, image segmentation is the process of dividing an image into non-overlapping regions. These regions, which may correspond, e.g., to different objects, are fundamental for the correct interpretation and classification of the scene represented by the image. The division into regions is not unique, but it depends on the application, i.e., it must be driven by the final goal of the segmentation and hence by the most significant features with respect to that goal. Thus, image segmentation can be regarded as a strongly ill-posed problem. A classical approach to deal with ill posedness consists in incorporating in the model a-priori information about the solution, e.g., in the form of penalty terms. In this work we provide a brief overview of basic computational models for image segmentation, focusing on edge-based and region-based variational models, as well as on statistical and machine-learning approaches. We also sketch numerical methods that are applied in computing solutions to these models. In our opinion, our view can help the readers identify suitable classes of methods for solving their specific problems.
A novel authentication scheme for edge computing-enabled Internet of Vehicles providing anonymity and identity tracing with drone-assistance

As an element of the network edge for the smart city, the smart vehicle with computation capability undertakes information collection and transmission and serves as a node of the Internet of Vehicles (IoV). It is emergent to keep available communication for IoV in rural or disaster areas under 5G/beyond 5G environments, where drones play a critical part in the above cases. Also, edge computing is a good method to complete the incentive computing for high-speed vehicles. In this paper, an authentication scheme is constructed for an edge computing-enabled Internet of Vehicles with drone assistance. The vehicles can keep their identities anonymous in the data transmission. The roadside unit, as the mobile edge computing server, deals with data from vehicles. If any vehicle’s identity is required, the trusted authority will disclose it via the corresponding message. The formal proof demonstrates that the messages between participants cannot be forged by the attacker. Compared with some recent schemes of drone-assisted IoV, our scheme is the only one that maintains all security features. Moreover, our scheme is sound and practical via the expression of performance discussion and network simulation.
Implementation of a Distributed Framework for Permissioned Blockchain-Based Secure Automotive Supply Chain Management

An automotive supply chain includes a range of activities from the concept of the product to its final transfer to a customer and subsequent vehicle maintenance. The three distinct stages of this chain are production, sales, and maintenance. In many countries, automobile records are not available to the public and anyone who has access to the central database or government systems can tamper with these records. In addition, used vehicle maintenance and transfer histories remain unavailable or inaccessible. These issues can be overcome by incorporating state-of-the-art blockchain technology into automotive supply chain management. Blockchain technology uses a chain of blocks for distributed transfer and storage of information, creating a decentralized data register that makes records of any digital asset tamper-proof and transparent. In this paper, we implement a permissioned blockchain-based framework for secure and efficient supply chain management of the automobile industry. We employed Hyperledger Fabric; an enterprise-grade distributed ledger platform for developing solutions. In our solution, the blockchain is customized and private in order to ensure system security. We evaluated our system in terms of memory cost, monetary cost, and speed of execution. Our results demonstrate that only 346 MB of extra memory space is required for storing the automotive data of 1 million users, thus rendering the memory cost negligible. The monetary cost is insignificant as all open source blockchain resources are employed, and the speed of record update is also fast. Our results also show that the decentralization of the automotive supply chain using blockchain can implement system security with minor modifications in the established configuration of the web application database.
The effect of quantum memory on quantum speed limit time for CP-(in)divisible channels

Quantum speed limit time defines the limit on the minimum time required for a quantum system to evolve between two states. Investigation of bounds on speed limit time of quantum system under non-unitary evolution is of fundamental interest, as it reveals interesting connections to quantum (non-)Markovianity. Here, we discuss the characteristics of quantum speed limit time as a function of quantum memory, quantified as the deviation from temporal self-similarity of quantum dynamical maps for CP-divisible as well as indivisible maps, and show that the presence of quantum memory can speed up quantum evolution. This demonstrates the enhancement of the speed of quantum evolution in the presence of quantum memory for a wider class of channels than indicated by the CP-indivisibility criterion.
Gudermannian Function Induced Banach Space Valued Ordinary and Fractional Neural Network Approximations

Here we investigate the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional derivatives. Our operators are defined by using a density function generated by the Gudermannian sigmoid function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer. It relies on [14].