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  • Study of Degenerate Poly-Bernoulli Polynomials by λ-Umbral Calculus
  • Abstract Recently, degenerate poly-Bernoulli polynomials are defined in terms of degenerate polyexponential functions by Kim-Kim-Kwon-Lee. The aim of this paper is to further examine some properties of the degenerate poly-Bernoulli polynomials by using three formulas from the recently developed ‘λ-umbral calculus.’ In more detail, we represent the degenerate poly-Bernoulli polynomials by Carlitz Bernoulli polynomials and degenerate Stirling numbers of the first kind, by fully degenerate Bell polynomials and degenerate Stirling numbers of the first kind, and by higherorder degenerate Bernoulli polynomials and degenerate Stirling numbers of the second kind.
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  • Code Transform Model Producing High-Performance Program
  • Abstract This paper introduces a novel transform method to produce the newly generated programs through code transform model called the second generation of Generative Pre-trained Transformer (GPT-2) reasonably, improving the program execution performance significantly. Besides, a theoretical estimation in statistics has given the minimum number of generated programs as required, which guarantees to find the best one within them. The proposed approach can help the voice assistant machine resolve the problem of inefficient execution of application code. In addition to GPT-2, this study develops the variational Simhash algorithm to check the code similarity between sample program and newly generated program, and…
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  • Simulating the Effect of Temperature Gradient on Grain Growth of 6061-T6 Aluminum Alloy via Monte Carlo Potts Algorithm
  • Abstract During heat treatment or mechanical processing, most polycrystalline materials experience grain growth, which significantly affects their mechanical properties. Microstructure simulation on a mesoscopic scale is an important way of studying grain growth. A key research focus of this type of method has long been how to efficiently and accurately simulate the grain growth caused by a non-uniform temperature field with temperature gradients. In this work, we propose an improved 3D Monte Carlo Potts (MCP) method to quantitatively study the relationship between non-uniform temperature fields and final grain morphologies. Properties of the aluminum alloy AA6061-T6 are used to establish a trial…
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  • A Novel Named Entity Recognition Scheme for Steel E-Commerce Platforms Using a Lite BERT
  • Abstract In the era of big data, E-commerce plays an increasingly important role, and steel E-commerce certainly occupies a positive position. However, it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs. In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms, we propose a novel deep learning-based loss function for named entity recognition (NER). Considering the impacts of small sample and imbalanced data, in our NER scheme, the focal loss, the label smoothing, and the cross entropy are incorporated into…
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  • Neutrosophic N-Structures Applied to Sheffer Stroke BL-Algebras
  • Abstract In this paper, we introduce a neutrosophic N-subalgebra, a (ultra) neutrosophic N-filter, level sets of these neutrosophic N-structures and their properties on a Sheffer stroke BL-algebra. By defining a quasi-subalgebra of a Sheffer stroke BL-algebra, it is proved that the level set of neutrosophic N-subalgebras on the algebraic structure is its quasi-subalgebra and vice versa. Then we show that the family of all neutrosophic N-subalgebras of a Sheffer stroke BL-algebra forms a complete distributive lattice. After that a (ultra) neutrosophic N-filter of a Sheffer stroke BL-algebra is described, we demonstrate that every neutrosophic N-filter of a Sheffer stroke BL-algebra is…
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  • Deep Learning Applications for COVID-19 Analysis: A State-of-the-Art Survey
  • Abstract The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the absence of a vaccine, the…
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  • IDV: Internet Domain Name Verification Based on Blockchain
  • Abstract The rapid development of blockchain technology has provided new ideas for network security research. Blockchain-based network security enhancement solutions are attracting widespread attention. This paper proposes an Internet domain name verification method based on blockchain. The authenticity of DNS (Domain Name System) resolution results is crucial for ensuring the accessibility of Internet services. Due to the lack of adequate security mechanisms, it has always been a challenge to verify the authenticity of Internet domain name resolution results. Although the solution represented by DNSSEC (Domain Name System Security Extensions) can theoretically solve the domain name verification problem, it has not been…
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  • Subdivision Surface-Based Isogeometric Boundary Element Method for Steady Heat Conduction Problems with Variable Coefficient
  • Abstract The present work couples isogeometric analysis (IGA) and boundary element methods (BEM) for three dimensional steady heat conduction problems with variable coefficients. The Computer-Aided Design (CAD) geometries are built by subdivision surfaces, and meantime the basis functions of subdivision surfaces are employed to discretize the boundary integral equations for heat conduction analysis. Moreover, the radial integration method is adopted to transform the additional domain integrals caused by variable coefficients to the boundary integrals. Several numerical examples are provided to demonstrate the correctness and advantages of the proposed algorithm in the integration of CAD and numerical analysis.
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  • The New Neutrosophic Double and Triple Exponentially Weighted Moving Average Control Charts
  • Abstract The concept of neutrosophic statistics is applied to propose two monitoring schemes which are an improvement of the neutrosophic exponentially weighted moving average (NEWMA) chart. In this study, two control charts are designed under the uncertain environment or neutrosophic statistical interval system, when all observations are undermined, imprecise or fuzzy. These are termed neutrosophic double and triple exponentially weighted moving average (NDEWMA and NTEWMA) control charts. For the proficiency of the proposed chart, Monte Carlo simulations are used to calculate the run-length characteristics (such as average run length (ARL), standard deviation of the run length (SDRL), percentiles (P25, P50, P75))…
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