Machine learning (ML)-enabled is one of the appealing characteristics of modern software systems, which usually contain ML components to make the system more intelligent for easier living. Requirements for ML-enabled ...
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Machine learning (ML)-enabled is one of the appealing characteristics of modern software systems, which usually contain ML components to make the system more intelligent for easier living. Requirements for ML-enabled software systems involve functional, quality, environmental, and data requirements. UML is a de facto approach for requirements analysis and system design, but its current modeling capabilities do not yet cover ML-enabled software systems to describe software quality requirements, environmental requirements, and data requirements. In this paper, we propose a requirements model for ML-enabled software systems and a modeling process for this model based on an extension of UML. In addition, we demonstrate the proposed model and modeling process through the case of the Tesla Autopilot system. The results show that the proposed model is expressive and usable and has a low learning curve when the software developers have basic knowledge of UML. Our proposed model can be further implemented and used in industrial settings.
In this work, the theoretical study of magnetic strength of pairing interaction in two-band model high-temperature iron-based superconductors Ba(Fe1−xCox)2As2 and Ba1−xNaxFe2As2 is conducted. With zero applied externa...
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This work presents a concurrent topology optimization method for the multiscale design of fiber-reinforced composites. Under the moving morphable component (MMC) method framework, the fiber-winding angle at the micros...
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Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronar...
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Myocardial perfusion imaging(MPI),which uses single-photon emission computed tomography(SPECT),is a well-known estimating tool for medical diagnosis,employing the classification of images to show situations in coronary artery disease(CAD).The automatic classification of SPECT images for different techniques has achieved near-optimal accuracy when using convolutional neural networks(CNNs).This paper uses a SPECT classification framework with three steps:1)Image denoising,2)Attenuation correction,and 3)Image *** denoising is done by a U-Net architecture that ensures effective image *** correction is implemented by a convolution neural network model that can remove the attenuation that affects the feature extraction process of ***,a novel multi-scale diluted convolution(MSDC)network is *** merges the features extracted in different scales and makes the model learn the features more *** scales of filters with size 3×3 are used to extract *** three steps are compared with state-of-the-art *** proposed denoising architecture ensures a high-quality image with the highest peak signal-to-noise ratio(PSNR)value of *** proposed classification method is compared with the five different CNN models,and the proposed method ensures better classification with an accuracy of 96%,precision of 87%,sensitivity of 87%,specificity of 89%,and F1-score of 87%.To demonstrate the importance of preprocessing,the classification model was analyzed without denoising and attenuation correction.
Advancements in artificial intelligence, notably the groundbreaking efforts in deep learning exemplified by physics-informed neural networks, have opened up innovative pathways for addressing intricate ocean acoustic ...
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Advancements in artificial intelligence, notably the groundbreaking efforts in deep learning exemplified by physics-informed neural networks, have opened up innovative pathways for addressing intricate ocean acoustic problems. However, conventional physics-informed neural networks are limited in solving high-frequency forward and inverse problems. This paper introduces a novel physics-informed generative adversarial network integrating a forward-solving network (generator) and an inverse parameter-estimating network (discriminator). The generator network incorporates convolutional neural networks with hard-constrained boundary conditions and optimized loss functions to effectively predict the solution governed by the time-domain wave equation. For inverse problems, a discriminator is introduced for parameter estimation to complete the generative adversarial network. Furthermore, customized optimization strategies and an adaptive weighting loss function are devised to boost the training performance further. The test results of both forward and reverse cases show the advantage of our model over existing methods in terms of accuracy. The result indicates its vast potential for applications in ocean acoustics engineering.
Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-based entity matching approaches typical...
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Automated modulation recognition is a challenging task in communication systems. Leveraging recent advancements in transfer learning, this paper proposes a novel method for automatic modulation recognition using trans...
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This paper examines the escalating ransomware threats faced by government-managed educational institutions, focusing on their vulnerabilities, case studies, and mitigation strategies. With the adoption of Bring Your O...
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As blockchain technology advances, the use of smart contracts has increased dramatically across many different industries, and Ethereum has become the most popular smart contract platform. However, frequent smart cont...
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In video compression, motion estimation and motion compensation are critical for achieving efficient encoding. Although the commonly used SpyNet and bilinear interpolation have contributed in improving the compression...
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