Self-Sovereign Identity (SSI) systems aim to enable citizens to control their personal data, allowing them to manage and share their identity information securely and privately. They have promising features compared t...
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Deep learning has significantly advanced object detection, with transformer-based models achieving notable improvements in both accuracy and efficiency. Despite their success, these models are inherently complex, maki...
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This paper proposes an artificial intelligence model designed to identify the positions of the fingers on the hands, assigning them corresponding musical notes. The creation of this model involved the development of a...
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Wildlife on public roads presents a public safety problem as well as an environmental concern. The field of object detection within computer Vision offers some approaches that can be used either in autonomous vehicles...
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In this article, we perform continual reinforcement learning (CRL) experiments using two benchmarks that rely on a physically simulated robot with a differential drive. CRL is a flavor of reinforcement learning (RL) i...
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We propose Area Disagreement, a novel uncertainty estimation method for Active Learning in the context of Semantic Segmentation, which capitalizes on the unique characteristics of the task. It relies on the assumption...
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With the rapid development of computer-related technology in China, the application range of computer simulation technology is becoming more and more extensive. As one of the important research methods of computer sim...
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With the development of globalization, communication among people has become more and more frequent. In this case, the barrier of language communication has become an important problem. In order to solve this problem,...
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Fundamental in numerical computing, mean square (MS) and mean squared error (MSE) calculations play a vital role, especially in artificial intelligence (AI) and signal processing applications. This paper introduces a ...
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ISBN:
(数字)9798350349597
ISBN:
(纸本)9798350349603;9798350349597
Fundamental in numerical computing, mean square (MS) and mean squared error (MSE) calculations play a vital role, especially in artificial intelligence (AI) and signal processing applications. This paper introduces a versatile, low-complexity, and adaptable approach to implement MS and MSE calculations. The central concept involves employing in-memory computing (IMC) technique and executing computations through a set of memristor devices. Specifically, the necessary multiplications and additions leverage the inherent properties of memristor devices, adhering to Ohm's law and Kirchhoff's current law. This innovative method enhances flexibility by programming memristors and emphasizes processing efficiency by reducing computational complexity and latency, surpassing traditional implementation methods.
In this paper, we introduce a multitask learning architecture for simultaneous depth estimation and surface normals prediction, built upon a shared encoder framework. Our approach integrates well-established depth est...
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