Due to the development of convolutional neural networks (CNNs), which have contributed extensively to significant advances in computer vision over the past decade, image segmentation has been profoundly influenced by ...
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The rise of end-user applications powered by large language models (LLMs), including both conversational interfaces and add-ons to existing graphical user interfaces (GUIs), introduces new privacy challenges. However,...
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This paper introduces a nanophotonic edge computing (NEC) system built on a bilayer AlN/Si waveguide platform, optimized for AI processing in wearable sensors. Utilizing AlN electro-optic micro-ring resonators (EOMRRs...
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Sentiment analysis is a key element for expert reviews but is at a loss in terms of detection of sarcasm and irony, which distort the actual sentiments. This paper proposes an approach that will fuse syntactic and con...
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In corporate settings, conferences, or classrooms, an orator relies on manual slide transitions, which can disrupt their presentation flow. Presentation devices can be inaccessible due to the physical limitations of i...
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The growth of 6G results in rising data transmission rate in static scenario. To achieve fast communication and computation with quality service in high-speed train is a challenge. This is due to fast handover and Dop...
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This study introduces the 'AI-Assisted E-Consult' initiative. The rapid digitization of healthcare is reshaping how medical services are delivered. This paper outlines the design of an AI-Driven E-Consult plat...
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Traditional knowledge graph (KG) completion models learn embeddings to predict missing facts. Recent works attempt to complete KGs in a text-generation manner with large language models (LLMs). However, they need to g...
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The approach encompassed under this model introduces stock trend forecasting using a new approach that involves Convolutional Neural Networks (CNNs) supplemented with technical indicators like the Exponential Moving A...
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This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the e...
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ISBN:
(数字)9798331523657
ISBN:
(纸本)9798331523664
This work presents a hybrid method for answer validation in question answering systems, combining neural network methods with explicit logical rules. The Rule-Augmented Neural Network (RANN) framework integrates the expressiveness of neural networks and precision of rule-based systems for improving answer validation accuracy. Using a sample of the Natural Questions dataset, we demonstrate how we can achieve more reliable and interpretable answer validation by complementing traditional neural architectures with domain-specific rules. The experimental results validate the RANN framework. The experimental results emphasize that the hybrid approach outperforms both neural-based models and rule-based reasoning by achieving the best accuracy, precision, recall, and F1 score.
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