Feature engineering is critical for improving machine learning performance (ML), especially when handling categorical data. Traditional encoding methods, such as one-hot and label encoding, often result in challenges ...
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This paper centers on leveraging Convolution-Augmented Transformer Models originally designed for Automatic Speech Recognition (ASR) in the realm of Sign Language—specifically, American Sign Language Fingerspelling R...
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With rapidly expanding cloud-enabled big data environments, there is an imperative need for efficient data-sharing mechanisms that are multidimensional and balance both speed and security. In this connection, high-spe...
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Recently, there has been interest in classifying emotions using audio inputs and machine learning methods. Because a single statement might be delivered in a variety of emotional circumstances, textual data alone is i...
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Chronic liver damage is believed to be mostly caused by the Hepatitis C virus (HCV). About 90% of hepatitis C infections progress to chronic hepatitis. Acute HCV infection is a condition that frequently progresses to ...
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In the evolving landscape of supply chain management, the integration of radio-frequency identification (RFID) technology has marked a significant milestone. This development has led to the emergence of a new system i...
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This research proposes an integrated framework of a digital twin, incorporating artificial intelligence and the Internet of Things to optimize energy management and prolong the lifespan of the battery in electric vehi...
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The research community has traditionally concentrated on emotion detection in emotion modeling, while emotion generation has garnered less focus. With the rise of artificial intelligence, numerous chatbots have been d...
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In the digital world, text data is produced in an unstructured manner across various communication channels. Extracting valuable information from such data with security is crucial and requires the development of tech...
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Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks ofte...
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Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.
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