The event management mechanism matches messages that have been subscribed to and events that have been published. To identify the subscriptions that correspond to the occurrence inside the category, it must first run ...
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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.
With the emphasis on healthcare, early childhood education, and fitness, noninvasive measurement and recognition methods have received more attention. Pressure sensing has been extensively studied because of its advan...
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Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overl...
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The Narrowband Internet of Things (NB-IoT) communication plays a significant role in the IoT due to the capability of generating broad exploration with the usage of limited power. Over the past few years, the Low Powe...
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Stereotypes constitute a widely used technique for creating user models. This paper explores the potential of stereotype-based models in virtual environments in order to enhance user engagement and learning outcomes. ...
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In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of...
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In an era dominated by information dissemination through various channels like newspapers,social media,radio,and television,the surge in content production,especially on social platforms,has amplified the challenge of distinguishing between truthful and deceptive *** news,a prevalent issue,particularly on social media,complicates the assessment of news *** pervasive spread of fake news not only misleads the public but also erodes trust in legitimate news sources,creating confusion and polarizing *** the volume of information grows,individuals increasingly struggle to discern credible content from false narratives,leading to widespread misinformation and potentially harmful *** numerous methodologies proposed for fake news detection,including knowledge-based,language-based,and machine-learning approaches,their efficacy often diminishes when confronted with high-dimensional datasets and data riddled with noise or *** study addresses this challenge by evaluating the synergistic benefits of combining feature extraction and feature selection techniques in fake news *** employ multiple feature extraction methods,including Count Vectorizer,Bag of Words,Global Vectors for Word Representation(GloVe),Word to Vector(Word2Vec),and Term Frequency-Inverse Document Frequency(TF-IDF),alongside feature selection techniques such as Information Gain,Chi-Square,Principal Component Analysis(PCA),and Document *** comprehensive approach enhances the model’s ability to identify and analyze relevant features,leading to more accurate and effective fake news *** findings highlight the importance of a multi-faceted approach,offering a significant improvement in model accuracy and ***,the study emphasizes the adaptability of the proposed ensemble model across diverse datasets,reinforcing its potential for broader application in real-world *** introduce a pioneering ensemble
The text's legibility can dramatically influence any device's usability, and a wealth of research has examined the ideal font characteristics for various displays regarding legibility and readability. However,...
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For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but faul...
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For achieving Energy-Efficiency in wireless sensor networks(WSNs),different schemes have been proposed which focuses only on reducing the energy consumption.A shortest path determines for the Base Station(BS),but fault tolerance and energy balancing gives equal importance for improving the network *** saving energy in WSNs,clustering is considered as one of the effective methods for Wireless Sensor *** of the excessive overload,more energy consumed by cluster heads(CHs)in a cluster based WSN to receive and aggregate the information from member sensor nodes and it leads to *** increasing the WSNs’lifetime,the CHs selection has played a key role in energy consumption for sensor *** Energy Efficient Unequal Fault Tolerant Clustering Approach(EEUFTC)is proposed for reducing the energy utilization through the intelligent methods like Particle Swarm Optimization(PSO).In this approach,an optimal Master Cluster Head(MCH)-Master data Aggregator(MDA),selection method is proposed which uses the fitness values and they evaluate based on the PSO for two optimal nodes in each cluster to act as Master Data Aggregator(MDA),and Master Cluster *** data from the cluster members collected by the chosen MCH exclusively and the MDA is used for collected data reception from MCH transmits to the ***,the MCH overhead *** the heavy communication of data,overhead controls using the scheduling of Energy-Efficient Time Division Multiple Access(EE-TDMA).To describe the proposed method superiority based on various performance metrics,simulation and results are compared to the existing methods.
Graph data represents information efficiently and can be used to learn subsequent tasks easily. In the domain of biological science, recommender systems, social network analysis graph representation learning has becom...
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