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Cloud Computing: 10th EAI International Conference, CloudComp 2020, Qufu, China, December 11-12, 2020, Proceedings

Cloud Computing: 10th EAI International Conference, CloudComp 2020, Qufu, China, December 11-12, 2020, Proceedings

Publisher Springer, Berlin
Year
Pages 191
Version paperback
Language English
ISBN 9783030699918
Categories Network hardware
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Book description

This book constitutes the refereed proceedings of the 10th International Conference on Cloud Computing, CloudComp 2020, held in Qufu, China, in December 2020. Due to COVID-19 pandemic the conference conference was held virtually.The 14 full papers were carefully reviewed and selected from 49 submissions. The book is organized in four general areas of cyber-physical intelligent computing, secure cloud systems and cloud-based privacy, cloud-based IoT architecture, and cloud cCmputing applications.


Cloud Computing: 10th EAI International Conference, CloudComp 2020, Qufu, China, December 11-12, 2020, Proceedings

Table of contents

Cyber-Physical Intelligent Computing.- Knowledge Graphs Meet Crowdsourcing: A Brief Survey.- A Dual-Index Based Representation for Processing XPath Queries on Very Large XML Documents.- IAS-BERT: An Information Gain Association Vector Semi-supervised BERT Model for Sentiment Analysis.- A Concept Lattice Method for Eliminating Redundant Features.- Secure Cloud Systems and Cloud-Based Privacy 5 Exploring Self-Attention Mechanism of Deep Learning in Cloud Intrusion Detection.- A fraud detection approach based on combined feature weighting.- Data Privacy Protection of Industrial Blockchain.- Cloud-Based IoT Architecture Lightweight Anonymous Communication Model Based on Anonymous IBE.- Reseach on Distributed Trust Management in IoT.- Cost-aware Big Data Stream Processing in Cloud Environment.- Cloud Computing Applications.- Research on The Development of Natural Human-Computer Interaction for Mobile Terminals.- Encoding Dual Semantic Knowledge for Text-enhanced Cloud Services.- Personalized Medical Diagnosis Recommendation Based on Neutrosophic Sets and Spectral Clustering.- ECG Arrhythmia Heartbeat Classification Using Deep Learning Networks.

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