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Collaborative Computing

Networking, Applications and Worksharing: 19th EAI International Conference, CollaborateCom...

Honghao Gao Xinheng Wang Nikolaos Voros

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English
Springer International Publishing AG
23 February 2024
The three-volume set LNICST 561, 562  563 constitutes the refereed post-conference proceedings of the 19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023, held in Corfu Island, Greece, during October 4-6, 2023.

The 72 full papers presented in these proceedings were carefully reviewed and selected from 176 submissions. The papers are organized in the following topical sections:

Volume I : Collaborative Computing, Edge Computing & Collaborative working, Blockchain applications, Code Search and Completion, Edge Computing Scheduling and Offloading. Volume II: Deep Learning and Application, Graph Computing, Security and Privacy Protection and Processing and Recognition. Volume III: Onsite Session Day2, Federated learning and application, Collaborative working, Edge Computing and Prediction, Optimization and Applications.
Edited by:   , ,
Imprint:   Springer International Publishing AG
Country of Publication:   Switzerland
Edition:   2024 ed.
Volume:   562
Dimensions:   Height: 235mm,  Width: 155mm, 
ISBN:   9783031545276
ISBN 10:   3031545273
Series:   Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
Pages:   536
Publication Date:  
Audience:   Professional and scholarly ,  Undergraduate
Format:   Paperback
Publisher's Status:   Active
Deep Learning and Application.- Task Offloading in UAV-to-Cell MEC Networks: Cell Clustering and Path Planning.LAMB: Label-induced Mixed-level Blending for Multimodal Multi-label Emotion Detection.- MSAM: Deep Semantic Interaction Network for Visual Question Answering.- Defeating the non-stationary opponent using deep reinforcement learning and opponent modeling.- Multi-agent Deep Reinforcement Learning-based Approach to Mobility-aware Caching.- D-AE: A Discriminant Encode-Decode Nets For Data Generation.- ECCRG: A Emotion- and Content-controllable Response Generation Model.- Origin-Destination Convolution Recurrent Network: A Novel OD Matrix Prediction Framework.- MD-TransUNet: TransUNet with Multi-Attention and Dilated Convolution for Brain Stroke Lesion Segmentation.- Graph Computing.- DGFormer: An Effective Dynamic Graph Transformer based Anomaly Detection Model for IoT Time Series.- STAPointGNN: Spatial-Temporal Attention Graph Neural Network for Gesture Recognition Using Millimeter-Wave Radar.- NPGraph: An Efficient Graph Computing Model in NUMA-Based Persistent Memory Systems.- tHR-Net: A Hybrid Reasoning Framework for Temporal Knowledge Graph.- Improving Code Representation Learning via Multi-view Contrastive Graph Pooling for Abstract Syntax Tree.- Security and Privacy Protection.- Protect applications and data in use in IoT environment using collaborative computing.- Robustness-enhanced assertion generation method based on code mutation and attack defense.- Secure Traffic Data Sharing in UAV-Assisted VANETs.- A Lightweight PUF-Based Group Authentication Scheme for Privacy-Preserving Metering Data Collection in Smart Grid.- A Semi-Supervised Learning Method for Malware Traffic Classification with Raw Bitmaps.- Secure and Private Approximated Coded Distributed Computing Using Elliptic Curve Cryptography.- A Novel Semi-supervised IoT Time Series Anomaly Detection Model using Graph Structure Learning.- Structural Adversarial Attack for Code Representation Models.- An Efficient Authentication and Key Agreement Scheme for CAV Internal Applications.- Processing and Recognition.- SimBPG: A comprehensive similarity evaluation metric for business process graphs.- Probabilistic Inference Based Incremental Graph Index for Similarity Search on Social Networks.- Cloud-Edge-Device Collaborative Image Retrieval and Recognition for Mobile Web.- Contrastive Learning-based Finger-Vein Recognition with Automatic Adversarial Augmentation.- Multi-Dimensional Sequential Contrastive Learning for QoS Prediction.

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