Buch, Englisch, 494 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 774 g
20th EAI International Conference, CollaborateCom 2024, Wuzhen, China, November 14-17, 2024, Proceedings, Part I
Buch, Englisch, 494 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 774 g
ISBN: 978-3-031-93250-2
Verlag: Springer
The three-volume set LNICST 624, 625, 626 constitutes the refereed proceedings of the 20th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2024, held in Wuzhen, China, during November 14–17, 2024.
The 62 full papers were carefully reviewed and selected from 173 submissions. They are categorized under the topical sections as follows:
Edge computing & Task scheduling
Deep Learning and application
Blockchain applications
Security and Privacy Protection
Representation learning & Collaborative working
Graph neural networks & Recommendation systems
Federated Learning and application
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
- Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
- Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering
- Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
Weitere Infos & Material
Edge Computing & Task Scheduling.- Latency Energy aware Heterogeneous Resource Allocation and Task Scheduling in Industrial Cloud Edge Computing.- Backpressure-based Federated Learning Model Scheduling in Edge Computing.- Minimizing the Age of Knowledge in Application-oriented Mobile Edge Computing System with DRL-based Scheduling.- Dependency-Aware Task Offloading in Dynamic Network Environment with D2D Collaboration.- Delay Minimization for Downlink PD-NOMA Transmission with Index Coding in Cache-Aided Wireless Networks.- Fast Adaptive Caching Algorithm for Mobile Edge Networks Based on Meta-Reinforcement Learning.- Delay- and Cost-Aware Dynamic Service Migration in Collaborative Satellite Computing.- Towards Efficient Scheduling in Large Clusters Leveraging the Small-World Network Model.- A Dynamic Prioritization Task Offloading Strategy with Delay Constraints.- Task Scheduling Strategy among Multiple Local Mobile Clouds in Pervasive Edge Computing.- A Task Scheduling Strategy Based on Computing-Aware and Multi-Agent Collaborative Services in Pervasive Edge Computing.-Collaborative Vehicular Edge Cloud Computing Task Offloading Optimization Scheme Based on Deep Reinforcement Learning.- Deep Learning and Application.- NL-ATD: Spatio-Temporal Few-Shot Learning via Attention Transfer and Denoising Model.- A GCN-based DRL Approach for task migration and resource allocation in Heterogeneous Edge-Cloud Environments.- A Multi-Document Summarization Method for Customer Feedback Based on Large Language Models.- KaRe: Towards Flexible and Effective Machine Unlearning with Knowledge Alignment and Repair.- SWGCNN-BiLSTM: A Method for Detecting Unknown Attack Traffic within Imbalanced Samples.- Two-stage workflow scheduling based on deep reinforcement learning.- GRASP-SLAM: Gmapping-augmented DRL for Active SLAM using Policy gradient.- WiLDID:Low-Collaboration WiFi-Based Person Identification Via A Lightweight Deep Neural Network.- Dialogue Summarization by Integrating Structural Features and Improving Factual Consistency through Post-Editing.- TransAware: An Automatic Parallel Method for Deep Learning Model Training with Global Model Structure Awareness.- A Reliability Enhancement Scheme for Distributed Cloud Service Systems Based on Deep Reinforcement Learning.- Contrastive Learning-Based Finger-Vein Recognition Using Frequency-Mixup Augmentation and Time-Frequency Feature Fusion.- BACE-RUL: A Bi-directional Adversarial Network with Covariate Encoding for Machine Remaining Useful Life Prediction.
