Collaborative Computing: Networking, Applications and Worksharing

Name: Collaborative Computing: Networking, Applications and Worksharing | 17th EAI International Conference, CollaborateCom 2021, Virtual Event, October 16-18, 2021, Proceedings, Part II
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17th EAI International Conference, CollaborateCom 2021, Virtual Event, October 16-18, 2021, Proceedings, Part II

Honghao Gao Xinheng Wang(Editor)

Springer (Publisher)

Published on 1. January 2022

XIX, 470 pages

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978-3-030-92638-0 (ISBN)

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Content

Intro
Preface
Organization
Contents - Part II
Contents - Part I
Edge Computing
Energy-Efficient Cooperative Offloading for Multi-AP MEC in IoT Networks
1 Introduction
2 Related Work
3 System Model
3.1 Local Processing Phase
3.2 Relay MD Processing Phase
3.3 MEC Server Processing Phase
4 Problem Formulation and Optimal Solution
4.1 Energy Efficient Problem and Optimal Solution
4.2 Supported Maximum Task Length
5 Numerical Analysis
6 Conclusions
References
Multi-truth Discovery with Correlations of Candidates in Crowdsourcing Systems
1 Introduction
2 Related Work
2.1 Truth Discovery
2.2 Truth Discovery with Different Correlations
2.3 Multi-truth Discovery
3 Preliminaries
4 The Design of MTD-CC
4.1 Metric for Candidate Correlations
4.2 Construction of MRF
4.3 Transformation of MRF
4.4 Min-cut Based Graph Separation
4.5 MTD-CC Algorithm
5 Performance Evaluation
5.1 Metrics and Baselines
5.2 Experiment on Real Dataset
5.3 Experiment on Synthetic Dataset
6 Conclusion
References
D2D-Based Multi-relay-Assisted Computation Offloading in Edge Computing Network
1 Introduction
2 Motivating Example
3 System Model and Problem Formulation
3.1 Local Model
3.2 Edge Model
3.3 Incentive Model
3.4 Computation Offloading Model
4 D2D-Based Multi-relay-Assisted Computation Offloading Method
4.1 Relay Selection Algorithm
4.2 D2D-Based Multi-relay-Assisted Computation Offloading
4.3 Complexity Analysis
5 Performance Evaluation
5.1 Simulation Setting
5.2 Experimental Results
6 Related Work and Comparison Analysis
7 Conclusion
References
Delay-Sensitive Slicing Resources Scheduling Based on Multi-MEC Collaboration in IoV
1 Introduction
2 System Model
2.1 Slicing Resources Model
2.2 Mobility and Communication Model
2.3 Pricing Model
3 Proposed Solution Strategy
4 Simulation Results
4.1 Simulation Settings
4.2 Parametric Analysis
4.3 Scheme Comparison
5 Conclusion
References
An OO-Based Approach of Computing Offloading and Resource Allocation for Large-Scale Mobile Edge Computing Systems
1 Introduction
2 System Model and Problem Formulation
2.1 Communication Model
2.2 Computing Model
2.3 Problem Formulation
3 An Efficient Offloading Algorithm Based on Ordinal Optimization
3.1 Computation Resource Allocation Problem
3.2 Task Placement Problem and An OO-Based Offloading Algorithm
4 Performance Evaluation
4.1 Simulation Setup
4.2 Determine Crude Model and OPC Class
4.3 Comparison with Baseline Methods
5 Conclusion
References
Edge Computing and Collaborative Working
Joint Location-Value Privacy Protection for Spatiotemporal Data Collection via Mobile Crowdsensing
1 Introduction
2 Related Work
2.1 DP-Based Approaches in MCS
2.2 LDP-Based Approaches in MCS
3 Motivation
4 System Model and Preliminaries
4.1 System Model
4.2 Preliminaries
5 Methodology
5.1 Overview
5.2 Location Privacy Preserving Mechanism (LPPM)
5.3 Value Privacy Preserving Mechanism (VPPM)
6 Theoretical Analysis
7 Performance Evaluation
7.1 Simulation Setup
7.2 Performance Evaluation
8 Conclusion
References
Hybrid Semantic Conflict Prevention in Real-Time Collaborative Programming
1 Introduction
2 Related Work: Review of Prior Work on Semantic Conflict Prevention with Dependency-Based Automatic Locking (DAL)
3 Major Constraints of the Prior DAL Scheme
4 HSCP: Hybrid Semantic Conflict Prevention
5 Major Technical Issues and Solutions
5.1 Customizable Locking Scope Determination
5.2 HSCP Three-Level Awareness Mechanism
5.3 HSCP Implementation: Architecture and Components
6 Prototype Implementation and Evaluations
6.1 Major User Interfaces of HSCP Prototype System
6.2 Preliminary User Evaluations
6.3 Experimental Evaluations
7 Conclusions and Future Work
References
Supporting Cross-Platform Real-Time Collaborative Programming: Architecture, Techniques, and Prototype System
1 Introduction
2 Related Work
3 Design Objectives and Rationales
3.1 Design Objective A: Supporting Cross-Platform Real-Time Collaborative Programming
3.2 Design Objective B: Supporting Unconstrained Multi-level Consistency Maintenance
3.3 Design Objective C: Supporting Flexible Extensibility and Reusability in Design and Implementation
4 CP-ROOF: A Novel and Generic Cross-Platform Real-Time Collaborative Programming Framework
4.1 Workflow and Functional Design
4.2 Architectural Overview of CP-ROOF
4.3 CP-ROOF Core: Fundamental Real-Time Collaborative Programming Support
4.4 CP-ROOF Server: Collaboration Coordinator
4.5 CP-ROOF Client: Transparent Collaboration Client Adaptor
5 Major Technical Issues and Solutions
5.1 Multi-level Operational Transformation
5.2 Client Design and Implementations
6 Experimental Evaluations
6.1 Cross-Platform Collaboration and Evaluations
6.2 Performance of Major Procedures During Collaboration
7 Conclusions and Further Work
References
Collaborative Computing Based on Truthful Online Auction Mechanism in Internet of Things
1 Introduction
2 System Model and Problem Formulation
2.1 Cost Model
2.2 Utility Model
2.3 Optimization Problem
3 Revenue Maximization Online Auction Algorithm Design
3.1 Pricing Strategy
3.2 Evaluation of Computation Tasks
3.3 Online Algorithm
4 Algorithm Analysis
5 Experiments Results
6 Conclusion
References
A Hashgraph-Based Knowledge Sharing Approach for Mobile Robot Swarm
1 Introduction
2 Related Work
2.1 Robot Swarm
2.2 Consensus Algorithms in Robot Swarms
2.3 Hashgraph
3 Motivated Scenarios
4 Hashgraph-Based Knowledge Sharing Approach
4.1 Enhanced Hashgraph in the Mobile Network Environment
4.2 Hashgraph Approach in the Single-Feature Scenario
4.3 Hashgraph Approach in the Multi-feature Scenario
5 Experiments
5.1 Single-Feature Experiments
5.2 Multi-feature Experiments
5.3 Experiments on Consensus Time of Hashgraph Approach
5.4 Experiments of CPU Utilization
6 Conclusions and Future Work
References
Collaborative Working and Deep Learning and Application
CASE: Predict User Behaviors via Collaborative Assistant Sequence Embedding Model
1 Introduction
1.1 Top-N Behavior Prediction
1.2 Limitations of Previous Work
2 Proposed Model
2.1 Constructing Graphs
2.2 Learn Behavior Embedding
2.3 Predict the Future Behavior
3 Experiments
3.1 Datasets
3.2 Evaluation Metric
3.3 Experiment Design
3.4 Result and Analysis
4 Conclusion
References
A Collaborative Optimization-Guided Entity Extraction Scheme
1 Introduction
2 Related Work
2.1 The Rule and Vocabulary-Based Entity Extraction
2.2 The Traditional Machine Learning-Based Entity Extraction
2.3 The Deep Learning-Based Entity Extraction
3 The Proposed Scheme
3.1 The Use of BERT Model
3.2 The Design of CRF Layer
3.3 The PSO-Based Collaborative Optimization
4 Experiments
4.1 Experimental Dataset
4.2 Metric
4.3 Experimental Comparison
5 Conclusion
References
A Safe Topological Waypoints Searching-Based Conservative Adaptive Motion Planner in Unknown Cluttered Environment
1 Introduction
2 Related Works
2.1 Standard Pathfinding Algorithm
2.2 Trajectory Replanning
3 Geometrically Topological Waypoints Searching
3.1 Topological Points Searching
3.2 Optimal State Transition Waypoint
4 Conservative Trajectory Replanning
4.1 Adaptive Trajectory Replanning
4.2 B-Spline Trajectory Representation
4.3 Problem Formulation
5 Experiments
5.1 Implementation Details
5.2 Topological Waypoints Searching
5.3 Conservative Trajectory Replanning
5.4 Comparisons of Planning Efficiency
6 Conclusions
References
Multi-D3QN: A Multi-strategy Deep Reinforcement Learning for Service Composition in Cloud Manufacturing
Abstract
1 Introduction
2 Related Work
2.1 Meta-heuristics-based Service Composition
2.2 RL/DRL-Based Service Composition
3 Service Composition Problem Description and MDP-Based CMfg Service Composition
3.1 Problem Description
3.2 MDP-Based CMfg Service Composition
4 Proposed Algorithm Framework
4.1 DQN Algorithm
4.2 The Dueling Architecture
4.3 The Double Estimator
4.4 The Prioritized Replay Mechanism
4.5 The Strategy for Adaptability
5 Experiments
5.1 Experiment Setting
5.2 Result Analysis
6 Conclusions
Acknowledgments
References
Transfer Knowledge Between Cities by Incremental Few-Shot Learning
Abstract
1 Introduction
2 Related Work
2.1 Spatial-Temporal Prediction
2.2 Transfer Learning
3 Preliminaries
4 Methodology
4.1 The Spatial-Temporal Network Architecture (ST-Net)
4.2 Incremental Few-Shot Learning
4.3 Memory Regularizer Combine Base and Novel Knowledge
4.4 Parameter Optimization (Knowledge Transfer)
5 Experiment
5.1 Datasets
5.2 Data Preprocessing
5.3 Baselines
5.4 Evaluation Metric
5.5 Experimental Settings
5.6 Experimental Result
5.7 Parameter Sensitivity
6 Conclusions
Acknowledgement
References
Deep Learning and Application
Multi-view Representation Learning with Deep Features for Offline Signature Verification
1 Introduction
2 Related Work
2.1 Feature Extraction in Offline Signature Verification Systems
2.2 Multi-view Representation Learning
3 Multi-view Representation Learning for Offline Signature Verification Systems
3.1 Generating the Second View from Deep Features
3.2 CCA-based Multi-view Representation Learning Approaches
3.3 Training the Writer-Dependent Classifiers
4 Experiment
4.1 Dataset
4.2 Experimental Settings
4.3 Evaluation Measurements
4.4 Experiments on the GPDS Dataset
4.5 Experiments on the CEDAR and Brazilian PUC-PR Datasets
5 Conclusion
References
Backdoor Attack of Graph Neural Networks Based on Subgraph Trigger
Abstract
1 Introduction
2 Related Works
3 Preliminaries
3.1 Concepts
3.2 Attack Model
4 Backdoor Attack on GNN
4.1 Attack Overview
4.2 Generation of Trigger
4.3 Selection of Attack Nodes
4.4 Backdoor Insertion
5 Experiment and Evaluation
5.1 Datasets
5.2 Evaluation Metrics
5.3 Experimental Setup
5.4 Result and Analysis
6 Conclusions and Future Work
Acknowledgement
References
A UniverApproCNN with Universal Approximation and Explicit Training Strategy
1 Introduction
2 Preliminaries
3 A CNN Structure with Universal Approximation: UniverApproCNN
3.1 Model Design
3.2 UniverApproCNN for Multiple Outputs
3.3 Normalized CNN and UniverApproCNN
4 UniverApproCNN for Two-Dimensional Input
4.1 Proof of the Approximation of the CNN Suitable for Two-dimensional Input
4.2 Model Design
5 Performance Experiment of UniverApproCNN for Inertial Guidance
6 Approximation Coefficients of UniverApproCNN
7 Conclusion
A Appendix
A.1 Proof of the Theorem 3
A.2 Model Training Results in the Trajectory Prediction Experiments
A.3 Back Propagation Process of UniverApproCNN
References
MS-BERT: A Multi-layer Self-distillation Approach for BERT Compression Based on Earth Mover's Distance
1 Introduction
2 Preliminaries
2.1 Self-distillation
2.2 Sample-wise Adaptive Inference
3 Methodology
3.1 Overview of MS-BERT
3.2 Self-distillation with Earth Mover's Distance
3.3 Student Classifiers Splicing Strategy
3.4 Top-K Uncertainty
4 Experiments
4.1 Datasets
4.2 Evaluation Metrics
4.3 Experimental Result
4.4 Ablation Study
5 Related Work
6 Conclusion
References
Smart Contract Vulnerability Detection Based on Dual Attention Graph Convolutional Network
1 Introduction
2 Related Works
3 Problem Description
4 Smart Contract Vulnerability Detection Method
4.1 Attributes Generation
4.2 Construction of DA-GCN Model
5 Experiment
5.1 Experimental Settings
5.2 Experimental Results
6 Conclusion
References
Crowdturfing Detection in Online Review System: A Graph-Based Modeling
Abstract
1 Introduction
2 Related Work
3 Crowdturfing Detection Model
3.1 Modeling and Initialization
3.2 CrowdDet Framework
3.3 Improved Cost-Sensitive Loss Function
4 Experiment
4.1 Dataset and Evaluation Metrics
4.2 Baseline Methods
4.3 Data Preparation and Experiment Settings
4.4 Performance Evaluation
5 Conclusion
Acknowledgement
References
Attention-Aware Actor for Cooperative Multi-agent Reinforcement Learning
1 Introduction
2 Background
2.1 Attention Mechanism
2.2 Attention-Based Algorithms for MARL
2.3 Graph Network
3 Our Approach
3.1 Multi-agent Mutual Interplay Graph Structure
3.2 Attention-Aware Actor Architecture
4 Experimental Evaluation
4.1 Settings
4.2 Validation
4.3 Attention Analysis
5 Conclusion
References
Geographic and Temporal Deep Learning Method for Traffic Flow Prediction in Highway Network
1 Introduction
2 Related Work
3 Feature Pre-processing and Prediction Method
3.1 Feature Engineering
3.2 Graph Construction
3.3 Traffic Flow Prediction
4 Evaluation
4.1 Settings
4.2 Experiments
5 Conclusion
References
How are You Affected? A Structural Graph Neural Network Model Predicting Individual Social Influence Status
1 Introduction
2 Related Work
2.1 Social Influence
2.2 Graph Neural Networks
3 Preliminaries
4 Our Model: SGN
4.1 Friendship Interacting Module
4.2 Influence Propagating Module
4.3 Global Attention and Output
5 Experiment
5.1 Experiment Setings
5.2 Experiment Result of Prediction Performances
5.3 Attention Analysis
6 Conclusion
References
Multi-order Proximity Graph Structure Embedding
1 Introduction
2 Related Work
3 Proposed Method
3.1 Notation
3.2 Structure Preserving Model
3.3 Random Walk Sampling
3.4 Negative Sampling
3.5 Algorithm
4 Evaluation
4.1 Experimental Setup
4.2 Experimental Results
4.3 Ablation Study of Negative Sampling
4.4 Study of Hyper Parameters
5 Conclusion
References
Deep Learning and Application and UVA
PATR: A Novel Poisoning Attack Based on Triangle Relations Against Deep Learning-Based Recommender Systems
1 Introduction
2 Related Work
3 Proposed Model
3.1 Problem Formulation
3.2 Pre-training Module
3.3 Reconstruction Module
4 Experiments and Analysis
4.1 Experimental Setup
4.2 Experimental Results and Analysis
5 Conclusion
References
Low-Cost LiDAR-Based Vehicle Detection for Self-driving Container Trucks at Seaport
1 Introduction
2 Related Work
2.1 LiDAR 3D Point Cloud Detection
2.2 LiDAR Projection-Based Detection
3 Dual-LiDAR Perceptive System
3.1 Data Collection and BEV Map Projection
3.2 Lightweight CNN Detector
4 Experimental Study
4.1 Data Augmentation
4.2 Comparison of Models
4.3 Tracking Test
5 Conclusion
References
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Collaborative Computing: Networking, Applications and Worksharing

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