
Collaborative Computing: Networking, Applications and Worksharing
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The 62 full papers and 7 short papers presented were carefully reviewed and selected from 206 submissions. The papers reflect the conference sessions as follows: Optimization for Collaborate System; Optimization based on Collaborative Computing; UVA and Traffic system; Recommendation System; Recommendation System & Network and Security; Network and Security; Network and Security & IoT and Social Networks; IoT and Social Networks & Images handling and human recognition; Images handling and human recognition & Edge Computing; Edge Computing; Edge Computing & Collaborative working; Collaborative working & Deep Learning and application; Deep Learning and application; Deep Learning and application; Deep Learning and application & UVA.
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Content
- Intro
- Preface
- Organization
- Contents - Part I
- Contents - Part II
- Optimization for Collaborate System (Workshop Papers)
- Chinese Named Entity Recognition Based on Dynamically Adjusting Feature Weights
- 1 Introduction
- 2 Model Approach
- 2.1 BERT
- 2.2 CNN
- 2.3 BILSTM
- 2.4 CRF
- 3 BERT+EL-LGWF+CRF
- 3.1 Weighted Fusion According to CNN and BILSTM
- 3.2 EL
- 3.3 Loss Function
- 4 Experiment and Analysis
- 4.1 Dataset
- 4.2 Evaluation Indices
- 4.3 Experimental Results and Analysis
- 5 Summary
- References
- Location Differential Privacy Protection in Task Allocation for Mobile Crowdsensing Over Road Networks
- Abstract
- 1 Introduction
- 2 Preliminary
- 2.1 Privacy Model
- 2.2 Adversary Model
- 3 PPTA Framework
- 3.1 Location Obfuscation
- 3.2 Task Allocation Based on Obfuscated Locations
- 3.3 Speed-Up with d-Spanner Graph
- 4 Evaluation
- 4.1 Experiment Configurations
- 4.2 Experimental Results
- 5 Related Work
- 6 Conclusion
- Acknowledgments
- References
- "Failure" Service Pattern Mining for Exploratory Service Composition
- Abstract
- 1 Introduction
- 2 Related Work
- 2.1 Log-Based Service Pattern Mining
- 2.2 Process-Based Service Pattern Mining
- 3 Model Definition
- 3.1 Exploratory Service Composition Instance Model
- 3.2 Service Pattern Model
- 4 FSPMA
- 5 Prototype Implementation
- 6 Experiment
- 6.1 Dataset and Environment
- 6.2 Experimental Verification
- 7 Application
- 7.1 Service Recommendation Using "Failure" Service Patterns
- 7.2 An Example
- 8 Conclusion
- Acknowledgements
- References
- Optimal Control and Reinforcement Learning for Robot: A Survey
- 1 Introduction
- 2 Optimal Control Problem Statement
- 3 Solutions of Optimal Control for Robot
- 3.1 Overview the Related Approaches
- 3.2 Improve Precision and System Complexity
- 3.3 Overcome Model Bias
- 3.4 Reduce Computation
- 4 Future Prospects and Discussion
- 5 Summary and Conclusions
- References
- KTOBS: An Approach of Bayesian Network Learning Based on K-tree Optimizing Ordering-Based Search
- Abstract
- 1 Introduction
- 2 Bayesian Network and k-tree
- 2.1 Bayesian Network
- 2.2 Tree Width and k-tree
- 3 BN Learning Based on K-tree Optimizing Ordering-Based Search
- 3.1 Obtaining Candidate Parent Set
- 3.2 Initial Network Construction
- 3.3 Optimizing Network Using Ordering-Based Search
- 4 Experiment
- 4.1 Dataset and Evaluation Method
- 4.2 Experiment Results and Analysis
- 5 Conclusions
- Acknowledgement
- References
- Recommendation Model Based on Social Homogeneity Factor and Social Influence Factor
- Abstract
- 1 Introduction
- 2 Related Work
- 3 Preliminary and Problem Definition
- 3.1 Novel Graph Attention Network
- 3.2 Notations
- 3.3 Problem Definition
- 4 The Proposed Model
- 4.1 Model Details
- 4.1.1 Original Input and Similar Item Network
- 4.1.2 Embedding Layer
- 4.1.3 NGAT Layer
- 4.1.4 Pairwise Neural Interaction Layer
- 4.1.5 Policy-Based Fusion Layer
- 4.1.6 Output Layer and Loss Function
- 4.2 Model Training
- 4.2.1 Mini-Batch Training
- 4.2.2 Alleviate Overfitting
- 5 Experiments
- 5.1 Dataset Introduction
- 5.2 Experimental Setup
- 5.2.1 Experimental Environment Setting
- 5.2.2 Evaluation Metrics
- 5.2.3 Compare Models
- 5.3 Comparative Experiments: RQ1
- 5.4 Ablation Experiments: RQ2
- 5.5 Parameter Sensitivity Experiments: RQ3
- 6 Conclusion
- References
- Attention Based Spatial-Temporal Graph Convolutional Networks for RSU Communication Load Forecasting
- 1 Introduction
- 2 Related Works
- 3 System Model
- 4 Communication Load Evaluation
- 5 The Communication Load Prediction Model
- 5.1 Temporal Attention
- 5.2 Spatial Attention
- 5.3 Graph Convolution in Spatial Dimension
- 5.4 Convolution in Temporal Dimension
- 5.5 Fully Connected Layer
- 6 Simulation and Analysis
- 6.1 Dataset
- 6.2 Model Parameters
- 6.3 Comparison and Result Analysis
- 7 Conclusion
- References
- UVA and Traffic System
- Mobile Encrypted Traffic Classification Based on Message Type Inference
- 1 Introduction
- 2 Related Work
- 2.1 Traditional Unencrypted Traffic Classification
- 2.2 Sequence Feature-Based Encrypted Traffic Classification
- 2.3 Attribute Feature-Based Encrypted Traffic Classification
- 3 System Introduction
- 3.1 System Overview
- 3.2 Data Preprocessing
- 3.3 Message Type Inference
- 3.4 Machine Learning
- 4 Evaluation
- 4.1 Preliminary
- 4.2 Analysis of the Message Type Inference
- 4.3 Analysis of Adopted Features
- 4.4 Comparisons with Existing Approaches
- 5 Discussion and Conclusion
- References
- Fine-Grained Spatial-Temporal Representation Learning with Missing Data Completion for Traffic Flow Prediction
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 3.1 Feature Extractors
- 3.2 Data Completer
- 4 Experiments and Analysis
- 4.1 Experimental Settings
- 4.2 Performance of Traffic Flow Prediction
- 4.3 Effect of Data Completer
- 5 Conclusion and Future Work
- References
- Underwater Information Sensing Method Based on Improved Dual-Coupled Duffing Oscillator Under Lévy Noise Description
- Abstract
- 1 Introduction
- 2 Related Work
- 2.1 Lévy Noise Model
- 2.2 Chaotic Oscillator Signal Sensing System
- 3 Approach
- 3.1 Lévy Noise Model Describes Underwater Natural Environment Interference
- 3.2 Improved Signal Sensing Method of Dual Coupling Duffing Oscillator
- 4 Experiment and Analysis
- 4.1 Experiment Deployment
- 4.2 Performance
- 5 Conclusion
- Acknowledgement
- References
- Unpaired Learning of Roadway-Level Traffic Paths from Trajectories
- 1 Introduction
- 2 Related Work
- 3 Preliminary Concepts
- 4 Overview
- 4.1 Definition
- 4.2 Problem Analysis and Approach Overview
- 5 Trajectory Data Transition
- 5.1 Feature Extraction
- 5.2 Orientation Converted to Color Information
- 6 Training Model
- 7 Experiment and Analysis
- 7.1 Dataset and Experimental Environment
- 7.2 Parameter Setting and Data Division
- 7.3 Results and Performance Comparison
- 8 Conclusion
- References
- Multi-UAV Cooperative Exploring for the Unknown Indoor Environment Based on Dynamic Target Tracking
- 1 Introduction
- 2 Related Work and Scenario Description
- 2.1 Related Work
- 2.2 Scenario Description
- 3 Method
- 3.1 Wall-Around Algorithm
- 3.2 Tracking-D*Lite
- 4 Experiments
- 4.1 Tracking-D*Lite Algorithm Experiment
- 4.2 Simulation Experiment
- 5 Conclusion
- References
- Recommendation System
- MR-FI: Mobile Application Recommendation Based on Feature Importance and Bilinear Feature Interaction
- Abstract
- 1 Introduction
- 2 Related Work
- 3 Proposed Method
- 3.1 Embedding Layer
- 3.2 SENET Layer
- 3.3 Bilinear-Interaction Layer
- 3.4 Connectivity Layer
- 3.5 Deep Network
- 3.6 Prediction Layer
- 4 Experimental Result and Analysis
- 4.1 Data Set and Experiment Setup
- 4.2 Evaluation Metrics
- 4.3 Baseline Methods
- 4.4 Experimental Performance
- 4.5 Hyperparameters Analysis
- 5 Conclusion and Future Work
- Acknowledgement
- References
- Dual-Channel Graph Contextual Self-Attention Network for Session-Based Recommendation
- Abstract
- 1 Introduction
- 2 Related Work
- 2.1 Neural Network Model
- 2.2 Attention Mechanism
- 3 Proposed Method
- 3.1 Problem Statement
- 3.2 Model Overview
- 3.3 Session Graph Construction
- 3.4 Item Embedding Learning
- 3.5 Self-attention Network
- 3.6 Prediction Layer
- 4 Experiments and Analyses
- 4.1 Datasets
- 4.2 Evaluation Metric
- 4.3 Experiment Settings
- 4.4 Comparison with Baseline Methods
- 4.5 The Influence of Model Parameters on Experimental Results
- 5 Conclusions
- References
- Context-aware Graph Collaborative Recommendation Without Feature Entanglement
- 1 Introduction
- 2 Problem Formulation
- 3 Methodology
- 3.1 General Framework
- 3.2 Optimization
- 3.3 Time Complexity Analysis of CGCR
- 4 Experiments
- 4.1 Dataset Description
- 4.2 Experimental Settings
- 4.3 RQ1: Does the Proposed Method Perform Better Than Other Comparison Methods?
- 4.4 RQ2: Does the Proposed Method Elucidate the Meanings of Each Dimension of the Embedding?
- 4.5 RQ3: How Does Number of Graph Convolution Layer Impact?
- 4.6 RQ4: How Does Non-sampling Strategy Impact?
- 5 Related Work
- 5.1 Collaborative Filtering
- 5.2 Non-sampling Learning for Top-K Recommendation
- 6 Conclusion and Future Work
- References
- Improving Recommender System via Personalized Reconstruction of Reviews
- 1 Introduction
- 2 Related Work
- 2.1 Review-based Recommendation with Topic Modeling
- 2.2 Document-Level Recommendation
- 2.3 Review-Level Recommendation
- 3 Methodology
- 3.1 Probem Definition
- 3.2 Overall Framework of PPRR
- 3.3 Review Document Reconstruction Network (Re-Doc-Net)
- 3.4 Document-Level Encode Network(Doc-Net)
- 3.5 Review-Level Encode Network(Review-Net)
- 3.6 Rating Score Prediction Layer
- 4 Experiments and Analysis
- 4.1 DataSets and Experiments Settings
- 4.2 Performance Evaluation
- 4.3 Discussion
- 4.4 Hyper-Parameters Analyses
- 5 Conclusion
- References
- Recommendation System and Network and Security
- Dynamic Traffic Network Based Multi-Modal Travel Mode Fusion Recommendation
- Abstract
- 1 Introduction
- 2 Concepts Used in the Paper
- 2.1 Definition of the Fusion Recommendation Problem
- 2.2 Heterogeneous Transport Travel Networks
- 2.3 Meta-paths Extraction Based on User Trajectory
- 2.4 Meta-path Guided Neighbors
- 3 Heterogeneous Transport Travel Network Recommendation Model
- 3.1 Initial Embedding
- 3.2 Practice of Meta-path
- 3.3 Meta-path Aggregation Functions
- 3.4 Semantic Aggregation
- 3.5 Evaluation Prediction
- 4 Experiments and Analysis
- 4.1 Dataset
- 4.2 Experimental Setup
- 4.3 Result Analysis
- 4.4 Result Analysis
- 4.5 Effect of Aggregation Functions on Recommended Performance
- 4.6 Performance of the Model on Specific Datasets
- 5 Conclusion
- Acknowledgment
- References
- Improving Personalized Project Recommendation on GitHub Based on Deep Matrix Factorization
- 1 Introduction
- 2 Related Work
- 2.1 GitHub Project Recommendations
- 2.2 Deep Learning in Recommendation Systems
- 3 Proposed Methods
- 3.1 Data Collection
- 3.2 Recommender System
- 3.3 Result and Evaluation
- 4 Experimental Setup
- 4.1 Datasets
- 4.2 Evaluation and Metrics
- 4.3 Statistic Test
- 5 Experimental Results
- 5.1 RQ1:Does the Proposed Method Perform Better Than Other Comparison Methods?
- 5.2 RQ2:What Is the Effect of the Dimension of the Low-Dimensional Vector and the Number of Recommended Lists on the Performance of the Proposed Method?
- 6 Threats to Validity
- 6.1 Internal Validity
- 6.2 External Validity
- 7 Conclusions
- References
- An Intelligent SDN DDoS Detection Framework
- 1 Introduction
- 2 Related Works
- 3 Security-oriented Flow Monitoring and Sampling
- 3.1 Performance Analysis of Security-Oriented Flow Table Sampling
- 3.2 Flow Monitoring and Sampling with Low-Latency Based on Optimization Theory
- 4 Service Flow-Oriented Attack Recognition Model
- 4.1 Service Flow Features Required by the Model
- 4.2 DDoS Attack Detection Model Based on Clustering and VAE
- 4.3 DDoS Attack Defense Based on Recognition Result
- 5 Simulation and Performance Evaluation
- 5.1 Simulation Setup
- 5.2 Network Traffic Sampling Efficiency Evaluation
- 5.3 Attack Detection Model Evaluation
- 6 Conclusion
- References
- Inspector: A Semantics-Driven Approach to Automatic Protocol Reverse Engineering
- 1 Introduction
- 2 Related Work
- 3 System Design
- 3.1 Overview
- 3.2 Length Field Inference
- 3.3 Message Type Field Inference
- 3.4 Protocol Format Inference
- 4 Evaluation
- 4.1 Datasets
- 4.2 Evaluation Metrics
- 4.3 Tunable Parameters
- 4.4 Experimental Results
- 5 Conclusions
- References
- MFF-AMD: Multivariate Feature Fusion for Android Malware Detection
- 1 Introduction
- 2 Related Work
- 3 Multivariate Feature Extraction
- 3.1 Static Feature Extraction
- 3.2 Dynamic Feature Extraction
- 3.3 Application Coverage
- 3.4 Feature Selection
- 4 Implementation
- 4.1 Architecture
- 4.2 Weight Distribution Algorithm
- 5 Evaluation
- 5.1 Dataset and Setup
- 5.2 Results and Analysis
- 6 Conclusion and Future Work
- References
- Network and Security
- PSG: Local Privacy Preserving Synthetic Social Graph Generation
- 1 Introduction
- 2 Related Work
- 2.1 Social Network Privacy Protection
- 2.2 Synthetic Graph Generation
- 3 Preliminaries
- 3.1 System Overview
- 3.2 Problem Statement
- 4 Design Details
- 4.1 Privacy Protection Mechanism Design
- 4.2 Privacy Analysis
- 4.3 Synthetic Network Generation
- 5 Performance Evaluation
- 5.1 Datasets and Models
- 5.2 Evaluation Metrics
- 5.3 Experimental Results
- 6 Conclusion
- References
- Topology Self-optimization for Anti-tracking Network via Nodes Distributed Computing
- 1 Introduction
- 2 Related Works
- 3 Introduction to Convex-Polytope Topology
- 3.1 Basic Properties
- 3.2 The Optimum Structure of CPT
- 4 Topology Self-optimization
- 4.1 Calculation of Optimum Local Topology
- 4.2 Topology Self-optimization via Nodes' Collaboration
- 5 Performance Evaluation
- 5.1 Evaluation of Network Optimization
- 5.2 Evaluation of Network Resilience
- 6 Conclusion
- References
- An Empirical Study of Model-Agnostic Interpretation Technique for Just-in-Time Software Defect Prediction
- 1 Introduction
- 2 Related Work
- 2.1 Just-in-time Software Defect Prediction
- 2.2 Explainability in Software Defect Prediction
- 3 Classifier-Agnostic Interpretation Technique
- 3.1 LIME
- 3.2 BreakDown
- 3.3 SHAP
- 4 Experimental Setup
- 4.1 Data Sets
- 4.2 Building Classification Models
- 4.3 Evaluation Metrics
- 5 Experimental Results and Analysis
- 5.1 Analysis for RQ1
- 5.2 Analysis for RQ2
- 6 Conclusion and Future Work
- References
- Yet Another Traffic Black Hole: Amplifying CDN Fetching Traffic with RangeFragAmp Attacks
- 1 Introduction
- 2 Background
- 2.1 CDN Overview
- 2.2 HTTP Range Request Mechanism on CDN
- 2.3 Differences in CDNs Handling Range Requests
- 2.4 Amplification Attacks
- 3 RangeFragAmp Attack
- 3.1 Threat Model
- 3.2 S-RFA Attack
- 3.3 O-RFA Attack
- 4 Real-World Evaluation
- 4.1 Consideration in Selecting CDN Providers
- 4.2 S-RFA Attack Evaluation
- 4.3 O-RFA Attack Evaluation
- 4.4 Severity Assessment
- 4.5 CDN Providers Feedback
- 5 Mitigation
- 5.1 Root Cause Analysis
- 5.2 Limitation
- 5.3 Solutions
- 6 Related Work
- 7 Conclusion
- References
- DCNMF: Dynamic Community Discovery with Improved Convex-NMF in Temporal Networks
- 1 Introduction
- 2 Related Work
- 3 Algorithm
- 3.1 Notation
- 3.2 The Unified DCNMF Model Formulation
- 3.3 Optimization
- 4 Experiments and Results
- 4.1 Evaluation Measures
- 4.2 Synthetic Dataset 1: Dynamic-GN Dataset
- 4.3 Synthetic Dataset 2: Dynamic-LFR Dataset
- 4.4 KIT-Email Data
- 5 Discussion and Conclusion
- References
- Network and Security and IoT and Social Networks
- Loopster++: Termination Analysis for Multi-path Linear Loop
- 1 Introduction
- 2 Preliminaries
- 2.1 Scope of Our Work
- 2.2 Path Dependency Automaton (PDA)
- 2.3 The Structure of Loopster
- 2.4 Termination of Linear Loop Program
- 3 Methodology
- 3.1 Path Termination Analysis
- 3.2 Inter-Path Analysis
- 3.3 Cycle Analysis
- 4 Implementation and Evaluation
- 4.1 Effectiveness of Loopster++
- 4.2 Performance of Loopster++
- 5 Relate Work
- 6 Conclusion
- References
- A Stepwise Path Selection Scheme Based on Multiple QoS Parameters Evaluation in SDN
- 1 Introduction
- 2 Related Work
- 3 Proposed Scheme: SWQoS
- 3.1 SWQoS Scheme Architecture
- 3.2 Path Finding
- 3.3 QoS Requirements of Services
- 3.4 Path Selection
- 4 Experiments and Performance Evaluation
- 4.1 The Experimental Environment and Topology
- 4.2 The First Group Experiment: Simulating the Network Status of Selecting the Preferred Paths
- 4.3 The Second Group Experiment: Simulating the Network Status of Obtaining Satisfied Paths
- 4.4 The Third Group Experiment: Simulating the Network Status of Obtaining Reluctant Paths
- 5 Conclusions
- References
- A Novel Approach to Taxi-GPS-Trace-Aware Bus Network Planning
- 1 Introduction
- 2 Related Work
- 3 Main Steps
- 3.1 Candidate Bus Stop Identification
- 3.2 Bus Network Generation
- 4 Simulations
- 5 Conclusion
- References
- Community Influence Maximization Based on Flexible Budget in Social Networks
- 1 Introduction
- 2 Related Work
- 3 System Model and Problem Formulation
- 3.1 System Model
- 3.2 Problem Formulation
- 4 Our Solutions
- 4.1 General Solution
- 4.2 FBCIM Algorithm
- 4.3 FBBCIM Algorithm
- 5 Performance Evaluation
- 5.1 Datasets and Parameters Setting
- 5.2 Comparison of Algorithms and Metrics
- 5.3 Evaluation Results
- 6 Conclusion
- References
- An Online Truthful Auction for IoT Data Trading with Dynamic Data Owners
- 1 Introduction
- 2 System Model and Problem Formulation
- 2.1 System Model
- 2.2 Data Trading Model Based on an Auction Mechanism
- 2.3 Problem Formulation
- 3 Online Data Trading Algorithm
- 3.1 Online Matching Algorithm Based on a Greedy Strategy
- 3.2 Computing Trading Prices Based on Critical Data Owners
- 3.3 Theoretical Analysis
- 4 Numerical Illustration
- 4.1 Methodology and Simulation Settings
- 4.2 Numerical Results
- 5 Related Work
- 5.1 Decentralized Data Trading Based on the Blockchain Technology
- 5.2 Trading Data with Different Levels of Privacy
- 6 Conclusion
- References
- IoT and Social Networks and Images Handling and Human Recognition
- Exploiting Heterogeneous Information for IoT Device Identification Using Graph Convolutional Network
- 1 Introduction
- 2 Preliminaries
- 2.1 TLS Basics
- 2.2 Graph Convolutional Networks
- 2.3 Problem Definition
- 3 The THG-IoT Framework
- 3.1 Data Preprocessing
- 3.2 Graph Generation
- 3.3 GCN Classifier
- 4 Experimental Evaluation
- 4.1 Dataset
- 4.2 Evaluation Metrics
- 4.3 Experimental Setting
- 4.4 Parameter Study
- 4.5 Comparison Experiments
- 4.6 Variant of THG-IoT
- 5 Related Work
- 6 Conclusions and Future Work
- References
- Data-Driven Influential Nodes Identification in Dynamic Social Networks
- 1 Introduction
- 2 Related Work
- 3 Data-Driven Model for Influential Nodes Identification in Social Networks
- 3.1 Multi-scale Comprehensive Metric System
- 3.2 Data-Driven Weight Optimization Algorithm
- 3.3 Influential Nodes Identification Based on Data-Driven Weighted TOPSIS
- 4 Experiments and Analysis
- 4.1 Experimental Setup
- 4.2 Performance Comparison
- 5 Conclusion and Future Work
- References
- Human Motion Recognition Based on Wi-Fi Imaging
- Abstract
- 1 Introduction
- 2 Wi-Fi Imaging Algorithm Based on 3D Virtual Array
- 2.1 Imaging Algorithm Based on Virtual 3D Array
- 2.2 Description of Improved 3D Decoherence Algorithm
- 3 Environment Adaptive Human Continuous Motion Recognition
- 3.1 Continuous Action Segmentation
- 3.2 Action Feature Extraction
- 3.3 SVM Classification Based on GA Algorithm Optimization
- 4 Experiment and Result Analysis
- 4.1 Experimental Configuration
- 4.2 Human Imaging and Motion Recognition
- 4.3 Result Analysis
- 4.4 Model Test
- 4.5 Comparison of Different Models
- 5 Conclusion
- Acknowledgment
- References
- A Pervasive Multi-physiological Signal-Based Emotion Classification with Shapelet Transformation and Decision Fusion
- Abstract
- 1 Introduction
- 2 Related Works
- 2.1 Emotion Classification Based on Physiological Signals
- 2.2 Shapelet-Based Algorithms
- 3 Methods
- 3.1 Overview
- 3.2 Data Preprocessing
- 3.3 Sub-classification Methods
- 3.3.1 Shapelet Transformation Algorithm
- 3.3.2 Feature Extraction
- 3.3.3 Sub-classifiers
- 3.4 Decision-Level Fusion Strategy
- 4 Experimental Results and Analysis
- 4.1 Database
- 4.2 Results of Emotion Classification of a Single Physiological Signal
- 4.3 Results Comparisons
- 5 Conclusion and Future Work
- Acknowledgments
- References
- A Novel and Efficient Distance Detection Based on Monocular Images for Grasp and Handover
- 1 Introduction
- 2 Related Works
- 2.1 RGB-D-Based Methods
- 2.2 Analytic-Based Methods
- 2.3 Model-Based Methods
- 3 Method
- 3.1 Distance Detection A
- 3.2 Distance Detection B
- 4 Experiments and Results
- 4.1 Experimental Equipment
- 4.2 Preliminary Work
- 4.3 Grasping Tests
- 4.4 Human-Robot Handover Tests
- 4.5 Time Cost
- 4.6 Qualitative Results and Future Work
- 5 Conclusion
- References
- Images Handling and Human Recognition and Edge Computing
- A Novel Gaze-Point-Driven HRI Framework for Single-Person
- 1 Introduction
- 2 Related Work
- 2.1 Gaze Point Estimation
- 2.2 Application of Gaze Points in HRI
- 3 Methods
- 3.1 Overview
- 3.2 Object Locations Distribution Obtaining
- 3.3 Gaze Points Distribution Estimating
- 3.4 Gaze Target Reasoning and Entity Matching
- 3.5 Moving and Grabbing
- 4 Results
- 4.1 Experimental Equipment and Setup
- 4.2 Experimental Results
- 5 Conclusion
- References
- Semi-automatic Segmentation of Tissue Regions in Digital Histopathological Image
- 1 Introduction
- 2 Related Work
- 2.1 Methods Based on Hand-Crafted Features
- 2.2 Methods Based on Deep Learning
- 3 Preliminaries
- 3.1 Methodology Overview
- 3.2 Histopathological Images Preprocessing: Staining Normalization
- 3.3 Pre-segmentation of Tissue Regions
- 3.4 Automatic Segmentation of Tissue Regions
- 4 Experiments and Results Analysis
- 4.1 Experimental Objective
- 4.2 Dataset
- 4.3 Experimental Setup
- 4.4 Experimental Results and Analysis
- 5 Conclusion and Future Work
- References
- T-UNet: A Novel TC-Based Point Cloud Super-Resolution Model for Mechanical LiDAR
- 1 Introduction
- 2 Related Works
- 3 Model Architecture
- 3.1 Point Cloud Projection and Back-Projection
- 3.2 T-UNet Model
- 4 Experimental Study
- 4.1 Datasets
- 4.2 Implementation Details
- 4.3 Model Evaluation
- 5 Conclusion
- References
- Computation Offloading for Multi-user Sequential Tasks in Heterogeneous Mobile Edge Computing
- 1 Introduction
- 2 Related Work
- 3 MUST Model and Problem Formulation
- 3.1 System Model
- 3.2 Problem Formulation
- 4 Regular Expression Based Algorithm for MUST
- 5 Performance Evaluation
- 5.1 Setup
- 5.2 Results
- 6 Conclusion
- References
- Model-Based Evaluation and Optimization of Dependability for Edge Computing Systems
- 1 Introduction
- 2 Related Work
- 3 Dependability Modeling and Analysis of Edge/Cloud Server
- 3.1 System State Transition Model
- 3.2 Analysis of Dependability Attributes
- 4 Dependability Modeling and Analysis of Edge Computing System
- 4.1 State Aggregation Technique
- 4.2 Dependability Model of Edge Computing Systems
- 5 Model and Approach of Dependability Optimization
- 6 Empirical Evaluation
- 6.1 Data Set and Experimental Settings
- 6.2 Experimental Results
- 7 Conclusion
- References
- Author Index
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