Green, Pervasive, and Cloud Computing: 15th International Conference, GPC 2020, Xi'an, China, November 13–15, 2020, Proceedings

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This book constitutes the refereed proceedings of the 15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020, held in Xi'an, China, in November 2020. The 30 full papers presented in this book together with 8 short papers were carefully reviewed and selected from 96 submissions. They cover the following topics: Device-free Sensing; Machine Learning; Recommendation Systems; Urban Computing; Human Computer Interaction; Internet of Things and Edge Computing; Positioning; Applications of Computer Vision; CrowdSensing; and Cloud and Related Technologies.

Author(s): Zhiwen Yu, Christian Becker, Guoliang Xing
Series: Lecture Notes in Computer Science, 12398
Publisher: Springer
Year: 2021

Language: English
Pages: 502
City: Cham

Preface
Organization
Contents
Device-Free Sensing
Wavelet Analysis Based Noncontact Vital Signal Measurements Using mm-Wave Radar
1 Introduction
2 FMCW Radar Signal
3 Noncontact Vital Signals Detection Program
3.1 Theory of FMCW Radar Vital Signals Measurement
3.2 Instantaneous Vital Signals Detection Algorithm
4 Experiments and Results
4.1 Experimental Equipment and Parameters
4.2 Measurement Results
5 Conclusion
References
Study on Feasibility of Remote Metal Detection Using Millimeter Wave Radar for Convenient and Efficient Security Check
1 Introduction
2 Related Work
3 Overview
4 Methodology
4.1 Preliminaries: Radar Fundamental
4.2 The Establishment of Human Body Model
4.3 Metal Detection
5 Experiment
5.1 Dataset
5.2 Implementation Details
5.3 System Performance
5.4 System Practicability
6 Discussion and Future Work
7 Conclusion
References
Long-Range Gesture Recognition Using Millimeter Wave Radar
1 Introduction
2 Related Work
3 Overview
4 Methodology
4.1 Point Cloud Model of Gesture
4.2 The Model of Convolutional Neural Network
5 Experiment
5.1 Implementation Details
5.2 Performance Analysis
5.3 Forecast Accuracy for New Users
5.4 Practical Application Analysis
6 Discussion
6.1 Distance Influence
6.2 Multiple People Influence
6.3 Real-Time Consideration
7 Conclusion
References
Towards Fine-Grained Indoor White Space Sensing
1 Introduction
2 Indoor TV Spectra Measurement
2.1 Equipment and Setup
2.2 Synchronous Measurement
2.3 Asynchronous Measurement
3 System Design
3.1 System Overview
3.2 Multitask Gaussian Process Based Spatial Interpolation
3.3 Determining the Positions of Candidate Locations
4 Performance Evaluation
4.1 Evaluation Setup
4.2 Performance of Gaussian Process Based Interpolation
4.3 Different Positions of Candidate Locations
5 Related Work
6 Conclusion
References
Evaluating mmWave Sensing Ability of Recognizing Multi-people Under Practical Scenarios
1 Introduction
2 Related Works
2.1 Application Prospect of MmWave
2.2 Recognition Using mmWave Signals
3 System Design and Experimental Process
3.1 Overview of Main Experiment
3.2 Hardware Configuration and Data Acquisition
4 Experiment Evaluation and Analysis
4.1 Basic Working Performance of mmWave FMCW Radar
4.2 Minimum Effective Clustering Distance Between Two Persons
4.3 Occlusion Relationship Effect
5 Limitations
5.1 Reflecting by Flat Surfaces
5.2 Scope of Monitoring
6 Conclusion
References
Machine Learning
GradSA: Gradient Sparsification and Accumulation for Communication-Efficient Distributed Deep Learning
1 Introduction
2 Background and Problem Setting
3 Design Overview
4 Layer-Level Gradient Sparsification
5 Staleness-Compensated Historical Gradient Accumulation
5.1 Historical Gradient Approximation
5.2 Historical Gradient Accumulation
6 Evaluation
6.1 Experiment Setup
6.2 Overall Training Performance Comparison
6.3 Scalability Evaluation
7 Related Work
8 Conclusion
References
An Improved Artificial Bee Colony Algorithm with Multiple Search Strategy
1 Introduction
2 Original Artificial Bee Colony Algorithm
3 The Proposed ABC Algorithm
3.1 Initialization Phase
3.2 Employed Bees Phase
3.3 Onlooker Bees Phase
3.4 Scout Bee Phase
4 Validation and Comparison
4.1 Benchmark Functions
4.2 Simulation and Results
5 Conclusion
References
An Efficient Data Prefetch Strategy for Deep Learning Based on Non-volatile Memory
1 Introduction
2 Related Work
2.1 Optimizations for Data Bottleneck in DL Systems
2.2 Development of NVRAM
3 NVRAM-Based Cache Sliding Strategy for Data Prefetch
3.1 NVRAM Data Initialization
3.2 NVRAM Data Sliding
3.3 NVRAM Refilling for Next Sliding Stage
4 Evaluation
4.1 Experiment Environment
4.2 Performance Evaluation
5 Conclutions and Future Work
References
A Drift Detection Method Based on Diversity Measure and McDiarmid’s Inequality in Data Streams
1 Introduction
2 The Diversity Measure and McDiarmid Drift Detection Method
2.1 Diversity Measure
2.2 Drift Detection Method
2.3 McDiarmid’s Inequality
2.4 Parameters Analysis
3 Experiment Evaluation
4 Conclusion
References
An Improved Sparse Representation Classifier Based on Data Augmentation for Time Series Classification
1 Introduction
2 SRC and ESRC
3 Improved Extend SRC
4 Experimental Results
4.1 Experiments on UCR Archive
4.2 Experiments on Predicting the Effect of Aerobic Exercise Intervention on 24 Young Patients with Hypertension
5 Conclusion
References
Concept Stability Based Isolated Maximal Cliques Detection in Dynamic Social Networks
1 Introduction
2 Preliminaries
2.1 Clique
2.2 FCA
2.3 Concept Stability
3 Problem Statement
4 Concept Stability Based Isolated Maximal Clique Detection
4.1 Formal Context Construction from Dynamic Social Network
4.2 Isolated Maximal Clique Detection
5 Experiments
5.1 Data Set and Configurations
5.2 Experimental Results
6 Conclusions
References
Echo State Network Based on L0 Norm Regularization for Chaotic Time Series Prediction
1 Introduction
2 Preliminaries
3 ESN Based on L0 Norm Regularization
3.1 Orthogonal Matching Pursuit (OMP)
3.2 OMP-ESN Algorithm
4 Simulations
4.1 Datasets Description
4.2 Experimental Setup
4.3 Results Discussion
5 Conclusion
References
Recommendation Systems
MI-KGNN: Exploring Multi-dimension Interactions for Recommendation Based on Knowledge Graph Neural Networks
1 Introduction
2 Related Work
3 MI-KGNN
3.1 Problem Definition
3.2 Problem Analysis
3.3 Implementation of MI-KGNN
4 Experiments
4.1 Dataset Description
4.2 Baselines
4.3 Experimental Settings
4.4 Results and Discussion
5 Conclusions and Future Work
References
MGCN4REC: Multi-graph Convolutional Network for Next Basket Recommendation with Instant Interest
1 Introduction
2 Related Work
2.1 Representation Learning
2.2 Sequential Recommendation System
3 MGCN4REC Framework
3.1 Preliminaries
3.2 Multi-graph-Based User-Item Representation
3.3 User Preferences and Interests Modeling
3.4 Attention-Based Preferences Aggregating
3.5 Model Training
4 Experiment
4.1 Dataset
4.2 Baselines
4.3 Evaluation Metric
4.4 Experimental Results
5 Conclucion
References
Urban Computing
Using Deep Active Learning to Save Sensing Cost When Estimating Overall Air Quality
1 Introduction
2 Related Work
3 Problem Description
4 Data Preprocessing
4.1 Data Description and Missing Data Processing
4.2 Data Feature Processing
5 Proposed Method
5.1 Acquisition Function for the First Phase of Active Learning
5.2 The Second Phase of Active Learning
5.3 Active Variational Adversarial Model
6 Experiments
6.1 Learning of AQI Level
6.2 Active Learning of AQI Level
7 Conclusion
References
Interpretable Multivariate Time Series Classification Based on Prototype Learning
1 Introduction
2 Related Work
2.1 Multivariate Time Series Classification
2.2 Interpretability
3 Method
3.1 Model Architecture Overview
3.2 Data Encoding
3.3 Prototype Learning
3.4 Fully Connection
4 Experiment
4.1 Experimental Settings
4.2 Result
4.3 Interpretation
5 Conclusion
References
A Driver-Centric Vehicle Reposition Framework via Multi-agent Reinforcement Learning
1 Introduction
2 Related Works
3 Problem Statement
4 Methodologies
4.1 Multi-agent DDPG
4.2 Geographic-Based MADDPG
5 Experiments
5.1 Simulator
5.2 Performance Evaluation
5.3 Consideration of ORR
5.4 Passenger Satisfaction
5.5 Ablation Study
6 Conclusions
References
Demand-Responsive Windows Scheduling in Tertiary Hospital Leveraging Spatiotemporal Neural Networks
1 Introduction
2 Preliminary and Framework Overview
2.1 Preliminaries
2.2 Framework Overview
3 Hospital Visit Demand Graph Modeling
3.1 Taxi Trajectory and Hospital Location Based Hospital Visit Demand Extraction
3.2 Spatiotemporal Hospital Visit Demand Graph Modeling
4 Registration Windows Scheduling
4.1 ST-GNN Based Hospital Visit Demand Forecasting
4.2 Demand-Responsive Registration Windows Scheduling
5 Evaluation
5.1 Dataset Description
5.2 Evaluation on Visit Forecast
5.3 Case Studies on Windows Scheduling
6 Relative Work
6.1 Spatiotemporal Sequence Forecast
6.2 Graph Neural Networks
7 Conclusion and Future Work
References
Prediction Technology for Parking Occupancy Based on Multi-dimensional Spatial-Temporal Causality and ANN Algorithm
1 Introduction
2 Related Work
2.1 Real-Time On-Line Prediction
3 Prediction Methodology
3.1 Multidimensional Spatial-Temporal Causality Model
3.2 Parking Occupancy Prediction
4 Analysis of Experimental Results
4.1 Data Selection and Normalization
4.2 Neural Network Construction
4.3 Analysis of Experimental Results
5 Conclusion
References
An Improved Leaky-ESN for Electricity Load Forecasting
1 Introduction
2 Related Work
3 Preliminary
4 AESN for Electricity Load Forecasting
4.1 The Modular Control Strategy
4.2 AESN-I with Initial Sparsely-Connected Reservoir
4.3 AESN-II with Initial Fully-Connected Reservoir
5 Experiments and Results
5.1 Dataset Description
5.2 Experimental Settings
5.3 Results Discussion
6 Conclusions and Future Work
References
Human Computer Interaction
MateBot: The Design of a Human-Like, Context-Sensitive Virtual Bot for Harmonious Human-Computer Interaction
1 Introduction
2 Related Work
3 The MateBot Architecture
4 MateBot: Detailed Design
4.1 Context-Sensitive Network
4.2 Appearance Translation Network
4.3 Dialogue Network
5 Evaluation
5.1 Dataset
5.2 Evaluation Results
6 Conclusion
References
User Behavior Analysis Toward Adaptive Guidance for Machine Operation Tasks
1 Introduction
2 Related Work
3 Key Idea
4 Operational Behavior Detection
4.1 Visual Features
4.2 Behavioral Features
4.3 Correlation to Skill Level and Difficulty
5 Experimental Results
5.1 Experimental Conditions
5.2 Behavior Changes Through Skill Improvements
6 Discussion
7 Conclusion
References
Behavior Fingerprints Based Smartphone User Authentication: A Review
1 Introduction
2 Behavioral Biometrics Authentication Solutions on Smartphones
2.1 Uni-Modal Behavioral Authentication Systems
2.2 Multimodal Behavioral Authentication Systems
3 Discussion
4 Open Issues and Challenges
5 Conclusion
References
A System to Find the Change of One's Vision Implicitly
1 Introduction
2 Motivation and Challenges
2.1 Motivation
2.2 Challenges and Approach
3 System Overview
4 Activity Recognition
5 Eye-Screen Distance Estimation
5.1 Eye Localization
5.2 Eye-Screen Distance
5.3 Find Outliers
5.4 Median as Representation
5.5 Least Squares
6 Implementation and Evaluation
6.1 Dataset
6.2 Detection Result
7 Discussion
8 Conclusions
References
Internet of Things and Edge Computing
Energy-Aware Marginal Multi-attribute Federated Query in IoT Networks
1 Introduction
2 Energy Model
3 Marginal Multi-attribute Federated Query
3.1 Cost Calculation of Queried Packets Transmitting for Marginal Edge Nodes
3.2 Marginal Multi-attribute Federated Query Model
4 Implementation and Evaluation
4.1 Baseline Methods
4.2 Experiment Settings
4.3 Evaluation Results
5 Related Works and Comparison
5.1 Energy-Aware Routing Query
5.2 Spatial Keyword Query
6 Conclusion
References
A Novel UAV Charging Scheme for Minimizing Coverage Breach in Rechargeable Sensor Networks
1 Introduction
2 Problem Description
2.1 Network Model and Assumptions
2.2 Partial Recharging Model
2.3 Problem Definition
3 Complete Coverage and Energy Knowledge Partial Charging Scheme (Co-EPaCS)
3.1 Finding a Subset of Sensors to Charge and Deciding When to Start a Charging Tour
3.2 Planning UAV Charging Tour and Charging the Sensors
4 Performance Evaluation
4.1 Parameter Settings
4.2 Results and Discussions
5 Conclusion
References
A Parallel Tasks Scheduling Algorithm with Markov Decision Process in Edge Computing
1 Introduction
2 Related Work
2.1 Research on Edge Computing
2.2 Applications of Reinforcement Learning in Scheduling Problems
3 Problem Definition
4 Our Method
4.1 Algorithm Overview
4.2 Establishment of Batch
4.3 Q-learning Method
5 Experiment
5.1 Experiment Settings
5.2 Effectiveness Evaluation
6 Conclusion
References
Positioning
Localization Research Based on Low Cost Sensor
1 Introduction
2 System Overview
3 Agorithm Design
3.1 Flow Chart
3.2 Walking State Detection
3.3 Extended Kalman Filter Algorithm
4 Experimental Results and Analysis
5 Conclusion
References
Relative Floor Estimation for Indoor Co-navigation: A Machine Learning Approach
1 Introduction
2 Problem Description
3 Relative Floor Classification
3.1 Relative Feature
3.2 Relation Classifier
4 Experiments
4.1 Experiment Settings
4.2 Comparison of Different Classifiers
4.3 The Impact of Horizontal Distance by GPS
4.4 The Impact of Training Samples
4.5 The Impact of Different Signal Sources
5 Conclusion
References
Applications of Computer Vision
Defect Detection of Production Surface Based on CNN
1 Introduction
2 Related Work
3 Implement Details
3.1 Dataset
3.2 Data Augmentation
3.3 Backbone
3.4 Detector
3.5 Extra Module
3.6 Transfer Learning
4 Experiment
4.1 Experiment Setup
4.2 Multi-scale Anchors
4.3 Comparison
4.4 Analysis
5 Conclusion
References
An Image-Based Method for 3D Human Shapes Retrieval
1 Introduction
2 Method
2.1 The Spatial Coordinates of Human in the Image
2.2 3D Pose Estimation from 2D Human Pose
2.3 Similarity Computation for 3D Model Retrieval
3 Experiments
4 Conclusion
References
MobiVision: A Novel Energy-Efficient Mobile Deep Learning Framework for Computer Vision
1 Introduction
2 Related Work
3 Method
3.1 Solution Space
3.2 Large-Scale Solution Space Partitioning
3.3 Customized Neural Network Framework Designing
4 Model Performance
4.1 DataSets and Data Preprocessing
4.2 Coarse-Grained Clustering
4.3 Classifiers
5 Conclusion
References
CrowdSensing
Mobile Crowd-Sensing System Based on Participant Selection
1 Introduction
2 Relative Work
3 Problem Formulation
3.1 Definition
3.2 Assumptions
3.3 Problem Formulation
4 Method
4.1 Data Speculation
4.2 Participant Selection
5 Experiment
5.1 Data Preprocessing
5.2 State Setting Experiment
5.3 Air Quality Data Experiment
6 Conclusion
References
Quality-Aware and Penalty-Sensitive Opportunistic Crowdsensing in Mobile Relay Networks
1 Introduction
2 Related Works
3 Problem Formulation
3.1 System Overview
3.2 Relay Encouraging Coverage Maximization (RECM)
4 Mechanism Design
4.1 Basic-REG
4.2 Approximation Analysis
4.3 Enhanced-REG
4.4 Penalty Sensitive Scenario
5 Performance Evaluation
5.1 Simulation Setup
5.2 Evaluation of RECM
5.3 Evaluation of Penalty Sensitive Scenario
6 Conclusion
References
Cloud and Related Technologies
Failure Prediction with Hierarchical Approach in Private Cloud
1 Introduction
2 Related Work
3 System Architecture
3.1 Data Collection Module
3.2 Data Storage Module
3.3 VM Fault Monitoring Module
3.4 Processing Behavior Management
4 Proposed Method
4.1 Data Cleaning
4.2 Fast Filtering
4.3 Confidence Judgment
4.4 Complex Model
4.5 The HFP Algorithm
5 Result Analysis
5.1 Experiments Setup
5.2 Performance Analysis
5.3 Feature Analysis
6 Conclusions
References
Research on Job Scheduling Algorithms Based on Cloud Computing
1 Introduction
2 Related Work
3 Evaluation Model
3.1 Problem Definition
3.2 Deadline Estimation Model
4 (MQWAGS) Job Scheduling Algorithm
4.1 Deadline Estimation Model Design Goals
4.2 Examples of Job Scheduling Strategies
4.3 Multi-queue Load-Aware Greedy Scheduling (MQWAGS)
5 Experiment
5.1 Experimental and Simulation Setup
5.2 Experimental Results and Analysis and Simulation Setup
6 Conclusions
References
Recovering Cloud Services Using Hybrid Clouds Under Power Outage
1 Introduction
2 Model and Problem Formulation
3 Online Algorithm
3.1 Problem Transformation Using Lyapunov Optimization
3.2 Power-Aware Online Algorithm
4 Performance Evaluation
5 Conclusion
References
BED: A Block-Level Deduplication-Based Container Deployment Framework
1 Introduction
2 Background and Motivation
2.1 Deploying a Non-local Container
2.2 Large Image Size Caused by Data Redundancy
2.3 Deduplication for Container Image
3 System Design
3.1 Overview
3.2 Enabling Block-Level Deduplication During Pulling an Image
3.3 Reconstructing Image Layer with Blocks
3.4 Overlapping Decompression, Pull, and Reconstruction
4 Evaluation
4.1 Experimental Setups
4.2 Overall Performance When Pulling Images in Batch
4.3 Pull Time of Each Type of Images
4.4 Benefit of Overlapping
5 Related Work
5.1 Fast Deployment of Container
5.2 Deduplication in Virtualized Environments
6 Conclusion
References
Author Index