Data Science

Name: Data Science | 8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022, Chengdu, China, August 19-22, 2022, Proceedings, Part I
Brand: Springer
Price: 96.29 EUR
Availability: OnlineOnly

8th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2022, Chengdu, China, August 19-22, 2022, Proceedings, Part I

Yang Wang Guobin Zhu Qilong Han Hongzhi Wang Xianhua Song Zeguang Lu(Editor)

Springer (Publisher)

Published on 10. August 2022

XXIV, 437 pages

E-Book

PDF with digital watermarking

System requirements

978-981-19-5194-7 (ISBN)

€96.29incl. 7% vat

System requirements

for PDF with digital watermarking

E-Book Single Licence

Available for download

Description

More details

Other editions

Content

Intro
Preface
Organization
Contents - Part I
Contents - Part II
Big Data Mining and Knowledge Management
Self-attention Based Multimodule Fusion Graph Convolution Network for Traffic Flow Prediction
1 Introduction
2 Spatiotemporal Prediction in Deep Learning
2.1 Time Correlation Research
2.2 Time Correlation Research
3 Prediction Model of Traffic Flow Based on Multi-module Fusion
3.1 Model Frame Diagram
3.2 Space-Time Decoupling
3.3 Spatial Convolution
3.4 Spatial Self-attention
3.5 Temporal Convolution
3.6 Time Self-attention
3.7 Information Fusion and GRU
4 Experimental Analysis
4.1 Dataset
4.2 Analysis of Results
5 Conclusion
References
Data Analyses and Parallel Optimization of the Tropical-Cyclone Coupled Numerical Model
1 Introduction
1.1 A Subsection Sample
2 Model Setup
2.1 Atmospheric Model Setup
2.2 Hydrodynamic Model Setup
2.3 Ocean Wave Model Setup
2.4 HPC Facilities
2.5 Coupled Variables
3 Scaling Experiments
3.1 Parallel Tests Analysis
3.2 SWAN Model Parallel Algorithm Optimization
3.3 Ocean Model Grid Optimization
4 Parallel Test Results
5 Model Results Discussion
6 Conclusion
References
Factorization Machine Based on Bitwise Feature Importance for CTR Prediction
1 Introduction
2 Related Work
3 Our Approach
3.1 Embedding Layer
3.2 Learning
4 Experiments
4.1 Experimental Settings
4.2 Hyperparameter Study
4.3 Ablation Study
4.4 Performance Comparison
5 Conclusion
References
Focusing on the Importance of Features for CTR Prediction
1 Introduction
2 ECABiNet Model
2.1 Sparse Input and Embedding Layer
2.2 Layer Norm
2.3 ECANET Layer
2.4 Feature Cross Layer
2.5 DNN Layer
2.6 Output
3 Experiment
3.1 Experimental Setup
3.2 LayerNorm Effect Comparison
3.3 Comparison of the Effects of Different Attention Modules
3.4 Comparison of the Classic Model
3.5 Study HyperParameter
4 Related Work
5 Conclusions
References
Active Anomaly Detection Technology Based on Ensemble Learning
1 Introduction
2 Problem Statement
3 Proposed Model
3.1 Supervised Ensemble Learning Model
3.2 Human Participation
3.3 Model Self-training
3.4 Experiment
3.5 Conclusion
References
Automatic Generation of Graduation Thesis Comments Based on Multilevel Analysis
1 Introduction
2 Technical Principle
2.1 BERT Model Introduced
2.2 Basic Structure of the BERT Model
2.3 Comparison with Other Algorithms
3 Project Analysis
3.1 Technical Route
3.2 Technical Analysis
4 Project Implementation
4.1 Database Established Modification
4.2 Student Information Input
4.3 Neural Network Training
4.4 Automatically Generate Comments
5 Conclusion
References
A Survey of Malware Classification Methods Based on Data Flow Graph
1 Introduction
2 Data Flow Graph
2.1 Basic Concepts of the Data Flow Graph
2.2 Data Flow Graph Corresponding to Common APIs
2.3 Extension of Data Flow Graphs
3 Malware Classification Based on Data Flow Graph
3.1 User-Defined Data Flow Graph Feature-based Malware Classification
3.2 Data Flow Graph Similarity-Based Malware Classification
3.3 Graph Neural Network-Based Malware Classification
4 Discussion
5 Conclusion
References
Anomaly Detection of Multivariate Time Series Based on Metric Learning
1 Introduction
2 Preliminaries
3 Proposed Model
3.1 Preprocessing
3.2 Encoder for High-Dimensional Time Series Data
3.3 Attentional Center Learning
3.4 Loss Function
3.5 Semisupervised Learning
4 Experiments
4.1 Dataset
4.2 Setup
4.3 Result
5 Conclusion
References
Social Network Analysis of Coauthor Networks in Inclusive Finance in China
1 Introduction and Motivation
2 Data Collection and Preprocessing
3 Results
3.1 General Characteristics of the Coauthor Network
3.2 Ego Characteristics of the Coauthor Network
3.3 The Evolution of Cohesive Subgroups in the Coauthor Network
4 Conclusions
References
Multirelationship Aware Personalized Recommendation Model
1 Introduction
2 Preliminary Preparation
2.1 Problem Definition
2.2 Data Preprocessing
2.3 User Relationship Graphs
3 Modeling and Training
3.1 MrAPR Model
3.2 Model Training
4 Experiment
4.1 Dataset
4.2 Baselines and Evaluation Metrics
4.3 Parameter Settings
4.4 Ablation Experiments
5 Conclusion
References
Machine Learning for Data Science
Preliminary Study on Adapting ProtoPNet to Few-Shot Learning Using MAML
1 Introduction
2 Related Work
2.1 Few-Shot Learning
2.2 Interpretability
3 Proposed Methods
3.1 Adapting ProtoPNet to MAML
3.2 Evaluating Models
4 Experiments
4.1 Datasets
4.2 Experiment 1: Omniglot Few-Shot Classification
4.3 Experiment 2: MiniImagenet Few-Shot Classification
4.4 Experiment 3: Interpretability Analysis on Omngilot
4.5 Experiment 4: Preliminary Interpretability Analysis on MiniImagenet
5 Conclusion and Future Work
References
A Preliminary Study of Interpreting CNNs Using Soft Decision Trees
1 Introduction
2 Related Work
3 Proposed Methods
3.1 Model Foundations
3.2 Using Normal/Soft Decision Trees to Interpret CNNs
3.3 Evaluating Interpretability
4 Experiments
4.1 Dataset and Experimental Setup
4.2 Experiment 1: Classification Performance
4.3 Experiment 2: Visualization of Normal/Soft Decision Trees' Top Features
4.4 Experiment 3: Interpretability Performance
4.5 Experiment 4: Scores of Human Experts on Tag Clarity
5 Conclusion and Future Work
References
Deep Reinforcement Learning with Fuse Adaptive Weighted Demonstration Data
1 Introduction
2 Related Work
2.1 Deep Reinforcement Learning
2.2 Multiagent Reinforcement Learning
3 Methods
4 Experimental Results and Analysis
4.1 Experimental Environment and Data
4.2 Results and Analysis
5 Discussion
References
DRIB: Interpreting DNN with Dynamic Reasoning and Information Bottleneck
1 Introduction
2 Related Works
2.1 Explain the Existing Deep Learning Models
2.2 Construction of Interpretable Deep Learning Models
3 Method
3.1 Dynamic Reasoning Decision Module
3.2 Information Bottleneck Verification Module
4 Experiments
4.1 Experimental Settings
4.2 Interpretability of Calculation in Dynamic Reasoning Decision
4.3 Explainability of Attribution in the Information Bottleneck
4.4 Visualization of Understandability
5 Conclusion
References
Multimedia Data Management and Analysis
Advanced Generative Adversarial Network for Image Superresolution
1 Introduction
2 Related Work
3 GAN and SRGAN
4 Proposed Method
4.1 Generator Network Structure
4.2 Discriminator Network Structure
4.3 Loss Function
5 Experiment Results and Analysis
5.1 Implementation Details
5.2 Datasets and Evaluation Metrics
5.3 Experimental Results and Analysis
5.4 Ablation Study
6 Conclusions
References
Real-World Superresolution by Using Deep Degradation Learning
1 Introduction
2 Related Work
2.1 Real-World Superresolution
2.2 Contrastive Learning
3 PurPosed Method
3.1 Overview of the Unsupervised Framework
3.2 Degradation Model
3.3 Reconstruction Model
4 Experiments
4.1 Training Data
4.2 Training Details
4.3 Training Details
5 Conclusion
References
Probability Loop Closure Detection with Fisher Kernel Framework for Visual SLAM
1 Introduction
2 Related Works
3 Methodology
3.1 Fisher Vector Generation
3.2 Probability Visual Vocabulary
3.3 Loop Closure Detection
4 Results and Discussion
4.1 Dataset and Preprocessing
4.2 Evaluation Metrics
4.3 2D Motion
4.4 3D Motion
4.5 Bidirectional Loops
4.6 Ablation Study
5 Conclusions
References
A Complex Background Image Registration Method Based on the Optical Flow Field Algorithm
1 Introduction
2 The Proposed Method
3 Evaluation Functions
4 Experimental Results
5 Conclusion
References
Collaborative Learning Method for Natural Image Captioning
1 Introduction
2 Related Work
3 Method
3.1 P2PM: Pix2Pix Inverting Module
3.2 NLGM: Natural Language Generation Module
3.3 Collaborative Learning Framework
4 Experiments
4.1 Experimental Setup
4.2 Main Results
5 Conclusion
References
Visual Analysis of the National Characteristics of the COVID-19 Vaccine Based on Knowledge Graph
1 Introduction
2 Related Studies
3 Construction of the COVID-19 Vaccine Knowledge Graph
3.1 Data Acquisition
3.2 Entity Extraction to Construct a Relational Model
3.3 Knowledge Graph Establishment
4 Visual Analysis of the National Characteristics of the COVID-19 Vaccine
5 Conclusions and Recommendations
References
Speech Recognition for Parkinson's Disease Based on Improved Genetic Algorithm and Data Enhancement Technology
1 Introduction
2 The Proposed Methods
2.1 Method
3 Speech Recognition and Diagnosis
3.1 Data Preprocessing
3.2 Improved GA-SVM Model
3.3 Speech Recognition Algorithm
4 Experiment and Evaluation
4.1 Experiment Setup
4.2 Comparison and Analysis of Results
5 Conclusion
References
A Review of Animal Individual Recognition Based on Computer Vision
1 Introduction
2 Construction of Animal Datasets
2.1 Data Collection Method
2.2 Datasets Related to Individual Animal Identification
2.3 Data Preprocessing
3 Individual Animal Target Detection
3.1 Traditional Target Detection Algorithms
3.2 Animal Target Detection Algorithm Based on Deep Learning
4 Methods of Individual Identification of Animals
4.1 Typical Animal Individual Recognition Algorithms
4.2 Performance Comparison
5 Application of Animal Individual Identification Technology
6 Summary and Outlook
6.1 Major Problems
6.2 Outlook
References
CIRS: A Confidence Interval Radius Slope Method for Time Series Points Based on Unsupervised Learning
1 Introduction
2 Related Work
3 Definition
3.1 Multidimensional Time Series
3.2 Definition of Research Problem
4 Model
5 Algorithm Complexity and Feasibility Analysis
6 Experiment
6.1 Data Set
6.2 Set up
6.3 Accuracy
7 Conclusion
References
LSN: Long-Term Spatio-Temporal Network for Video Recognition
1 Introduction
2 Method
2.1 LSTM and ConvLSTM
2.2 Long-Term Spatio-Temporal Network
3 Experiments
3.1 Datasets
3.2 Implementation Details
3.3 Ablation Study
3.4 Comparison with Representative Methods
4 Conclusion
References
A Novel Fish Counting Method Based on Multiscale and Multicolumn Convolution Group Network
1 Introduction
1.1 Related Work
2 The Proposed Method
2.1 Fish Counting Network
2.2 Asymmetric Convolution
2.3 Spatial Pyramid Structure
2.4 Multicolumn Convolution Group
2.5 Predicted Density Plot for Count
2.6 Loss Function
2.7 Dataset Construction
3 Experiments
3.1 Evaluation Metric
3.2 Experimental Design
3.3 Results and Analysis
3.4 Ablation Experiment
4 Conclusion
References
A Low Spectral Bias Generative Adversarial Model for Image Generation
1 Introduction
2 Related Work
3 The Spectral Bias of Generated Images
3.1 Spectral Bias in the Resize-Convolution Generator
3.2 Spectral Bias in the Deconvolution Generator
4 The Reason of Different Spectral Bias
5 Solutions
6 Conclusion
References
Research on Video Object Detection Methods Based on YOLO with Motion Features
1 Introduction
2 Method Construction
2.1 Moving Object Detection Module
2.2 Object of Interest Detection Module
2.3 Background Decision Module
3 Experiment and Evaluation
3.1 Implementation Details
3.2 Dataset Preparation and YOLOv4 Model Validation
3.3 Motion Video Object Detection and Recognition
4 Conclusion
References
TU-Net: U-shaped Structure Based on Transformers for Medical Image Segmentation
1 Introduction
2 Method
2.1 Algorithm Framework
2.2 Patch Embedding
2.3 Skip Connection
3 Experiments
3.1 Dataset and Implementation Details
3.2 Experimental Results
3.3 Ablation Study
4 Conclusion
References
Reducing Video Transmission Cost of the Cloud Service Provider with QoS-Guaranteed
1 Introduction
2 Related Work
3 System Model and Problem Formulation
3.1 System Model
3.2 Formulaic Description of the Algorithm
4 Algorithm Design and Description
4.1 The Low Delay Algorithm for the Emergency Video Clips
4.2 The Minimum Cost Path Algorithm for the Nonemergency Video Clips
5 Evaluation
5.1 Simulation Setup
5.2 Description of Key Performance Indicators
5.3 Simulation Results
6 Conclusions
References
Association Strategy Graph Convolutional Neural Network for Human Skeletal Behavior Recognition
1 Introduction
2 Related Work
3 Algorithms and Construction
3.1 Equations
3.2 RCTR-GCN Model Definition of the New Association Policy
3.3 The Relational Architecture of the Model
4 Experiment and Analysis
4.1 Datasets
4.2 Comparison of RCTR-GCN Experiments with Different Association Strategies
4.3 Comparison with Other Popular Algorithms
5 Conclusion
References
Unsupervised Style Control for Image Captioning
1 Introduction
2 Related Work
2.1 CLIP Pretrained Model
2.2 Stylized Image Captioning
2.3 Text Style Control
3 Approach
3.1 Overview
3.2 Image Encoder
3.3 Stylized Caption Decoder
3.4 Image Sentiment Recognition
3.5 Training Strategy
4 Experiment
4.1 Datasets
4.2 Evaluation Metrics
4.3 Implementation Details
4.4 Results
4.5 Ablation Studies
4.6 Human Evaluation
5 Conclusion
References
AM-PSPNet: Pyramid Scene Parsing Network Based on Attentional Mechanism for Image Semantic Segmentation
1 Introduction
2 AM-PSPNet
2.1 Efficient Channel Attention Module
2.2 Deep Guidance Fusion Module
3 Experiments and Analysis
3.1 Experimental Design
3.2 Ablation Study
3.3 Performance Evaluation on PASCAL VOC 2012
3.4 Performance Evaluation on Cityscapes
4 Conclusions
References
Author Index

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Data Science

Description

More details

Other editions

Additional editions

Content

System requirements