Details

Related name

• Gao, Yang [Browse]
• Cao, Longbing [Browse]
• Xiao, Fu. [Browse]
• Cui, Yiping [Browse]
• Gu, Rong [Browse]
• Wang, Li. [Browse]
• Cui, Laizhong [Browse]
• Yang, Wanqi [Browse]

Series

• Communications in Computer and Information Science Series [More in this series]
• Communications in Computer and Information Science Series ; v.2005 [More in this series]

Source of description

Description based on publisher supplied metadata and other sources.

Contents

• Intro
• Preface
• Organization
• Contents
• Long-Term and Short-Term Perception in Knowledge Tracing
• 1 Introduction
• 2 Related Work
• 2.1 Knowledge Tracing
• 2.2 Recent Advances in MLP
• 3 Question Definition
• 3.1 Concepts and Data Representation
• 3.2 Interaction Record Representation
• 3.3 Objective of Knowledge Tracing
• 4 Method
• 4.1 2PL-IRT Based Embedding Layer
• 4.2 Long-Term and Short-Term Perception Layer
• 4.3 Response Prediction Layer
• 5 Experiments
• 5.1 Datasets
• 5.2 Baselines
• 5.3 Experimental Setup
• 5.4 Experimental Results
• 5.5 Ablation Study
• 5.6 Hyper-parameters Analysis
• 6 Conclusion and Future Work
• References
• A Transfer Learning Enhanced Decomposition-Based Hybrid Framework for Forecasting Multiple Time-Series
• 2 Method
• 2.1 Datasets
• 2.2 The Framework of Proposed Method
• 2.3 Baselines
• 2.4 Proposed Transfer Learning Strategy
• 2.5 Evaluation Metrics
• 3 Results and Discussion
• 3.1 Experimental Settings
• 3.2 Comparison of Time-Series and Its Sub-sequences
• 3.3 Comparison of Sub-ARIMA Models
• 3.4 Comparison of MVMD-Hybrid Framework
• 4 Conclusion
• Dataset Search over Integrated Metadata from China's Public Data Open Platforms
• 2 Crawling and Integration of Dataset Metadata
• 2.1 Crawling of Dataset Metadata
• 2.2 Integration of Dataset Metadata
• 3 Dataset Search over Integrated Metadata
• 3.1 Keyword-Based Retrieval
• 3.2 Diversity-Based Re-ranking
• 3.3 Attribute-Based Filtering
• 4 Experiments
• 4.1 Keyword-Based Retrieval
• 4.2 Diversity-Based Re-ranking
• 4.3 Data Catalog Consolidation
• 5 Related Work
• 5.1 National PDOPs in Other Countries
• 5.2 Dataset Search
• Integrating DCNNs with Genetic Algorithm for Diabetic Retinopathy Classification.
• 2.1 Single CNN for DR Classification
• 2.2 Multiple CNNs for DR Classification
• 3 Methodology
• 3.1 Overview of GA-DCNN
• 3.2 GCA-SA Module
• 3.3 The Strategy of Integrating DCNNs with GA
• 4 Experiment Results
• 4.1 Dataset
• 4.2 Evaluation Metrics
• 4.3 Results and Analysis
• 5 Conclusion
• The Convolutional Neural Network Combing Feature-Aligned and Attention Pyramid for Fine-Grained Visual Classification
• 2.1 Methods Using Multi-scale Information
• 2.2 Methods Using Attention Mechanisms
• 3 The Convolutional Neural Network Combing Feature-Aligned and Attention Pyramid
• 3.1 Bottom-Up Multi-scale Feature Module
• 3.2 Top-Down Attention Module
• 3.3 ROI Feature Refinement
• 4 Experimental Results and Analysis
• 4.1 Model Implementation Details
• 4.2 Comparison with State-of-the-art Methods
• 4.3 Ablation Studies
• 4.4 Visualization
• OCWYOLO: A Road Depression Detection Method
• 2.1 Object Detection Method
• 2.2 Intersection Over Union
• 2.3 Dynamic Weight Networks
• 2.4 Attention Mechanism
• 3 Methods
• 3.1 Network Architecture
• 3.2 Loss Function Optimization
• 3.3 Attention Mechanism
• 4.1 Datasets and Implementation Details
• 4.2 Comparative Experiments
• 4.3 Ablation Experiments
• 4.4 Visualize Results
• Explicit Exploring Geometric Modality for Shape-Enhanced Single-View 3D Face Reconstruction
• 2.1 Preliminary: 3DMM and Projection
• 3 Network
• 4 Loss Criteria
• 5.1 Training Details
• 5.2 3D Face Reconstruction
• 5.3 3D Face Alignment Results
• 5.4 Ablation Study
• 6 Conclusion
• References.
• Fine Edge and Texture Prior Guided Super Resolution Reconstruction Network
• 2 Related Works
• 2.1 Single Image Super-Resolution
• 2.2 Prior Information Assisted Image Reconstruction
• 3.1 Architecture
• 3.2 Shallow Feature Extraction Network (SFEN)
• 3.3 Fine Texture Reconstruction Network (FTRN)
• 3.4 Fine Edge Reconstruction Network (FERN)
• 3.5 Image Refinement Network (IRN)
• 4.1 Datasets
• 4.2 Implements Details
• 4.3 Qualitative Comparisons and Discussion
• 4.4 Quantitative Comparisons and Discussion
• 5 Analysis and Discussion
• 5.1 Effectiveness of the Prior Information
• 5.2 Study of
• UD-GCN: Uncertainty-Based Semi-supervised Deep GCN for Imbalanced Node Classification
• 1.1 Introduction
• 2 Methodology
• 2.1 Adaptive Under-Sampling
• 2.2 Recursive Optimization for Deep GCN
• 2.3 Algorithm Formalization
• 3 Experiments
• 3.1 Experimental Setup
• 3.2 Performance Comparison
• 3.3 Sensitivity to the Number of Model Layers
• Twin Support Vector Regression with Privileged Information
• 2.1 Support Vector Regression
• 2.2 Twin Support Vector Regression
• 3 Twin Support Vector Regression with Privileged Information
• 4 Experiment
• 4.1 Datasets and Setting
• 4.2 Experiments Analysis
• 4.3 Computing Time
• 5 Conclusions
• Detecting Social Robots Based on Multi-view Graph Transformer
• 3.1 Topic Graph Construction
• 3.2 Graph Augmentation
• 3.3 Mult-view Graph Transformer
• 3.4 Mult-view Attention
• 3.5 Training and Optimization
• 4.2 Baselines
• 4.3 Model Architecture Study
• Scheduling Containerized Workflow in Multi-cluster Kubernetes
• 3 Design
• 3.1 Scientific Workflow
• 3.2 Two-Level Scheduling Scheme
• 3.3 CWC Architecture
• 3.4 CWS Architecture
• 3.5 Workflow Injection Module
• 4 Experimental Evaluation
• 4.1 Experimental Setup
• 4.2 Workflow Example
• A Study of Electricity Theft Detection Method Based on Anomaly Transformer
• 2 Characteristic Analysis and Data Expansion
• 2.1 Data Analysis
• 2.2 Data Expansion Mechanism
• 2.3 Feature Analysis
• 3 Electricity Theft Detection Model
• 3.1 Electricity Theft Detection Methods
• 3.2 Electricity Theft Detection Specific Process
• 4.1 Data Expansion Performance Evaluation
• 4.2 Dataset Preparation
• 4.3 Evaluation Metrics
• 4.4 Model Parameters
• 4.5 Analysis of Results
• Application and Research on a Large Model Training Method Based on Instruction Fine-Tuning in Domain-Specific Tasks
• 2.1 LoRA
• 2.2 P-Tuning
• 2.3 Freeze Fine-Tuning
• 4.1 Objective
• 4.2 Dataset
• 4.3 Fine-Tuning Pre-trained Models
• 4.4 Experimental Environment
• 4.5 Experimental Process
• 5 Experimental Result and Analysis
• Author Index.

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ISBN

981-9989-79-5

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