Big Data : 11th CCF Conference, BigData 2023, Nanjing, China, September 8-10, 2023, Proceedings

• Autore: Chen Enhong
• Pubblicazione: Singapore : , : Springer, , 2024; ©2023
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (209 pages)
• Altri autori: GaoYang CaoLongbing XiaoFu CuiYiping GuRong WangLi CuiLaizhong YangWanqi
• Nota di contenuto: 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 -- 1 Introduction -- 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 -- References -- Dataset Search over Integrated Metadata from China's Public Data Open Platforms -- 1 Introduction -- 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 -- 6 Conclusion and Future Work -- References -- Integrating DCNNs with Genetic Algorithm for Diabetic Retinopathy Classification. ; 1 Introduction -- 2 Related Work -- 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 -- References -- The Convolutional Neural Network Combing Feature-Aligned and Attention Pyramid for Fine-Grained Visual Classification -- 1 Introduction -- 2 Related Work -- 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 -- 5 Conclusion -- References -- OCWYOLO: A Road Depression Detection Method -- 1 Introduction -- 2 Related Work -- 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 Experiments -- 4.1 Datasets and Implementation Details -- 4.2 Comparative Experiments -- 4.3 Ablation Experiments -- 4.4 Visualize Results -- 5 Conclusion -- References -- Explicit Exploring Geometric Modality for Shape-Enhanced Single-View 3D Face Reconstruction -- 1 Introduction -- 2 Method -- 2.1 Preliminary: 3DMM and Projection -- 3 Network -- 4 Loss Criteria -- 5 Experiments -- 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 -- 1 Introduction -- 2 Related Works -- 2.1 Single Image Super-Resolution -- 2.2 Prior Information Assisted Image Reconstruction -- 3 Methodology -- 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 Experiments -- 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 -- 6 Conclusion -- References -- UD-GCN: Uncertainty-Based Semi-supervised Deep GCN for Imbalanced Node Classification -- 1 Introduction -- 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 -- 4 Conclusion -- References -- Twin Support Vector Regression with Privileged Information -- 1 Introduction -- 2 Related Works -- 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 -- References -- Detecting Social Robots Based on Multi-view Graph Transformer -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 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 Experiments -- 4.1 Dataset -- 4.2 Baselines -- 4.3 Model Architecture Study -- 5 Conclusion -- References. ; Scheduling Containerized Workflow in Multi-cluster Kubernetes -- 1 Introduction -- 2 Related Work -- 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 -- 4.3 Results and Analysis -- 5 Conclusion -- References -- A Study of Electricity Theft Detection Method Based on Anomaly Transformer -- 1 Introduction -- 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 Experimental Evaluation -- 4.1 Data Expansion Performance Evaluation -- 4.2 Dataset Preparation -- 4.3 Evaluation Metrics -- 4.4 Model Parameters -- 4.5 Analysis of Results -- 5 Conclusion -- References -- Application and Research on a Large Model Training Method Based on Instruction Fine-Tuning in Domain-Specific Tasks -- 1 Introduction -- 2 Related Work -- 2.1 LoRA -- 2.2 P-Tuning -- 2.3 Freeze Fine-Tuning -- 3 Methodology -- 4 Experiment -- 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 -- 6 Conclusion -- References -- Author Index.
• Titolo autorizzato: Big Data
• ISBN: 981-9989-79-5
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910770269803321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science Series