top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.

Biblioteca

MARC Lista (tabellare)
Big Data : 11th CCF Conference, BigData 2023, Nanjing, China, September 8-10, 2023, Proceedings
Big Data : 11th CCF Conference, BigData 2023, Nanjing, China, September 8-10, 2023, Proceedings
Autore Chen Enhong
Edizione [1st ed.]
Pubbl/distr/stampa Singapore : , : Springer, , 2024
Descrizione fisica 1 online resource (209 pages)
Altri autori (Persone) GaoYang
CaoLongbing
XiaoFu
CuiYiping
GuRong
WangLi
CuiLaizhong
YangWanqi
Collana Communications in Computer and Information Science Series
ISBN 981-9989-79-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNISA-996574259303316
Chen Enhong  
Singapore : , : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Big Data : 11th CCF Conference, BigData 2023, Nanjing, China, September 8–10, 2023, Proceedings / / edited by Enhong Chen, Yang Gao, Longbing Cao, Fu Xiao, Yiping Cui, Rong Gu, Li Wang, Laizhong Cui, Wanqi Yang
Big Data : 11th CCF Conference, BigData 2023, Nanjing, China, September 8–10, 2023, Proceedings / / edited by Enhong Chen, Yang Gao, Longbing Cao, Fu Xiao, Yiping Cui, Rong Gu, Li Wang, Laizhong Cui, Wanqi Yang
Autore Chen Enhong
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (209 pages)
Disciplina 006.3
Altri autori (Persone) GaoYang
CaoLongbing
XiaoFu
CuiYiping
GuRong
WangLi
CuiLaizhong
YangWanqi
Collana Communications in Computer and Information Science
Soggetto topico Artificial intelligence
Data mining
Image processing - Digital techniques
Computer vision
Information technology - Management
Artificial Intelligence
Data Mining and Knowledge Discovery
Computer Imaging, Vision, Pattern Recognition and Graphics
Computer Application in Administrative Data Processing
ISBN 981-9989-79-5
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Long-term and Short-term Perception in Knowledge Tracing -- A Transfer Learning Enhanced Decomposition-Based Hybrid Framework for Forecasting Multiple Time-Series -- Dataset Search over Integrated Metadata from China's Public Data Open Platforms -- Integrating DCNNs with Genetic Algorithm for Diabetic Retinopathy Classification -- The Convolutional Neural Network Combing Feature-aligned and Attention Pyramid for Fine-Grained Visual Classification -- OCWYOLO:A Road Depression Detection Method -- Explicit Exploring Geometric Modality for Shape-enhanced Single-view 3D Face Reconstruction -- Fine edge and texture prior guided super resolution reconstruction network -- UD-GCN: Uncertainty-Based Semi-Supervised Deep GCN for Imbalanced Node Classification -- Twin Support Vector Regression with Privileged Information -- Detecting Social Robots Based on Multi-View Graph Transformer -- Scheduling Containerized Workflow in Multi-Cluster Kubernetes -- A Study of Electricity Theft Detection Method Based on Anomaly Transformer -- Application and Research on a Large Model Training Method Based on Instruction Fine-Tuning in Domain-Specific Tasks.
Record Nr. UNINA-9910770269803321
Chen Enhong  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui