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[null,"1.","Record Nr.",null,"UNISA996601561103316"]
[null,null,"Autore",null,"Song Xiangyu"]
[null,null,"Titolo",null,"Web and Big Data : 7th International Joint Conference, APWeb-WAIM 2023, Wuhan, China, October 6-8, 2023, Proceedings, Part I"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Singapore Pte. Limited, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"981-9723-03-5"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (533 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14331"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- A BERT-Based Semantic Enhanced Model for COVID-19 Fake News Detection -- 1 Introduction -- 2 Related Work -- 2.1 COVID-19 Fake News Collection -- 2.2 COVID-19 Fake News Detection -- 2.3 BERT Model -- 3 Methodology -- 3.1 Dataset -- 3.2 Problem Statement -- 3.3 Text Representation Learning -- 3.4 Topic Generation -- 3.5 Classifier Design -- 4 Experimental Results and Parameter Analysis -- 4.1 Experimental Results -- 4.2 Parameter Analysis -- 5 Conclusion -- References -- Mining Frequent Geo-Subgraphs in a Knowledge Graph -- 1 Introduction -- 2 Problem Definition -- 3 Frequent Geo-Subgraph Mining -- 4 Optimizations -- 4.1 Arc Consistency Based Candidate Generation -- 4.2 Image Vertex Reusage -- 4.3 Geo-Grid Based Vertex Ordering -- 5 Experimental Study -- 5.1 Setup -- 5.2 Performance Evaluations -- 6 Related Work -- 7 Conclusion -- References -- Locality Sensitive Hashing for Data Placement to Optimize Parallel Subgraph Query Evaluation -- 1 Introduction -- 2 Background -- 2.1 Preliminaries -- 2.2 Parallel Execution Model -- 3 Locality Sensitive Hashing for Data Placement -- 3.1 Vertex Similarity -- 3.2 Vertex MinHash -- 4 System Implementation -- 5 Experiments -- 5.1 Experimental Setting -- 5.2 Effect of Our Proposed Techniques -- 5.3 Comparison with Other Parallel Subgraph Query Systems -- 5.4 Data Placement Performance -- 6 Related Work -- 7 Conclusion -- References -- DUTD: A Deeper Understanding of Trajectory Data for User Identity Linkage -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 Proposed Model -- 4.1 Grid Feature Extractor -- 4.2 Tranformer-Based Encoder -- 4.3 Matcher -- 5 Experiment -- 5.1 "]
[{"$nulls":4},"Datasets -- 5.2 Baselines -- 5.3 Parameter Setting and Evaluation Metrics -- 5.4 Performance Comparison -- 5.5 Ablation Study -- 6 Conclusion -- References."]
[{"$nulls":4},"Large-Scale Rank Aggregation from Multiple Data Sources Based D3MOPSO Method -- 1 Introduction -- 2 Related Work -- 3 Definitions and Problem Formulation -- 4 Proposed Method -- 4.1 Strategy on Encoding Scheme and Multi-directional Search -- 4.2 Particle Swarm Initialization -- 4.3 Definition of Discrete Position and Velocity -- 4.4 Discrete Particle Statue Updating -- 4.5 Framework of the Proposed Algorithm -- 4.6 Complexity Analysis -- 5 Experimental Studies -- 5.1 Comparison Algorithms -- 5.2 Experimental Settings -- 5.3 Evaluation Metrics -- 5.4 The Results -- 6 Conclusion -- References -- Hierarchically Delegatable and Revocable Access Control for Large-Scale IoT Devices with Tradability Based on Blockchain -- 1 Introduction -- 2 Building Blocks -- 2.1 Blockchain and Ethereum -- 2.2 Digital Signature -- 2.3 BIP-32 Standard -- 3 System Assumption and Requirements -- 3.1 System Entities -- 3.2 System Assumption -- 3.3 System Requirements -- 4 The Proposed Framework -- 4.1 High-Level Overview -- 4.2 IoT Device Registration -- 4.3 Ownership Transfer/Trading of IoT Device -- 4.4 (Hierarchical) Delegation of Access Control -- 4.5 Access an IoT Device -- 4.6 Revocation -- 5 Experimental Results -- 6 Security Analysis -- 7 Conclusions -- References -- Distributed Deep Learning for Big Remote Sensing Data Processing on Apache Spark: Geological Remote Sensing Interpretation as a Case Study -- 1 Introduction -- 2 Related Works -- 2.1 Distributed Deep Learning's Development Status -- 2.2 DDL-Based Remote Sensing Data Processing -- 3 Distributed Deep Learning Frameworks -- 3.1 MLlib -- 3.2 SparkTorch and TensorflowOnSpark -- 3.3 DeepLearning4Java -- 3.4 BigDL -- 3.5 Horovod -- 4 D-AMSDFNet: Distributed Deep Learning-Based AMSDFNet for Geological Remote Sensing Interpretation -- 4.1 AMSDFNet -- 4.2 Design of Distributed AMSDFNet -- 5 Experiments."]
[{"$nulls":4},"5.1 Settings -- 5.2 Analysis of Experimental Results -- 6 Conclusions -- References -- Graph-Enforced Neural Network for Attributed Graph Clustering -- 1 Introduction -- 2 Related Works -- 3 Notations and Problem Formulation -- 4 Degradation Analysis -- 4.1 Intra-cluster Estrangement -- 4.2 Attribute Similarity Neglection -- 4.3 Blurred Cluster Boundaries -- 5 The Proposed Method -- 5.1 Multi-task Learning Framework -- 5.2 High-Order Structural Proximity Enforcement -- 5.3 Attribute Similarity Enforcement -- 5.4 Cluster Boundary Enforcement -- 5.5 Joint Objective Optimization -- 6 Experiments -- 6.1 Experiment Settings -- 6.2 Performance Comparison -- 6.3 Efficiency Comparison -- 6.4 Ablation Study -- 6.5 Hyperparameter Sensitivity Analysis -- 7 Conclusion -- References -- MacGAN: A Moment-Actor-Critic Reinforcement Learning-Based Generative Adversarial Network for Molecular Generation -- 1 Introduction -- 2 Related Work -- 3 MacGAN Overview -- 3.1 GAN -- 3.2 Autoregressive GAN for SMILES Strings -- 3.3 Moment Reward -- 4 Experiment -- 4.1 Dataset -- 4.2 Evaluation Measures -- 4.3 Desired Chemical Properties -- 4.4 Model Setup -- 4.5 Experimental Results -- 5 Conclusion -- References -- Multi-modal Graph Convolutional Network for Knowledge Graph Entity Alignment -- 1 Introduction -- 2 Related Work -- 2.1 Entity Alignment -- 2.2 Multi-modal Knowledge Graph -- 3 Methodology -- 3.1 Definition and Model Overview -- 3.2 Multi-modal Pre-trained Embedding -- 3.3 Multi-modal Enhancement Embedding Mechanism -- 3.4 Objective -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Baselines -- 4.4 Main Results -- 4.5 Ablation Study -- 4.6 Parameter Analysis -- 5 "]
[{"$nulls":4},"Conclusion and Future Work -- References -- Subgraph Federated Learning with Global Graph Reconstruction -- 1 Introduction -- 2 Related Work -- 2.1 Subgraph Federated Learning (SFL)."]
[{"$nulls":4},"2.2 Graph Structure Learning (GSL) -- 2.3 Split Learning -- 3 Problem Setting -- 4 Methodology -- 4.1 Framework Overview -- 4.2 Local Pre-training -- 4.3 The Local Graph Learning Module -- 4.4 The Global Graph Structure Learning Module -- 4.5 Objective and Training Procedure -- 5 Experiment -- 5.1 Experimental Setups -- 5.2 Comparison with State-of-the-art Methods (RQ1) -- 5.3 Ablation Study (RQ2) -- 5.4 Sensitivity Analysis (RQ3) -- 6 Conclusion -- References -- SEGCN: Structural Enhancement Graph Clustering Network -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Notations -- 3.2 Topology Enhancement Module -- 3.3 Improved Attention-Driven Graph Clustering Network with Global Structure Dynamic Fusion Module -- 3.4 Optimization Objective Function -- 4 Experiment -- 4.1 Benchmark Datasets -- 4.2 Experimental Setup and Evaluation -- 4.3 Clustering Results -- 4.4 Ablation Studies -- 4.5 Visualization Results -- 5 Conclusion -- References -- Designing a Knowledge Graph System for Digital Twin to Assess Urban Flood Risk -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 4 The Proposed UrbanFloodKG System -- 4.1 System Overview -- 4.2 Data Layer -- 4.3 Graph Layer -- 4.4 Algorithm Layer -- 4.5 Digital Twin Layer -- 5 Experiment and Discussion -- 5.1 Dataset and Environment -- 5.2 Link Prediction Analysis -- 5.3 Node Classification Analysis -- 6 Conclusion -- References -- TASML: Two-Stage Adaptive Semi-supervised Meta-learning for Few-Shot Learning -- 1 Introduction -- 2 Related Work -- 2.1 Brain-Inspired Model for Visual Object Recognition -- 2.2 Meta-learning for Few-Shot Learning -- 3 Methodology -- 3.1 Preliminary -- 3.2 The Two-Stage Semi-supervised Meta-learning Framework -- 3.3 Unsupervised Visual Representation Learning -- 3.4 Gradient-Based Meta-learning for Few-Shot Learning -- 3.5 Global Context-Aware Module -- 4 Experiments."]
[{"$nulls":4},"4.1 Few-Shot Image Classification -- 4.2 Ablation Study -- 4.3 Visualization -- 5 Conclusion -- References -- An Empirical Study of Attention Networks for Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 2.1 Enrich Contextual Information Based Methods -- 2.2 Reduce Computation Complexity Based Methods -- 3 Experiment -- 3.1 Datasets -- 3.2 Implementation Details -- 4 Analysis -- 5 Conclusions and Future Works -- References -- Epidemic Source Identification Based on Infection Graph Learning -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Description -- 2.2 Propagation Model -- 3 Related Work -- 4 Our Model -- 4.1 Architecture -- 4.2 Input Generation -- 4.3 GCN Layer -- 4.4 Graph Embedding Layer -- 4.5 Output Layer -- 4.6 Loss Function -- 4.7 Model Complexity -- 5 Experiment -- 5.1 Datasets and Baselines -- 5.2 Evaluation Metrics -- 5.3 Experimental Setting -- 5.4 Source Identification Performance -- 5.5 Ablation Study -- 5.6 Impact of Parameters -- 5.7 Model Efficiency -- 6 Conclusion and Future Work -- References -- Joint Training Graph Neural Network for the Bidding Project Title Short Text Classification -- 1 Introduction -- 2 Related Work -- 2.1 Text Classification -- 2.2 Short Text Classification -- 3 Method -- 3.1 Extracting Contextual Information -- 3.2 Graph Structure Construction -- 3.3 Feature Caching and Replacement -- 3.4 Graph Convolution Operation -- 3.5 Classification -- 4 Experiment -- 4.1 Datasets -- 4.2 Data Processing -- 4.3 Baseline Models -- 4.4 Experimental Settings -- 4.5 Results -- 4.6 Parameter Analysis -- 5 Conclusion -- References -- Hierarchical Retrieval of Ancient Chinese Character Images Based on Region Saliency and Skeleton Matching -- 1 Introduction -- 2 Related Works -- 3 "]
[{"$nulls":4},"Method -- 3.1 Visual Feature Extraction -- 3.2 Regional Channel Screening -- 3.3 Saliency Joint Weighting Method."]
[{"$nulls":4},"3.4 Shape Fine Matching Based on Skeleton Context."]