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[null,"1.","Record Nr.",null,"UNINA9910878977303321"]
[null,null,"Autore",null,"Huang De-Shuang"]
[null,null,"Titolo",null,"Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part XIII / / edited by De-Shuang Huang, Wei Chen, Qinhu Zhang"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9756-18-9"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (536 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science, , 1611-3349 ; ; 14874"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Computational intelligence"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computational Intelligence"]
[{"$nulls":4},"Machine Learning"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part XIII -- Knowledge Discovery and Data Mining -- LeadRec: Towards Personalized Sequential Recommendation via Guided Diffusion -- 1 Introduction -- 2 Related Work -- 2.1 Sequence Recommendation -- 2.2 Diffusion Model -- 3 Preliminary -- 3.1 Forward Process -- 3.2 Reverse Process -- 3.3 Optimization -- 4 Lead Diffusion Recommender Model -- 4.1 LeadRec -- 4.2 LeadRec Blocks -- 5 Experiments -- 5.1 Dataset and Evaluation Metrics -- 5.2 Baselines and Training Protocol -- 5.3 Main Results -- 5.4 Further Analysis -- 6 Conclusion and Limitations -- References -- Anomaly Detection Method for Multivariate Time Series Data Based on BLTranAD -- 1 Introduction -- 2 Related Work -- 3 Methods -- 4 Experiment -- 4.1 Baseline -- 4.2 Dataset -- 4.3 Data Preprocessing -- 4.4 Experimental Settings -- 4.5 Evaluation Indicators -- 4.6 Results -- 5 Conclusion -- References -- MANet: A Mining and Analysis Method of Air Pollutants Transmission Path Network -- 1 Introduction -- 2 Methods -- 2.1 Related Work -- 2.2 Framework -- 2.3 Select Valid Data -- 2.4 Single Source Diffusion Influence Factor -- "]
[{"$nulls":4},"2.5 Causal Mechanism-Oriented Construction Method -- 3 Experiment and Characteristic Analysis -- 3.1 The Generation of Pollutant Transmission Path Network -- 3.2 Validation and Analysis -- 4 Conclusion -- References -- HRMNN: Heterogeneous Relationship Mined Graph Neural Network -- 1 Introduction -- 2 Methodology -- 2.1 Node Features Projection -- 2.2 Relational Graph Generator -- 2.3 Object-Level Aggregation -- 2.4 Relation-Level Aggregation -- 3 Experiment -- 3.1 Datasets -- 3.2 Baseline -- 3.3 Node Classification -- 3.4 Node Clustering -- 3.5 Link Prediction -- 3.6 Ablation Study -- 4 Conclusion -- References -- Knowledge Completion Method Based on Relational Embedding with GNN -- 1 Introduction."]
[{"$nulls":4},"2 Knowledge Completion Based on GNN -- 2.1 Knowledge Graph Construction -- 2.2 Relational Embedding -- 2.3 Graph Neural Network (GNN) -- 2.4 Analysis of Classic Models -- 3 Discussion -- 3.1 Feature Learning Capability -- 3.2 Adaptability -- 3.3 Interpretability -- 3.4 Effective Processing of Graph-Structured Data -- 4 Conclusion -- References -- An Influence Blocking Maximization Algorithm Based on Community Division in Social Networks -- 1 Introduction -- 2 Problem Definition -- 3 Method -- 3.1 Community Division Based on Negative Seeds -- 3.2 Community Merging -- 3.3 The Allocation Process of the Positive Seeds -- 3.4 Finding Positive Seeds in the Community -- 4 Experiments and Result -- 4.1 Experimental Setting -- 4.2 Experimental Results -- 5 Conclusion -- References -- Research on Feature Selection Methods Based on Feature Clustering and Information Theory -- 1 Introduction -- 2 AP-MSU Feature Selection Model -- 2.1 Introduction to Related Work -- 2.2 Modeling Algorithm -- 3 Empirical Analysis -- 3.1 Analysis of the Effect of De-redundancy on the Dichotomous Dataset -- 3.2 De-redundancy Effect of Multi-categorization Dataset -- 4 Conclusions -- References -- A Redundant Relation Reduced Bidirectional Extraction Framework Based on SpanBERT for Relational Triple Extraction -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 SpanBERT-Based Encoder -- 3.2 Sentence Relation Prediction -- 3.3 Bidirectional Tagging Based Entity Extraction -- 3.4 Biaffine Based Relation Extraction -- 3.5 Training Strategy and Share-Aware Learning Mechanism -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Experiment Results -- 5 Conclusions -- References -- FEEL: A Framework for Evaluating Emotional Support Capability with Large Language Models -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Task Definition."]
[{"$nulls":4},"3.2 ESCEval: A Dataset of Human ESC Evaluation -- 3.3 Proposed FEEL -- 4 Experiments and Results -- 4.1 Implementation of FEEL -- 4.2 Comparative Results -- 4.3 Ablation Experiment -- 5 Conclusion -- References -- BACP: Bayesian Augmented CP Factorization for Traffic Data Imputation -- 1 Introduction -- 2 Preliminaries -- 3 Proposed Model: BACP -- 3.1 Model Analysis -- 3.2 Variational Inference of BACP -- 3.3 Algorithm Analysis -- 4 Experiments -- 4.1 Settings -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Improving Zero-Shot Stance Detection by Infusing Knowledge from Large Language Models -- 1 Introduction -- 2 Related Work -- 2.1 Zero-Shot Stance Detection -- 2.2 Data Augmentation Based on LLMs -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 LLM Knowledge Generation -- 3.3 Topic-Iterative Data Augmentation -- 3.4 Knowledge Infusion -- 4 Experiments -- 4.1 Datasets and Metrics -- 4.2 Implementation Details -- 5 Results and Analysis -- 5.1 Comparison Results -- 5.2 Ablation Results -- 5.3 Case Study -- 6 Conclusion -- References -- Robust Cyberbullying Detection in Diverse Textual Noise -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 "]
[{"$nulls":4},"Attention-Based Compositional Embedding -- 3.2 Capsule Network Based on k-Means Routing -- 3.3 Coherent Robustness Training -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setup -- 4.3 Evaluation Metrics -- 4.4 Baseline Methods -- 4.5 Main Results -- 4.6 Ablation Study -- 4.7 Hyperparameter Experiments -- 5 Conclusion -- References -- AVPS: Automatic Vertical Partitioning for Dynamic Workload -- 1 Introduction -- 2 AVPS Workflow -- 3 Query Encoding and Collection -- 4 Repartitioning System -- 4.1 Design of PPO Controller -- 4.2 Workload Selector -- 5 Experiment -- 5.1 Experiment Setup -- 5.2 Performance Analysis on Evaluation Model -- 5.3 Efficiency Analysis on PostgreSQL."]
[{"$nulls":4},"6 Conclusion -- References -- Multi-granularity Histories Merging Network for Temporal Knowledge Graph Reasoning -- 1 Introduction -- 2 Notations -- 3 Methodology -- 3.1 Local History Encoder -- 3.2 Attention-Based Decoder -- 3.3 Global History Gate -- 3.4 Parameter Learning -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results and Analysis -- 4.3 Ablation Study -- 4.4 Sensitivity Analysis of Global History Gate -- 4.5 Sensitivity Analysis of AttConvTransE -- 5 Conclusion -- References -- EntroMAGNN: An Entropy-Driven Metapath-Based Graph Neural Network for Maritime Emergency Event Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Graph-Based Event Prediction -- 2.2 Knowledge-Based Event Prediction -- 3 Method -- 3.1 Definition -- 3.2 Model -- 4 Experiment -- 4.1 Implementation Detail -- 4.2 Main Results -- 4.3 Ablation Study and Analysis -- 4.4 Case Study -- 5 Conclusion -- References -- Rumor Detection with News Environment Enhanced Propagation Structure -- 1 Introduction -- 2 Methodology -- 2.1 Formalization -- 2.2 Graph Construction -- 2.3 Graph Embedding -- 2.4 Classification -- 3 Experiment -- 3.1 Datasets -- 3.2 Baselines -- 3.3 Experimental Settings -- 3.4 Performance Comparison -- 3.5 Ablation Analysis -- 3.6 Case Study -- 4 Conclusion -- References -- HEAMWalk: Heterogeneous Network Embedding Based on Attribute Combined Multi-view Random Walks -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Random Walk with Attribute View -- 3.2 Selection Strategy of Structure Views Under Different Meta-Paths -- 3.3 Learning Node Embeddings by Skip-Gram -- 4 Experiment -- 4.1 Datasets -- 4.2 Baselines -- 4.3 Node Classification -- 4.4 Node Clustering -- 4.5 Parameter Analysis -- 5 Conclusion -- References -- Spatial-Temporal Dependency Based Multivariate Time Series Anomaly Detection for Industrial Processes."]
[{"$nulls":4},"1 Introduction -- 2 Method -- 2.1 Task Formalization -- 2.2 Preliminaries -- 2.3 Framework of MTVAE-GM -- 2.4 Data Preprocessing -- 2.5 Details of MTVAE-GM -- 2.6 Joint Optimization -- 2.7 Inference -- 3 Experiments -- 3.1 Datasets and Metrics -- 3.2 Setup -- 3.3 Performance Comparison -- 3.4 Ablation Study -- 3.5 Case Study -- 4 Conclusion -- References -- A Multi-Granularity Semantic Extraction Method for Text Classification -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Overview -- 3.2 Review of BERT -- 3.3 Multi-Granularity Semantic Extraction -- 3.4 Semantic Information Fusion -- 4 Experiments and Results -- 4.1 Experimental Settings -- 4.2 Comparison with the State-of-The-Arts -- 4.3 Ablation Study -- 5 Conclusion -- References -- Modality-Guided Collaborative Filtering for Recommendation -- 1 Introduction -- 2 Methodology -- 2.1 Preliminaries -- 2.2 Overview -- 2.3 Adaptive Graph Augmentation -- 2.4 Masked Graph Autoencoder -- 2.5 Model Optimization -- 3 Experimental Results -- 3.1 Datasets -- 3.2 Compared Baselines -- 3.3 Performance Comparison -- 3.4 Ablation Studies -- 3.5 Case Study -- 4 Conclusion -- References -- Reinforce Tokens for the Next "]
[{"$nulls":4},"Recommendation Generation -- 1 Introduction -- 2 Methodology -- 2.1 Preliminaries -- 2.2 Overview -- 2.3 Collaborative Signal Learning -- 2.4 Reinforced Token Generation -- 2.5 Instruction Tuning -- 3 Experimental Results -- 3.1 Datasets -- 3.2 Compared Baselines -- 3.3 Performance Comparison -- 3.4 Ablation Studies -- 3.5 Case Study -- 4 Conclusion -- References -- Structural Optimization and Sequence Interaction Enhancement for Hyper-Relational Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 2.1 Hypergraph Structural Learning -- 2.2 Hyper-Relational Knowledge Graphs Completion -- 3 Methodology -- 3.1 Model Generalization."]
[{"$nulls":4},"3.2 Importance Information Sampling for Hypergraph Structures."]
[null,null,null,"Sommario/riassunto","This 13-volume set LNCS 14862-14874 constitutes - in conjunction with the 6-volume set LNAI 14875-14880 and the two-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was \"Advanced Intelligent Computing Technology and Applications\". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology. ."]