Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part VI / / edited by De-Shuang Huang, Zhanjun Si, Wei Chen

• Autore: Huang De-Shuang
• Pubblicazione: Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (498 pages)
• Disciplina: 006.3
• Soggetto topico: Artificial intelligence; Computers; Computer networks; Data mining; Image processing - Digital techniques; Computer vision; Software engineering; Artificial Intelligence; Computing Milieux; Computer Communication Networks; Data Mining and Knowledge Discovery; Computer Imaging, Vision, Pattern Recognition and Graphics; Software Engineering
• Altri autori: SiZhanjun ChenWei
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part VI -- Knowledge Discovery and Data Mining -- LLM-Driven External Knowledge Integration Network for Rumor Detection -- 1 Introduction -- 2 Related Works -- 2.1 Rumor Detection -- 2.2 Large Language Models -- 2.3 Evidence Retrieval -- 3 Method -- 3.1 Representation -- 3.2 Entity Concept Extraction with ChatGPT -- 3.3 Evidence Retrieval with ChatGPT -- 3.4 Rumor Classification -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Setting -- 4.3 Compared Methods -- 4.4 Primary Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- The Degree of Symmetry of Fuzzy Relations -- 1 Introduction -- 2 Preliminaries -- 3 The Degree of the Symmetry of Fuzzy Relations -- 4 Concluding Remarks -- References -- LegalGPT: Legal Chain of Thought for the Legal Large Language Model Multi-agent Framework -- 1 Introduction -- 2 Relate Works -- 2.1 Language Agent -- 2.2 Large Language Models and Chain of Thought -- 3 Approach -- 3.1 Legal Examination Assistant COT -- 3.2 Legal Consultation COT -- 3.3 Judgment Prediction COT -- 3.4 Legal Large Language Model Fine Tuning -- 4 Experiments -- 4.1 Experiments Setting -- 4.2 Results -- 4.3 Analysis -- 5 Conclusion -- References -- Leverage Diagnosis Intensity in Medication Recommendations -- 1 Introduction -- 2 The Proposed Methodology -- 2.1 Input Representations -- 2.2 Diagnosis Intensity -- 2.3 The Diagnoses Encoder -- 2.4 The Procedure Encoder -- 2.5 EHR and DDI Graph -- 2.6 Drug-Level and Visit-Level Filtering -- 2.7 The Training Process -- 3 Experiments and Results -- 3.1 Dataset -- 3.2 Evaluation Metrics -- 3.3 Baseline Models -- 3.4 Tuning Parameters -- 3.5 Result Analysis -- 3.6 Ablation Study -- 4 Conclusion and Future Work -- References -- USST: Utilizing SimAM and SGA Techniques to Cassava Leaf Diseases Classification in Real Cultivation Environments. ; 1 Introduction -- 2 Materials and Methods -- 2.1 Dataset -- 2.2 SimAM: Three-Dimensional Attention Module -- 2.3 Integration of SGA-SimAM Model -- 3 Experiments and Results -- 3.1 Experimental Results and Analysis -- 3.2 Contrasting Various Models with the USST -- 3.3 Ablation Studies and Analysis -- 4 Discussion -- 5 Conclusion -- References -- An Entity Alignment Model for Echinococcosis Knowledge Graph -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Attribute Feature Extraction Module -- 3.2 Relation Feature Extraction Module -- 3.3 Joint Embedding Module -- 4 Results -- 4.1 Datasets -- 4.2 Evaluation Indicators -- 4.3 Comparative Experiment and Analysis -- 4.4 Ablation Experiment -- 5 Conclusion -- References -- SF-MCTS: Score Feedback Monte Carlo Tree Search for Digital Curling in Continuous State Space -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 LE-PV Network: Location Extraction Policy-Value Network -- 3.2 SF-MCTS: Score Feedback Monte Carlo Tree Search -- 4 Experiments -- 4.1 Curling Datasets -- 4.2 Experimental Results -- 5 Conclusion -- References -- FOKHic: A Framework of k-mer Based Hierarchical Classification -- 1 Introduction -- 2 Methods -- 2.1 Overall Flow of Algorithm -- 2.2 k-Mer Feature Vector -- 2.3 Principal Component Analysis -- 2.4 Attention Fusion -- 3 Results and Discussion -- 3.1 Dataset -- 3.2 Evaluation Metrics -- 3.3 Ablation Experiment -- 3.4 Virus Hierarchical Classification -- 3.5 Family Level Classification -- 4 Conclusion -- References -- Enhance Volatility of Denormalized Predictions in Time Series Forecasting -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Overall Structure of Friformer -- 3.2 Temporal Forecaster -- 3.3 Frequency Forecaster -- 3.4 Combination of Trend and Seasonal Component -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Main Results -- 4.3 Ablation Studies. ; 5 Conclusion and Future Work -- References -- A Semantic Fusion-Based Model for Infrared Small Target Detection -- 1 Introduction -- 2 Related Work -- 2.1 Network Structure -- 2.2 Dynamic One-Dimensional Aggregation Module Based on Residual Connection -- 2.3 Feature Fusion Module Based on Semantic Flow -- 3 Experimental Results and Analysis -- 3.1 Datasets and Training Details -- 3.2 Evaluation Metrics -- 3.3 Evaluation on Synthetic Datasets -- 3.4 Robustness Analysis -- 4 Conclusion -- References -- An Improved Label Propagation Algorithm Based on Motif and Critical Node for Community Detection -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 Motif Mining -- 3.2 Critical Nodes Finding -- 3.3 Motif Weighted Network Construction -- 3.4 Label Propagation -- 3.5 Algorithm Description -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Metrics -- 4.3 Baseline Methods -- 4.4 Effectiveness Experiments -- 4.5 Discussion -- 4.6 Parameter Analysis -- 5 Conclusion -- References -- TIFE: Tree-Structured Interactive Feature Enhancement for Multivariate Time Series Forecasting -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 4 Methodology -- 4.1 Interactive Feature Enhancement Block -- 4.2 Tree-Structured IFE -- 4.3 Linear Stack of TIFE -- 5 Experiments -- 5.1 The Forecasting Results of the ETT Dataset -- 5.2 The Forecasting Results for the PeMS Dataset -- 5.3 The Impact of Hyperparameters -- 6 Conclusion -- References -- Spatio-temporal Fusion of Transformer and Global Feature Mining for Traffic Flow Prediction -- 1 Introduction -- 2 Methodology -- 2.1 Spatio-temporal Fusion Transformer Layer -- 2.2 Global Feature Mining Module -- 3 Experiments -- 3.1 Datasets -- 3.2 Baselines -- 3.3 Experimental Setup -- 3.4 Performance Comparison -- 3.5 Ablation Study -- 3.6 Case Study -- 4 Conclusion -- References. ; Multi-task Online Course Recommendation Method Based on FDMA -- 1 Introduction -- 2 Model Design and Implementation -- 2.1 Notation -- 2.2 Input Layer -- 2.3 Embedding Layer -- 2.4 Feature Extraction Layer -- 2.5 Feature Fusion Layer -- 2.6 Autoencoder Layer -- 2.7 Task Prediction Layer -- 3 Experimental Setup and Results Discussion -- 3.1 Experimental Setup -- 3.2 Experimental Data -- 3.3 Evaluation Metrics -- 3.4 Loss and Performance Analysis -- 3.5 Ablation Experiment -- 3.6 Contrast Experiment -- 4 Conclusion -- References -- Evaluating Effect of Classroom Interior Layouts on Crowd Evacuation Efficiency Using Improved Evacuation Model -- 1 Introduction -- 2 The Improved Evacuation Model -- 2.1 Improved Evacuation Model Based on Reverse Calculation of Static Fields -- 2.2 Improved Evacuation Model -- 3 Experimental Results and Discussion -- 3.1 Static Field Calculation Experimental Results -- 3.2 Classroom Interior Layout Based on Improved Crowd Evacuation -- 4 Conclusion -- References -- Improving the Performance of Intelligent Photo Compliance Detection Method Based on Diffusion Model for Telecom User Management -- 1 Introduction -- 2 Related Work -- 2.1 Image Generation -- 2.2 Image Classification -- 2.3 Identity Authentication -- 3 Methodology -- 3.1 Fake Registration Photo Generation -- 3.2 Photo Compliance Detection -- 4 Experiments -- 4.1 Dataset -- 4.2 Experimental Results and Analysis -- 5 Conclusion -- References -- Deep Knowledge Tracking Integrating Programming Exercise Difficulty and Forgetting Factors -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Exercise Difficulty Analysis -- 3.2 Knowledge Relationship Representation -- 3.3 Information Aggregation and Propagation -- 3.4 State Update and Temporal Modeling -- 3.5 Knowledge State Prediction -- 4 Experiments -- 4.1 Dataset. ; 4.2 Experimental Environment and Parameter Settings -- 4.3 Experimental Results and Analysis -- 5 Conclusion -- References -- Large Language Model Based Intelligent Interaction for Digital Human -- 1 Introduction -- 2 Analysis of Digital Human Intelligent Interaction -- 2.1 Functional Requirements of Digital Human -- 2.2 Non-functional Requirements for Digital Human -- 3 LLM Based Digital Human Construction -- 3.1 Appearance and User Interface Design -- 3.2 Text and Speech Interaction Design -- 3.3 Behavior and Animation Design -- 4 Web Application Validation -- 4.1 Website Architecture Design -- 4.2 Embedding of Digital Human Model -- 4.3 Validation of the Intelligent Interaction of Digital Human -- 5 Conclusions -- References -- Machine Learning -- Exploiting Persona Perception for Diverse Generation from Limited Personalized Data -- 1 Introduction -- 2 Related Work -- 2.1 Persona-Based Conversation -- 2.2 Consistent Dialogue Generation -- 2.3 Reinforcement Learning -- 3 Proposed Approach -- 3.1 Task Definition -- 3.2 Method Overview -- 3.3 Consistency Enhancer (CE) -- 3.4 Persona Interactivate Generator (PIG) -- 3.5 Reinforcement Learning -- 3.6 Persona-Generative Adversarial Networks (PeGAN) -- 4 Experiments -- 4.1 Baselines -- 4.2 Experimental Settings -- 4.3 Result of Automatic Evaluation -- 4.4 Ablation Study -- 5 Conclusion -- References -- Speech Partial Spoofing Detection Using Conformer Blocks and Multiple Pooling Integration -- 1 Introduction -- 2 Related Work -- 2.1 Spoofed Speech Detection -- 2.2 Multi-instance Learning -- 3 Methodology -- 3.1 Network Structure -- 3.2 Multiple Pooling Integration -- 3.3 Conformer Block -- 3.4 Loss Function -- 4 Experiments -- 4.1 Dataset and Implementation Details -- 4.2 Experimental Results Based on the PartialSpoof Dataset -- 4.3 Cross-Dataset Detection Between PartialSpoof and ASVspoof2019LA. ; 4.4 Ablation Experiment.
• Sommario/riassunto: This 6-volume set LNAI 14875-14880 constitutes - in conjunction with the 13-volume set LNCS 14862-14874 and the 2-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. The intelligent computing annual conference primarily aims to promote research, development and application of advanced intelligent computing techniques by providing a vibrant and effective forum across a variety of disciplines. This conference has a further aim of increasing the awareness of industry of advanced intelligent computing techniques and the economic benefits that can be gained by implementing them. The intelligent computing technology includes a range of techniques such as Artificial Intelligence, Pattern Recognition, Evolutionary Computing, Informatics Theories and Applications, Computational Neuroscience & Bioscience, Soft Computing, Human Computer Interface Issues, etc.
• Titolo autorizzato: Advanced Intelligent Computing Technology and Applications
• ISBN: 981-9756-78-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910878983703321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 14880