{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":46,"n_cols":5,"rows_kept":46,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[11,2,11,3],[11,4,11,5],[12,2,12,3],[12,4,12,5],[13,2,13,3],[14,2,14,3],[15,2,15,3],[16,2,16,3],[17,2,17,3],[18,2,18,3],[19,2,19,3],[32,2,32,3],[33,2,33,3],[36,2,36,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA990002551780403321"]
[null,null,"Autore",null,"Bliss, Gilbert Ames <1876-1951>"]
[null,null,"Titolo",null,"Calculus of Variations / Gilbert Ames Bliss."]
[null,null,"Pubbl/distr/stampa",null,"<<La >>Salle : Open Court, 1962"]
[null,null,"Descrizione fisica",null,"xiii, 189 p. ; 18 cm"]
[null,null,"Disciplina",null,"516"]
[null,null,"Locazione",null,"MAS"]
[null,null,"Collocazione",null,"MXVI-A-26"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910878984903321"]
[null,null,"Titolo",null,"Advanced Intelligent Computing Technology and Applications : 20th International Conference, ICIC 2024, Tianjin, China, August 5–8, 2024, Proceedings, Part II / / edited by De-Shuang Huang, Chuanlei Zhang, Yijie Pan"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024"]
[null,null,"ISBN",null,"981-9756-66-9"]
[null,null,"Edizione",null,"[1st ed. 2024.]"]
[null,null,"Descrizione fisica",null,"1 online resource (508 pages)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 14876"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Computers"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Data mining"]
[{"$nulls":4},"Image processing - Digital techniques"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Software engineering"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computing Milieux"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Computer Imaging, Vision, Pattern Recognition and Graphics"]
[{"$nulls":4},"Software Engineering"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part II -- Intelligent Data Analysis and Prediction -- Long-Short-Term Expert Attention Neural Networks for Traffic Flow Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Conventional Traffic Flow Prediction -- 2.2 Graph Neural Networks Based Traffic Flow Forecasting -- 2.3 Attention Mechanism -- 3 Method -- 3.1 Overall Network Architecture -- 3.2 Input Embedding -- 3.3 Shared Mixture of Experts -- 3.4 Long-Term Expert Networks and Short-Term Expert Network -- 3.5 Integration and Information Interaction Module -- 4 Experiments -- 4.1 Datasets and Experimental Settings -- 4.2 Performance Comparison -- 4.3 Evaluation of Long-Term Prediction -- 4.4 Effectiveness of Model "]
[{"$nulls":4},"Parameters and Ablation Studies -- 5 Conclusion -- References -- Capturing Dynamic Dependencies and Temporal Fluctuations for Traffic Flow Forecasting -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Learnable Traffic Embedding -- 3.2 Graph Embedding Convolutional Recurrent Network -- 3.3 Temporal Interleaved Convolution -- 3.4 Dual-Branch Gated Attention -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Baselines -- 4.4 Comparison Results -- 4.5 Ablation Study -- 5 Conclusion -- References -- IFTNet: Interpolation Frequency- and Time-Domain Network for Long-Term Time Series Forecasting -- 1 Introduction -- 2 Preliminary -- 2.1 Picket Fence Effect and Frequency-Domain Interpolation -- 2.2 Problem Definition -- 3 Methods -- 3.1 Seasonal-Trend Decomposition -- 3.2 Feature Embedding Block -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental Settings -- 4.3 Results and Analysis -- 4.4 Ablation Study -- 4.5 Parameter Sensitivity -- 4.6 Apply Frequency-Domain Interpolation to Frequency-Domain Based Model -- 4.7 Showcases -- 5 Conclusion -- References."]
[{"$nulls":4},"UNetPlusTS: Decomposition-Mixing UNet++ Architecture for Long-Term Time Series Forecasting -- 1 Introduction -- 2 Related Works -- 2.1 Linear Long-Term Time Series Forecasting -- 2.2 U-Net Series Architectures -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Architecture -- 3.3 UNetPlus Mixing Module (UPM) -- 4 Experiments -- 4.1 Datasets -- 4.2 Experimental -- 4.3 Main Results -- 4.4 Ablation Study -- 4.5 Visualization -- 4.6 Parameter Sensitivity -- 5 Conclusion -- References -- Combining Multi-granularity Text Semantics with Graph Relational Semantics for Question Retrieval in CQA -- 1 Introduction -- 2 Related Works -- 3 Problem Definition -- 4 Methodology -- 4.1 Q-Q Connection Relations Learning -- 4.2 Q-a Relevance Learning by a BERT-Based Model -- 4.3 Q-q Similarity Learning by a Tag-Enhanced Multi-granularity Matching Model -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Experimental Results -- 6 Conclusion -- References -- Frequency Enhanced Carbon Dioxide Emissions Forecasting Model with Missing Values Encoding -- 1 Introduction -- 2 Backgrounds -- 3 Missing Information Encoding Module -- 3.1 Bidirectional gated Recurrent Unit -- 3.2 Delta Coding for Missing Value Processing -- 4 Frequency Enhancement Fusion Module -- 4.1 Discrete Cosine Transform Block -- 4.2 Channel Fusion Block -- 5 Experiments and Analysis of Prediction Models -- 5.1 Carbon Dioxide Emission Data Set -- 5.2 Experimental Design -- 5.3 Comparative Analysis of Results of Different Benchmark Models -- 6 Conclusion -- References -- Short-Term PV Output Forecasting Approach Based on Deep Learning and Singular Spectrum Analysis -- 1 Introduction -- 2 Fuzzy C-Means Clustering and Singular Spectrum Analysis Based on Ant Colony Optimization -- 2.1 Ant Colony Optimization -- 2.2 Fuzzy C-Means Clustering Based on Ant Colony Optimization."]
[{"$nulls":4},"2.3 Singular Spectrum Analysis Based on Ant Colony Optimization -- 3 Deep Learning Models -- 3.1 Attention Mechanism Based on Feature -- 3.2 The CNN-BIGRU Classification Model and the Regression Forecasting Model CNN-BIGRU-ATTENTION -- 3.3 The Overall Procedure of the Proposed Forecasting Approach -- 4 Case Study and Numerical Results -- 4.1 Data Clustering Analysis -- 4.2 Decomposition Strategy -- 4.3 Forecasting Result and Discussion -- 5 Conclusions -- References -- Enhancing Stock Similarity Analysis with Phase-Embedded Multivariate Similarity Measure -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Heterogeneity-Oriented Feature Fusion -- 3.3 Phase-Embedded Time Warping -- 3.4 Prediction Schemes -- 4 Experiment -- 4.1 "]
[{"$nulls":4},"Experimental Setup -- 4.2 Comparison of Methods -- 4.3 Results and Analysis -- 5 Conclusions -- References -- Enhancing Federated Learning: A Novel Approach of Shapley Value Computation in Smart Contract -- 1 Introduction -- 2 System Architecture -- 2.1 Federated Learning -- 2.2 Shapley Value -- 2.3 Smart Contract -- 3 Methodology -- 3.1 Overview -- 3.2 Initial Phase -- 3.3 Model Selection and Updates -- 3.4 Model Selection and Updates -- 4 Experimental Validation of Smart Contract-Based Shapley Value Computation -- 4.1 Setup and Methodological Synthesis -- 4.2 Results Integrating Smart Contract Computations -- 4.3 Parameter Sensitivity and System Robustness -- 5 Conclusion -- References -- CPMA: Spatio-Temporal Network Prediction Model Based on Convolutional Parallel Multi-head Self-attention -- 1 Introduction -- 2 Related Work -- 3 Improved LSTM Prediction Model Based on CPMA -- 4 Experiment -- 4.1 Experimental Data -- 4.2 Model Evaluation Criteria -- 4.3 Comparative Test of Different Network Structures in the Same Sample -- 4.4 Comparative Test of Different Batch Data Input."]
[{"$nulls":4},"4.5 Comparative Experiments of Different Data Volumes -- 4.6 Comparative Experiments at Different Monitoring Points in the Same Time Period -- 4.7 Comparison of Forecasting Errors of Different Forecasting Models -- 5 Conclusion -- References -- Attention Based Multi-scale Spatial-temporal Fusion Propagation Graph Network for Traffic Flow Prediction -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 Overall Architecture -- 3.2 Embedding Layer -- 3.3 Spatial-Temporal Attention -- 3.4 Multi-scale Spatial-Temporal Mix-hop Graph Convolution -- 4 Experiments -- 4.1 Datasets -- 4.2 Settings -- 4.3 Experimental Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Integrating Social and Knowledge Graphs with Time Decay Mechanisms -- 1 Introduction -- 2 Problem Formulation -- 3 Methodology -- 3.1 Initial Embedding Layer -- 3.2 Fusion Layer -- 3.3 Propagation Layers -- 3.4 Concatenation Layers -- 3.5 Prediction Layers -- 3.6 Optimization Method -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Recommendation Performance Comparison (RQ1) -- 4.3 Ablation Study of ISKG Framework (RQ2) -- 4.4 Impact of Time Decay Function TD(t) on ISKG-TD's Efficacy (RQ3) -- 5 Conclusion and Future Work -- References -- Multi-modal Quality Prediction Algorithm Based on Anomalous Energy Tracking Attention -- 1 Introduction -- 2 Relative Works -- 2.1 Research Status of Product Quality Prediction Technology -- 2.2 Research Status of Product Image Defect Detection -- 3 Model and Design -- 3.1 Fluctuation Energy Information Extraction Module -- 3.2 Multimodal Attention Fusion Module -- 3.3 Bilinear Pooling Fusion Module -- 4 Experiments and Analysis of Results -- 4.1 Datasets and Preprocessing -- 4.2 Design of Evaluation Indicators and Parameters -- 4.3 Performance Comparison Experiments -- 4.4 Ablation Experiments -- 5 Conclusion -- References."]
[{"$nulls":4},"Hybrid Convolution Based Online Multivariate Time Series Forecasting Algorithm -- 1 Introduction -- 2 Proposed Framework -- 2.1 Problem Definition -- 2.2 Model Architecture -- 2.3 Feature Mapping Module -- 2.4 Dual Convolution Model -- 2.5 Linear Mapping Module -- 3 Experiments -- 3.1 Experimental Setting -- 3.2 Forecasting Results -- 3.3 Ablation Studies -- 3.4 Cumulative Loss Analysis -- 4 Conclusion and Future Work -- References -- Daformer: A Novel Dimension-Augmented Transformer Framework for Multivariate Time Series Forecasting -- 1 Introduction -- 2 Methodology -- 2.1 Dimension-Augmented Module -- 2.2 Feature Fusion Module -- 2.3 CTE Block -- 3 Experiments -- 3.1 Experimental Design -- 3.2 Comparative Experiments -- 3.3 Specified Periods in the Dimension-Augmented "]
[{"$nulls":4},"Module -- 3.4 Specified Kernel Size in the Feature Fusion Module -- 3.5 Ablation Experiments -- 3.6 Efficiency Analysis -- 3.7 Hyperparameter Analysis -- 4 Conclusion -- References -- A Transformer-Based Model for Time Series Prediction of Remote Sensing Data -- 1 First Section -- 2 Related Work -- 3 DataSet -- 4 RSformer -- 4.1 Feature Extraction -- 4.2 De-normalization -- 4.3 Series Decomposition -- 5 Experiment -- 5.1 Result -- 5.2 Ablation Study -- 6 Conclusion -- References -- A Multi-scale Indicators Carbon Emission Prediction Method Based on Decision Forests -- 1 Introduction -- 2 Methods and Data Sources -- 2.1 Data Sources and Indicators -- 2.2 Models -- 3 Analysis -- 3.1 Indicator Relations and Data Mining -- 3.2 Carbon Emission Prediction Model -- 4 Discussion -- 5 Conclusion -- References -- GD-PTCF: Prompt-Tuning Based Classification Framework for Government Data -- 1 Introduction -- 2 Related Work -- 2.1 GD Classification -- 2.2 Pre-trained Classification Models -- 3 The Proposed GD-PTCF Framework -- 3.1 Overview -- 3.2 Sample Processing -- 3.3 CPP -- 3.4 RE-Coder."]
[{"$nulls":4},"3.5 CLM."]
[null,null,null,"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."]