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Record Nr. |
UNISA996550555303316 |
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Titolo |
Machine Learning and Knowledge Discovery in Databases : Applied Data Science and Demo Track / / edited by Gianmarco De Francisci Morales [and five others] |
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Pubbl/distr/stampa |
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Cham, Switzerland : , : Springer Nature Switzerland AG, , [2023] |
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©2023 |
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ISBN |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (427 pages) |
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Collana |
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Lecture Notes in Computer Science Series ; ; Volume 14175 |
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Disciplina |
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Soggetti |
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Data mining |
Databases |
Machine learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Preface -- Organization -- Invited Talks Abstracts -- Neural Wave Representations -- Physics-Inspired Graph Neural Networks -- Mapping Generative AI -- Contents - Part VII -- Sustainability, Climate, and Environment -- Continually Learning Out-of-Distribution Spatiotemporal Data for Robust Energy Forecasting -- 1 Introduction -- 2 Related Works -- 2.1 Energy Prediction in Urban Environments -- 2.2 Mobility Data as Auxiliary Information in Forecasting -- 2.3 Deep Learning for Forecasting -- 3 Problem Definition -- 3.1 Time Series Forecasting -- 3.2 Continual Learning for Time Series Forecasting -- 4 Method -- 4.1 Backbone-Temporal Convolutional Network -- 4.2 Fast Adaptation -- 4.3 Associative Memory -- 5 Datasets and Contextual Data -- 5.1 Energy Usage Data -- 5.2 Mobility Data -- 5.3 COVID Lockdown Dates -- 5.4 Temperature Data -- 5.5 Dataset Preprocessing -- 6 Experiments and Results -- 6.1 Experimental Setup -- 6.2 Mobility -- 6.3 Continual Learning -- 7 Conclusion -- References -- Counterfactual Explanations for Remote Sensing Time Series Data: An Application to Land Cover Classification -- 1 Introduction -- 2 Related Work -- 3 Study Area and Land Cover Classification -- 3.1 Study Area -- 3.2 Land Cover Classification -- 4 Proposed Method -- 4.1 Architecture Overview -- 4.2 Networks Implementation and Training -- |
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4.3 Class-Swapping Loss -- 4.4 GAN-Based Regularization for Plausibility -- 4.5 Unimodal Regularization for Time-Contiguity -- 5 Results -- 5.1 Experimental Settings -- 5.2 Comparative Analysis -- 5.3 CFE4SITS In-depth Analysis -- 6 Conclusion -- References -- Cloud Imputation for Multi-sensor Remote Sensing Imagery with Style Transfer -- 1 Introduction -- 2 Related Work -- 2.1 Multi-sensor Cloud Imputation -- 2.2 Style Transfer -- 3 Methodology -- 3.1 Adaptive Instance Normalization (AdaIN). |
3.2 Cluster-Based Attentional Instance Normalization (CAIN) -- 3.3 Composite Style Transfer Module, CAIN + AdaIN (CAINA) -- 3.4 The Deep Learning Network Architecture -- 4 Experiments -- 4.1 Dataset and Environmental Configuration -- 4.2 Experiment Settings -- 4.3 Quantitative Results of the First Set of Experiments -- 4.4 Quantitative Results of the Second Set of Experiments -- 4.5 Analysis on Variances Among the Compared Methods -- 4.6 Qualitative Results and Residual Maps -- 5 Conclusions -- References -- Comprehensive Transformer-Based Model Architecture for Real-World Storm Prediction -- 1 Introduction -- 2 Related Work -- 3 Problem Statement, Challenge, and Idea -- 3.1 Problem Statement -- 3.2 Challenges -- 3.3 Our Idea -- 4 Method -- 4.1 Representation Learning -- 4.2 Prediction -- 5 Experiments and Results -- 5.1 Experimental Setting -- 5.2 Overall Performance Under Storm Event Predictions -- 5.3 Significance of Our Design Components for Storm Predictions -- 5.4 Necessity of Content Embedding in Our MAE Encoder -- 5.5 Detailed Design Underlying Our Pooling Layer -- 5.6 Constructing Temporal Representations -- 5.7 Impact of Positional Embedding on Our ViT Encoder -- 6 Conclusion -- References -- Explaining Full-Disk Deep Learning Model for Solar Flare Prediction Using Attribution Methods -- 1 Introduction -- 2 Related Work -- 3 Data and Model -- 4 Attribution Methods -- 5 Experimental Evaluation -- 5.1 Experimental Settings -- 5.2 Evaluation -- 6 Discussion -- 7 Conclusion and Future Work -- References -- Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data -- 1 Introduction -- 2 Background and Problem Definition -- 2.1 Clustering Multi-dimensional Climate Data -- 2.2 Problem Definition -- 3 Related Works -- 4 Proposed Methodology -- 4.1 Overview of Our Deep Spatiotemporal Clustering (DSC) Approach. |
4.2 Clustering Assignment -- 4.3 Joint Optimization -- 5 Experiments -- 5.1 Dataset and Data Preprocessing -- 5.2 Baseline Methods -- 5.3 Evaluation Metrics -- 5.4 Experiment Results -- 5.5 Ablation Study -- 6 Conclusions -- References -- Circle Attention: Forecasting Network Traffic by Learning Interpretable Spatial Relationships from Intersecting Circles -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Definition -- 3.2 Circle Attention -- 3.3 Transformer Model -- 4 Experimental Settings -- 5 Results and Discussion -- 5.1 Experiment 1: Baseline Comparison -- 5.2 Experiment 2: Ablation Study of Circle Parameters -- 6 Conclusion -- 7 Ethical Statement -- References -- Transportation and Urban Planning -- Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations -- 1 Introduction -- 2 Preliminaries -- 3 Model -- 3.1 Geo-Tokenizer Embedding Layer -- 3.2 Causal Location Embedding Model -- 3.3 Pre-training Hierarchical Auto-Regressive Location Model -- 3.4 Fine-Tuning Downstream Tasks -- 4 Experiments -- 4.1 Datasets -- 4.2 Settings -- 4.3 Experimental Results (RQ1) -- 4.4 Ablation Study -- 4.5 Deployed Solution -- 5 Related Work -- 6 Conclusions -- References -- Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization -- 1 Introduction -- 2 Related Work -- 2.1 Traditional Methods -- 2.2 RL- |
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Based Methods -- 3 Preliminary -- 4 Method -- 4.1 Introduce Queue Length for TSC Methods -- 4.2 AttentionLight Agent Design -- 4.3 Network Design of AttentionLight -- 5 Experiment -- 5.1 Overall Performance -- 5.2 Queue Length Effectiveness Analysis -- 5.3 Reward Function Investigation -- 5.4 Action Duration Study -- 5.5 Model Generalization -- 6 Conclusion -- References. |
PICT: Precision-enhanced Road Intersection Recognition Using Cycling Trajectories -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Formulation -- 3.2 Framework Overview -- 3.3 Geometry Feature Extraction -- 3.4 Grid Topology Representation -- 3.5 Intersection Inference -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Main Results -- 4.3 Ablation Study -- 5 Conclusion -- References -- FDTI: Fine-Grained Deep Traffic Inference with Roadnet-Enriched Graph -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Problem Definition -- 4 Method -- 4.1 Fine-Grained Traffic Spatial-Temporal Graph -- 4.2 Dynamic Mobility Convolution -- 4.3 Flow Conservative Traffic State Transition -- 5 Experiment -- 5.1 Experiment Settings -- 5.2 Overall Performance -- 5.3 Graph Smooth Analysis -- 5.4 Ablation Study -- 5.5 Scalability -- 6 Conclusion -- References -- RulEth: Genetic Programming-Driven Derivation of Security Rules for Automotive Ethernet -- 1 Introduction -- 2 Background and Related Work -- 3 Threat Model -- 4 RulEth Language -- 5 RulEth System Architecture -- 6 Evaluation -- 7 Conclusion -- References -- Spatial-Temporal Graph Sandwich Transformer for Traffic Flow Forecasting -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Problem Formulation -- 3.2 Transformer Architecture -- 4 Proposed Method -- 4.1 Overall Design -- 4.2 Spatial-Temporal Sandwich Transformer -- 4.3 Multi-step Prediction -- 4.4 Training Procedure and Complexity -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Performance Comparison and Analysis (RQ1) -- 5.3 Ablation Study (RQ2) -- 5.4 Learning Stability (RQ3) -- 5.5 Parameter Sensitivity (RQ4) -- 6 Conclusion -- References -- Data-Driven Explainable Artificial Intelligence for Energy Efficiency in Short-Sea Shipping -- 1 Introduction -- 2 Related Work -- 3 Case Study Description -- 3.1 Problem Formulation. |
4 Modeling and Analysis -- 4.1 Exploratory Analysis -- 4.2 Optimizing the Model -- 4.3 Exploiting the Model -- 5 Conclusion -- References -- Multivariate Time-Series Anomaly Detection with Temporal Self-supervision and Graphs: Application to Vehicle Failure Prediction -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Addressing the Challenges: Our Methodology -- 2 Related Works -- 2.1 Vehicle Predictive Maintenance with Machine Learning -- 2.2 Time-Series Anomaly Detection -- 3 Proposed Model -- 3.1 Data Preprocessing and Feature Construction -- 3.2 Graph Autoencoder -- 3.3 Graph Generative Learning -- 3.4 Temporal Contrastive Learning -- 3.5 Anomaly Scoring -- 4 Experiments -- 4.1 Dataset -- 4.2 Evaluation Protocol -- 4.3 Experimental Results -- 5 Conclusion -- References -- Predictive Maintenance, Adversarial Autoencoders and Explainability -- 1 Introduction -- 2 Overview of Dataset -- 3 Proposed Solution -- 3.1 Autoencoder Models for Time Series Anomaly Detection -- 3.2 Failure Detection -- 3.3 Model Explainability -- 4 Results -- 4.1 Failure Detection Based on Compressor Cycles -- 4.2 Anomaly Detection on Data Chunks -- 4.3 Explainability -- 5 Discussion -- 6 Conclusion -- References -- TDCM: Transport Destination Calibrating Based on Multi-task Learning -- 1 Introduction -- 2 Related Work -- 3 Problem Definition -- 4 Overview -- 4.1 Waybill Trajectory Pre-processing -- 4.2 Stay Hotspot Detection -- 4.3 Hotspot Feature Extraction -- 4.4 Transport Destination Calibration -- 5 Experiments -- 5.1 Datasets |
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and Settings -- 5.2 Overall Evaluation -- 5.3 Ablation Analysis of Features -- 5.4 Multi-task Weight Selection -- 5.5 Case Study -- 6 Conclusion -- References -- Demo -- An Interactive Interface for Novel Class Discovery in Tabular Data -- 1 Introduction -- 2 Interface Description -- 3 Conclusion -- References. |
marl-jax: Multi-agent Reinforcement Leaning Framework for Social Generalization. |
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