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AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part II / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part II / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
Autore Liu Tongliang
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (509 pages)
Disciplina 006.3
Altri autori (Persone) WebbGeoff
YueLin
WangDadong
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Computer networks
Data mining
Application software
Computer vision
Artificial Intelligence
Computer Communication Networks
Data Mining and Knowledge Discovery
Computer and Information Systems Applications
Computer Vision
ISBN 981-9983-91-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Knowledge Representation and NLP -- Collaborative Qualitative Environment Mapping -- 1 Introduction -- 2 Qualitative Spatio-Temporal Reasoning -- 3 LH Interval Calculus -- 4 Collaborative Qualitative Environmental Mapping -- 5 Experiments -- 6 Related Work -- 7 Conclusion and Future Work -- References -- Towards Learning Action Models from Narrative Text Through Extraction and Ordering of Structured Events -- 1 Introduction -- 2 Related Work -- 3 Structured Event Extraction -- 4 Event Ordering -- 5 Narrative Chain Extraction -- 6 Challenges for NLP Research -- 7 Challenges for Model Acquisition -- 8 Conclusions -- References -- The Difficulty of Novelty Detection and Adaptation in Physical Environments -- 1 Introduction -- 2 Background and Related Work -- 2.1 Novelty Research -- 2.2 Difficulty Prediction -- 2.3 Learning Algorithms -- 2.4 Qualitative Spatial Relations (QSRs) -- 2.5 Experimental Domain -- 3 Novelty Difficulty Formulation -- 3.1 Dimensions of Novelty -- 3.2 Observational State -- 3.3 Action State -- 4 Discussion and Conclusion -- References -- Lateral AI: Simulating Diversity in Virtual Communities -- 1 Introduction -- 1.1 Large Language Models -- 1.2 Prompt Engineering -- 2 Lateral AI -- 2.1 Lateral AI Design -- 2.2 Comparison with Other Models -- 2.3 Key Features of Lateral AI -- 3 Lateral AI Demonstrations -- 3.1 Arnold Schwarzenegger AI Persona's Advice on Vitality -- 3.2 Creating Unconventional Thinkers -- 3.3 A Moral Dilemma -- 3.4 Seeking Recommendations from a Board of Experts -- 3.5 Pushing AI To Predict Beyond Its Factual Knowledge -- 4 Conclusion -- References -- Reports, Observations, and Belief Change -- 1 Introduction -- 2 Preliminaries -- 2.1 Motivating Example -- 2.2 Belief Revision -- 2.3 Trust -- 3 Revision by Reports.
3.1 Basic Definitions -- 3.2 Report Revision Operators -- 3.3 Honesty Sets -- 3.4 Representation Result -- 4 Observations -- 4.1 Conflict -- 4.2 Revision by Observations -- 4.3 Basic Properties -- 5 Discussion -- 5.1 Related Work -- 5.2 Future Work -- 5.3 Conclusion -- References -- A Prompting Framework to Enhance Language Model Output -- 1 Introduction -- 2 Prompting Techniques -- 3 Research Methods -- 3.1 Framework Formulation -- 4 Results -- 4.1 Experiments -- 4.2 Intrinsic Evaluation Results -- 4.3 Extrinsic Evaluation Results -- 4.4 Constraints -- 5 Conclusions -- References -- Epistemic Reasoning in Computational Machine Ethics -- 1 Introduction -- 2 Background -- 3 Ethical Principle Function -- 3.1 Goodness-Based Principle -- 3.2 Less Harm Principle -- 3.3 Deontological Principle -- 4 Aggregation Strategies -- 4.1 Maximum Average -- 4.2 Maximin Strategy -- 4.3 Coefficient of Optimism -- 4.4 Regret Minimisation -- 4.5 Illustration -- 5 Results and Discussions -- 5.1 Milnor's Axioms -- 5.2 Axiom Satisfaction -- 6 Conclusion -- References -- Using Social Sensing to Validate Flood Risk Modelling in England -- 1 Introduction -- 2 Methodology -- 2.1 Data Collection -- 2.2 Twitter Data Pre-processing -- 2.3 Flood Map Development -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Symbolic Data Analysis to Improve Completeness of Model Combination Methods -- 1 Introduction -- 2 Background -- 2.1 The Symbolic Data Analysis Paradigm -- 2.2 Consensus Models -- 3 Build a Decision Tree from a Symbolic Data Table -- 3.1 Build a Decision Tree from a Synthetic Dataset -- 3.2 Build a Decision Tree from Symbolic Distributional Data -- 4 Evaluation and Results -- 4.1 Datasets -- 4.2 Results and Discussion -- 5 Conclusions -- References -- CySpider: A Neural Semantic Parsing Corpus with Baseline Models for Property Graphs -- 1 Introduction.
2 Related Work -- 3 Notation and Task Formulation -- 4 SQL2Cypher: From SQL Queries to Cypher Queries -- 5 Text-to-Cypher Neural Models -- 5.1 Pipeline -- 5.2 End-to-End Training -- 5.3 Evaluation Metric -- 6 Experiment Results -- 6.1 Dataset Statistics -- 6.2 Models Evaluation Result -- 6.3 Error Analysis -- 7 Conclusion and Future Work -- References -- S5TR: Simple Single Stage Sequencer for Scene Text Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparisons with the State-of-the-arts -- 4.4 Qualitative Results -- 5 Conclusions -- References -- Explainable AI -- Coping with Data Distribution Shifts: XAI-Based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Energy Consumption Prediction -- 2.2 XAI-Based Model Improvement -- 2.3 SHapley Additive ExPlanations (SHAP) -- 3 SHAP Clustering-Based Adaptive Learning (SCAL) -- 3.1 Building Block 1: SHAP Clustering in Explanation Space -- 3.2 Building Block 2: Extraction of SHAP Clustering Characteristics -- 3.3 Building Block 3: Adaptive Model Refinement Based on SHAP Clustering Characteristics -- 4 Experimental Setup and Data Set -- 5 Results -- 5.1 SCAL Performance -- 5.2 Cluster Analysis in Explanation Space -- 6 Transferability to Other Use Cases -- 6.1 Financial Distress Data Set (Classification Problem) -- 6.2 Power Data Set (Regression Problem) -- 7 Conclusion and Future Work -- References -- Concept-Guided Interpretable Federated Learning -- 1 Introduction -- 2 Related Work -- 2.1 Interpretable Federated Learning -- 2.2 Concept-Related Interpretability -- 3 Problem Settings -- 3.1 Federated Learning -- 3.2 Concept Bottleneck Model -- 4 Proposed Method -- 4.1 Concept Bank -- 4.2 Linear Predictor.
4.3 Training Algorithm -- 5 Experiment -- 5.1 Datasets -- 5.2 Performance Analysis -- 5.3 Reasoning Process -- 6 Conclusion and Limitations -- References -- Systematic Analysis of the Impact of Label Noise Correction on ML Fairness -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experiments -- 5 Results -- 6 Discussion -- 7 Conclusions -- References -- Part-Aware Prototype-Aligned Interpretable Image Classification with Basic Feature Domain -- 1 Introduction -- 2 Method -- 2.1 The Overview of PaProtoPNet Architecture -- 2.2 Basic Feature Domain and Prototype Alignment -- 2.3 Feature Separation Module -- 2.4 Computing Scores for Classification -- 2.5 Overall Loss Function -- 3 Experiments -- 3.1 Performance Comparison -- 3.2 Model Analysis -- 3.3 Reasoning Process -- 4 Discussion and Future Work -- References -- Hybrid CNN-Interpreter: Interprete Local and Global Contexts for CNN-Based Models -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network Structures -- 2.2 Visual Interpretabilities of CNN-Based Models -- 3 Method -- 3.1 Stacking Forward Propagation -- 3.2 Linear Regression Module -- 3.3 Filter Importance Analysis Module -- 4 Experiment and Discussion -- 4.1 Local Interpretability for CNN-Based Models -- 4.2 Global Interpretability for CNN-Based Models -- 5 Conclusion and Future Work -- References -- Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust -- 1 Introduction -- 2 Related Work -- 2.1 User Trust -- 2.2 Fidelity -- 2.3 Robustness -- 3 Hypotheses -- 4 Methodology -- 4.1 Fidelity-Based Scenario Study Design -- 4.2 Robustness-Based Scenario Study Design -- 4.3 Metrics -- 5 Experiment -- 5.1 Dataset -- 5.2 Participants -- 5.3 Experimental Procedure -- 6 Results -- 6.1 Correlations Between User Trust and Fidelity -- 6.2 Correlations Between User Trust and Robustness -- 7 Discussion.
8 Conclusion and Future Work -- References -- Interpretable Drawing Psychoanalysis via House-Tree-Person Test -- 1 Introduction -- 2 Related Works -- 2.1 Drawing Psychoanalysis -- 2.2 Class Activation Mapping -- 3 Method -- 3.1 Quantization of the Size -- 3.2 Quantization of the Position -- 4 Experiments -- 4.1 The HTP Dataset -- 4.2 Implementation Detials -- 4.3 Experiment Results -- 5 Conclusion -- References -- A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection -- 1 Introduction -- 1.1 Main Contributions -- 2 Collection of Polynomial SGaBloME Models -- 2.1 Variable Selection via Selecting Relevant Variables -- 2.2 Variable Selection via Rank Sparse Models -- 2.3 Collection of Polynomial SGaBloME Models -- 3 Main Theoretical Results -- 3.1 Boundedness Conditions on the Parameter Space -- 3.2 Loss Function -- 3.3 Penalized Maximum Likelihood Estimation (PMLE) -- 3.4 Oracle Inequality -- 4 Conclusion and Perspectives -- References -- Reinforcement Learning -- Auction-Based Allocation of Location-Specific Tasks -- 1 Introduction -- 2 Setup -- 3 Auction-Based Algorithms -- 3.1 Bidding Rule -- 3.2 BidSumPath Bidding Rule -- 3.3 BidSumTree Bidding Rule -- 4 Theoretical Analysis Under Task Capacities -- 5 Experimental Comparison of Algorithms -- 5.1 Experimental Setup and Design -- 5.2 Impact of Feasibility Constraints -- 5.3 Performance Against Optimal -- 6 Conclusions -- References -- Generalized Bargaining Protocols -- 1 Introduction -- 2 Automated Negotiation -- 3 Proposed Framework -- 3.1 Evaluating Negotiation Protocols -- 3.2 Tentative Agreements Unique Offers (TAU) -- 4 Empirical Evaluation -- 5 Conclusion -- References -- SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 SAGE.
4.1 Meta-controller.
Record Nr. UNISA-996565865403316
Liu Tongliang  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part I / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part I / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
Autore Liu Tongliang
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (574 pages)
Disciplina 006.3
Altri autori (Persone) WebbGeoff
YueLin
WangDadong
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Computer networks
Data mining
Application software
Computer vision
Artificial Intelligence
Computer Communication Networks
Data Mining and Knowledge Discovery
Computer and Information Systems Applications
Computer Vision
ISBN 981-9983-88-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Computer Vision -- Multi-graph Laplacian Feature Mapping Incorporating Tag Information for Image Annotation -- 1 Introduction -- 2 Related Work -- 3 Propoesd Method -- 3.1 Multi-graph Laplacian Incorporating Tag Information -- 3.2 Tag Graph Laplacian with Visual Content -- 3.3 Loss Function and Objective Function -- 4 Optimization -- 5 Experimental Results -- 5.1 Experiment Settings -- 5.2 Experimental Performance -- 5.3 The Analysis Parameters -- 6 Conclusion -- References -- Short-Term Solar Irradiance Forecasting from Future Sky Images Generation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Nowcasting Model -- 3.2 Image Prediction Model -- 3.3 The Forecasting Framework -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Evaluate Metrics, Data Processing and Hyper-parameters -- 4.3 Nowcasting Results -- 4.4 Forecasting Results -- 5 Conclusion -- References -- No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Preliminaries -- 3.2 Token Selection and Idling -- 3.3 Token Cut Loss -- 3.4 Finetuning -- 4 Experiments -- 4.1 Implementation Settings -- 4.2 Results -- 4.3 Analysis of Token Cut Loss -- 4.4 Analysis of Token Idle Strategy -- 5 Conclusion -- References -- Story Sifting Using Object Detection Techniques -- 1 Introduction -- 2 Background and Related Work -- 3 Approach -- 3.1 Recasting Story Sifting as Object Detection -- 3.2 Representing Story Arcs as Images -- 3.3 Choice of YOLOv5 Model -- 4 Model Development -- 5 Evaluating Model Performance -- 5.1 Model Performance -- 6 Evaluating Time Efficiency -- 7 Detection from a Virtual Storyworld Environment -- 8 Discussion -- 9 Conclusion -- References.
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline -- 1 Introduction -- 2 Related Study -- 3 New Dataset: SimMining3D -- 3.1 Data Collection at Simulated Environment -- 3.2 Automatic Annotation -- 4 Perception: Baseline Experiment -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Oyster Mushroom Growth Stage Identification: An Exploration of Computer Vision Technologies -- 1 Introduction -- 2 Related Works -- 3 The Monitoring System -- 3.1 The Problem -- 3.2 The Label Map -- 4 Empirical Studies -- 4.1 Settings -- 4.2 Performances -- 5 Conclusion and Future Works -- References -- Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Framework Overview -- 3.2 Anchor-Free Head-Body Detection -- 3.3 Joint SimOTA -- 3.4 Tracking Framework -- 3.5 Loss Function -- 3.6 Training Details -- 4 Experiments -- 4.1 MOT Challenge -- 4.2 Qualitative Result on MOT20 -- 4.3 Ablation Study on Joint SimOTA -- 4.4 Crowdhuman -- 5 Conclusion -- References -- SAR2EO: A High-Resolution Image Translation Framework with Denoising Enhancement -- 1 Introduction -- 2 Related Work -- 2.1 GAN -- 2.2 Image-to-Image Translation -- 3 Proposed Method -- 3.1 Preliminary: Pix2pixHD -- 3.2 SAR and EO Images -- 3.3 Denoising Enhanced SAR2EO Framework -- 4 Experiments -- 4.1 Dataset -- 4.2 Metrics -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Studies -- 5 Conclusion -- References -- A New Perspective of Weakly Supervised 3D Instance Segmentation via Bounding Boxes -- 1 Introduction -- 2 Related Work -- 2.1 Fully Supervised Method -- 2.2 Weakly Supervised Method -- 3 Methodology -- 3.1 Problem Description -- 3.2 Cluster-Based Candidate Points Filtering.
3.3 Smallest-Box Heuristic -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Dataset -- 4.3 Evaluation Metrics and Experiment Results -- 4.4 Ablation Study -- 4.5 Robustness -- 5 Conclusion -- References -- Large-Kernel Attention Network with Distance Regression and Topological Self-correction for Airway Segmentation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Prediction Head -- 2.3 Implementation Details -- 3 Experimental Results -- 3.1 Metrics -- 3.2 Comparison with Other Methods -- 3.3 Ablation Study -- 4 Conclusion -- References -- Deep Learning -- WeightRelay: Efficient Heterogeneous Federated Learning on Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning for Time Series Classification -- 2.2 Federated Learning on Heterogeneous Devices -- 3 Motivation -- 4 Weight Relay -- 4.1 Heterogeneous Models -- 4.2 Weight Alignment -- 5 Analysis of Weight Relay -- 5.1 Consistency Proof for the Alignment -- 5.2 Macro Explanation of the Training Acceleration -- 5.3 Micro Explanation of the Training Acceleration -- 6 Experiment -- 6.1 Benchmarks -- 6.2 Evaluation Criteria -- 6.3 Experiment Setup -- 6.4 Experiment Result -- 7 Conclusion -- References -- Superpixel Attack -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Definition -- 2.2 Related Work -- 3 Research on Update Areas -- 3.1 Update Areas of Existing Methods -- 3.2 Color Variance of Update Areas -- 3.3 Compactness of Update Areas -- 3.4 Superpixel Calculated by SLIC -- 3.5 Analysis of Color Variance and Compactness -- 4 Superpixel Attack -- 4.1 Update Areas Using Superpixels -- 4.2 Procedure of Versatile Search -- 5 Experiments -- 6 Conclusion -- References -- Cross Domain Pulmonary Nodule Detection Without Source Data -- 1 Introduction -- 2 Method -- 2.1 Feature Extractor Adaptation -- 2.2 Detection Head Adaptation -- 3 Experiments.
3.1 Benchmark and Evaluation -- 3.2 Implementation Details -- 3.3 Results -- 4 Related Works -- 5 Conclusion -- References -- 3RE-Net: Joint Loss-REcovery and Super-REsolution Neural Network for REal-Time Video -- 1 Introduction -- 2 Related Work -- 3 Model Design -- 4 Experiments -- 5 Conclusion -- References -- Neural Networks in Forecasting Financial Volatility -- 1 Introduction -- 2 Related Work -- 3 Experimental Comparison of Forecasting Models -- 3.1 Posing the Problem as a Shared Task -- 3.2 Methods -- 3.3 Result Evaluation and Analysis -- 4 Discussion -- References -- CLIP-Based Composed Image Retrieval with Comprehensive Fusion and Data Augmentation -- 1 Introduction -- 2 Related Work -- 2.1 Composed Image Retrieval -- 2.2 Vision-Language Pre-training -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 CLIP-CD -- 4 Experiments -- 4.1 Datasets and Metrics -- 4.2 Implementation Details -- 4.3 Performance Comparison -- 4.4 Ablation Study -- 4.5 Case Study -- 5 Conclusions -- References -- LiDAR Inpainting of UAV Based 3D Point Cloud Using Supervised Learning -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Problem Definition -- 5 Methodology -- 5.1 Simulator -- 5.2 Extracting Individual Point Clouds -- 5.3 Point Cloud Inpainting Model -- 5.4 Inpainting Complete Environments -- 6 Experimental Results -- 7 Conclusion and Future Work -- References -- A Sampling Method for Performance Predictor Based on Contrastive Learning -- 1 Introduction -- 2 Background -- 2.1 Contrastive Learning -- 2.2 Graph Data Sampling Methods -- 3 Approach -- 3.1 Architecture Augmentation -- 3.2 Architecture Maximal Agreement -- 4 Experiments -- 4.1 Overall Performance -- 4.2 Performance Evaluation in NAS Datasets -- 4.3 Ablation Study -- 5 Conclusion -- References.
AdaptMatch: Adaptive Consistency Regularization for Semi-supervised Learning with Top-k Pseudo-labeling and Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Consistency Regularization -- 2.2 Contrastive Learning -- 3 Our Approach: AdaptMatch -- 3.1 Data Augmentation -- 3.2 Top-k Label Guessing -- 3.3 Contrastive Learning -- 3.4 Summarization of the Framework -- 4 Experiments -- 4.1 Datasets and Experimental Setup -- 4.2 Main Results -- 5 Ablation Study -- 6 Conclusion -- References -- Estimation of Unmasked Face Images Based on Voice and 3DMM -- 1 Introduction -- 2 Related Research -- 2.1 Studies on Mask Removal -- 2.2 Studies on Estimating Facial Shape from Voice -- 2.3 3D Morphable Model (3DMM) -- 3 Proposed Method -- 3.1 Overview of the Proposal Method -- 3.2 Extraction of Voice Embedding -- 3.3 Combining Voice Embedding and Intermediate Features -- 3.4 Training of Multitasking Module -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Training Details -- 4.3 Qualitative Evaluation -- 4.4 Quantitative Evaluation -- 5 Discussion -- 5.1 On Qualitative Evaluation -- 5.2 On Quantitative Evaluation -- 6 Conclusion and Future Works -- References -- Aging Contrast: A Contrastive Learning Framework for Fish Re-identification Across Seasons and Years -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning for Fish Recognition -- 2.2 Contrastive Learning -- 3 Dataset -- 4 Proposed Method -- 4.1 Segmentation and Feature Extraction -- 4.2 Aging Contrast Framework -- 5 Experiments -- 6 Conclusion -- References -- Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Methodology -- 4.1 Spatial Bottleneck Transformer -- 4.2 ST-InducedTran Model -- 5 Experiments -- 5.1 Dataset -- 5.2 Baseline -- 5.3 Implementation Details -- 5.4 Evaluation Metrics.
6 Results and Discussion.
Record Nr. UNISA-996565865503316
Liu Tongliang  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part II / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part II / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
Autore Liu Tongliang
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (509 pages)
Disciplina 006.3
Altri autori (Persone) WebbGeoff
YueLin
WangDadong
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Computer networks
Data mining
Application software
Computer vision
Artificial Intelligence
Computer Communication Networks
Data Mining and Knowledge Discovery
Computer and Information Systems Applications
Computer Vision
ISBN 981-9983-91-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Knowledge Representation and NLP -- Collaborative Qualitative Environment Mapping -- 1 Introduction -- 2 Qualitative Spatio-Temporal Reasoning -- 3 LH Interval Calculus -- 4 Collaborative Qualitative Environmental Mapping -- 5 Experiments -- 6 Related Work -- 7 Conclusion and Future Work -- References -- Towards Learning Action Models from Narrative Text Through Extraction and Ordering of Structured Events -- 1 Introduction -- 2 Related Work -- 3 Structured Event Extraction -- 4 Event Ordering -- 5 Narrative Chain Extraction -- 6 Challenges for NLP Research -- 7 Challenges for Model Acquisition -- 8 Conclusions -- References -- The Difficulty of Novelty Detection and Adaptation in Physical Environments -- 1 Introduction -- 2 Background and Related Work -- 2.1 Novelty Research -- 2.2 Difficulty Prediction -- 2.3 Learning Algorithms -- 2.4 Qualitative Spatial Relations (QSRs) -- 2.5 Experimental Domain -- 3 Novelty Difficulty Formulation -- 3.1 Dimensions of Novelty -- 3.2 Observational State -- 3.3 Action State -- 4 Discussion and Conclusion -- References -- Lateral AI: Simulating Diversity in Virtual Communities -- 1 Introduction -- 1.1 Large Language Models -- 1.2 Prompt Engineering -- 2 Lateral AI -- 2.1 Lateral AI Design -- 2.2 Comparison with Other Models -- 2.3 Key Features of Lateral AI -- 3 Lateral AI Demonstrations -- 3.1 Arnold Schwarzenegger AI Persona's Advice on Vitality -- 3.2 Creating Unconventional Thinkers -- 3.3 A Moral Dilemma -- 3.4 Seeking Recommendations from a Board of Experts -- 3.5 Pushing AI To Predict Beyond Its Factual Knowledge -- 4 Conclusion -- References -- Reports, Observations, and Belief Change -- 1 Introduction -- 2 Preliminaries -- 2.1 Motivating Example -- 2.2 Belief Revision -- 2.3 Trust -- 3 Revision by Reports.
3.1 Basic Definitions -- 3.2 Report Revision Operators -- 3.3 Honesty Sets -- 3.4 Representation Result -- 4 Observations -- 4.1 Conflict -- 4.2 Revision by Observations -- 4.3 Basic Properties -- 5 Discussion -- 5.1 Related Work -- 5.2 Future Work -- 5.3 Conclusion -- References -- A Prompting Framework to Enhance Language Model Output -- 1 Introduction -- 2 Prompting Techniques -- 3 Research Methods -- 3.1 Framework Formulation -- 4 Results -- 4.1 Experiments -- 4.2 Intrinsic Evaluation Results -- 4.3 Extrinsic Evaluation Results -- 4.4 Constraints -- 5 Conclusions -- References -- Epistemic Reasoning in Computational Machine Ethics -- 1 Introduction -- 2 Background -- 3 Ethical Principle Function -- 3.1 Goodness-Based Principle -- 3.2 Less Harm Principle -- 3.3 Deontological Principle -- 4 Aggregation Strategies -- 4.1 Maximum Average -- 4.2 Maximin Strategy -- 4.3 Coefficient of Optimism -- 4.4 Regret Minimisation -- 4.5 Illustration -- 5 Results and Discussions -- 5.1 Milnor's Axioms -- 5.2 Axiom Satisfaction -- 6 Conclusion -- References -- Using Social Sensing to Validate Flood Risk Modelling in England -- 1 Introduction -- 2 Methodology -- 2.1 Data Collection -- 2.2 Twitter Data Pre-processing -- 2.3 Flood Map Development -- 3 Results -- 4 Discussion -- 5 Conclusion -- References -- Symbolic Data Analysis to Improve Completeness of Model Combination Methods -- 1 Introduction -- 2 Background -- 2.1 The Symbolic Data Analysis Paradigm -- 2.2 Consensus Models -- 3 Build a Decision Tree from a Symbolic Data Table -- 3.1 Build a Decision Tree from a Synthetic Dataset -- 3.2 Build a Decision Tree from Symbolic Distributional Data -- 4 Evaluation and Results -- 4.1 Datasets -- 4.2 Results and Discussion -- 5 Conclusions -- References -- CySpider: A Neural Semantic Parsing Corpus with Baseline Models for Property Graphs -- 1 Introduction.
2 Related Work -- 3 Notation and Task Formulation -- 4 SQL2Cypher: From SQL Queries to Cypher Queries -- 5 Text-to-Cypher Neural Models -- 5.1 Pipeline -- 5.2 End-to-End Training -- 5.3 Evaluation Metric -- 6 Experiment Results -- 6.1 Dataset Statistics -- 6.2 Models Evaluation Result -- 6.3 Error Analysis -- 7 Conclusion and Future Work -- References -- S5TR: Simple Single Stage Sequencer for Scene Text Recognition -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Comparisons with the State-of-the-arts -- 4.4 Qualitative Results -- 5 Conclusions -- References -- Explainable AI -- Coping with Data Distribution Shifts: XAI-Based Adaptive Learning with SHAP Clustering for Energy Consumption Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Energy Consumption Prediction -- 2.2 XAI-Based Model Improvement -- 2.3 SHapley Additive ExPlanations (SHAP) -- 3 SHAP Clustering-Based Adaptive Learning (SCAL) -- 3.1 Building Block 1: SHAP Clustering in Explanation Space -- 3.2 Building Block 2: Extraction of SHAP Clustering Characteristics -- 3.3 Building Block 3: Adaptive Model Refinement Based on SHAP Clustering Characteristics -- 4 Experimental Setup and Data Set -- 5 Results -- 5.1 SCAL Performance -- 5.2 Cluster Analysis in Explanation Space -- 6 Transferability to Other Use Cases -- 6.1 Financial Distress Data Set (Classification Problem) -- 6.2 Power Data Set (Regression Problem) -- 7 Conclusion and Future Work -- References -- Concept-Guided Interpretable Federated Learning -- 1 Introduction -- 2 Related Work -- 2.1 Interpretable Federated Learning -- 2.2 Concept-Related Interpretability -- 3 Problem Settings -- 3.1 Federated Learning -- 3.2 Concept Bottleneck Model -- 4 Proposed Method -- 4.1 Concept Bank -- 4.2 Linear Predictor.
4.3 Training Algorithm -- 5 Experiment -- 5.1 Datasets -- 5.2 Performance Analysis -- 5.3 Reasoning Process -- 6 Conclusion and Limitations -- References -- Systematic Analysis of the Impact of Label Noise Correction on ML Fairness -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 4 Experiments -- 5 Results -- 6 Discussion -- 7 Conclusions -- References -- Part-Aware Prototype-Aligned Interpretable Image Classification with Basic Feature Domain -- 1 Introduction -- 2 Method -- 2.1 The Overview of PaProtoPNet Architecture -- 2.2 Basic Feature Domain and Prototype Alignment -- 2.3 Feature Separation Module -- 2.4 Computing Scores for Classification -- 2.5 Overall Loss Function -- 3 Experiments -- 3.1 Performance Comparison -- 3.2 Model Analysis -- 3.3 Reasoning Process -- 4 Discussion and Future Work -- References -- Hybrid CNN-Interpreter: Interprete Local and Global Contexts for CNN-Based Models -- 1 Introduction -- 2 Related Work -- 2.1 Convolutional Neural Network Structures -- 2.2 Visual Interpretabilities of CNN-Based Models -- 3 Method -- 3.1 Stacking Forward Propagation -- 3.2 Linear Regression Module -- 3.3 Filter Importance Analysis Module -- 4 Experiment and Discussion -- 4.1 Local Interpretability for CNN-Based Models -- 4.2 Global Interpretability for CNN-Based Models -- 5 Conclusion and Future Work -- References -- Impact of Fidelity and Robustness of Machine Learning Explanations on User Trust -- 1 Introduction -- 2 Related Work -- 2.1 User Trust -- 2.2 Fidelity -- 2.3 Robustness -- 3 Hypotheses -- 4 Methodology -- 4.1 Fidelity-Based Scenario Study Design -- 4.2 Robustness-Based Scenario Study Design -- 4.3 Metrics -- 5 Experiment -- 5.1 Dataset -- 5.2 Participants -- 5.3 Experimental Procedure -- 6 Results -- 6.1 Correlations Between User Trust and Fidelity -- 6.2 Correlations Between User Trust and Robustness -- 7 Discussion.
8 Conclusion and Future Work -- References -- Interpretable Drawing Psychoanalysis via House-Tree-Person Test -- 1 Introduction -- 2 Related Works -- 2.1 Drawing Psychoanalysis -- 2.2 Class Activation Mapping -- 3 Method -- 3.1 Quantization of the Size -- 3.2 Quantization of the Position -- 4 Experiments -- 4.1 The HTP Dataset -- 4.2 Implementation Detials -- 4.3 Experiment Results -- 5 Conclusion -- References -- A Non-asymptotic Risk Bound for Model Selection in a High-Dimensional Mixture of Experts via Joint Rank and Variable Selection -- 1 Introduction -- 1.1 Main Contributions -- 2 Collection of Polynomial SGaBloME Models -- 2.1 Variable Selection via Selecting Relevant Variables -- 2.2 Variable Selection via Rank Sparse Models -- 2.3 Collection of Polynomial SGaBloME Models -- 3 Main Theoretical Results -- 3.1 Boundedness Conditions on the Parameter Space -- 3.2 Loss Function -- 3.3 Penalized Maximum Likelihood Estimation (PMLE) -- 3.4 Oracle Inequality -- 4 Conclusion and Perspectives -- References -- Reinforcement Learning -- Auction-Based Allocation of Location-Specific Tasks -- 1 Introduction -- 2 Setup -- 3 Auction-Based Algorithms -- 3.1 Bidding Rule -- 3.2 BidSumPath Bidding Rule -- 3.3 BidSumTree Bidding Rule -- 4 Theoretical Analysis Under Task Capacities -- 5 Experimental Comparison of Algorithms -- 5.1 Experimental Setup and Design -- 5.2 Impact of Feasibility Constraints -- 5.3 Performance Against Optimal -- 6 Conclusions -- References -- Generalized Bargaining Protocols -- 1 Introduction -- 2 Automated Negotiation -- 3 Proposed Framework -- 3.1 Evaluating Negotiation Protocols -- 3.2 Tentative Agreements Unique Offers (TAU) -- 4 Empirical Evaluation -- 5 Conclusion -- References -- SAGE: Generating Symbolic Goals for Myopic Models in Deep Reinforcement Learning -- 1 Introduction -- 2 Background -- 3 Related Work -- 4 SAGE.
4.1 Meta-controller.
Record Nr. UNINA-9910766896603321
Liu Tongliang  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part I / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
AI 2023: Advances in Artificial Intelligence [[electronic resource] ] : 36th Australasian Joint Conference on Artificial Intelligence, AI 2023, Brisbane, QLD, Australia, November 28–December 1, 2023, Proceedings, Part I / / edited by Tongliang Liu, Geoff Webb, Lin Yue, Dadong Wang
Autore Liu Tongliang
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (574 pages)
Disciplina 006.3
Altri autori (Persone) WebbGeoff
YueLin
WangDadong
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Computer networks
Data mining
Application software
Computer vision
Artificial Intelligence
Computer Communication Networks
Data Mining and Knowledge Discovery
Computer and Information Systems Applications
Computer Vision
ISBN 981-9983-88-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Computer Vision -- Multi-graph Laplacian Feature Mapping Incorporating Tag Information for Image Annotation -- 1 Introduction -- 2 Related Work -- 3 Propoesd Method -- 3.1 Multi-graph Laplacian Incorporating Tag Information -- 3.2 Tag Graph Laplacian with Visual Content -- 3.3 Loss Function and Objective Function -- 4 Optimization -- 5 Experimental Results -- 5.1 Experiment Settings -- 5.2 Experimental Performance -- 5.3 The Analysis Parameters -- 6 Conclusion -- References -- Short-Term Solar Irradiance Forecasting from Future Sky Images Generation -- 1 Introduction -- 2 Related Works -- 3 Method -- 3.1 Nowcasting Model -- 3.2 Image Prediction Model -- 3.3 The Forecasting Framework -- 4 Experiments and Results -- 4.1 Datasets -- 4.2 Evaluate Metrics, Data Processing and Hyper-parameters -- 4.3 Nowcasting Results -- 4.4 Forecasting Results -- 5 Conclusion -- References -- No Token Left Behind: Efficient Vision Transformer via Dynamic Token Idling -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Preliminaries -- 3.2 Token Selection and Idling -- 3.3 Token Cut Loss -- 3.4 Finetuning -- 4 Experiments -- 4.1 Implementation Settings -- 4.2 Results -- 4.3 Analysis of Token Cut Loss -- 4.4 Analysis of Token Idle Strategy -- 5 Conclusion -- References -- Story Sifting Using Object Detection Techniques -- 1 Introduction -- 2 Background and Related Work -- 3 Approach -- 3.1 Recasting Story Sifting as Object Detection -- 3.2 Representing Story Arcs as Images -- 3.3 Choice of YOLOv5 Model -- 4 Model Development -- 5 Evaluating Model Performance -- 5.1 Model Performance -- 6 Evaluating Time Efficiency -- 7 Detection from a Virtual Storyworld Environment -- 8 Discussion -- 9 Conclusion -- References.
SimMining-3D: Altitude-Aware 3D Object Detection in Complex Mining Environments: A Novel Dataset and ROS-Based Automatic Annotation Pipeline -- 1 Introduction -- 2 Related Study -- 3 New Dataset: SimMining3D -- 3.1 Data Collection at Simulated Environment -- 3.2 Automatic Annotation -- 4 Perception: Baseline Experiment -- 4.1 Experimental Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Oyster Mushroom Growth Stage Identification: An Exploration of Computer Vision Technologies -- 1 Introduction -- 2 Related Works -- 3 The Monitoring System -- 3.1 The Problem -- 3.2 The Label Map -- 4 Empirical Studies -- 4.1 Settings -- 4.2 Performances -- 5 Conclusion and Future Works -- References -- Handling Heavy Occlusion in Dense Crowd Tracking by Focusing on the Heads -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Framework Overview -- 3.2 Anchor-Free Head-Body Detection -- 3.3 Joint SimOTA -- 3.4 Tracking Framework -- 3.5 Loss Function -- 3.6 Training Details -- 4 Experiments -- 4.1 MOT Challenge -- 4.2 Qualitative Result on MOT20 -- 4.3 Ablation Study on Joint SimOTA -- 4.4 Crowdhuman -- 5 Conclusion -- References -- SAR2EO: A High-Resolution Image Translation Framework with Denoising Enhancement -- 1 Introduction -- 2 Related Work -- 2.1 GAN -- 2.2 Image-to-Image Translation -- 3 Proposed Method -- 3.1 Preliminary: Pix2pixHD -- 3.2 SAR and EO Images -- 3.3 Denoising Enhanced SAR2EO Framework -- 4 Experiments -- 4.1 Dataset -- 4.2 Metrics -- 4.3 Implementation Details -- 4.4 Main Results -- 4.5 Ablation Studies -- 5 Conclusion -- References -- A New Perspective of Weakly Supervised 3D Instance Segmentation via Bounding Boxes -- 1 Introduction -- 2 Related Work -- 2.1 Fully Supervised Method -- 2.2 Weakly Supervised Method -- 3 Methodology -- 3.1 Problem Description -- 3.2 Cluster-Based Candidate Points Filtering.
3.3 Smallest-Box Heuristic -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Dataset -- 4.3 Evaluation Metrics and Experiment Results -- 4.4 Ablation Study -- 4.5 Robustness -- 5 Conclusion -- References -- Large-Kernel Attention Network with Distance Regression and Topological Self-correction for Airway Segmentation -- 1 Introduction -- 2 Method -- 2.1 Network Architecture -- 2.2 Prediction Head -- 2.3 Implementation Details -- 3 Experimental Results -- 3.1 Metrics -- 3.2 Comparison with Other Methods -- 3.3 Ablation Study -- 4 Conclusion -- References -- Deep Learning -- WeightRelay: Efficient Heterogeneous Federated Learning on Time Series -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning for Time Series Classification -- 2.2 Federated Learning on Heterogeneous Devices -- 3 Motivation -- 4 Weight Relay -- 4.1 Heterogeneous Models -- 4.2 Weight Alignment -- 5 Analysis of Weight Relay -- 5.1 Consistency Proof for the Alignment -- 5.2 Macro Explanation of the Training Acceleration -- 5.3 Micro Explanation of the Training Acceleration -- 6 Experiment -- 6.1 Benchmarks -- 6.2 Evaluation Criteria -- 6.3 Experiment Setup -- 6.4 Experiment Result -- 7 Conclusion -- References -- Superpixel Attack -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Definition -- 2.2 Related Work -- 3 Research on Update Areas -- 3.1 Update Areas of Existing Methods -- 3.2 Color Variance of Update Areas -- 3.3 Compactness of Update Areas -- 3.4 Superpixel Calculated by SLIC -- 3.5 Analysis of Color Variance and Compactness -- 4 Superpixel Attack -- 4.1 Update Areas Using Superpixels -- 4.2 Procedure of Versatile Search -- 5 Experiments -- 6 Conclusion -- References -- Cross Domain Pulmonary Nodule Detection Without Source Data -- 1 Introduction -- 2 Method -- 2.1 Feature Extractor Adaptation -- 2.2 Detection Head Adaptation -- 3 Experiments.
3.1 Benchmark and Evaluation -- 3.2 Implementation Details -- 3.3 Results -- 4 Related Works -- 5 Conclusion -- References -- 3RE-Net: Joint Loss-REcovery and Super-REsolution Neural Network for REal-Time Video -- 1 Introduction -- 2 Related Work -- 3 Model Design -- 4 Experiments -- 5 Conclusion -- References -- Neural Networks in Forecasting Financial Volatility -- 1 Introduction -- 2 Related Work -- 3 Experimental Comparison of Forecasting Models -- 3.1 Posing the Problem as a Shared Task -- 3.2 Methods -- 3.3 Result Evaluation and Analysis -- 4 Discussion -- References -- CLIP-Based Composed Image Retrieval with Comprehensive Fusion and Data Augmentation -- 1 Introduction -- 2 Related Work -- 2.1 Composed Image Retrieval -- 2.2 Vision-Language Pre-training -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 CLIP-CD -- 4 Experiments -- 4.1 Datasets and Metrics -- 4.2 Implementation Details -- 4.3 Performance Comparison -- 4.4 Ablation Study -- 4.5 Case Study -- 5 Conclusions -- References -- LiDAR Inpainting of UAV Based 3D Point Cloud Using Supervised Learning -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Problem Definition -- 5 Methodology -- 5.1 Simulator -- 5.2 Extracting Individual Point Clouds -- 5.3 Point Cloud Inpainting Model -- 5.4 Inpainting Complete Environments -- 6 Experimental Results -- 7 Conclusion and Future Work -- References -- A Sampling Method for Performance Predictor Based on Contrastive Learning -- 1 Introduction -- 2 Background -- 2.1 Contrastive Learning -- 2.2 Graph Data Sampling Methods -- 3 Approach -- 3.1 Architecture Augmentation -- 3.2 Architecture Maximal Agreement -- 4 Experiments -- 4.1 Overall Performance -- 4.2 Performance Evaluation in NAS Datasets -- 4.3 Ablation Study -- 5 Conclusion -- References.
AdaptMatch: Adaptive Consistency Regularization for Semi-supervised Learning with Top-k Pseudo-labeling and Contrastive Learning -- 1 Introduction -- 2 Related Work -- 2.1 Consistency Regularization -- 2.2 Contrastive Learning -- 3 Our Approach: AdaptMatch -- 3.1 Data Augmentation -- 3.2 Top-k Label Guessing -- 3.3 Contrastive Learning -- 3.4 Summarization of the Framework -- 4 Experiments -- 4.1 Datasets and Experimental Setup -- 4.2 Main Results -- 5 Ablation Study -- 6 Conclusion -- References -- Estimation of Unmasked Face Images Based on Voice and 3DMM -- 1 Introduction -- 2 Related Research -- 2.1 Studies on Mask Removal -- 2.2 Studies on Estimating Facial Shape from Voice -- 2.3 3D Morphable Model (3DMM) -- 3 Proposed Method -- 3.1 Overview of the Proposal Method -- 3.2 Extraction of Voice Embedding -- 3.3 Combining Voice Embedding and Intermediate Features -- 3.4 Training of Multitasking Module -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Training Details -- 4.3 Qualitative Evaluation -- 4.4 Quantitative Evaluation -- 5 Discussion -- 5.1 On Qualitative Evaluation -- 5.2 On Quantitative Evaluation -- 6 Conclusion and Future Works -- References -- Aging Contrast: A Contrastive Learning Framework for Fish Re-identification Across Seasons and Years -- 1 Introduction -- 2 Related Work -- 2.1 Deep Learning for Fish Recognition -- 2.2 Contrastive Learning -- 3 Dataset -- 4 Proposed Method -- 4.1 Segmentation and Feature Extraction -- 4.2 Aging Contrast Framework -- 5 Experiments -- 6 Conclusion -- References -- Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Methodology -- 4.1 Spatial Bottleneck Transformer -- 4.2 ST-InducedTran Model -- 5 Experiments -- 5.1 Dataset -- 5.2 Baseline -- 5.3 Implementation Details -- 5.4 Evaluation Metrics.
6 Results and Discussion.
Record Nr. UNINA-9910768469403321
Liu Tongliang  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
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DSDE 2022 : proceedings of 2022 5th International Conference on Data Storage and Data Engineering : February 25-27, 2022, Sanya, China / / conference chairs, Geoff Webb, Peiquan Jin
DSDE 2022 : proceedings of 2022 5th International Conference on Data Storage and Data Engineering : February 25-27, 2022, Sanya, China / / conference chairs, Geoff Webb, Peiquan Jin
Pubbl/distr/stampa New York : , : Association for Computing Machinery, , 2022
Descrizione fisica 1 online resource (124 pages) : illustrations
Disciplina 025.04
Collana ACM Other conferences
Soggetto topico Information storage and retrieval systems
Data mining
Neural networks (Computer science)
Computer engineering
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910580192903321
New York : , : Association for Computing Machinery, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
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PRICAI 2006: Trends in Artificial Intelligence [[electronic resource] ] : 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, August 7-11, 2006, Proceedings / / edited by Quiang Yang, Geoff Webb
PRICAI 2006: Trends in Artificial Intelligence [[electronic resource] ] : 9th Pacific Rim International Conference on Artificial Intelligence, Guilin, China, August 7-11, 2006, Proceedings / / edited by Quiang Yang, Geoff Webb
Edizione [1st ed. 2006.]
Pubbl/distr/stampa Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006
Descrizione fisica 1 online resource (XXVIII, 1263 p. Also available online.)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Artificial Intelligence
ISBN 3-540-36668-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Keynote Speech -- Regular Papers -- Computer Vision -- Short Papers Part -- Natural Language Processing and Speech Recognition -- Computer Vision -- Perception and Animation -- Evolutionary Computing -- Industrial Applications -- Short Papers Part -- Automated Reasoning -- Evolutionary Computing -- Game -- Machine Learning and Data Mining -- Industrial Applications -- Information Retrieval -- Natural Language Processing -- Neural Networks -- Computer Vision.
Record Nr. UNISA-996465866403316
Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2006
Materiale a stampa
Lo trovi qui: Univ. di Salerno
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