Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

Name: Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track | European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part IX
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European Conference, ECML PKDD 2024, Vilnius, Lithuania, September 9-13, 2024, Proceedings, Part IX

Albert Bifet Tomas Krilavicius Ioanna Miliou Slawomir Nowaczyk(Editor)

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Published on 1. September 2024

LVII, 470 pages

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This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024.

The papers presented in these proceedings are from the following three conference tracks: -

Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII.

Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII.

Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X.

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Content

Intro
Preface
Organization
Invited Talks Abstracts
The Dynamics of Memorization and Unlearning
The Emerging Science of Benchmarks
Enhancing User Experience with AI-Powered Search and Recommendations at Spotify
How to Utilize (and Generate) Player Tracking Data in Sport
Resource-Aware Machine Learning-A User-Oriented Approach
Contents - Part IX
Applied Data Science Track
VulEXplaineR: XAI for Vulnerability Detection on Assembly Code
1 Introduction
2 Related Work
2.1 Vulnerability Detection
2.2 XAI for Vulnerability Detection
3 Methodology
3.1 Method of Communication for Vulnerability Detection
3.2 Graph Explanation
3.3 VulEXplaineR
4 Experimental Results
4.1 Dataset
4.2 Experimental Setup
4.3 Results
4.4 Graph Explanation
5 Conclusion
References
Guiding Catalogue Enrichment with User Queries
1 Introduction
2 Background and Related Work
3 Query-Guided Triplet Prediction
3.1 Prediction from KGE Using Rejection Sampling (RS)
3.2 Guided Prediction with Queries (QG)
4 Evaluation and Results
4.1 Experimental Setup
4.2 Automatic Evaluation
4.3 Human Evaluation
4.4 Comparison with Other Types of Guidance
5 Conclusions and Limitations
References
PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning
1 Introduction
2 Background and Related Work
3 Enhancing Task Offloading with MARL
4 PeersimGym
4.1 System Modeling
4.2 Implementation Details
4.3 Reducing the Reality Gap
5 Experimental Results and Analysis
5.1 Experimental Setup
5.2 Results Analysis
6 Conclusion and Future Work
References
Robust Interaction-Based Relevance Modeling for Online e-Commerce Search
1 Introduction
2 Related Work
3 Methodology
3.1 Dynamic-Length Representation Scheme
3.2 Professional Terms Recognition Strategy
3.3 Contrastive Adversarial Training Mechanism
4 Experiments
4.1 Offline Evaluation
4.2 Online Experiments
5 Conclusion
References
Learning Optimal Linear Precoding for Cell-Free Massive MIMO with GNN
1 Introduction
2 Related Work
3 System Model and Notation
3.1 Notation
3.2 Cell-Free Massive MIMO
3.3 Precoding Matrix and Downlink SINR Calculation
3.4 Optimal Linear Precoding
3.5 Zero Forcing and Maximum Ratio Precoding
4 Graph Neural Network
4.1 Graph Representation
4.2 Data Preprocessing and Postprocessing
4.3 Structure of the Neural Network
4.4 Training and Loss Function
5 Numerical Results
5.1 Simulation Parameters and Performance Metrics
5.2 Spectral Efficiency
5.3 Computational Complexity and Runtime
6 Conclusions
References
Multiple Hypergraph Learning for Ephemeral Group Recommendation
1 Introduction
2 Related Work
3 Methodology
3.1 Model Overview
3.2 Hypergraph Construction
3.3 Hypergraph Convolution
3.4 Cross-Hypergraph Contrastive Learning
3.5 Model Optimization
3.6 Complexity Analysis
4 Experiments
4.1 Experimental Settings
4.2 Performance Comparison
4.3 Ablation Study
4.4 Hyperparameter Sensitivity Analysis
5 Conclusion
References
Spoofing Transaction Detection with Group Perceptual Enhanced Graph Neural Network
1 Introduction
2 Related Works
2.1 Spoofing Detection
2.2 Graph Learning in Finance
3 The Proposed Method
3.1 Model Architecture Overview
3.2 Transaction Graph Construction
3.3 Node and Edge Representation Learning
3.4 Group-Enhanced Representation Layer
3.5 Prediction Network Layer
4 Experiments
4.1 Datasets
4.2 Baseline Models
4.3 Experimental Parameter Settings
4.4 Spoofing Detection Experiment
4.5 Parameter Sensitivity Experiment
4.6 Ablation Study
4.7 Case Study in Industry-Level Application Scenarios
5 Conclusion
References
Self-SLAM: A Self-supervised Learning Based Annotation Method to Reduce Labeling Overhead
1 Introduction
2 Dataset Description
2.1 Wheelchair
2.2 CASE
3 SSLAM: Self-supervised Label Generation Framework
3.1 Log-Cosh Loss in SSLAM Framework
4 Evaluation
4.1 Experimental Configuration
4.2 Results: CASE Dataset
4.3 Results on Wheelchair Dataset
5 Discussion
6 Relevant Literature
7 Conclusion and Future Works
References
Multi-intent Driven Contrastive Sequential Recommendation
1 Introduction
2 Related Work
2.1 Sequential Recommendation
2.2 Contrastive Learning
3 Problem Formulation
4 Our Model
4.1 Overview
4.2 Sequence Encoding
4.3 Intent Prototype Identifying and Updating
4.4 Multi-intent Guided Contrastive Learning
4.5 Overall Loss
4.6 Discussion
5 Experiments
5.1 Experimental Setting
5.2 Performance Comparison
5.3 Ablation Study
5.4 Robustness Analysis
5.5 Impact of Flase Negative Sample Elimination
5.6 Hyper-parameter Sensitivity
6 Conclusion
References
KAT5: Knowledge-Aware Transfer Learning with a Text-to-Text Transfer Transformer
1 Introduction
2 Related Work
3 Model
3.1 Baseline
3.2 KAT5: Knowledge-Aware Text-to-Text Transfer Transformer
3.3 Pre-training Data Creation
3.4 KAT5 Fine-Tuning
4 Experimental Settings
4.1 Datasets
4.2 Training
5 Results
5.1 Performance on Joint Entity-Relation Extraction
5.2 Performance on Summarization
5.3 Performance on Machine Translation
6 Discussion
7 Conclusion
References
Asymmetric Graph-Based Deep Reinforcement Learning for Portfolio Optimization
1 Introduction
2 Related Work
3 The Proposed Method
3.1 Problem Formulation
3.2 Framework Overview
3.3 Construction of Asymmetric Graphs
3.4 Multi-dimensional Relation Representation
3.5 Generator for Portfolio Optimization
4 Experiments
4.1 Experimental Setup
4.2 Overall Results
4.3 Ablation Study
4.4 Interpretability Analysis
4.5 Hyperparameter Sensitivity
5 System Observation
6 Conclusion
References
Code Summarization with Project-Specific Features
1 Introduction
2 Related Work
3 The CSWPS Method
3.1 Stage 1: Learning Latent Summary Representations
3.2 Stage 2: Sampling Latent Summary Representations for Code Summarization
4 Experiment Settings
4.1 Datasets
4.2 Hyperparameters in Our Model
4.3 Evaluation Metrics
4.4 The Comparative Models
5 Experimental Results
5.1 RQ1: Our Main Model Vs. SOTA Models
5.2 RQ2: Can CSWPS Improve Existing Models?
5.3 RQ3: The Effect of the Modules Pertaining to Project-Specific Features
5.4 RQ4: Are the Generated Summaries More Project-Specific?
6 Discussion and Conclusion
References
Unraveling Anomalies in Time: Unsupervised Discovery and Isolation of Anomalous Behavior in Bio-Regenerative Life Support System Telemetry
1 Introduction
2 Related Work
3 Methodology
3.1 Anomaly Detection
3.2 Feature Extraction
3.3 Time Series Clustering
3.4 Quality Measures
4 Experimental Results
4.1 Dataset
4.2 RQ1: Are the Results of MDI and DAMP Complementary?
4.3 RQ2: Which Features Yield the Best Results?
4.4 RQ3: Which Algorithm Yields Better Results?
4.5 RQ4: What Anomaly Types can be Isolated?
4.6 RQ5: Can We Identify Recurring Abnormal Behavior?
5 Conclusions and Outlook
References
Long-Term Fairness in Ride-Hailing Platform
1 Introduction
2 Related Work
3 Preliminaries
3.1 Problem Formulation
3.2 Efficiency
3.3 Long-Term Fairness
3.4 Balance of Long-Term Fairness and Total Utility
4 Approach: Optimising Efficiency and Long-Term Fairness for Ride-Hailing
4.1 Overview
4.2 Time-Series Forecasting
4.3 Multi-objective Multi-agent Q Learning
4.4 Scalarisation Function
5 Experiments
5.1 Datasets
5.2 Experimental Details
5.3 Results and Analysis
5.4 Ablation Study
6 Conclusion
References
A Merge Sort Based Ranking System for the Evaluation of Large Language Models
1 Introduction
2 Related Work
3 Methodologies
3.1 Evaluation Platform
3.2 Transitive Merge Sort
4 Experiments
4.1 Experiment Settings
4.2 Experiment Results
5 Conclusion
References
Enhancing HVAC Control Efficiency: A Hybrid Approach Using Imitation and Reinforcement Learning
1 Introduction
2 Previous Work
3 Methodology
3.1 Behavioural Cloning from Demonstrations
3.2 Reinforcement Learning Fine-Tuning
4 Experimental Setup
4.1 Environment
4.2 Rewards
4.3 Training Setup
5 Results
5.1 Performance Analysis
5.2 Policy Analysis
6 Conclusion and Future Work
References
Synthesis of Standard 12-Lead ECG from Single-Lead ECG Using Shifted Diffusion Models
1 Introduction
2 Related Work
2.1 Synthesize ECG Using Diffusion Models
2.2 Synthesis of 12-Lead ECG from Reduced Leads
3 Preliminary
3.1 Synthesis of 12-Lead ECG from Single-Lead ECG
3.2 Diffusion Models
4 Time-Frequency Shifted Diffusion for 12-Lead ECG Synthesis
4.1 Motivation
4.2 Forward Process and Reverse Process
4.3 Training and Sampling
5 SD-ECG Architecture
5.1 Main Network
5.2 Shift Network
6 Experiments
6.1 Experimental Setup
6.2 Experimental Results
6.3 Model Analysis
7 Conclusion
References
SAGS-DynamicBio: Integrating Semantic-Aware and Graph Structure-Aware Embedding for Dynamic Biological Data with Knowledge Graphs
1 Introduction
2 Related Work
2.1 Knowledge Graph Embedding
2.2 Knowledge Graph Embedding for DTIs
3 Methodology
3.1 Knowledge Graph Embedding
3.2 Semantic-Aware Learning Based on Attention Mechanism
3.3 Graph Structure-Aware Learning
3.4 Training for Joint Semantic-Aware and Graph Structure-Aware
4 Experiments
4.1 Experiment Setup
4.2 Drug-Target Interactions Task
4.3 Link Prediction Task
4.4 Ablation Studies
5 Conclusion
References
Graph Machine Learning for Fast Product Development from Formulation Trials
1 Introduction
2 Background
3 Dataset and Case-Study
4 Methodology
4.1 DGM-Based Model
4.2 Explainer Model
4.3 Explore and Simulate Product Development
5 Experiments
6 Conclusion
References
HPExplorer: XAI Method to Explore the Relationship Between Hyperparameters and Model Performance
1 Introduction
2 Related Work
2.1 HP Optimization in ML Research
2.2 XAI Research
2.3 Visual Analytics Research
3 Analytical Needs and Requirements for HPExplorer
3.1 Analytical Needs of Users
3.2 Requirements for HPExplorer
4 HPExplorer
4.1 HP Discovery Algorithm
4.2 Interactive Visual Exploration Component
5 Real-World Applications of HPExplorer
5.1 Use Case 1: Mortality Rate Prediction
5.2 Use Case 2: Soil Organic Carbon Prediction
5.3 Use Case 3: Flood Detection
6 Discussion
7 Conclusion
References
Boosting Patient Representation Learning via Graph Contrastive Learning
1 Introduction
2 Related Work
3 Method
3.1 Basic Notations and Problem Definitions
3.2 Architecture Overview
4 Experiments
4.1 Datasets, Tasks, Evaluation Metrics
4.2 Comparison Approaches
5 Results and Discussion
6 Conclusions and Future Works
References
Time Series Clustering for Enhanced Dynamic Allocation in A/B Testing
1 Introduction
2 State of the Art
2.1 Classical MAB Problem
2.2 Contextual Multi Armed Bandit
2.3 Large-Scale Context
3 Contribution
3.1 Illustrative Dataset
3.2 Two New Algorithms DBA-Ctree-Ucb and DBA-LinUCB for Handling Times Series for A/B-Tests
4 Experiments
5 Discussion of Results and Conclusion
References
Reinforcement Learning Meets Microeconomics: Learning to Designate Price-Dependent Supply and Demand for Automated Trading
1 Introduction
2 Related Work
3 Problem Definition
3.1 Day-Ahead Electricity Market
3.2 Reinforcement Learning to Bid
4 Method
4.1 Analysis
4.2 Price-Dependent Supply and Demand in Bids
4.3 Parametric Representation of a Collection of Bids
4.4 Bidding Policy
4.5 Bidding Policy Optimization with Reinforcement Learning
4.6 Alternative Bidding Strategies
5 Simulations
5.1 Simulation Environment
5.2 Experiments
5.3 Different Operation Scenarios
5.4 Results
6 Conclusions
References
Spatial Transfer Learning for Estimating PM2.5 in Data-Poor Regions
1 Introduction
2 Related Work
3 Problem Formulation
4 Methodology
4.1 Neighborhood Cloud Generation
4.2 Generating Latent Dependency Factor (LDF)
4.3 Transfer Learning and Multivariate Regression
5 Evaluation
5.1 Datasets
5.2 Prediction Models
5.3 Optimal k for Neighborhood Cloud
5.4 Results and Analysis
5.5 Qualitative Analysis
5.6 Ablation Study
6 Discussion
6.1 Limitations and Future Work
7 Conclusion
References
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality
1 Introduction
2 Related Work
3 Methodology
3.1 Problem Formulation
3.2 Local Data Preprocessing
3.3 Local Training
3.4 Server Aggregation
4 Experiments
4.1 Experiment Setting
4.2 Empirical Results
5 Conclusion
References
Contrastive Learning Enhanced Diffusion Model for Improving Tropical Cyclone Intensity Estimation with Test-Time Adaptation
1 Introduction
2 Related Work
3 Methodology
3.1 Preliminaries
3.2 Conditional Diffusion Model for a Regression Task
3.3 Contrastive Learning Enhanced Diffusion Model
4 Experiments
4.1 Dataset
4.2 Models and Metrics
4.3 Implementation Details
4.4 Overall Performance
4.5 Diffusion Loss Analysis
4.6 Parameter Study
4.7 Case Study
5 Conclusion
References
BESTMVQA: A Benchmark Evaluation System for Medical Visual Question Answering
1 Introduction
2 Research Scope and Task Description
3 Related Work
4 System Overview
5 Empirical Study
5.1 Considered Models
5.2 Experimental Setup
5.3 Evaluation Metrics
5.4 Results
5.5 Detailed Analysis
5.6 Qualitative Analysis
6 Conclusion
References
AeroINR: Meta-learning for Efficient Generation of Aerodynamic Geometries
1 Introduction
2 Existing Approaches and Related Work
2.1 Traditional Parameterisations
2.2 Deep Learning-Based Approaches
2.3 Grid-Based Geometry Representation
2.4 Approaches for Learning Priors over Geometry Space
3 Framework of AeroINR
3.1 Implementation
4 Experiments
4.1 Dataset
4.2 Benchmarks
4.3 Metrics
4.4 Experimental Setting
4.5 Results
5 Conclusions and Future Work
References
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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track

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