Annotation Practices in Societally Impactful Machine Learning Applications: What are Popular Recommender Systems Models Actually Trained On?

Operationalizing modular autonomous customised buses based on different demand prediction scenarios

Machine learning-based identification of vulnerability factors for masonry buildings in aggregate: The historicalcentre of casentino hit by the 2009 l'aquila earthquake

Machine learning based parameter sensitivity of regional climate models: a case study of the WRF model for heat extremes over Southeast Australia

Advecting Superspecies: Efficiently Modeling Transport of Organic Aerosol With a Mass-Conserving Dimensionality Reduction Method

Self-Supervised Learning for Enhancing Angular Resolution in Automotive MIMO Radars

Recent advancements of landslide hydrology

Explainable Artificial Intelligence to Investigate the Contribution of Design Variables to the Static Characteristics of Bistable Composite Laminates

Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA

Simulated Design-Build-Test-Learn Cycles for Consistent Comparison of Machine Learning Methods in Metabolic Engineering

Value-Based Hybrid Intelligence

Radar Sensing in Healthcare: Challenges and Achievements in Human Activity Classification & Vital Signs Monitoring

Machine learning and digital twins: monitoring and control for dynamic security in power systems

Paving the way for the integration of synthesis, assessment, and design tools within an ontological framework

Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue

NLICE: Synthetic Medical Record Generation for Effective Primary Healthcare Differential Diagnosis

Attribute Inference Attacks in Online Multiplayer Video Games: A Case Study on DOTA2

“How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware

The Human Activity Radar Challenge: Benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

High-speed predictions of post-burn contraction using a neural network trained on 2D-finite element simulations