Image steganalysis using active learning and hyperparameter optimization

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This study integrates deep learning and geospatial analysis to enhance soil loss estimation in the Moulouya Watershed, a region prone to erosion due to diverse topography and climatic conditions. Traditional models like the Universal Soil Loss Equation (US ...

Monitoring of cardiac output (CO) is a mainstay of hemodynamic management in the acutely or critically ill patient. Invasive determination of CO using thermodilution, albeit regarded as the gold standard, is cumbersome and bears risks inherent to catheteri ...

Faithful human performance capture and free-view rendering from sparse RGB observations is a long-standing problem in Vision and Graphics. The main challenges are the lack of observations and the inherent ambiguities of the setting, e.g. occlusions and dep ...

Machine learning is increasingly integrated with decision making by powering automated systems and informing human decision makers in applications such as CV screening, portfolio allocation, loan risk assessment, and insurance approval. In this thesis, I e ...

This work aims to develop a scalable surrogate modelling framework for robust multidisciplinary design optimisation (RMDO) in complex engineering systems. Focusing on gas-bearing supported rotors, it addresses the need to maintain system performance under ...

Non-Orthogonal Multiple Access (NOMA) is a Resource Sharing Mechanism (RSM) initially studied for 5G cellular networks and brought back to the agenda for 6G networks. While NOMA’s benefit at the level of a single cell has been properly established, assessi ...

Interactive simulations enhance science education and foster inquiry skills, but their open-ended nature can be cognitively overloading. While adaptive systems offer timely support, research on predicting conceptual understanding in these environments is l ...

We consider a network of agents that must solve an online optimization problem from continual observation of streaming data. To this end, the agents implement a distributed cooperative strategy where each agent is allowed to perform local exchange of infor ...

Small scale ultrasonic piezoelectric actuators performance strongly depends on not well-known contact dynamics. Deep Neural Network (DNN) sees their use in physic simulation growing as their flexibility allows better performance especially when dynamics la ...

By offering a large number of highly diverse resources, learning platforms have been attracting lots of participants, and the interactions with these systems have generated a vast amount of learning-related data. Their collection, processing and analysis h ...

Noise in the data is one of the main cause of model performance drop. Denoising is therefore a critical step in most data pipelines. In this paper we propose to fuse the learning abilities of Neural Network with the well-proven efficiency of the wavelet pa ...

The contribution of school curricula to public understanding and acceptance of evolution is still mostly unknown, due to the scarcity of studies that compare the learning goals present in different curricula. To overcome this lack of data we analysed 19 sc ...

Deep learning (DL) has recently been proposed as a novel approach for 21cm foreground removal. Before applying DL to real observations, it is essential to assess its consistency with established methods, its performance across various simulation models, an ...

The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with their properties, and what can be done to improve the training efficiency w ...

A review is given on the highlights of a scatter-shot approach of developing machine-learning methods and artificial neural networks based fast predictors for the application to fusion exhaust. The aim is to enable and facilitate optimized and improved mod ...

Writing high-quality procedural texts is a challenging task for many learners. While example-based learning has shown promise as a feedback approach, a limitation arises when all learners receive the same content without considering their individual input ...

Image steganalysis using active learning and hyperparameter optimization

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Application of Deep Learning and Geospatial Analysis in Soil Loss Risk in the Moulouya Watershed, Morocco

Machine learning-enabled estimation of cardiac output from peripheral waveforms is independent of blood pressure measurement location in an in silico population

MetaCap: Meta-learning Priors from Multi-view Imagery for Sparse-View Human Performance Capture and Rendering

Machine Learning in Decision-Making Systems: Fairness, Robustness, and Data Bias

Dimensionless Group-Driven Ensemble Neural Networks for Robust Design Optimisation in Engineering

Assessing the Performance of NOMA in a Multi-Cell Context: A General Evaluation Framework

One Code to Predict Them All: Universal Encoding for Inquiry Modeling

Distributed Adaptive Learning Under Communication Constraints

Normal Contact Force Estimation Using Deep Learning

1st International Workshop on User-Centered Practices of Knowledge Discovery in Educational Data

Robust time series denoising with learnable wavelet packet transform

Evolution in European and Israeli school curricula - a comparative analysis

The stability of deep learning for 21cm foreground removal across various sky models and frequency-dependent systematics

Prediction rigidities for data-driven chemistry

Data-driven models in fusion exhaust: AI methods and perspectives

Enhancing Procedural Writing Through Personalized Example Retrieval: A Case Study on Cooking Recipes