An Interpretable Approach to Identify Performance Indicators Within Unstructured Learning Environments

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Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

Despite the importance of using artifacts during the design thinking process, there is a limited understanding of the influence of tangibles with lower perceptual richness on design thinking skills within educational settings. It is speculated that tangibl ...

The increasing prevalence of personal devices motivates the design of algorithms that can leverage their computing power, together with the data they generate, in order to build privacy-preserving and effective machine learning models. However, traditional ...

Computational thinking (CT) is considered an emerging competence domain linked to 21st-century competences, and educational robotics (ER) is increasingly recognised as a tool to develop CT competences. This is why researchers recommend developing intervent ...

Whilst, in many countries, the sudden enforced transfer to large-scale distance learning in state schools as a result of COVID_19 lockdown measures caused turmoil, it also provided an unprecedented opportunity to understand more about key factors associate ...

Convolutional neural networks perform a local and translationally-invariant treatment of the data: quantifying which of these two aspects is central to their success remains a challenge. We study this problem within a teacher-student framework for kernel r ...

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system work ...

Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum fü ...

Learning in the brain is poorly understood and learning rules that respect biological constraints, yet yield deep hierarchical representations, are still unknown. Here, we propose a learning rule that takes inspiration from neuroscience and recent advances ...

We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial loss functions under different adversarial budgets. We then demonstrate tha ...

Computational thinking courses are no longer exclusive to engineering and computer science students in higher education but have become a requirement in other fields, as well as for students in secondary, primary, and even early childhood education. Comput ...

Porous molecular crystals are an emerging class of porous materials formed by crystallisation of molecules with weak intermolecular interactions, which distinguishes them from extended nanoporous materials like metal–organic frameworks (MOFs). To aid disco ...

An Interpretable Approach to Identify Performance Indicators Within Unstructured Learning Environments

Chattez avec Graph Search

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

What Role Do Tangibles Play in Fostering Design Thinking Skills? An Exploratory Study

Democratizing Machine Learning

The role of feedback and guidance as intervention methods to foster computational thinking in educational robotics learning activities for primary school

Emergency adaptation to distance learning: no magic bullet, but where there’s a will, there’s a way

Locality defeats the curse of dimensionality in convolutional teacher-student scenarios*

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Automatic Content Curation of Visual Heritage

Local plasticity rules can learn deep representations using self-supervised contrastive predictions

On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

Bringing Computational Thinking to non-STEM Undergraduates through an Integrated Notebook Application

Geometric landscapes for material discovery within energy–structure–function maps