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

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Image steganalysis, detecting hidden data in digital images, is essential for enhancing digital security. Traditional steganalysis methods typically rely on large, pre-labeled image datasets, which are difficult and costly to compile. To address this, this ...

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 ...

Scientific Machine Learning (SciML) is a recently emerged research field which combines physics-based and data-driven models for the numerical approximation of differential problems. Physics-based models rely on the physical understanding of the problem at ...

A data-driven inverse design method based on neural networks is proposed for turbomachinery. In the devised methodology, design parameters are provided as input to the neural network, and performance attributes, e.g. efficiency, as output. Once trained, th ...

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 ...

Introduction: Brain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensi ...

Mapping the neural patterns that drive human behavior is a key challenge in neuroscience. Even the simplest of our everyday actions stem from the dynamic and complex interplay of multiple neural structures across the central nervous system (CNS). Yet, most ...

Algorithms deployed in education can shape the learning experience and success of a student. It is therefore important to understand whether and how such algorithms might create inequalities or amplify existing biases. In this paper, we analyze the fairnes ...

Interactive simulations allow students to discover the underlying principles of a scientific phenomenon through their own exploration. Unfortunately, students often struggle to learn effectively in these environments. Classifying students’ interaction data ...

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment. We propose H-MARL (Hallucinated Multi-Agent Reinforcement Learning), a no ...

Despite the importance of learning analytics in digital education, there is limited support for researchers in education to generate, access, and share experimental data while complying with ethical and privacy legislation. We propose a set of related tool ...

In a typical fusion experiment, the plasma can have several possible confinement modes. At the tokamak a configuration variable, aside from the low (L) and high (H) confinement modes, an additional mode, dithering (D), is frequently observed. Developing me ...

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs. However, research on relational representation learning has so far mostly focused on th ...

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to shar ...

Learning socially-aware motion representations is at the core of recent advances in multi-agent problems, such as human motion forecasting and robot navigation in crowds. Despite promising progress, existing representations learned with neural networks sti ...

Despite extensive research efforts in the field of laser welding, the imperfect repeatability of the weld quality still represents an open topic. Indeed, the inherent complexity of the underlying physical phenomena prevents the implementation of an effecti ...

Machine learning techniques have been used extensively in several domains of Science and Engineering for decades. These powerful tools have been applied also to the domain of high-energy physics, in the analysis of the data from particle collisions, for ye ...

Deep neural networks have been empirically successful in a variety of tasks, however their theoretical understanding is still poor. In particular, modern deep neural networks have many more parameters than training data. Thus, in principle they should over ...

The identification of accident hot spots is a central task of road safety management. Bayesian count data models have emerged as the workhorse method for producing probabilistic rankings of hazardous sites in road networks. Typically, these methods assume ...

Social practices are well-known mediators in the adoption of educational innovations during professional learning, as postulated by the Knowledge Appropriation Model (KAM). However, understanding how teachers adopt new pedagogical approaches at scale is of ...

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

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Image steganalysis using active learning and hyperparameter optimization

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

Combining physics-based and data-driven models: advancing the frontiers of research with scientific machine learning

DATA-DRIVEN INVERSE DESIGN METHOD FOR TURBOMACHINERY

Distributed Adaptive Learning Under Communication Constraints

Hyperdimensional computing with holographic and adaptive encoder

Decoding cerebro-spinal signatures of human behavior: Application to motor sequence learning

Protected Attributes Tell Us Who, Behavior Tells Us How: A Comparison of Demographic and Behavioral Oversampling for Fair Student Success Modeling

Generalisable Methods for Early Prediction in Interactive Simulations for Education

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

An End-to-End Data Pipeline for Managing Learning Analytics

Plasma confinement mode classification using a sequence-to-sequence neural network with attention

Representation Learning for Multi-relational Data

Graph-Homomorphic Perturbations For Private Decentralized Learning

Social NCE: Contrastive Learning of Socially-aware Motion Representations

Adaptive Laser Welding Control: A Reinforcement Learning Approach

Application of machine learning techniques at the CERN Large Hadron Collider

On Vacuous and Non-Vacuous Generalization Bounds for Deep Neural Networks

A new spatial count data model with Bayesian additive regression trees for accident hot spot identification

Combining the Knowledge Appropriation Model and epistemic networks to understand co-creation and adoption of learning designs using log data