Collaboration and abstract representations: towards predictive models based on raw speech and eye-tracking data

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Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmark ...

Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of prod ...

Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their a ...

Despite their high sensitivity and their suitability for miniaturization, biosensors are still limited for clinical applications due to the lack of reproducibility and specificity of their detection performance. The design and preparation of sensing surfac ...

Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Com ...

When humans interact with each other, eye gaze movements have to support motor control as well as communication. On the one hand, we need to fixate the task goal to retrieve visual information required for safe and precise action-execution. On the other ha ...

The mechanical performance-including deformation, fracture and radiation damage-of zirconium is determined at the atomic scale. With Zr and its alloys extensively used in the nuclear industry, understanding that atomic scale behavior is crucial. The defect ...

Innovations in statistical technology, in functions including credit-screening, have raised concerns about distributional impacts across categories such as race. Theoretically, distributional effects of better statistical technology can come from greater f ...

The hardware complexity of modern machines makes the design of adequate programming models crucial for jointly ensuring performance, portability, and productivity in high-performance computing (HPC). Sequential task-based programming models paired with adv ...

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...

Collaboration and abstract representations: towards predictive models based on raw speech and eye-tracking data

Chattez avec Graph Search

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Frugal Decentralized Learning

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Design and Preparation of Sensing Surfaces for Capacitive Biodetection

Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning

The Gaze Dialogue Model: Nonverbal Communication in HHI and HRI

Machine learning for metallurgy V: A neural-network potential for zirconium

Predictably Unequal? The Effects of Machine Learning on Credit Markets

Decentralized in-order execution of a sequential task-based code for shared-memory architectures

Predicting involuntary hospitalization in psychiatry: A machine learning investigation