Brain Signatures of Time Perception in Virtual Reality.

Achieving a high level of immersion and adaptation in virtual reality (VR) requires precise measurement and representation of user state. While extrinsic physical characteristics such as locomotion and pose can be accurately tracked in real-time, reliably capturing mental states is more challenging. Quantitative psychology allows considering more intrinsic features like emotion, attention, or cognitive load.

Brainsourcing for temporal visual attention estimation.

The concept of visual attention in dynamic contents, such as videos, has been much less studied than its counterpart, i.e., visual salience.

On-device query intent prediction with lightweight LLMs to support ubiquitous conversations.

Conversational Agents (CAs) have made their way to providing interactive assistance to users. However, the current dialogue modelling techniques for CAs are predominantly based on hard-coded rules and rigid interaction flows, which negatively affects their flexibility and scalability. Large Language Models (LLMs) can be used as an alternative, but unfortunately they do not always provide good levels of privacy protection for end-users since most of them are running on cloud services.

Sneaky emotions: impact of data partitions in affective computing experiments with brain-computer interfacing.

Brain-Computer Interfacing (BCI) has shown promise in Machine Learning (ML) for emotion recognition. Unfortunately, how data are partitioned in training/test splits is often overlooked, which makes it difficult to attribute research findings to actual modeling improvements or to partitioning issues. We introduce the "data transfer rate" construct (i.

No Interface, No Problem: Gesture Recognition on Physical Objects Using Radar Sensing.

Physical objects are usually not designed with interaction capabilities to control digital content. Nevertheless, they provide an untapped source for interactions since every object could be used to control our digital lives. We call this problem: Instead of embedding computational capacity into objects, we can simply detect users' gestures on them.

Glimpse: A Gaze-Based Measure of Temporal Salience.

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data.

Determinants of masticatory performance assessed by mixing ability tests.

Statement Of Problem: Studies determining the main predictors of masticatory performance by using mixing ability tests are sparse.

Purpose: The purpose of this clinical study was to identify potential determinants of masticatory performance assessed by analyzing a patient's masticatory ability using bicolored chewing gum and visual, quantitative, and interactive methods.

Material And Methods: Nondental participants attending healthcare centers were consecutively recruited in Granada, Spain.

Chewing Performance Calculator: An interactive clinical method for quantifying masticatory performance.

Statement Of Problem: There is a need to quantitatively differentiate between impaired and normal mastication by using straightforward and reliable methods because currently available methods are expensive, complex, and time-consuming.

Purpose: The purpose of this clinical study was to assess the reliability, validity, and clinical utility of a new Web-based software program designed to calculate masticatory performance, the Chewing Performance Calculator (CPC) measuring masticatory performance (MP), by analyzing the area of mixed bicolored chewing gum.

Material And Methods: One hundred and ten participants were consecutively recruited from the School of Dentistry of the University of Salamanca.