260851 research outputs found
Sort by
Erasure and resistance. Everyday practices of Palestinian workers in the settler economy
No full textEMBARGOED – expected end date 04.03.2029</p
A VARK learning style-based recommendation system for adaptive e-learning – pilot study
No full textThis research investigates an adaptive e-learning system designed to offer personalized learning experiences based on each learner’s needs, including learning style, and knowledge gaps. The system utilizes the VARK model and a recommendation engine with fuzzy logic approaches based on rules to customize materials. The system involved administering a VARK assessment to identify each learner’s learning style, monitored by pre/posttests to detect knowledge gaps. Findings from a pilot study revealed that this system significantly improved learner enhancement, engagement, satisfaction and motivation, in contrast with the standard e-learning system. These outcomes emphasize the importance of recognizing individual learning styles and addressing targeted knowledge shortages. By delivering tailored learning paths that suit each student’s unique learning, the proposed system overcomes certain limitations in existing studies, offering a scalable option for better educational results. This approach shows potential for improving e-learning by reducing gaps in personalization and efficiency and finding learning preferences for students.</p
Physically plausible data augmentations for wearable IMU-based human activity recognition using physics simulation
No full textThe scarcity of high-quality labeled data in sensor-based Human Activity Recognition (HAR) hinders model performance and limits generalization across real-world scenarios. Data augmentation is a key strategy to mitigate this issue by enhancing the diversity of training datasets. Signal Transformation-based Data Augmentation (STDA) techniques have been widely used in HAR. However, these methods are often physically implausible and can produce data misaligned with activity labels. In this study, we propose Physically Plausible Data Augmentation (PPDA) enabled by physics simulation. PPDA leverages human body movement data from motion capture or video-based pose estimation and incorporates realistic variability through physics simulation, including changes in body movements, sensor placements, and hardware-related effects. We compare the performance of PPDA methods with traditional STDA methods on three public datasets of activities of daily living and fitness workouts: REALDISP (34 classes), REALWORLD (8 classes), and MM-Fit (11 classes). First, we compare each PPDA method with its closest STDA counterpart, showing that PPDA improves macro F1 scores by an average of 3.7 percentage points (pp), with gains up to 13 pp. Second, we assess to what extent combining PPDAs can reduce the need for initial data collection, and in comparison with combined STDAs, we observe that PPDA achieves competitive performance with up to 60% fewer training subjects. As the first systematic study of PPDA in HAR, these results highlight the advantages of pursuing physical plausibility in data augmentation and the potential of physics simulation for generating synthetic Inertial Measurement Unit (IMU) data for training deep learning HAR models. This cost-effective and scalable approach therefore helps address the annotation scarcity challenge in HAR.</p
Computational modelling reveals slower safety learning and threat extinction are associated with higher anxiety severity in remote fear conditioning
No full textAnxiety disorders are chronic, pervasive, and debilitating; characterised by a persistent or exaggerated response to distal or abstract threats. Impaired threat discrimination (distinguishing safe from threatening stimuli) and impaired threat extinction (learning a once threatening stimulus is now safe), are known risk factors in the development and persistence of anxiety disorders. These effects can be experimentally elicited through fear conditioning. First, repeated trials of paired aversive and neutral stimuli are delivered during a fear acquisition phase, followed by repeated trials with no aversive stimuli in a fear extinction phase. The effects are typically measured through comparison of end-phase data points, or simple descriptive or statistical models. Computational modelling, by contrast, can offer a hypothesis-driven, trial-by-trial mechanistic account of fear conditioning. This unmasks within subject task variance by estimating the rate of threat learning, safety learning, and threat extinction, examining individual differences in the cognitive mechanisms behind anxiety. A normative sample (n = 145) underwent a differential fear conditioning task on a bespoke smartphone app, in addition to completing an anxiety severity measure (GAD-7). Computational models fitted to task data estimated learning rates. Whilst the threat learning rate showed no association, the threat extinction and safety learning rates showed small negative associations with anxiety severity (⍴ = -0.22, p = 0.01 & ⍴ = -0.21, p = 0.01 respectively). These findings are in keeping with prior studies using traditional analytical approaches, and indicate that anxious individuals are not quicker to develop fear of a stimulus, but take more time than their non-anxious counterparts to learn that a stimulus is safe. This study strengthens the evidence for impairments in fear extinction in those with anxiety, and the importance of learning rates as an index of anxiety severity, a previously hidden cognitive mechanism underlying anxiety persistence.</p
Childhood attention deficit hyperactivity disorder (ADHD) traits, societal exclusion, and midlife psychological distress
No full textNo description supplied</p
Understanding caregiver stress to inform community-based stress monitoring and supportive interventions: a qualitative, descriptive study
No full textTo inform community-based stress monitoring tools and supportive interventions, this study aimed to understand caregiver stress as experienced by a diverse group of informal caregivers guided by the Pearlin’s stress process model. We used a qualitative descriptive design conducting semistructured interviews with informal caregivers (≥ 18 years) currently or previously caring for an adult with health issues at home. Data were analysed using the framework approach. We recruited 27 caregivers (19 current and 8 former) from various geographic locations within the United Kingdom. In terms of background and context, poor caregiver health increased stress whereas prior employment in health or social care, and access to trusted supports and resources reduced stress. Common primary stressors included rapidly changing or palliative care care-recipient needs, loneliness and loss (i.e., loss of their normal life, or of the life and future plans they had expected). Family conflict, occupational/economic strains and social/recreational life constraints were important secondary stressors. Guilt contributed to intrapsychic strain resulting in low self-esteem and feelings of role captivity. Few participants discussed positive elements of caregiving such as mastery or gain. Stress mediators included coping strategies such as taking control, humour, taking brief respite, social activities, access to peer and other forms of social support, and trusted support for caring. Common outcomes of stress included exhaustion, physical injuries, weight loss, difficulty sleeping, depression and anxiety. Despite growing recognition of issues facing informal caregivers and policies or services put in place to support them, our data indicate key stressors remain. Future supportive initiatives should reflect dynamic and individual caregiver needs, thereby enabling caregivers to prioritise their mental and physical wellbeing and receive brief respite from caregiving responsibilities. Stress monitoring tools and accompanying supportive interventions, if codesigned with caregivers with lived experience, offer the potential to identify high-stress periods, enable timely interventions and guide more efficient resource allocation.</p
Safe fairness guarantees without demographics in classification: spectral uncertainty set perspective
No full textAs automated classification systems become increasingly prevalent, concerns have emerged over their potential to reinforce and amplify existing societal biases. In the light of this issue, many methods have been proposed to enhance the fairness guarantees of classifiers. Most of the existing interventions assume access to group information for all instances, a requirement rarely met in practice. Fairness without access to demographic information has often been approached through robust optimization techniques, which target worst-case outcomes over a set of plausible distributions known as the uncertainty set. However, their effectiveness is strongly influenced by the chosen uncertainty set. In fact, existing approaches often overemphasize outliers or overly pessimistic scenarios, compromising both overall performance and fairness. To overcome these limitations, we introduce SPECTRE, a minimax-fair method that adjusts the spectrum of a simple Fourier feature mapping and constrains the extent to which the worst-case distribution can deviate from the empirical distribution. We perform extensive experiments on the American Community Survey datasets involving 20 states. The safeness of SPECTRE comes as it provides the highest average values on fairness guarantees together with the smallest interquartile range in comparison to state-of-the-art approaches, even compared to those with access to demographic group information. In addition, we provide a theoretical analysis that derives computable bounds on the worst-case error for both individual groups and the overall population, as well as characterizes the worst-case distributions responsible for these extremal performances.</p
Epigames: a novel approach to generate real-life contact networks and behavioural data through experimental epidemic games in naturalistic settings
No full textInfectious diseases that spread from person to person by direct transmission, respiratory pathogens such as influenza and coronaviruses among them, impose a large global health burden and remain the most likely causative agents for future devastating pandemics1. For many such diseases, transmission occurs when individuals are in close proximity for a sufficient time and through highly structured social contact networks2,3. Data on the properties of these networks, including their temporal and spatial structure, how pathogens spread on them, and how interventions may alter this spread are scarce4 or inconsistent5, and seldom incorporate behavioural features. This produces a knowledge gap between policy-relevant models of pathogen transmission and the data they require: contact networks in high spatial and temporal resolution and their variability and malleability under different conditions.</p
