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Gabriella Engdahl in conversation with Alexandra Kolb
Get PDFInterview conducted as part of the BA/Leverhulme-funded project 'On Air: Exploring the historical, scientific and political impacts of inflatables in choreography
Uncovering the latent structure of human time perception
No full textOne of the ongoing controversies in interval timing concerns whether human time perception relies on multiple distinct mechanisms. This debate centres around whether subsecond and suprasecond timing may be attributed to a single semi-uniform timing system or separate and interacting cognitive systems. Whereas past studies offer valuable insights, this study overcomes previous limitations by adopting multiple convergent statistical approaches in a design with strong statistical power. We conducted two online experiments involving participants reproducing temporal intervals ranging from 400 to 2400 ms (Experiment 1; N = 302) and 1000 to 2000 ms (Experiment 2; N = 302). We contrasted the application of exploratory factor analysis and structural equation modelling to differentiate distinct latent structures underlying duration reproduction patterns. Additionally, we compared the model outcomes with results from changepoint analysis models fitted to individual participants' data. In both experiments, these analyses yielded evidence for a two-factor model comprising a general timing factor spanning the full interval range and a second factor capturing the regression to the mean of presented stimulus intervals (central tendency bias). We observed a low proportion of detected changepoints, further supporting the limited evidence for a hypothesized discontinuity between distinct underlying systems, while also finding that changepoint detection patterns were predicted by latent factor scores. These results suggest that the central tendency bias should be considered when investigating potential discontinuities in interval timing systems. Our work contributes to the integration of factor analytic and computational modelling approaches in the study of time perception and has implications for the measurement and interpretation of interval timing in a range of contexts
CID‐RPL: Clone ID Attack Detection Using Deep Neural Network for RPL‐Based IoT Networks
No full textThe proliferation of the Internet of Things (IoT) has reshaped industries based on seamless connectivity. However, it has also brought about immense security challenges, especially in the communication protocol of routing protocol for low‐power and lossy networks (RPL). One of these security threats vital to the RPL‐based IoT networks includes the Clone ID attack on malicious nodes when they clone the identity of legitimate nodes to access their sensitive data without authorization. Detecting Clone ID attacks in RPL‐based IoT networks is complex because network traffic data has high dimensions and substantial data imbalances while facing limited resources in these environments. The unmanaged control message system and insufficient identity authentication methods within the RPL protocol directly expose networks to state‐of‐the‐art cyber security threats. This paper proposes a new edge layer‐based deep neural network (DNN) approach to detect Clone ID attacks from IoT sensor networks by network traffic pattern analysis. The proposed method is based on deep data features to distinguish legitimate nodes from cloned nodes and improve the overall security, resilience, and operational efficiency of RPL‐based IoT networks. To check the efficiency of our proposed method, we designed a synthetic dataset called CID‐RPL. The CID‐RPL dataset consists of 25 attributes and 2,131,328 samples. The experimental results are best to describe that our proposed approach outperformed the previously designed methods by offering an accuracy improvement of 5.06%, precision improvement of 7.60%, recall increment of 7.0%, and F1 score enhancement of 11.0%. Similarly, residual energy at the network level increased by 32.84%, which infers that the lifetime of the network will be extended and its energy efficiency increased under attack situations. Thus, the results testify to the effectiveness of the DL‐based solution proposed herein to detect Clone ID attacks in dynamic and evolving network environments
Adaptive Fisher Behavior Alone Can Induce Tipping Points and Stock Collapse: A Synthesis of Bioeconomic Theory That Applies to All Capture Fisheries Under Open Access
No full textBeliefs About Naturists Scale: A Standardised Measure of Personal Stigma Towards Naturists
No full textThe aim of this study was to fill in the gap in the existing literature when it comes to investigating stigma towards naturism and the individuals who engage in it by creating a psychometric instrument that considers both the experiences of naturists and the beliefs of non-naturists. This quantitative study was cross-sectional in its nature. For the first part of the study, a total of 151 participants participated. After data cleaning, the final sample size was 126. The participants were aged between 18 and 69. The participants were asked to report their gender, sexual orientation, and previous engagement in naturist activities. For the second part of the study, 347 participants completed the survey. The participants in this section were aged between 20 and 76 years old. An explorative principal axis factor analysis was conducted on the 34 items of the developed beliefs about naturists scale (BANS). The analysis indicated that the items loaded onto three factors, with a total of 46.686% of the variance explained, which broadly related to the attitudes, beliefs, and behaviour components of stigma. The first validation indicated that the BANS should be reduced to 29 items. The attitudes factor consisted of 13 items; the beliefs factor consisted of 10 items; and the behaviours factor consisted of six items. A further quantitative study was then carried out to re-evaluate the construct validity of the improved scale and to explore its concurrent validity. The revalidation of the BANS aimed to evaluate the concurrent validity, construct validity, and reliability of the scale. The correlation analysis indicated that the BANS has a high concurrent validity, as it was highly correlated with similar theoretical concepts and empirical predictors of stigma. Although more research is necessary to further evaluate the predictive validity, as well as the validity and reliability across different populations, this preliminary validation suggests a good concurrent and convergent validity, making this the most valid scale in existence to date when it comes to exploring the relationship between stigma and naturism
Editorial: Holistically healthy humans: championing mental and physical wellbeing in education
Get PDFTransforming Smart Healthcare Systems with AI-Driven Edge Computing for Distributed IoMT Networks
No full textThe Internet of Medical Things (IoMT) with edge computing provides opportunities for the rapid growth and development of a smart healthcare system (SHM). It consists of wearable sensors, physical objects, and electronic devices that collect health data, perform local processing, and later forward it to a cloud platform for further analysis. Most existing approaches focus on diagnosing health conditions and reporting them to medical experts for personalized treatment. However, they overlook the need to provide dynamic approaches to address the unpredictable nature of the healthcare system, which relies on public infrastructure that all connected devices can access. Furthermore, the rapid processing of health data on constrained devices often leads to uneven load distribution and affects the system’s responsiveness in critical circumstances. Our research study proposes a model based on AI-driven and edge computing technologies to provide a lightweight and innovative healthcare system. It enhances the learning capabilities of the system and efficiently detects network anomalies in a distributed IoMT network, without incurring additional overhead on a bounded system. The proposed model is verified and tested through simulations using synthetic data, and the obtained results prove its efficacy in terms of energy consumption by 53%, latency by 46%, packet loss rate by 52%, network throughput by 56%, and overhead by 48% than related solutions
