All Outputs (2058)

The value of information for dynamic decentralised criticality computation (2022)
Conference Proceeding
Proselkov, Y., Herrera, M., Hernandez, M. P., Kumar Parlikad, A. K., & Brintrup, A. (2022). The value of information for dynamic decentralised criticality computation. In IFAC-PapersOnLine (408-413). https://doi.org/10.1016/j.ifacol.2022.04.228

Smart manufacturing uses advanced data-driven solutions to improve performance and operations resilience requiring large amounts of data delivered quickly, enabled by telecom networks and network elements such as routers or switches. Disruptions can... Read More about The value of information for dynamic decentralised criticality computation.

Deep synthesis of cloud lighting (2022)
Journal Article
Satilmis, P., Marnerides, D., Debattista, K., & Bashford-Rogers, T. (2022). Deep synthesis of cloud lighting. IEEE Computer Graphics and Applications, 42(5; 01 Sept.-Oct. 2022), 8 - 18. https://doi.org/10.1109/MCG.2022.3172846

Current appearance models for the sky are able to represent clear sky illumination to a high degree of accuracy. However, these models all lack a common feature of real-skies: clouds. These are an essential component for many applications which rely... Read More about Deep synthesis of cloud lighting.

Ego-graph replay based continual learning for misinformation engagement prediction (2022)
Conference Proceeding
Bo, H., Mcconville, R., Hong, J., & Liu, W. (in press). Ego-graph replay based continual learning for misinformation engagement prediction. . https://doi.org/10.48550/arXiv.2207.12105

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinfor-mation detection systems to classify if a post is misinformation. Instead of po... Read More about Ego-graph replay based continual learning for misinformation engagement prediction.

Studying how digital luthiers choose their tools (2022)
Conference Proceeding
Renney, N., Renney, H., Mitchell, T. J., & Gaster, B. R. (2022). Studying how digital luthiers choose their tools. . https://doi.org/10.1145/3491102.3517656

Digital lutherie is a sub-domain of digital craft focused on creating digital musical instruments: high-performance devices for musical expression. It represents a nuanced and challenging area of human-computer interaction that is well established an... Read More about Studying how digital luthiers choose their tools.

Machine in the middle: Exploring dark patterns of emotional human-computer integration through media art (2022)
Conference Proceeding
Dickinson, R., Semertzidis, N., & Mueller, F. F. (2022). Machine in the middle: Exploring dark patterns of emotional human-computer integration through media art. In S. Barbosa, C. Lampe, C. Appert, & D. A. Shamma (Eds.), CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3491101.3503555

As our relationship with machines becomes evermore intimate, we observe increasing efforts in the quantification of human emotion, which has historically generated unintended consequences. We acknowledge an amplification of this trend through recent... Read More about Machine in the middle: Exploring dark patterns of emotional human-computer integration through media art.

Sensitive periods for the effect of childhood adversity on DNA methylation: Updated results from a prospective, longitudinal study (2022)
Journal Article
Lussier, A. A., Zhu, Y., Smith, B. J., Simpkin, A. J., Smith, A. D. A. C., Suderman, M. J., …Dunn, E. C. (2023). Sensitive periods for the effect of childhood adversity on DNA methylation: Updated results from a prospective, longitudinal study. Biological Psychiatry: Global Open Science, 3(3), 567-571. https://doi.org/10.1016/j.bpsgos.2022.04.002

Disclosure risks in odds ratios and logistic regression (2022)
Presentation / Conference
Derrick, B., Green, E., Ritchie, F., & White, P. (2022, April). Disclosure risks in odds ratios and logistic regression. Paper presented at Scottish Economic Society Annual Conference 2022: Special session 'Protecting confidentiality in social science research outputs', Glasgow

When publishing statistics from confidential data, there exists a risk that the statistic might inadvertently reveal confidential information. Statistical disclosure control (SDC) aims to reduce that risk to an acceptable level. Most SDC theory is co... Read More about Disclosure risks in odds ratios and logistic regression.

Safety in numbers: Minimum thresholding, Maximum bounds and Little White Lies : The case of the mean and standard deviation (2022)
Presentation / Conference
Derrick, B., Green, E., Kember, K., Ritchie, F., & White, P. (2022, April). Safety in numbers: Minimum thresholding, Maximum bounds and Little White Lies : The case of the mean and standard deviation. Paper presented at Scottish Economic Society, Glasgow

Reporting the sample mean, sample standard deviation and sample size could in some cases lead to the unique identification of the underpinning sample. The likelihood of this reveal via direct enumeration of the possible search space decreases with i... Read More about Safety in numbers: Minimum thresholding, Maximum bounds and Little White Lies : The case of the mean and standard deviation.

Vehicle detection using improved region convolution neural network for accident prevention in smart roads (2022)
Journal Article
Djenouri, Y., Belhadi, A., Srivastava, G., Djenouri, D., & Line, J. C. (2022). Vehicle detection using improved region convolution neural network for accident prevention in smart roads. Pattern Recognition Letters, 158, 42-47. https://doi.org/10.1016/j.patrec.2022.04.012

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. T... Read More about Vehicle detection using improved region convolution neural network for accident prevention in smart roads.

Urinary Volatile Organic Compound Testing in Fast-Track Patients with Suspected Colorectal Cancer (2022)
Journal Article
Boulind, C. E., Gould, O., Costello, B. D. L., Allison, J., White, P., Ewings, P., …Francis, N. K. (2022). Urinary Volatile Organic Compound Testing in Fast-Track Patients with Suspected Colorectal Cancer. Cancers, 14(9), 2127. https://doi.org/10.3390/cancers14092127

Colorectal symptoms are common but only infrequently represent serious pathology, including colorectal cancer (CRC). A large number of invasive tests are presently performed for reassurance. We investigated the feasibility of urinary volatile organic... Read More about Urinary Volatile Organic Compound Testing in Fast-Track Patients with Suspected Colorectal Cancer.

Statistical modeling of sensitive period effects using the structured life course modelling approach (SLCMA) (2022)
Book Chapter
Smith, B. J., Smith, A. D., & Dunn, E. C. (2022). Statistical modeling of sensitive period effects using the structured life course modelling approach (SLCMA). In Sensitive Periods of Brain Development and Preventive Interventions (215-234). Springer. https://doi.org/10.1007/7854_2021_280

Sensitive periods are times during development when life experiences can have a greater impact on outcomes than at other periods during the life course. However, a dearth of sophisticated methods for studying time-dependent exposure-outcome relations... Read More about Statistical modeling of sensitive period effects using the structured life course modelling approach (SLCMA).

Spontaneous and driven growth of multicellular lipid compartments to millimeter size from porous polymer structures** (2022)
Journal Article
Nomura, S. M., Shimizu, R., Archer, R. J., Hayase, G., Toyota, T., Mayne, R., & Adamatzky, A. (2022). Spontaneous and driven growth of multicellular lipid compartments to millimeter size from porous polymer structures**. ChemSystemsChem, 4(5), https://doi.org/10.1002/syst.202200006

This report describes a method to obtain multicellular shaped compartments made by lipids growing from a sponge-like porous structure. Each compartment is several tens of micrometers in diameter and separated by membranes comprised of phospholipid an... Read More about Spontaneous and driven growth of multicellular lipid compartments to millimeter size from porous polymer structures**.

Computing on wheels: A deep reinforcement learning-based approach (2022)
Journal Article
Kazmi, S. M. A., Ho, T. M., Nguyen, T. T., Fahim, M., Khan, A., Piran, M. J., & Baye, G. (2022). Computing on wheels: A deep reinforcement learning-based approach. IEEE Transactions on Intelligent Transportation Systems, 23(11), 22535-22548. https://doi.org/10.1109/TITS.2022.3165662

Future generation vehicles equipped with modern technologies will impose unprecedented computational demand due to the wide adoption of compute-intensive services with stringent latency requirements. The computational capacity of the next generation... Read More about Computing on wheels: A deep reinforcement learning-based approach.

Hybrid RESNET and regional convolution neural network for accident estimation (2022)
Journal Article
Djenouri, Y., Srivastava, G., Djenouri, D., Belhadi, A., & Jerry, C. L. (2022). Hybrid RESNET and regional convolution neural network for accident estimation. IEEE Transactions on Intelligent Transportation Systems, 23(12), 25335-25344. https://doi.org/10.1109/TITS.2022.3165156

Road safety is tackled and an intelligent deep learning framework is proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. The road state is first collected, while an intelligent filter, based on SIFT ex... Read More about Hybrid RESNET and regional convolution neural network for accident estimation.

Video based convolutional neural networks forecasting for rainfall forecasting (2022)
Journal Article
Barnes, A., Rodding Kjeldsen, T., & McCullen, N. (2022). Video based convolutional neural networks forecasting for rainfall forecasting. IEEE Geoscience and Remote Sensing Letters, 19, https://doi.org/10.1109/LGRS.2022.3167456

This study presents a new methodology for improving forecasts of current monthly, regional precipitation using video-based convolutional neural networks (CNNs). Using 13 administrative regions of Great Britain as a case study, three CNN architectures... Read More about Video based convolutional neural networks forecasting for rainfall forecasting.

Empirical studies in end-user computer-generated music composition systems (2022)
Thesis
Hunt, S. Empirical studies in end-user computer-generated music composition systems. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/7239594

Computer music researchers dream of the perfect algorithm, in which the music generated is indistinguishable from, or even superior to, that composed by the world’s most talented composers. However, the fulfilment of this aim remains ambitious. This t... Read More about Empirical studies in end-user computer-generated music composition systems.

LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST (2022)
Conference Proceeding
Mustapha, K., Djenouri, D., Jianguo, D., & Djenouri, Y. (2022). LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST. In 2021 17th International Conference on Mobility, Sensing and Networking (MSN) (694-699). https://doi.org/10.1109/MSN53354.2021.00107

The present paper considers emerging Internet of Things (IoT) applications and proposes a Long Short Term Memory (LSTM) based neural network for predicting the end of the broadcasting period under slotted CSMA (Carrier Sense Multiple Access) based MA... Read More about LSTM for periodic broadcasting in green IoT applications over energy harvesting enabled wireless networks: Case study on ADAPCAST.

Exploring collaborative working of undergraduate students through virtual worlds: A study conducted in a Sri Lankan context (2022)
Thesis
Somaratne, R. Exploring collaborative working of undergraduate students through virtual worlds: A study conducted in a Sri Lankan context. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/7604202

Collaborative work is a type of learning activity given to students. This type of learning activity encourages students to work actively with their group members. Therefore, the participation of group members is important during group-based activitie... Read More about Exploring collaborative working of undergraduate students through virtual worlds: A study conducted in a Sri Lankan context.

Language of fungi derived from their electrical spiking activity (2022)
Journal Article
Adamatzky, A. (2022). Language of fungi derived from their electrical spiking activity. Royal Society Open Science, 9(4), 211926. https://doi.org/10.1098/rsos.211926

Fungi exhibit oscillations of extracellular electrical potential recorded via differential electrodes inserted into a substrate colonized by mycelium or directly into sporocarps. We analysed electrical activity of ghost fungi (Omphalotus nidiformis),... Read More about Language of fungi derived from their electrical spiking activity.

Short-Term safety outcomes of mastectomy and immediate prepectoral implant-based breast reconstruction: Pre-BRA prospective multicentre cohort study (2022)
Journal Article
Harvey, K. L., Sinai, P., Mills, N., White, P., Holcombe, C., & Potter, S. (2022). Short-Term safety outcomes of mastectomy and immediate prepectoral implant-based breast reconstruction: Pre-BRA prospective multicentre cohort study. British Journal of Surgery, 109(6), 530-538. https://doi.org/10.1093/bjs/znac077

Background: Prepectoral breast reconstruction (PPBR) has recently been introduced to reduce postoperative pain and improve cosmetic outcomes in women having implant-based procedures. High-quality evidence to support the practice of PPBR, however, is... Read More about Short-Term safety outcomes of mastectomy and immediate prepectoral implant-based breast reconstruction: Pre-BRA prospective multicentre cohort study.