All Outputs (102)

Comonadic semantics for guarded fragments (2021)
Presentation / Conference Contribution
Abramsky, S., & Marsden, D. (2021, June). Comonadic semantics for guarded fragments. Presented at 2021 36th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), Rome, Italy

In previous work ([1], [2], [3]), it has been shown how a range of model comparison games which play a central role in finite model theory, including Ehrenfeucht-Fraïssé, pebbling, and bisimulation games, can be captured in terms of resource-indexed... Read More about Comonadic semantics for guarded fragments.

Internal ∞-Categorical Models of Dependent Type Theory: Towards 2LTT Eating HoTT (2021)
Presentation / Conference Contribution
Kraus, N. (2021, June). Internal ∞-Categorical Models of Dependent Type Theory: Towards 2LTT Eating HoTT. Presented at LICS'21 - ACM/IEEE Symposium on Logic in Computer Science, Rome, Italy

Using dependent type theory to formalise the syntax of dependent type theory is a very active topic of study and goes under the name of "type theory eating itself" or "type theory in type theory." Most approaches are at least loosely based on Dybjer'... Read More about Internal ∞-Categorical Models of Dependent Type Theory: Towards 2LTT Eating HoTT.

Placing AI in the Creative Industries: The Case for Intelligent Music Production (2021)
Book Chapter
McGarry, G., Chamberlain, A., Crabtree, A., & Greenhalgh, C. (2021). Placing AI in the Creative Industries: The Case for Intelligent Music Production. . Springer. https://doi.org/10.1007/978-3-030-78635-9_72

The digital transformation of the recording industry is one of the most well-known in recent times, with the Digital Audio Workstation (DAW) making new methods of music production available to amateurs and professionals in almost any setting beyond t... Read More about Placing AI in the Creative Industries: The Case for Intelligent Music Production.

Developing a cyber security culture: Current practices and future needs (2021)
Journal Article
Uchendu, B., Nurse, J. R., Bada, M., & Furnell, S. (2021). Developing a cyber security culture: Current practices and future needs. Computers and Security, 109, Article 102387. https://doi.org/10.1016/j.cose.2021.102387

While the creation of a strong security culture has been researched and discussed for decades, it continues to elude many businesses. Part of the challenge faced is distilling pertinent, recent academic findings and research into useful guidance. In... Read More about Developing a cyber security culture: Current practices and future needs.

The Digital Network of Networks: Regulatory Risk and Policy Challenges of Vaccine Passports (2021)
Journal Article
Wilford, S. H., McBride, N., Brooks, L., Eke, D. O., Akintoye, S., Owoseni, A., Leach, T., Flick, C., Fisk, M., & Stacey, M. (2021). The Digital Network of Networks: Regulatory Risk and Policy Challenges of Vaccine Passports. European Journal of Risk Regulation, 12(2), 393-403. https://doi.org/10.1017/err.2021.35

The extensive disruption to and digital transformation of travel administration across borders largely due to COVID-19 mean that digital vaccine passports are being developed to resume international travel and kick-start the global economy. Currently... Read More about The Digital Network of Networks: Regulatory Risk and Policy Challenges of Vaccine Passports.

Sounding out the System: Multidisciplinary Web Science Platforms for Creative Sonification (2021)
Presentation / Conference Contribution
Emsley, I., & Chamberlain, A. (2021, June). Sounding out the System: Multidisciplinary Web Science Platforms for Creative Sonification. Presented at 13th ACM Web Science Conference, Virtual

In this paper, we present our initial findings in using digital methods to consider the way that different devices can connect to the same object. We take a more experimental view of the ways in which network data might be used in compositions to hel... Read More about Sounding out the System: Multidisciplinary Web Science Platforms for Creative Sonification.

Safety Assessment Review of a Dressing Assistance Robot (2021)
Journal Article
Delgado Bellamy, D., Chance, G., Caleb-Solly, P., & Dogramadzi, S. (2021). Safety Assessment Review of a Dressing Assistance Robot. Frontiers in Robotics and AI, 8, Article 667316. https://doi.org/10.3389/frobt.2021.667316

Hazard analysis methods such as HAZOP and STPA have proven to be effective methods for assurance of system safety for years. However, the dimensionality and human factors uncertainty of many assistive robotic applications challenges the capability of... Read More about Safety Assessment Review of a Dressing Assistance Robot.

Metaheuristics “In the Large” (2021)
Journal Article
Swan, J., Adriaensen, S., Johnson, C. G., Kheiri, A., Krawiec, F., Merelo, J. J., Minku, L. L., Özcan, E., Pappa, G. L., García-Sánchez, P., Sörensen, K., Voß, S., Wagner, M., & White, D. R. (2022). Metaheuristics “In the Large”. European Journal of Operational Research, 297(2), 393-406. https://doi.org/10.1016/j.ejor.2021.05.042

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need... Read More about Metaheuristics “In the Large”.

Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images (2021)
Journal Article
Calderon-Ramirez, S., Yang, S., Moemeni, A., Colreavy-Donnelly, S., Elizondo, D. A., Oala, L., Rodríguez-Capitán, J., Jiménez-Navarro, M., López-Rubio, E., & Molina-Cabello, M. A. (2021). Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images. IEEE Access, 9, 85442 - 85454. https://doi.org/10.1109/ACCESS.2021.3085418

In this work we implement a COVID-19 infection detection system based on chest Xray images with uncertainty estimation. Uncertainty estimation is vital for safe usage of computer aided diagnosis tools in medical applications. Model estimations with h... Read More about Improving Uncertainty Estimation With Semi-supervised Deep Learning for COVID-19 Detection Using Chest X-ray Images.

Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS (2021)
Journal Article
Deveci, M., Özcan, E., John, R., Pamucar, D., & Karaman, H. (2021). Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS. Applied Soft Computing, 109, Article 107532. https://doi.org/10.1016/j.asoc.2021.107532

Over the past 20 years, the development of offshore wind farms has become increasingly important across the world. One of the most crucial reasons for that is offshore wind turbines have higher average speeds than those onshore, producing more electr... Read More about Offshore wind farm site selection using interval rough numbers based Best-Worst Method and MARCOS.

Playing Games with Tito: Designing Hybrid Museum Experiences for Critical Play (2021)
Journal Article
Løvlie, A. S., Ryding, K., Spence, J., Rajkowska, P., Waern, A., Wray, T., Benford, S., Preston, W., & Clare-Thorn, E. (2021). Playing Games with Tito: Designing Hybrid Museum Experiences for Critical Play. Journal on Computing and Cultural Heritage, 14(2), 1-26. https://doi.org/10.1145/3446620

This article brings together two distinct, but related perspectives on playful museum experiences: Critical play and hybrid design. The article explores the challenges involved in combining these two perspectives, through the design of two hybrid mus... Read More about Playing Games with Tito: Designing Hybrid Museum Experiences for Critical Play.

Designing to Support and Extend the Competencies of People with Visual Impairments (2021)
Presentation / Conference Contribution
Reyes Cruz, G. (2021, May). Designing to Support and Extend the Competencies of People with Visual Impairments. Presented at CHI '21: CHI Conference on Human Factors in Computing Systems, Yokohama, Japan

This work sits in the fields of Human Computer Interaction and accessibility research dedicated to the study and development of technology used by people who are blind or visually impaired. Increasingly, researchers have stated the need to get away f... Read More about Designing to Support and Extend the Competencies of People with Visual Impairments.

An Examination of the Work Practices of Crowdfarms (2021)
Presentation / Conference Contribution
Wang, Y., Papangelis, K., Saker, M., Lykourentzou, I., Khan, V.-J., Chamberlain, A., & Grudin, J. (2021, May). An Examination of the Work Practices of Crowdfarms. Presented at CHI '21: CHI Conference on Human Factors in Computing Systems, Yokohama, Japan (online)

Crowdsourcing is a new value creation business model. Annual revenue of the Chinese market alone is hundreds of millions of dollars, yet few studies have focused on the practices of the Chinese crowdsourcing workforce, and those that do mainly focus... Read More about An Examination of the Work Practices of Crowdfarms.

From Responsible Research and Innovation to responsibility by design (2021)
Journal Article
Stahl, B. C., Akintoye, S., Bitsch, L., Bringedal, B., Eke, D., Farisco, M., Grasenick, K., Guerrero, M., Knight, W., Leach, T., Nyholm, S., Ogoh, G., Rosemann, A., Salles, A., Trattnig, J., & Ulnicane, I. (2021). From Responsible Research and Innovation to responsibility by design. Journal of Responsible Innovation, 8(2), 175-198. https://doi.org/10.1080/23299460.2021.1955613

Drawing on more than eight years working to implement Responsible Research and Innovation (RRI) in the Human Brain Project, a large EU-funded research project that brings together neuroscience, computing, social sciences, and the humanities, and one... Read More about From Responsible Research and Innovation to responsibility by design.

Multi-Objective Feature Selection With Missing Data in Classification (2021)
Journal Article
Xue, Y., Tang, Y., Xu, X., Liang, J., & Neri, F. (2022). Multi-Objective Feature Selection With Missing Data in Classification. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2), 355-364. https://doi.org/10.1109/TETCI.2021.3074147

Feature selection (FS) is an important research topic in machine learning. Usually, FS is modelled as a bi-objective optimization problem whose objectives are: 1) classification accuracy; 2) number of features. One of the main issues in real-world ap... Read More about Multi-Objective Feature Selection With Missing Data in Classification.

Hypoalbuminemia in patients following their recovery from severe coronavirus disease 2019 (2021)
Journal Article
Ali, K. M., Ali, A. M., Tawfeeq, H. M., Figueredo, G. P., & Rostam, H. M. (2021). Hypoalbuminemia in patients following their recovery from severe coronavirus disease 2019. Journal of Medical Virology, 93(7), 4532-4536. https://doi.org/10.1002/jmv.27002

Coronavirus disease 2019 (COVID-19) is caused by a contagious virus that has spread to more than 200 countries, territories, and regions. Thousands of studies to date have examined all aspects of this disease, yet little is known about the postrecove... Read More about Hypoalbuminemia in patients following their recovery from severe coronavirus disease 2019.

First Steps Towards RAT: A Protocol for Documenting Data Use in the Agent-Based Modeling Process (2021)
Presentation / Conference Contribution
Siebers, P.-O., Achter, S., Palaretti Bernardo, C., Borit, M., & Chattoe-Brown, E. (2019, September). First Steps Towards RAT: A Protocol for Documenting Data Use in the Agent-Based Modeling Process. Presented at Social Simulation Conference 2019, Mainz, Germany

While there is a number of frameworks and protocols in Agent-Based Modeling (ABM) that support the documentation of different aspects of a simulation study, it is surprising to find only a small number dealing with the handling of data. Here we prese... Read More about First Steps Towards RAT: A Protocol for Documenting Data Use in the Agent-Based Modeling Process.

Recovery agenda for sustainable development post COVID-19 at the country level: developing a fuzzy action priority surface (2021)
Journal Article
Ranjbari, M., Shams Esfandabadi, Z., Scagnelli, S. D., Siebers, P.-O., & Quatraro, F. (2021). Recovery agenda for sustainable development post COVID-19 at the country level: developing a fuzzy action priority surface. Environment, Development and Sustainability, 23, 16646-16673. https://doi.org/10.1007/s10668-021-01372-6

As a response to the urgent call for recovery actions against the COVID-19 crisis, this research aims to identify action priority areas post COVID-19 toward achieving the targets of the sustainable development goals (SDGs) within the 2030 Agenda for... Read More about Recovery agenda for sustainable development post COVID-19 at the country level: developing a fuzzy action priority surface.

Realising a Push Button Modality for Video-Based Forensics (2021)
Journal Article
Zawali, B., Ikuesan, R. A., Kebande, V. R., Furnell, S., & A-Dhaqm, A. (2021). Realising a Push Button Modality for Video-Based Forensics. Infrastructures, 6(4), Article 54. https://doi.org/10.3390/infrastructures6040054

Complexity and sophistication among multimedia-based tools have made it easy for perpetrators to conduct digital crimes such as counterfeiting, modification, and alteration without being detected. It may not be easy to verify the integrity of video c... Read More about Realising a Push Button Modality for Video-Based Forensics.

Nonlocal graph theory based transductive learning for hyperspectral image classification (2021)
Journal Article
Huang, B., Ge, L., Chen, G., Radenkovic, M., Wang, X., Duan, J., & Pan, Z. (2021). Nonlocal graph theory based transductive learning for hyperspectral image classification. Pattern Recognition, 116, Article 107967. https://doi.org/10.1016/j.patcog.2021.107967

Hyperspectral Image classification plays an important role in the maintenance of remote image analysis, which has been attracting a lot of research interest. Although various approaches, including unsupervised and supervised methods, have been propos... Read More about Nonlocal graph theory based transductive learning for hyperspectral image classification.