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All Outputs (300)

Linking gameplay metrics to computational thinking (2020)
Presentation / Conference Contribution
Panayotov, N., Donald, I., Falconer, R., & Kempe, V. (2020, September). Linking gameplay metrics to computational thinking. Presented at 14th European Conference on Games Based Learning, ECGBL 2020

Computational thinking (CT) is considered to be a fundamental skill underlying not only programming ability, but also an entire array of computational problem-solving competencies in a data-driven world. The need for accessible and engaging education... Read More about Linking gameplay metrics to computational thinking.

Defining animation terminology with BSL users (2020)
Presentation / Conference Contribution
Mortimer, J. (2020, February). Defining animation terminology with BSL users. Presented at Educating animators conference, University of Salford

Exploring the gap in animation/games terminology available to BSL users, and working with BSL focus groups to propose new signs for animation. Reviewing the challenges and gaps in University policy and procedure for supporting both the student and de... Read More about Defining animation terminology with BSL users.

Artificial Neural Networks Training Acceleration Through Network Science Strategies (2020)
Presentation / Conference Contribution
Cavallaro, L., Bagdasar, O., De Meo, P., Fiumara, G., & Liotta, A. (2019, June). Artificial Neural Networks Training Acceleration Through Network Science Strategies. Presented at NUMTA: International Conference on Numerical Computations: Theory and Algorithms, Crotone, Italy

Deep Learning opened artificial intelligence to an unprecedented number of new applications. A critical success factor is the ability to train deeper neural networks, striving for stable and accurate models. This translates into Artificial Neural Net... Read More about Artificial Neural Networks Training Acceleration Through Network Science Strategies.

An Online Learning Approach to a Multi-player N-armed Functional Bandit (2020)
Presentation / Conference Contribution
O’Neill, S., Bagdasar, O., & Liotta, A. (2020). An Online Learning Approach to a Multi-player N-armed Functional Bandit. In Numerical Computations: Theory and Algorithms (438-445). https://doi.org/10.1007/978-3-030-40616-5_41

Congestion games possess the property of emitting at least one pure Nash equilibrium and have a rich history of practical use in transport modelling. In this paper we approach the problem of modelling equilibrium within congestion games using a decen... Read More about An Online Learning Approach to a Multi-player N-armed Functional Bandit.

Restorative Justice in Scotland (2020)
Presentation / Conference Contribution
Maglione, G., Buchan, J., & Robertson, L. (2020, February). Restorative Justice in Scotland

2020: Community Justice Scotland Academic Advisory Group, invited speaker on ‘Restorative Justice in Scotland’, Edinburgh, UK (with J. Buchan and L. Robertson).

AC Electrical Motor (2020)
Presentation / Conference Contribution
Muhammad Sukki, F. (2020, February). AC Electrical Motor. Presented at Teaching Presentation, Glasgow, UK

Delegation of Authentication to the Data Plane in Software-Defined Networks (2020)
Presentation / Conference Contribution
Almaini, A., Al-Dubai, A., Romdhani, I., & Schramm, M. (2019, October). Delegation of Authentication to the Data Plane in Software-Defined Networks. Presented at 2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS), Shenyang, China

OpenFlow is considered as the most known protocol for Software Defined Networking (SDN). The main drawback of OpenFlow is the lack of support of new header definitions, which is required by network operators to apply new packet encapsulations. While... Read More about Delegation of Authentication to the Data Plane in Software-Defined Networks.

(Not) costing the earth? Theorizing flight shame, train bragging, and campervan travel (2020)
Presentation / Conference Contribution
Stanley, P. (2020, February). (Not) costing the earth? Theorizing flight shame, train bragging, and campervan travel. Paper presented at European Congress of Qualitative Inquiry, Malta

Flygskam (flight-shame), a Swedish neologism, hints at an emerging climate-smart tourist movement: closer-to-home, flight-free travel1. But going overland is more expensive and time consuming than flying, as capitalism does not price in environmental... Read More about (Not) costing the earth? Theorizing flight shame, train bragging, and campervan travel.

Reality inspired games: expanding the lens of games' claims to authenticity (2020)
Presentation / Conference Contribution
McMillan, R., Jayemanne, D., & Donald, I. (2020). Reality inspired games: expanding the lens of games' claims to authenticity. In D. Leorke (Ed.), DiGRA '20

This paper considers the potentials of contemporary games staking claims to realism through documentary and journalistic techniques as part of a wide-ranging cultural and technological phenomenon– ‘Reality Inspired Games’ or RIGs (Maurin, 2018). We a... Read More about Reality inspired games: expanding the lens of games' claims to authenticity.

A theoretical framework for game jams in applied contexts (2020)
Presentation / Conference Contribution
Reid, A. J., Smy, P., & Donald, I. (2020). A theoretical framework for game jams in applied contexts. In D. Leorke (Ed.), DiGRA '20

Game jams encourage participants to define, explore, create, and disseminate games with respect to a pre-defined time-period and under specified constraints. Various methods and approaches have helped with establishing conventions, rules, and process... Read More about A theoretical framework for game jams in applied contexts.

Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances (2020)
Presentation / Conference Contribution
Ahmed, R., Dashtipour, K., Gogate, M., Raza, A., Zhang, R., Huang, K., Hawalah, A., Adeel, A., & Hussain, A. (2019, July). Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances. Presented at 10th International Conference, BICS 2019, Guangzhou, China

In pattern recognition, automatic handwriting recognition (AHWR) is an area of research that has developed rapidly in the last few years. It can play a significant role in broad-spectrum of applications rending from, bank cheque processing, applicati... Read More about Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances.

Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification (2020)
Presentation / Conference Contribution
Yang, G., Huang, K., Zhang, R., Goulermas, J. Y., & Hussain, A. (2019, July). Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification. Presented at 10th International Conference, BICS 2019, Guangzhou, China

Zero-shot learning (ZSL), i.e. classifying patterns where there is a lack of labeled training data, is a challenging yet important research topic. One of the most common ideas for ZSL is to map the data (e.g., images) and semantic attributes to the s... Read More about Self-focus Deep Embedding Model for Coarse-Grained Zero-Shot Classification.

Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning (2020)
Presentation / Conference Contribution
Ilyas, M., Ahmad, J., Lawson, A., Khan, J. S., Tahir, A., Adeel, A., Larijani, H., Kerrouche, A., Shaikh, M. G., Buchanan, W., & Hussain, A. (2019, July). Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning. Presented at 10th International Conference, BICS 2019, Guangzhou, China

Prospective studies using longitudinal patient data can be used to help to predict responsiveness to Growth Hormone (GH) therapy and assess any suspected risks. In this paper, a novel Clinical Decision Support System (CDSS) is developed to predict gr... Read More about Height Prediction for Growth Hormone Deficiency Treatment Planning Using Deep Learning.

Privacy-Aware Cloud Ecosystems and GDPR Compliance (2020)
Presentation / Conference Contribution
Barati, M., Rana, O., Theodorakopoulos, G., & Burnap, P. (2020). Privacy-Aware Cloud Ecosystems and GDPR Compliance. In 2019 7th International Conference on Future Internet of Things and Cloud (FiCloud). https://doi.org/10.1109/ficloud.2019.00024

Understanding how cloud providers support the European General Data Protection Regulation (GDPR) remains an imporant challenge for new providers emerging on the market. GDPR influences access to, storage, processing and tranmission of data, requiring... Read More about Privacy-Aware Cloud Ecosystems and GDPR Compliance.

Generalized Adversarial Training in Riemannian Space (2020)
Presentation / Conference Contribution
Zhang, S., Huang, K., Zhang, R., & Hussain, A. (2020). Generalized Adversarial Training in Riemannian Space. In 2019 IEEE International Conference on Data Mining (ICDM) (826-835). https://doi.org/10.1109/icdm.2019.00093

Adversarial examples, referred to as augmented data points generated by imperceptible perturbations of input samples, have recently drawn much attention. Well-crafted adversarial examples may even mislead state-of-the-art deep neural network (DNN) mo... Read More about Generalized Adversarial Training in Riemannian Space.