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Outputs (177)

Evaluating UK offset agreements (2015–17) (2019)
Report
Lawson, S. (2019). Evaluating UK offset agreements (2015–17). Jisc

This report is the final summary of a three-year evaluation of Jisc Collections offset agreements. The work has been sponsored by Jisc as part of the Jisc Collections Studentship Award at Birkbeck, University of London.

LEGO Café: Let’s Build a Learning Community (2019)
Presentation / Conference Contribution
Ennis, L., & Yorkstone, S. (2019, June). LEGO Café: Let’s Build a Learning Community. Presented at Learning and Teaching Conference, Edinburgh

Trying something new can be scary. Often it helps to float ideas with friendly colleagues before taking the leap and branching out. We created our monthly LEGO Café as a space where academic and professional staff could come together to experiment wi... Read More about LEGO Café: Let’s Build a Learning Community.

Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters (2019)
Journal Article
Lu, Y., Liu, L., Panneerselvam, J., Zhai, X., Sun, X., & Antonopoulos, N. (2020). Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters. IEEE Transactions on Sustainable Computing, 5(3), 308-318. https://doi.org/10.1109/TSUSC.2019.2905728

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess ene... Read More about Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters.

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence (2019)
Journal Article
Yuan, B., Panneerselvam, J., Liu, L., Antonopoulos, N., & Lu, Y. (2019). An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence. IEEE Transactions on Industrial Informatics, 15(7), 4295-4305. https://doi.org/10.1109/tii.2019.2902877

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effect... Read More about An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence.

Helping learned societies explore Plan S-compliant business models - A proposal (2019)
Other
Fyfe, A., Lawson, S., Moore, S., Neylon, C., & Eve, M. P. (2019). Helping learned societies explore Plan S-compliant business models - A proposal. https://doi.org/10.5281/zenodo.2551448

This proposal was submitted to the funding call 'Helping learned societies explore Plan S-compliant business models', from the Wellcome Trust in partnership with UK Research and Innovation (UKRI) and the Association of Learned & Professional Society... Read More about Helping learned societies explore Plan S-compliant business models - A proposal.

Playing games in academic libraries (2018)
Presentation / Conference Contribution
Ennis, L. (2018, December). Playing games in academic libraries. Presented at Edinburgh Library and Information Services Agency Open Forum, National Library of Scotland

This presentation and workshop explored the place of games, play and laughter in library settings.

Participating in a Faculty Learning Community (2018)
Presentation / Conference Contribution
Ennis, L. (2018, November). Participating in a Faculty Learning Community. Presented at Library TeachMeet: teaching, training and information skills within academic libraries, Glasgow, Scotland

This presentation is a quick look at the structure and purpose of the first Faculty Learning Community (FLC) at Edinburgh Napier University. Established in April 2018, the theme of the FLC is "Supporting one another in the university". The purpose of... Read More about Participating in a Faculty Learning Community.

Cloud-based video analytics using convolutional neural networks (2018)
Journal Article
Yaseen, M. U., Anjum, A., Farid, M., & Antonopoulos, N. (2019). Cloud-based video analytics using convolutional neural networks. Software: Practice and Experience, 49(4), 565-583. https://doi.org/10.1002/spe.2636

Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of... Read More about Cloud-based video analytics using convolutional neural networks.