Novel visualisation techniques for working with multiple, overlapping classification hierarchies
Journal Article
Graham, M., Watson, M. F., & Kennedy, J. (2002)
Novel visualisation techniques for working with multiple, overlapping classification hierarchies. Taxon, 51, 351-358
A Java-based program is presented that provides a visualisation tool for display of and comparison between classification hierarchies. Taxa, or groups of taxa, can be tracked ...
MaTSE: the gene expression time-series explorer.
Journal Article
Craig, P., Cannon, A., Kukla, R., & Kennedy, J. (2013)
MaTSE: the gene expression time-series explorer. BMC bioinformatics, 14, https://doi.org/10.1186/1471-2105-14-S19-S1
Background
High throughput gene expression time-course experiments provide a perspective on biological functioning recognized as having huge value for the diagnosis, treatmen...
On-Line Building Energy Optimization Using Deep Reinforcement Learning
Journal Article
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019)
On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Transactions on Smart Grid, 10(4), 3698-3708. https://doi.org/10.1109/tsg.2018.2834219
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the ...
A Review of Predictive Quality of Experience Management in Video Streaming Services
Journal Article
Torres Vega, M., Perra, C., De Turck, F., & Liotta, A. (2018)
A Review of Predictive Quality of Experience Management in Video Streaming Services. IEEE Transactions on Broadcasting, 64(2), 432-445. https://doi.org/10.1109/tbc.2018.2822869
Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but als...
Self-Learning Power Control in Wireless Sensor Networks
Journal Article
Chincoli, M., & Liotta, A. (2018)
Self-Learning Power Control in Wireless Sensor Networks. Sensors, 18(2), 1-29. https://doi.org/10.3390/s18020375
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This...
A Task Taxonomy for Temporal Graph Visualisation
Journal Article
Kerracher, N., Kennedy, J., & Chalmers, K. (2015)
A Task Taxonomy for Temporal Graph Visualisation. IEEE Transactions on Visualization and Computer Graphics, 21(10), 1160-1172. https://doi.org/10.1109/tvcg.2015.2424889
By extending and instantiating an existing formal task framework, we define a task taxonomy and task design space for temporal graph visualisation. We discuss the process invo...
Visualization of Online Datasets
Journal Article
Peng, T., & Downie, C. (2017)
Visualization of Online Datasets. International Journal of Networked and Distributed Computing, 6(1), 11-23. https://doi.org/10.2991/ijndc.2018.6.1.2
As computing technology advances, computers are being used to orchestrate and advance wide spectrums of commercial and personal life, information visualization becomes even mo...
Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines
Journal Article
Mocanu, D. C., Bou Ammar, H., Puig, L., Eaton, E., & Liotta, A. (2017)
Estimating 3D trajectories from 2D projections via disjunctive factored four-way conditional restricted Boltzmann machines. Pattern Recognition, 69, 325-335. https://doi.org/10.1016/j.patcog.2017.04.017
Estimation, recognition, and near-future prediction of 3D trajectories based on their two dimensional projections available from one camera source is an exceptionally difficul...
A topological insight into restricted Boltzmann machines
Journal Article
Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2016)
A topological insight into restricted Boltzmann machines. Machine Learning, 104(2-3), 243-270. https://doi.org/10.1007/s10994-016-5570-z
Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep artificial neural networks for automatic feature...
BayesPiles: Visualisation Support for Bayesian Network Structure Learning
Journal Article
Vogogias, A., Kennedy, J., Archambault, D., Bach, B., Smith, V. A., & Currant, H. (2018)
BayesPiles: Visualisation Support for Bayesian Network Structure Learning. ACM transactions on intelligent systems and technology, 10(1), 1-23. https://doi.org/10.1145/3230623
We address the problem of exploring, combining and comparing large collections of scored, directed networks for understanding inferred Bayesian networks used in biology. In th...