Adaptivity - and Regular Cartesian Patches for the Shallow Water Equations and Vectorisation of an Augmented Riemann Solver for the Shallow Water Equations
(2014)
Presentation / Conference Contribution
Bader, M., & Weinzierl, T. (2014, December). Adaptivity - and Regular Cartesian Patches for the Shallow Water Equations and Vectorisation of an Augmented Riemann Solver for the Shallow Water Equations. Paper presented at SIAM Annual Meeting, Chicago, IL
Outputs (127)
A game-based learning approach to road safety: the code of everand (2014)
Presentation / Conference Contribution
Dunwell, I., de Freitas, S., Petridis, P., Hendrix, M., Arnab, S., Lameras, P., & Stewart, C. (2014, December). A game-based learning approach to road safety: the code of everand. Presented at Proceedings of the SIGCHI Conference on Human Factors in Computing Systems ACM
Towards Self-healing SDN (2014)
Presentation / Conference Contribution
Chockler, G., & Trehan, A. (2014, December). Towards Self-healing SDN. Presented at Distributed Software Defined Net- works (DSDN) workshop, Principles of Distributed Computing (PODC) 2014
Determining Majority in Networks with Local Interactions and Very Small Local Memory (2014)
Book Chapter
Mertzios, G., Nikoletseas, S., Raptopoulos, C., & Spirakis, P. (2014). Determining Majority in Networks with Local Interactions and Very Small Local Memory. In J. Esparza, P. Fraigniaud, T. Husfeldt, & E. Koutsoupias (Eds.), Automata, languages, and programming : 41st international colloquium, ICALP 2014, Copenhagen, Denmark, July 8-11, 2014, proceedings, part I (871-882). Springer Verlag. https://doi.org/10.1007/978-3-662-43948-7_72We study here the problem of determining the majority type in an arbitrary connected network, each vertex of which has initially two possible types (states). The vertices may have a few additional possible states and can interact in pairs only if the... Read More about Determining Majority in Networks with Local Interactions and Very Small Local Memory.
Improved routing in the data centre networks HCN and BCN (2014)
Presentation / Conference Contribution
Stewart, I. (2014, December). Improved routing in the data centre networks HCN and BCN. Presented at 2nd International Symposium on Computing and Networking - Across Practical Development and Theoretical Research, Shizuoka, Japan
On the privacy of private browsing-A forensic approach (2014)
Journal Article
Satvat, K., Forshaw, M., Hao, F., & Toreini, E. (2014). On the privacy of private browsing-A forensic approach. Journal of Information Security and Applications, 19(1), 88-100
Intersection Graphs of L-Shapes and Segments in the Plane (2014)
Book Chapter
Felsner, S., Knauer, K., Mertzios, G., & Ueckerdt, T. (2014). Intersection Graphs of L-Shapes and Segments in the Plane. In E. Csuhaj-Varjú, M. Dietzfelbinger, & Z. Ésik (Eds.), Mathematical foundations of computer science 2014 : 39th international symposium, MFCS 2014, Budapest, Hungary, August 25-29, 2014. Proceedings, part II (299-310). Springer Verlag. https://doi.org/10.1007/978-3-662-44465-8_26An L-shape is the union of a horizontal and a vertical segment with a common endpoint. These come in four rotations: ⌊,⌈,⌋ and ⌉. A k-bend path is a simple path in the plane, whose direction changes k times from horizontal to vertical. If a graph adm... Read More about Intersection Graphs of L-Shapes and Segments in the Plane.
Refined particle swarm intelligence method for abrupt motion tracking (2014)
Journal Article
Lim, M. K., Chan, C. S., Monekosso, D., & Remagnino, P. (2014). Refined particle swarm intelligence method for abrupt motion tracking. Information Sciences, 283, 267-287. https://doi.org/10.1016/j.ins.2014.01.003
Protein classification using Hidden Markov models and randomised decision trees (2014)
Presentation / Conference Contribution
Lacey, A., Deng, J., & Xie, X. (2014, December). Protein classification using Hidden Markov models and randomised decision trees. Presented at 2014 7th International Conference on Biomedical Engineering and Informatics IEEE
Active Learning of Hyperparameters: An Expected Cross Entropy Criterion for Active Model Selection (2014)
Report
Kulick, J., Lieck, R., & Toussaint, M. (2014). Active Learning of Hyperparameters: An Expected Cross Entropy Criterion for Active Model Selection. [No known commissioning body]