SLOPE: A Self Learning Optimization and Prediction Ensembler for Task Scheduling
(2018)
Presentation / Conference Contribution
Kapoor, L., Jindal, A., Benslimane, A., Aujla, G. S., Chaudhary, R., Kumar, N., & Zomaya, A. Y. (2018). SLOPE: A Self Learning Optimization and Prediction Ensembler for Task Scheduling. . https://doi.org/10.1109/wimob.2018.8589108
All Outputs (217)
Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer (2018)
Presentation / Conference Contribution
Atapour-Abarghouei, A., & Breckon, T. (2018). Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer. In Proc. Computer Vision and Pattern Recognition (2800-2810). https://doi.org/10.1109/CVPR.2018.00296Monocular depth estimation using learning-based approaches has become promising in recent years. However, most monocular depth estimators either need to rely on large quantities of ground truth depth data, which is extremely expensive and difficult t... Read More about Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer.
CAM: A Combined Attention Model for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., Bradley, S., & McGough, A. S. (2018). CAM: A Combined Attention Model for Natural Language Inference. In N. Abe, H. Liu, C. Pu, X. Hu, N. Ahmed, M. Qiao, …J. Saltz (Eds.), 2018 IEEE International Conference on Big Data (Big Data) ; proceedings (1009-1014). https://doi.org/10.1109/bigdata.2018.8622057Natural Language Inference (NLI) is a fundamental step towards natural language understanding. The task aims to detect whether a premise entails or contradicts a given hypothesis. NLI contributes to a wide range of natural language understanding appl... Read More about CAM: A Combined Attention Model for Natural Language Inference.
A linear-time algorithm for maximum-cardinality matching on cocomparability graphs (2018)
Journal Article
Mertzios, G., Nichterlein, A., & Niedermeier, R. (2018). A linear-time algorithm for maximum-cardinality matching on cocomparability graphs. SIAM Journal on Discrete Mathematics, 32(4), 2820-2835. https://doi.org/10.1137/17m1120920Finding maximum-cardinality matchings in undirected graphs is arguably one of the most central graph problems. For general $m$-edge and $n$-vertex graphs, it is well known to be solvable in $O(m\sqrt{n})$ time. We present a linear-time algorithm to f... Read More about A linear-time algorithm for maximum-cardinality matching on cocomparability graphs.
GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training (2018)
Presentation / Conference Contribution
Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019). GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training. In C. Jawahar, H. Li, G. Mori, & K. Schindler (Eds.), Computer Vision – ACCV 2018 : 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III (622-637). https://doi.org/10.1007/978-3-030-20893-6_39Anomaly detection is a classical problem in computer vision, namely the determination of the normal from the abnormal when datasets are highly biased towards one class (normal) due to the insufficient sample size of the other class (abnormal). While... Read More about GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training.
Image recoloring for home scene (2018)
Presentation / Conference Contribution
Lin, X., Wang, X., Li, F. W., Yang, B., Zhang, K., & Wei, T. (2018). Image recoloring for home scene. In VRCAI '18 Proceedings of the 16th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry. https://doi.org/10.1145/3284398.3284404Indoor home scene coloring technology is a hot topic for home design, helping users make home coloring decisions. Image based home scene coloring is preferable for e-commerce customers since it only requires users to describe coloring expectations or... Read More about Image recoloring for home scene.
TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text (2018)
Presentation / Conference Contribution
Medhat, F., Mohammadi, M., Jaf, S., Willcocks, C., Breckon, T., Matthews, P., …Obara, B. (2018). TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text.—Text recognition of scanned documents is usually dependent upon the type of text, being handwritten or machine-printed. Accordingly, the recognition involves prior classification of the text category, before deciding on the recognition method to be... Read More about TMIXT: A process flow for Transcribing MIXed handwritten and machine-printed Text.
Surjective H-Colouring over reflexive digraphs (2018)
Journal Article
Larose, B., Martin, B., & Paulusma, D. (2018). Surjective H-Colouring over reflexive digraphs. ACM Transactions on Computation Theory, 11(1), Article 3. https://doi.org/10.1145/3282431The Surjective H-Colouring problem is to test if a given graph allows a vertex-surjective homomorphism to a fixed graph H. The complexity of this problem has been well studied for undirected (partially) reflexive graphs. We introduce endo-triviality,... Read More about Surjective H-Colouring over reflexive digraphs.
SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems (2018)
Journal Article
Jindal, A., Aujla, G. S., Kumar, N., Chaudhary, R., Obaidat, M. S., & You, I. (2018). SeDaTiVe: SDN-Enabled Deep Learning Architecture for Network Traffic Control in Vehicular Cyber-Physical Systems. IEEE Network, 32(6), https://doi.org/10.1109/mnet.2018.1800101
Hereditary graph classes: when the complexities of coloring and clique cover coincide (2018)
Journal Article
Blanché, A., Dabrowski, K., Johnson, M., & Paulusma, D. (2019). Hereditary graph classes: when the complexities of coloring and clique cover coincide. Journal of Graph Theory, 91(3), 267-289. https://doi.org/10.1002/jgt.22431graph is (H1;H2)-free for a pair of graphs H1;H2 if it contains no induced subgraph isomorphic to H1 or H2. In 2001, Král’, Kratochvíl, Tuza, and Woeginger initiated a study into the complexity of Colouring for (H1;H2)-free graphs. Since then, others... Read More about Hereditary graph classes: when the complexities of coloring and clique cover coincide.
On the parameterized complexity of (k,s)-SAT (2018)
Journal Article
Paulusma, D., & Szeider, S. (2019). On the parameterized complexity of (k,s)-SAT. Information Processing Letters, 43, 34-36. https://doi.org/10.1016/j.ipl.2018.11.005Let (k, s)-SAT be the k-SAT problem restricted to formulas in which each variable occurs in at most s clauses. It is well known that (3, 3)-SAT is trivial and (3, 4)-SAT is NP-complete. Answering a question posed by Iwama and Takaki (DMTCS 1997), Ber... Read More about On the parameterized complexity of (k,s)-SAT.
Automatic Musculoskeletal and Neurological Disorder Diagnosis With Relative Joint Displacement From Human Gait (2018)
Journal Article
Rueangsirarak, W., Zhang, J., Aslam, N., Ho, E. S., & Shum, H. P. (2018). Automatic Musculoskeletal and Neurological Disorder Diagnosis With Relative Joint Displacement From Human Gait. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(12), 2387-2396. https://doi.org/10.1109/tnsre.2018.2880871Musculoskeletal and neurological disorders are common devastating companions of ageing, leading to a reduction in quality of life and increased mortality. Gait analysis is a popular method for diagnosing these disorders. However, manually analyzing t... Read More about Automatic Musculoskeletal and Neurological Disorder Diagnosis With Relative Joint Displacement From Human Gait.
Strategies for Multimedia Learning Object Recommendation in a Language Learning Support System: Verbal Learners Vs. Visual Learners (2018)
Journal Article
Wang, J., Mendori, T., & Hoel, T. (2018). Strategies for Multimedia Learning Object Recommendation in a Language Learning Support System: Verbal Learners Vs. Visual Learners. International Journal of Human-Computer Interaction, 35(4-5), https://doi.org/10.1080/10447318.2018.1543085
Colouring (Pr+Ps)-free graphs (2018)
Presentation / Conference Contribution
Klimošová, T., Malík, J., Masařík, T., Novotná, J., Paulusma, D., & Slívová, V. (2018). Colouring (Pr+Ps)-free graphs. In W. Hsu, D. Lee, & C. Liao (Eds.), 29th International Symposium on Algorithms and Computation (ISAAC 2018) (5:1-5:13). https://doi.org/10.4230/lipics.isaac.2018.5The k-Colouring problem is to decide if the vertices of a graph can be coloured with at most k colours for a fixed integer k such that no two adjacent vertices are coloured alike. If each vertex u must be assigned a colour from a prescribed list L(u)... Read More about Colouring (Pr+Ps)-free graphs.
Cutting-Edge VR/AR Display Technologies (Gaze-, Accommodation-, Motion-aware and HDR-enabled) (2018)
Presentation / Conference Contribution
Koulieris, G., Aksit, K., Richardt, C., & Mantiuk, R. (2018). Cutting-Edge VR/AR Display Technologies (Gaze-, Accommodation-, Motion-aware and HDR-enabled). In SIGGRAPH Asia 2018 Courses. https://doi.org/10.1145/3277644.3277771Near-eye (VR/AR) displays suffer from technical, interaction as well as visual quality issues which hinder their commercial potential. This tutorial will deliver an overview of cutting-edge VR/AR display technologies, focusing on technical, interacti... Read More about Cutting-Edge VR/AR Display Technologies (Gaze-, Accommodation-, Motion-aware and HDR-enabled).
Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses (2018)
Presentation / Conference Contribution
Cristea, A., Alamri, A., Kayama, M., Stewart, C., Alsheri, M., & Shi, L. (2018). Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund UniversityWhilst a high dropout rate is a well-known problem in MOOCs, few studies take a data-driven approach to understand the reasons of such a phenomenon, and to thus be in the position to recommend and design possible adaptive solutions to alleviate it. I... Read More about Earliest Predictor of Dropout in MOOCs: A Longitudinal Study of FutureLearn Courses.
Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses (2018)
Presentation / Conference Contribution
Shi, L., & Cristea, A. (2018). Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Designing Digitalization (ISD2018 Proceedings). Lund, Sweden: Lund UniversityBig data and analytics for educational information systems, despite having gained researchers’ attention, are still in their infancy and will take years to mature. Massive open online courses (MOOCs), which record learner-computer interactions, bring... Read More about Demographic Indicators Influencing Learning Activities in MOOCs: Learning Analytics of FutureLearn Courses.
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning (2018)
Presentation / Conference Contribution
Bonner, S., Brennan, J., Kureshi, I., Theodoropoulos, G., McGough, S., & Obara, B. (2018). Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning.Graphs are a commonly used construct for representing relationships between elements in complex high dimensional datasets. Many real-world phenomenon are dynamic in nature, meaning that any graph used to represent them is inherently temporal. However... Read More about Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation Learning.
How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers (2018)
Presentation / Conference Contribution
Cristea, A., Alshehri, M., Alamri, A., Kayama, M., Stewart, C., & Shi, L. (2018). How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers. In B. Andersson, B. Johansson, S. Carlsson, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Proceedings of the 27th International Conference on Information Systems Development (ISD2018), Education Track, Lund, Sweden, August 22-24, 2018Data-intensive analysis of massive open online courses (MOOCs) is popular. Researchers have been proposing various parameters conducive to analysis and prediction of student behaviour and outcomes in MOOCs, as well as different methods to analyse and... Read More about How is learning fluctuating? FutureLearn MOOCs fine-grained temporal Analysis and Feedback to Teachers.
Thetis coastal ocean model: discontinuous Galerkin discretization for the three-dimensional hydrostatic equations (2018)
Journal Article
Kärnä, T., Kramer, S. C., Mitchell, L., Ham, D. A., Piggott, M. D., & Baptista, A. M. (2018). Thetis coastal ocean model: discontinuous Galerkin discretization for the three-dimensional hydrostatic equations. Geoscientific Model Development, 11(11), 4359-4382. https://doi.org/10.5194/gmd-11-4359-2018Unstructured grid ocean models are advantageous for simulating the coastal ocean and river–estuary–plume systems. However, unstructured grid models tend to be diffusive and/or computationally expensive, which limits their applicability to real-life p... Read More about Thetis coastal ocean model: discontinuous Galerkin discretization for the three-dimensional hydrostatic equations.