All Outputs (216)

Can Learner Characteristics Predict Their Behaviour on MOOCs? (2018)
Presentation / Conference Contribution
Cristea, A. I., Alamri, A., Alshehri, M., Kayama, M., Foss, J., Shi, L., & Stewart, C. D. (2018). Can Learner Characteristics Predict Their Behaviour on MOOCs?. In 10th International Conference on Education Technology and Computers (119-125). https://doi.org/10.1145/3290511.3290568

Stereotyping is the first type of adaptation in education ever proposed. However, the early systems have never dealt with the numbers of learners that current MOOCs provide. Thus, the umbrella question that this work tackles is if learner characteris... Read More about Can Learner Characteristics Predict Their Behaviour on MOOCs?.

On the influence of affect in EEG-based subject identification (2018)
Journal Article
Arnau-González, P., Arevalillo-Herráez, M., Katsigiannis, S., & Ramzan, N. (2021). On the influence of affect in EEG-based subject identification. IEEE Transactions on Affective Computing, 12(2), 391-401. https://doi.org/10.1109/taffc.2018.2877986

A Bayesian framework for assessing the strength distribution of composite structures with random defects (2018)
Journal Article
Sandhu, A., Reinarz, A., & Dodwell, T. (2018). A Bayesian framework for assessing the strength distribution of composite structures with random defects. Composite Structures, 205, 58-68. https://doi.org/10.1016/j.compstruct.2018.08.074

This paper presents a novel stochastic framework to quantify the knock down in strength from out-of-plane wrinkles at the coupon level. The key innovation is a Markov Chain Monte Carlo algorithm which rigorously derives the stochastic distribution of... Read More about A Bayesian framework for assessing the strength distribution of composite structures with random defects.

On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding (2018)
Presentation / Conference Contribution
Alshammari, N., Akcay, S., & Breckon, T. (2018). On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding. In Proc. Intelligent Vehicles Symposium (1027-1032). https://doi.org/10.1109/IVS.2018.8500664

Illumination changes in outdoor environments under non-ideal weather conditions have a negative impact on automotive scene understanding and segmentation performance. In this paper, we present an evaluation of illuminationinvariant image transforms a... Read More about On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding.

In-depth Exploration of Engagement Patterns in MOOCs (2018)
Presentation / Conference Contribution
Lei, S., & Cristea, A. (2018, December). In-depth Exploration of Engagement Patterns in MOOCs. Presented at Web Information Systems Engineering (WISE 2018), Dubai

With the advent of ‘big data’, various new methods have been proposed, to explore data in several domains. In the domain of learning (and e-learning, in particular), the outcomes lag somewhat behind. This is not unexpected, as e-learning has the addi... Read More about In-depth Exploration of Engagement Patterns in MOOCs.

When can graph hyperbolicity be computed in linear time? (2018)
Journal Article
Fluschnik, T., Komusiewicz, C., Mertzios, G., Nichterlein, A., Niedermeier, R., & Talmon, N. (2019). When can graph hyperbolicity be computed in linear time?. Algorithmica, 81(5), 2016-2045. https://doi.org/10.1007/s00453-018-0522-6

Hyperbolicity is a distance-based measure of how close a given graph is to being a tree. Due to its relevance in modeling real-world networks, hyperbolicity has seen intensive research over the last years. Unfortunately, the best known algorithms use... Read More about When can graph hyperbolicity be computed in linear time?.

Critical vertices and edges in H-free graphs (2018)
Journal Article
Paulusma, D., Picouleau, C., & Ries, B. (2019). Critical vertices and edges in H-free graphs. Discrete Applied Mathematics, 257, 361-367. https://doi.org/10.1016/j.dam.2018.08.016

A vertex or edge in a graph is critical if its deletion reduces the chromatic number of the graph by one. We consider the problems of deciding whether a graph has a critical vertex or edge, respectively. We give a complexity dichotomy for both proble... Read More about Critical vertices and edges in H-free graphs.

The multiscale bowler-hat transform for blood vessel enhancement in retinal images (2018)
Journal Article
Sazak, C., Nelson, C. J., & Obara, B. (2019). The multiscale bowler-hat transform for blood vessel enhancement in retinal images. Pattern Recognition, 88, 739-750. https://doi.org/10.1016/j.patcog.2018.10.011

Enhancement, followed by segmentation, quantification and modelling of blood vessels in retinal images plays an essential role in computer-aided retinopathy diagnosis. In this paper, we introduce the bowler-hat transform method a new approach based o... Read More about The multiscale bowler-hat transform for blood vessel enhancement in retinal images.

Modeling Detailed Cloud Scene from Multi-source Images (2018)
Presentation / Conference Contribution
Cen, Y., Liang, X., Chen, J., Yang, B., & Li, F. W. (2018). Modeling Detailed Cloud Scene from Multi-source Images. In H. Fu, A. Ghosh, & J. Kopf (Eds.), Pacific graphics short papers, (49-52). https://doi.org/10.2312/pg.20181278

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For e... Read More about Modeling Detailed Cloud Scene from Multi-source Images.

Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery (2018)
Book Chapter
Payen de La Garanderie, G., Atapour Abarghouei, A., & Breckon, T. P. (2018). Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery. In V. Ferrari, M. Hebert, C. Sminchisescu, & Y. Weiss (Eds.), Computer Vision – ECCV 2018 : 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XII (812-830). Springer Verlag. https://doi.org/10.1007/978-3-030-01261-8_48

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360... Read More about Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery.

Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers (2018)
Presentation / Conference Contribution
Vissol-Gaudin, E., Kotsialos, A., Groves, C., Pearson, C., Zeze, D., Petty, M., & Al-moubayed, N. (2018). Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers. In 2018 IEEE Congress on Evolutionary Computation (CEC) : 8-13 July 2018, Rio de Janeiro, Brazil ; proceedings (646-653). https://doi.org/10.1109/cec.2018.8477779

This paper focuses on a performance analysis of single-walled-carbon-nanotube / liquid crystal classifiers produced by evolution in materio. A new confidence measure is proposed in this paper. It is different from statistical tools commonly used to e... Read More about Confidence Measures for Carbon-Nanotube / Liquid Crystals Classifiers.

An Exploration of Dropout with RNNs for Natural Language Inference (2018)
Presentation / Conference Contribution
Gajbhiye, A., Jaf, S., Al-Moubayed, N., McGough, A. S., & Bradley, S. (2018, December). An Exploration of Dropout with RNNs for Natural Language Inference. Presented at ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In thi... Read More about An Exploration of Dropout with RNNs for Natural Language Inference.

Defining an alert mechanism for detecting likely threats to national security (2018)
Presentation / Conference Contribution
Cardenas-Canto, P., Theodoropoulos, G., & Obara, B. (2018). Defining an alert mechanism for detecting likely threats to national security.

Attribute relaxation from class level to instance level for zero-shot learning (2018)
Journal Article
Zhang, H., Long, Y., & Zhao, C. (2018). Attribute relaxation from class level to instance level for zero-shot learning. Electronics Letters, 54(20), 1170-1172. https://doi.org/10.1049/el.2018.5027

Conventional zero-shot learning (ZSL) methods usually use class-level attribute, which corresponds to a batch of images of same category. This setting is not reasonable since the images even though belong to same category still have variances in thei... Read More about Attribute relaxation from class level to instance level for zero-shot learning.

Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum (2018)
Presentation / Conference Contribution
Tsakalidis, A., Aletras, N., Cristea, A., & Liakata, M. (2018). Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (367-376). https://doi.org/10.1145/3269206.3271783

Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising. Previous approaches mainly focus on predicting national general election... Read More about Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum.

On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks (2018)
Presentation / Conference Contribution
Guo, T., Akcay, S., Adey, P., & Breckon, T. (2018). On The Impact Of Varying Region Proposal Strategies For Raindrop Detection And Classification Using Convolutional Neural Networks. In Proc. Int. Conf. on Image Processing (3413-3417). https://doi.org/10.1109/ICIP.2018.8451453

Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks (2018)
Journal Article
Lee, S. H., Chan, C. S., & Remagnino, P. (2018). Multi-Organ Plant Classification Based on Convolutional and Recurrent Neural Networks. IEEE Transactions on Image Processing, 27(9), 4287-4301. https://doi.org/10.1109/tip.2018.2836321

DROpS: A demand response optimization scheme in SDN-enabled smart energy ecosystem (2018)
Journal Article
Aujla, G. S., Garg, S., Batra, S., Kumar, N., You, I., & Sharma, V. (2019). DROpS: A demand response optimization scheme in SDN-enabled smart energy ecosystem. Information Sciences, 476, https://doi.org/10.1016/j.ins.2018.09.047

Scores of Scores An OpenScore project to encode and share sheet music (2018)
Presentation / Conference Contribution
Gotham, M., Bosworth, W., Jonas, P., Rootham, D., Bower, B., & VanHandel, L. (2018). Scores of Scores An OpenScore project to encode and share sheet music. In DLfM '18: Proceedings of the 5th International Conference on Digital Libraries for Musicology (87-95). https://doi.org/10.1145/3273024.3273026

A population protocol for exact majority with $O(\log^{5/3} n)$ stabilization time and asymptotically optimal number of states (2018)
Presentation / Conference Contribution
Berenbrink, P., Elsässer, R., Friedetzky, T., Kaaser, D., Kling, P., & Radzik, T. (2018). A population protocol for exact majority with $O(\log^{5/3} n)$ stabilization time and asymptotically optimal number of states. In U. Schmid, & J. Widder (Eds.), 32nd International Symposium on Distributed Computing (DISC 2018) (10:1-10:18). https://doi.org/10.4230/lipics.disc.2018.10

A population protocol is a sequence of pairwise interactions of n agents. During one interaction, two randomly selected agents update their states by applying a deterministic transition function. The goal is to stabilize the system at a desired outpu... Read More about A population protocol for exact majority with $O(\log^{5/3} n)$ stabilization time and asymptotically optimal number of states.