All Outputs (221)

Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information Visualization (2017)
Journal Article
Mcnabb, L., & Laramee, R. S. (2017). Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information Visualization. Computer Graphics Forum, 36(3), 589-617. https://doi.org/10.1111/cgf.13212

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within... Read More about Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information Visualization.

The continuity of monadic stream functions (2017)
Conference Proceeding
Capretta, V., & Fowler, J. (2017). The continuity of monadic stream functions. In Proceedings - 2017 32nd Annual ACM/IEEE Symposium on Logic in Computer Science (LICS 2017) (658-669). https://doi.org/10.1109/LICS.2017.8005119

© 2017 IEEE. Brouwer's continuity principle states that all functions from infinite sequences of naturals to naturals are continuous, that is, for every sequence the result depends only on a finite initial segment. It is an intuitionistic axiom that... Read More about The continuity of monadic stream functions.

The Functional Dendritic Cell Algorithm: A formal specification with Haskell (2017)
Conference Proceeding
Greensmith, J., & Gale, M. B. (2017). The Functional Dendritic Cell Algorithm: A formal specification with Haskell. In Proceedings - 2017 IEEE Congress on Evolutionary Computation (CEC 2017) (1787-1794). https://doi.org/10.1109/CEC.2017.7969518

The Dendritic Cell Algorithm (DCA) has been described in a number of different ways, sometimes resulting in incorrect implementations. We believe this is due to previous, imprecise attempts to describe the algorithm. The main contribution of this pap... Read More about The Functional Dendritic Cell Algorithm: A formal specification with Haskell.

Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data (2017)
Conference Proceeding
Jaiswal, S., Valstar, M. F., Gillott, A., & Daley, D. (2017). Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data. In Proceedings - 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) (762-769). https://doi.org/10.1109/FG.2017.95

Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are neurodevelopmental conditions which impact on a significant number of children and adults. Currently, the diagnosis of such disorders is done by experts who employ... Read More about Automatic Detection of ADHD and ASD from Expressive Behaviour in RGBD Data.

Small Sample Deep Learning for Newborn Gestational Age Estimation (2017)
Conference Proceeding
Torres Torres, M., Valstar, M. F., Henry, C., Ward, C., & Sharkey, D. (2017). Small Sample Deep Learning for Newborn Gestational Age Estimation. In Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017) (79-86). https://doi.org/10.1109/FG.2017.19

A baby’s gestational age determines whether or not they are preterm, which helps clinicians decide on suitable post-natal treatment. The most accurate dating methods use Ultrasound Scan (USS) machines, but these machines are expensive, require traine... Read More about Small Sample Deep Learning for Newborn Gestational Age Estimation.

Young people's policy recommendations on algorithm fairness (2017)
Conference Proceeding
Perez, E., Koene, A., Portillo, V., Dowthwaite, L., & Cano, M. (2017). Young people's policy recommendations on algorithm fairness. In WebSci '17: Proceedings of the 2017 ACM on Web Science Conference (247-251). https://doi.org/10.1145/3091478.3091512

This paper explores the policy recommendations made by young people regarding algorithm fairness. It describes a piece of ongoing research developed to bring children and young people to the front line of the debate regarding children's digital right... Read More about Young people's policy recommendations on algorithm fairness.

Data Work: How Energy Advisors and Clients Make IoT Data Accountable (2017)
Journal Article
Fischer, J. E., Crabtree, A., Colley, J., Rodden, T., & Costanza, E. (2017). Data Work: How Energy Advisors and Clients Make IoT Data Accountable. Computer Supported Cooperative Work, 26(4-6), 597-626. https://doi.org/10.1007/s10606-017-9293-x

© 2017, The Author(s). We present fieldwork findings from the deployment of an interactive sensing system that supports the work of energy advisors who give face-to-face advice to low-income households in the UK. We focus on how the system and the da... Read More about Data Work: How Energy Advisors and Clients Make IoT Data Accountable.

CRNN: A Joint Neural Network for Redundancy Detection (2017)
Conference Proceeding
Fu, X., Ch’ng, E., Aickelin, U., & See, S. (2017). CRNN: A Joint Neural Network for Redundancy Detection. In 2017 IEEE International Conference on Smart Computing (SMARTCOMP) (1-8). https://doi.org/10.1109/SMARTCOMP.2017.7946996

This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character-aware convolutional neural network (Char-CNN) with character-aware rec... Read More about CRNN: A Joint Neural Network for Redundancy Detection.

Enabling hand-crafted visual markers at scale (2017)
Conference Proceeding
Preston, W., Benford, S., Thorn, E., Koleva, B., Rennick-Egglestone, S., Mortier, R., …Worboys, M. (2017). Enabling hand-crafted visual markers at scale. . https://doi.org/10.1145/3064663.3064746

As locative media and augmented reality spread into the everyday world so it becomes important to create aesthetic visual markers at scale. We explore a designer-centred approach in which skilled designers handcraft seed designs that are automaticall... Read More about Enabling hand-crafted visual markers at scale.

Algorithmic bias: addressing growing concerns (2017)
Journal Article
Koene, A. (2017). Algorithmic bias: addressing growing concerns. IEEE Technology and Society Magazine, 36(2), 31-32. https://doi.org/10.1109/MTS.2017.2697080

In the context of the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems, and with support from its executive director, the author have proposed the development of a new IEEE Standard on Algorithmic Bi... Read More about Algorithmic bias: addressing growing concerns.

Accountable Internet of things?: outline of the IoT Databox model (2017)
Conference Proceeding
Crabtree, A., Lodge, T., Colley, J., Greenhalgh, C., & Mortier, R. (2017). Accountable Internet of things?: outline of the IoT Databox model.

This paper outlines the IoT Databox model as a means of making the Internet of Things (IoT) accountable to individuals. Accountability is a key to building consumer trust and mandated in data protection legislation. We briefly outline the ‘external’... Read More about Accountable Internet of things?: outline of the IoT Databox model.

Touchomatic: Interpersonal touch gaming in the wild (2017)
Conference Proceeding
Marshall, J., & Tennent, P. (2017). Touchomatic: Interpersonal touch gaming in the wild. In Proceedings of the 2017 Conference on Designing Interactive Systems - DIS '17 (417-428). https://doi.org/10.1145/3064663.3064727

Direct touch between people is a key element of social behaviour. Recently a number of researchers have explored games which sense aspects of such interpersonal touch to control interaction with a multiplayer computer game. In this paper, we describe... Read More about Touchomatic: Interpersonal touch gaming in the wild.

The Rough Mile: Testing a Framework of Immersive Practice (2017)
Conference Proceeding
Spence, J., Hazzard, A., McGrath, S., Greenhalgh, C., & Benford, S. (2017). The Rough Mile: Testing a Framework of Immersive Practice. In Proceedings of the 2017 Conference on Designing Interactive Systems - DIS '17 (877-888). https://doi.org/10.1145/3064663.3064756

We present our case study on gifting digital music, The Rough Mile, as an example of a Framework of Immersive Practice, intended for researchers and practitioners in HCI and interaction design. Although immersion is a frequently used term in the HCI... Read More about The Rough Mile: Testing a Framework of Immersive Practice.

A portable image-based cytometer for rapid malaria detection and quantification (2017)
Journal Article
Yang, D., Subramanian, G., Duan, J., Gao, S., Bai, L., Chandramohanadas, R., & Ai, Y. (2017). A portable image-based cytometer for rapid malaria detection and quantification. PLoS ONE, 12(6), Article e0179161. https://doi.org/10.1371/journal.pone.0179161

Increasing resistance by malaria parasites to currently used antimalarials across the developing world warrants timely detection and classification so that appropriate drug combinations can be administered before clinical complications arise. However... Read More about A portable image-based cytometer for rapid malaria detection and quantification.

A hybrid EDA for load balancing in multicast with network coding (2017)
Journal Article
Xing, H., Li, S., cui, Y., Yan, L., Pan, W., & Qu, R. (2017). A hybrid EDA for load balancing in multicast with network coding. Applied Soft Computing, 59, https://doi.org/10.1016/j.asoc.2017.06.003

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) i... Read More about A hybrid EDA for load balancing in multicast with network coding.

Learning heuristic selection using a time delay neural network for open vehicle routing (2017)
Conference Proceeding
Tyasnurita, R., Özcan, E., & John, R. (2017). Learning heuristic selection using a time delay neural network for open vehicle routing.

A selection hyper-heuristic is a search method that controls a prefixed set of low-level heuristics for solving a given computationally difficult problem. This study investigates a learning-via demonstrations approach generating a selection hyper-heu... Read More about Learning heuristic selection using a time delay neural network for open vehicle routing.

Exploring the landscape of the space of heuristics for local search in SAT (2017)
Conference Proceeding
Burnett, A. W., & Parkes, A. J. (2017). Exploring the landscape of the space of heuristics for local search in SAT.

Local search is a powerful technique on many combinatorial optimisation problems. However, the effectiveness of local search methods will often depend strongly on the details of the heuristics used within them. There are many potential heuristics, an... Read More about Exploring the landscape of the space of heuristics for local search in SAT.

Editorial responsibilities arising from personalization algorithms (2017)
Journal Article
Koene, A., Perez, E., Webb, H., Patel, M., Ceppi, S., Jirotka, M., & McAuley, D. (2017). Editorial responsibilities arising from personalization algorithms. Orbit, 1(1),

A modified indicator-based evolutionary algorithm (mIBEA) (2017)
Conference Proceeding
Li, W., Özcan, E., John, R., Drake, J. H., Neumann, A., & Wagner, M. (2017). A modified indicator-based evolutionary algorithm (mIBEA).

Multi-objective evolutionary algorithms (MOEAs) based on the concept of Pareto-dominance have been successfully applied to many real-world optimisation problems. Recently, research interest has shifted towards indicator-based methods to guide the sea... Read More about A modified indicator-based evolutionary algorithm (mIBEA).

Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation (2017)
Conference Proceeding
Egede, J. O., Valstar, M. F., & Martinez, B. (2017). Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation.

Automatic continuous time, continuous value assessment of a patient's pain from face video is highly sought after by the medical profession. Despite the recent advances in deep learning that attain impressive results in many domains, pain estimation... Read More about Fusing deep learned and hand-crafted features of appearance, shape, and dynamics for automatic pain estimation.