Outputs (118)

Setoid Type Theory—A Syntactic Translation (2019)
Presentation / Conference Contribution
Altenkirch, T., Boulier, S., Kaposi, A., & Tabereau, N. (2019, October). Setoid Type Theory—A Syntactic Translation. Presented at 13th International Conference on Mathematics of Program Construction (MPC 2019), Porto, Portugal

We introduce setoid type theory, an intensional type theory with a proof-irrelevant universe of propositions and an equality type satisfying functional extensionality and propositional extensionality. We justify the rules of setoid type theory by a s... Read More about Setoid Type Theory—A Syntactic Translation.

Optimising Decarbonisation Investment for Firms towards Environmental Sustainability (2019)
Journal Article
Tran, T.-H., Mao, Y., & Siebers, P.-O. (2019). Optimising Decarbonisation Investment for Firms towards Environmental Sustainability. Sustainability, 11(20), Article 5718. https://doi.org/10.3390/su11205718

We develop a mixed-integer non-linear programming model for firms’ decarbonisation investment decision-making towards a sustainable environment. Our model seeks the optimal investment for a firm to achieve maximum profit under constraints derived fro... Read More about Optimising Decarbonisation Investment for Firms towards Environmental Sustainability.

Privacy Engineering for Domestic IoT: Enabling Due Diligence (2019)
Journal Article
Lodge, T., & Crabtree, A. (2019). Privacy Engineering for Domestic IoT: Enabling Due Diligence. Sensors, 19(20), Article 4380. https://doi.org/10.3390/s19204380

The EU’s General Data Protection Regulation (GDPR) has recently come into effect and insofar as IoT applications touch EU citizens or their data, developers are obliged to exercise due diligence and ensure they undertake Data Protection by Design and... Read More about Privacy Engineering for Domestic IoT: Enabling Due Diligence.

Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms (2019)
Journal Article
Schweiger, G., Nilsson, H., Schoeggl, J., Birk, W., & Posch, A. (2020). Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms. Applied Mathematics and Computation, 365, Article 124713. https://doi.org/10.1016/j.amc.2019.124713

A trend across most areas where simulation-driven development is used is the ever increasing size and complexity of the systems under consideration, pushing established methods of modeling and simulation towards their limits. This paper complements e... Read More about Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms.

A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes (2019)
Journal Article
Chen, B., Qu, R., Bai, R., & Laesanklang, W. (2020). A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes. RAIRO: Operations Research, 54(5), 1467-1494. https://doi.org/10.1051/ro/2019080

Based on a real-life container transport problem, a model of Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is proposed in this paper. In a wide planning horizon, which is divided into a number of shifts, a fixed number of trucks a... Read More about A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes.

Deep Reinforcement Learning based Patch Selection for Illuminant Estimation (2019)
Journal Article
Xu, B., Liu, J., Hou, X., Liu, B., & Qiu, G. (2019). Deep Reinforcement Learning based Patch Selection for Illuminant Estimation. Image and Vision Computing, 91, Article 103798. https://doi.org/10.1016/j.imavis.2019.08.002

Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model. However, such practices would inevitably lead to information loss or the... Read More about Deep Reinforcement Learning based Patch Selection for Illuminant Estimation.

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study (2019)
Journal Article
Canizo, M., Triguero, I., Conde, A., & Onieva, E. (2019). Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study. Neurocomputing, 363, 246-260. https://doi.org/10.1016/j.neucom.2019.07.034

Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of... Read More about Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study.

A comparison of presentation methods for conducting youth juries (2019)
Journal Article
Dowthwaite, L., Perez Vallejos, E., Koene, A., Cano, M., & Portillo, V. (2019). A comparison of presentation methods for conducting youth juries. PLoS ONE, 14(6), Article e0218770. https://doi.org/10.1371/journal.pone.0218770

The 5Rights Youth Juries are an educational intervention to promote digital literacy by engaging participants (i.e. jurors) in a deliberative discussion around their digital rights. The main objective of these jury-styled focus groups is to encourage... Read More about A comparison of presentation methods for conducting youth juries.

The Living Room of the Future (2019)
Presentation / Conference Contribution
Sailaja, N., Colley, J., Crabtree, A., Gradinar, A., Coulton, P., Forrester, I., Kerlin, L., & Stenton, P. (2019, June). The Living Room of the Future. Presented at 2019 ACM International Conference on Interactive Experiences for TV and Online Video (TVX 19), Salford, Manchester, United Kingdom

Emergent media services are turning towards the use of audience data to deliver more personalised and immersive experiences. We present the Living Room of The Future (LRoTF), an embodied design fiction built to both showcase future adaptive physicall... Read More about The Living Room of the Future.

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective (2019)
Journal Article
Angarita-Zapata, J. S., Masegosa, A. D., & Triguero, I. (2019). A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective. IEEE Access, 7, 68185 -68205. https://doi.org/10.1109/ACCESS.2019.2917228

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computatio... Read More about A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective.