Outputs (130)

“Research in the Wild”: Approaches to Understanding the Unremarkable as a Resource for Design (2019)
Book Chapter
Crabtree, A., Tolmie, P., & Chamberlain, A. (2019). “Research in the Wild”: Approaches to Understanding the Unremarkable as a Resource for Design. In A. Chamberlain, & A. Crabtree (Eds.), Into the Wild: Beyond the Design Research Lab (31-53). Springer. https://doi.org/10.1007/978-3-030-18020-1_3

This chapter outlines some key approaches towards understanding the unremarkable. It focuses first on a sociological orientation to the everyday world as key to the enterprise, and then on a variety of complimentary approaches for elaborating or surf... Read More about “Research in the Wild”: Approaches to Understanding the Unremarkable as a Resource for Design.

Research ‘In the Wild’ (2019)
Book Chapter
Chamberlain, A., & Crabtree, A. (2020). Research ‘In the Wild’. In A. Chamberlain, & A. Crabtree (Eds.), Into the Wild: Beyond the Design Research Lab (1-6). Springer International Publishing. https://doi.org/10.1007/978-3-030-18020-1_1

Over recent years the term `in the wild' has increasingly appeared in publications within the field of Human Computer Interaction (HCI). The phrase has become synonymous with a range of approaches that focus upon carrying out research-based studies r... Read More about Research ‘In the Wild’.

Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty (2019)
Journal Article
He, F., Chaussalet, T., & Qu, R. (2019). Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty. Operations Research Perspectives, 6, Article 100119. https://doi.org/10.1016/j.orp.2019.100119

Nursing workforce management is a challenging decision-making task in hospitals. The decisions are made across different timescales and levels from strategic long-term staffing budget to mid-term scheduling. These decisions are interconnected and imp... Read More about Controlling understaffing with conditional Value-at-Risk constraint for an integrated nurse scheduling problem under patient demand uncertainty.

DECSYS - Discrete and Ellipse-based response Capture SYStem (2019)
Presentation / Conference Contribution
Ellerby, Z., McCulloch, J., Young, J., & Wagner, C. (2019, June). DECSYS - Discrete and Ellipse-based response Capture SYStem. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Data-driven techniques that capture uncertainty through intervals or fuzzy sets can substantially improve systematic reasoning about uncertain information. Recent years have seen renewed interest in the capture of intervals from a variety of sources-... Read More about DECSYS - Discrete and Ellipse-based response Capture SYStem.

Evolving Deep CNN-LSTMs for Inventory Time Series Prediction (2019)
Presentation / Conference Contribution
Xue, N., Triguero, I., Figueredo, G. P., & Landa-Silva, D. (2019, June). Evolving Deep CNN-LSTMs for Inventory Time Series Prediction. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the mo... Read More about Evolving Deep CNN-LSTMs for Inventory Time Series Prediction.

Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses (2019)
Presentation / Conference Contribution
Maciel Guerra, A., Figueredo, G. P., Von Zuben, F., Marti, E., Twycross, J., & Alcocer, M. J. (2019, June). Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Microarrays can be employed to better characterise allergies, as interactions between antibodies and allergens in mammals can be monitored. Once the joint dynamics of these elements in both healthy and diseased animals are understood, a model to pred... Read More about Microarray Feature Selection and Dynamic Selection of Classifiers for Early Detection of Insect Bite Hypersensitivity in Horses.

On Comparing and Selecting Approaches to Model Interval-Valued Data as Fuzzy Sets (2019)
Presentation / Conference Contribution
McCulloch, J., Ellerby, Z., & Wagner, C. (2019, June). On Comparing and Selecting Approaches to Model Interval-Valued Data as Fuzzy Sets. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

The capture of interval-valued data is becoming an increasingly common approach in data collection (from survey based research to the collation of sensor data) as an efficient method of obtaining information about uncertainty associated with the data... Read More about On Comparing and Selecting Approaches to Model Interval-Valued Data as Fuzzy Sets.

A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening (2019)
Presentation / Conference Contribution
Figueredo, G. P., Shi, P., Parkes, A. J., Evans, K., Garibaldi, J. M., Negm, O., Tighe, P. J., Sewell, H. F., & Robertson, J. (2019, June). A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening. Presented at 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand

Current methods to identify cutoff values for tumour-associated molecules (antigens) discrimination are based on statistics and brute force. These methods applied to cancer screening problems are very inefficient, especially with large data sets with... Read More about A Hybrid Evolutionary Strategy to Optimise Early-Stage Cancer Screening.

Path Spaces of Higher Inductive Types in Homotopy Type Theory (2019)
Presentation / Conference Contribution
Kraus, N., & von Raumer, J. (2019, June). Path Spaces of Higher Inductive Types in Homotopy Type Theory. Presented at 2019 34th Annual ACM/IEEE Symposium on Logic in Computer Science (LICS), Vancouver, BC, Canada

The study of equality types is central to homotopy type theory. Characterizing these types is often tricky, and various strategies, such as the encode-decode method, have been developed. We prove a theorem about equality types of coequalizers and pus... Read More about Path Spaces of Higher Inductive Types in Homotopy Type Theory.

A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification (2019)
Presentation / Conference Contribution
Jimenez, M., Torres, M. T., John, R., & Triguero, I. (2019, June). A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification. Presented at 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), New Orleans, LA, USA

Citizen science is becoming mainstream in a wide variety of real-world applications in astronomy or bioinformatics, in which, for example, classification tasks by experts are very time consuming. These projects engage amateur volunteers that are task... Read More about A Preliminary Approach for the Exploitation of Citizen Science Data for Fast and Robust Fuzzy k-Nearest Neighbour Classification.