Outputs (135)

Identifying Variation in the Newborn Life Support Procedure: An Automated Method (2023)
Conference Proceeding
Tan, A., Remenyte-Prescott, R., Egede, J., Valstar, M., & Sharkey, D. (2023). Identifying Variation in the Newborn Life Support Procedure: An Automated Method. In M. P. Brito, T. Aven, P. Baraldi, M. Čepin, & E. Zio (Eds.), Proceedings of the 33rd European Safety and Reliability Conference (ESREL 2023) (607-614)

This research is conducted for developing an automated method to recognize variations in the Newborn Life Support (NLS) procedure. Compliance with the NLS standard guideline is essential to prevent any adverse consequences for the newborn. Video reco... Read More about Identifying Variation in the Newborn Life Support Procedure: An Automated Method.

Automated design of local search algorithms: Predicting algorithmic components with LSTM (2023)
Journal Article
Meng, W., & Qu, R. (2024). Automated design of local search algorithms: Predicting algorithmic components with LSTM. Expert Systems with Applications, 237(Part A), Article 121431. https://doi.org/10.1016/j.eswa.2023.121431

With a recently defined AutoGCOP framework, the design of local search algorithms has been defined as the composition of elementary algorithmic components. The effective compositions of the best algorithms thus retain useful knowledge of effective al... Read More about Automated design of local search algorithms: Predicting algorithmic components with LSTM.

Automated design of search algorithms based on reinforcement learning (2023)
Journal Article
Yi, W., & Qu, R. (2023). Automated design of search algorithms based on reinforcement learning. Information Sciences, 649, Article 119639. https://doi.org/10.1016/j.ins.2023.119639

Automated algorithm design has attracted increasing research attention recently in the evolutionary computation community. The main design decisions include selection heuristics and evolution operators in the search algorithms. Most existing studies,... Read More about Automated design of search algorithms based on reinforcement learning.

“It becomes more of an abstract idea, this privacy” – Informing the design for communal privacy experiences in smart homes (2023)
Journal Article
Kraemer, M. J., Chalhoub, G., Webb, H., & Flechais, I. (2023). “It becomes more of an abstract idea, this privacy” – Informing the design for communal privacy experiences in smart homes. International Journal of Human-Computer Studies, 180, Article 103138. https://doi.org/10.1016/j.ijhcs.2023.103138

In spite of research recognizing the home as a shared space and privacy as inherently social, privacy in smart homes has mainly been researched from an individual angle. Sometimes contrasting and comparing perspectives of multiple individuals, resear... Read More about “It becomes more of an abstract idea, this privacy” – Informing the design for communal privacy experiences in smart homes.

Calculating Compilers for Concurrency (2023)
Journal Article
Bahr, P., & Hutton, G. (2023). Calculating Compilers for Concurrency. Proceedings of the ACM on Programming Languages, 7(ICFP), 740-767. https://doi.org/10.1145/3607855

Choice trees have recently been introduced as a general structure for defining the semantics of programming languages with a wide variety of features and effects. In this article we focus on concurrent languages, and show how a codensity version of c... Read More about Calculating Compilers for Concurrency.

Deep Contrastive Representation Learning With Self-Distillation (2023)
Journal Article
Xiao, Z., Xing, H., Zhao, B., Qu, R., Luo, S., Dai, P., …Zhu, Z. (2024). Deep Contrastive Representation Learning With Self-Distillation. IEEE Transactions on Emerging Topics in Computational Intelligence, 8(1), 3-15. https://doi.org/10.1109/tetci.2023.3304948

Recently, contrastive learning (CL) is a promising way of learning discriminative representations from time series data. In the representation hierarchy, semantic information extracted from lower levels is the basis of that captured from higher level... Read More about Deep Contrastive Representation Learning With Self-Distillation.

Time to consider animal data governance: perspectives from neuroscience (2023)
Journal Article
Eke, D., Ogoh, G., Knight, W., & Stahl, B. (in press). Time to consider animal data governance: perspectives from neuroscience. Frontiers in Neuroinformatics, 17, Article 1233121. https://doi.org/10.3389/fninf.2023.1233121

Introduction: Scientific research relies mainly on multimodal, multidimensional big data generated from both animal and human organisms as well as technical data. However, unlike human data that is increasingly regulated at national, regional and int... Read More about Time to consider animal data governance: perspectives from neuroscience.

Human-AI Musicking: A Framework for Designing AI for Music Co-creativity (2023)
Conference Proceeding
Vear, C., Benford, S., Avila, J. M., & Moroz, S. (2023). Human-AI Musicking: A Framework for Designing AI for Music Co-creativity.

In this paper, we present a framework for understanding human-AI musicking. This framework prompts a series of questions for reflecting on various aspects of the creative interrelationships between musicians and AI and thus can be used as a tool for... Read More about Human-AI Musicking: A Framework for Designing AI for Music Co-creativity.

“They’re not going to do all the tasks we do”: Understanding Trust and Reassurance towards a UV-C Disinfection Robot (2023)
Conference Proceeding
Trigo, M. J. G., Reyes-Cruz, G., Maior, H. A., Pepper, C., Price, D., Leonard, P., …Fischer, J. E. (2023). “They’re not going to do all the tasks we do”: Understanding Trust and Reassurance towards a UV-C Disinfection Robot. In 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). https://doi.org/10.1109/RO-MAN57019.2023.10309364

Increasingly, robots are adopted for routine tasks such as cleaning and disinfection of public spaces, raising questions about attitudes and trust of professional cleaners who might in future have robots as teammates, and whether the general public f... Read More about “They’re not going to do all the tasks we do”: Understanding Trust and Reassurance towards a UV-C Disinfection Robot.

Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data (2023)
Conference Proceeding
Dave, R., Angarita-Zapata, J. S., & Triguero, I. (2023). Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data. In Machine Learning and Knowledge Extraction (82-102). https://doi.org/10.1007/978-3-031-40837-3_6

The emergence of Machine Learning (ML) has altered how researchers and business professionals value data. Applicable to almost every industry, considerable amounts of time are wasted creating bespoke applications and repetitively hand-tuning models t... Read More about Hyper-Stacked: Scalable and Distributed Approach to AutoML for Big Data.