All Outputs (1967)

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.

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.

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.

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.

“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.

Automatically Labeling Cyber Threat Intelligence reports using Natural Language Processing (2023)
Conference Proceeding
Abdi, H., Bagley, S. R., Furnell, S., & Twycross, J. (2023). Automatically Labeling Cyber Threat Intelligence reports using Natural Language Processing. In DocEng ’23 : Proceedings of the 2023 ACM Symposium on Document Engineering. https://doi.org/10.1145/3573128.3609348

Attribution provides valuable intelligence in the face of Advanced Persistent Threat (APT) attacks. By accurately identifying the culprits and actors behind the attacks, we can gain more insights into their motivations, capabilities, and potential fu... Read More about Automatically Labeling Cyber Threat Intelligence reports using Natural Language Processing.

A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study (2023)
Journal Article
Horne, K. L., Viramontes-Hörner, D., Packington, R., Monaghan, J., Shaw, S., Akani, A., …Selby, N. M. (2023). A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study. Kidney International, 104(6), 1185-1193. https://doi.org/10.1016/j.kint.2023.08.005

Acute kidney injury (AKI) is associated with adverse long-term outcomes, but many studies are retrospective, focused on specific patient groups or lack adequate comparators. The ARID (AKI Risk in Derby) Study was a five-year prospective parallel-grou... Read More about A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study.

LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems (2023)
Conference Proceeding
Fatehi, K., & Kucukyilmaz, A. (2023). LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems. In Interspeech 2023

With advances in deep learning methodologies, Automatic Speech Recognition (ASR) systems have seen impressive results. However, ASR in Low-Resource Environments (LREs) are challenged by a lack of training data for the specific target domain. We propo... Read More about LABERT: A Combination of Local Aggregation and Self-Supervised Speech Representation Learning for Detecting Informative Hidden Units in Low-Resource ASR Systems.

Digitally Un/Free: the everyday impact of social media on the lives of young people (2023)
Journal Article
Bibizadeh, R. E., Procter, R., Girvan, C., Webb, H., & Jirotka, M. (in press). Digitally Un/Free: the everyday impact of social media on the lives of young people. Learning, Media and Technology, 1-14. https://doi.org/10.1080/17439884.2023.2237883

This article offers an original contribution to the crucial question of how digital media impacts children and young people’s everyday lives. Focus groups with young people aged 11–21 years, and interviews with teachers in schools in England revealed... Read More about Digitally Un/Free: the everyday impact of social media on the lives of young people.

Navigating the labyrinth of RI through a practical application — A case study in a cross-disciplinary research project (2023)
Journal Article
Zhao, J., Patel, M., Inglesant, P., Portillo, V., Webb, H., Dowthwaite, L., …Jirotka, M. (2023). Navigating the labyrinth of RI through a practical application — A case study in a cross-disciplinary research project. Journal of Responsible Technology, 15, Article 100064. https://doi.org/10.1016/j.jrt.2023.100064

Responsible Innovation (RI) aims to enable research and innovation to take a more systematic approach to anticipating potential risks and consequences of planned research/innovative outputs. The Anticipation, Reflection, Engagement and Action (AREA)... Read More about Navigating the labyrinth of RI through a practical application — A case study in a cross-disciplinary research project.

Apartness, sharp elements, and the Scott topology of domains (2023)
Journal Article
de Jong, T. (2023). Apartness, sharp elements, and the Scott topology of domains. Mathematical Structures in Computer Science, 33(7), 573-604. https://doi.org/10.1017/S0960129523000282

Working constructively, we study continuous directed complete posets (dcpos) and the Scott topology. Our two primary novelties are a notion of intrinsic apartness and a notion of sharp elements. Being apart is a positive formulation of being unequal,... Read More about Apartness, sharp elements, and the Scott topology of domains.

Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning (2023)
Journal Article
Aickelin, U., Khorshidi, H. A., Qu, R., & Charkhgard, H. (2023). Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning. IEEE Transactions on Evolutionary Computation, 27(4), 746-748. https://doi.org/10.1109/tevc.2023.3292528

We are very pleased to introduce this special issue on multiobjective evolutionary optimization for machine learning (MOML). Optimization is at the heart of many machine-learning techniques. However, there is still room to exploit optimization in mac... Read More about Guest Editorial Special Issue on Multiobjective Evolutionary Optimization in Machine Learning.

The Münchhausen Method in Type Theory (2023)
Journal Article
Altenkirch, T., Kaposi, A., Šinkarovs, A., & Végh, T. (2023). The Münchhausen Method in Type Theory. LIPIcs, 269, https://doi.org/10.4230/LIPIcs.TYPES.2022.10

In one of his long tales, after falling into a swamp, Baron Münchhausen salvaged himself and the horse by lifting them both up by his hair. Inspired by this, the paper presents a technique to justify very dependent types. Such types reference the ter... Read More about The Münchhausen Method in Type Theory.

The #longcovid revolution: A reflexive thematic analysis (2023)
Journal Article
Turner, M., Beckwith, H., Spratt, T., Vallejos, E. P., & Coughlan, B. (2023). The #longcovid revolution: A reflexive thematic analysis. Social Science and Medicine, 333, Article 116130. https://doi.org/10.1016/j.socscimed.2023.116130

Research has identified long COVID as the first virtual patient-made condition (Callard and Perego, 2021). It originated from Twitter users sharing their experiences using the hashtag #longcovid. Over the first two years of the pandemic, long COVID a... Read More about The #longcovid revolution: A reflexive thematic analysis.

Generative AI Considered Harmful (2023)
Conference Proceeding
Fischer, J. E. (2023). Generative AI Considered Harmful. In Proceedings of the 5th International Conference on Conversational User Interfaces (CUI '23). https://doi.org/10.1145/3571884.3603756

The recent months have seen an explosion of interest, hype, and concern about generative AI, driven by the release of ChatGPT. In this article I seek to explicate some potential and actual harms of the engineering and use of generative AI such as Cha... Read More about Generative AI Considered Harmful.

RoboClean: Contextual Language Grounding for Human-Robot Interactions in Specialised Low-Resource Environments (2023)
Conference Proceeding
Fuentes, C., Porcheron, M., & Fischer, J. E. (2023). RoboClean: Contextual Language Grounding for Human-Robot Interactions in Specialised Low-Resource Environments. In Proceedings of the 5th International Conference on Conversational User Interfaces (CUI '23). https://doi.org/10.1145/3571884.3597137

Building effective voice interfaces for the instruction of service robots in specialised environments is difficult due to the local knowledge of workers, such as specific terminology for objects and space, leading to limited data to train language mo... Read More about RoboClean: Contextual Language Grounding for Human-Robot Interactions in Specialised Low-Resource Environments.