All Outputs (24)

User Engagement Triggers in Social Media Discourse on Biodiversity Conservation (2024)
Journal Article
Dethlefs, N., & Cuayáhuitl, H. (online). User Engagement Triggers in Social Media Discourse on Biodiversity Conservation. ACM Transactions on Social Computing, https://doi.org/10.1145/3662685

Studies in digital conservation have increasingly used social media in recent years as a source of data to understand the interactions between humans and nature, model and monitor biodiversity, and analyse online discourse about the conservation of s... Read More about User Engagement Triggers in Social Media Discourse on Biodiversity Conservation.

This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy (2023)
Journal Article
Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy. Patterns, 4(1), Article 100676. https://doi.org/10.1016/j.patter.2022.100676

We explore the recently released ChatGPT model, one of the most powerful conversational AI models that has ever been developed. This opinion provides a perspective on its strengths and weaknesses and a call to action for the AI community (including a... Read More about This new conversational AI model can be your friend, philosopher, and guide ... and even your worst enemy.

Deep learning for the early detection of harmful algal blooms and improving water quality monitoring (2022)
Thesis
Dagtekin, O. Deep learning for the early detection of harmful algal blooms and improving water quality monitoring. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4220252

Climate change will affect how water sources are managed and monitored. The frequency of algal blooms will increase with climate change as it presents favourable conditions for the reproduction of phytoplankton. During monitoring, possible sensory fa... Read More about Deep learning for the early detection of harmful algal blooms and improving water quality monitoring.

Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines (2022)
Journal Article
Chatterjee, J., & Dethlefs, N. (2022). Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines. IEEE Access, 10, 84710-84737. https://doi.org/10.1109/ACCESS.2022.3197167

Intelligent question-answering (QA) systems have witnessed increased interest in recent years, particularly in their ability to facilitate information access, data interpretation or decision support. The wind energy sector is one of the most promisin... Read More about Automated Question-Answering for Interactive Decision Support in Operations & Maintenance of Wind Turbines.

Facilitating a smoother transition to renewable energy with AI (2022)
Journal Article
Chatterjee, J., & Dethlefs, N. (2022). Facilitating a smoother transition to renewable energy with AI. Patterns, 3(6), Article 100528. https://doi.org/10.1016/j.patter.2022.100528

Artificial intelligence (AI) can help facilitate wider adoption of renewable energy globally. We organized a social event for the AI and renewables community to discuss these aspects at the International Conference on Learning Representations (ICLR),... Read More about Facilitating a smoother transition to renewable energy with AI.

A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms (2022)
Journal Article
Walker, C., Rothon, C., Aslansefat, K., Papadopoulos, Y., & Dethlefs, N. (2022). A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms. Lecture notes in computer science, 13525 LNCS, 189-203. https://doi.org/10.1007/978-3-031-15842-1_14

With an increasing emphasis on driving down the costs of Operations and Maintenance (O &M) in the Offshore Wind (OSW) sector, comes the requirement to explore new methodology and applications of Deep Learning (DL) to the domain. Condition-based monit... Read More about A Deep Learning Framework for Wind Turbine Repair Action Prediction Using Alarm Sequences and Long Short Term Memory Algorithms.

Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future (2021)
Journal Article
Chatterjee, J., & Dethlefs, N. (2021). Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future. Renewable & sustainable energy reviews, 144, Article 111051. https://doi.org/10.1016/j.rser.2021.111051

Wind energy has emerged as a highly promising source of renewable energy in recent times. However, wind turbines regularly suffer from operational inconsistencies, leading to significant costs and challenges in operations and maintenance (O&M). Condi... Read More about Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future.

XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines (2021)
Journal Article
Chatterjee, J., & Dethlefs, N. XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines. https://doi.org/10.48550/arXiv.2012.10489. Manuscript submitted for publication

Condition-based monitoring (CBM) has been widely utilised in the wind industry for monitoring operational inconsistencies and failures in turbines, with techniques ranging from signal processing and vibration analysis to artificial intelligence (AI)... Read More about XAI4Wind: A Multimodal Knowledge Graph Database for Explainable Decision Support in Operations & Maintenance of Wind Turbines.

Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes (2021)
Journal Article
Schoene, A. M., Turner, A. P., De Mel, G., & Dethlefs, N. (in press). Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes. IEEE Transactions on Affective Computing, https://doi.org/10.1109/TAFFC.2021.3057105

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furth... Read More about Hierarchical Multiscale Recurrent Neural Networks for Detecting Suicide Notes.

A divide-and-conquer approach to neural natural language generation from structured data (2021)
Journal Article
Dethlefs, N., Schoene, A., & Cuayáhuitl, H. (2021). A divide-and-conquer approach to neural natural language generation from structured data. Neurocomputing, 433, 300-309. https://doi.org/10.1016/j.neucom.2020.12.083

Current approaches that generate text from linked data for complex real-world domains can face problems including rich and sparse vocabularies as well as learning from examples of long varied sequences. In this article, we propose a novel divide-and-... Read More about A divide-and-conquer approach to neural natural language generation from structured data.

The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines (2020)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020, August). The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines. Paper presented at Fragile Earth: Data Science for a Sustainable Planet. KDD 2020, Virtual Conference

The global pursuit towards sustainable development is leading to increased adaptation of renewable energy sources. Wind turbines are promising sources of clean energy, but regularly suffer from failures and down-times, primarily due to the complex en... Read More about The Promise of Causal Reasoning in Reliable Decision Support for Wind Turbines.

Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines (2020)
Journal Article
Chatterjee, J., & Dethlefs, N. (2020). Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines. Wind energy, 23(8), 1693-1710. https://doi.org/10.1002/we.2510

The last decade has witnessed an increased interest in applying machine learning techniques to predict faults and anomalies in the operation of wind turbines. These efforts have lately been dominated by deep learning techniques which, as in other fie... Read More about Deep learning with knowledge transfer for explainable anomaly prediction in wind turbines.

Natural Language Generation for Operations and Maintenance in Wind Turbines (2019)
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2019, December). Natural Language Generation for Operations and Maintenance in Wind Turbines. Paper presented at NeurIPS 2019 Workshop: Tackling Climate Change with Machine Learning, Vancouver Convention Center, British Columbia, Canada

Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines and associated... Read More about Natural Language Generation for Operations and Maintenance in Wind Turbines.

Domain transfer for deep natural language generation from abstract meaning representations (2017)
Journal Article
Dethlefs, N. (2017). Domain transfer for deep natural language generation from abstract meaning representations. IEEE computational intelligence magazine, 12(3), 18-28. https://doi.org/10.1109/mci.2017.2708558

Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail... Read More about Domain transfer for deep natural language generation from abstract meaning representations.

A natural language-based presentation of cognitive stimulation to people with dementia in assistive technology : a pilot study (2017)
Journal Article
Cuayahuitl, H., Dethlefs, N., Milders, M., Cuayáhuitl, H., Al-Salkini, T., & Douglas, L. (2017). A natural language-based presentation of cognitive stimulation to people with dementia in assistive technology : a pilot study. Informatics for Health and Social Care, 42(4), 349-360. https://doi.org/10.1080/17538157.2016.1255627

Currently, an estimated 36 million people worldwide are affected by Alzheimer’s disease or related dementias. In the absence of a cure, non-pharmacological interventions, such as cognitive stimulation, which slow down the rate of deterioration can be... Read More about A natural language-based presentation of cognitive stimulation to people with dementia in assistive technology : a pilot study.

Information density and overlap in spoken dialogue (2015)
Journal Article
Dethlefs, N., Hastie, H., Cuayáhuitl, H., Yu, Y., Rieser, V., & Lemon, O. (2016). Information density and overlap in spoken dialogue. Computer speech & language, 37, 82-97. https://doi.org/10.1016/j.csl.2015.11.001

Incremental dialogue systems are often perceived as more responsive and natural because they are able to address phenomena of turn-taking and overlapping speech, such as backchannels or barge-ins. Previous work in this area has often identified disti... Read More about Information density and overlap in spoken dialogue.

Hierarchical reinforcement learning for situated natural language generation (2014)
Journal Article
Dethlefs, N., & Cuayáhuitl, H. (2015). Hierarchical reinforcement learning for situated natural language generation. Natural language engineering, 21(3), 391-435. https://doi.org/10.1017/S1351324913000375

Natural Language Generation systems in interactive settings often face a multitude of choices, given that the communicative effect of each utterance they generate depends crucially on the interplay between its physical circumstances, addressee and in... Read More about Hierarchical reinforcement learning for situated natural language generation.

Transparency Of Execution Using Epigenetic Networks
Presentation / Conference Contribution
Dethlefs, N., & Turner, A. (2017, September). Transparency Of Execution Using Epigenetic Networks. Presented at 14th European Conference on Artificial Life, ECAL 2017, Lyon, France

This paper describes how the recurrent connectionist architecture epiNet, which is capable of dynamically modifying its topology, is able to provide a form of transparent execution. EpiNet, which is inspired by eukaryotic gene regulation in nature, i... Read More about Transparency Of Execution Using Epigenetic Networks.

Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI. Presented at The Science of Making Torque from Wind (TORQUE 2020), Online, Netherlands

© 2020 Published under licence by IOP Publishing Ltd. Machine learning techniques have been widely used for condition-based monitoring of wind turbines using Supervisory Control & Acquisition (SCADA) data. However, many machine learning models, inclu... Read More about Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI.

A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines
Presentation / Conference Contribution
Chatterjee, J., & Dethlefs, N. (2020, July). A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK

© 2020 IEEE. Wind energy is one of the fastest-growing sustainable energy sources in the world but relies crucially on efficient and effective operations and maintenance to generate sufficient amounts of energy and reduce downtime of wind turbines an... Read More about A Dual Transformer Model for Intelligent Decision Support for Maintenance of Wind Turbines.