[65]	William Leeney and Ryan McConville. An investigation into using unsupervised metrics to optimise gnns for node clustering. arXiv preprint arXiv:2402.07845, 2024. [ bib ]
[64]	William Leeney and Ryan McConville. A framework for exploring federated community detection. 4th workshop on Graphs and more Complex structures for Learning and Reasoning (GCLR), colocated with AAAI, 2024. [ bib ]
[63]	Catherine Morgan, Emma L Tonkin, Alessandro Masullo, Ferdian Jovan, Arindam Sikdar, Pushpajit Khaire, Majid Mirmehdi, Ryan McConville, Gregory JL Tourte, Alan Whone, et al. A multimodal dataset of real world mobility activities in parkinson’s disease. Scientific data, 10(1):918, 2023. [ bib ]
[62]	Ferdian Jovan, Catherine Morgan, Ryan McConville, Emma L Tonkin, Ian Craddock, and Alan Whone. Multimodal indoor localisation in parkinson's disease for detecting medication use: Observational pilot study in a free-living setting. In KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4273--4283. ACM, 2023. [ bib ]
[61]	Catherine Morgan, Alessandro Masullo, Majid Mirmehdi, Hanna Kristiina Isotalus, Ferdian Jovan, Ryan McConville, Emma L Tonkin, Alan Whone, and Ian Craddock. Automated real-world video analysis of sit-to-stand transitions predicts parkinson’s disease severity. Digital Biomarkers, 7(1):92--103, 2023. [ bib ]
[60]	Harry Emerson, Ryan McConville, and Matthew Guy. The safety challenges of deep learning in real-world type 1 diabetes management. arXiv preprint arXiv:2310.14743, 2023. [ bib ]
[59]	Harry Emerson, Matthew Guy, and Ryan McConville. Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes. Journal of Biomedical Informatics, 142:104376, 2023. [ bib ]
[58]	Andrés Domínguez Hernández, Richard Owen, Dan Saattrup Nielsen, and Ryan McConville. Ethical, political and epistemic implications of machine learning (mis) information classification: insights from an interdisciplinary collaboration between social and data scientists. Journal of Responsible Innovation, 10(1):2222514, 2023. [ bib ]
[57]	William Leeney and Ryan McConville. Uncertainty in gnn learning evaluations: A comparison between measures for quantifying randomness in gnn community detection. Entropy, 26(1):78, 2024. [ bib ]
[56]	William Leeney and Ryan McConville. Uncertainty in gnn learning evaluations: The importance of a consistent benchmark for community detection. In International Conference on Complex Networks and Their Applications, pages 112--123. Springer Nature Switzerland Cham, 2023. [ bib ]
[55]	Andrés Domínguez Hernández, Richard Owen, Dan Saattrup Nielsen, and Ryan Mcconville. Addressing contingency in algorithmic (mis) information classification: Toward a responsible machine learning agenda. In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, pages 971--971, 2023. [ bib ]
[54]	Alex Iacob, Pedro PB Gusmão, Nicholas D Lane, Armand K Koupai, Mohammud J Bocus, Raúl Santos-Rodríguez, Robert J Piechocki, and Ryan McConville. Privacy in multimodal federated human activity recognition. In 3rd On-Device Intelligence Workshop at MLSys 2023, 2023. [ bib ]
[53]	Hongbo Bo, Yiwen Wu, Zinuo You, Ryan McConville, Jun Hong, and Weiru Liu. What will make misinformation spread: An xai perspective. In World Conference on Explainable Artificial Intelligence, pages 321--337. Springer Nature Switzerland Cham, 2023. [ bib ]
[52]	Luke Gassmann, Ryan McConville, and Matthew Edwards. Predicting interpersonal influence from conversational features. In 2023 10th International Conference on Behavioural and Social Computing (BESC), pages 1--7. IEEE, 2023. [ bib ]
[51]	Luke Gassmann, Matthew Edwards, and Ryan McConville. A methodology for surveying gradients of influence on social media platforms using multi-media content. In European Starting AI Researchers’ Symposium, 2023. [ bib ]
[50]	Dominika Nadia Wojtczak, Claudia Peersman, Luisa Zuccolo, and Ryan McConville. Characterising discourse and engagement across topics of misinformation on twitter. IEEE Access, 2023. [ bib ]
[49]	Yiwen Wu, Kevin McAreavey, Weiru Liu, and Ryan McConville. A comparative analysis of information cascade prediction using dynamic heterogeneous and homogeneous graphs. In The 12th International Conference on Complex Networks and their Applications. Springer, 2023. [ bib ]
[48]	Hongnan Ma, Kevin McAreavey, Ryan McConville, and Weiru Liu. Explainable ai for non-experts: Energy tariff forecasting. In 2022 27th International Conference on Automation and Computing (ICAC), pages 1--6. IEEE, 2022. [ bib ]
[47]	Armand K Koupai, Mohammud J Bocus, Raul Santos-Rodriguez, Robert J Piechocki, and Ryan McConville. Self-supervised multimodal fusion transformer for passive activity recognition. IET Wireless Sensor Systems, 12(5-6):149--160, 2022. [ bib ]
[46]	Thanaphon Suwannaphong, Ryan McConville, and Ian Craddock. Radio signal strength indication augmentation for one-shot learning in indoor localisation. In Proceedings of the 1st ACM Workshop on Smart Wearable Systems and Applications, pages 7--12, 2022. [ bib ]
[45]	C Morgan, A Masullo, H Isotalus, E Tonkin, M Mirmehdi, F Jovan, T Whone, G Oikonomou, R McConville, and G Tourte. Real-world sit-to-stand evaluation. Movement Disorders, 37(S2):S195--S196, 2022. [ bib ]
[44]	James Pope, Jinyuan Liang, Vijay Kumar, Francesco Raimondo, Xinyi Sun, Ryan McConville, Thomas Pasquier, Rob Piechocki, George Oikonomou, Bo Luo, et al. Resource-interaction graph: Efficient graph representation for anomaly detection. arXiv preprint arXiv:2212.08525, 2022. [ bib ]
[43]	Mohammud J Bocus, Hok-Shing Lau, Ryan McConville, Robert J Piechocki, and Raul Santos-Rodriguez. Self-supervised wifi-based activity recognition. In 2022 IEEE Globecom Workshops (GC Wkshps), pages 552--557. IEEE, 2022. [ bib ]
[42]	Mohammud Junaid Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raúl Santos-Rodríguez, Kevin Chetty, and Robert J. Piechocki. Operanet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors. Scientific Data, 9, 2022. [ bib \| arXiv ]
[41]	Hongbo Bo, Ryan McConville, Jun Hong, and Weiru Liu. Ego-graph replay based continual learning for misinformation engagement prediction. In 2022 International Joint Conference on Neural Networks (IJCNN), 2022. [ bib \| arXiv ]
[40]	Ferdian Jovan, Ryan McConville, Catherine Morgan, Emma Tonkin, Alan Whone, and Ian Craddock. Multimodal indoor localisation for measuring mobility in parkinson's disease using transformers. 2022. [ bib \| arXiv ]
[39]	Harry Emerson, Matt Guy, and Ryan McConville. Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes. 2022. [ bib \| arXiv ]
[38]	Dan Saattrup Nielsen and Ryan McConville. Mumin: A large-scale multilingual multimodal fact-checked misinformation social network dataset. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). ACM, 2022. [ bib \| arXiv ]
[37]	Jonas Paulavičius, Seifallah Jardak, Ryan McConville, Robert Piechocki, and Raul Santos-Rodriguez. Temporal self-supervised learning for rssi-based indoor localization. In ICC 2022 - IEEE International Conference on Communications, pages 3046--3051, 2022. [ bib \| DOI ]
[36]	Roisin McNaney, Catherine Morgan, Pranav Kulkarni, Julio Vega, Farnoosh Heidarivincheh, Ryan McConville, Alan Whone, Mickey Kim, Reuben Kirkham, and Ian Craddock. Exploring perceptions of cross-sectoral data sharing with people with parkinson's. CHI Conference on Human Factors in Computing Systems (CHI'22), 2022. [ bib ]
[35]	Kiyoung Shin, Ryan McConville, Oussama Metatla, Minhye Chang, Chiyoung Han, Junhaeng Lee, and Anne Roudaut. Outdoor localization using ble rssi and accessible pedestrian signals for the visually impaired at intersections. Sensors, 22(1), 2022. [ bib \| DOI \| http ]
[34]	Taku Yamagata, Ryan McConville, and Raul Santos-Rodriguez. Reinforcement learning with feedback from multiple humans with diverse skills. NeurIPS 2021 Workshop on Safe and Robust Control of Uncertain Systems, 2021. [ bib \| arXiv ]
[33]	James Pope, Francesco Raimondo, Vijay Kumar, Ryan McConville, Robert J. Piechocki, George C. Oikonomou, Thomas Pasquier, Bo Luo, Dan Howarth, Ioannis Mavromatis, Pietro Edoardo Carnelli, Adrián Sánchez-Mompó, Theodoros Spyridopoulos, and Aftab Khan. Container escape detection for edge devices. Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems, 2021. [ bib ]
[32]	Atis Elsts and Ryan McConville. Are microcontrollers ready for deep learning-based human activity recognition? Electronics, 10(21), 2021. [ bib \| DOI \| http ]
[31]	Mohammud Junaid Bocus, Wenda Li, Shelly Vishwakarma, Roget Kou, Chong Tang, Karl Woodbridge, Ian Craddock, Ryan McConville, Raúl Santos-Rodríguez, Kevin Chetty, and Robert J. Piechocki. Operanet: A multimodal activity recognition dataset acquired from radio frequency and vision-based sensors. 2021. [ bib \| arXiv ]
[30]	James Neve and Ryan McConville. Photos are all you need for reciprocal recommendation in online dating. arXiv, 2021. [ bib \| arXiv ]
[29]	Farnoosh Heidarivincheh, Ryan McConville, Catherine Morgan, Roisin McNaney, Alessandro Masullo, Majid Mirmehdi, Alan L. Whone, and Ian Craddock. Multimodal classification of parkinson’s disease in home environments with resiliency to missing modalities. Sensors, 21(12), 2021. [ bib \| DOI \| http ]
[28]	Hok-Shing Lau, Ryan McConville, Mohammud J. Bocus, Robert J. Piechocki, and Raul Santos-Rodriguez. Self-supervised wifi-based activity recognition. arXiv, 2021. [ bib \| arXiv ]
[27]	Hongbo Bo, Ryan McConville, Jun Hong, and Weiru Liu. Social influence prediction with train and test time augmentation for graph neural networks. In 2021 International Joint Conference on Neural Networks (IJCNN), 2021. [ bib \| arXiv ]
[26]	Catherine Morgan, Farnoosh Heidarivincheh, Ian Craddock, Ryan McConville, Miquel Perello Nietó, Emma L. Tonkin, Alessandro Masullo, Antonis Vafeas, Mickey Kim, Roisin McNaney, Gregory Tourte, and Alan Whone. Data labelling in the wild: Annotating free-living activities and parkinson’s disease symptoms. In 2021 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2021. [ bib ]
[25]	Ryan McConville, Gareth Archer, Ian Craddock, Michał Kozłowski, Robert Piechocki, James Pope, and Raul Santos-Rodriguez. Vesta: A digital health analytics platform for a smart home in a box. Future Generation Computer Systems, 114:106 -- 119, 2021. [ bib \| DOI \| http ]
[24]	Catherine Morgan, Ian Craddock, Emma L Tonkin, Kirsi M Kinnunen, Roisin McNaney, Sam Whitehouse, Majid Mirmehdi, Farnoosh Heidarivincheh, Ryan McConville, Julia Carey, Alison Horne, Michal Rolinski, Lynn Rochester, Walter Maetzler, Helen Matthews, Oliver Watson, Rachel Eardley, and Alan L Whone. Protocol for pd sensors: Parkinsons disease symptom evaluation in a naturalistic setting producing outcome measures using sphere technology. an observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in parkinson’s disease. BMJ Open, 10(11), 2020. [ bib \| DOI \| http ]
[23]	James Neve and Ryan McConville. Imrec: Learning reciprocal preferences using images. In Fourteenth ACM Conference on Recommender Systems, RecSys '20, page 170–179, New York, NY, USA, 2020. Association for Computing Machinery. [ bib \| DOI \| http ]
[22]	Junaid J Bocus, Jonas Paulavicius, Ryan McConville, Raul Santos-Rodriguez, and Robert J Piechocki. Low cost localisation in residential environments using high resolution cir information. In 2020 IEEE Global Communications Conference (GLOBECOM), Taipei, Taiwan, 2020, pages 1--6, United States, August 2020. Institute of Electrical and Electronics Engineers (IEEE). [ bib ]
[21]	Atis Elsts, Niall Twomey, Ryan McConville, and Ian Craddock. Energy-efficient activity recognition framework using wearable accelerometers. Journal of Network and Computer Applications, 168:102770, 2020. [ bib \| DOI \| http ]
[20]	Ryan McConville, Raul Santos-Rodriguez, Robert J Piechocki, and Ian Craddock. N2d:(not too) deep clustering via clustering the local manifold of an autoencoded embedding. In 25th International Conference on Pattern Recognition, ICPR 2020. IEEE Computer Society, 2020. [ bib \| .pdf ]
[19]	Mohammud J. Bocus, Wenda Li, Jonas Paulavicius, Ryan McConville, Raul Santos-Rodriguez, Kevin Chetty, and Robert Piechocki. Translation resilient opportunistic wifi sensing. In 25th International Conference on Pattern Recognition, ICPR 2020. IEEE Computer Society, 2020. [ bib ]
[18]	Alexander Hepburn, Valero Laparra, Jesús Malo, Ryan McConville, and Raul Santos-Rodriguez. Perceptnet: A human visual system inspired neural network for estimating perceptual distance. In The 27th IEEE International Conference on Image Processing (ICIP 2020), 2020. [ bib \| .pdf ]
[17]	Hongbo Bo, Ryan McConville, Jun Hong, and Weiru Liu. Social network influence ranking via embedding network interactions for user recommendation. In Companion Proceedings of the Web Conference 2020, WWW ’20, page 379–384, New York, NY, USA, 2020. Association for Computing Machinery. [ bib \| DOI \| http ]
[16]	Taku Yamagata, Raúl Santos-Rodríguez, Ryan McConville, and Atis Elsts. Online Feature Selection for Activity Recognition using Reinforcement Learning with Multiple Feedback. page arXiv:1908.06134, Aug 2019. [ bib \| arXiv ]
[15]	Alex Hepburn, Valero Laparra, Ryan McConville, and Raul Santos-Rodriguez. Enforcing perceptual consistency on generative adversarial networks by using the normalised laplacian pyramid distance. In Proceedings of the Northern Lights Deep Learning Workshop 2020, volume 1. Septentrio Academic Publishing, February 2020. [ bib \| DOI ]
[14]	Ryan McConville, Dallan Byrne, Ian Craddock, Robert Piechocki, James Pope, and Raul Santos-Rodriguez. A dataset for room level indoor localization using a smart home in a box. Data in Brief, 22:1044 -- 1051, 2019. [ bib \| DOI \| .pdf ]
[13]	Michal Kozlowski, Ryan McConville, Raul Santos-Rodriguez, and Robert J Piechocki. Energy efficiency in reinforcement learning for wireless sensor networks. In Green Data Mining, 6 2018. [ bib \| .pdf ]
[12]	Alexander Hepburn, Ryan McConville, Raul Santos-Rodriguez, Jesús Cid-Sueiro, and Darío García-García. Proper losses for learning with example-dependent costs. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018). CEUR Workshop Proceedings, 6 2018. [ bib \| .pdf ]
[11]	Ryan McConville, Gareth Archer, Ian Craddock, Herman ter Horst, Robert Piechocki, James Pope, and Raul Santos-Rodriguez. Online heart rate prediction using acceleration from a wrist worn wearable. In KDD Workshop on Machine Learning for Medicine and Healthcare, 8 2018. [ bib \| .pdf ]
[10]	Niall Twomey, Tom Diethe, Xenofon Fafoutis, Atis Elsts, Ryan McConville, Peter Flach, and Ian Craddock. A comprehensive study of activity recognition using accelerometers. Informatics, 5(2), 6 2018. [ bib \| DOI \| .pdf ]
[9]	Ryan McConville, Raul Santos-Rodriguez, and Niall Twomey. Person identification and discovery with wrist worn accelerometer data. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018, pages 615--620, 3 2018. [ bib \| .pdf ]
[8]	Ryan McConville, Weiru Liu, and Jun Hong. Vertex deduplication based on string similarity and community membership. In Complex Networks and Their Applications VI - Proceedings of Complex Networks 2017 (The 6th International Conference on Complex Networks and Their Applications), volume 689 of Studies in Computational Intelligence, pages 178--189. Springer-Verlag Berlin, 2018. [ bib \| DOI \| .pdf ]
[7]	Atis Elsts, Ryan McConville, Xenofon Fafoutis, Niall Twomey, Robert Piechocki, Raul Santos-Rodriguez, and Ian Craddock. On-board feature extraction from acceleration data for activity recognition. In Proceedings of the International Conference on Embedded Wireless Systems and Networks, pages 163--168, United States, 12 2017. Association for Computing Machinery (ACM). [ bib \| .pdf ]
[6]	Ryan McConville, Dallan Byrne, Ian Craddock, Robert Piechocki, James Pope, and Raul Santos-Rodriguez. Understanding the quality of calibrations for indoor localisation. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT 2018), United States, 11 2017. Institute of Electrical and Electronics Engineers (IEEE). [ bib \| DOI \| .pdf ]
[5]	Alex Hepburn, Ryan McConville, and Raul Santos-Rodriguez. Album cover generation from genre tags. In 10th International Workshop on Machine Learning and Music, 10 2017. [ bib \| .pdf ]
[4]	James Pope, Ryan McConville, Michal Kozlowski, Xenofon Fafoutis, Raul Santos-Rodriguez, Robert Piechocki, and Ian Craddock. Sphere in a box: Practical and scalable eurvalve activity monitoring smart home kit. In 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops 2017), pages 128--135, United States, 10 2017. Institute of Electrical and Electronics Engineers (IEEE). [ bib \| DOI \| .pdf ]
[3]	Leonie Tanczer, Ryan McConville, and Peter Maynard. Censorship and surveillance in the digital age: The technological challenges for academics. Journal of Global Security Studies, 1(4):346--355, 11 2016. [ bib \| DOI \| .pdf ]
[2]	Ryan McConville, Xin Cao, Weiru Liu, and Paul Miller. Accelerating large scale centroid-based clustering with locality sensitive hashing. In 2016 IEEE 32nd International Conference on Data Engineering (ICDE 2016), pages 649--660, United States, 8 2016. Institute of Electrical and Electronics Engineers (IEEE). [ bib \| DOI \| .pdf ]
[1]	Ryan McConville, Weiru Liu, and Paul Miller. Vertex clustering of augmented graph streams. In Proceedings of the 2015 SIAM International Conference on Data Mining, pages 109--117, United States, 3 2015. Society for Industrial and Applied Mathematics. [ bib \| DOI \| .pdf ]

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