Property:References

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Thermal Detection of Subtle Human Cues for a Robot Magic Performance +
Martin Cooney, & Alexey Vinel. “Magic in Human-Robot Interaction (HRI).” In the 34th annual workshop of the Swedish Artificial Intelligence Society (SAIS 2022), 2022. Cho, Y., Bianchi-Berthouze, N., Marquardt, N., & Julier, S. J. (2018, April). Deep thermal imaging: Proximate material type recognition in the wild through deep learning of spatial surface temperature patterns. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-13). Xu, Z., Wang, Q., Li, D., Hu, M., Yao, N., & Zhai, G. (2020). Estimating departure time using thermal camera and heat traces tracking technique. Sensors, 20(3), 782. Cooney, M., & Bigun, J. (2017). PastVision+: Thermovisual inference of recent Medicine intake by Detecting heated Objects and cooled lips. Frontiers in Robotics and AI, 4, 61.  +
Thermal Profiling of Residential Energy Consumption for Heat Pump and District Heating Customers +
H. Gadd and S. Werner, "Heat load patterns in district heating substations", Applied Energy, vol. 108, pp. 176-183, 2013. A. Albert and R. Rajagopal, "Thermal Profiling of Residential Energy Use", IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 602-611, 2015. A. Albert and R. Rajagopal, "Building dynamic thermal profiles of energy consumption for individuals and neighborhoods", 2013 IEEE International Conference on Big Data, 2013.  +
Time Series Motif/Discord Discovery Under Context +
https://www.cs.ucr.edu/~eamonn/Matrix_Profile_Tutorial_Part1.pdf https://www.cs.ucr.edu/~eamonn/Matrix_Profile_Tutorial_Part2.pdf  +
Time series anomaly detection for Heavy-duty vehicles (in collaboration with Volvo) +
- Carvalho, J., Zhang, M., Geyer, R., Cotrini, C., & Buhmann, J. M. (2024). Invariant anomaly detection under distribution shifts: a causal perspective. Advances in Neural Information Processing Systems, 36. - Wang, Z., & Veitch, V. (2022). A unified causal view of domain invariant representation learning. -Ikonomovska, E., Gama, J., & Džeroski, S. (2015). Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing, 150, 458-470. - Muallem, A., Shetty, S., Pan, J. W., Zhao, J., & Biswal, B. (2017). Hoeffding tree algorithms for anomaly detection in streaming datasets: A survey. Journal of Information Security, 8(4). - Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016, July). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1). - Gallicchio, C. (2024). Euler state networks: Non-dissipative reservoir computing. Neurocomputing, 579, 127411. - Foumani, N. M., Tan, C. W., Webb, G. I., Rezatofighi, H., & Salehi, M. (2024). Series2vec: similarity-based self-supervised representation learning for time series classification. Data Mining and Knowledge Discovery, 1-25. - Baevski, A., Hsu, W. N., Xu, Q., Babu, A., Gu, J., & Auli, M. (2022, June). Data2vec: A general framework for self-supervised learning in speech, vision and language. In International Conference on Machine Learning (pp. 1298-1312). PMLR.  +
Time series anomaly detection for heavy-duty vehicles +
Ruiz, C., Menasalvas, E., & Spiliopoulou, M. (2009). C-denstream: Using domain knowledge on a data stream. In Discovery Science: 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009 12 (pp. 287-301). Springer Berlin Heidelberg. Cao, F., Estert, M., Qian, W., & Zhou, A. (2006, April). Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining (pp. 328-339). Society for industrial and applied mathematics. Fan, Y., Nowaczyk, S., & Antonelo, E. A. (2016). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1). Fan, Y., Nowaczyk, S., & Rögnvaldsson, T. (2020). Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliability Engineering & System Safety, 203, 107098. Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134-147. Lavin, A., & Ahmad, S. (2015, December). Evaluating real-time anomaly detection algorithms--the Numenta anomaly benchmark. In 2015 IEEE 14th international conference on machine learning and applications (ICMLA) (pp. 38-44). IEEE. Wu, K., Zhang, K., Fan, W., Edwards, A., & Philip, S. Y. (2014, December). Rs-forest: A rapid density estimator for streaming anomaly detection. In 2014 IEEE international conference on data mining (pp. 600-609). IEEE. Hendrickx, K., Meert, W., Mollet, Y., Gyselinck, J., Cornelis, B., Gryllias, K., & Davis, J. (2020). A general anomaly detection framework for fleet-based condition monitoring of machines. Mechanical Systems and Signal Processing, 139, 106585.  +
Timeseries XAI in Cybersecurity and Industry +
https://www.sciencedirect.com/science/article/pii/S1566253523001148 https://www.nature.com/articles/s41597-025-04603-x  +
Timeseries representation learning for EHR +
Data: https://mimic.mit.edu/docs/about/ Papers: https://arxiv.org/pdf/1907.05321.pdf https://www.ijcai.org/proceedings/2021/0324.pdf https://proceedings-of-deim.github.io/DEIM2022/papers/H23-4.pdf https://proceedings.neurips.cc/paper/2019/file/53c6de78244e9f528eb3e1cda69699bb-Paper.pdf  +
Transfer Learning for Network Security +
Li D, Yuan Q, Li T, Chen S, Yang J. Cross-domain Network Traffic Classification Using Unsupervised Domain Adaptation. In2020 International Conference on Information Networking (ICOIN) 2020 Jan 7 (pp. 245-250). IEEE. Sun G, Liang L, Chen T, Xiao F, Lang F. Network traffic classification based on transfer learning. Computers & electrical engineering. 2018 Jul 1;69:920-7. Taghiyarrenani Z, Fanian A, Mahdavi E, Mirzaei A, Farsi H. Transfer learning based intrusion detection. In2018 8th International Conference on Computer and Knowledge Engineering (ICCKE) 2018 Oct 25 (pp. 92-97). IEEE.  +
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Uncertainty quantification for data driven clinical decision making +
Sensoy, Murat, Lance Kaplan, and Melih Kandemir. "Evidential deep learning to quantify classification uncertainty." arXiv preprint arXiv:1806.01768 (2018). Amini, Alexander, et al. "Deep evidential regression." arXiv preprint arXiv:1910.02600 (2019).  +
Understand Patterns in Volvo Truck Lifetime Repair History +
http://www.sciencedirect.com/science/article/pii/S000437029800023X http://www.pomdp.org/index.shtml http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.7714 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.147.1619 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.335.8737  +
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V2X Intention Sharing for E-Bikes and E-Scooters +
Gradient navigation model for pedestrian dynamics https://link.aps.org/doi/10.1103/PhysRevE.89.062801 Intelligent Transport Systems (ITS); Vehicular communications; Basic set of applications; Part 2: Specification of Cooperative Awareness basic service (EN 302 637-2 - V1.4.1) Intelligent Transport Systems (ITS); Vulnerable Road Users (VRU) awareness; Part 3: Specification of VRU awareness basic service; Release 2 (TS 03.300.3.v2.1.1)  +
Vehicle Operation Classification +
Time series classification Unsupervised and semi-supervised clustering ...  +
Visual Transformers for 3D medical images Classification: use-case neurodegenerative disorders +
Attention is all you need: https://arxiv.org/abs/1706.03762 Bert: Pre-training of deep bidirectional transformers for language understanding: https://arxiv.org/abs/1810.04805 Transformers in Vision: A Survey: https://arxiv.org/pdf/2101.01169.pdf ViT-V-Net: https://pythonrepo.com/repo/junyuchen245-ViT-V-Net_for_3D_Image_Registration_Pytorch Our paper, adopting VGG architecture for classification of 3D brain scans: https://link.springer.com/article/10.1007/s00259-021-05483-0  +
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Zero-Shot Learning for Semantic Segmentation +
https://arxiv.org/pdf/1707.00600.pdf https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/41473.pdf https://arxiv.org/pdf/1906.00817.pdf https://openaccess.thecvf.com/content_ICCVW_2019/papers/MDALC/Kato_Zero-Shot_Semantic_Segmentation_via_Variational_Mapping_ICCVW_2019_paper.pdf https://github.com/daooshee/Few-Shot-Learning  +
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