Property:References

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Conditional GAN for better embedding and generation of medical codes +	Data: https://mimic.mit.edu/docs/about/ papers: https://dspace.mit.edu/handle/1721.1/128349 https://proceedings.neurips.cc/paper/2019/file/254ed7d2de3b23ab10936522dd547b78-Paper.pdf https://www.sciencedirect.com/science/article/pii/S0957417421000233
Consensus clustering for categorizing orthogonal vehicle operations +	- Some slides: https://www.siam.org/meetings/sdm11/clustering.pdf - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012, April). Discovering multiple clustering solutions: Grouping objects in different views of the data. In Data Engineering (ICDE), 2012 IEEE 28th International Conference on (pp. 1207-1210). IEEE. - Hu, J., & Pei, J. (2017). Subspace multi-clustering: a review. Knowledge and Information Systems, 1-28. - Yang, S., & Zhang, L. (2017). Non-redundant multiple clustering by nonnegative matrix factorization. Machine Learning, 106(5), 695-712. - Dang, X. H., & Bailey, J. (2015). A framework to uncover multiple alternative clusterings. Machine Learning, 98(1-2), 7-30. - Gionis, A., Mannila, H., & Tsaparas, P. (2007). Clustering aggregation. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(1), 4. - Qi, Z., & Davidson, I. (2009, June). A principled and flexible framework for finding alternative clusterings. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 717-726). ACM. - Muller, E., Gunnemann, S., Farber, I., & Seidl, T. (2012). Discovering multiple clustering solutions: Grouping objects in different views of the data. In IEEE 28th International Conference on Data Engineering (ICDE), (pp. 1207-1210). - Cui, Y., Fern, X. Z., & Dy, J. G. (2007). Non-redundant multi-view clustering via orthogonalization. In IEEE International Conference on Data Mining (ICDM), (pp. 133-142). - Strehl, A., & Ghosh, J. (2002). Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, pp. 583-617.
Constrained dynamic path planning for truck and trailer +	R. Siegwart and I. R. Nourbakhsh,Introduction to Autonomous Mobile Robots. Scituate, MA, USA: Bradford Company, 2004 A. Nozad, Heavy vehicle path stability control for collision avoidance applications," Master's thesis, Chalmers university of technology, 2011 J. G. Fernandez, A vehicle dynamics model for driving simulators," Master's thesis, Chalmers university of technology, 2012
Courteous robot guide for visitors to an intelligent home +	Yusuke Kato, Takayuki Kanda, Hiroshi Ishiguro. May I help you? Design of Human-like Polite Approaching Behavior. HRI 2015: 35-42 Tomoko Yonezawa, Hirotake Yamazoe, Akira Utsumi, Shinji Abe. Anthropomorphic awareness of partner robot to user’s situation based on gaze and speech detection. International Journal of Autonomous and Adaptive Communications Systems. Volume 5, Issue 1. DOI: 10.1504/IJAACS.2012.044782
Cross-Spectrum Ocular Identity Recognition via Deep Learning +	R. Jillela and A. Ross, "Matching face against iris images using periocular information," 2014 IEEE International Conference on Image Processing (ICIP), Paris, 2014, pp. 4997-5001. doi: 10.1109/ICIP.2014.7026012: https://ieeexplore.ieee.org/document/7026012 P. R. Nalla and A. Kumar, "Toward More Accurate Iris Recognition Using Cross-Spectral Matching," in IEEE Transactions on Image Processing, vol. 26, no. 1, pp. 208-221, Jan. 2017. doi: 10.1109/TIP.2016.2616281: https://ieeexplore.ieee.org/document/7587438
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Data Heterogeneity in Federated Learning +	Advances and Open Problems in Federated Learning: https://hal.inria.fr/hal-02406503/document FedML: A Research Library and Benchmark for Federated Machine Learning: https://arxiv.org/pdf/2007.13518.pdf
Data Mining In a Warehouse Inventory +	Zeynep Akata, Florent Perronnin, Zaid Harchaoui, Cordelia Schmid. Good Practice in Large-Scale Learning for Image Classi cation. IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, 2014, 36 (3), pp.507-520.<10.1109/TPAMI.2013.146>.<hal-00835810> Florent Perronnin, Zeynep Akata, Zaid Harchaoui, Cordelia Schmid. Towards Good Practice in Large-Scale Learning for Image Classification. CVPR 2012 - IEEE Computer Vision and Pattern Recognition, Jun 2012, Providence (RI), United States. IEEE, pp.3482-3489, 2012,<10.1109/CVPR.2012.6248090>.<hal-00690014> Raphael Puget, Nicolas Baskiotis, Patrick Gallinari. Sequential Dynamic Classi cation for Large Scale Multi-class Problems. Extreme Classi cation Workshop at ICML, Jul 2015, Lille,France. 2015.<hal-01207428>
Deep feature analysis and extraction on Logged Vehicle data for the task of predictive maintenance +	• Doquet, Guillaume, and Michele Sebag. "Agnostic feature selection." The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2019 • Prytz, Rune, et al. "Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data." Engineering applications of artificial intelligence 41 (2015): 139-150.
Deep stacked ensemble +	1- David H.Wolpert, "Stacked generalisation" https://doi.org/10.1016/S0893-6080(05)80023-1 2- Jason Brownle, "How to Develop a Stacking Ensemble for Deep Learning Neural Networks in Python With Keras", https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/ 3 - PS Mashhadi, S Nowaczyk, S Pashami. "Parallel orthogonal deep neural network" Neural Networks 140, 167-183
Deepfake Detection +	1- Tolosana, Ruben, et al. "Deepfakes and beyond: A survey of face manipulation and fake detection." Information Fusion 64 (2020): 131-148. 2- Liu, Xin, and Xiao Chen. "A Survey of GAN-Generated Fake Faces Detection Method Based on Deep Learning." Journal of Information Hiding and Privacy Protection 2.2 (2020): 87. 3- Hsu, Chih-Chung, Yi-Xiu Zhuang, and Chia-Yen Lee. "Deep fake image detection based on pairwise learning." Applied Sciences 10.1 (2020): 370. 4- Khodabakhsh, Ali, et al. "Fake face detection methods: Can they be generalized?." 2018 international conference of the biometrics special interest group (BIOSIG). IEEE, 2018. 5- Mashhadi, Peyman Sheikholharam, Sławomir Nowaczyk, and Sepideh Pashami. "Parallel orthogonal deep neural network." Neural Networks 140 (2021): 167-183.
Detecting Faults and Estimating Missing Values in Smart Meter Data +	http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5524054 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1425550
Detecting Points of Interest for Robotic First Aid +	-pose recognition Jamie Shotton, Ross Girshick, Andrew Fitzgibbon, Toby Sharp, Mat Cook, Mark Finocchio, Richard Moore, Pushmeet Kohli, Antonio Criminisi, Alex Kipman, Andrew Blake, "Efficient Human Pose Estimation from Single Depth Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 12, pp. 2821-2840, Dec. 2013, doi:10.1109/TPAMI.2012.241 -first aid Travers, A. H., Rea, T. D., Bobrow, B. J., et al. (2010). Part 4: CPR overview 2010 American Heart Association guidelines for cardiopulmonary resuscitation and emergency cardiovascular care. Circulation, 122(18 suppl 3), S676-S684.
Detecting changes in causal relations +	Structural causal discovery techniques: https://arxiv.org/pdf/1211.3295.pdf Change detection in Granger causality: http://cowles.yale.edu/sites/default/files/files/pub/d20/d2059.pdf
Detecting different types of machines based on usage +	1.- Bengio Y, Courville A, P Vincent P. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence. Volume: 35, Issue: 8, Aug. 2013. 2.- Kotsiantis S. Supervised Machine Learning: A Review of Classification Techniques. Informatica 31 (2007) 249-268 3.- Grira N, Crucianu M, Boujemaa N. Unsupervised and Semi-supervised Clustering: a Brief Survey. 4.- Taskar B, Segal E, Koller D. Probabilistic Classification and Clustering in Relational Data.
Detection and intention prediction of pedestrians in zebra crossings +	Computer Vision Datasets: http://clickdamage.com/sourcecode/cv_datasets.php Computer Vision Resources: http://cvisioncentral.com/vision-resources/ Caffee Model Zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo R. Klette, “Concise Computer Vision”, Springer, 2014
Detection of smart cars cyber attacks +	Weber et al: Embedded Hybrid Anomaly Detection for Automotive CAN Communication, Weber et al: Online Detection of Anomalies in Vehicle Signals using Replicator Neural Networks
Digital Twin - AFRY +	Victor Svahn
Dynamic Objects Detection and Tracking +	Petrovskaya, Anna, and Sebastian Thrun. "Model based vehicle detection and tracking for autonomous urban driving." Autonomous Robots 26.2-3 (2009): 123-139. Wojke, N.; Haselich, M., "Moving vehicle detection and tracking in unstructured environments," Robotics and Automation (ICRA), 2012 IEEE International Conference on , vol., no., pp.3082,3087, 14-18 May 2012. Moras, J.; Cherfaoui, V.; Bonnifait, P., "A lidar perception scheme for intelligent vehicle navigation," Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on , vol., no., pp.1809,1814, 7-10 Dec. 2010 Golovinskiy, Aleksey, Vladimir G. Kim, and Thomas Funkhouser. "Shape-based recognition of 3D point clouds in urban environments." Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009. Granstrom, K.; Lundquist, C.; Gustafsson, F.; Orguner, U., "Random Set Methods: Estimation of Multiple Extended Objects," Robotics & Automation Magazine, IEEE , vol.21, no.2, pp.73,82, June 2014 Data Association and Tracking a survey RoboEarth. Rusu, Radu Bogdan, and Steve Cousins. "3d is here: Point cloud library (pcl)." Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, 2011. Brostow, Gabriel J., et al. "Segmentation and recognition using structure from motion point clouds." Computer Vision–ECCV 2008. Springer Berlin Heidelberg, 2008. 44-57. Drost, Bertram, et al. "Model globally, match locally: Efficient and robust 3D object recognition." Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010. Biasotti, S. ; Falcidieno, B. ; Giorgi, D. ; Spagnuolo, M. “Mathematical Tools for Shape Analysis and Description”, 2014, Publisher :Morgan & Claypool, Edition:1, ISBN:1627053646 Börcs, Attila, et al. "A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds." (2014).
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Embedding DNN models on mobile robots for object detection +	• Pan, Sinno Jialin, and Qiang Yang. "A survey on transfer learning." IEEE Transactions on knowledge and data engineering 22.10 (2009): 1345-1359. • Yosinski, Jason, et al. "How transferable are features in deep neural networks?." Advances in neural information processing systems. 2014.
Evaluating the Digital Tools for Promoting Sustainable Food Consumption +	McClements, D. J., Barrangou, R., Hill, C., Kokini, J. L., Lila, M. A., Meyer, A. S., & Yu, L. (2021). Building a resilient, sustainable, and healthier food supply through innovation and technology. Annual review of food science and technology, 12(1), 1-28. Samoggia, A., Monticone, F., & Bertazzoli, A. (2021). Innovative digital technologies for purchasing and consumption in urban and regional agro-food systems: A systematic review. Foods, 10(2), 208.
Evaluating the Effects of Social Media on Educational Sustainability in Sweden +	- Abbas, J., Aman, J., Nurunnabi, M., & Bano, S. (2019). The impact of social media on learning behavior for sustainable education: Evidence of students from selected universities in Pakistan. Sustainability, 11(6), 1683. - Ebrahimi, P., Khajeheian, D., & Fekete-Farkas, M. (2021). A SEM-NCA approach towards social networks marketing: Evaluating consumers’ sustainable purchase behavior with the moderating role of eco-friendly attitude. International Journal of Environmental Research and Public Health, 18(24), 13276. - Li, J., & Xue, E. (2022). A social networking analysis of education policies of creating world-class universities for higher education sustainability in China. Sustainability, 14(16), 10243.
Evolutionary Behavior Trees for Multi-Agent Task-Oriented Environment +	http://frail.ii.pwr.edu.pl/
Explainable AI and poverty prediction +	(1) Lee and Braithwaite (2021), "High-Resolution Poverty Maps in Sub-Saharan Africa", https://arxiv.org/abs/2009.00544 (2) Jean, Burke, Xie, Davis, Lobell, and Ermon (2016), "Combining satellite imagery and machine learning to predict poverty", Science, https://www.science.org/doi/10.1126/science.aaf7894 (3) Roscher, Bohn, Duarte, and Garcke (2020), "Explainable Machine Learning for Scientific Insights and Discoveries", IEEE Access, https://ieeexplore.ieee.org/document/9007737
Explainable AI for predictive maintenance in collaboration with Volvo +	1- Adadi, Amina, and Mohammed Berrada. "Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)." IEEE access 6 (2018): 52138-52160. 2- Arrieta, Alejandro Barredo, et al. "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI." Information Fusion 58 (2020): 82-115. 3- https://dl.acm.org/doi/abs/10.1145/3292500.3332281 4- Cortez, Paulo, and Mark J. Embrechts. "Using sensitivity analysis and visualization techniques to open black box data mining models." Information Sciences 225 (2013): 1-17. 5- Gilpin, Leilani H., et al. "Explaining explanations: An overview of interpretability of machine learning." 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA). IEEE, 2018.
Explainable Anomaly Detection +	Li, Z., Zhu, Y., & Van Leeuwen, M. (2023). A survey on explainable anomaly detection. ACM Transactions on Knowledge Discovery from Data, 18(1), 1-54. Jacob, V., Song, F., Stiegler, A., Rad, B., Diao, Y., & Tatbul, N. (2020). Exathlon: A benchmark for explainable anomaly detection over time series. arXiv preprint arXiv:2010.05073. Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., & Zhang, A. (2021). A survey on causal inference. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(5), 1-46. Chatterjee, J., & Dethlefs, N. (2020, September). Temporal causal inference in wind turbine scada data using deep learning for explainable AI. In Journal of Physics: Conference Series (Vol. 1618, No. 2, p. 022022). IOP Publishing. Liu, Y., Ding, K., Lu, Q., Li, F., Zhang, L. Y., & Pan, S. (2024). Towards self-interpretable graph-level anomaly detection. Advances in Neural Information Processing Systems, 36. Deng, A., & Hooi, B. (2021, May). Graph neural network-based anomaly detection in multivariate time series. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 5, pp. 4027-4035). Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., ... & Akoglu, L. (2021). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12012-12038. Rad, B., Song, F., Jacob, V., & Diao, Y. (2021, June). Explainable anomaly detection on high-dimensional time series data. In Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems (pp. 2-14).

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