CIS 640: Advanced Topics in Software Systems:
Data-Driven IoT/Edge Computing
Spring 2020
Reading List (perpetually under construction)
Time Series Analysis
- Time Series Analysis and Its Applications with R Examples. Robert H. Shumway, David S. Stoffer. 4th Edition, Springer 2016. [pdf]
- Time Series Analysis: With Applications in R, 2nd edition. J. D. Cryer and K.-S. Chan. New York: Springer, 2010. [pdf]
- Time Series: Theory and Methods. P. J. Brockwell. 2nd ed. 1991, 2nd printing 2009. New York, NY: Springer, 2009. [pdf]
- Introductory Time Series with R. P. S. P. Cowpertwait and A. V. Metcalfe. 2009 edition. Dordrecht; New York: Springer, 2009. [pdf]
- Suchi Saria, Andrew Duchi, and Daphne Koller. Discovering deformable
motifs in continuous time series data. In IJCAI International Joint
Conference on Artificial Intelligence, pages 1465-1471, 2011. doi:
10.5591/978-1-57735-516-8/IJCAI11-247. [pdf]
Anomaly Detection
- Outlier Analysis. (2nd ed.) 2016. Aggarwal, Charu C. Springer Publishing Company, Incorporated.
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Anomaly detection: A survey. Varun Chandola, Arindam Banerjee, Vipin Kumar. CSUR '09 (https://dl.acm.org/citation.cfm?id=1541882)
- Precision and Recall for Time Series.
Nesime Tatbul, Tae Jun Lee, Stan Zdonik, Mejbah Alam, Justin Gottschlich.
NeurlPS 2018 (https://arxiv.org/abs/1803.03639/)
Anomaly Detection Techniques
- Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles.
Franco van Wyk, Yiyang Wang, Anahita Khojandi, and Neda Masoud.
IEEE Trans. on Intelligent Transportation Systems, April 2019.
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Security of Cyber-Physical Systems in the Presence of Transient Sensor Faults.
Junkil Park, Radoslav Ivanov, James Weimer, Miroslav Pajic, Sang Hyuk Son, and Insup Lee.
ACM Transactions on Cyber-Physical Systems: 1(3), 2017.
Confidence Evaluation
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Guo, Chuan, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. "On calibration of modern neural networks." In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 1321-1330. JMLR. org, 2017.
https://arxiv.org/abs/1706.04599
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Song, Hao, Tom Diethe, Meelis Kull, and Peter Flach. "Distribution calibration for regression." arXiv preprint arXiv:1905.06023 (2019).
https://arxiv.org/abs/1905.06023
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Kuleshov, Volodymyr, Nathan Fenner, and Stefano Ermon. "Accurate uncertainties for deep learning using calibrated regression." arXiv preprint arXiv:1807.00263 (2018).
https://arxiv.org/abs/1807.00263
Data Generation for Machine Learning and Anomaly Detection
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Evaluating Real-Time Anomaly Detection Algorithms - The Numenta Anomaly Benchmark.
Alexander Lavin, Subutai Ahmad.
IEEE ICMLA, 2015.
(https://arxiv.org/abs/1510.03336)
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Demystifying Numenta Anomaly Benchmark.
Nidhi Singh, Craig Olinsky.
IJCNN, 2017.
(https://ieeexplore.ieee.org/abstract/document/7966038)
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Paranom: A Parallel Anomaly Dataset Generator. (DATSA)
Justin Gottschlich.
(https://arxiv.org/pdf/1801.03164.pdf)
- Snorkel: the systems for Programmatically Building and Managing Training Data.
(https://www.snorkel.org/)
Interpretable (Machine Learning and) Anomaly Detection
- Techniques for Interpretable Machine Learning. M. Du, N. Liu, and X. Hu.
CACM 63(1), Jan 2020.
- The Mythos of Model Interpretability. Zachary C. Lipton. 2016. (https://arxiv.org/abs/1606.03490)
- Towards A Rigorous Science of Interpretable Machine Learning.
Finale Doshi-Velez, Been Kim. 2017. (https://arxiv.org/abs/1702.08608)
Medical Applications
- Challenges and Research Directions in Medical Cyber–Physical Systems. I. Lee et al. Proceedings of the IEEE, vol. 100, no. 1, pp. 75–90, Jan. 2012, doi: 10.1109/JPROC.2011.2165270.
[pdf]
- Medical Cyber-Physical Systems. I. Lee et al. Book chapter draft [pdf]
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Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care. Patrick Schwab, Emanuela Keller, Carl Muroi, David J. Mack, Christian Strassle, Walter Karlen. ICML. 2018. [pdf]
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Reducing Pulse Oximetry False Alarms Without Missing Life-Threatening Events. Hung Nguyen, Sooyong Jang, Radoslav Ivanov, Christopher P. Bonafide, James Weimer, Insup Lee. CHASE 2018. [pdf]
Physical Modeling
- A hybrid neural network‐first principles approach to process modeling. Psichogios, D.C. and Ungar, L.H. 1992. AIChE J., 38: 1499-1511. doi:10.1002/aic.690381003
- Physics-informed neural networks: A deep
learning framework for solving forward and inverse problems involving nonlinear partial di erential
equations. Maziar Raissi, Paris Perdikaris, and George E Karniadakis. Journal of Computational Physics, 378:686{707}, 2019.
Last updated 1/16/20 by Ivan Ruchkin.