CIS 640: Advanced Topics in Software Systems:
Data-Driven IoT/Edge Computing
Spring 2020

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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.
• 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.
• 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

• 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
• 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
• 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

• Evaluating Real-Time Anomaly Detection Algorithms - The Numenta Anomaly Benchmark. Alexander Lavin, Subutai Ahmad. IEEE ICMLA, 2015. (https://arxiv.org/abs/1510.03336)
• Demystifying Numenta Anomaly Benchmark. Nidhi Singh, Craig Olinsky. IJCNN, 2017. (https://ieeexplore.ieee.org/abstract/document/7966038)
• 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]
• 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]
• 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.