A Novel Warning Identification Framework for Risk-Informed Anomaly Detection

[1]  Katherine Fraser,et al.  Challenges for unsupervised anomaly detection in particle physics , 2021, Journal of High Energy Physics.

[2]  Mary Ann Lundteigen,et al.  Reliable Unmanned Autonomous Systems: Conceptual Framework for Warning Identification during Remote Operations , 2021, 2021 IEEE International Symposium on Systems Engineering (ISSE).

[3]  Catuscia Palamidessi,et al.  On the Applicability of Machine Learning Fairness Notions , 2021, SIGKDD Explor..

[4]  Shuilong He,et al.  Intelligent fault diagnosis of machines with small & imbalanced data: A state-of-the-art review and possible extensions. , 2021, ISA transactions.

[5]  Olga Fink,et al.  Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer , 2020, Knowl. Based Syst..

[6]  Ralph Foorthuis.  On the nature and types of anomalies: a review of deviations in data , 2020, International Journal of Data Science and Analytics.

[7]  Catuscia Palamidessi,et al.  On the Applicability of ML Fairness Notions , 2020, ArXiv.

[8]  Tilmann Rabl,et al.  Optimizing Machine Learning Workloads in Collaborative Environments , 2020, SIGMOD Conference.

[9]  Paul Fearnhead,et al.  Subset Multivariate Collective and Point Anomaly Detection , 2019, J. Comput. Graph. Stat..

[10]  John McDermid,et al.  Towards a Framework for Safety Assurance of Autonomous Systems , 2019, AISafety@IJCAI.

[11]  Shikha Verma,et al.  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2019, Vikalpa: The Journal for Decision Makers.

[12]  Ali S. Hadi,et al.  Anomaly Detection Methods for Categorical Data , 2019, ACM Comput. Surv..

[13]  Shai Ben-David,et al.  When can unlabeled data improve the learning rate? , 2019, COLT.

[14]  Toniann Pitassi,et al.  Fairness through Causal Awareness: Learning Causal Latent-Variable Models for Biased Data , 2018, FAT.

[15]  Enrico Zio,et al.  The future of risk assessment , 2018, Reliab. Eng. Syst. Saf..

[16]  Xue-qiu He,et al.  Rockburst occurrences and microseismicity in a longwall panel experiencing frequent rockbursts , 2018, Geosciences Journal.

[17]  George J. Vachtsevanos,et al.  Resilient Design and Operation of Cyber Physical Systems with Emphasis on Unmanned Autonomous Systems , 2018, Journal of Intelligent & Robotic Systems.

[18]  Kishan G. Mehrotra,et al.  Anomaly Detection Principles and Algorithms , 2017, Terrorism, Security, and Computation.

[19]  Tony Doyle,et al.  Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy , 2017, Inf. Soc..

[20]  Charu C. Aggarwal,et al.  LODES: Local Density Meets Spectral Outlier Detection , 2016, SDM.

[21]  Erik Hollnagel,et al.  Resilience Engineering: A New Understanding of Safety , 2016 .

[22]  John Schulman,et al.  Concrete Problems in AI Safety , 2016, ArXiv.

[23]  Seiichi Uchida,et al.  A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data , 2016, PloS one.

[24]  Miguel Angel Veganzones,et al.  Hyperspectral Anomaly Detectors Using Robust Estimators , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Phillip Jeff. Durst,et al.  Levels of autonomy and autonomous system performance assessment for intelligent unmanned systems , 2014 .

[26]  Hamid H. Jebur,et al.  Machine Learning Techniques for Anomaly Detection: An Overview , 2013 .

[27]  Charu C. Aggarwal,et al.  Outlier Analysis , 2013, Springer New York.

[28]  Marvin Rausand,et al.  Risk Assessment: Rausand/Risk Assessment , 2011 .

[29]  Marvin Rausand,et al.  Risk Assessment: Theory, Methods, and Applications , 2011 .

[30]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[31]  Jung-Min Park,et al.  An overview of anomaly detection techniques: Existing solutions and latest technological trends , 2007, Comput. Networks.

[32]  Sanjay Ranka,et al.  Conditional Anomaly Detection , 2007, IEEE Transactions on Knowledge and Data Engineering.

[33]  R. Wears.  Resilience Engineering: Concepts and Precepts , 2006 .

[34]  M. Bukowska.  The probability of rockburst occurrence in the Upper Silesian Coal Basin area dependent on natural mining conditions , 2006 .

[35]  Sameer Singh,et al.  Novelty detection: a review - part 1: statistical approaches , 2003, Signal Process..

[36]  R. Sekar,et al.  Specification-based anomaly detection: a new approach for detecting network intrusions , 2002, CCS '02.

[37]  Hoda M. O. Mokhtar,et al.  A Hybrid Model for Documents Representation , 2021 .

[38]  D. Makajic-Nikolic.  ISO 31000: Risk Management Guidelines , 2020, Encyclopedia of Sustainable Management.

[39]  Gereon Weiss,et al.  Managing Uncertainty of AI-based Perception for Autonomous Systems , 2019, AISafety@IJCAI.

[40]  E. L. Harder,et al.  The Institute of Electrical and Electronics Engineers, Inc. , 2019, 2019 IEEE International Conference on Software Architecture Companion (ICSA-C).

[41]  Gideon J. Mellenbergh.  Outliers , 2019, Counteracting Methodological Errors in Behavioral Research.

[42]  Felix Bießmann,et al.  On Challenges in Machine Learning Model Management , 2018, IEEE Data Eng. Bull..

[43]  Lisa,et al.  background in , 2017 .

[44]  Marek Sikora,et al.  Application of rule-based models for seismic hazard prediction in coal mines , 2014 .

[45]  D. Hawkins.  Identification of Outliers , 1980, Monographs on Applied Probability and Statistics.