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 2022

  1. S. Ramakrishna, B. Luo, Y. Barve, G. Karsai, and A. Dubey, Risk-Aware Scene Sampling for Dynamic Assurance of Autonomous Systems, in 2022 IEEE International Conference on Assured Autonomy (ICAA), 2022, pp. 107–116.
  2. D. M. Lopez, T. T. Johnson, S. Bak, H.-D. Tran, and K. Hobbs, Evaluation of Neural Network Verification Methods for Air to Air Collision Avoidance, AIAA Journal of Air Transportation (JAT), Oct. 2022.
  3. D. Manzanas Lopez, P. Musau, N. P. Hamilton, and T. T. Johnson, Reachability Analysis of a General Class of Neural Ordinary Differential Equations, in Formal Modeling and Analysis of Timed Systems, Cham, 2022, pp. 258–277.
  4. X. Yang, T. Yamaguchi, H.-D. Tran, B. Hoxha, T. T. Johnson, and D. Prokhorov, Neural Network Repair with Reachability Analysis, in Formal Modeling and Analysis of Timed Systems, Cham, 2022, pp. 221–236.
  5. T. Bao, S. Chen, T. T. Johnson, P. Givi, S. Sammak, and X. Jia, Physics Guided Neural Networks for Spatio-temporal Super-resolution of Turbulent Flows, in The 38th Conference on Uncertainty in Artificial Intelligence, 2022.
  6. N. Hamilton, P. K. Robinette, and T. T. Johnson, Training Agents to Satisfy Timed and Untimed Signal Temporal Logic Specifications with Reinforcement Learning, in Software Engineering and Formal Methods, Cham, 2022, pp. 190–206.
  7. N. Hamilton, P. Musau, D. M. Lopez, and T. T. Johnson, Zero-Shot Policy Transfer in Autonomous Racing: Reinforcement Learning vs Imitation Learning, in 2022 IEEE International Conference on Assured Autonomy (ICAA), 2022, pp. 11–20.
  8. P. Musau, N. Hamilton, D. M. Lopez, P. Robinette, and T. T. Johnson, On Using Real-Time Reachability for the Safety Assurance of Machine Learning Controllers, in 2022 IEEE International Conference on Assured Autonomy (ICAA), 2022, pp. 1–10.
  9. B. Serbinowski and T. T. Johnson, BehaVerify: Verifying Temporal Logic Specifications for Behavior Trees, in Software Engineering and Formal Methods, Cham, 2022, pp. 307–323.
  10. D. Boursinos and X. Koutsoukos, Selective Classification of Sequential Data Using Inductive Conformal Prediction, in 2022 IEEE International Conference on Assured Autonomy (ICAA), 2022, pp. 46–55.
  11. F. Cai, Z. Zhang, J. Liu, and X. Koutsoukos, Open Set Recognition using Vision Transformer with an Additional Detection Head. arXiv, 2022.
  12. H.-D. Tran, W. Xiang, and T. T. Johnson, Verification Approaches for Learning-Enabled Autonomous Cyber–Physical Systems, IEEE Design Test, vol. 39, no. 1, pp. 24–34, 2022.

 2021

  1. S. Ramakrishna, Z. RahimiNasab, G. Karsai, A. Easwaran, and A. Dubey, Efficient Out-of-Distribution Detection Using Latent Space of β-VAE for Cyber-Physical Systems, ACM Trans. Cyber-Phys. Syst., 2021.
  2. D. Stojcsics, D. Boursinos, N. Mahadevan, X. Koutsoukos, and G. Karsai, Fault-Adaptive Autonomy in Systems with Learning-Enabled Components, Sensors (Basel, Switzerland), vol. 21, no. 18, p. 6089, Sep. 2021.
  3. C. Hartsell, S. Ramakrishna, A. Dubey, D. Stojcsics, N. Mahadevan, and G. Karsai, ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems, in 16th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021, 2021.
  4. F. Cai, A. I. Ozdagli, N. Potteiger, and X. Koutsoukos, Inductive Conformal Out-of-distribution Detection based on Adversarial Autoencoders, in 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 2021, pp. 1–6.
  5. F. Cai, A. I. Ozdagli, and X. Koutsoukos, Detection of Dataset Shifts in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression, arXiv preprint arXiv:2104.06613, 2021.
  6. D. Boursinos and X. Koutsoukos, Assurance monitoring of learning-enabled cyber-physical systems using inductive conformal prediction based on distance learning, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol. 35, no. 2, pp. 251–264, 2021.
  7. D. Boursinos and X. Koutsoukos, Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning, in 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS), 2021, pp. 1–7.
  8. T. T. Johnson, D. M. Lopez, L. Benet, M. Forets, S. Guadalupe, C. Schilling, R. Ivanov, T. J. Carpenter, J. Weimer, and I. Lee, ARCH-COMP21 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants, in 8th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH21), 2021, vol. 80, pp. 90–119.
  9. T. T. Johnson, ARCH-COMP21 Repeatability Evaluation Report, in 8th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH21), 2021, vol. 80, pp. 153–160.
  10. S. Bak, C. Liu, and T. T. Johnson, The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results, CoRR, vol. abs/2109.00498, 2021.
  11. W. Xiang, H.-D. Tran, X. Yang, and T. T. Johnson, Reachable Set Estimation for Neural Network Control Systems: A Simulation-Guided Approach, IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 1821–1830, 2021.
  12. H.-D. Tran, N. Pal, P. Musau, D. M. Lopez, N. Hamilton, X. Yang, S. Bak, and T. T. Johnson, Robustness Verification of Semantic Segmentation Neural Networks Using Relaxed Reachability, in Computer Aided Verification, Cham, 2021, pp. 263–286.
  13. J. A. Rosenfeld, B. P. Russo, R. Kamalapurkar, and T. T. Johnson, The Occupation Kernel Method for Nonlinear System Identification, arXiv: Optimization and Control, vol. abs/1909.11792, 2021.
  14. X. Yang, O. A. Beg, M. Kenigsberg, and T. T. Johnson, A Framework for Identification and Validation of Affine Hybrid Automata from Input-Output Traces, ACM Trans. Cyber-Phys. Syst., Jun. 2021.

 2020

  1. S. Ramakrishna, C. Hartsell, A. Dubey, P. Pal, and G. Karsai, A Methodology for Automating Assurance Case Generation, in Thirteenth International Tools and Methods of Competitive Engineering Symposium (TMCE 2020), 2020.
  2. C. Hartsell, N. Mahadevan, H. Nine, T. Bapty, A. Dubey, and G. Karsai, Workflow Automation for Cyber Physical System Development Processes, in 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2020.
  3. S. Ramakrishna, C. Harstell, M. P. Burruss, G. Karsai, and A. Dubey, Dynamic-weighted simplex strategy for learning enabled cyber physical systems, Journal of Systems Architecture, vol. 111, p. 101760, 2020.
  4. F. Cai and X. Koutsoukos, Real-time out-of-distribution detection in learning-enabled cyber-physical systems, in 2020 ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), 2020, pp. 174–183.
  5. F. Cai, J. Li, and X. Koutsoukos, Detecting adversarial examples in learning-enabled cyber-physical systems using variational autoencoder for regression, in 2020 IEEE Security and Privacy Workshops (SPW), 2020, pp. 208–214.
  6. V. Sundar, S. Ramakrishna, Z. Rahiminasab, A. Easwaran, and A. Dubey, Out-of-Distribution Detection in Multi-Label Datasets using Latent Space of β-VAE, in 2020 IEEE Security and Privacy Workshops (SPW), Los Alamitos, CA, USA, 2020, pp. 250–255.
  7. D. Boursinos and X. Koutsoukos, Trusted Confidence Bounds for Learning Enabled Cyber-Physical Systems, in 2020 IEEE Security and Privacy Workshops (SPW), 2020, pp. 228–233.
  8. D. Boursinos and X. Koutsoukos, Assurance Monitoring of Cyber-Physical Systems with Machine Learning Components, in Thirteenth International Tools and Methods of Competitive Engineering Symposium (TMCE 2020), 2020.
  9. D. Boursinos and X. Koutsoukos, Improving Prediction Confidence in Learning-Enabled Autonomous Systems, in Dynamic Data Driven Applications Systems, Cham, 2020, pp. 217–224.
  10. H.-D. Tran, D. M. Lopez, X. Yang, P. Musau, L. V. Nguyen, W. Xiang, S. Bak, and T. T. Johnson, Demo: The Neural Network Verification (NNV) Tool, in 2020 IEEE Workshop on Design Automation for CPS and IoT (DESTION), 2020, pp. 21–22.
  11. T. T. Johnson, D. M. Lopez, P. Musau, H.-D. Tran, E. Botoeva, F. Leofante, A. Maleki, C. Sidrane, J. Fan, and C. Huang, ARCH-COMP20 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants, in ARCH20. 7th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH20), 2020, vol. 74, pp. 107–139.
  12. H.-D. Tran, X. Yang, D. Manzanas Lopez, P. Musau, L. V. Nguyen, W. Xiang, S. Bak, and T. T. Johnson, NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systems, in Computer Aided Verification, Cham, 2020, pp. 3–17.
  13. T. T. Johnson, ARCH-COMP20 Repeatability Evaluation Report, in ARCH20. 7th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH20), 2020, vol. 74, pp. 175–183.
  14. H.-D. Tran, S. Bak, W. Xiang, and T. T. Johnson, Verification of Deep Convolutional Neural Networks Using ImageStars, in Computer Aided Verification, Cham, 2020, pp. 18–42.
  15. S. Bak, H.-D. Tran, K. Hobbs, and T. T. Johnson, Improved Geometric Path Enumeration for Verifying ReLU Neural Networks, in Computer Aided Verification, Cham, 2020, pp. 66–96.
  16. F. Cai, J. Li, and X. Koutsoukos, Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression, in 2020 IEEE Security and Privacy Workshops (SPW), 2020, pp. 208–214.
  17. D. M. Lopez, P. Musau, N. Hamilton, H.-D. Tran, and T. T. Jonhson, Case Study: Safety Verification of an Unmanned Underwater Vehicle, in 2020 IEEE Security and Privacy Workshops (SPW), 2020, pp. 189–195.
  18. U. Yu, Combining Reachable Set Computation with Neuron Coverage, PhD thesis, 2020.

 2019

  1. C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, and G. Karsai, A CPS toolchain for learning-based systems: demo abstract, in Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2019, Montreal, QC, Canada, 2019, pp. 342–343.
  2. C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, T. T. Johnson, X. D. Koutsoukos, J. Sztipanovits, and G. Karsai, Model-based design for CPS with learning-enabled components, in Proceedings of the Workshop on Design Automation for CPS and IoT, DESTION@CPSIoTWeek 2019, Montreal, QC, Canada, 2019, pp. 1–9.
  3. H.-D. Tran, F. Cai, M. L. Diego, P. Musau, T. T. Johnson, and X. Koutsoukos, Safety verification of cyber-physical systems with reinforcement learning control, ACM Transactions on Embedded Computing Systems (TECS), vol. 18, no. 5s, pp. 1–22, 2019.
  4. S. Ramakrishna, A. Dubey, M. P. Burruss, C. Hartsell, N. Mahadevan, S. Nannapaneni, A. Laszka, and G. Karsai, Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots, in IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Valencia, Spain, May 7-9, 2019, 2019, pp. 108–117.
  5. C. Hartsell, N. Mahadevan, S. Ramakrishna, A. Dubey, T. Bapty, T. T. Johnson, X. D. Koutsoukos, J. Sztipanovits, and G. Karsai, CPS Design with Learning-Enabled Components: A Case Study, in Proceedings of the 30th International Workshop on Rapid System Prototyping, RSP 2019, New York, NY, USA, October 17-18, 2019, 2019, pp. 57–63.
  6. D. M. Lopez, P. Musau, H.-D. Tran, S. Dutta, T. J. Carpenter, R. Ivanov, and T. T. Johnson, ARCH-COMP19 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants, in ARCH19. 6th International Workshop on Applied Verification of Continuous and Hybrid Systems, 2019, vol. 61, pp. 103–119.
  7. T. T. Johnson, ARCH-COMP19 Repeatability Evaluation Report, in ARCH19. 6th International Workshop on Applied Verification of Continuous and Hybrid Systems, 2019, vol. 61, pp. 162–169.
  8. H.-D. Tran, D. Manzanas Lopez, P. Musau, X. Yang, L. V. Nguyen, W. Xiang, and T. T. Johnson, Star-Based Reachability Analysis of Deep Neural Networks, in Formal Methods – The Next 30 Years: Third World Congress, FM 2019, Porto, Portugal, October 7–11, 2019, Proceedings, Berlin, Heidelberg, 2019, pp. 670–686.
  9. H.-D. Tran, P. Musau, D. Manzanas Lopez, X. Yang, L. V. Nguyen, W. Xiang, and T. T. Johnson, Parallelizable Reachability Analysis Algorithms for Feed-Forward Neural Networks, in 2019 IEEE/ACM 7th International Conference on Formal Methods in Software Engineering (FormaliSE), 2019, pp. 51–60.
  10. H.-D. Tran, F. Cai, M. L. Diego, P. Musau, T. T. Johnson, and X. Koutsoukos, Safety Verification of Cyber-Physical Systems with Reinforcement Learning Control, ACM Trans. Embed. Comput. Syst., vol. 18, no. 5s, Oct. 2019.
  11. W. Xiang, H.-D. Tran, and T. T. Johnson, Nonconservative Lifted Convex Conditions for Stability of Discrete-Time Switched Systems Under Minimum Dwell-Time Constraint, IEEE Transactions on Automatic Control, vol. 64, no. 8, pp. 3407–3414, 2019.
  12. S. Bak, H.-D. Tran, and T. T. Johnson, Numerical Verification of Affine Systems with up to a Billion Dimensions, in Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control, New York, NY, USA, 2019, pp. 23–32.
  13. H.-D. Tran, L. V. Nguyen, P. Musau, W. Xiang, and T. T. Johnson, Decentralized Real-Time Safety Verification for Distributed Cyber-Physical Systems, in Formal Techniques for Distributed Objects, Components, and Systems, Cham, 2019, pp. 261–277.
  14. W. Xiang, D. M. Lopez, P. Musau, and T. T. Johnson, Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems, in Safe, Autonomous and Intelligent Vehicles, H. Yu, X. Li, R. M. Murray, S. Ramesh, and C. J. Tomlin, Eds. Cham: Springer International Publishing, 2019, pp. 123–144.
  15. H.-D. Tran, L. V. Nguyen, P. Musau, W. Xiang, and T. T. Johnson, Real-Time Verification for Distributed Cyber-Physical Systems, CoRR, vol. abs/1909.09087, 2019.
  16. J. A. Rosenfeld, R. Kamalapurkar, B. Russo, and T. T. Johnson, Occupation Kernels and Densely Defined Liouville Operators for System Identification, in 2019 IEEE 58th Conference on Decision and Control (CDC), 2019.

 2018

  1. W. Xiang, P. Musau, A. A. Wild, D. M. Lopez, N. Hamilton, X. Yang, J. Rosenfeld, and T. T. Johnson, Verification for machine learning, autonomy, and neural networks survey, arXiv preprint arXiv:1810.01989, 2018.
  2. W. Xiang, H.-D. Tran, and T. T. Johnson, Output reachable set estimation and verification for multilayer neural networks, IEEE transactions on neural networks and learning systems, vol. 29, no. 11, pp. 5777–5783, 2018.
  3. T. T. Johnson, ARCH-COMP18 Repeatability Evaluation Report, in ARCH18. 5th International Workshop on Applied Verification of Continuous and Hybrid Systems, 2018, vol. 54, pp. 128–134.