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Cyber-Physical Systems (CPSs), e.g., elevator systems and autonomous driving systems, are progressively permeating our everyday lives.
1912
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2021
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G. P. Meyer, “An Alternative Probabilistic Interpretation of the Huber Loss,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Los Alamitos, CA, USA: IEEE Computer Society, Jun. 2021, pp. 5257–5265. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/CVPR46437.2021.00522
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2018
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V. Damjanovic-Behrendt, “A Digital Twin-based Privacy Enhancement Mechanism for the Automotive Industry,” in 2018 International Conference on Intelligent Systems (IS) . IEEE Press, 2018, pp. 272–279, place: Funchal - Madeira, Portugal. [Online]. Available: https://doi.org/10.1109/IS.2018.8710526
2018
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2018
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T. K. Lee, T. W. Wang, W. X. Wu, Y. C. Kuo, S. H. Huang, G. S. Wang, C. Y. Lin, J. J. Chen, and Y. C. Tseng, “Building a V2X Simulation Framework for Future Autonomous Driving,” 2019 20th Asia-Pacific Network Operations and Management Symposium: Management in a Cyber-Physical World, APNOMS 2019 , pp. 1–6, 2019, publisher: IEICE
2019
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X. Gao, C. Shan, C. Hu, Z. Niu, and Z. Liu, “An Adaptive Ensemble Machine Learning Model for Intrusion Detection,” IEEE Access , vol. 7, pp. 82 512–82 521, 2019. [Online]. Available: https://ieeexplore.ieee.org/document/8740962/
2019
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K.-C. Li and B. B. Gupta, “Recent Advances in Security, Privacy and Trust for Internet-of-Things (IoT) and Cyber-Physical Systems (CPS),” 2020, publisher: Chapman and Hall/CRC
2020
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C. V. Nguyen, T. Hassner, M. Seeger, and C. Archambeau, “LEEP: a new measure to evaluate transferability of learned representations,” in Proceedings of the 37th International Conference on Machine Learning , ser. ICML’20. JMLR.org, 2020
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J. M. Zhang, M. Harman, L. Ma, and Y. Liu, “Machine Learning Testing: Survey, Landscapes and Horizons,” IEEE Transactions on Software Engineering , vol. X, no. X, pp. 1–1, 2020
2020
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Q. Xu, S. Ali, T. Yue, and M. Arratibel, “Uncertainty-aware transfer learning to evolve digital twins for industrial elevators,” in Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . Singapore Singapore: ACM, Nov. 2022, pp. 1257–1268. [Online]. Available: https://dl.acm.org/doi/10.1145/3540250.3558957
2022
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X. Liu, K. Ji, Y. Fu, W. Tam, Z. Du, Z. Yang, and J. Tang, “P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Dublin, Ireland: Association for Computational Linguistics, 2022, pp. 61–68. [Online]. Available: https://aclanthology.org/2022.acl-short.8
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2022
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2022
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F. O. Catak, T. Yue, and S. Ali, “Uncertainty-Aware Prediction Validator in Deep Learning Models for Cyber-Physical System Data,” ACM Trans. Softw. Eng. Methodol. , vol. 31, no. 4, Jul. 2022, place: New York, NY, USA Publisher: Association for Computing Machinery. [Online]. Available: https://doi.org/10.1145/3527451
2022
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——, “Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems,” ACM Trans. Softw. Eng. Methodol. , vol. 32, no. 5, Jul. 2023, place: New York, NY, USA Publisher: Association for Computing Machinery. [Online]. Available: https://doi.org/10.1145/3582571
2023
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Q. Xu, S. Ali, T. Yue, Z. Nedim, and I. Singh, “KDDT: Knowledge Distillation-Empowered Digital Twin for Anomaly Detection,” in Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering , ser. ESEC/FSE 2023. New York, NY, USA: Association for Computing Machinery, 2023, pp. 1867–1878. [Online]. Available: https://doi.org/10.1145/3611643.3613879
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T. Yue and S. Ali, “Evolve the Model Universe of a System Universe,” in 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) , Sep. 2023, pp. 1726–1731, journal Abbreviation: 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE)
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2023
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L. Han, S. Ali, T. Yue, A. Arrieta, and M. Arratibel, “Uncertainty-Aware Robustness Assessment of Industrial Elevator Systems,” ACM Trans. Softw. Eng. Methodol. , vol. 32, no. 4, May 2023, place: New York, NY, USA Publisher: Association for Computing Machinery. [Online]. Available: https://doi.org/10.1145/3576041
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