60 BIBLIOGRAPHY
Conference on Computer Vision and Pattern Recognition, pages 427–436, 2015. DOI:
10.1109/cvpr.2015.7298640 31, 37
[168] Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian
Goodfellow, and Rob Fergus. Intriguing properties of neural networks. ArXiv Preprint
ArXiv:1312.6199, 2013.
[169] Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi, and Pascal Frossard. Deepfool: A
simple and accurate method to fool deep neural networks. In Proc. of IEEE Conference on
Computer Vision and Pattern Recognition (CVPR), number EPFL-CONF-218057, 2016.
DOI: 10.1109/cvpr.2016.282
[170] Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing
adversarial examples. ArXiv Preprint ArXiv:1412.6572, 2014.
[171] Nicolas Papernot, Patrick McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik,
and Ananthram Swami. e limitations of deep learning in adversarial settings. In Secu-
rity and Privacy (EuroS&P), IEEE European Symposium on, pages 372–387, 2016. DOI:
10.1109/eurosp.2016.36 31
[172] Vijay D’silva, Daniel Kroening, and Georg Weissenbacher. A survey of automated tech-
niques for formal software verification. IEEE Transactions on Computer-Aided Design of
Integrated Circuits and Systems, 27(7):1165–1178, 2008. DOI: 10.1109/tcad.2008.923410
32
[173] Mukul R. Prasad, Armin Biere, and Aarti Gupta. A survey of recent advances in sat-
based formal verification. International Journal on Software Tools for Technology Transfer,
7(2):156–173, 2005. DOI: 10.1007/s10009-004-0183-4
[174] Brian J. Taylor, Marjorie A. Darrah, and Christina D. Moats. Verification and validation
of neural networks: A sampling of research in progress. In Intelligent Computing: eory
and Applications, vol. 5103, pages 8–17, International Society for Optics and Photonics,
2003. DOI: 10.1117/12.487527 35, 37
[175] Sanjit A. Seshia, Dorsa Sadigh, and S. Shankar Sastry. Towards verified artificial intel-
ligence. ArXiv Preprint ArXiv:1606.08514, 2016. 32, 33
[176] Charles Pecheur and Stacy Nelson. V&V of advanced systems at Nasa. Produced for the
Space Launch Initiative 2nd Generation RLV TA-5 IVHM Project, 2002. 32
[177] Jeannette M. Wing. A specifier’s introduction to formal methods. Computer, 23(9):8–22,
1990. DOI: 10.1109/2.58215 32
BIBLIOGRAPHY 61
[178] Zeshan Kurd and Tim P. Kelly. Using fuzzy self-organising maps for safety criti-
cal systems. Reliability Engineering and System Safety, 92(11):1563–1583, 2007. DOI:
10.1016/j.ress.2006.10.005 32
[179] Zeshan Kurd, Tim Kelly, and Jim Austin. Developing artificial neural networks for
safety critical systems. Neural Computing and Applications, 16(1):11–19, 2007. DOI:
10.1007/s00521-006-0039-9 ix, 32, 41
[180] Luca Pulina and Armando Tacchella. An abstraction-refinement approach to verification
of artificial neural networks. In International Conference on Computer Aided Verification,
pages 243–257, Springer, 2010. DOI: 10.1007/978-3-642-14295-6_24 32
[181] Guy Katz, Clark Barrett, David L. Dill, Kyle Julian, and Mykel J. Kochenderfer. Relu-
plex: An efficient SMT solver for verifying deep neural networks. In International Confer-
ence on Computer Aided Verification, pages 97–117, Springer, 2017. DOI: 10.1007/978-3-
319-63387-9_5 32
[182] Xiaowei Huang, Marta Kwiatkowska, Sen Wang, and Min Wu. Safety verification of
deep neural networks. In International Conference on Computer Aided Verification, pages 3–
29, Springer, 2017. DOI: 10.1007/978-3-319-63387-9_1 32
[183] Shankar Sastry. Lyapunov stability theory. In Nonlinear Systems, pages 182–234, Springer,
1999. DOI: 10.1007/978-1-4757-3108-8_5 33
[184] Insup Lee, Oleg Sokolsky, John Regehr, et al. Statistical runtime checking of probabilistic
properties. In International Workshop on Runtime Verification, pages 164–175, Springer,
2007. DOI: 10.1007/978-3-540-77395-5_14 34
[185] A. Prasad Sistla and Abhigna R. Srinivas. Monitoring temporal properties of stochastic
systems. In International Workshop on Verification, Model Checking, and Abstract Interpreta-
tion, pages 294–308, Springer, 2008. DOI: 10.1007/978-3-540-78163-9_25
[186] Lars Grunske and Pengcheng Zhang. Monitoring probabilistic properties. In Proc. of
the 7th Joint Meeting of the European Software Engineering Conference and the ACM SIG-
SOFT Symposium on the Foundations of Software Engineering, pages 183–192, 2009. DOI:
10.1145/1595696.1595724
[187] A. Prasad Sistla, Miloš Žefran, and Yao Feng. Monitorability of stochastic dynamical sys-
tems. In International Conference on Computer Aided Verification, pages 720–736, Springer,
2011. DOI: 10.1007/978-3-642-22110-1_58
[188] Zhiwei Wang, Mohamed H. Zaki, and Sofiene Tahar. Statistical runtime verification of
analog and mixed signal designs. In Signals, Circuits and Systems (SCS), 3rd International
Conference on, pages 1–6, IEEE, 2009. DOI: 10.1109/icscs.2009.5412620
62 BIBLIOGRAPHY
[189] Dennis K. Peters and David Lorge Parnas. Requirements-based monitors for real-
time systems. IEEE Transactions on Software Engineering, 28(2):146–158, 2002. DOI:
10.1145/347324.348874 34
[190] Xiaowan Huang, Justin Seyster, Sean Callanan, Ketan Dixit, Radu Grosu, Scott A.
Smolka, Scott D. Stoller, and Erez Zadok. Software monitoring with controllable over-
head. International Journal on Software Tools for Technology Transfer, 14(3):327–347, 2012.
DOI: 10.1007/s10009-010-0184-4 34
[191] Karl Heckemann, Manuel Gesell, omas Pfister, Karsten Berns, Klaus Schneider, and
Mario Trapp. Safe automotive software. In International Conference on Knowledge-Based
and Intelligent Information and Engineering Systems, pages 167–176, Springer, 2011. DOI:
10.1007/978-3-642-23866-6_18 34
[192] Philip Koopman and Michael Wagner. Challenges in autonomous vehicle testing and
validation. SAE International Journal of Transportation Safety, 4(1):15–24, 2016. DOI:
10.4271/2016-01-0128 35
[193] Esther Levin, Naftali Tishby, and Sara A. Solla. A statistical approach to learning and
generalization in layered neural networks. Proc. of the IEEE, 78(10):1568–1574, 1990.
DOI: 10.1016/b978-0-08-094829-4.50020-9 35
[194] Lars Kai Hansen and Peter Salamon. Neural network ensembles. IEEE Transactions on
Pattern Analysis and Machine Intelligence, 12(10):993–1001, 1990. DOI: 10.1109/34.58871
35
[195] Anders Krogh and Jesper Vedelsby. Neural network ensembles, cross validation, and
active learning. In Advances in Neural Information Processing Systems, pages 231–238, 1995.
35
[196] Shenkai Gu, Ran Cheng, and Yaochu Jin. Multi-objective ensemble generation. Wi-
ley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 5(5):234–245, 2015.
DOI: 10.1002/widm.1158 35
[197] David H. Wolpert. Stacked generalization. Neural Networks, 5(2):241–259, 1992. DOI:
10.1016/s0893-6080(05)80023-1 35
[198] Kexin Pei, Yinzhi Cao, Junfeng Yang, and Suman Jana. Deepxplore: Automated white-
box testing of deep learning systems. In Proc. of the 26th Symposium on Operating Systems
Principles, pages 1–18, ACM, 2017. DOI: 10.1145/3132747.3132785 36, 38
[199] Yann LeCun. e mnist database of handwritten digits. http://yann.lecun.com/exd
b/mnist/, 1998. 36
BIBLIOGRAPHY 63
[200] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet:
A large-scale hierarchical image database. In Computer Vision and Pattern Recognition,
(CVPR). IEEE Conference on, pages 248–255, 2009. DOI: 10.1109/cvpr.2009.5206848
36
[201] Udacity Challenge 2016. Using deep learning to predict steering angles, 2016.
https://medium.com/udacity/challenge-2-using-deep-learning-to-predict-
steering-angles-f42004a36ff3 36
[202] Contagio 2010. Contagio, pdf malware dump, 2010. http://contagiodump.blogspo
t.com/ 36
[203] VirusTotal 2004. Virustotal, free service that analyzes suspicious files and urls and fa-
cilitates the quick detection of viruses, worms, trojans, and all kinds of malware, 2004.
https://www.virustotal.com 36
[204] Daniel Arp, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and
CERT Siemens. Drebin: Effective and explainable detection of android malware in your
pocket. In NDSS, vol. 14, pages 23–26, 2014. DOI: 10.14722/ndss.2014.23247 36
[205] Michael Spreitzenbarth, Felix Freiling, Florian Echtler, omas Schreck, and Johannes
Hoffmann. Mobile-sandbox: Having a deeper look into android applications. In Proc.
of the 28th Annual ACM Symposium on Applied Computing, pages 1808–1815, 2013. DOI:
10.1145/2480362.2480701 36
[206] Mark W. Craven and Jude W. Shavlik. Visualizing learning and computation in artificial
neural networks. International Journal on Artificial Intelligence Tools, 1(03):399–425, 1992.
DOI: 10.1142/s0218213092000260 37
[207] Jakub Wejchert and Gerald Tesauro. Neural network visualization. In Advances in Neural
Information Processing Systems, pages 465–472, 1990. 37
[208] Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional
networks: Visualising image classification models and saliency maps. ArXiv Preprint
ArXiv:1312.6034, 2013. 37
[209] Jason Yosinski, Jeff Clune, Anh Nguyen, omas Fuchs, and Hod Lipson. Understanding
neural networks through deep visualization. ArXiv Preprint ArXiv:1506.06579, 2015. 37
[210] Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard
Firner, Lawrence Jackel, and Urs Muller. Explaining how a deep neural network trained
with end-to-end learning steers a car. ArXiv Preprint ArXiv:1704.07911, 2017. 37
64 BIBLIOGRAPHY
[211] Matthew D. Zeiler and Rob Fergus. Visualizing and understanding convolutional net-
works. In European Conference on Computer Vision, pages 818–833, Springer, 2014. DOI:
10.1007/978-3-319-10590-1_53 38
[212] Shivani Acharya and Vidhi Pandya. Bridge between black box and white box—gray box
testing technique. International Journal of Electronics and Computer Science Engineering,
2(1):175–185, 2012. 38
[213] Yuchi Tian, Kexin Pei, Suman Jana, and Baishakhi Ray. Deeptest: Automated testing of
deep-neural-network-driven autonomous cars. ArXiv Preprint ArXiv:1708.08559, 2017.
DOI: 10.1145/3180155.3180220 38
[214] Johann Schumann, Pramod Gupta, and Yan Liu. Application of neural networks in high
assurance systems: A survey. In Applications of Neural Networks in High Assurance Systems,
pages 1–19, Springer, 2010. DOI: 10.1007/978-3-642-10690-3_1 39
[215] C. Wilkinson, J. Lynch, R. Bharadwaj, and K. Woodham. Verification of adaptive sys-
tems. Technical Report, Technical report, FAA, 2013. 39
[216] Kush R. Varshney and Homa Alemzadeh. On the safety of machine learning: Cyber-
physical systems, decision sciences, and data products. Big Data, 5(3):246–255, 2017.
DOI: 10.1089/big.2016.0051 39
[217] Rick Salay, Rodrigo Queiroz, and Krzysztof Czarnecki. An analysis of ISO 26262: Using
machine learning safely in automotive software. ArXiv Preprint ArXiv:1709.02435, 2017.
40
[218] Fabio Falcini, Giuseppe Lami, and Alessandra Mitidieri Costanza. Deep learning in
automotive software. IEEE Software, 34(3):56–63, 2017. DOI: 10.1109/ms.2017.79 41
[219] Tim Kelly and Rob Weaver. e goal structuring notation—a safety argument nota-
tion. In Proc. of the Dependable Systems and Networks Workshop on Assurance Cases, page 6,
Citeseer, 2004. 41
..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset