nach oben

The Journal of Supercomputing

Erschienen in:

05.03.2020

Pseudo-random number generation using LSTMs

verfasst von: Young-Seob Jeong, Kyo-Joong Oh, Chung-Ki Cho, Ho-Jin Choi

Erschienen in: The Journal of Supercomputing | Ausgabe 10/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Previous studies have developed pseudo-random number generators, where a pseudo-random number is not perfectly random but is practically useful. In this paper, we propose a new system for pseudo-random number generation. Recurrent neural networks with long short-term memory units are used to mimic the appearance of a given sequence of irrational number (e.g., pi), and these are intended to generate pseudo-random numbers in an iterative manner. We design algorithms to ensure that the output sequence contains no repetition or pattern. Through experimental results, we can observe the potential of the proposed system in terms of its randomness and stability. As this system can be used for parameter approximation in machine learning techniques, we believe that it will contribute to various industrial fields such as traffic management and frameworks for sensor networks.

Vorheriger Artikel An accelerated rendering scheme for massively large point cloud data

Nächster Artikel Predator–prey approach in modeling users’ data packets forwarding

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. In: Proceedings of NIPS 2014 Deep Learning and Representation Learning Workshop, Montreal, Quebec, Canada, pp 1–9

Desai V, Patil R, Rao D (2012) Using layer recurrent neural network to generate pseudo random number sequences. Int J Comput Sci Issues 9(2):324–334

Fischer A, Igel C (2012) An introduction to restricted Boltzmann machines. In: Proceedings of 17th Iberoamerican Congress: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Buenos Aires, Argentina, pp 14–36

Gholipour A, Mirzakuchaki S (2011) A pseudorandom number generator with Keccak hash function. Int J Comput Electr Eng 3(6):896–899CrossRef

Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Proceedings of 28th Annual Conference on Neural Information Processing Systems, Montreal, Quebec, Canada, pp 2672–2680

Graves A (2012) Supervised sequence labelling with recurrent neural networks, vol 385. Springer, BerlinCrossRef

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRef

Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int J Uncertain Fuzziness Knowl Based Syst 6(2):107–116MathSciNetCrossRef

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef

10.

Jeong YS, Oh K, Cho CK, Choi, HJ (2018) Pseudo random number generation using LSTMS and irrational numbers. In: Proceedings of the 26th IEEE International Conference on Big Data and Smart Computing, Shanghai, China, pp 541–544

11.

Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar, pp 1746–1751

12.

Kim ZM, Jeong YS, Oh HR, Oh KJ, Lim CG, Iraqi Y, Choi HJ (2016) Investigating the impact of possession-way of a smartphone on action recognition. Sensors 16(6):1–15CrossRef

13.

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of 26th Annual Conference on Neural Information Processing Systems, Nevada, USA, pp 1106–1114

14.

Lee HG, Kim JS, Shin JH, Lee J, Quan YX, Jeong YS (2016) papago: a machine translation service withword sense disambiguation and currency conversion. In: Proceedings of the 26th International Conference on Computational Linguistics: System Demonstrations, Osaka, Japan, pp 185–188

15.

Lee KS, Lee SR, Kim Y, Lee CG (2017) Deep learning-based real-time query processing for wireless sensor network. Int J Distrib Sens Netw 13(5):1–10CrossRef

16.

Mikolov T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Proceedings of the 27st Annual Conference on Neural Information Processing Systems, Nevada, United States, pp 3111–3119

17.

Milinkovic L, Antic M, Cica Z (2011) Pseudo-random number generator based on irrational numbers. In: Proceedings of 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services, Serbia, pp 719–722

18.

Mitchell TM (1997) Machine learning, 1st edn. McGraw-Hill, New YorkMATH

19.

National Institute of Standard and Technology: Data encryption standard. Federal Information Processing Standard (FIPS) publication 46(3) (1999)

20.

National Institute of Standard and Technology: Advanced encryption standard. Federal Information Processing Standard (FIPS) publication 197 (2001)

21.

National Institute of Standard and Technology: A statistical test suite for random and pseudorandom number generators for cryptographic applications (2010)

22.

Neumann JV (1951) Various techniques used in connection with random digits. In: Monte Carlo method, vol 12. National Bureau of Standards Applied Mathematics Series, pp 36–38

23.

Olah C (2015) Understanding LSTM networks . http://colah.github.io/posts/2015-08-Understanding-LSTMs/

24.

Rumelhart D, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition, vol 1. MIT Press, Cambridge, pp 318–362

25.

Schneier B (1996) Applied cryptography, 2nd edn. Wiley, New YorkMATH

26.

Stallings W (2010) Cryptography and network security, principles and practices, 5th edn. Pearson, London

27.

Sutskever I, Martens J, Hinton G (2011) Generating text with recurrent neural networks. In: Proceedings of the 28th International Conference on Machine Learning, Bellevue, WA, USA, pp 1017–1024

28.

Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, pp 3104–3112

29.

Sys M, Riha Z, Matyas V, Marton K, Suciu A (2015) On the interpretation of results from the nist statistical test suite. Rom J Inf Sci Technol 18(1):18–32

30.

Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp 1–9

31.

Tirad K (2010) Developing pseudo random number generator based on neural networks and neurofuzzy systems. Master’s thesis, Ryerson University

32.

Tirdad K (2010) Developing pseudo random number generator based on neural networks and neurofuzzy systems. Master degree dissertation, Ryerson University

33.

Wang XY, Qin X (2012) A new pseudo-random number generator based on CML and chaotic iteration. Nonlinear Dyn 70(2):1589–1592MathSciNetCrossRef

34.

Wang Y, Wang G, Zhang H (2010) Random number generator based on Hopfield neural network and SHA-2 (512). In: Lecture notes in electrical engineering, vol 56. Springer, Berlin, pp 198–205

35.

Yao S, Hu S, Zhao Y, Zhang A, Abdelzaher T (2017) Deepsense: a unified deep learning framework for time-series mobile sensing data processing. In: Proceedings of the 26th International World Wide Web Conference, Perth, Australia, pp 351–360

Titel: Pseudo-random number generation using LSTMs
verfasst von: Young-Seob Jeong
Kyo-Joong Oh
Chung-Ki Cho
Ho-Jin Choi
Publikationsdatum: 05.03.2020
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 10/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-020-03229-7

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 10/2020

SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming

Hash-tree PCA: accelerating PCA with hash-based grouping

Improvement and performance analysis of a power-saving mechanism considering traffic patterns

δ-Transitive closures and triangle consistency checking: a new way to evaluate graph pattern queries in large graph databases

An efficient continuous range query processing scheme in mobile P2P networks

Efficient processing of recommendation algorithms on a single-machine-based graph engine