Investigation of determinism-related issues in the Sobol′ low-discrepancy sequence for producing sound global sensitivity analysis indices

A computationally efficient and robust sampling scheme can support a sensitivity analysis of models to discover their behaviour through Quasi Monte Carlo approximation. This is especially useful for complex models, as often occur in environmental domains when model runtime can be prohibitive. The Sobol' sequence is one of the most used quasi-random low-discrepancy sequences as it can explore the parameter space significantly more evenly than pseudo-random sequences. The built-in determinism of the Sobol' sequence assists in achieving this attractive property. However, the Sobol' sequence tends to deteriorate in the sense that the estimated errors are distributed inconsistently across model parameters as the dimensions of a model increase. By testing multiple Sobol' sequence implementations, it is clear that the deterministic nature of the Sobol' sequence occasionally introduces relatively large errors in sensitivity indices produced by well-known global sensitivity analysis methods, and that the errors do not diminish by averaging through multiple replications. Problematic sensitivity indices may mistakenly guide modellers to make type I and II errors in trying to identify sensitive parameters, and this will potentially impact model reduction attempts based on these sensitivity measurements. This work investigates the cause of the Sobol' sequence's determinism-related issues. References I. A. Antonov and V. M. Saleev. An economic method of computing LPτ-sequences. USSR Comput. Math. Math. Phys. 19.1 (1979), pp. 252–256. doi: 10.1016/0041-5553(79)90085-5 P. Bratley and B. L. Fox. Algorithm 659: Implementing Sobol’s quasirandom sequence generator. ACM Trans. Math. Soft. 14.1 (1988), pp. 88–100. doi: 10.1145/42288.214372 J. Feinberg and H. P. Langtangen. Chaospy: An open source tool for designing methods of uncertainty quantification. J. Comput. Sci. 11 (2015), pp. 46–57. doi: 10.1016/j.jocs.2015.08.008 on p. C90). S. Joe and F. Y. Kuo. Constructing Sobol sequences with better two-dimensional projections. SIAM J. Sci. Comput. 30.5 (2008), pp. 2635–2654. doi: 10.1137/070709359 S. Joe and F. Y. Kuo. Remark on algorithm 659: Implementing Sobol’s quasirandom sequence generator. ACM Trans. Math. Soft. 29.1 (2003), pp. 49–57. doi: 10.1145/641876.641879 W. J. Morokoff and R. E. Caflisch. Quasi-random sequences and their discrepancies. SIAM J. Sci. Comput. 15.6 (1994), pp. 1251–1279. doi: 10.1137/0915077 X. Sun, B. Croke, S. Roberts, and A. Jakeman. Comparing methods of randomizing Sobol’ sequences for improving uncertainty of metrics in variance-based global sensitivity estimation. Reliab. Eng. Sys. Safety 210 (2021), p. 107499. doi: 10.1016/j.ress.2021.107499 S. Tarantola, W. Becker, and D. Zeitz. A comparison of two sampling methods for global sensitivity analysis. Comput. Phys. Com. 183.5 (2012), pp. 1061–1072. doi: 10.1016/j.cpc.2011.12.015 S. Tezuka. Discrepancy between QMC and RQMC, II. Uniform Dist. Theory 6.1 (2011), pp. 57–64. url: https://pcwww.liv.ac.uk/~karpenk/JournalUDT/vol06/no1/5Tezuka11-1.pdf I. M. Sobol′. On the distribution of points in a cube and the approximate evaluation of integrals. USSR Comput. Math. Math. Phys. 7.4 (1967), pp. 86–112. doi: 10.1016/0041-5553(67)90144-9 I. M. Sobol′. Sensitivity estimates for nonlinear mathematical models. Math. Model. Comput. Exp 1.4 (1993), pp. 407–414.

Authenticated encryption mode with blocks skipping

Block symmetric ciphers are one of the most important components of modern information security systems. At the same time, in addition to the structure of the applied block symmetric cipher, the cryptographic strength and performance of the information protection system is largely determined by the applied encryption mode. In addition to high performance and high-quality destruction of block statistics, modern encryption modes should also protect encrypted information from occurred or intentionally introduced errors. In this paper, we have developed an encryption mode with blocks skipping and using a pseudo-random key sequence generator, which allows checking the integrity of encrypted information with accurate detection of the place where an error was introduced. In this case, the error detection accuracy is determined by the adjustable parameter of the macroblock size and can be set depending on the level of importance of the protected information. The developed encryption mode is characterized by the following key advantages: reducing the number of required encryption operations by half, while providing a high level of cryptographic quality; more effective destruction of macroblock statistics due to the use of an additional generator of pseudo-random key sequences, the impossibility of propagation of the occurred (intentionally introduced) error outside the macroblock, as well as higher values of the number of protection levels due to the possibility of classifying the initial states of the applied generators of pseudo-random key sequences. As proposed in this paper, the mode of authenticated encryption with blocks skipping can be recommended for use on mobile platforms that are demanding both in terms of the quality and reliability of the protected information and are limited in terms of computing and power resources.

The Dynamic Analysis of a Novel Reconfigurable Cubic Chaotic Map and Its Application in Finite Field

Dynamic degradation occurs when chaotic systems are implemented on digital devices, which seriously threatens the security of chaos-based pseudorandom sequence generators. The chaotic degradation shows complex periodic behavior, which is often ignored by designers and seldom analyzed in theory. Not knowing the exact period of the output sequence is the key problem that affects the application of chaos-based pseudorandom sequence generators. In this paper, two cubic chaotic maps are combined, which have symmetry and reconfigurable form in the digital circuit. The dynamic behavior of the cubic chaotic map and the corresponding digital cubic chaotic map are analyzed respectively, and the reasons for the complex period and weak randomness of output sequences are studied. On this basis, the digital cubic chaotic map is optimized, and the complex periodic behavior is improved. In addition, a reconfigurable pseudorandom sequence generator based on the digital cubic chaotic map is constructed from the point of saving consumption of logical resources. Through theoretical and numerical analysis, the pseudorandom sequence generator solves the complex period and weak randomness of the cubic chaotic map after digitization and makes the output sequence have better performance and less resource consumption, which lays the foundation for applying it to the field of secure communication.

CRISPR-VAE: A Method for Explaining CRISPR/Cas12a Predictions, and an Efficiency-aware gRNA Sequence Generator

Motivation: Sizeable research has been conducted to facilitate the usage of CRISPR-Cas systems in genome editing, in which deep learning-based methods among others have shown great promise in the prediction of the gRNA efficiency. An accurate prediction of gRNA efficiency helps practitioners optimize their engineered gRNAs, maximizing the on-target efficiency, and minimizing the off-target effects. However, the black box prediction of deep learning-based methods does not provide adequate explanation to the factors that make a sequence efficient; rectifying this issue can help promote the usage of CRISPR-Cas systems in numerous domains. Results: We put forward a framework for interpreting gRNA efficiency prediction, dubbed CRISPR-VAE, and apply it to CRISPR/Cpf1. We thus help open the door to a better interpretability of the factors that make a certain gRNA efficient. We further lay out a semantic articulation of such factors into position-wise k-mer rules. The paradigm consists of building an efficiency-aware gRNA sequence generator trained on available real data, and using it to generate a large amount of synthetic sequences with favorable traits, upon which the explanation of the gRNA prediction is based. CRISPR-VAE can further be used as a standalone sequence generator, where the user has access to a low-level editing control. The framework can be readily integrated with different CRISPR-Cas tools and datasets, and its efficacy is confirmed in this paper.

Betalogger: Smartphone Sensor-based Side-channel Attack Detection and Text Inference Using Language Modeling and Dense MultiLayer Neural Network

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.

The New Scramble for Faure Sequence Based on Irrational Numbers

This article intends to review quasirandom sequences, especially the Faure sequence to introduce a new version of scrambled of this sequence based on irrational numbers, as follows to prove the success of this version of the random number sequence generator and use it in future calculations. We introduce this scramble of the Faure sequence and show the performance of this sequence in employed numerical codes to obtain successful test integrals. Here, we define a scrambling matrix so that its elements are irrational numbers. In addition, a new form of radical inverse function has been defined, which by combining it with our new matrix, we will have a sequence that not only has a better close uniform distribution than the previous sequences but also is a more accurate and efficient tool in estimating test integrals.

Nonparametric Bifurcation and Anti-Control of Hyperchaos in a Memristor–Memcapacitor-Based Circuit

In this paper, a new memristor model and a new memcapacitor model are proposed. Based on the two models, a simple chaotic circuit is constructed. Due to the special characteristics of the memristor and memcapacitor, the proposed circuit has two-dimensional normally hyperbolic manifolds of equilibria, and nonparametric bifurcation can occur when the conditions supporting the normal hyperbolicity of such manifolds are not satisfied. By adding a nonlinear controller to the proposed circuit, an anti-controlled system is realized, which has hyperchaotic dynamic behaviors under some suitable control parameters. The stability of equilibrium points and dynamic properties of the original system and the anti-controlled system are explored by Lyapunov exponents, bifurcation diagrams and so on. Furthermore, the anti-controlled system is applied to design a random sequence generator on digital signal processor platform.