Random Number Generator (RNG)

A novel Mathematical Random Number Generator (MRNG) is presented here. In this case, "mathematical" refers to the fact that to construct that generator it is not necessary to resort to a physical phenomenon, such as the thermal noise of an electronic device, but rather to a mathematical procedure. The MRNG generates binary strings { in principle, as long as desired { which may be considered genuinely random in the sense that they pass the statistical tests currently accepted to evaluate the randomness of those strings. From those strings, the MRNG also generates random numbers expressed in base 10. An MRNG has been installed as a facility on the following web page: www.appliedmathgroup.org. This generator may be used for applications in tasks in: a) computational simulation of probabilistic-type systems, and b) the random selection of samples of dierent populations. Users interested in applications in cryptography can build another MRNG, but they would have to withhold inf...

Cheap micro-controllers, such as the Arduino or other controllers based on the Atmel AVR CPUs are being deployed in a wide variety of projects, ranging from sensors networks to robotic submarines. In this paper, we investigate the feasibility of using the Arduino as a true random number generator (TRNG). The Arduino Reference Manual recommends using it to seed a pseudo random number generator (PRNG) due to its ability to read random atmospheric noise from its analog pins. This is an enticing application since true bits of entropy are hard to come by. Unfortunately, we show by statistical methods that the atmospheric noise of an Arduino is largely predictable in a variety of settings, and is thus a weak source of entropy. We explore various methods to extract true randomness from the micro-controller and conclude that it should not be used to produce randomness from its analog pins.

Random number generators for several distributions, both discrete and continuous, are here discussed and exemplified by scripts and examples. Random generation of uniform distributions is then applied to the problem of numerical integration in one or more dimensions. Convergent series are then derived for elliptic integrals and for the measure of the surface of the triaxial ellipsoid. Text in Italian.

Road Accidents are global phenomenon which is occurring all over the world but are very severe in mixed traffic conditions as prevailing in Indian road conditions. Many lives are lost and huge amount of property damage occurs due to accidents. Road safety audit is formal procedure for assessing potential and safety performance in the provision of new road schemes, the improvement and rehabilitation of existing road and in maintenance of roads. The role of auditor is to provide independent advice in the form of recommendation.It is proved that RSA has the potential to save lives. RSA appears to be an ideal tool for improving road safety. Transportation through road networks satisfies the basic need of people. Many lives are lost and huge amount of property damage occurs due to accidents.

This paper discusses and demonstrate the proposed method, for random number generator. The random number generator trying to discard the relationship between consecutive digit occurrences, moreover, these random sets can't be repeated except if the equivalent generator algorithm with same initial values are used. The designed generator stunned the earlier problems of an initial key size, minimal periodic and the capability to regenerate the similar sequence. The proposed generator employing the idea of Futoshiki puzzle game, this puzzle originated from Japan, trying to ll the equal dimension board with numbers (1 . . . board size) without any repetition of digits in the row and column, also there is an expansion method to meet the users demands. The generated random numbers can be used in several aspects of security, such as encryption or pass word generator for network administrators. The generated random numbers must have subjected to several randomness tests called NIST statistical test suite to check the level of randomness of the produced sets, whereas, successfully passed all statistical tests were crossing the threshold P-value. The statistical properties indicate that the Futoshiki generator is substantially successful in producing random number with respectable statistical outcomes, high linear complexity and provides a small initial value with the capability to regenerate the equivalent sequence when feed up with the same initial value.

Many experiments have been conducted over the past eight decades to explore whether the ostensible psychic ability of psychokinesis (PK, or "mind over matter") might be a genuine human potential, and the most extensive of these have involved attempts to mentally influence the output of electronic, binary-bit random number generators (RNGs). Research of this type can generally be divided into two lines: proof-oriented (concerned with the accumulation and statistical evaluation of data from controlled experiments designed specifically to test for the presence of PK effects on the microscopic scale) and process-oriented (concerned with conducting exploratory experiments designed to systematically vary certain test conditions in order to search for and identify any physical, biological, and psychological factors which might have a role in improving or moderating PK effects). To help orient novice investigators and cross-disciplinary researchers who may be considering work along these lines (as well as to offer some initial guiding insight on possible directions for future research), this paper provides a general review of some of the notable proof-and process-oriented findings that have been obtained to date in experimental microscopic PK research using RNGs. The review generally indicates that although a considerable amount of proof-oriented data for micro-PK has accumulated over the years, the relatively sparse amount of process-oriented data available at present leaves many open questions regarding the underlying factors involved, providing ample opportunity for novice investigators and cross-disciplinary researchers to make valuable research contributions in the future.

This paper offers an overview of quantum random number generators (QRNG). We first explain why random numbers are important for (quantum) cryptography and scientific purposes such as Monte Carlo simulations. We argue that QRNG are better than random numbers generated through software, which are only pseudo-random. We then explore how QRNGs evolved from using entropy sources based on radioactivity or circuitry to using optical phenomena. We explain in detail some of the working principles of optical QRNGs, and finish by arguing theoretically and experimentally that generators based on Amplified Stimulated Emission are the most promising type of QRNG. This overview and experimental discussion are based on the papers by Collantes and Garcia-Escartin (2017) [7] and Cao et al. (2021) [4].

Abstract. Functional form and dysfunctional form of Kano model are considered as customer need regarding attribute of product. Both functional and dysfunctional forms are: Like, Must-be Neutral, Live-with and Dislike. The answers of customer regarding a product of functional and dysfunctional forms have been applied for selection of customer needs regarding product attribute (Kano evaluation). Filling–up and returning the Questionnaires by the individuals are essential for determining Kano evaluation. But many Questionnaires have not been returned in that case. Moreover, many possible consumers could not get opportunity to fill-up questionnaire. These uncertain or unknown consumers’ opinions are also essential for product development. The choices of Kano evaluations have been outlined by: Attractive, One-dimensional, Must-be, Indifferent and Reverse. In this study, choices of evaluation of unknown customer are considered uniform cumulative vector probability (scenario 1). This study is based on the Monte Carlo simulation method, concept of probability and Kano model. This model has also been tested for its soundness and found fairly consistent including existing Kano model (scenario 2) and case survey for headlight of bicycle (scenario 3).

Keywords: Kano Model, Probability, Product Attributes, Customer satisfaction and dissatisfaction.

Size biraz garip gelebilir, ancak rastgele bir sürecin ne olduğunu tam olarak tanımlayamayız. Ancak her ne kadar rastgele bir şeyin ne olduğunu birebir olarak tanımlayamıyor olsak da ne olmadığını tanımlayabiliriz. Eğer bir süreç tahmin edilebilir değilse ya da belirli bir paterne sahip değil ise ona rastgele deriz. Burada şunu belirtmemiz gerekir ki matematikteki rastgelelik kavramı ile fizikteki rastgelelik kavramı tamı tamına aynı şeyler değildir, çoğu zaman bir sürece rastgele dememizin sebebi o süreç hakkındaki bilgisizliğimizden kaynaklanır. Örneğin yazı tura ve zar atmayı ele alalım, bunların " rastgele " olmasının tek sebebi bizim bilgisizliğimizden kaynaklanır. Eğer ki bozuk paranın atıldığı anda, dönme hızı ve dönme açısı, çevrilme hızı ve çevrilme açısı, paraya uygulanan net kuvvet ve benzeri başlangıç değişkenlerini biliyor olsaydık teorik olarak para yere düşmeden hangi yüzün üstte kalacağını hesaplayabilirdik. Kaldı ki araştırmacılar bozuk parayı atmadan önce yazı ya da tura geleceğini tahmin edebileceğimiz araçlar geliştirmiştir. [1] Şimdi asıl soruya gelelim, her değişkeni bildiğimiz halde tahmin edemeyeceğimiz, nedensiz rastgele bir süreç var mı? Ve nasıl gördüğümüz şeylerin ardında böyle rastgele bir sürecin olmadığından emin olabiliriz, belki de henüz doğru paterne bakmamışızdır? Ya da belki de şu anda rastgele bir sürecin sonuçlarına bakıyor ancak yeterince uzun süredir inceleyemediğimiz için bunu fark etmemişizdir? Sanki kamuflaj olmuşçasına rastgele bir süreç ile eninde sonunda rastgele değilmiş gibi gözüken bazı paternler oluşturabilir. Örneğin bir torbada 1'den 10'a kadar numaralandırılmış kağıtları rastgele çektiğimizde sırasıyla 1,2,3…,10 numaralarını elde edebiliriz, ancak bu deneyi tekrar ettikçe bahsettiğimiz paternin de diğer paternler gibi sadece rastgele bir sürecin ürünü olduğunu görürüz. Bunun başka güzel bir örneği de Youtube videolarının bağlantılarında rastgele bir şekilde oluşan kelimelerdir, Youtube'a her bir video yüklendiğinde o videonun bağlantı adresi rastgele diyebileceğimiz bir şekilde belirlenir, bu bağlantılar genellikle anlamsızdır ancak bu bağlantılardan o kadar çok üretilir ki arada bir rastgele harflerin yan yana gelmesiyle " sex " gibi bazı kelimelerin oluştuğunu gözlemleriz. Bu, o videoların arkasında özel bir sebep aramamız gerektiği anlamına gelmez, böyle durumların gerçekleşmesi olasılık dahilinde oldukça normaldir.[2] Sonuç olarak bir sürecin aslında rastgele olup olmadığını sadece bir kaç sonuca bakarak söyleyemeyiz, çünkü rastgele süreçler bazen hiç de rastgele gibi gözükmeyen sonuçları ortaya çıkarabilir. BİLGİSAYARLAR VE RASTGELE SAYI ÜRETMENİN ZORLUĞU İşin temeline inecek olduğumuzda bilgisayarların oldukça basit şeyler yapan basit bileşenlerden meydana geldiğini görürüz. Bu bileşenler ya da parçalar bilgiyi sunar, işler ve mekanizmaları kontrol eder. Bilgi depolayan, bu bilgiye bir dizi mantıksal işlem uygulayan ve istenildiğinde bu bilginin sonucunu bilgi olarak sunan aygıtlara bilgisayar çipi denir. Bu bilgisayar çipleri modüllerden meydana gelmiştir, modüller mantık kapılarından ve mantık kapıları da transistörlerden meydana gelmiştir. Bir transistör bilgisayarlardaki bilgi işlemcisinin en basit formudur. Temel olarak transistör dediğimiz şey aslında gelen bilginin yolunu açıp kapatabilen bir anahtardır. Bu arada bir bilgi " sıfır " veya " bir " olarak ifade edilebilen " bit " lerden oluşur. Bitlerin bir araya gelip oluşturdukları farklı kombinasyonlar daha

Games come in three flavors; Chance, Skill and Both. The outcomes of Games of Chance are driven by randomness. The outcomes of Games of Skill are driven by Player choice. The outcomes of Game of Both are driven by a combination of Chance and Skill. Distrust overshadows online gambling (iGaming) because Players suspect Operators of rigging games by subverting their randomness. A True Random Number Generator, Cryptography, and a Blockchain could mitigate this situation by enabling Operators to pre-publish game inputs prior to turns in such a way that Players could not defraud the Operators by using that information to influence their game play. In this paper, we discuss The BigBang Survival Run, an online variant of a parimutuel Game of Chance, where the above approaches are beginning to be implemented.

The paper gives an introduction to function and quality evaluation of the Infineon random number generator (RNG) for smart cards and security ICs. The RNG consists of a physical source of noise and a digital post-processing unit. The physical source of noise uses phase noise to generate a bit stream with an entropy of almost 100%. The quality of the RNG is evaluated by using the RIPE test suite.

A true random number generator (TRNG) is proposed, harvesting entropy from multicore CPUs to generate non-deterministic outputs. The entropy source is the unpredictable sequence of thread access when parallel threads attempt to access the same memory location, known as race condition or data races. Although prior work using the same entropy source exists, they either have low efficiency or insufficient security analysis. The novelty of this work lies in its use of chaotic networks capable of extracting entropy while postprocessing outputs simultaneously. These networks are formulated by coupling chaotic maps in the form of chaotic coupled map lattices which have the capability to amplify minor uncertainties, leading to better performance as compared to other CPU-based TRNGs. We first perform experiments to depict the unpredictable nature of thread access due to race conditions through entropy and scale index analysis. Next, the proposed generator is scrutinized based on a standardized set of evaluation criteria which includes the use of multiple statistical test suites followed by an analysis of its non-deterministic property. We also perform an in-depth entropy analysis of the generator’s outputs and measure its degree of non-periodicity. Results indicate that the proposed chaos-based TRNG is fast, evenly distributed, and is secure enough for applications that have high security requirements.

What I was thinking yesterday is a strange way to arrive at a random number: first break it into N partitions, then for each partition we break it further into random number of parts. For each part we partition it into random number of partitions then ask the computer to generate a random number smaller then its length. At the end we add up all those partitions to arrive at a new total(that maybe the results of ten such a loop) that is the output of the program. And we could repeat the same process until it reach the lower limit(or many different criteria or condition interacting with each others).
Notice we could took the time it take(the number of iterations) to reach for a certain limit as an output, or the numbers within the sequences(or the sum/difference in various forms within a sequence or between different sequence in single iteration or more than one iteration), or directly take that number as the output, or taking the difference between this number and the next number, or the slope/the rate of decreasing as the output, or sum/difference of the slope(s)...etc.
Then we can use those parameters indirectly extracted or appeared in the above process to feedback into itself so the next iteration would be slightly different from this iteration, (or one sequence would have some effects on other sequences). This self-adjustment code could be either simple or really complicated and convoluted then the program may appear that it has some form of intelligence and self-awareness. Those code could be monitoring the performance parameters of the above process then adjust the parameters, then an upper manager could also managing and fine-tuning the fine-tuning process.
What we known as chaos or intelligence is based on the idea of self-adjustment or fine-tuning. That the result is interacting with the processes that produce it, or the processes themselves interacting with each other thus it is unpredictable of the actual process by any A.I. I suppose I can write an A.I. observer to monitor this process then try to stimulate it, while the process inside it try to be difference from what is predicted of it. The back and fro between them allowed us to create a perfectly random and unpredictable process.

The program to make sequence via usage of imaginary has completed its stage 1: Create a non-repeating decimal number that is more complicated than necessary;
Second step is to start with random position then take four successive number from the sequence then do multiplication with another imaginary number created in the same way.
Step three: Once it start its calculation then each iteration would use those two parameters as two initial position to do next calculation. Once it fulfill the criteria for edit the content of first sequence then first sequence is edited, Also new members of next sequence is created.
Fourth step: If the new sequence is completed, then the process repeated itself until altogether there are 3 sequence. Then new sequences are formed using numerical methods using those sequences.
Fifth step: Once 3 such sequences are formed, that would go back to form sequences using initial calculation method. Until maybe 12 sequences are created. New sequence, and random number is created using third method.

A hardware electronic random numbers generator, designed with cheap, easily obtainable and ubiquitous components. No special components or materials required. Generates random sequences and records them on an SD card. Passed testing by FIPS and  NIST standards. Circuit assembled and fully tested, published in the Elektor magazine. Used in applied  cryptography.

Reliable evidence from the Jahn-Dunne studies conducted over several decades indicated that human proximity can affect the dynamics of certain processes that strongly depend upon “random” processes. Random Event Generators (REG) operate through “random” electron tunneling through spaces that are within the same order of magnitude as synapses. If the mechanisms by which these human-machine interactions occur involve electromagnetic processes, then application of specific temporally patterned magnetic fields to the human volume should affect the strength of the deviation from “random” variations. Whole-body exposure to ~400 nT, complex-patterned magnetic fields based upon 3 ms point durations reversed the effects of normal “intention” upon the operation of REGs. The energies generated within the cerebral volume by that field if emitted as irradiative power were within the range of the mass equivalent of an electron at the level of p-n junction of the semiconductor. These results suppo...

New theoretical and traditional quantitative solutions involving a pervasive unit quantum of ~10-20 J within biological and large-scale physical systems predicted that the mass of the human subject, subtle changes in gravitational phenomena, and the energy available within the cerebral volume should affect proximal random number variations produced by electron tunneling. In a series of experiments application of a specific, physiologically-patterned weak magnetic field over the right temporal lobe significantly enhanced the effects of intention upon deviations from random variations created by electron tunneling devices at a distance of 1 m. These variations were strongly (r~0.80) correlated with the coupling between the forces from the background free oscillations of the earth and the energy differences across the cell width between lunar perigee and apogee. The results support the approach that complex cognitive processes including “intention” can be described by physicochemical p...

Lemniscate chaotic map (LCM) provides a wide range of control parameters, canceling the need for several rounds of substitutions, and excellent performance in the confusion process. Unfortunately, the hardware model of LCM is complex and consumes high power. This paper presents a proposed low power hardware model of LCM called practical lemniscate chaotic map (P-LCM) depending on trigonometric identities to reduce the complexity of the conventional model. The hardware model designed and implement into the field programmable gate array (FPGA) board, Spartan-6 SLX45FGG484-3. The proposed model achieves a 48.3 % reduction in used resources and a 34.6 % reduction in power consumption compared to the conventional LCM. We also introduce a new pseudo-random number generator based on a proposed low power P-LCM model and perform the randomization tests for the proposed encryption system. Keywords: Chaotic map FPGA Lemniscate chaotic map Random number generator Read-only memory (ROM) This is an open access article under the CC BY-SA license. 1. INTRODUCTION Pseudo chaotic random number generators (PCRNG) is a vital cryptography application of nonlinear chaotic systems. Many researchers present the software implementation of PCRNGs using MATLAB. The most common way to generate PCRNG is to use a feedback shift register method in different ways such as a linear feedback shift register, carry forward feedback shift register, and nonlinear feedback shift register [1-3]. Another technique used a coupled map lattice with time-varying delay [4]. The researchers in [5] proposed a PRNG based on Lorenz systems with FPGA implementation achieving an operating frequency of 78.149 MHz. In [6], the authors investigate the fixed-point arithmetic representation random effect, and they presented a PRNG based on coupled skew tent maps and provides its hardware implementation. The FPGA implementations of different PRNGs based on chaotic maps presented in [7]. In [8], A pseudo-random generator generated using a chaotic quadratic map with FPGA implementation. In [9], another idea used to make PRNG with a function based on ring oscillator and chaotic logistic map. Another design to PRNG based on a logistic map that changes its chaotic parameters presented and implemented with FPGA in [10]. In [11], a secured PRNG based on a piecewise linear chaotic map presented and implemented using FPGA. The sensitivity of the chaos-based encryption systems is affected by two factors; the first factor is the use of binary streams extracted from a single orbit of a chaotic map while the second factor is the use of

What ERTP do is to provide a structure in combination with fixed or changeable criteria to make random decision(which the programmer has no idea of the result), and of course that is not the only method to provide a structure. We could use natural non-repeating decimal number(s) or by breaking such a number into different sequences of ERTP, or even use ERTP to make random sequence base on its output then feedback to itself n-th times(using old structure to build new structure), we could mix naturally occurring parameter like Pi with random number generation mechanism in the computer then use some internal or external criteria to control how different party contribute to the making of the final random number. Now suppose we have n-th sequences of ERTP that are from single naturally occurring parameter then each of them somehow controlled a formation of a random number, and each random number is 'racing' with each other. Then each time also acknowledging the result of the race in each turn and adjust accordingly via interaction with their own ERTP sequence. What we have is an ERTP based sequence generation mechanism(base on the result of each the race each turn), and also a mechanism to generate random via interaction of those structures of ERTP sequences. Of course, there maybe some mechanism for the result of the race in each turn to feedback and somehow to change that ERTP sequence if certain (simple/complex/self-evolving) criteria is met.

Entropy in computer security is associated with the unpredictability of a source of randomness. The random source with high entropy tends to achieve a uniform distribution of random values. Random number generators are one of the most important building blocks of cryptosystems. In constrained devices of the Internet of Things ecosystem, high entropy random number generators are hard to achieve due to hardware limitations. For the purpose of the random number generation in constrained devices, this work proposes a solution based on the least-significant bits concatenation entropy harvesting method. As a potential source of entropy, on-board integrated sensors (i.e., temperature, humidity and two different light sensors) have been analyzed. Additionally, the costs (i.e., time and memory consumption) of the presented approach have been measured. The results obtained from the proposed method with statistical fine tuning achieved a Shannon entropy of around 7.9 bits per byte of data for ...

A novel Mathematical Random Number Generator (MRNG) is presented here. In this case, "mathematical" refers to the fact that to construct that generator it is not necessary to resort to a physical phenomenon, such as the thermal noise of an electronic device, but rather to a mathematical procedure. The MRNG generates binary strings - in principle, as long as desired - which may be considered genuinely random in the sense that they pass the statistical tests currently accepted to evaluate the randomness of those strings. From those strings, the MRNG also generates random numbers expressed in base 10. An MRNG has been installed as a facility on the following web page: http://www.appliedmathgroup.org. This generator may be used for applications in tasks in: a) computational simulation of probabilistic-type systems, and b) the random selection of samples of different populations. Users interested in applications in cryptography can build another MRNG, but they would have to wit...

In this paper, the performance of single-tone Radio over Fiber (RoF) system has been analyzed by employing different duobinary modulation formats. This single-tone RoF system has been modeled and analyzed using OptiSystem (14.0) software. To evaluate the transmission performance of RoF system, various performance metrics such as Q-factor, BER, and Eye Height are considered. Simulation results indicate that duobinary Hyperbolic-Secant pulse generator format with Single Drive Mach-Zehnder modulator provides better Q-factor and minimum BER as compared to existing modulation format in RoF system.

A novel Mathematical Random Number Generator (MRNG) is presented here. In this case, "mathematical" refers to the fact that to construct that generator it is not necessary to resort to a physical phenomenon, such as the thermal noise of an electronic device, but rather to a mathematical procedure. The MRNG generates binary strings — in principle, as long as desired — which may be considered genuinely random in the sense that they pass the statistical tests currently accepted to evaluate the randomness of those strings. From those strings, the MRNG also generates random numbers expressed in base 10. An MRNG has been installed as a facility on this web page. This generator may be used for applications in tasks in: a) computational simulation of probabilistic-type systems, and b) the random selection of samples of different populations. Users interested in applications in cryptography can build another MRNG, but they would have to withhold information — specified in section 5...

Face
recognition system is one of the robust
means of authentication. It involves comparing the faces
of an individual against a set of images in the training
database. Thus the security issues pertaining to the
training database is very critical. This paper ai
ms at
providing security to the images in the training database
by empowering the encryption algorithms using a secure
Random Number Generator (RNG). To facilitate this, the
seismic waves are used as seeds to drive the Pseudo
-
Random Number Generators (PRNG
s). The efficiency of
seismic waves as a True Random Number Generator
(TRNG) was evaluated using two statistical suites. Also,
the proposed TRNG is compared against other existing
RNGs. It was found that the degree of randomness
rendered by the proposed sy
stem was in good agreement
like the other existing generators. The proposed system
was found to be cost
-
effective, portable and easy to
maintain.

A high-density random number generator (RNG) based on spin signals in a multidomain ferromagnetic layer in a magnetic tunnel junction (MTJ) is proposed and fabricated. Unlike conventional spin-based RNGs, the proposed method does not require one to control an applied current, leading to a time delay in the system. RNG demonstrations are performed at room temperature. The randomness of the bit sequences generated by the proposed RNG is verified using the FIPS 140-2 statistical test suite provided by the NIST. The test results validate the effectiveness of the proposed RNGs. Our results suggest that we can obtain high-density, ultrafast RNGs if we can achieve high integration on the chip.

A high-density random number generator (RNG) based on spin signals in a multidomain ferromagnetic layer in a magnetic tunnel junction (MTJ) is proposed and fabricated. Unlike conventional spin-based RNGs, the proposed method does not require one to control an applied current, leading to a time delay in the system. RNG demonstrations are performed at room temperature. The randomness of the bit sequences generated by the proposed RNG is verified using the FIPS 140-2 statistical test suite provided by the NIST. The test results validate the effectiveness of the proposed RNGs. Our results suggest that we can obtain high-density, ultrafast RNGs if we can achieve high integration on the chip.

Whereas a multitude of solar and geomagnetic variables were not correlated with significant deviations in continuous measurements from random physical systems (Random Event Generators), these variables were moderately correlated with REG output during periods of intention. The scalar components (2 –10 nT) of the Interplanetary Magnetic Field (r = ~0.50) and global geomagnetic activity (r = ~0.55) were significantly correlated with REG deviations during the second minute of intention. Significance compared with nonsuccessful deviations occurred during periods of intention when the Solar Radio Flux was about 20 units (2∙10−21 W∙m −2 Hz −1) higher. The polarity of the deviation was different within a Faraday (echoic) chamber than in a normal environment as well as between genders. The amount of energy associated with the increase in geomagnetic activity within the volume of human cerebrum is remarkably similar to the gravitational energy within this mass because of minute variations in...

The purpose of this paper is to present a novel Hybrid Random Number Generator (HRNG). Here “hybrid” refers to the fact that to construct this generator it is necessary to use 1) physical components–texts–and a physical process, and 2) a mathematical procedure. ...