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Figure 8. Prototype Front-End-Generative Learning Objects Instantiated with Random Numbers Based Expressions

We modeled classes for each AGLO section: scenario, theory, questions, and feedbacks. We modeled domain specific classes for the learned concepts like trees and graphs having generative methods controllable through parameters. Figure 8 depicts our prototype front-end.

Figure 7. Generated Tree Example-Generative Learning Objects Instantiated with Random Numbers Based Expressions

In this subsection we give an example of a model instantiation. We describe the instantiation process for all the symbols in the AGLO model. The nNoOfElements symbol was instantiated with the generated value of 10. The tabKeys symbol was instantiated with the [A,B,C,D,E,F,G,H,I] generated array. The tree symbol was instantiated with a ”Tree” object built with arguments equal with previously instantiated symbols. Class ”Tree” is part of the auxiliary JavaScript library.The treeSVG symbol was assigned to the tree SVG string representation obtained by a method call and is depicted in figure 7.

Figure 5. AGLO Scenario Symbols-Generative Learning Objects Instantiated with Random Numbers Based Expressions

analyzed AGLO that is displayed to the learner for localization and selection purposes. The second XML element is the scenario element containing a text description of the AGLO and a set of symbols. The description is expressed in natural language and we can notice that it contains four main steps: i) random tree generation; ii) index computation for presentation; iii) parent index computation for answer validation; iv) access to the first two keys for particular feedback generation. In the scenario section depicted in figure 5 several symbols are defined with the following semantics.

Figure 6. AGLO Presentation Sections-Generative Learning Objects Instantiated with Random Numbers Based Expressions

The seventh and eighth symbols named node1 and node2, both of type character, are initialized with the first two values from the keys array. These symbols will be used later in the feedback section. Their initialization is based on accessing the keys using the array indexing operator. Next, we discuss about the presentation sections of the AGLO depicted in figure 6.

Figure 3. Autogenerative Learning Object Model Definition (2)Generative Learning Objects Instantiated with Random Numbers Based Expressions

Formally, the theory section (see line 11) contains only static data. Here the educator places theoretical information about the learned concept in HTML format. The question, answers and feedbacks sections (see lines 12-13, 14-17, 18-21) contain a mix of static data and dynamic values referring the previously computed symbol values. The semantic of these sections is to create a dynamic content to be presented to the learner. Formalisms for specific details are also present, like: i) correctness - because we need to know which is the correct answer if they are multiple; ii) feedbacks answer identifier list - because feedbacks may be related to certain answers and showed to the learner only in certain conditions.

Figure 2. Autogenerative Learning Object Model Definition (1)-Generative Learning Objects Instantiated with Random Numbers Based Expressions

The proposed model is defined using the EBNF meta-language resulting in the concise grammar from figures 2 and 3. We will not present the full XML Schema because of clarity and space reasons. Structurally, the AGLO model has six sections: i) name; ii) scenario; iii) theory; iv) question; v) answers; vi) feedbacks. Next, we will explain each section in details.

Figure 4. AGLO Name and Description-Generative Learning Objects Instantiated with Random Numbers Based Expressions

In this subsection we describe an AGLO model example. Figure 4 presents in details all the XML elements containing AGLO metadata.

Figure 1. The AGLO Workflow-Generative Learning Objects Instantiated with Random Numbers Based Expressions

E-learning is a key area of research with a great influence on the developments of several industries. For example, the nowadays ITC industry is in a continuous growth because of its applications in almost all industrial domains. Companies tend to lack qualified human resources and because of that they reject high economical value projects. In response to this lack of human resource problem, universities started to develop several alternative study programs, many of them are based on e-learning technology and namely on electronic learning materials. Learning objects (LO) are considered to be digital resources that support learning and can be delivered across networks in large or small sizes (Wiley, 2000). In order to increase the reusability and interoperability of LOs, standards were developed by several organizations (IEEE Learning Technology Standards Committee) (e.g. LOM http://ltsc.ieee.org/doc/wg12/LOMv4.1.htm.)

opencc-by-4.0Oct 2018View details →

The impact of pulsed Electromagnetic Fault Injection on true random number generators

Random number acquisition from HECTOR daughter board and the clock signals of both decimator and Priority Encoder. The acquisition where done with a shift register between the output of Priority Encoder’s output and the scope (the same applies for the decimator’s output). Folder is class by type of faults: <ol> <li>tmp_00_wb means two of Priority Encoder’s bits are stuck at 00.</li> <li>tmp_01_wb means two of Priority Encoder’s bits are stuck at 01.</li> <li>tmp_10_wb means two of Priority Encoder’s bits are stuck at 10.</li> <li>tmp_11_wb means two of Priority Encoder’s bits are stuck at 11.</li> <li>tmp_0_wb means one of Priority Encoder’s bit is stuck at 0.</li> <li>tmp_1_wb means one of Priority Encoder’s bit is stuck at 1.</li> <li>Decim_0_wb means one bit of decimator’s output stuck at 0.</li> <li>Decim_1_wb means one bit of decimator’s output stuck at 1.</li> </ol> Curves are class as follows:  (Index means the acquisition number) <ol> <li>CIdecimIndex_0.trc files are the clock signal before decimation, i.e. the clock used to sample Priority encoder’s output.</li> <li>COdecimIndex_0.trc files are the clock signal after decimation, i.e. the clock used to sample decimator’s output.</li> <li>IrandDecim_Index_0.trc is the output of the Priority Encoder.</li> <li>OrandDecim_Index_0.trc is the output of the Priority Encoder.</li> </ol> All trc files are binary files. The file conditions_wb recap all the injection parameter used to obtain the different fault.

opencc-by-4.0Sep 2024View details →

Datasets used in "Assesing the quality of random number generators through neural networks", Machine Learning: Science and Technology 5 (2024) 025072

Datasets corresponding to the bits generated by different random number generators used in J. L. Crespo et al, Machine Learning: Science and Technology 5 (2024) 025072. VCSEL_QRNG_postprocessed_bits.txt: postprocessed bits from the random generator based on gain-switching of VCSELs  EC_LCG_bits.txt:  bits from the linear congruential generator on elliptic curves LCG_32_bits.txt:: bits from the linear congruential generator with 32 bits VCSEL_QRNG_raw_bits.txt: raw bits from the random generator based on gain-switching of VCSELs

dryad40/100

Randomized controlled clinical trials with tagged information regarding the number of participants

Open the record for dataset details and reuse information.

publicSep 2024View details →

zenodo36/100

D1D-TRNS4 Truly Random Number Seeds with Verified Statistical Quality

========================================================        Dataset: D1D-TRNS4     Truly Random Number Seeds with Verified         Statistical Quality (Jul.2016)               Release Notes     Copyright (c) 2016 by Harris V. Georgiou ========================================================    Release:     Jul 31, 2016    - Version:  1.1a    - Format:   .wav/.bin ======================================================== This file contains important information about the current version of the dataset package. Downloading and using this material hints that you accept the EULA/Terms-of-Use (please read carefully). We welcome your comments and suggestions. _______________________________________________ WHAT'S IN THIS PACKAGE? -  Overview -  Available file formats -  Files and Datasets -  License Agreement _______________________________________________ OVERVIEW The generation of truly random number streams is a task of fundamental importance in cryptography and computer simulations. The term "truly" refers to the use of a natural phenomenon that is inherently random, e.g. the decay of a radio-active element. Instead, a more practical approach is to use pseudo-random number generators (PRNG) that closely resembles such random process (output), as long as they have a very long period and their initial seed is inherently random. Additionally, some cryptographically strong algorithms, such as hashing/digest or encryption functions can also be used, since they act by design as "entropy diffusers". This package contains four sets of data that combine (a) truly random sources as seed and (b) cryptography for additional randomness in the output. Specifically, low-quality (noisy) sound recordings of rainfall and RF static are used as input for multiple PGP/GPG encryption steps with random keys. The result is a set of random binary data of very high statistical quality (sizes 690KB-1.27MB), which can be used as-is for one-time pads or as seeds for high-quality PRNG implementations. The source data is low-quality noisy waveforms: two rainfall samples with (approximately) mean=1.3KHz and stdev=600Hz in both cases; and two RF static samples with (approximately) mean=1.6KHz and stdev=800Hz. These can also be used as-is, but for quality PRNG seeds these raw data have to be properly pre-processed (at least whitened). The final data are evaluated using the ENT command-line tool and the appropriate statistics in Excel/LibreOffice format. In essence, ENT compares the bit-level statistics of the binary (PGP/GPG) files to the theoretically optimal values if the data are perfectly random. _______________________________________________ AVAILABLE FILE FORMATS The datasets are available in the following formats (included): *.wav    : source sound files (4bit/mono/8kHz/32kbps) *.bin   : binary files ready for use (PGP/GPG output)

opencc-by-nc-sa-4.0Jul 2016View details →

zenodo36/100

More random numbers

Random numbers used to showcase showyourwork! Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="https://github.com/rodluger/showyourwork-template/tree/80752fbc03784089b4485935a974f4de2751a252">this GitHub repo</a> using the following command: <pre><code class="language-bash">cd src/analysis && bash random.sh</code></pre>

opencc-zeroDec 2021View details →

zenodo36/100

Mbit/s-range alkali vapour spin noise quantum random number generators - DATASETS

Two example datasets. Each dataset consists of 2 files; one, where spin noise is present in the spectrum (noise_on.npy) and one, where spin noise is absent (noise_off.npy). The former is used generate random numbers, while the latter is used to 1. find a baseline threshold \Sigma, as described in the paper, and 2. to find the spin noise spectrum.  The spin noise spectrum can be found by subtracting the spectra of the first and second file, as it is only present in the first file. Every dataset should be filtered around the Larmor frequency prior to any bit generation. The width of the band-pass filter should be such that the entirety of the spin noise spectrum is contained. Bit generation is to be done by protocols described in the paper "Mbit/s-range alkali vapour spin noise quantum random number generators". All bitrates are calculated at \Sigma = 5 \sigma, where \sigma is the standard deviation of noise_off.npy for the given experiment. Cs12: Sample rate 50 MHz.  Measured at a temperature of approximately 140 degrees C.  The Larmor frequency is approximately 670 kHz. There is a large induced gradient present in the system, so the spin noise spectrum is not a Lorentzian shape, but is spread with a full-width half max of 500 kHz . Protocol 1 raw bitrates are approximately 40 kb/s, whereas protocol 2 raw bitrates are approximately 2.40 Mb/s. Cs14: Sample rate 100 MHz. Spin noise measurement (noise_on.npy) has 1G samples (16 bit signed). Background measurement (noise_off.npy) has 10M samples (16 bit signed). Measured at a temperature of approximately 140 degrees C. The Larmor frequency is approximately 440kHz  ,T2 is approximately 2e-6 s. This is the dataset presented in the paper. Protocol 1 raw bitrates are approximately 40 kb/s, protocol 2 raw bitrates are approximately 1.97 Mb/s.

opencc-by-4.0May 2023View details →

opencc-zeroNov 2021View details →

Random numbers

This is a collection of ten datasets, each containing ten iid zero-mean, unit-variance random numbers. These are used in an example of the showyourwork open source scientific article workflow. Created using <a href="https://github.com/rodluger/showyourwork">showyourwork</a> from <a href="https://github.com/rodluger/showyourwork-template/tree/d24bc06388bd4be5038b2eef6dbc681d9d786ecf">this GitHub repo</a> using the following command: <pre><code class="language-bash">cd src/figures && python src/analysis/run_simulation.py</code></pre>

opencc-by-4.0Feb 2024View details →

Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"

Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test" Includes scripts for generating raw results, postprocessing scripts, as well as scripts/notebooks for generating plots for the manuscript

opencc-by-4.0Oct 2022View details →

More Flexible Number Formats for High-Accuracy Simulations - Randomized Inputs

The random input matrices and vectors used in the thesis "More Flexible Number Formats for High-Accuracy Simulations".