• Repository author: Tommaso Zanotti* email: tommaso.zanotti@unimore.it or francescomaria.puglisi@unimore.it * Version v1.0

This repository includes MATLAB files and datasets related to the IEEE IIRW 2023 conference proceeding:T. Zanotti et al., "Reliability Analysis of Random Telegraph Noisebased True Random Number Generators," 2023 IEEE International Integrated Reliability Workshop (IIRW), South Lake Tahoe, CA, USA, 2023, pp. 1-6, doi: 10.1109/IIRW59383.2023.10477697

The repository includes:

The data of the bitmaps reported in Fig. 4, i.e., the results of the simulation of the ideal RTN-based TRNG circuit for different reseeding strategies. To load and plot the data use the "plot_bitmaps.mat" file.

The result of the circuit simulations considering the EvolvingRTN from the HfO2 device shown in Fig. 7, for two Rgain values. Specifically, the data is contained in the following csv files:

"Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_11n.csv" (lower Rgain)

"Sim_TRNG_Circuit_HfO2_3_20s_Vth_210m_no_Noise_Ibias_4_8n.csv" (higher Rgain)

The result of the circuit simulations considering the temporary RTN from the SiO2 device shown in Fig. 8. Specifically, the data is contained in the following csv files:

"Sim_TRNG_Circuit_SiO2_1c_300s_Vth_180m_Noise_Ibias_1.5n.csv" (ref. Rgain)

"Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.575n.csv" (lower Rgain)

"Sim_TRNG_Circuit_SiO2_1c_100s_200s_Vth_180m_Noise_Ibias_1.425n.csv" (higher Rgain)

Excel sheets in order: The sheet entitled “Hens Original Data” contains the results of an experiment conducted to study the response of laying hens during initial phase of egg production subjected to different intakes of dietary threonine. The sheet entitled “Simulated data & fitting values” contains the 10 simulated data sets that were generated using a standard procedure of random number generator. The predicted values obtained by the new three-parameter and conventional four-parameter logistic models were also appeared in this sheet. (XLSX)

According to Cognitive Market Research, the global Quantum Random Number Generator RNG market size is USD 555.9 million in 2024. It will expand at a compound annual growth rate (CAGR) of 72.60% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 222.36 million in 2024 and will grow at a compound annual growth rate (CAGR) of 70.8% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 166.77 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 127.86 million in 2024 and will grow at a compound annual growth rate (CAGR) of 74.6% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 27.80 million in 2024 and will grow at a compound annual growth rate (CAGR) of 72.0% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 11.12 million in 2024 and will grow at a compound annual growth rate (CAGR) of 72.3% from 2024 to 2031.
Cloud held the dominant segment in the Quantum Random Number Generator RNG market in 2024.

Market Dynamics of Quantum Random Number Generator RNG Market

Key Drivers for Quantum Random Number Generator RNG Market

Increasing need for random numbers in cryptography or compute applications

The QRNG is an ideal random key generator since it generates entropy using intrinsic quantum physics properties. Nowadays, applications demand a huge number of keys and randomization to achieve total security. It could include key vaults, games, IoT devices, AI/ML, blockchains, simulations, and vital infrastructure. QRNG is the source of these applications in which trust in randomness is prevalent. Furthermore, it is utilized in encryption for a wide range of applications, including cryptography, numerical simulation, gambling, and game design.

Growing adoption of quantum computing

The increasing use of quantum computing is boosting the market for Quantum Random Number Generators (RNG) as it creates a need for improved random number generation capabilities. The accurate abilities of quantum computing enable RNGs to produce truly random numbers, essential for secure communication and encryption. Advancements in quantum computing will lead to a higher demand for dependable RNGs, driving market expansion to meet the changing requirements of cybersecurity and data encryption.

Restraint Factor for the Quantum Random Number Generator RNG Market

High initial investment

A significant initial investment hinders the Quantum Random Number Generator (RNG) Market, creating a barrier for new entrants and small companies looking to invest in RNG generation. The significant initial costs involved in the research, development, and deployment of quantum RNG solutions may discourage potential entrants from joining the market. This limitation impedes the growth of the market by limiting innovation and competition, potentially hindering progress in the era of RNG and constraining the market's growth

Impact of Covid-19 on the Quantum Random Number Generator RNG Market

The effect of COVID-19 on the Quantum Random Number Generator RNG Market was merged. Although the pandemic initially caused disruptions in supply chains and slowed down certain trends, the increased focus on cybersecurity and data protection during remote work and digital interactions enhanced the need for secure communication solutions such as quantum RNGs. With a focus on safeguarding information, both organizations and governments fueled growth in the Quantum RNG market despite pandemic-related obstacles. Introduction of the Quantum Random Number Generator RNG Market

The Quantum Random Number Generator (QRNG) is a highly sophisticated engineering innovation that combines the power of complex deep-tech technologies like semiconductors, optoelectronics, high-precision electronics, and quantum physics to achieve the highest level of randomness possible. QRNG has shown to be a critical enabling technology for quantum-level security in mobile devices, data centres, and medical implants. They provide consumers with a significant enhancement over ordinary random number generators (RNGs), which have been utilized for years in a variety of business applications. Several factors, including th...

Abstract A library containing highly portable implementations of most algorithms for (pseudo) random number generation has been developed, which might be used in any area of simulation which requires random number generators. Each generator is freely configurable by the user, so the RANEXP library is particularly well-suited for applications requiring different random number generators. The algorithms are implemented in C, but are callable from Fortran application program also. Title of program: RANEXP Catalogue Id: ACTB_v1_0 Nature of problem Any Monte Carlo simulation or statistical test requiring uniform pseudorandom numbers. Versions of this program held in the CPC repository in Mendeley Data ACTB_v1_0; RANEXP; 10.1016/0010-4655(94)90072-8 This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

Underpinning data for manuscript entitled "Generation of random numbers by measuring on a silicon-on-insulator chip phase fluctuations from a laser diode"

The global Random Outfit Generator market was valued at USD 1.2 billion in 2025 and is projected to grow at a CAGR of 10.5% during the forecast period, reaching USD 2.5 billion by 2033. This growth is attributed to increasing demand for personalized and time-saving fashion solutions, rising disposable income, and growing awareness of fashion trends. The market is predominantly driven by fashion enthusiasts and professionals who seek efficient ways to generate unique and stylish outfits. Among the key segments of the Random Outfit Generator market are: Application: Fashion Designer, Fashion Enthusiasts, Photography Stylist, Others Types: Cloud-based, On-premises Geography: North America, South America, Europe, Middle East & Africa, Asia Pacific Key players in the market include Fashmates, Stylicious, Your Closet, Combyne, My Dressing, Acloset, My Wardrobe, Smart Closet, Pureple, Twelve70, Roll For Fantasy, Randommer, The Fashion Robot, and Picrew. These companies offer innovative solutions to cater to the growing demand for random outfit generators, engaging users with interactive features and personalized experiences.

Comparison of results between NSA-GA, NSA, and random testing on all programs using integer data type and different range.

OpenCon 2015 Application Open Data

The purpose of this document is to accompany the public release of data collected from OpenCon 2015 applications.Download & Technical Information The data can be downloaded in CSV format from GitHub here: https://github.com/RightToResearch/OpenCon-2015-Application-Data The file uses UTF8 encoding, comma as field delimiter, quotation marks as text delimiter, and no byte order mark.

License and Requests

This data is released to the public for free and open use under a CC0 1.0 license. We have a couple of requests for anyone who uses the data. First, we’d love it if you would let us know what you are doing with it, and share back anything you develop with the OpenCon community (#opencon / @open_con ). Second, it would also be great if you would include a link to the OpenCon 2015 website (www.opencon2015.org) wherever the data is used. You are not obligated to do any of this, but we’d appreciate it!

Data Fields

Unique ID

This is a unique ID assigned to each applicant. Numbers were assigned using a random number generator.

Timestamp

This was the timestamp recorded by google forms. Timestamps are in EDT (Eastern U.S. Daylight Time). Note that the application process officially began at 1:00pm EDT June 1 ended at 6:00am EDT on June 23. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. [a]

Gender

Mandatory. Choose one from list or fill-in other. Options provided: Male, Female, Other (fill in).

Country of Nationality

Mandatory. Choose one option from list.

Country of Residence

Mandatory. Choose one option from list.

What is your primary occupation?

Mandatory. Choose one from list or fill-in other. Options provided: Undergraduate student; Masters/professional student; PhD candidate; Faculty/teacher; Researcher (non-faculty); Librarian; Publisher; Professional advocate; Civil servant / government employee; Journalist; Doctor / medical professional; Lawyer; Other (fill in).

Select the option below that best describes your field of study or expertise

Mandatory. Choose one option from list.

What is your primary area of interest within OpenCon’s program areas?

Mandatory. Choose one option from list. Note: for the first approximately 24 hours the options were listed in this order: Open Access, Open Education, Open Data. After that point, we set the form to randomize the order, and noticed an immediate shift in the distribution of responses.

Are you currently engaged in activities to advance Open Access, Open Education, and/or Open Data?

Mandatory. Choose one option from list.

Are you planning to participate in any of the following events this year?

Optional. Choose all that apply from list. Multiple selections separated by semi-colon.

Do you have any of the following skills or interests?

Mandatory. Choose all that apply from list or fill-in other. Multiple selections separated by semi-colon. Options provided: Coding; Website Management / Design; Graphic Design; Video Editing; Community / Grassroots Organizing; Social Media Campaigns; Fundraising; Communications and Media; Blogging; Advocacy and Policy; Event Logistics; Volunteer Management; Research about OpenCon's Issue Areas; Other (fill-in).

Data Collection & Cleaning

This data consists of information collected from people who applied to attend OpenCon 2015. In the application form, questions that would be released as Open Data were marked with a caret (^) and applicants were asked to acknowledge before submitting the form that they understood that their responses to these questions would be released as such. The questions we released were selected to avoid any potentially sensitive personal information, and to minimize the chances that any individual applicant can be positively identified. Applications were formally collected during a 22 day period beginning on June 1, 2015 at 13:00 EDT and ending on June 23 at 06:00 EDT. Some applications have timestamps later than this date, and this is due to a variety of reasons including exceptions granted for technical difficulties, error corrections (which required re-submitting the form), and applications sent in via email and later entered manually into the form. Applications were collected using a Google Form embedded at http://www.opencon2015.org/attend, and the shortened bit.ly link http://bit.ly/AppsAreOpen was promoted through social media. The primary work we did to clean the data focused on identifying and eliminating duplicates. We removed all duplicate applications that had matching e-mail addresses and first and last names. We also identified a handful of other duplicates that used different e-mail addresses but were otherwise identical. In cases where duplicate applications contained any different information, we kept the information from the version with the most recent timestamp. We made a few minor adjustments in the country field for cases where the entry was obviously an error (for example, electing a country listed alphabetically above or below the one indicated elsewhere in the application). We also removed one potentially offensive comment (which did not contain an answer to the question) from the Gender field and replaced it with “Other.”

About OpenCon

OpenCon 2015 is the student and early career academic professional conference on Open Access, Open Education, and Open Data and will be held on November 14-16, 2015 in Brussels, Belgium. It is organized by the Right to Research Coalition, SPARC (The Scholarly Publishing and Academic Resources Coalition), and an Organizing Committee of students and early career researchers from around the world. The meeting will convene students and early career academic professionals from around the world and serve as a powerful catalyst for projects led by the next generation to advance OpenCon's three focus areas—Open Access, Open Education, and Open Data. A unique aspect of OpenCon is that attendance at the conference is by application only, and the majority of participants who apply are awarded travel scholarships to attend. This model creates a unique conference environment where the most dedicated and impactful advocates can attend, regardless of where in the world they live or their access to travel funding. The purpose of the application process is to conduct these selections fairly. This year we were overwhelmed by the quantity and quality of applications received, and we hope that by sharing this data, we can better understand the OpenCon community and the state of student and early career participation in the Open Access, Open Education, and Open Data movements.

Questions

For inquires about the OpenCon 2015 Application data, please contact Nicole Allen at nicole@sparc.arl.org.

Advanced lottery random number generator is a company.

This paper studies the flexible large-scale supplier selection and order allocation problem with various quantity discounts, i.e., no discount, all-unit discount, incremental discount, and carload discount. It fills a literature gap that models usually formulate one or seldom two types because of the modeling and solution difficulty. All suppliers offering the same discount are far from reality, especially when the number of suppliers is large. The proposed model is a variant of the NP-hard knapsack problem. The greedy algorithm, which solves the fractional knapsack problem optimally, is applied to cope with the challenge. Three greedy algorithms are developed using a problem property and two sorted lists. Simulations show the average optimality gaps are 0.1026%, 0.0547%, and 0.0234% and the model can be solved in centiseconds, densiseconds, and seconds for supplier numbers 1000, 10000, and 100000. This allows the full use of data in the big data era.

For many real-world problems, there exist non-deterministic heuristics which generate valid but possibly sub-optimal solutions. The program optimisation with dependency injection method, introduced here, allows such a heuristic to be placed under evolutionary control, allowing search for the optimum. Essentially, the heuristic is “fooled” into using a genome, supplied by a genetic algorithm, in place of the output of its random number generator. The method is demonstrated with generative heuristics in the domains of 3D design and communications network design. It is also used in novel approaches to genetic programming. 16th European Conference, EuroGP 2013, Vienna, Austria, April 3-5, 2013 Author has checked copyright

The dataset contains all gathered data from the experiment from Wednesday, March 16, 2022 11:58:41.929 AM UTC+0 (1647431921929) until Sunday, April 3, 2022 1:08:35.353 PM UTC+0 (1648991315353). The experiment was executed during physical presence within the Arctic Circle in Tromsø, Norway 69° 40' 53.117'' N 18° 58' 36.027'' E at 35m elevation above sea level. The dataset was gathered with a prototype [1] based on the CREDO android application [2]. The main research is to use Ultra High Energy Cosmic Rays (UHECR) as an entropy source for a Random Bit Generator (RBG).

The associated publication will probably have the title "Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator"

In order to reproduce the results the SQLite3 database "mrng_arctic_experiment_2022.db" is needed. To get the visual representations of the detections use "image_decoding_and_codesnippets.py" to generate the cleaned (414 detections / ~15MB) or the uncleaned (5567 detections / ~195 MB) dataset. The compressed folder "raw_data_incl_space_weather.7z" contains all raw data as gathered with the MRNG prototype, unprocessed, uncleaned, and unmerged.

[1] https://github.com/StefanKutschera/mrng-prototype, visited on 27.03.2023

[2] https://github.com/credo-science/credo-detector-android, visited on 27.03.2023

Abstract The Scalable Parallel Random Number Generators library (SPRNG) supports fast and scalable random number generation with good statistical properties for parallel computational science applications. In order to accelerate SPRNG in high performance reconfigurable computing systems, we present the Hardware Accelerated SPRNG library (HASPRNG). Ported to the Xilinx University Program (XUP) and Cray XD1 reconfigurable computing platforms, HASPRNG includes the reconfigurable logic for Field Programma...

Title of program: HASPRNG Catalogue Id: AEER_v1_0

Nature of problem Many computational science applications are able to consume large numbers of random numbers. For example, Monte Carlo simulations such as α-estimation are able to consume limitless random numbers for the computation as long as hardware resources for the computing are supported. Moreover, parallel computational science applications require independent streams of random numbers to attain statistically significant results. The SPRNG library provides this capability, but at a significant computation ...

Versions of this program held in the CPC repository in Mendeley Data AEER_v1_0; HASPRNG; 10.1016/j.cpc.2009.07.002

This program has been imported from the CPC Program Library held at Queen's University Belfast (1969-2019)

Introduction

This datasets have SQL injection attacks (SLQIA) as malicious Netflow data. The attacks carried out are SQL injection for Union Query and Blind SQL injection. To perform the attacks, the SQLMAP tool has been used.

NetFlow traffic has generated using DOROTHEA (DOcker-based fRamework fOr gaTHering nEtflow trAffic). NetFlow is a network protocol developed by Cisco for the collection and monitoring of network traffic flow data generated. A flow is defined as a unidirectional sequence of packets with some common properties that pass through a network device.

Datasets

The firts dataset was colleted to train the detection models (D1) and other collected using different attacks than those used in training to test the models and ensure their generalization (D2).

The datasets contain both benign and malicious traffic. All collected datasets are balanced.

The version of NetFlow used to build the datasets is 5.

    Dataset
    Aim
    Samples
    Benign-malicious
    traffic ratio




    D1
    Training
    400,003
    50%


    D2
    Test
    57,239
    50%

Infrastructure and implementation

Two sets of flow data were collected with DOROTHEA. DOROTHEA is a Docker-based framework for NetFlow data collection. It allows you to build interconnected virtual networks to generate and collect flow data using the NetFlow protocol. In DOROTHEA, network traffic packets are sent to a NetFlow generator that has a sensor ipt_netflow installed. The sensor consists of a module for the Linux kernel using Iptables, which processes the packets and converts them to NetFlow flows.

DOROTHEA is configured to use Netflow V5 and export the flow after it is inactive for 15 seconds or after the flow is active for 1800 seconds (30 minutes)

Benign traffic generation nodes simulate network traffic generated by real users, performing tasks such as searching in web browsers, sending emails, or establishing Secure Shell (SSH) connections. Such tasks run as Python scripts. Users may customize them or even incorporate their own. The network traffic is managed by a gateway that performs two main tasks. On the one hand, it routes packets to the Internet. On the other hand, it sends it to a NetFlow data generation node (this process is carried out similarly to packets received from the Internet).

The malicious traffic collected (SQLI attacks) was performed using SQLMAP. SQLMAP is a penetration tool used to automate the process of detecting and exploiting SQL injection vulnerabilities.

The attacks were executed on 16 nodes and launch SQLMAP with the parameters of the following table.

    Parameters
    Description




    '--banner','--current-user','--current-db','--hostname','--is-dba','--users','--passwords','--privileges','--roles','--dbs','--tables','--columns','--schema','--count','--dump','--comments', --schema'
    Enumerate users, password hashes, privileges, roles, databases, tables and columns


    --level=5
    Increase the probability of a false positive identification


    --risk=3
    Increase the probability of extracting data


    --random-agent
    Select the User-Agent randomly


    --batch
    Never ask for user input, use the default behavior


    --answers="follow=Y"
    Predefined answers to yes

Every node executed SQLIA on 200 victim nodes. The victim nodes had deployed a web form vulnerable to Union-type injection attacks, which was connected to the MYSQL or SQLServer database engines (50% of the victim nodes deployed MySQL and the other 50% deployed SQLServer).

The web service was accessible from ports 443 and 80, which are the ports typically used to deploy web services. The IP address space was 182.168.1.1/24 for the benign and malicious traffic-generating nodes. For victim nodes, the address space was 126.52.30.0/24. The malicious traffic in the test sets was collected under different conditions. For D1, SQLIA was performed using Union attacks on the MySQL and SQLServer databases.

However, for D2, BlindSQL SQLIAs were performed against the web form connected to a PostgreSQL database. The IP address spaces of the networks were also different from those of D1. In D2, the IP address space was 152.148.48.1/24 for benign and malicious traffic generating nodes and 140.30.20.1/24 for victim nodes.

To run the MySQL server we ran MariaDB version 10.4.12. Microsoft SQL Server 2017 Express and PostgreSQL version 13 were used.

Our goal is to explore the feasibility and usefulness of using a combination of covering arrays and machine learning models for predicting results of an agent- based simulation model within the vast parameter value combination space. The challenge is to select parameter values that are representative of the overall behavior of the model, so that we can train the machine learning model to be able to correctly predict behavior on previously untested areas of the parameter space. We have chosen Wilensky's Heat Bugs model in NetLogo for our study. It is a simple model, amenable to quick data generation, with a limited number of outputs to predict, and with emergent behavior. This model therefore allows exploration of this new approach.We utilize covering arrays to reduce the parameter value space systematically, run the model for each parameter set in the 2-way and 3-way covering arrays, train a random forest model on the 2-way data (33, 351 parameter combinations), and test its ability to predict the outcome of the simulation on the significantly larger 3-way data that was not seen during the training of the model (3, 971, 955 parameter combinations).

Synthetic datasets (training/validation) for end-to-end Relation Extraction of relationships between Organisms and Natural-Products. The datasets are provided for reproducibility purposes, but, can also be used to train new models. As in the corresponding article, 3 subtypes of synthetic datasets are provided:

Diversity-synt: The seed literature references used in the generation process correspond to the top-500 extracted items per biological kingdoms using the GME-sampler. Random-synt: 5 datasets of equivalent sizes as Diversity-synt, but using randomly sampled seed literature references. Extended-synt: A merge of Diversity-synt and the 5 Random-synt datasets. All datasets were produced with Vicuna-13b-v1.3. Like the model, the produced synthetic data are also submitted to the License of the model used for generation, see the original LLaMA model card. LLaMA is licensed under the LLaMA License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.

Random numbers are an important resource for applications such as numerical simulation and secure communication. However, it is difficult to certify whether a physical random number generator is truly unpredictable. Here, we exploit the phenomenon of quantum nonlocality in a loophole-free photonic Bell test experiment for the generation of randomness that cannot be predicted within any physical theory that allows one to make independent measurement choices and prohibits superluminal signaling. To certify and quantify the randomness, we describe a new protocol that performs well in an experimental regime characterized by low violation of Bell inequalities. Applying an extractor function to our data, we obtained 256 new random bits, uniform to within 0.001. arXiv:1702.05178

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

1000 random number

Peptide-expressing phage display libraries are widely used for the interrogation of antibodies. Affinity selected peptides are then analyzed to discover epitope mimetics, or are subjected to computational algorithms for epitope prediction. A critical assumption for these applications is the random representation of amino acids in the initial naïve peptide library. In a previous study we implemented Next Generation Sequencing to evaluate a naïve library and discovered severe deviations from randomness in UAG codon overrepresentation as well as in high G phosphoramidite abundance causing amino acid distribution biases. In this study we demonstrate that the UAG overrepresentation can be attributed to the burden imposed on the phage upon the assembly of the recombinant Protein 8 subunits. This was corrected by constructing the libraries using supE44-containing bacteria which suppress the UAG driven abortive termination. We also demonstrate that the overabundance of G stems from variant synt...