Dataset Card for test-data-generator

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/franciscoflorencio/test-data-generator/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/franciscoflorencio/test-data-generator.

Card Random Number Generator Market Outlook

The global card random number generator market size was valued at USD 1.5 billion in 2023 and is projected to reach USD 3.8 billion by 2032, expanding at a compound annual growth rate (CAGR) of 11.2% during the forecast period. This growth is driven by the increasing demand for secure and fair gaming experiences, as well as the rising need for robust security mechanisms in financial transactions. The rapid digitalization and expansion of online gaming platforms further fuel the market's growth, offering numerous opportunities for advancements in random number generation technology.

One of the primary growth factors for the card random number generator market is the booming online gaming industry. As gaming platforms strive to provide fair and transparent gaming environments, the demand for sophisticated random number generators is surging. These generators ensure that card shuffling and other game mechanics are unpredictable and free from tampering, enhancing user trust and engagement. Additionally, advancements in cryptographic techniques have expanded the application of random number generators in secure online transactions, protecting user data and financial information from cyber threats.

The financial sector also plays a significant role in propelling the growth of the card random number generator market. Financial institutions rely on random number generators for various applications, including secure encryption, authentication processes, and transaction verification. As the frequency and sophistication of cyber-attacks increase, the need for advanced security solutions becomes more critical. Random number generators provide an essential layer of security, ensuring that sensitive information remains protected against fraudulent activities and unauthorized access.

Technological advancements, particularly in quantum computing, are another crucial driver of market growth. The development of quantum random number generators (QRNGs) promises unprecedented levels of randomness and security, making them highly attractive for use in critical applications such as cryptography, research simulations, and secure communications. These cutting-edge technologies are expected to revolutionize the random number generation landscape, paving the way for more reliable and tamper-proof systems across various industries.

When examining the regional outlook, North America is poised to dominate the card random number generator market, owing to its strong presence of leading technology companies and robust online gaming industry. The region's advanced technological infrastructure and high adoption rate of digital solutions further contribute to its market leadership. Asia Pacific is anticipated to showcase significant growth during the forecast period, driven by the expanding online gaming market, rising internet penetration, and increasing investments in cybersecurity. Europe is also expected to experience steady growth, supported by stringent regulatory requirements for data protection and secure digital transactions.

Type Analysis

The card random number generator market can be segmented by type into hardware random number generators (RNGs) and software RNGs. Hardware RNGs generate random numbers based on physical processes, such as electronic noise, which are inherently unpredictable. This type of RNG is favored for applications requiring high levels of security and integrity, such as cryptographic applications and secure communications. The increasing recognition of hardware RNGs' superior security features is driving their adoption in sectors like finance, where data protection is paramount.

Software RNGs, on the other hand, use algorithms to produce random numbers. While generally easier to implement and more cost-effective than hardware RNGs, software RNGs can be less secure due to their deterministic nature—they can potentially be predicted if the algorithm or seed value is compromised. Despite this, software RNGs are widely used in applications where high security is not as critical, such as gaming and lotteries. Their flexibility and ease of integration make them a popular choice for online gaming platforms and simulation applications.

The competition between hardware and software RNGs in the market is intense, as each type has its distinct advantages and applications. Innovations in both categories are continuously emerging, with hardware RNGs incorporating quantum technology to enhance randomness and security, while software RNGs are improving their algorithms to reduce

• 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)

The performance of statistical methods is frequently evaluated by means of simulation studies. In case of network meta-analysis of binary data, however, available data- generating models are restricted to either inclusion of two-armed trials or the fixed-effect model. Based on data-generation in the pairwise case, we propose a framework for the simulation of random-effect network meta-analyses including multi-arm trials with binary outcome. The only of the common data-generating models which is directly applicable to a random-effects network setting uses strongly restrictive assumptions. To overcome these limitations, we modify this approach and derive a related simulation procedure using odds ratios as effect measure. The performance of this procedure is evaluated with synthetic data and in an empirical example.

This dataset contains 56,964 conversations between een AI assistant and a (fake) "Human" (generated) in Dutch, specifically in the domain of programming (Stack Overflow). They are translations of Baize's machine-generated answers to the Stack Overflow dataset.

Data Instances

{'id': 9, 'input': 'Het gesprek tussen de mens en de AI-assistent. ' '[|Human|] Ik heb wat hulp nodig met Spark SQL union en het ordenen ' 'van kolommen. ' '[|AI|] Natuurlijk, ik help je graag. Waar heb je specifiek hulp ' 'bij nodig? ' '[|Human|] Ik wil de output van een UNION-query ordenen op een ' 'kolom die niet aanwezig is in de SELECT-instructie. Is dat ' 'mogelijk? ' '[|AI|] Ja, het is mogelijk met Spark SQL. Je kunt de ordinale ' 'positie van de kolom in de ORDER BY-clausule gebruiken in plaats ' 'van de kolomnaam. Als je bijvoorbeeld wilt ordenen op de derde ' 'kolom in de SELECT-instructie, kun je ORDER BY 3 gebruiken. ' '[|Human|] Dat is nuttig. Kan ik ook een alias gebruiken in de ' 'ORDER BY-clausule? ' '[|AI|] Ja, je kunt een alias gebruiken in de ORDER BY-clausule. De ' 'alias moet worden gedefinieerd in de SELECT-instructie. Als je ' 'bijvoorbeeld de derde kolom als "col3" alias geeft, kun je ordenen ' 'met ORDER BY col3. ' '[|Human|] Oké, ik denk dat ik het begrijp. Kan ik dezelfde alias ' 'gebruiken voor kolommen uit beide tabellen in de UNION? ' '[|AI|] Nee, je kunt dezelfde alias niet gebruiken voor kolommen ' 'uit beide tabellen in de UNION. Elke tabel in de UNION moet een ' 'unieke alias hebben voor de kolommen. ' '[|Human|] ', 'topic': 'Spark SQL UNION - ORDER BY kolom niet in SELECT'},

Data Fields

id: the ID of the item. The following 82 IDs are not included because they could not be translated: [1713, 1937, 1960, 4326, 4356, 8357, 8542, 8827, 9137, 9782, 11560, 11961, 12244, 12362, 12488, 13259, 13621, 14445, 14835, 15006, 17746, 18808, 19285, 19426, 19491, 21270, 21661, 22098, 23352, 23840, 23869, 25148, 25928, 27102, 27856, 28387, 29942, 30041, 30251, 32396, 32742, 32941, 33628, 34116, 34648, 34859, 35977, 35987, 36035, 36456, 37028, 37238, 37640, 38107, 38735, 39015, 40984, 41115, 41567, 42397, 43219, 43783, 44599, 44980, 45239, 47676, 48922, 49534, 50282, 50683, 50804, 50919, 51076, 51211, 52000, 52183, 52489, 52595, 53884, 54726, 55795, 56992]

input: the machine-generated conversation between AI and "Human". Always starts with Het gesprek tussen de mens en de AI-assistent. and has at least one occurrence of both [|AI|] and [|Human|].

topic: the topic description

Dataset Creation

Both the translations and the topics were translated with OpenAI's API for gpt-3.5-turbo. max_tokens=1024, temperature=0 as parameters.

The prompt template to translate the input is (where src_lang was English and tgt_lang Dutch):

CONVERSATION_TRANSLATION_PROMPT = """You are asked to translate a conversation between an AI assistant and a human from {src_lang} into {tgt_lang}.

Here are the requirements that you should adhere to: 1. maintain the format: the conversation consists of the AI (marked as [|AI|]) and the human ([|Human|]) talking in turns and responding to each other; 2. do not translate the speaker identifiers [|AI|] and [|Human|] but always copy them into the translation in appropriate places; 3. ensure accurate translation and keep the correctness of the conversation; 4. make sure that text is fluent to read and does not contain grammatical errors. Use standard {tgt_lang} without regional bias; 5. translate the human's text using informal, but standard, language; 6. make sure to avoid biases (such as gender bias, grammatical bias, social bias); 7. if the human asks to correct grammar mistakes or spelling mistakes then you have to generate a similar mistake in {tgt_lang}, and then also generate a corrected output version for the AI in {tgt_lang}; 8. if the human asks to translate text from one to another language, then you only translate the human's question to {tgt_lang} but you keep the translation that the AI provides in the language that the human requested; 9. do not translate code fragments but copy them as they are. If there are English examples, variable names or definitions in code fragments, keep them in English.

Now translate the following conversation with the requirements set out above. Do not provide an explanation and do not add anything else.

"""

The prompt to translate the topic is:

TOPIC_TRANSLATION_PROMPT = "Translate the following title of a conversation from {src_lang} to {tgt_lang} in a succinct,"
" summarizing manner. Translate accurately and formally. Do not provide any explanation"
" about the translation and do not include the original title.

"

The system message was:

You are a helpful assistant that translates English to Dutch to the requirements that are given to you.

Note that 82 items (0.1%) were not successfully translated. The translation was missing the AI identifier [|AI|] and/or the human one [|Human|]. The IDs for the missing items are [1713, 1937, 1960, 4326, 4356, 8357, 8542, 8827, 9137, 9782, 11560, 11961, 12244, 12362, 12488, 13259, 13621, 14445, 14835, 15006, 17746, 18808, 19285, 19426, 19491, 21270, 21661, 22098, 23352, 23840, 23869, 25148, 25928, 27102, 27856, 28387, 29942, 30041, 30251, 32396, 32742, 32941, 33628, 34116, 34648, 34859, 35977, 35987, 36035, 36456, 37028, 37238, 37640, 38107, 38735, 39015, 40984, 41115, 41567, 42397, 43219, 43783, 44599, 44980, 45239, 47676, 48922, 49534, 50282, 50683, 50804, 50919, 51076, 51211, 52000, 52183, 52489, 52595, 53884, 54726, 55795, 56992].

The translation quality has not been verified. Use at your own risk!

Licensing Information

Licensing info for Stack Overflow Questions is listed as Apache 2.0. If you use the current dataset, you should also adhere to the original license.

This text was generated (either in part or in full) with GPT-3 (gpt-3.5-turbo), OpenAI’s large-scale language-generation model. Upon generating draft language, the author reviewed, edited, and revised the language to their own liking and takes ultimate responsibility for the content of this publication.

If you use this dataset, you must also follow the Sharing and Usage policies.

As clearly stated in their Terms of Use, specifically 2c.iii, "[you may not] use output from the Services to develop models that compete with OpenAI". That means that you cannot use this dataset to build models that are intended to commercially compete with OpenAI. As far as I am aware, that is a specific restriction that should serve as an addendum to the current license.

This dataset is also available on the Hugging Face hub with the same DOI and license. See that README for more info.

Quantum-AI Synthetic Data Generator Market Outlook

According to our latest research, the global Quantum-AI Synthetic Data Generator market size reached USD 1.82 billion in 2024, reflecting a robust expansion driven by technological advancements and increasing adoption across multiple industries. The market is projected to grow at a CAGR of 32.7% from 2025 to 2033, reaching a forecasted market size of USD 21.69 billion by 2033. This growth trajectory is primarily fueled by the rising demand for high-quality synthetic data to train artificial intelligence models, address data privacy concerns, and accelerate digital transformation initiatives across sectors such as healthcare, finance, and retail.

One of the most significant growth factors for the Quantum-AI Synthetic Data Generator market is the escalating need for vast, diverse, and privacy-compliant datasets to train advanced AI and machine learning models. As organizations increasingly recognize the limitations and risks associated with using real-world data, particularly regarding data privacy regulations like GDPR and CCPA, the adoption of synthetic data generation technologies has surged. Quantum computing, when integrated with artificial intelligence, enables the rapid and efficient creation of highly realistic synthetic datasets that closely mimic real-world data distributions while ensuring complete anonymity. This capability is proving invaluable for sectors like healthcare and finance, where data sensitivity is paramount and regulatory compliance is non-negotiable. As a result, organizations are investing heavily in Quantum-AI synthetic data solutions to enhance model accuracy, reduce bias, and streamline data sharing without compromising privacy.

Another key driver propelling the market is the growing complexity and volume of data generated by emerging technologies such as IoT, autonomous vehicles, and smart devices. Traditional data collection methods are often insufficient to keep pace with the data requirements of modern AI applications, leading to gaps in data availability and quality. Quantum-AI Synthetic Data Generators address these challenges by producing large-scale, high-fidelity synthetic datasets on demand, enabling organizations to simulate rare events, test edge cases, and improve model robustness. Additionally, the capability to generate structured, semi-structured, and unstructured data allows businesses to meet the specific needs of diverse applications, ranging from fraud detection in banking to predictive maintenance in manufacturing. This versatility is further accelerating market adoption, as enterprises seek to future-proof their AI initiatives and gain a competitive edge.

The integration of Quantum-AI Synthetic Data Generators into cloud-based platforms and enterprise IT ecosystems is also catalyzing market growth. Cloud deployment models offer scalability, flexibility, and cost-effectiveness, making synthetic data generation accessible to organizations of all sizes, including small and medium enterprises. Furthermore, the proliferation of AI-driven analytics in sectors such as retail, e-commerce, and telecommunications is creating new opportunities for synthetic data applications, from enhancing customer experience to optimizing supply chain operations. As vendors continue to innovate and expand their service offerings, the market is expected to witness sustained growth, with new entrants and established players alike vying for market share through strategic partnerships, product launches, and investments in R&D.

From a regional perspective, North America currently dominates the Quantum-AI Synthetic Data Generator market, accounting for over 38% of the global revenue in 2024, followed by Europe and Asia Pacific. The strong presence of leading technology companies, robust investment in AI research, and favorable regulatory environment contribute to North America's leadership position. Europe is also witnessing significant growth, driven by stringent data privacy regulations and increasing adoption of AI across industries. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding IT infrastructure, and government initiatives promoting AI innovation. As regional markets continue to evolve, strategic collaborations and cross-border partnerships are expected to play a pivotal role in shaping the global landscape of the Quantum-AI Synthetic Data Generator market.

Component Analysis

Artifacts for the paper titled Root Cause Analysis for Microservice System based on Causal Inference: How Far Are We?.

This artifact repository contains 9 compressed folders, as follows:

ID File Name Description

1 syn_circa.zip CIRCA10, and CIRCA50 datasets for Causal Discovery

2 syn_rcd.zip RCD10, and RCD50 datasets for Causal Discovery

3 syn_causil.zip CausIL10, and CausIL50 datasets for Causal Discovery

4 rca_circa.zip CIRCA10, and CIRCA50 datasets for RCA

5 rca_rcd.zip RCD10, and RCD50 datasets for RCA

6 online-boutique.zip Online Boutique dataset for RCA

7 sock-shop-1.zip Sock Shop 1 dataset for RCA

8 sock-shop-2.zip Sock Shop 2 dataset for RCA

9 train-ticket.zip Train Ticket dataset for RCA

Each zip file contains the generated/collected data from the corresponding data generator or microservice benchmark systems (e.g., online-boutique.zip contains metrics data collected from the Online Boutique system).

Details about the generation of our datasets

1. Synthetic datasets

We use three different synthetic data generators from three previous RCA studies [15, 25, 28] to create the synthetic datasets: CIRCA, RCD, and CausIL data generators. Their mechanisms are as follows:1. CIRCA datagenerator [28] generates a random causal directed acyclic graph (DAG) based on a given number of nodes and edges. From this DAG, time series data for each node is generated using a vector auto-regression (VAR) model. A fault is injected into a node by altering the noise term in the VAR model for two timestamps. 2. RCD data generator [25] uses the pyAgrum package [3] to generate a random DAG based on a given number of nodes, subsequently generating discrete time series data for each node, with values ranging from 0 to 5. A fault is introduced into a node by changing its conditional probability distribution.3. CausIL data generator [15] generates causal graphs and time series data that simulate the behavior of microservice systems. It first constructs a DAG of services and metrics based on domain knowledge, then generates metric data for each node of the DAG using regressors trained on real metrics data. Unlike the CIRCA and RCD data generators, the CausIL data generator does not have the capability to inject faults.To create our synthetic datasets, we first generate 10 DAGs whose nodes range from 10 to 50 for each of the synthetic data generators. Next, we generate fault-free datasets using these DAGs with different seedings, resulting in 100 cases for the CIRCA and RCD generators and 10 cases for the CausIL generator. We then create faulty datasets by introducing ten faults into each DAG and generating the corresponding faulty data, yielding 100 cases for the CIRCA and RCD data generators. The fault-free datasets (e.g. syn_rcd, syn_circa) are used to evaluate causal discovery methods, while the faulty datasets (e.g. rca_rcd, rca_circa) are used to assess RCA methods.

1. Data collected from benchmark microservice systems

We deploy three popular benchmark microservice systems: Sock Shop [6], Online Boutique [4], and Train Ticket [8], on a four-node Kubernetes cluster hosted by AWS. Next, we use the Istio service mesh [2] with Prometheus [5] and cAdvisor [1] to monitor and collect resource-level and service-level metrics of all services, as in previous works [ 25 , 39, 59 ]. To generate traffic, we use the load generators provided by these systems and customise them to explore all services with 100 to 200 users concurrently. We then introduce five common faults (CPU hog, memory leak, disk IO stress, network delay, and packet loss) into five different services within each system. Finally, we collect metrics data before and after the fault injection operation. An overview of our setup is presented in the Figure below.

Code

The code to reproduce the experimental results in the paper is available at https://github.com/phamquiluan/RCAEval.

References

As in our paper.

Snapshot img

Synthetic Data Generation Market Size 2025-2029

The synthetic data generation market size is forecast to increase by USD 4.39 billion, at a CAGR of 61.1% between 2024 and 2029.

The market is experiencing significant growth, driven by the escalating demand for data privacy protection. With increasing concerns over data security and the potential risks associated with using real data, synthetic data is gaining traction as a viable alternative. Furthermore, the deployment of large language models is fueling market expansion, as these models can generate vast amounts of realistic and diverse data, reducing the reliance on real-world data sources. However, high costs associated with high-end generative models pose a challenge for market participants. These models require substantial computational resources and expertise to develop and implement effectively. Companies seeking to capitalize on market opportunities must navigate these challenges by investing in research and development to create more cost-effective solutions or partnering with specialists in the field. Overall, the market presents significant potential for innovation and growth, particularly in industries where data privacy is a priority and large language models can be effectively utilized.

What will be the Size of the Synthetic Data Generation Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by the increasing demand for data-driven insights across various sectors. Data processing is a crucial aspect of this market, with a focus on ensuring data integrity, privacy, and security. Data privacy-preserving techniques, such as data masking and anonymization, are essential in maintaining confidentiality while enabling data sharing. Real-time data processing and data simulation are key applications of synthetic data, enabling predictive modeling and data consistency. Data management and workflow automation are integral components of synthetic data platforms, with cloud computing and model deployment facilitating scalability and flexibility. Data governance frameworks and compliance regulations play a significant role in ensuring data quality and security. Deep learning models, variational autoencoders (VAEs), and neural networks are essential tools for model training and optimization, while API integration and batch data processing streamline the data pipeline. Machine learning models and data visualization provide valuable insights, while edge computing enables data processing at the source. Data augmentation and data transformation are essential techniques for enhancing the quality and quantity of synthetic data. Data warehousing and data analytics provide a centralized platform for managing and deriving insights from large datasets. Synthetic data generation continues to unfold, with ongoing research and development in areas such as federated learning, homomorphic encryption, statistical modeling, and software development. The market's dynamic nature reflects the evolving needs of businesses and the continuous advancements in data technology.

How is this Synthetic Data Generation Industry segmented?

The synthetic data generation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userHealthcare and life sciencesRetail and e-commerceTransportation and logisticsIT and telecommunicationBFSI and othersTypeAgent-based modellingDirect modellingApplicationAI and ML Model TrainingData privacySimulation and testingOthersProductTabular dataText dataImage and video dataOthersGeographyNorth AmericaUSCanadaMexicoEuropeFranceGermanyItalyUKAPACChinaIndiaJapanRest of World (ROW)

By End-user Insights

The healthcare and life sciences segment is estimated to witness significant growth during the forecast period.In the rapidly evolving data landscape, the market is gaining significant traction, particularly in the healthcare and life sciences sector. With a growing emphasis on data-driven decision-making and stringent data privacy regulations, synthetic data has emerged as a viable alternative to real data for various applications. This includes data processing, data preprocessing, data cleaning, data labeling, data augmentation, and predictive modeling, among others. Medical imaging data, such as MRI scans and X-rays, are essential for diagnosis and treatment planning. However, sharing real patient data for research purposes or training machine learning algorithms can pose significant privacy risks. Synthetic data generation addresses this challenge by producing realistic medical imaging data, ensuring data privacy while enabling research

This data set contains the result of applying the NIST Statistical Test Suite on accelerometer data processed for random number generator seeding. The NIST Statistical Test Suite can be downloaded from: http://csrc.nist.gov/groups/ST/toolkit/rng/documentation_software.html. The format of the output is explained in http://csrc.nist.gov/publications/nistpubs/800-22-rev1a/SP800-22rev1a.pdf.

Static torque, no load, constant speed, and sinusoidal oscillation test data for a 10hp, 300rpm magnetically-geared generator prototype using either an adjustable load bank for a fixed resistance or an output power converter.

Synthetic Healthcare Dataset

  Overview

This dataset is a synthetic healthcare dataset created for use in data analysis. It mimics real-world patient healthcare data and is intended for applications within the healthcare industry.

  Data Generation

The data has been generated using the Faker Python library, which produces randomized and synthetic records that resemble real-world data patterns. It includes various healthcare-related fields such as patient… See the full description on the dataset page: https://huggingface.co/datasets/vrajakishore/dummy_health_data.

Market Overview:

The global quantum random number generator RNG sales market is expected to grow at a CAGR of 7.5% during the forecast period from 2022 to 2030. The growth of the quantum RNG market can be attributed to the increasing demand for secure communication and data security. In addition, the growing adoption of Quantum Cryptography in various applications. However, a lack of awareness about quantum cryptography among end users may restrain the growth of this market during the forecast period.

Product Definition:

Quantum Random Number Generator Sales is the process of selling quantum random number generators. These are devices that generate random numbers using the principles of quantum mechanics. They are used for security applications, such as generating cryptographic keys, and in other settings where true randomness is important.

PCIe Type:

PCIe is a high-speed I/O interconnect standard for external cards. It is used in computers, servers, storage devices, and other electronic devices. PCIe provides better performance over PCI and also uses less power; making it an ideal choice for high-end systems that require more than basic functions such as graphics adapters.

USB Type:

USB Type is a specification for a type of connector used on portable devices, such as personal computers. USB Connectors are typically rectangular with a protrusion in one corner that fits into the corresponding receptacle on the device. It has three major interfaces, namely USB Mass Storage (MS), Universal Serial Bus (USB) Power Delivery, and USB Host Control.

Application Insights:

Quantum communication is expected to be the fastest-growing application segment over the forecast period. Quantum communication offers enhanced security and privacy as compared to classical communication systems due to characteristics of quantum mechanics such as uncertainty principle, non-locality, and entanglement. Traditional Information Security is expected to be the second-fastest growing application segment over the forecast period. Traditional Information Security applications use classical security methods such as passwords, firewalls, and intrusion detection systems to protect information.

Cryptography is expected to be the third-fastest growing application segment over the forecast period. Cryptography uses mathematical algorithms to secure data and communication. The betting industry is expected to be the fourth-fastest growing application segment over the forecast period. The betting industry uses cryptography for security purposes such as preventing fraud and ensuring fairness in gambling transactions. Other is expected to be the slowest growing application segment over the forecast period. Other includes applications that are not classified into any other category

Regional Analysis:

North America dominated the global market in terms of revenue share in 2019. The region is expected to continue its dominance over the forecast period owing to the high demand for secure and private communication channels among enterprises and government agencies. Moreover, the growing adoption of PCIe-type RNGs by several key companies for their critical applications is also likely to drive the regional growth over the forecast period. Europe is expected to witness modest growth over the forecast period owing to the increasing demand for quantum-safe cryptography and other applications in the region. The Asia Pacific is expected to grow at a faster pace than other regions due to the growing adoption of blockchain technology and increased investment in RNGs by key companies in this region. The Middle East & Africa is expected to account for a small share of the global market over the forecast period, as there are limited opportunities for quantum-safe cryptography and other key applications in this region.

Growth Factors:

• Increasing demand for secure data transmission and storage.
• A growing number of cyber-attacks and data breaches.
• The proliferation of IoT devices and big data analytics.
• Development of quantum computing technology.
• Rising awareness about the benefits of using quantum random number generators.

Report Scope

Data generation in machine learning involves creating or manipulating data to train and evaluate machine learning models. The purpose of data generation is to provide diverse and representative examples that cover a wide range of scenarios, ensuring the model's robustness and generalization. Data augmentation techniques involve applying various transformations to existing data samples to create new ones. These transformations include: random rotations, translations, scaling, flips, and more. Augmentation helps in increasing the dataset size, introducing natural variations, and improving model performance by making it more invariant to specific transformations. The dataset contains GENERATED USA passports, which are replicas of official passports but with randomly generated details, such as name, date of birth etc. The primary intention of generating these fake passports is to demonstrate the structure and content of a typical passport document and to train the neural network to identify this type of document. Generated passports can assist in conducting research without accessing or compromising real user data that is often sensitive and subject to privacy regulations. Synthetic data generation allows researchers to develop and refine models using simulated passport data without risking privacy leaks.

Introduction

This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called BeamNG.tech. Virtual roads consist of sequences of road points interpolated using Cubic splines.

Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.

This dataset builds on top of our previous work in this area, including work on

test generation (e.g., AsFault, DeepJanus, and DeepHyperion) and the SBST CPS tool competition (SBST2021),

test selection: SDC-Scissor and related tool

test prioritization: automated test cases prioritization work for SDCs.

Dataset Overview

The TRAVEL dataset is available under the data folder and is organized as a set of experiments folders. Each of these folders is generated by running the test-generator (see below) and contains the configuration used for generating the data (experiment_description.csv), various statistics on generated tests (generation_stats.csv) and found faults (oob_stats.csv). Additionally, the folders contain the raw test cases generated and executed during each experiment (test..json).

The following sections describe what each of those files contains.

Experiment Description

The experiment_description.csv contains the settings used to generate the data, including:

Time budget. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.

The size of the map. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.

The test subject. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.

The test generator. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.

The speed limit. The maximum speed at which the driving agent under test can travel.

Out of Bound (OOB) tolerance. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.

Experiment Statistics

The generation_stats.csv contains statistics about the test generation, including:

Total number of generated tests. The number of tests generated during an experiment. This number is broken down into the number of valid tests and invalid tests. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.

Test outcome. The test outcome contains the number of passed tests, failed tests, and test in error. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.

The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (total oob) and its breakdown into OOB that happened while driving left or right. Further statistics about the diversity (i.e., sparseness) of the failures are also reported.

Test Cases and Executions

Each test..json contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.

The data about the test case definition include:

The road points. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (interpolated_points)

The test ID. The unique identifier of the test in the experiment.

Validity flag and explanation. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., the road contains sharp turns or the road self intersects)

The test data are organized according to the following JSON Schema and can be interpreted as RoadTest objects provided by the tests_generation.py module.

{ "type": "object", "properties": { "id": { "type": "integer" }, "is_valid": { "type": "boolean" }, "validation_message": { "type": "string" }, "road_points": { §\label{line:road-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "interpolated_points": { §\label{line:interpolated-points}§ "type": "array", "items": { "$ref": "schemas/pair" }, }, "test_outcome": { "type": "string" }, §\label{line:test-outcome}§ "description": { "type": "string" }, "execution_data": { "type": "array", "items": { "$ref" : "schemas/simulationdata" } } }, "required": [ "id", "is_valid", "validation_message", "road_points", "interpolated_points" ] }

Finally, the execution data contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).

The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the simulation_data.py module.

{ "$id": "schemas/simulationdata", "type": "object", "properties": { "timer" : { "type": "number" }, "pos" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel" : { "type": "array", "items":{ "$ref" : "schemas/triple" } } "vel_kmh" : { "type": "number" }, "steering" : { "type": "number" }, "brake" : { "type": "number" }, "throttle" : { "type": "number" }, "is_oob" : { "type": "number" }, "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§ }, "required": [ "timer", "pos", "vel", "vel_kmh", "steering", "brake", "throttle", "is_oob", "oob_percentage" ] }

Dataset Content

The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (SDC-Scissor work and tool) and test prioritization (automated test cases prioritization work for SDCs).

SBST CPS Tool Competition Data

The data collected during the SBST CPS tool competition are stored inside data/competition.tar.gz. The file contains the test cases generated by Deeper, Frenetic, AdaFrenetic, and Swat, the open-source test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    DEFAULT
    200 × 200
    120
    5 (real time)
    0.95
    BeamNG.AI - 0.7


    SBST
    200 × 200
    70
    2 (real time)
    0.5
    BeamNG.AI - 0.7

Specifically, the TRAVEL dataset contains 8 repetitions for each of the above configurations for each test generator totaling 64 experiments.

SDC Scissor

With SDC-Scissor we collected data based on the Frenetic test generator. The data is stored inside data/sdc-scissor.tar.gz. The following table summarizes the used parameters.

    Name
    Map Size (m x m)
    Max Speed (Km/h)
    Budget (h)
    OOB Tolerance (%)
    Test Subject




    SDC-SCISSOR
    200 × 200
    120
    16 (real time)
    0.5
    BeamNG.AI - 1.5

The dataset contains 9 experiments with the above configuration. For generating your own data with SDC-Scissor follow the instructions in its repository.

Dataset Statistics

Here is an overview of the TRAVEL dataset: generated tests, executed tests, and faults found by all the test generators grouped by experiment configuration. Some 25,845 test cases are generated by running 4 test generators 8 times in 2 configurations using the SBST CPS Tool Competition code pipeline (SBST in the table). We ran the test generators for 5 hours, allowing the ego-car a generous speed limit (120 Km/h) and defining a high OOB tolerance (i.e., 0.95), and we also ran the test generators using a smaller generation budget (i.e., 2 hours) and speed limit (i.e., 70 Km/h) while setting the OOB tolerance to a lower value (i.e., 0.85). We also collected some 5, 971 additional tests with SDC-Scissor (SDC-Scissor in the table) by running it 9 times for 16 hours using Frenetic as a test generator and defining a more realistic OOB tolerance (i.e., 0.50).

Generating new Data

Generating new data, i.e., test cases, can be done using the SBST CPS Tool Competition pipeline and the driving simulator BeamNG.tech.

Extensive instructions on how to install both software are reported inside the SBST CPS Tool Competition pipeline Documentation;

The Insider Threat Test Dataset is a collection of synthetic insider threat test datasets that provide both background and malicious actor synthetic data.The CERT Division, in partnership with ExactData, LLC, and under sponsorship from DARPA I2O, generated a collection of synthetic insider threat test datasets. These datasets provide both synthetic background data and data from synthetic malicious actors.For more background on this data, please see the paper, Bridging the Gap: A Pragmatic Approach to Generating Insider Threat Data.Datasets are organized according to the data generator release that created them. Most releases include multiple datasets (e.g., r3.1 and r3.2). Generally, later releases include a superset of the data generation functionality of earlier releases. Each dataset file contains a readme file that provides detailed notes about the features of that release.The answer key file answers.tar.bz2 contains the details of the malicious activity included in each dataset, including descriptions of the scenarios enacted and the identifiers of the synthetic users involved.

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)

The synthetic data generation market is projected to be worth USD 0.3 billion in 2024. The market is anticipated to reach USD 13.0 billion by 2034. The market is further expected to surge at a CAGR of 45.9% during the forecast period 2024 to 2034.

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FARLEAD2 receives a test scenario from the developer

The dataset contains 350 features engineered from the phasor measurements (PMU-type) signals from the IEEE New England 39-bus power system test case network, which are generated from the 9360 systematic MATLAB®/Simulink electro-mechanical transients simulations. It was prepared to serve as a convenient and open database for experimenting with different types of machine learning techniques for transient stability assessment (TSA) of electrical power systems.

Different load and generation levels of the New England 39-bus benchmark power system were systematically covered, as well as all three major types of short-circuit events (three-phase, two-phase and single-phase faults) in all parts of the network. The consumed power of the network was set to 80%, 90%, 100%, 110% and 120% of the basic system load levels. The short-circuits were located on the busbar or on the transmission line (TL). When they were located on a TL, it was assumed that they can occur at 20%, 40%, 60%, and 80% of the line length. Features were obtained directly from the time-domain signals at the pickup time (pre-fault value) and at the trip time (post-fault value) of the associated distance protection relays.

This is a stochastic dataset of 3120 cases, created from the population of 9360 systematic simulations, which features a statistical distribution of different fault types, as follows: single-phase (70%), double-phase (20%) and three-phase faults (10%). It also features a class imbalance, with less than 20% of cases belonging to the unstable class. Dataset is a compressed CSV file.

List of feature names in the dataset:

WmGx - rotor speed for each generator Gx, from G1 to G10,

DThetaGx - rotor angle deviation for each generator Gx, from G1 to G10,

ThetaGx - rotor mechanical angle for each generator Gx, from G1 to G10,

VtGx - stator voltage for each generator Gx, from G1 to G10,

IdGx - stator d-component current for each generator Gx, from G1 to G10,

IqGx - stator q-component current for each generator Gx, from G1 to G10,

LAfvGx - pre-fault power load angle for each generator Gx, from G1 to G10,

LAlvGx - post-fault power load angle for each generator Gx, from G1 to G10,

PfvGx - pre-falut value of the generator active power for each generator Gx, from G1 to G10,

PlvGx - post-falut value of the generator active power for each generator Gx, from G1 to G10,

QfvGx - pre-falut value of the generator reactive power for each generator Gx, from G1 to G10,

QlvGx - post-falut value of the generator reactive power for each generator Gx, from G1 to G10,

VAfvBx - pre-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,

VBfvBx - pre-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,

VCfvBx - pre-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,

VAlvBx - post-fault bus voltage magnitude in phase A for each bus Bx, from B1 to B39,

VBlvBx - post-fault bus voltage magnitude in phase B for each bus Bx, from B1 to B39,

VClvBx - post-fault bus voltage magnitude in phase C for each bus Bx, from B1 to B39,

Stability - binary indicator (0/1) that determines if the power system was stable or unstable (0 - stable, 1 - unstable); this is the label variable.

License: Creative Commons CC-BY.

Disclaimer: This dataset is provided "as is", without any warranties of any kind.

Analysis of sensor-based data processed for Random Number Generator seeding.