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.

Context

In my short research, there are no datasets related to Name Country of origin. The next step was to scrape data from individual common names lists in Wikipedia. Faker library is used to create fake data where data is scraped from publicly available data, when it comes to names they're scraped from the Wikipedia common names and other name sources.

Content

Dataset consists of 404062 full names from 63 different countries namely -- Bulgaria,Egypt,Canada,Laos,Thailand,Slovakia,Indonesia,Bosnia and Herzegovina,Ukraine,Japan,Israel,United Arab Emirates,Austria,Armenia,Lithuania,Turkey,Croatia,Luxembourg,Sweden,Latvia,Switzerland,Jordan,United Kingdom,Colombia,Portugal,Bangladesh,Palestine,France,Azerbaijan,Estonia,New Zealand,Saudi Arabia,India,Russia,Finland,United States,Slovenia,Mexico,Australia,Malta,Belgium,Taiwan,Philippines,Romania,Nepal,Poland,Greece,Norway,China,Cyprus,Brazil,Spain,Ireland,Czech Republic,Georgia,Italy,Hungary,Ghana,South Korea,Iran,Germany,Netherlands,Denmark.

Acknowledgements

This dataset wouldn't be made without the libraries faker (https://pypi.org/project/Faker/) and googletrans (https://pypi.org/project/googletrans/).

Inspiration

This dataset can be widely used in solving NLP problems and many text-related problems in determining Ontologies, Knowledge graphs etc.

Fake Email Address Generator Market Analysis The global market for Fake Email Address Generators is expected to reach a value of XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. Key drivers of this growth include the increasing demand for privacy and anonymity online, the growing prevalence of spam and phishing attacks, and the proliferation of digital marketing campaigns. Additionally, the adoption of cloud-based solutions and the emergence of new technologies, such as artificial intelligence (AI), are further fueling market expansion. Key trends in the Fake Email Address Generator market include the growing popularity of enterprise-grade solutions, the emergence of disposable email services, and the increasing integration with other online tools. Restraints to market growth include concerns over security and data protection, as well as the availability of free or low-cost alternatives. The market is dominated by a few major players, including Burnermail, TrashMail, and Guerrilla Mail, but a growing number of smaller vendors are emerging with innovative solutions. Geographically, North America and Europe are the largest markets, followed by the Asia Pacific region.

For benchmarking, please refer to its variant UPFD-POL and UPFD-GOS.

The dataset has been integrated with Pytorch Geometric (PyG) and Deep Graph Library (DGL). You can load the dataset after installing the latest versions of PyG or DGL.

The UPFD dataset includes two sets of tree-structured graphs curated for evaluating binary graph classification, graph anomaly detection, and fake/real news detection tasks. The dataset is dumped in the form of Pytorch-Geometric dataset object. You can easily load the data and run various GNN models using PyG.

The dataset includes fake&real news propagation (retweet) networks on Twitter built according to fact-check information from Politifact and Gossipcop. The news retweet graphs were originally extracted by FakeNewsNet. Each graph is a hierarchical tree-structured graph where the root node represents the news; the leaf nodes are Twitter users who retweeted the root news. A user node has an edge to the news node if he/she retweeted the news tweet. Two user nodes have an edge if one user retweeted the news tweet from the other user.

We crawled near 20 million historical tweets from users who participated in fake news propagation in FakeNewsNet to generate node features in the dataset. We incorporate four node feature types in the dataset, the 768-dimensional bert and 300-dimensional spacy features are encoded using pretrained BERT and spaCy word2vec, respectively. The 10-dimensional profile feature is obtained from a Twitter account's profile. You can refer to profile_feature.py for profile feature extraction. The 310-dimensional content feature is composed of a 300-dimensional user comment word2vec (spaCy) embedding plus a 10-dimensional profile feature.

The dataset statistics is shown below:

Please refer to the paper for more details about the UPFD dataset.

Due to the Twitter policy, we could not release the crawled user's historical tweets publicly. To get the corresponding Twitter user information, you can refer to the news lists under \data in our github repo and map the news id to FakeNewsNet. Then, you can crawl the user information by following the instruction on FakeNewsNet. In the UPFD project, we use Tweepy and Twitter Developer API to get the user information.

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.

[Real or Fake] : Fake Job Description Prediction This dataset contains 18K job descriptions out of which about 800 are fake. The data consists of both textual information and meta-information about the jobs. The dataset can be used to create classification models which can learn the job descriptions which are fraudulent.

Fake audio detection is a growing concern and some relevant datasets have been designed for research. However, there is no standard public Chinese dataset under complex conditions.

In this paper, we aim to fill in the gap and design a Chinese fake audio detection dataset (CFAD) for studying more generalized detection methods. Twelve mainstream speech-generation techniques are used to generate fake audio. To simulate the real-life scenarios, three noise datasets are selected for noise adding at five different signal-to-noise ratios, and six codecs are considered for audio transcoding. CFAD dataset can be used not only for fake audio detection but also for detecting the algorithms of fake utterances for audio forensics. Baseline results are presented with analysis. The results that show fake audio detection methods with generalization remain challenging. The CFAD dataset is publicly available on GitHub https://github.com/ADDchallenge/CFAD

CFAD dataset considers 12 types of fake audio, 11 of which are generated by different speech synthesis techniques and the remaining one is partially fake type. Partially fake audio is completely different from synthesis speech and thus can better evaluate the generalization of the detection model to unknown types. The real audio is collected from 6 different corpora to increase the diversity of real category distributions, which makes model less prone to artifact from a single database. For robustness evaluation, we additionally simulate background noise and media codecs that might occur in real life and provide detailed labels, including fake type, real source, noise type, signal noise ratio (SNR), and media codecs. Overall, CFAD dataset consists of three different versions, named clean, noisy, and codec versions.

Each version of the dataset is divided into disjoint training, development, and test sets in the same way. There is no speaker overlap across these three subsets. Each test set is further divided into seen and unseen test sets. Unseen test sets can evaluate the generalization of the methods to unknown types. It is worth mentioning that both real audio and fake audio in the unseen test set are unknown to the model.

For the noisy speech part, we select three noise databases for simulation. Additive noises are added to each audio in the clean dataset at 5 different SNRs. The additive noises of the unseen test set and the remaining subsets come from different noise databases.

For the codec speech part, we select six different codecs. Two of them are applied for unseen test set.

In each version (clean, noisy, and codec versions) of the CFAD dataset, there are 138400 utterances in training set, 14400 utterances in development set, 42000 utterances in seen test set, and 21000 utterances in unseen test set.

Clean Real Audios Collection

From the point of eliminating the interference of irrelevant factors, we collect clean real audios from two aspects: 5 open resources from OpenSLR platform (http://www.openslr.org/12/) and one self-recording dataset.

Clean Fake Audios Generation

We select 11 representative speech synthesis methods to generate the fake audios and one partially fake audios.

Noisy Audios Simulation

Noisy audios aim to quantify the robustness of the methods under noisy conditions. To simulate the real-life scenarios, we artificially sample the noise signals and add them to clean audios at 5 different SNRs, which are 0dB, 5dB, 10dB, 15dB and 20dB. Additive noises are selected from three noise databases: PNL 100 Nonspeech Sounds, NOISEX-92, and TAU Urban Acoustic Scenes.

Audio Transcoding

The Codec version aims to quantify the robustness of the methods under different format conversions. We select a total of six codecs. For the training, development, and seen test sets in codec version, mp3, flac, ogg, and m4a are used. For the unseen test set of the codec version, aac, and wma are used.

Audio transcoding operation is operated on the audio in the clean version. Each clean audio will be randomly transformed with one of the candidate codecs and converted back to original WAV files using ffmpeg toolkits.

This data set is licensed with a CC BY-NC-ND 4.0 license.

You can cite the data using the following BibTeX entry.

The market for fake email address generators is experiencing robust growth, driven by increasing concerns over privacy and online security. Individuals and businesses alike are seeking temporary email addresses to avoid spam, phishing attempts, and unwanted marketing communications. The anonymity offered by these services appeals to those seeking to maintain their privacy online, particularly when registering for websites or online services that may engage in intrusive data collection practices. The market's expansion is further fueled by the rising prevalence of data breaches and the subsequent heightened awareness of the risks associated with sharing personal information online. This trend is expected to continue, with increasing adoption across various demographics and sectors. The market's competitive landscape is characterized by a multitude of providers, ranging from simple, free services to more sophisticated options offering enhanced privacy features. This diversity contributes to the market's growth, providing consumers with choices based on their specific needs and budget. While regulatory changes and increased efforts to combat fraudulent activities present potential restraints, the underlying demand for privacy and security is likely to sustain market growth in the coming years. We project a continued positive CAGR, reflecting both the ongoing expansion of internet usage and the increasing need for protective measures against online threats. The segmentation of the market reflects the various needs and preferences of users. Some users require only basic temporary email addresses for short-term purposes, while others need more advanced features for greater security and anonymity. This diversity in user needs creates opportunities for both simple and complex solutions. The leading companies in this space are continuously innovating to offer new features and improve their services, further fueling competition and market expansion. Geographic segmentation reveals varying levels of adoption across different regions, reflecting factors such as internet penetration, digital literacy, and regulatory environments. Regions with higher levels of internet penetration and awareness of online security threats generally demonstrate higher market adoption rates. Future growth will likely be driven by continued technological advancements, increased regulatory scrutiny of data privacy, and growing user demand for privacy-centric solutions in an increasingly digital world.

Credit Card Generator Market Outlook

The global credit card generator market is projected to experience robust growth with a market size of approximately USD 580 million in 2023, and it is anticipated to reach USD 1.2 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 8.5%. The rising need for secure and efficient credit card testing tools, driven by the expansion of e-commerce and digital transactions, forms a significant growth catalyst for this market. As online retail and digital financial services burgeon, the demand for reliable credit card generators continues to escalate, underscoring the importance of this market segment.

One of the pivotal growth drivers for the credit card generator market is the increasing complexity and sophistication of online payment systems. As e-commerce platforms and digital payment solutions proliferate worldwide, there is a growing need for comprehensive testing tools to ensure the reliability and security of these systems. Credit card generators play a crucial role in this context by providing developers and testers with the means to simulate various credit card scenarios, thereby enhancing the robustness of payment processing systems. Additionally, the rise in cyber threats and fraud necessitates stringent testing, further propelling market growth.

Another significant factor contributing to the market's expansion is the growing emphasis on fraud prevention and security. Financial institutions and businesses are increasingly investing in sophisticated tools to combat fraud and secure financial transactions. Credit card generators offer a practical solution for testing the efficacy of anti-fraud measures and ensuring that security protocols are adequately robust. By enabling the simulation of fraudulent activities and various transaction scenarios, these tools help organizations better prepare for and mitigate potential security breaches.

Furthermore, the marketing and promotional applications of credit card generators are also driving market growth. Companies leveraging digital marketing strategies use these tools to create dummy credit card numbers for various promotional activities, such as offering free trials or discounts, without exposing real customer data. This capability not only aids in marketing efforts but also ensures compliance with data privacy regulations, thereby enhancing consumer trust and brand reputation. The versatility of credit card generators in supporting both operational and marketing functions underscores their growing importance in the digital age.

Regionally, North America holds a significant share of the credit card generator market, driven by the high penetration of digital payment systems and advanced cybersecurity measures in the region. The presence of numerous financial institutions and technology companies further bolsters the market in North America. Meanwhile, Asia Pacific is expected to witness the fastest growth, fueled by the rapid digitalization of economies, increasing internet penetration, and burgeoning e-commerce activities. Europe also presents substantial opportunities due to stringent data protection regulations and the widespread adoption of digital transaction systems.

Type Analysis

The credit card generator market can be segmented by type into software and online services. Software-based credit card generators are widely used by developers and testers within organizations to simulate credit card transactions and validate payment processing systems. These tools are typically integrated into the development and testing environments, providing a controlled and secure platform for generating valid credit card numbers. The demand for software-based generators is driven by their ability to offer customizable options and advanced features, such as bulk generation and API integration, which enhance the efficiency of testing processes.

Online services, on the other hand, cater to a broader audience, including individual users, small businesses, and marketers. These services are accessible via web platforms and provide an easy-to-use interface for generating credit card numbers for various purposes, such as testing, fraud prevention, and marketing promotions. The growing popularity of online credit card generators can be attributed to their convenience, accessibility, and the increasing need for temporary and disposable credit card numbers in the digital economy. These services are particularly useful for busin

The invoice dataset provided is a mock dataset generated using the Python Faker library. It has been designed to mimic the format of data collected from an online store. The dataset contains various fields, including first name, last name, email, product ID, quantity, amount, invoice date, address, city, and stock code. All of the data in the dataset is randomly generated and does not represent actual individuals or products. The dataset can be used for various purposes, including testing algorithms or models related to invoice management, e-commerce, or customer behavior analysis. The data in this dataset can be used to identify trends, patterns, or anomalies in online shopping behavior, which can help businesses to optimize their online sales strategies.

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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 dataset allows you to try your hand at detecting fake images from real images. I trained a model on images that I collected from the Minecraft video game. From the provided link, you have access to my trained model, and can generate more fake data, if you like. However, if you would like additional real data, you will need to capture it from Minecraft yourself.

The following is a real image from Minecraft: https://github.com/jeffheaton/jheaton_images/blob/main/kaggle/spring-2021/mc-34.jpg?raw=true" alt="Real Minecraft">

This Minecraft image is obviously fake:

https://github.com/jeffheaton/jheaton_images/blob/main/kaggle/spring-2021/mc-202.jpg?raw=true" alt="alt">

Some images are not as easily guessed, such as this fake image:

https://github.com/jeffheaton/jheaton_images/blob/main/kaggle/spring-2021/mc-493.jpg?raw=true" alt="alt">

You will also have to contend with multiple times of the day. Darker images will be more difficult for your model.

https://github.com/jeffheaton/jheaton_images/blob/main/kaggle/spring-2021/mc-477.jpg?raw=true" alt="alt">

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. The dataset contains GENERATED Vietnamese 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.

Fake audio detection is a growing concern and some relevant datasets have been designed for research. But there is no standard public Chinese dataset under additive noise conditions. In this paper, we aim to fill in the gap and design a
Chinese fake audio detection dataset (FAD) for studying more generalized detection methods. Twelve mainstream speech generation techniques are used to generate fake audios. To simulate the real-life scenarios, three noise datasets are selected for
noisy adding at five different signal noise ratios. FAD dataset can be used not only for fake audio detection, but also for detecting the algorithms of fake utterances for
audio forensics. Baseline results are presented with analysis. The results that show fake audio detection methods with generalization remain challenging.
The FAD dataset is publicly available. The source code of baselines is available on GitHub https://github.com/ADDchallenge/FAD

The FAD dataset is designed to evaluate the methods of fake audio detection and fake algorithms recognition and other relevant studies. To better study the robustness of the methods under noisy
conditions when applied in real life, we construct the corresponding noisy dataset. The total FAD dataset consists of two versions: clean version and noisy version. Both versions are divided into
disjoint training, development and test sets in the same way. There is no speaker overlap across these three subsets. Each test sets is further divided into seen and unseen test sets. Unseen test sets can
evaluate the generalization of the methods to unknown types. It is worth mentioning that both real audios and fake audios in the unseen test set are unknown to the model.
For the noisy speech part, we select three noise database for simulation. Additive noises are added to each audio in the clean dataset at 5 different SNRs. The additive noises of the unseen test set and the
remaining subsets come from different noise databases. In each version of FAD dataset, there are 138400 utterances in training set, 14400 utterances in development set, 42000 utterances in seen test set, and 21000 utterances in unseen test set. More detailed statistics are demonstrated in the Tabel 2.

Clean Real Audios Collection
From the point of eliminating the interference of irrelevant factors, we collect clean real audios from
two aspects: 5 open resources from OpenSLR platform (http://www.openslr.org/12/) and one self-recording dataset.

Clean Fake Audios Generation
We select 11 representative speech synthesis methods to generate the fake audios and one partially fake audios.

Noisy Audios Simulation
Noisy audios aim to quantify the robustness of the methods under noisy conditions. To simulate the real-life scenarios, we artificially sample the noise signals and add them to clean audios at 5 different
SNRs, which are 0dB, 5dB, 10dB, 15dB and 20dB. Additive noises are selected from three noise databases: PNL 100 Nonspeech Sounds, NOISEX-92, and TAU Urban Acoustic Scenes.

This data set is licensed with a CC BY-NC-ND 4.0 license.
You can cite the data using the following BibTeX entry:
@inproceedings{ma2022fad,
title={FAD: A Chinese Dataset for Fake Audio Detection},
author={Haoxin Ma, Jiangyan Yi, Chenglong Wang, Xunrui Yan, Jianhua Tao, Tao Wang, Shiming Wang, Le Xu, Ruibo Fu},
booktitle={Submitted to the 36th Conference on Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks },
year={2022},
}

The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

The quality of AI-generated images has rapidly increased, leading to concerns of authenticity and trustworthiness.

CIFAKE is a dataset that contains 60,000 synthetically-generated images and 60,000 real images (collected from CIFAR-10). Can computer vision techniques be used to detect when an image is real or has been generated by AI?

Dataset details The dataset contains two classes - REAL and FAKE. For REAL, we collected the images from Krizhevsky & Hinton's CIFAR-10 dataset For the FAKE images, we generated the equivalent of CIFAR-10 with Stable Diffusion version 1.4 There are 100,000 images for training (50k per class) and 20,000 for testing (10k per class)

References If you use this dataset, you must cite the following sources

Krizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images.

Bird, J.J., Lotfi, A. (2023). CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images. arXiv preprint arXiv:2303.14126.

Real images are from Krizhevsky & Hinton (2009), fake images are from Bird & Lotfi (2023). The Bird & Lotfi study is a preprint currently available on ArXiv and this description will be updated when the paper is published.

License This dataset is published under the same MIT license as CIFAR-10:

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The aim of this work is to generate simulations of various sampling protocols of a longitudinal study of binary data. The conservation status of a site is evaluated as good or bad. This evaluation is repeated on several sites and repeated over time.The main interest is to evaluate the proportion of sites in good or bad conservation state during the first year (initial situation) and then to evaluate how the situationchange over time (trends).We want to evaluate the influence of the sampling size, frequency, repetition, etc... on the statistical power and the size of the confidence intervals.A first function generate the simulations, analyze the fake dataset and stores the model parameters.A grid of function parameters is generated to apply this first function with various combinations of options corresponding to various sampling protocols.A second function aggregate these results for each combination of parameters and compute descriptive statistics like the power of the tests and the confidenceintervals of the parametersThe outputs are saved on the disc and are available for data visualization (produced in another script).The pdf report present a graphical exploration of the results of these simulations.The "results" directory contains the ouput of the raw simulations : output_simulations_initial.csv are simulation for one year only to estimate the initial proportion of sites in bad conservation state. output_simulations_trends.csv contains simulations of dataset over several years to explore the statistical power of the slopes/trends over time. there are 50 simulations for each combination of parameters.The 2 other files are aggregated versions of these files. The 50 simulations for each combination of parameters are grouped to compute the statistical power and confidence intervals. This approach of power analysis is described by Gelman & Hill (2007) : Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Cambridge University Press

TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.

Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.

These data are used for training, testing, and evaluating the CycleGAN project related to coin reconstructions. Data are used to train the given CycleGAN model and coins are used to evaluate the model both internally, computationally, and by other users.trainingData.zip: This file provides training data obtained used to train the CycleGAN. There is a training folder which contains two folders (badCoins and goodCoins). The goodCoins folder contains coins that are better preserved and used to train what a coin should look like. The badCoins folder contains damaged coins and attempts to train to reconstruct from these coins. The name of each file corresponds to coins that can be found using the identifier for coins referenced on http://numismatics.org.testCoins.zip: These are image data used to test the developed CycleGAN model. The images folder contains the testing data used to generate model test data. The names of the coins correspond to the identifier on http://numismatics.org.CoinsEvaluation.pdf: This file is used by testers to evaluate if they think given reconstructed coins shows improved quality or not from the real coin. The users are asked to evaluate coins on a 1-5 scale, with 1 indicating no difference and 5 indicating much improved reconstructions.testCoinReconstruction.zip: This folder contains real and fake (i.e., reconstructed coins) coins used to determine if a given coin is real or reconstructed (i.e., fake). It was used for evaluators to determine if they can identify which coins were real or fake. In addition to the real and reconstructed coins, a pdf (Real_or_Fake.pdf) is used to ask judges to determine which given coins are real and which are generated reconstructions.model1MixedLatest.zip: This is the final CycleGAN model used for reconstructions applied in the paper. Notes on parameters for training are included.Model1Mixed.zip: This is a zip file containing real and fake (generated) coins from the model output.Coins_Used_In_Figures.csv: This list helps to correspond the number (in the paper's figures) and identifier used in http://numismatics.org. Figure 2 and 3 are used to show these example in the paper.Coin_Reconstruction_CycleGan: This is the final Google Colab settings used.

The Synthea Generated Synthetic Data in FHIR hosts over 1 million synthetic patient records generated using Synthea in FHIR format. Exported from the Google Cloud Healthcare API FHIR Store into BigQuery using analytics schema . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery . This public dataset is also available in Google Cloud Storage and available free to use. The URL for the GCS bucket is gs://gcp-public-data--synthea-fhir-data-1m-patients. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage. Please cite SyntheaTM as: Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, Scott McLachlan, Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record, Journal of the American Medical Informatics Association, Volume 25, Issue 3, March 2018, Pages 230–238, https://doi.org/10.1093/jamia/ocx079