19,997 machine learning datasets
19,997 dataset results
In this dataset, we provide detailed traffic stream data for the Spot robot, including both the Spot robot control traffic stream and the Spot video stream. The Spot robot traffic streams provide realistic traffic data for communication network evaluations, e.g., for measurements with the TSN FlexText testbed. Furthermore, we share data for the tactile internet including audio, video, and robotic communication. Finally, the dataset includes generic data streams for three different intervals (0.2ms, 0.3ms, and 0.5ms) with two different Ethernet frame sizes. The data is provided as .*pcap which can be replayed with various tools or be analyzed, e.g., with Wireshark. The Spot data streams are split into two directions and are based on Spot API calls.
The FathomNet2023 competition dataset is a subset of the broader FathomNet marine image repository. The training and test images for the competition were all collected in the Monterey Bay Area between the surface and 1300 meters depth by the Monterey Bay Aquarium Research Institute. The images contain bounding box annotations of 290 categories of bottom dwelling animals. The training and validation data are split across an 800 meter depth threshold: all training data is collected from 0-800 meters, evaluation data comes from the whole 0-1300 meter range. Since an organisms' habitat range is partially a function of depth, the species distributions in the two regions are overlapping but not identical. Test images are drawn from the same region but may come from above or below the depth horizon. The competition goal is to label the animals present in a given image (i.e. multi-label classification) and determine whether the image is out-of-sample.
LLNeRF Dataset is a real-world dataset as a benchmark for model learning and evaluation. To obtain real low-illumination images with real noise distributions, photos are taken at nighttime outdoor scenes or low-light indoor scenes containing diverse objects. Since the ISP operations are device dependent and the noise distributions across devices are also different, the data is collected using a mobile phone camera and a DSLR camera to enrich the diversity of the dataset.
Blockchain has empowered computer systems to be more secure using a distributed network. However, the current blockchain design suffers from fairness issues in transaction ordering. Miners are able to reorder transactions to generate profits, the so-called miner extractable value (MEV). Existing research recognizes MEV as a severe security issue and proposes potential solutions, including prominent Flashbots. However, previous studies have mostly analyzed blockchain data, which might not capture the impacts of MEV in a much broader AI society. Thus, in this research, we applied natural language processing (NLP) methods to comprehensively analyze topics in tweets on MEV. We collected more than 20000 tweets with #MEV and #Flashbots hashtags and analyzed their topics. Our results show that the tweets discussed profound topics of ethical concern, including security, equity, emotional sentiments, and the desire for solutions to MEV. We also identify the co-movements of MEV activities on blo
As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction act
Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction
Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (
The IC13 dataset contains 561 images: 420 for training and 141 for testing. It inherits data from the IC03 dataset and extends it with new images. Similar to IC03 dataset, the IC13 dataset contains 1,015 cropped text instance images after removing the words with non-alphanumeric characters.
We provide the code to generate base and query vector datasets for similarity search benchmarking and evaluation on high-dimensional vectors stemming from large language models. With the dense passage retriever (DPR) [1], we encode text snippets from the C4 dataset [2] to generate 768-dimensional vectors:
SOEVAL is created by us by mining questions from StackOverflow. Our goal was to create a prompt dataset that reflects the real-life needs of software developers. To build this dataset, we first collected 500 popular and recent questions with Python and Java tags for each. From these 1,000 questions, we applied a set of inclusion and exclusion criteria. The inclusion criteria were: the question has to (1) explicitly ask “how to do X” in Python or Java; (2) include code in its body; (3) have an accepted answer that includes code. We excluded questions that were (1) open-ended and asking for best practices/guidelines for a specific problem in Python/Java; (2) related to finding a specific API/module for a given task; (3) related to errors due to environment configuration (e.g., missing dependency library); (4) related to configuring libraries/API; (5) syntax specific types of questions. By applying the criteria above to these 1K questions, we obtained 28 and 42 prompts for Java and Python
OpenGDA is a benchmark for evaluating graph domain adaptation models. It provides abundant pre-processed and unified datasets for different types of tasks (node, edge, graph). They originate from diverse scenarios, covering web information systems, urban systems and natural systems. Furthermore, it integrates state-of-the-art models with standardized and end-to-end pipelines. Overall, OpenGDA provides a user-friendly, scalable and reproducible benchmark
Replay is a collection of multi-view, multi-modal videos of humans interacting socially. Each scene is filmed in high production quality, from different viewpoints with several static cameras, as well as wearable action cameras, and recorded with a large array of microphones at different positions in the room. The full Replay dataset consists of 68 scenes of social interactions between people, such as playing boarding games, exercising, or unwrapping presents. Each scene is about 5 minutes long and filmed with 12 cameras, static and dynamic. Audio is captured separately by 12 binaural microphones and additional near-range microphones for each actor and for each egocentric video. All sensors are temporally synchronized, undistorted, geometrically calibrated, and color calibrated.
The ARTE database, so far, contains 13 acoustic environments that were recorded with a purpose-built 62-channel microphone array in various locations around Sydney (Australia), and was decoded into the higher-order Ambisonics (HOA) format.
Internet Archive Scholar Reference Dataset.
Problem Statement
This archive contains raw data, intermediate results, statistics, and figures for the manuscript "Naïve individuals promote collective exploration in homing pigeons"
Persian Font Recognition (PFR)
Persian Text Image Segmentation (PTI SEG)
CoverageEval is a dataset specifically designed for evaluating LLMs on this task. To create CoverageEval, we parse the code coverage logs generated during the execution of the test cases. This parsing step enables us to extract the relevant coverage annotations. We then carefully structure and export the dataset in a format that facilitates its use and evaluation by researchers and practitioners alike.
Downloadable zip file containing raw data (simulated and real) as well as model fits / saved parameters.