298 machine learning datasets
298 dataset results
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
The code that created this dataset can be seen in https://github.com/nitzanfarhi/SecurityPatchDetection and can be reproduced by running: console python data_collection\create_dataset.py --all -o data_collection\data Notice that this dataset doesn't include the commits' generated data as it is very big. This can be generated by running only : console python data_collection\create_dataset.py --commits -data_collection\data
Overview: This dataset encompasses a compilation of 6,700 executed scoops (excavations), mapped across a vast spectrum of materials, terrain topography, and compositions.
This dataset contains information on application install interactions of users in the Myket android application market. The dataset was created for the purpose of evaluating interaction prediction models, requiring user and item identifiers along with timestamps of the interactions. Hence, the dataset can be used for interaction prediction and building a recommendation system. Furthermore, the data forms a dynamic network of interactions, and we can also perform network representation learning on the nodes in the network, which are users and applications.
The Automated Surface Observing Systems (ASOS) program is a joint effort of the National Weather Service (NWS), the Federal Aviation Administration (FAA), and the Department of Defense (DOD). These automated systems collect observations on a continual basis, 24 hours a day.
All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The duration of the measurement was 117 seconds. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analysing the video frames. '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological order with the first measured value at the top of the data.
Continuous EEG activity was recorded from each member of the dyad using an ActiveTwo head cap and the ActiveTwo Biosemi system (BioSemi, Amsterdam, Netherlands). Recordings were collected from 64 Ag-AgCl scalp electrodes and from bilateral mastoids. Two electrodes were placed next to each other 1 cm below the right eye to record eye-blink responses. A ground electrode was established by BioSemi’s common Mode Sense active electrode and Driven Right Leg passive electrode. EEG activity was digitized with ActiView software (BioSemi) and sampled at 2048 Hz. Data was downsampled post-acquisition and analyzed at 512 Hz.
In this dataset an uppertorso humanoid robot with 7-DOF arm explored 100 different objects belonging to 20 different categories using 10 behaviors: Look, Crush, Grasp, Hold, Lift, Drop, Poke, Push, Shake and Tap.
This dataset contains the ground truth for urban changes occurred in Mariupol, Ukraine for the time frame 2017-2020. This is useful for transferring the urban change monitoring network ERCNN-DRS (https://github.com/It4innovations/ERCNN-DRS_urban_change_monitoring) to that region.
The CapMIT1003 database contains captions and clicks collected for images from the MIT1003 database, for which reference eye scanpath are available. The database is distributed as a single SQLite3 database named capmit1003.db. For convenience, a lightweight Python class to access the database is provided in the official repository
The primary data of the SaGA corpus are made up of 25 dialogs of interlocutors (50), who engage in a spatial communication task combining direction-giving and sight description. Six of those dialogues with data only from the direction giver are available including audio (.wav) and video (.mp4) data. The secondary data consists of annotations (*.eaf) of gestures and speech-gesture referents, which have been completely and systematically annotated based on an annotation grid (cf. the SaGA documentation). The corpus is comprised of of 9881 isolated words and 1764 isolated gestures. The stimulus is a model of a town presented in a Virtual Reality (VR) environment. Upon finishing a "bus ride" through the VR town along five landmarks, a router explained the route as well as the wayside landmarks to an unknown and naive follower. The SaGA Corpus was curated for CLARIN as part of the Curation Project "Editing and Integration of Multimodal Resources in CLARIN-D" by the CLARIN-D Working Group 6
Studying how human drivers react differently when following autonomous vehicles (AV) vs. human-driven vehicles (HV) is critical for mixed traffic flow. This dataset contains extracted and enhanced two categories of car-following data, HV-following-AV (H-A) and HV-following-HV (H-H), from the open Lyft level-5 dataset.
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The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.
A dataset composed of 308 medium sized fires from the years 2018-2021, complete with both time series airborne based inference and ground operational estimation of fire extent, and operational mitigation data such as control line construction.
The dataset comprises motion sensor data of 19 daily and sports activities each performed by 8 subjects in their own style for 5 minutes. Five Xsens MTx units are used on the torso, arms, and legs.
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Hand-labelled dataset of crop and non-crop labels distributed throughout Nigeria with respective hd5f data arrays.
MOSAD (Mobile Sensing Human Activity Data Set) is a multi-modal, annotated time series (TS) data set that contains 14 recordings of 9 triaxial smartphone sensor measurements (126 TS) from 6 human subjects performing (in part) 3 motion sequences in different locations. The aim of the data set is to facilitate the study of human behaviour and the design of TS data mining technology to separate individual activities using low-cost sensors in wearable devices.