Machine Learning

A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC).

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Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems.

Last Updated On: 
Wed, 02/19/2025 - 11:11

EEG signals in the NMT-5k dataset were captured using a 21-channel setup following the 10/20 electrode placement system, maintaining a sampling rate of 200 Hz. Data collection was conducted using the KT88-2400 system from Contec Medical Systems.

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The proposed method is rigorously evaluated against several state-of-the-art algorithms, including ISACITD3IPPO, and IDDPG, to ensure a comprehensive performance analysis. The experimental data, which is publicly available [here], provides detailed insights into the training and evaluation processes of each algorithm.

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Dataset Description

This dataset is designed for analyzing and predicting comeback victories in Multiplayer Online Battle Arena (MOBA) games. It is derived from match data where an objective bounty mechanism was active, providing features that highlight differences between teams with and without the bounty advantage. The dataset is ideal for machine learning tasks, such as binary classification and feature importance analysis, and it enables researchers and analysts to explore factors influencing comeback scenarios in competitive gaming.

Dataset Contents: 

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Comprehensive dataset (5000 spectra) of simulated grating biosensor reflections in Excel format. Generated via Lumerical FDTD, it includes 11 parameters (thickness, RI, peak wavelength, FWHM, reflectance, etc.). It is ideal for data visualization, sensor response exploration, and AI/ML benchmarking. The full dataset in Excel format is coming soon! Follow this repository to be notified when it's released. In the meantime, feel free to browse the README for more information about the project.

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DLSF is the first dedicated dataset for Text-Image Synchronization Forgery (TISF) in multimodal media. The source data for this dataset is scraped from the Chinese news aggregation platform, Toutiao. This dataset includes extensive text, image, and audio-video data from news articles involving politicians and celebrities, featuring samples of both entity-level and attribute-level TISF. It provides comprehensive annotations, including labels for text-image authenticity, types of TISF, image forgery regions, and text forgery tokens.

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This dataset provides a comprehensive record of wind power generation and its relationship with oceanic-atmospheric indices, facilitating advanced forecasting and analytical research in renewable energy. The dataset comprises 12 input parameters, including average wind speed, which serves as a crucial predictor, while wind power generation acts as the output variable.

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The recent developments in the field of the Internet of Things (IoT) bring alongside them quite a few advantages. Examples include real-time condition monitoring, remote control and operation and sometimes even remote fault remediation. Still, despite bringing invaluable benefits, IoT-enriched entities inherently suffer from security and privacy issues. This is partially due to the utilization of insecure communication protocols such as the Open Charge Point Protocol (OCPP) 1.6. OCPP 1.6 is an application-layer communication protocol used for managing electric vehicle chargers.

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This dataset provides measurements of cerebral blood flow using Radio Frequency (RF) sensors operating in the Ultra-Wideband (UWB) frequency range, enabling non-invasive monitoring of cerebral hemodynamics. It includes blood flow feature data from two arterial networks, Arterial Network A and Arterial Network B. Statistical features were manually extracted from the RF sensor data, while autonomous feature extraction was performed using a Stacked Autoencoder (SAE) with architectures such as 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128.

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