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This dataset contains detailed player end-game statistics (e.g., number of kills) and in-game events (e.g., player kills) from all professional League of Legends matches held between September 15, 2019, and September 15, 2024. It encompasses a total of 37,388 matches across 392 tournaments, featuring 4,927 unique players. Matches come from all regions and tiers of play.
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This dataset is used for the automated assessment of open-ended exam questions in the online course Introduction to Software Engineering at Constantine the Philosopher University in Nitra. The dataset originates from the Moodle Learning Management System (LMS) and includes responses to eight open-ended questions centered on fundamental terminology related to the Scrum framework, a key methodology in agile software development.
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This dataset was developed using the MOBATSim simulator in MATLAB 2020b, designed to mimic real-world autonomous vehicle (AV) environments. It focuses on providing high-quality data for research in anomaly detection and cybersecurity, particularly addressing False Data Injection Attacks (FDIA). The dataset includes comprehensive sensor information, such as speed, rotational movements, positional coordinates, and labelled attack data, enabling supervised learning.
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277 Views
- The dataset consists of feature vectors belonging to 12,330 sessions. The dataset was formed so that each session would belong to a different user in a 1-year period to avoid any tendency to a specific campaign, special day, user profile, or period.
- Of the 12,330 sessions in the dataset, 84.5% (10,422) were negative class samples that did not end with shopping, and the rest (1908) were positive class samples ending with shopping.
- The dataset consists of 10 numerical and 8 categorical attributes. The 'Revenue' attribute can be used as the class label.
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92 Views
Currently, existing public datasets based on peripheral physiological signals are limited, and there is a lack of emotion recognition (ER) datasets specifically customized for smart classroom scenarios. Therefore, we have collected and constructed the I+ Lab Emotion (ILEmo) dataset, which is specifically designed for the emotion monitoring of students in classroom. The raw data of the ILEmo dataset is collected by the I+ Lab at Shandong University, using custom multi-modal wristbands and computing suites.
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176 Views
The SDUITC database is a multi-modal resourse developed at the Shandong Cooperative Vehicle-Infrastructure Test Base, which uses roadside cameras and LiDAR to monitor road targets and collect point cloud information. Following ground segmentation (target point cloud extraction), target identification and tracking, and feature extraction, the target point cloud information is refined and summarized into the following content: 1. Video snapshot of the captured target; 2. Point cloud clustering information for the target; 3. Feature tables.
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101 Views
This dataset provides turbidity measurements collected during a Moringa oleifera leaf water treatment process for compound extraction. The extraction process was conducted over a 15-minute duration, capturing key changes in turbidity to reflect the dynamics of the process. The raw data has been preprocessed, upsampled, and annotated for time series analysis, enabling detailed investigation of extraction patterns. Additionally, the dataset has been optimized using the ForGAN (Forecasting GAN) algorithm to enhance data granularity and support predictive modeling.
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32 Views
In-vehicle networks are responsible for safety-critical control applications, depending on data communication between electronic control units, and most are based on the CAN protocol. A huge amount of data is necessary for reliability, safety, and cybersecurity analysis in today's automotive solutions, especially to feed machine learning models. It is relevant to provide comprehensive datasets about CAN communication and different driving situations, which represents a lack in recent research because most public datasets are very limited.
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40 Views