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Performance models identified at run-time can be used by self-adaptive software systems to execute decisions on a cloud environment. These performance models are built by measuring the control inputs, disturbances, and outputs of the controlled system. These models have been shown to accurately interpolate for data already seen by the model identification method. However, automation in cloud operations can push the environment into operational regions the system has not seen, thus the performance model may not accurately extrapolate into unseen regions.
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We present the SynSUM benchmark, a synthetic dataset linking unstructured clinical notes to structured background variables. The dataset consists of 10,000 artificial patient records containing tabular variables (like symptoms, diagnoses and underlying conditions) and associated clinical notes describing the fictional patient encounter in the domain of respiratory diseases. The tabular portion of the data is generated through a Bayesian network, where both the causal structure between the variables and the conditional probabilities are proposed by an expert based on domain knowledge.
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27 Views
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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211 Views
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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299 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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106 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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177 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