Machine Learning
This dataset is designed for research on 2D Multi-frequency Electrical Impedance Tomography (mfEIT). It includes:
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Style Transfer Evaluation Benchmark (StyleBench) in submitted paper "StyleShot: A SnapShot on any Style".
To comprehensively evaluate the effectiveness and generalization ability of style transfer methods, we build StyleBench that covers 73 distinct styles, ranging from paintings, flat illustrations, 3D rendering to sculptures with varying materials. For each style, we collect 5-7 distinct images with variations.
In total, our StyleBench contains 490 images across diverse styles.
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During the study period, with the help of 292 participants, we were able to collect 599,635 app usage records. Here, we summarize the main characteristics of the participants based on the submitted surveys. 59% of the participants were female and 50% aged between 25 and 34. Participants were from all kinds of educational backgrounds ranging from high school diploma to PhD. In particular, 32% of them had a college degree, followed by 30% with a bachelor's degree. Smartphone was the main device used for connecting to the Internet for 53% of the participants, followed by laptop (25%).
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The Tsinghua App Usage Dataset is a large-scale mobile application usage dataset collected over one week in one of China’s largest cities. It contains anonymized app usage logs from 1,000 users, capturing detailed information on 2,000 identified apps across 9,800 base stations. Each record includes user ID, timestamp, base station location, app ID, and traffic consumption, allowing for comprehensive analysis of individual and regional mobile usage patterns.
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This data set is related to gas sensing, time-series classification. It has three columns: Time, I1, and Target. The Target column refers to the type of gas measured with the following classification:
•1 for Ammonia (NH₃)
•2 for Acetone (C₃H₆O) & Formaldehyde (CH₂O)
The data has been processed in a number of stages for accuracy and consistency. Raw data was first gathered using a Metal Oxide Semiconductor (MOS) sensor. After acquisition, the data were cleaned and processed, such as Savitzky-Golay filtering to remove noise or artifacts and smooth the signal.
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New technology solutions including tablets and advanced applications exist in modern classrooms across Indonesia but typically they miss the core educational objectives. The process of elementary school children memorizing the emoticon "happy" represents a lack of comprehension while high school students find themselves overwhelmed by IoT data and teachers work to adapt to AI-based educational requirements.
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The Fuyong dataset records 134 stroke patients who received treatment in the Shenzhen Fuyong People's Hospital between March 1, 2022, and September 31, 2024. Besides, medical records of 435 stroke patients treated in the Affiliated Taizhou People's Hospital of Nanjing Medical University between January 1, 2020, and December 31, 2023, are included in the Taizhou dataset. These two datasets use the pre- and post-thrombolysis of the NIHSS scores as a metric for evaluating the immediate efficacy of the thrombolytic intervention.
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This dataset supports the LookCursor AI project, which implements eye-tracking-based cursor control using OpenCV and Dlib. The primary file included is shape_predictor_68_face_landmarks.dat, a pre-trained model used to detect and map 68 facial landmarks essential for tracking eye movements. The dataset enables accurate facial feature detection, which is critical for cursor movement based on eye gaze.
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The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.
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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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