*.csv (zip); *.json (zip); *.pickle (zip); *.npz (zip);
Surface electromyography (EMG) can be used to interact with and control robotic systems via intent recognition. However, most machine learning algorithms used to decode EMG signals have been trained on relatively small datasets with limited subjects, which can affect their widespread generalization across different users and activities. Motivated by these limitations, we developed EMGNet - a large-scale dataset to support research and development in EMG neural decoding, with an emphasis on human locomotion.
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This dataset is the outcome of an observation on Millet traits under seed coating and covering. For covering we rely on Germination Percentage (FGP), Germination Index (GI),Mean Germination Time (MGT), Seedling Length( SL) and Seedling Vigour Index (SVI) and Abnormal Seedling have been measured. Moreover, different enzyme levels including catalase, peroxidase, and Malondialdehyde (MDA) are measured.
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In this study, an equatorial telescope with an aperture of 310 mm, which will be installed in Antarctica in 2024, is chosen as the research subject. The Hour angle that the telescope pointing at is in the range of t[0, 360], and that for the declination axis is [-90, 30].The dataset contains around 3,000 images. The overall workflow is to collect images of the telescope in various poses and then collect two of each pose of the telescope from the TCS side of the telescope
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QuaN is a collection of specially designed datasets for exploring the impact of noise quantum machine learning and other applications. The presented work focuses on the transformation of clean datasets into noisy counterparts across diverse domains, including MNIST-handwritten digits datasets, Medical MNIST, IRIS datasets and Mobile Health datasets. The dataset is created using noise from classical and quantum domains.
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Simulated dataset for deriving parametric constraints for Bayesian Knowedge Tracing. The classical Expectation-Maximization method results in degenerate parameters (i.e., parameters that violate the conceptual interpretation of the model, such as by saying that a learner with no knowledge of a skill is more likely to get an answer correct than a learner with knowledge). A novel approach based on Newton's method rescues these paramters using mathematically derived constraints on the parameter space.
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This research studies the stance classification task of parliamentary debates with the aims to analyse how parliamentarians argue on different debate topic, what is their political stance, and the impact of homophily with respect to their party affiliation. A state-level Australian Hansard data is collected focusing on debates related to obesity and food marketing policies in Australia. It covers 6 states and 1 territory (NT is excluded) from the period 1/1/2000 to 1/1/ 2022.
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Visual storytelling refers to the manner of describing a set of images rather than a single image, also known as multi-image captioning. Visual Storytelling Task (VST) takes a set of images as input and aims to generate a coherent story relevant to the input images. In this dataset, we bridge the gap and present a new dataset for expressive and coherent story creation. We present the Sequential Storytelling Image Dataset (SSID), consisting of open-source video frames accompanied by story-like annotations.
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Soft robots are a promising area of research due to their potential use in various applications. Learning the kinematics of soft robots is crucial for their advancement and application. This dataset is designed to provide training data for the development of machine learning models that can learn the kinematics of soft robots with different actuation types. The dataset includes the positional data of three soft robots, specifically the simulated pneumatic soft robot, simulated tendon-driven soft robot, and real-world tendon-driven soft robot.
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