Machine Learning
This data set is the result of model test trained on the basis of the Stanford earthquake dataset (stead): a global data set of seismic signals for AI, which can effectively get the seismic signal and the arrival time of seismic phase from the image, so as to prove the effectiveness of this model
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Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Dataset is a Dataset composed of Hardware Performance Counters extracted from a Multicore Real-Time Embedded System. This Dataset encompasses every Monitorable Performance counters in a Cortex-A53 quad-core processor, totaling 54 performance counters, which are sampled periodically through a non-Intrusive Monitoring Framework implemented over Embedded Parallel Operating System (EPOS), a Real-Time Operating System.
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The current dataset – crowdbot – presents outdoor pedestrian tracking from onboard sensors on a personal mobility robot navigating in crowds. The robot Qolo, a personal mobility vehicle for people with lower-body impairments was equipped with a reactive navigation control operating in shared-control or autonomous mode when navigating on three different streets of the city of Lausanne, Switzerland during farmer’s market days and Christmas market. Full Dataset here: DOI:10.21227/ak77-d722
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India is known for its highly disciplined foreign policies, strategic location, vibrant and massive Diaspora. India envisages enhancing its scope of cooperation, trade and widens its sphere of relations with the Pacific. As a result, the world is witnessing the rise of Indo-Pacific ties. Before the 1980’s the keystone of the universe was called the Atlantic, but now a radical shift to the east is noticed by the term “Indo-Pacific‟.
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YonseiStressImageDatabase is a database built for image-based stress recognition research. We designed an experimental scenario consisting of steps that cause or do not cause stress; Native Language Script Reading, Native Language Interview, Non-native Language Script Reading, Non-native Language Interview. And during the experiment, the subjects were photographed with Kinect v2. We cannot disclose the original image due to privacy issues, so we release feature maps obtained by passing through the network.
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Accurate and efficient anomaly detection is a key enabler for the cognitive management of optical networks, but traditional anomaly detection algorithms are computationally complex and do not scale well with the amount of monitoring data. Therefore, this dataset enables research on new optical spectrum anomaly detection schemes that exploit computer vision and deep unsupervised learning to perform optical network monitoring relying only on constellation diagrams of received signals.
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The purpose of this data collection was for the validation of a cuffless blood pressure estimation model during activities of daily living. Data were collected on five young healthy individuals (four males, age 28 ± 6.6 yrs) of varied fitness levels, ranging from sedentary to regularly active, and free of cardiovascular and peripheral vascular disease. Arterial blood pressure was continuously measured using finger PPG (Portapres; Finapres Medical Systems, the Netherlands).
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The data-set used in the paper titled "Short-Term Load Forecasting Using an LSTM Neural Network."
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