Machine Learning
each application has up to two files. One for memory dataset and another for control flow dataset. Each dataset is composed of JSON objects. Each instruction is a JSON object.
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eLearning, or online learning, has reached every corner of the globe in this era of digitization. As a result of the COVID-19 pandemic, the value of eLearning has increased substantially. In eLearning recommendation systems, information overload, personalised suggestion, sparsity, and accuracy are all major problems. The correct eLearning Recommendation System is necessary to tailor the course recommendation according to the user's needs. To create this model, dataset of the User Profile and User Rating is needed.
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BillionCOV is a global billion-scale English-language COVID-19 tweets dataset with more than 1.4 billion tweets originating from 240 countries and territories between October 2019 and April 2022. This dataset has been curated by hydrating the 2 billion tweets present in COV19Tweets.
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CAD-EdgeTune dataset is acquired using a Husarion ROSbot 2.0 and ROSbot 2.0 Pro with the collection speed set to 5 frames per second from a suburban university environment. We may split the information into subgroups for noon, dusk, and dawn in order to depict our surroundings under various lighting situations. We have assembled 17 sequences totaling 8080 frames, of which 1619 have been manually analyzed using an open-source pixel annotation program. Since nearby photographs are highly similar to one another, we decide to annotate every five images.
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This dataset aims to identify the polarity of tweets—whether they are supportive, oppositional, or neutral—towards the current government. It comprises a total of 26,000 tweets: 15,000 in English and 11,000 in Urdu. These tweets were collected from 80 different political users' accounts to ensure a diverse and comprehensive representation of opinions.
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We collected data to train the ML module to determine the user’s device's location based on beacon frame characteristics and RSSI values from Wi-Fi APs. To collect the data, we defined a threshold distance of 7 feet as the maximum allowable distance between the user’s devices. We then collected two datasets: one with data collected while the two Raspberry Pis were within 7 feet or less of each other named ”authentic”, and another with data collected while the distance between the two Raspberry Pis was over 7 feet named ”unauthorized”.
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Here is the most fresh dataset used for clustering in TOM.
It contains time series for 1659 GitHub repos of the metrics.
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Sample data set
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42 stimulus pictures are presented separately on the screen in the same sequences for all participants, including landscapes, people, social scenes and composite pictures. The eye tracker records the participants' gaze data on the stimulus pictures. Based on the gaze fixation position and duration, the fixation map could be visualized. We applies a 2-d convolution with a gauss filter on the fixation maps to get the visual heatmaps. The participants consist of schizophrenic patients and healthy controls.
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