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Physiological database including 7 pairs of facial surface EMG (left/right frontalis, left/right zygomaticus, left/right orbicularis and centre corrugator) muscles, forehead PPG, IMU (Accelerometer, Gyroscope, Magnetometer)  and continuous self-ratings of arousal and valence while watching video stimuli. 37 participants watched an average of 23 minutes worth of videos. This included blocks of positive, negative and neutral segments. Each segment included 10 videos, 30 seconds long each. Relaxation video was played between each segment.

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Grasp intention recognition is a vital problem for controlling assistive robots to help the elderly and infirm people restore arm and hand function. This dataset contains gaze data and scene image data of healthy individuals and hemiplegic patients while performing different grasping tasks. It can be used for gaze-based grasp intention recognition studies.

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This dataset was used in the implementation of a machine learning classification methodology to discriminate Fog computing applications as a function of their QoS requirements.

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Supplementary material to the article "Improving the teaching of real-world software practices by means of course integration"

 

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Since the longitudes and latitudes of the drivers in the Gaia dataset are mainly in city of Chengdu, it is not in the same area as the longitudes and latitudes in the EUA-dataset from Australia, we translate the latitudes and longitudes of drivers to Melbourne, Australia.The drivers will be located around the users and the base stations of the Melbourne subset of EUA-dataset. 

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This dataset (MegaGeoCOV Extended), which is an extended version of MegaGeoCOV, was introduced in this paper: A Twitter narrative of the COVID-19 pandemic in Australia (the paper will appear in proceedings of the 20th ISCRAM conference, Omaha, Nebraska, USA May 2023). Please refer to the paper for more details (e.g., keywords and hashtags used, descriptive statistics, etc.).

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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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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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(a) Refractive index and (b) extinction coefficient spectra of Ge2Sb2Te3S2 (GSTS) measured by spectroscopic ellipsometry in the wavelength range from 200 nm to 2500 nm.

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The dataset is generated from the ice-cream factory simulation environmen that is composed of six modules (Mixer, Pasteurizer, Homogenizer, Aeging Cooling, Dynamic Freezer, and Hardening). The values of analog sensors for level and temperature are modified using three anomaly injection options: freezing value, step change and ramp change. The dataset is composed of 1000 runs, out of which 258 were executed without anomalies.

Link to github: https://github.com/vujicictijana/MIDAS

 

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