Machine Learning
# RSS data from smartwatch for Contact Tracing
This dataset was collected for the purpose to understand the proximity between any two smartwatches worn by human.
We used the Google's Wear OS based smartwatch, powered by a Qualcomm Snapdragon Wear 3100 processor, from Fossil sport to collect the data.
The smartwatch is powered by a Qualcomm Snapdragon Wear 3100 processor and has an internal memory of up to 1GB.
Two volunteers were required to wear the smartwatch on different hand and stand at a certain distance from each other.
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A Dataset Bundle for Building Automation and Control Systems
useful for Security Analysis and to study the normal operation of these systems
This document describes a dataset bundle with diverse types of attacks, and also a not poisoned dataset. The capture was obtained in a real house with a complete Building Automation and Control System (BACS). This document describes the several included datasets and how their data can be employed in security analysis of KNX based building Automation.
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This is dataset from shopee, bukalapak, and tokopedia which is used to classify whether a seller is a drop shipper or not.
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This document shows all the original data used in our paper.
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This is the First Arabic voice Commands Dataset to provide personalized control of devices at smart homes for elder persons and persons with disabilities. The dataset contains 12 speakers, each saying 36 different phrases or words in Arabic language. The goal of this dataset is to use it in an Arabic smart home system to control home devices through voice. Participants were asked to say each phrase multiple times. The phrases to record were presented in a random order.
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The proliferation of efficient edge computing has enabled a paradigm shift of how we monitor and interpret urban air quality. Coupled with the dense spatiotemporal resolution realized from large-scale wireless sensor networks, we can achieve highly accurate realtime local inference of airborne pollutants. In this paper, we introduce a novel Deep Neural Network architecture targeted at latent time-series regression tasks from continuous, exogenous sensor measurements, based on the Transformer encoder scheme and designed for deployment on low-cost power-efficient edge processors.
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This is a protein negative interaction dataset, generated by our proposed method the “Features Dissimilarity-based Negative Generation” approach to generate protein negative sampling based on sequence data. It measures similarity of sequence characteristics without alignment based on Protein similarity. It achieved results of 97% compared to randomly generated negative dataset.
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The dataset represents the input data on which the article Bayesian CNN-BiLSTM and Vine-GMCM Based Probabilistic Forecasting of Hour-Ahead Wind Farm Power Outputs, is based. The data consist of a two-year hourly time series of measured wind speed and direction, air density, and production of two wind farms (WTs) in Croatia (Bruška and Jelinak). In addition to the two listed WTs, measurements of two nearby WTs (Glunca and Zelengrad) are also attached in training files (these WPPs are not directly analyzed in the article).
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