Machine Learning

This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. In the ‘5D_Data_Extractor.py’ file this can be set up to 6-Dimension, by the ‘fields_included’ variable. Two folders are included, one ready for preprocessing (‘subjects raw’) and the other already preprocessed ‘subjects preprocessed’.

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  • Machine Learning
  • Last Updated On: 
    Fri, 05/29/2020 - 16:24

    The ship motion data collected in this paper is actually measured when the ship is sailing at sea. The heave displacement is measured by a laser ranging sensor. The roll and pitch angles are collected by an electronic inclinometer. The sampling time of the data set exceeds 1000s, the collection interval is 0.176s, the collection frequency is 6 times per second, and there are more than 6000 sets of data in total.

    21 views
  • Machine Learning
  • Last Updated On: 
    Sun, 04/05/2020 - 13:37

    We develop a general group-based continuous-time Markov epidemic model (GgroupEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic epidemic spreading over large complex networks while being informative about the state of groups.

    50 views
  • Machine Learning
  • Last Updated On: 
    Sun, 04/05/2020 - 10:53

    This dataset is associated with the paper entitled "DeepWiPHY: Deep Learning-Based IEEE 802.11ax Receiver". It has synthetic and real-word IEEE 802.11ax OFDM symbols. The synthetic dataset has around 110 million OFDM symbols and the real-world dataset has more than 14 million OFDM symbols. Our comprehensive synthetic dataset has specifically considered typical indoor channel models and RF impairments. The real-world dataset was collected under a wide range of signal-to-noise ratio (SNR) levels and at various locations in a large office. To create this real-world dataset, we built a passive

    150 views
  • Artificial Intelligence
  • Last Updated On: 
    Sat, 04/11/2020 - 01:38

    Machine learning offers a wide range of techniques to predict medicine expenditures using historical expenditures data as well as other healthcare variables. For example, researchers have developed multi-layer perceptron (MLP), long-short term memory (LSTM), and convolutional neural networks (CNN) for predicting healthcare outcomes. However, recently proposed generative approaches (e.g., generative adversarial networks; GANs) are yet to be explored for time-series prediction of medicine-related expenditures.

    93 views
  • Machine Learning
  • Last Updated On: 
    Tue, 03/31/2020 - 15:04

    This dataset is a collection of images and their respective labels containing examples of multiple Brazilian coins, the primary purpose is to support the development of Computer Vision techniques for automatic detection of such objects, i.e., localization and classification tasks. 

    209 views
  • Computer Vision
  • Last Updated On: 
    Wed, 05/06/2020 - 11:13

    Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso system, which automatically generates PICO-based evidence summaries from medical literature.

    17 views
  • Machine Learning
  • Last Updated On: 
    Sun, 03/29/2020 - 04:54

    Tactile perception of the material properties in real-time using tiny embedded systems is a challenging task and of grave importance for dexterous object manipulation such as robotics, prosthetics and augmented reality [1-4] . As the psychophysical dimensions of the material properties cover a wide range of percepts, embedded tactile perception systems require efficient signal feature extraction and classification techniques to process signals collected by tactile sensors in real-time.

    363 views
  • Machine Learning
  • Last Updated On: 
    Mon, 05/18/2020 - 20:10

    The dataset consists of two populations of fetuses: 160 healthy and 102 Late Intra Uterine Growth Restricted (IUGR). Late IUGR is an adverse pathological condition encompassing chronic hypoxia as a consequence of placental insufficiency, resulting in an abnormal rate of fetal growth. In standard clinical practice, Late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This data collection comprises of a set of 31 Fetal Heart Rate (FHR) indices computed at different time scales and domains accompanied by the clinical diagnosis.

    309 views
  • Machine Learning
  • Last Updated On: 
    Tue, 03/24/2020 - 21:46

    Cyber-Physical Production Systems (CPPS) are the key enabling for industrial businesses and economic growth. The introduction of the Internet of Things (IoT) in industrial processes represents a new Internet revolution, mostly known as 4th Industrial Revolution, towards the Smart Manufacturing concept. Despite the huge interest from the industry side to innovate their production systems, in order to increase revenues at lower costs, the IoT concept is still immature and fuzzy, which increases security related risks in industrial systems.

    527 views
  • IoT
  • Last Updated On: 
    Wed, 05/06/2020 - 11:13

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