Machine Learning

iSignDB: A biometric signature database created using smartphone

Suraiya Jabin, Sumaiya Ahmad, Sarthak Mishra, and Farhana Javed Zareen

Department of Computer Science, Jamia Millia Islamia, New Delhi-110025, India

It's a database of biometric signatures recorded using sensors present in a smartphone. ​The dataset iSignDB is created to implement a novel anti-spoof biometric signature authentication for smartphone users.

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  • Artificial Intelligence
  • Last Updated On: 
    Tue, 05/26/2020 - 12:38

     

     

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  • Machine Learning
  • Last Updated On: 
    Mon, 04/20/2020 - 09:40

    Wine has been popular with the public for centuries; in the market, there are a variety of wines to choose from. Among all, Bordeaux, France, is considered as the most famous wine region in the world. In this paper, we try to understand Bordeaux wines made in the 21st century through Wineinformatics study. We developed and studied two datasets: the first dataset is all the Bordeaux wine from 2000 to 2016; and the second one is all wines listed in a famous collection of Bordeaux wines, 1855 Bordeaux Wine Official Classification, from 2000 to 2016.

    157 views
  • Artificial Intelligence
  • Last Updated On: 
    Thu, 04/30/2020 - 16:14

     

    Intending to cover the existing gap regarding behavioral datasets modelling interactions of users with individual a multiple devices in Smart Office to later authenticate them continuously, we publish the following collection of datasets, which has been generated after having five users interacting for 60 days with their personal computer and mobile devices. Below you can find a brief description of each dataset.

     

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  • Artificial Intelligence
  • Last Updated On: 
    Fri, 04/17/2020 - 19:38

    Stable and efficient walking strategies for humanoid robots usually relies on assumptions regarding terrain characteristics. If the robot is able to classify the ground type at the footstep moment, it is possible to take preventive actions to avoid falls and to reduce energy consumption. 

    This dataset contains raw data from 10 inertial and torque sensors of a humanoid robot, sampled after the impact between foot and ground. There are two types of data: simulated using gazebo and data from a real robot.

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  • Artificial Intelligence
  • Last Updated On: 
    Sat, 04/18/2020 - 15:55

    [17-APR-2020: WE ARE STILL UPLOADING THE DATASET, PLEASE WAIT UNTIL IT IS COMPLETED] -The dataset comprises a set of 11 different actions performed by 17 subjects that is created for multimodal fall detection. Five types of falls and six daily activities were considered in the experiment. Data collection comes from five wearable sensors, one brainwave helmet sensor, six infrared sensors around the room and two RGB-cameras. Three attempts per action were recorded. The dataset contains raw signals as well as three windowing-based feature sets.

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  • Artificial Intelligence
  • Last Updated On: 
    Mon, 05/11/2020 - 20:12

    While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election.

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  • Machine Learning
  • Last Updated On: 
    Wed, 04/15/2020 - 04:01

    Extracting the boundaries of Photovoltaic (PV) plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, Operation and Maintenance (O&M) service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. 

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  • Artificial Intelligence
  • Last Updated On: 
    Sat, 05/30/2020 - 14:15

    This work develops a novel power control framework for energy-efficient powercontrol in wireless networks. The proposed method is a new branch-and-boundprocedure based on problem-specific bounds for energy-efficiency maximizationthat allow for faster convergence. This enables to find the global solution forall of the most common energy-efficient power control problems with acomplexity that, although still exponential in the number of variables, is muchlower than other available global optimization frameworks.

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  • Machine Learning
  • Last Updated On: 
    Fri, 07/10/2020 - 04:25

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  • Machine Learning
  • Last Updated On: 
    Wed, 04/08/2020 - 10:35

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