Computational Intelligence

The database contains PROCON metrics values extracted from more than 30400 source code files (with 14950 bug reports) of GitHub repository.  Various Machine earning (ML) models trained using PROCON metrics outperform the ones trained using OO metrics of PROMISE repository.

  • Reliability
  • Computational Intelligence
  • Last Updated On: 
    Tue, 03/05/2019 - 06:02
    Citation Author(s): 
    Ritu Kapur, Balwinder Sodhi

    This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.

    The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.

  • Computational Intelligence
  • Transportation
  • Wearable Sensing
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Hristijan Gjoreski, Mathias Ciliberto, Lin Wang, Francisco Javier Ordoñez Morales, Sami Mekki, Stefan Valentin, Daniel Roggen

    To investigate the generalization performance of the evolved scheduling policies(SPs), which are generated by the hyper-heuristic coevolution, the evolutionary SPs extracted from the aggerate Pareto front were applied to 64 testing scenarios to compare with the combinations of 320 existing man-made SPs which include 32 job sequencing rules and 10 machine assignment rules. This dataset provides the simulation performance of the evolved SPs and the 320 existing man-made SPs on the multi-objective dynamic flexible job shop scheduling problem.

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Yong Zhou

    The experimental dataset involves the interval values for the bed slope (m/m) measured with a theodolite, the river flow rate (m^3/s), and the average surface velocity (m/s) from the Ter River (Spain).

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Remei Cal, Miguel Sainz, Josep Vehi

    There are no explicit labelled errors in FB15K. Considering the experience that most errors in real-world KG derive from the misunderstanding between similar entities, we consider the methods described in paper "DoesWilliamShakespeareREALLYWrite Hamlet? Knowledge Representation Learning with Confidence" to generate fake triples as negative examples automatically with less human annotation. Three kinds of fake triples may be constructed for each true triple: one by replacing head entity, one by replacing relationship, and one by replacing tail entity.

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Shengbin JIA

    The simplicity of deployment and perpetual operation of energy harvesting devices provides a compelling proposition for a new class of edge devices for the Internet of Things. In particular, Computational Radio Frequency Identification (CRFID) devices are an emerging class of battery-free, computational, sensing enhanced devices that harvest all of their energy for operation.

  • Computational Intelligence
  • Last Updated On: 
    Sun, 07/29/2018 - 04:10
    Citation Author(s): 
    Yang Su, YansongGao, Michael Chesser, Omid Kavehei, Alanson Sample and Damith C. Ranasinghe

    The goal of the benchmark test library is to offer a set of tests which are wide in feature coverage, progressive and stable. It serves the purpose of evaluating the strength and weakness of matchers (by being progressive and wide coverage) and measuring the progress of matchers (by being stable and reusable over the years).

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Ontology Alignment Evaluation Initiative Team

    test

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Kun-Chou Lee

    This is the code and data for IS-RNN validation and research

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Fei Wang

    Dataset used in paper "Machine Learning Cryptanalysis of a Quantum Random Number Generator" published at IEEE TIFS.

     

  • Computational Intelligence
  • Security
  • Last Updated On: 
    Mon, 05/13/2019 - 20:16
    Citation Author(s): 
    Nhan Duy Truong, Jing Yan Haw

    Pages