Computational Intelligence

Context: Multiple types of processing units (e.g., CPUs, GPUs and FPGAs) can be used jointly to achieve better performance in computational systems. However, these units are built with fundamentally different characteristics and demand attention especially towards software deployment. 

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Hugo Andrade, Jan Schröder, Ivica Crnkovic

     # of original news:30;# of candidate news:25899;# of reprinted news (no source label):4234 (537)

     

  • Computational Intelligence
  • Last Updated On: 
    Sun, 11/18/2018 - 22:57
    Citation Author(s): 
    Yin Luo, Fangfang Wang, Jun Chen, Lei Wang, Daniel Dajun Zeng

    The whole data set will be published after the acceptance of our paper via the same url as shown in the paper.

     

    When using PackageRank software to analyze our data set, please do not change the name of the .net files.

     

    The .net file has the following format:

    Node count: *Vertices count

     

    Node List:

        number "node name"

    EX:   1    "org.apache.tools.ant.taskdefs.optional.sitraka"

     

    Arc List:

    node1 node2 weight

    EX: 1 2 3

    Meaning: from node 1 to node 2 with weight 3

     

  • Computational Intelligence
  • Other
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Weifeng Pan, Zijiang Yang, Hua Ming, Carl K. Chang, and Bi Chen

    Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintain cellular functions. Recently, it has become evident that metabolism is not only responsible for generating the required energy and controlling the abundance of metabolites within a cell, but also has an important role in and influence on cellular fate specification.

  • Computational Intelligence
  • Biomedical and Health Sciences
  • Cancer Data
  • Last Updated On: 
    Tue, 11/13/2018 - 10:56
    Citation Author(s): 
    Youssef Hamadi, Claudio Angione, Hillel Kugler, Christoph Wintersteiger, Boyan Yordanov

    We provide results of "Robust Visual Tracking Based on Adaptive Extraction and Enhancement of Correlation Filter" on OTB2015 dataset, including the results of proposed tracker with HOG, HOGCN, and deep CNN features.

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

    Vibration data collected from handpump handles in Kwale, Kenya.

  • Computational Intelligence
  • Mechanical Sensing
  • Last Updated On: 
    Mon, 10/08/2018 - 12:39
    Citation Author(s): 
    H. Greeff, A. Manandhar, P. Thomson, R. Hope, D.A. Clifton

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

    D U C 2 0 0 2 dataset (https://www-nlpir.nist.gov/projects/duc/guidelines/2002.html) processed through doc2vec (https://github.com/jhlau/doc2vec) This dataset includes the documents embeddings of the full DUC 2002 in the following configurations:

  • Computational Intelligence
  • Last Updated On: 
    Thu, 11/08/2018 - 10:34
    Citation Author(s): 
    Sandra J. Gutiérrez-Hinojosa, Hiram Calvo, Marco A. Moreno-Armendáriz, Carlos A. Duchanoy

    Our dataset includes three parts: MNIST-rot,  MNIST-scale, and MNIST-rand. MNIST-rot is generated by randomly rotating each sample in the MNIST testing dataset in $[0,2\pi]$. We generated MNIST-scale by randomly scaling the ratio of the area occupied by the symbol over that of the entire image by a factor in $[0.5,1]$, and generated MNIST-rand by scaling and rotating images in MNIST testing dataset simultaneously.

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

    Deep learning belongs to the scope of artificial intelligence, which has attracted researchers all over the world. The CNN is one the most popular techniques of deep learning. In this paper, the WLAN (wireless local area network) localization is given by SVD (singular value decomposition) noise-reduction and CNN (convolutional neural network).

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

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