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Award ID contains: 1828363

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  1. ; (, Proc. of the IEEE Conference on Communications and Network Security)
    Full Text Available 
  2. ; ; ; ; ; (, Journal of Network and Computer Applications)
    Full Text Available 
  3. ; (, IEEE Transactions on Dependable and Secure Computing)
    Full Text Available 
  4. ; ; ; ; ; (, Journal of Network and Computer Applications)
    Accepted Manuscript Full Text Available
  5. ; (, IEEE International Conference on Networking, Architecture and Storage)
    Full Text Available 
  6. (, Proc. Of the 15th IEEE International Conference on Networking, Architecture, and Storage (NAS))
    null (Ed.)
    As the storage overhead of high-performance computing (HPC) data reaches into the petabyte or even exabyte scale, it could be useful to find new methods of compressing such data. The compression autoencoder (CAE) has recently been proposed to compress HPC data with a very high compression ratio; however, this machine learning-based method suffers from the major drawback of lengthy training times. In this paper, we attempt to mitigate this problem by proposing a proportioning scheme that reduces the amount of data that is used for training relative to the amount of data to be compressed. We show that this method drastically reduces the training time without, in most cases, significantly increasing the error. We further explain how this scheme can even improve the accuracy of the CAE on certain datasets. Finally, we provide some guidance on how to determine a suitable proportion of the training dataset to use in order to train the CAE for a given dataset. 
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    Accepted Manuscript Full Text Available
  7. (, USENIX Security 2021)
    null (Ed.)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, IEEE Internet of Things Journal)
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  9. ; ; (, in Proc. of the IEEE ICC 2021, Montreal, Canada)
    null (Ed.)
    Accepted Manuscript Full Text Available
  10. ; ; ; (, Network and Distributed Systems Security (NDSS) Symposium 2021)
    null (Ed.)
    Full Text Available