The Experts below are selected from a list of 15 Experts worldwide ranked by ideXlab platform

Darja Fišer - One of the best experts on this subject based on the ideXlab platform.

  • Dataset and baseline model of moderated content FRENK-STYRIA-24sata 1.0
    2018
    Co-Authors: Nikola Ljubešić, Tomaž Erjavec, Darja Fišer
    Abstract:

    FRENK-STYRIA-24sata is a dataset of moderated newspaper comments from the website 24sata.hr with metadata on the time of publishing, user Identifier, Thread Identifier and whether the comment was deleted by the moderators or not. The full text of each comment is encrypted via a character-replacement method so that the comments are not readable by humans. Basic punctuation is not encrypted in order to enable tokenization. The main use of this dataset are experiments on automating comment moderation. For real-world usage, a fastText classification model trained on non-encrypted data is made available as well.

  • Dataset and baseline model of moderated content FRENK-MMC-RTV 1.0
    2018
    Co-Authors: Nikola Ljubešić, Tomaž Erjavec, Darja Fišer
    Abstract:

    FRENK-MMC-RTV is a dataset of moderated newspaper comments from the website rtvslo.si with metadata on the time of publishing, user Identifier, Thread Identifier and whether the comment was deleted by the moderators or not. The full text of each comment is encrypted via a character-replacement method so that the comments are not readable by humans. Basic punctuation is not encrypted in order to enable tokenization. The main use of this dataset are experiments on automating comment moderation. For real-world usage, a fastText classification model trained on non-encrypted data is made available as well.

Nikola Ljubešić - One of the best experts on this subject based on the ideXlab platform.

  • Dataset and baseline model of moderated content FRENK-STYRIA-24sata 1.0
    2018
    Co-Authors: Nikola Ljubešić, Tomaž Erjavec, Darja Fišer
    Abstract:

    FRENK-STYRIA-24sata is a dataset of moderated newspaper comments from the website 24sata.hr with metadata on the time of publishing, user Identifier, Thread Identifier and whether the comment was deleted by the moderators or not. The full text of each comment is encrypted via a character-replacement method so that the comments are not readable by humans. Basic punctuation is not encrypted in order to enable tokenization. The main use of this dataset are experiments on automating comment moderation. For real-world usage, a fastText classification model trained on non-encrypted data is made available as well.

  • Dataset and baseline model of moderated content FRENK-MMC-RTV 1.0
    2018
    Co-Authors: Nikola Ljubešić, Tomaž Erjavec, Darja Fišer
    Abstract:

    FRENK-MMC-RTV is a dataset of moderated newspaper comments from the website rtvslo.si with metadata on the time of publishing, user Identifier, Thread Identifier and whether the comment was deleted by the moderators or not. The full text of each comment is encrypted via a character-replacement method so that the comments are not readable by humans. Basic punctuation is not encrypted in order to enable tokenization. The main use of this dataset are experiments on automating comment moderation. For real-world usage, a fastText classification model trained on non-encrypted data is made available as well.

Tomaž Erjavec - One of the best experts on this subject based on the ideXlab platform.

  • Dataset and baseline model of moderated content FRENK-STYRIA-24sata 1.0
    2018
    Co-Authors: Nikola Ljubešić, Tomaž Erjavec, Darja Fišer
    Abstract:

    FRENK-STYRIA-24sata is a dataset of moderated newspaper comments from the website 24sata.hr with metadata on the time of publishing, user Identifier, Thread Identifier and whether the comment was deleted by the moderators or not. The full text of each comment is encrypted via a character-replacement method so that the comments are not readable by humans. Basic punctuation is not encrypted in order to enable tokenization. The main use of this dataset are experiments on automating comment moderation. For real-world usage, a fastText classification model trained on non-encrypted data is made available as well.

  • Dataset and baseline model of moderated content FRENK-MMC-RTV 1.0
    2018
    Co-Authors: Nikola Ljubešić, Tomaž Erjavec, Darja Fišer
    Abstract:

    FRENK-MMC-RTV is a dataset of moderated newspaper comments from the website rtvslo.si with metadata on the time of publishing, user Identifier, Thread Identifier and whether the comment was deleted by the moderators or not. The full text of each comment is encrypted via a character-replacement method so that the comments are not readable by humans. Basic punctuation is not encrypted in order to enable tokenization. The main use of this dataset are experiments on automating comment moderation. For real-world usage, a fastText classification model trained on non-encrypted data is made available as well.

Yannis Smaragdakis - One of the best experts on this subject based on the ideXlab platform.

  • Portable and Efficient Distributed Threads for Java
    Springer-Verlag, 2004
    Co-Authors: Eli Tilevich, Yannis Smaragdakis
    Abstract:

    Java middleware mechanisms, such as Java RMI or CORBA implementations, do not support Thread coordination over the network: synchronizing on remote objects does not work correctly and Thread identity is not preserved for executions spanning multiple machines. The current approaches dealing with the problem suffer from one of two weaknesses: either they require a new middleware mechanism, making them less portable, or they add overhead to the execution to propagate a Thread Identifier through all method calls. In this paper we present an approach that works with an unmodified middleware implementation, yet does not impose execution overhead. The key to our technique is the bytecode transformation of only stub routines, instead of the entire client application

Eli Tilevich - One of the best experts on this subject based on the ideXlab platform.

  • Portable and Efficient Distributed Threads for Java
    Springer-Verlag, 2004
    Co-Authors: Eli Tilevich, Yannis Smaragdakis
    Abstract:

    Java middleware mechanisms, such as Java RMI or CORBA implementations, do not support Thread coordination over the network: synchronizing on remote objects does not work correctly and Thread identity is not preserved for executions spanning multiple machines. The current approaches dealing with the problem suffer from one of two weaknesses: either they require a new middleware mechanism, making them less portable, or they add overhead to the execution to propagate a Thread Identifier through all method calls. In this paper we present an approach that works with an unmodified middleware implementation, yet does not impose execution overhead. The key to our technique is the bytecode transformation of only stub routines, instead of the entire client application