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ItemTrends in Detection and Characterization of Propaganda Bots( 2019-01-08)Since the revelations of interference in the 2016 US Presidential elections, the UK’s Brexit referendum, the Catalan independence vote in 2017 and numerous other major political discussions by malicious online actors and propaganda bots, there has been increasing interest in understanding how to detect and characterize such threats.We focus on some of the recent research in algorithms for detection of propaganda botnets and metrics by which their impact can be measured
ItemSiMoNa: A Proof-of-concept Domain-Specific Modeling Language for IoT Infographics( 2019-01-08)The Internet of Things (IoT)is a prominent concept in academic and technology business discourse in recent times reflecting a wider trend to connect physical objects to the Internet and to each other. This idea of connect things started in the beginning of the 2008 through RFID. But after the open hardware initiatives (as Arduino), it gained more visibility and access to experiments with sensors in the environment. The IoT is already generating an unprecedented volume of data in greater varieties and higher velocities. Making sense of such data is an emerging and significant challenge. Infographics are visual representations that provide a visual space for end users to compare and analyze data, information, and knowledge in a more efficient form than traditional forms. The nature of IoT requires a continuum modification in how end users see information to achieve such efficiency gains. Conceptualizing and implementing infographics in an IoT system can thus require significant planning and development for both data scientists, graphic designers and developers resulting in both costs in terms of time and effort. To address this problem, this paper presents SiMoNa, a domain-specific modeling language (DSML) to create, connect, interact, and build interactive infographic presentations for IoT systems efficiently based on the model-driven development (MDD) paradigm. The language and approach are validated using real-world use cases.
ItemBig Data SAVE: Secure Anonymous Vault Environment( 2019-01-08)There has been great progress in taming the volume, velocity and variation of Big Data. Its volume creates need for increased storage space and improved data handling. Its velocity is concern for the speed and efficiency of applied algorithms and processes. Its variation requires flexibility to handle assorted data-types. However, as with many emerging fields, security has taken a backseat to benchmarks. This has led to retrofitting traditional security techniques ill-suited for Big Data protection, or high-performance setups exposed to data breach. Proposed is an innovative storage system that can provide large-scale, low-overhead data security, akin to safe-deposit boxes. This approach allows for anonymously-shared storage space, discrete levels of access, plausible deniability, and customizable degrees of overall protection (including warrant-proof). A promising factor of this new model is the use of a simple encryption algorithm (proven faster than industry-standard ciphers), that provides inherent attack resiliency and strong backward secrecy.