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Found 5 essays matching the above criteria.
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1. The state of WebAssembly in distributed systems : With a focus on Rust and Arc-Lang
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : With the current developments in modern web browsers, WebAssembly has been a rising trend over the last four years. Aimed at replacing bits of JavaScript functionality, it attempts to bring extra features to achieve portability and sandboxing through virtualisation. READ MORE
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2. A Scala DSL for Rust code generation
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Continuous Deep Analytics (CDA) is a new form of analytics with performance requirements exceeding what the current generation of distributed systems can offer. This thesis is part of a five year project in collaboration between RISE SICS and KTH to develop a next generation distributed system capable of CDA. READ MORE
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3. Design of a Network Library for Continuous Deep Analytics
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Data-intensive stream processing applications have escalated in popularity in recent years, producing numerous designs and implementations for handling unbounded streams of high-volume data. The sheer size and dimensionality of these types of data requires multiple machines to push processing throughput of hundreds of millions events per second at low latencies. READ MORE
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4. Dynamic Configuration of a Relocatable Driver and Code Generator for Continuous Deep Analytics
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Modern stream processing engines usually use the Java virtual machine (JVM) as execution platform. The JVM increases portability and safety of applications at the cost of not fully utilising the performance of the physical machines. READ MORE
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5. Adversarial Deep Learning Against Intrusion Detection Classifiers
University essay from Luleå tekniska universitet/DatavetenskapAbstract : Traditional approaches in network intrusion detection follow a signature-based ap- proach, however the use of anomaly detection approaches based on machine learning techniques have been studied heavily for the past twenty years. The continuous change in the way attacks are appearing, the volume of attacks, as well as the improvements in the big data analytics space, make machine learning approaches more alluring than ever. READ MORE