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This paper describes experiments in Machine Learning for text classification using a new representation of text based on WordNet hypernyms. Six binary classification tasks of varying difficulty are defined, and the Ripper system is used to produce discrimination rules for each task using the new hypernym density representation. Rules are also produced with(More)
The Learning with Errors (LWE) problem has become a central building block of modern cryptographic constructions. This work collects and presents hardness results for concrete instances of LWE. In particular, we discuss algorithms proposed in the literature and give the expected resources required to run them. We consider both generic instances of LWE as(More)
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