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Combining behavioural types with security analysis
Today's software systems are highly distributed and interconnected, and they
increasingly rely on communication to achieve their goals; due to their
societal importance, security and trustworthiness are crucial aspects for the
correctness of these systems. Behavioural types, which extend data types by
describing also the structured behaviour of programs, are a widely studied
approach to the enforcement of correctness properties in communicating systems.
This paper offers a unified overview of proposals based on behavioural types
which are aimed at the analysis of security properties
Deriving Verb Predicates By Clustering Verbs with Arguments
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993)
have proved useful, but have limited coverage. Verb classes automatically
induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other
hand, can give clusters with much larger coverage, and can be adapted to
specific corpora such as Twitter. We present a method for clustering the
outputs of VerbKB: verbs with their multiple argument types, e.g.
"marry(person, person)", "feel(person, emotion)." We make use of a novel
low-dimensional embedding of verbs and their arguments to produce high quality
clusters in which the same verb can be in different clusters depending on its
argument type. The resulting verb clusters do a better job than hand-built
clusters of predicting sarcasm, sentiment, and locus of control in tweets
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