16,833 research outputs found
Machine Learning of User Profiles: Representational Issues
As more information becomes available electronically, tools for finding
information of interest to users becomes increasingly important. The goal of
the research described here is to build a system for generating comprehensible
user profiles that accurately capture user interest with minimum user
interaction. The research described here focuses on the importance of a
suitable generalization hierarchy and representation for learning profiles
which are predictively accurate and comprehensible. In our experiments we
evaluated both traditional features based on weighted term vectors as well as
subject features corresponding to categories which could be drawn from a
thesaurus. Our experiments, conducted in the context of a content-based
profiling system for on-line newspapers on the World Wide Web (the IDD News
Browser), demonstrate the importance of a generalization hierarchy and the
promise of combining natural language processing techniques with machine
learning (ML) to address an information retrieval (IR) problem.Comment: 6 page
Information Security as Strategic (In)effectivity
Security of information flow is commonly understood as preventing any
information leakage, regardless of how grave or harmless consequences the
leakage can have. In this work, we suggest that information security is not a
goal in itself, but rather a means of preventing potential attackers from
compromising the correct behavior of the system. To formalize this, we first
show how two information flows can be compared by looking at the adversary's
ability to harm the system. Then, we propose that the information flow in a
system is effectively information-secure if it does not allow for more harm
than its idealized variant based on the classical notion of noninterference
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