Semi-Automatic Entity Set Refinement

Fine-grained Entity Set Refinement with User Feedback

State of the art semi-supervised entity set expansion algorithms produce noisy results, which need to be refined manually. Sets expanded for intended fine-grained concepts are especially noisy because these concepts are not well represented by the limited number of seeds. Such sets are usually incorrectly expanded to contain elements of a more general concept. We show that fine-grained control ...

Concept Detector Refinement on Social Videos

The explosion of the social video sharing sites gives new challenges on video search and indexing technique. Because of the concept diversity in social videos, it is very hard to build a well annotated dataset that provides good coverage over the whole meaning of concepts. However, the prosperity of social video also make it easy to obtain a huge number of videos, which gives an opportunity to ...

Heuristics on the Definition of UML Refinement Patterns

In this article we present a strategy to formalize frequently occurring forms of refinement that take place in UML model construction. Such strategy consists in recognizing a set of well founded refinement structures in a formal language which are then immersed into a UML-based development, giving origin to a set of UML refinement patterns. Apart from providing semi-formal evidence on the prese...

EASEAndroid: Automatic Policy Analysis and Refinement for Security Enhanced Android via Large-Scale Semi-Supervised Learning

Mandatory protection systems such as SELinux and SEAndroid harden operating system integrity. Unfortunately, policy development is error prone and requires lengthy refinement using audit logs from deployed systems. While prior work has studied SELinux policy in detail, SEAndroid is relatively new and has received little attention. SEAndroid policy engineering differs significantly from SELinux:...

Semi-automatic Program Refinement from Specifications Using Library Modules