CLaC-CORE, an exhaustive feature combination system ranked 4th among 34 teams in the Semantic Textual Similarity shared task STS 2013. Using a core set of 11 lexical features of the most basic kind, it uses a support vector regressor which uses a combination of these lexical features to train a model for predicting similarity between sentences in a two phase method, which in turn uses all combi...

This year, we participated in TRECVID 2010 content-based copy detection task. In this notebook paper we will describe our work in details. Different from last year when we just used SURF (speeded up robust feature) as visual feature, this year we employed a combination of four different features for our rough detection process: global SURF, center SURR, global color correlogram and center corre...

The culmination of the 2002 RoboFlag Summer Undergraduate Research Fellowship program, jointly operated between California Institute of Technology and Cornell University, was a final competition between two teams of three undergraduate researchers. After ten weeks of preparation, Team Pasadena defeated Team Ithaca in two of the three final games. This paper provides the detailed results of the ...

This study aimed at investigating the English textese of Iranian EFL learners by scrutinizing the linguistic features through a qualitative design. In doing so, 700 messages were collected from 43 MA Iranian EFL learners of both genders. The features were categorized and analyzed calculating the frequency and percentage. The findings of the study showed that Iranian EFL students used different ...

This paper gives a pictorial view of the relevance of lean thinking, particularly the applicationof muda as a supplement to the sustainable improvement diagnosis technique of existing public office buildings,for a fuller assessment of users’ requirement in Nigeria. The impact of perceived muda was related to the triplebottom line of sustainable development on perceived job productivity and desi...