A fuzzy-nets based in-process adaptive surface roughness control (FNASRC) system was developed in this research. The FNASRC system was able to adapt cutting parameters in-process and in a real time fashion to improve the surface roughness of machined parts when the surface roughness quality was not meeting customer requirements in the end milling operations. The FNASRC system was comprised of t...

An event chronicle provides people with an easy and fast access to learn the past. In this paper, we propose the first novel approach to automatically generate a topically relevant event chronicle during a certain period given a reference chronicle during another period. Our approach consists of two core components – a timeaware hierarchical Bayesian model for event detection, and a learning-to...

This paper presents our work on the 2016 CLEF eHealth Task 3.We used Indri to conduct our experiments. We used CHV to expand query and proposed a learning-to-rank algorithm to re-rank the result.

Most learning to rank research has assumed that the utility of different documents is independent, which results in learned ranking functions that return redundant results. The few approaches that avoid this have rather unsatisfyingly lacked theoretical foundations, or do not scale. We present a learning-torank formulation that optimizes the fraction of satisfied users, with a scalable algorith...

The use of phrases in retrieval models has been proven to be helpful in the literature, but no particular research addresses the problem of discriminating phrases that are likely to degrade the retrieval performance from the ones that do not. In this paper, we present a retrieval framework that utilizes both words and phrases flexibly, followed by a general learning-to-rank method for learning ...

We describe how we build the system for NTCIR-13 Short Text Conversation (STC) Chinese subtask. In our system, we use the retrieval-based method and the generationbased method respectively. For the retrieval-based method, we develop several features to match the candidates and then apply a learning to rank algorithm to get properly ranked results. For the generation-based method, we first gener...

This paper describes the approach proposed by UNIFESP for the MediaEval 2016 Predicting Media Interestingness Task and for its video subtask only. The proposed approach is based on combining learning-to-rank algorithms for predicting the interestingness of videos by their visual content.