Delayed Enhancement Magnetic Resonance Imaging (DE-MRI) can be used to non-invasively differentiate viable from non-viable myocardium within the Left Ventricle (LV) in patients suffering from myocardial diseases. Automated segmentation of scarified tissue can be used to quantify accurately the percentage of myocardium affected. This paper presents a method for cardiac scar detection and segment...

Climate type is one of the potentially most relevant pieces of metadata for identifying studies in evidence-based environmental management. In this paper, we propose a method for automatically predicting the climate type in environmental science literature using NLP techniques, relative to a pre-existing set of climate type categories. Our main approaches combine toponym detection and resolutio...

One of the main difficulties in sentiment analysis of the Arabic language is the presence of the colloquialism. In this paper, we examine the effect of using objective words in conjunction with sentimental words on sentiment classification for the colloquial Arabic reviews, specifically Jordanian colloquial reviews. The reviews often include both sentimental and objective words; however, the mo...

We present a new kind of pattern classifier, the Polynomial Learning Machine (PLM). The PLM is derived, and its construction detailed. We compare the performance of the PLM to that of the Support Vector Machine (SVM) in classifying binary-classed data sets, and find that the PLM consistently trains and tests in a shorter time than the SVM while maintaining comparable classification accuracy; in...

Background models are often used in video surveillance systems to find moving objects in an image sequence from a static camera. These models are often built under the assumption that the foreground objects are not known in advance. This assumption has led us to model background using one-class SVM classifiers. Our model belongs to a family of block-based nonparametric models that can be used e...

This paper outlines the UU-SVM system for Task 1 of the WMT16 Shared Task in Quality Estimation. Our system uses Support Vector Machine Regression to investigate the impact of a series of features aiming to convey translation quality. We propose novel features measuring reordering and noun translation errors. We show that we can outperform the baseline when we combine it with a subset of our ne...

Echocardiography plays an important part in diagnostic aid in cardiac diseases. A critical step in echocardiography-aided diagnosis is to extract the standard planes since they tend to provide promising views to present different structures that are benefit to diagnosis. To this end, this paper proposes a spatial-temporal embedding framework to extract the standard view planes from 4D STIC (spa...

This paper investigates directional relationships, regime variances, transition probabilities and expected regime durations for a system of economic and financial risk variables in the U.S. markets. The system is based on monthly data, and encompasses credit, and market risks and economic activity variables. The methodology is based on the Markov-Switching cointegrated VAR model and their impul...

The Melbourne team was a collaboration between academic and industry groups. The team participated in both the learning and the interactive tasks of this year’s Legal Track. The baseline run for the learning track employed true-relevance feedback, achieving respectable outcomes; the experimental runs added additional features and employed an SVM classifier, with poor results. The techniques dev...

In this paper we present two systems that address the issues of disorder recognition and normalization submitted by the authors as defined by the CLEF/ShARe Evaluation Lab. The first approach to the tasks formed a baseline approach using the cTakes system. Our second approach leveraged Structural Support Vector Machines with an array of feature types including lexical, semantic and cluster base...