Collocation has been considered a problematic area for L2 learners. Various studies have been conducted to investigate native speakers‘ (NS) and non-native speakers‘ (NNS) use of different types of collocations (e.g., Durrant and Schmitt, 2009; Laufer and Waldman, 2011).These studies have indicated that, unlike NS, NNS rely on a limited set of collocations and tend to overuse them. This raises ...

Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem. Bayesian NNs, which learn a distribution over weights, are currently the state-of-the-art for estimating predictive uncertainty; however these require significant modifica...

Sweetness is one of the 5 prototypical tastes and is activated by sugars and non-nutritive sweeteners (NNS). The aim of this study was to investigate measures of sweet taste function [detection threshold (DT), recognition threshold (RT), and suprathreshold intensity ratings] across multiple sweeteners. Sixty participants, 18-52 years of age (mean age in years = 26, SD = ±7.8), were recruited to...

This paper reports on a corpus-based comparison of syntactic complexity in NNS and NS university students’ writing. We analysed 600 essays from the Written English Corpus of Chinese Learners and the Louvain Corpus of Native English Essays using 10 syntactic complexity measures to investigate whether and the extent to which NNS and NS university students’ writing differs with respect to length o...

The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs. While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs. To t...

Credit scoring has gained more and more attentions both in academic world and the business community today. Many modeling techniques have been developed to tackle the credit scoring tasks. This paper presents a Structuretuning Particle Swarm Optimization (SPSO) approach for training feed-forward neural networks (NNs). The algorithm is successfully applied to a real credit problem. By simultaneo...

OBJECTIVE To assess how non-nutritive sucking (NNS) using a pacifier affected physiological and behavioral outcomes of preterm infants. DESIGN Short-term longitudinal, experimental design. SETTING The study took place at the neonatal intensive care unit at Al-Mansoura, Egypt. METHODS Forty-seven preterm infants were divided into intervention and control groups. Preterm infants in the inte...

Negative Selection Algorithms (NSAs) have been widely used in anomaly detection. As the security issue becomes more complex, more and more anomaly detection schemes involve high-dimension data. NSAs however perform poorly on effectiveness and efficiency when dealing with high-dimension data. To address these issues, we propose a Neighborhood Negative Selection (NNS) algorithm in this paper. Ins...

Based on negative correlation learning and evolutionary learning, this paper presents evolutionary ensembles with negative correlation learning (EENCL) to address the issues of automatic determination of the number of individual neural networks (NNs) in an ensemble and the exploitation of the interaction between individual NN design and combination. The idea of EENCL is to encourage di erent in...

Models in conventional decision support systems (DSSs) are best suited for problem solutions in domains with well defined/structured (mathematical) or partially defined/semi-structured (heuristic) domain models. Nonconservative/unstructured domains are those which either lack a known model or have a poorly defined domain model. Neural networks (NNs) represent an alternative modelling technique ...