The idea of the ‘digital natives’, a generation of tech-savvy young people immersed in digital technologies for which current education systems cannot cater, has gained widespread popularity on the basis of claims rather than evidence. Recent research has shown flaws in the argument that there is an identifiable generation or even a single type of highly adept technology user. For educators, th...

This paper considers the creation and consumption of content on user generated content platforms, e.g., reviews, articles, chat, videos, etc. On these platforms, users’ expectations regarding the amount and timing of participation by others becomes germane to their own involvement levels. Accordingly, we develop a dynamic rational expectations equilibrium model of joint consumption and generati...

The financial industry is in midst of a fundamental transformation. Changes in customer behavior and the impact of information technology are believed to change the financial industry from hierarchical to market-based structures. Although electronic markets in the financial services industry are well known in B2B processes such as e.g. stock exchanges, only little research has been undertaken T...

The technological capability of digital natives is thought to have considerable implications on the way they communicate, socialize, think and learn. Some researchers have even suggested that fundamental changes to the educational system are required to cater for the needs of this new cohort of learner, although such claims have little empirical support. In this study, we adopt a structural app...

Background “Canada’s Immigration Program” [1] reports that Canada has the highest per capita immigration rate in the world. Given the finding that pregnant immigrant women display higher prevalence of depression and anxiety disorders [2], the investigation of health outcomes in this population is warranted. The present study will examine whether prenatal perceived stress and/or physiologic mate...

Modeling informal inference in natural language is very challenging. With the recent availability of large annotated data, it has become feasible to train complex models such as neural networks to perform natural language inference (NLI), which have achieved state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform NLI...

We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and in...

Our efforts in the 2013 NLI shared task focused on the potential benefits of external corpora. We show that including training data from multiple corpora is highly effective at robust, cross-corpus NLI (i.e. open-training task 1), particularly when some form of domain adaptation is also applied. This method can also be used to boost performance even when training data from the same corpus is av...

This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI). The system was submitted to the NLI Shared Task 2017 fusion track which featured students essays and spoken responses in form of audio transcriptions and iVectors by non-native English speakers of eleven native languages. Our system competed in the challenge under the...