Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, ...

The article by Johns et. al. on indoor residual spraying (IRS) for malaria control in Ethiopia, published in this issue of GHSP, presents a variation on standard IRS operating procedures by essentially replacing the “squad leader,” usually recruited from the district town, with the health extension worker (HEW) from the community. The squad leader supervises the spray operators, who are also re...

PURPOSE A user-centered design approach was used to refine the mHealth Pain Squad+ real-time pain self-management app for adolescents with cancer for its usability (defined as being easy to use, easy to understand, efficient to complete, and acceptable). METHOD Three iterative usability testing cycles involving adolescent observation and interview were used to achieve this objective. During e...

There is evident lack of studies which examined anthropological determinants of success in water polo. The aim of this investigation was to study the physical fitness differences between two qualitative levels of junior water polo players (males; 16-18 years of age; 6+ year of experience in water polo). The sample (N = 54) comprised of 13 members of the junior national-squad (5 centers and 8 pe...

Current end-to-end machine reading and question answering (Q&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q&A architecture that does not require recurrent networks: Its encoder consists exclusively of convolution and self-...

Current end-to-end machine reading and question answering (Q&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q&A architecture that does not require recurrent networks: Its encoder consists exclusively of convolution and self-...

Six members of the Irish Olympic Road Cycling Squad underwent a comprehensive medical, nutritional, psychological and biochemical assessment in January 1983. They were given specific medical and dietary recommendations and were reassessed in January 1984 after a period spanning the competitive racing season. The cyclists' diets at both sessions were comparable and generally conformed with recom...

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There are many state-of-the-art deep learning architectures that have been used to develop reading comprehension systems using the Stanford Question Answering Dataset (SQuAD). In this work, we provide an overview of some of the state-ofthe-art models and compare their performance across several variations of encoder and decoder architectures and different attention mechanisms. We take inspirati...

As the non-playable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviours that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective team behaviours for shooter games. GP has been used to evolve teams capable of defeating a single powerful...