BIOMEDICINE: Mobilizing the Rescue Squad

Governing the Mod Squad

Computer games have increasingly been the focus of user-led innovations in form of games mods. This paper examines how different kinds of socio-technical affordances serve to organize and govern the actions of the people who develop and share their game mods. The affordances examined include customization and tailoring mechanisms, software and content copyright licenses, game software infrastru...

Mobilizing the Industrial Base

Question Answering on the SQuAD Dataset

We develop a deep learning framework for question answering on the Stanford Question Answering Dataset (SQuAD), blending ideas from existing state-of-theart models to achieve results that surpass the original logistic regression baselines. Using a dynamic coattention encoder and an LSTM decoder, we achieved an F1 score of 55.9% on the hidden SQuAD test set. In this paper, we present the methodo...

Question Answering on SQuAD

In this project, we exploit several deep learning architectures in Question Answering field, based on the newly released Stanford Question Answering dataset (SQuAD)[7]. We introduce a multi-stage process that encodes context paragraphs at different levels of granularity, uses co-attention mechanism to fuse representations of questions and context paragraphs, and finally decodes the co-attention...

SQuAD Reading Comprehension

One important task in Natural Language Understanding is Reading Comprehension. Given a piece of text, we want to be able to answer any relevant questions. Using Stanford Question Answering Dataset(SQuAD), which is a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, we built a reading comprehension model that attains 75.2% F1...