Reading comprehension has been widely studied. One of the most representative reading comprehension tasks is Stanford Question Answering Dataset (SQuAD), on which machine is already comparable with human. On the other hand, accessing large collections of multimedia or spoken content is much more difficult and time-consuming than plain text content for humans. It’s therefore highly attractive to...

The SQuAD dataset provides a reading comprehension task, with (question, answer, context) pairs. This paper attempts to develop a model to predict the answer for a given question, context pair. In a model inspired by ”Multi-Perspective Context Matching for Machine Comprehension” by Wang et al, this paper achieves 0.47 F1 score on a validation set.

In soft robotics, one of the trending topics is using sensors to have feedback from robot's body. This not an easy process accomplish since are often nonlinear, so researchers use different methods generate information data such as filters, machine learning algorithms, and optimization algorithms. this paper, we show that, with good electronic mechanical design, it possible for detecting...