The problem of finding association rules from a dataset is to find all possible associations that hold among the items, given a minimum support value and a minimum confidence. This involves finding frequent sets first and then the association rules that hold within the items in the frequent sets. The problem of mining temporal association rules from temporal dataset is to find association rules...

We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines and state-of-the-art neural readers to the dataset, and observe a considerable gap in performance (20% F1) between the best human and machine readers. We analyze the skills required for successful a...

We construct a unique geocoded dataset on temples, dynasties and battles in medieval India and propose a test to identify the motive for observed temple desecrations in that period. We test two competing historical narratives of temple desecrations. The first focuses on the idea of iconoclasm, i.e. destruction of religious sites and imagery deemed heretical, as embedded in Islamic theology. The...

The rapid growth in stored time-oriented data necessitates the development of new methods for handling, processing, and interpreting large amounts of temporal data. One important example of such processing is detecting anomalies in time-oriented data. The Knowledge-Based Temporal Abstraction method was previously proposed for intelligent interpretation of temporal data based on predefined domai...