99 research outputs found
Enabling Interactive Analytics of Secure Data using Cloud Kotta
Research, especially in the social sciences and humanities, is increasingly
reliant on the application of data science methods to analyze large amounts of
(often private) data. Secure data enclaves provide a solution for managing and
analyzing private data. However, such enclaves do not readily support discovery
science---a form of exploratory or interactive analysis by which researchers
execute a range of (sometimes large) analyses in an iterative and collaborative
manner. The batch computing model offered by many data enclaves is well suited
to executing large compute tasks; however it is far from ideal for day-to-day
discovery science. As researchers must submit jobs to queues and wait for
results, the high latencies inherent in queue-based, batch computing systems
hinder interactive analysis. In this paper we describe how we have augmented
the Cloud Kotta secure data enclave to support collaborative and interactive
analysis of sensitive data. Our model uses Jupyter notebooks as a flexible
analysis environment and Python language constructs to support the execution of
arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing,
Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by
(1) conducting deep understanding of a large collection of human-written papers
in a target domain and constructing comprehensive background knowledge graphs
(KGs); (2) creating new ideas by predicting links from the background KGs, by
combining graph attention and contextual text attention; (3) incrementally
writing some key elements of a new paper based on memory-attention networks:
from the input title along with predicted related entities to generate a paper
abstract, from the abstract to generate conclusion and future work, and finally
from future work to generate a title for a follow-on paper. Turing Tests, where
a biomedical domain expert is asked to compare a system output and a
human-authored string, show PaperRobot generated abstracts, conclusion and
future work sections, and new titles are chosen over human-written ones up to
30%, 24% and 12% of the time, respectively.Comment: 12 pages. Accepted by ACL 2019 Code and resource is available at
https://github.com/EagleW/PaperRobo
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