12,482 research outputs found
Mathematical practice, crowdsourcing, and social machines
The highest level of mathematics has traditionally been seen as a solitary
endeavour, to produce a proof for review and acceptance by research peers.
Mathematics is now at a remarkable inflexion point, with new technology
radically extending the power and limits of individuals. Crowdsourcing pulls
together diverse experts to solve problems; symbolic computation tackles huge
routine calculations; and computers check proofs too long and complicated for
humans to comprehend.
Mathematical practice is an emerging interdisciplinary field which draws on
philosophy and social science to understand how mathematics is produced. Online
mathematical activity provides a novel and rich source of data for empirical
investigation of mathematical practice - for example the community question
answering system {\it mathoverflow} contains around 40,000 mathematical
conversations, and {\it polymath} collaborations provide transcripts of the
process of discovering proofs. Our preliminary investigations have demonstrated
the importance of "soft" aspects such as analogy and creativity, alongside
deduction and proof, in the production of mathematics, and have given us new
ways to think about the roles of people and machines in creating new
mathematical knowledge. We discuss further investigation of these resources and
what it might reveal.
Crowdsourced mathematical activity is an example of a "social machine", a new
paradigm, identified by Berners-Lee, for viewing a combination of people and
computers as a single problem-solving entity, and the subject of major
international research endeavours. We outline a future research agenda for
mathematics social machines, a combination of people, computers, and
mathematical archives to create and apply mathematics, with the potential to
change the way people do mathematics, and to transform the reach, pace, and
impact of mathematics research.Comment: To appear, Springer LNCS, Proceedings of Conferences on Intelligent
Computer Mathematics, CICM 2013, July 2013 Bath, U
Meta-F*: Proof Automation with SMT, Tactics, and Metaprograms
We introduce Meta-F*, a tactics and metaprogramming framework for the F*
program verifier. The main novelty of Meta-F* is allowing the use of tactics
and metaprogramming to discharge assertions not solvable by SMT, or to just
simplify them into well-behaved SMT fragments. Plus, Meta-F* can be used to
generate verified code automatically.
Meta-F* is implemented as an F* effect, which, given the powerful effect
system of F*, heavily increases code reuse and even enables the lightweight
verification of metaprograms. Metaprograms can be either interpreted, or
compiled to efficient native code that can be dynamically loaded into the F*
type-checker and can interoperate with interpreted code. Evaluation on
realistic case studies shows that Meta-F* provides substantial gains in proof
development, efficiency, and robustness.Comment: Full version of ESOP'19 pape
Semantic processing of EHR data for clinical research
There is a growing need to semantically process and integrate clinical data
from different sources for clinical research. This paper presents an approach
to integrate EHRs from heterogeneous resources and generate integrated data in
different data formats or semantics to support various clinical research
applications. The proposed approach builds semantic data virtualization layers
on top of data sources, which generate data in the requested semantics or
formats on demand. This approach avoids upfront dumping to and synchronizing of
the data with various representations. Data from different EHR systems are
first mapped to RDF data with source semantics, and then converted to
representations with harmonized domain semantics where domain ontologies and
terminologies are used to improve reusability. It is also possible to further
convert data to application semantics and store the converted results in
clinical research databases, e.g. i2b2, OMOP, to support different clinical
research settings. Semantic conversions between different representations are
explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which
can also generate proofs of the conversion processes. The solution presented in
this paper has been applied to real-world applications that process large scale
EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015,
preprint versio
Oblivion: Mitigating Privacy Leaks by Controlling the Discoverability of Online Information
Search engines are the prevalently used tools to collect information about
individuals on the Internet. Search results typically comprise a variety of
sources that contain personal information -- either intentionally released by
the person herself, or unintentionally leaked or published by third parties,
often with detrimental effects on the individual's privacy. To grant
individuals the ability to regain control over their disseminated personal
information, the European Court of Justice recently ruled that EU citizens have
a right to be forgotten in the sense that indexing systems, must offer them
technical means to request removal of links from search results that point to
sources violating their data protection rights. As of now, these technical
means consist of a web form that requires a user to manually identify all
relevant links upfront and to insert them into the web form, followed by a
manual evaluation by employees of the indexing system to assess if the request
is eligible and lawful.
We propose a universal framework Oblivion to support the automation of the
right to be forgotten in a scalable, provable and privacy-preserving manner.
First, Oblivion enables a user to automatically find and tag her disseminated
personal information using natural language processing and image recognition
techniques and file a request in a privacy-preserving manner. Second, Oblivion
provides indexing systems with an automated and provable eligibility mechanism,
asserting that the author of a request is indeed affected by an online
resource. The automated ligibility proof ensures censorship-resistance so that
only legitimately affected individuals can request the removal of corresponding
links from search results. We have conducted comprehensive evaluations, showing
that Oblivion is capable of handling 278 removal requests per second, and is
hence suitable for large-scale deployment
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