3,305 research outputs found
Welfare Maximization Entices Participation
We consider randomized mechanisms with optional participation. Preferences
over lotteries are modeled using skew-symmetric bilinear (SSB) utility
functions, a generalization of classic von Neumann-Morgenstern utility
functions. We show that every welfare-maximizing mechanism entices
participation and that the converse holds under additional assumptions. Two
important corollaries of our results are characterizations of an attractive
randomized voting rule that satisfies Condorcet-consistency and entices
participation. This stands in contrast to a well-known result by Moulin (1988),
who proves that no deterministic voting rule can satisfy both properties
simultaneously
Mass Displacement Networks
Despite the large improvements in performance attained by using deep learning
in computer vision, one can often further improve results with some additional
post-processing that exploits the geometric nature of the underlying task. This
commonly involves displacing the posterior distribution of a CNN in a way that
makes it more appropriate for the task at hand, e.g. better aligned with local
image features, or more compact. In this work we integrate this geometric
post-processing within a deep architecture, introducing a differentiable and
probabilistically sound counterpart to the common geometric voting technique
used for evidence accumulation in vision. We refer to the resulting neural
models as Mass Displacement Networks (MDNs), and apply them to human pose
estimation in two distinct setups: (a) landmark localization, where we collapse
a distribution to a point, allowing for precise localization of body keypoints
and (b) communication across body parts, where we transfer evidence from one
part to the other, allowing for a globally consistent pose estimate. We
evaluate on large-scale pose estimation benchmarks, such as MPII Human Pose and
COCO datasets, and report systematic improvements when compared to strong
baselines.Comment: 12 pages, 4 figure
Privacy-Preserving Electronic Ticket Scheme with Attribute-based Credentials
Electronic tickets (e-tickets) are electronic versions of paper tickets,
which enable users to access intended services and improve services'
efficiency. However, privacy may be a concern of e-ticket users. In this paper,
a privacy-preserving electronic ticket scheme with attribute-based credentials
is proposed to protect users' privacy and facilitate ticketing based on a
user's attributes. Our proposed scheme makes the following contributions: (1)
users can buy different tickets from ticket sellers without releasing their
exact attributes; (2) two tickets of the same user cannot be linked; (3) a
ticket cannot be transferred to another user; (4) a ticket cannot be double
spent; (5) the security of the proposed scheme is formally proven and reduced
to well known (q-strong Diffie-Hellman) complexity assumption; (6) the scheme
has been implemented and its performance empirically evaluated. To the best of
our knowledge, our privacy-preserving attribute-based e-ticket scheme is the
first one providing these five features. Application areas of our scheme
include event or transport tickets where users must convince ticket sellers
that their attributes (e.g. age, profession, location) satisfy the ticket price
policies to buy discounted tickets. More generally, our scheme can be used in
any system where access to services is only dependent on a user's attributes
(or entitlements) but not their identities.Comment: 18pages, 6 figures, 2 table
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