2,298 research outputs found
Minimum Cuts in Near-Linear Time
We significantly improve known time bounds for solving the minimum cut
problem on undirected graphs. We use a ``semi-duality'' between minimum cuts
and maximum spanning tree packings combined with our previously developed
random sampling techniques. We give a randomized algorithm that finds a minimum
cut in an m-edge, n-vertex graph with high probability in O(m log^3 n) time. We
also give a simpler randomized algorithm that finds all minimum cuts with high
probability in O(n^2 log n) time. This variant has an optimal RNC
parallelization. Both variants improve on the previous best time bound of O(n^2
log^3 n). Other applications of the tree-packing approach are new, nearly tight
bounds on the number of near minimum cuts a graph may have and a new data
structure for representing them in a space-efficient manner
Linear-Time Poisson-Disk Patterns
We present an algorithm for generating Poisson-disc patterns taking O(N) time
to generate points. The method is based on a grid of regions which can
contain no more than one point in the final pattern, and uses an explicit model
of point arrival times under a uniform Poisson process.Comment: 4 pages, 2 figure
Does Confidence Reporting from the Crowd Benefit Crowdsourcing Performance?
We explore the design of an effective crowdsourcing system for an -ary
classification task. Crowd workers complete simple binary microtasks whose
results are aggregated to give the final classification decision. We consider
the scenario where the workers have a reject option so that they are allowed to
skip microtasks when they are unable to or choose not to respond to binary
microtasks. Additionally, the workers report quantized confidence levels when
they are able to submit definitive answers. We present an aggregation approach
using a weighted majority voting rule, where each worker's response is assigned
an optimized weight to maximize crowd's classification performance. We obtain a
couterintuitive result that the classification performance does not benefit
from workers reporting quantized confidence. Therefore, the crowdsourcing
system designer should employ the reject option without requiring confidence
reporting.Comment: 6 pages, 4 figures, SocialSens 2017. arXiv admin note: text overlap
with arXiv:1602.0057
Viscoelastic behaviour and fracture toughness of linear-low-density polyethylene reinforced with synthetic boehmite alumina nanoparticles
The benefits of coding over routing in a randomized setting
A novel randomized network coding approach for robust, distributed transmission and compression of information in networks is presented, and its advantages over routing-based approaches is demonstrated
Analytic Methods for Optimizing Realtime Crowdsourcing
Realtime crowdsourcing research has demonstrated that it is possible to
recruit paid crowds within seconds by managing a small, fast-reacting worker
pool. Realtime crowds enable crowd-powered systems that respond at interactive
speeds: for example, cameras, robots and instant opinion polls. So far, these
techniques have mainly been proof-of-concept prototypes: research has not yet
attempted to understand how they might work at large scale or optimize their
cost/performance trade-offs. In this paper, we use queueing theory to analyze
the retainer model for realtime crowdsourcing, in particular its expected wait
time and cost to requesters. We provide an algorithm that allows requesters to
minimize their cost subject to performance requirements. We then propose and
analyze three techniques to improve performance: push notifications, shared
retainer pools, and precruitment, which involves recalling retainer workers
before a task actually arrives. An experimental validation finds that
precruited workers begin a task 500 milliseconds after it is posted, delivering
results below the one-second cognitive threshold for an end-user to stay in
flow.Comment: Presented at Collective Intelligence conference, 201
Byzantine modification detection in multicast networks using randomized network coding
Distributed randomized network coding, a robust approach to multicasting in distributed network settings, can be extended to provide Byzantine modification detection without the use of cryptographic functions is presented in this paper
Byzantine Modification Detection in Multicast Networks With Random Network Coding
An information-theoretic approach for detecting Byzantine or adversarial modifications in networks employing random linear network coding is described. Each exogenous source packet is augmented with a flexible number of hash symbols that are obtained as a polynomial function of the data symbols. This approach depends only on the adversary not knowing the random coding coefficients of all other packets received by the sink nodes when designing its adversarial packets. We show how the detection probability varies with the overhead (ratio of hash to data symbols), coding field size, and the amount of information unknown to the adversary about the random code
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