5,108 research outputs found
Test Derivation from Timed Automata
A real-time system is a discrete system whose state changes occur in real-numbered time [AH97]. For testing real-time systems, specification languages must be extended with constructs for expressing real-time constraints, the implementation relation must be generalized to consider the temporal dimension, and the data structures and algorithms used to generate tests must be revised to operate on a potentially infinite set of states
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The Artificial Intelligence Quotient Measuring Machine Intelligence Based on Multi-Domain Complexity, Similarity, and Diversity
The growth and development of AI systems and benchmarks have been rapidly increasing, yet there is a disproportionately small amount of examination into the domains used to evaluate these systems. Even the domains that consider a larger scope of evaluation are often not generalizable, and the implemented AI systems are often not usable for different domains. To address the previously discussed issues within the AI community, we are putting forward a notion of machine intelligence that can be intuitively understood and effectively utilized. This notion will allow us to compute new metrics for both the agents evaluated and the domains used in the evaluation. The intelligence of a given system is determined by measuring its performance on a multi-domain test with measurable complexity, similarity, and diversity. The Artificial Intelligence Quotient (AIQ) structures these domain side measurements into a clear and consistent framework that can be utilized to measure an AI system’s intelligence. These domain side measurements allow for the creation of an intelligence space. Once a test is located within the space, its accompanying performance metric can be used to effectively scale the location allowing for a notion of agent capacitance. That is, the agent’s capability to achieve a certain understanding of the domain. An agent that achieves a higher understanding of the domain will be given a higher AIQ score than one that achieves a lower understanding. Agent capacitance also scales with domain complexity. An agent that achieves a comparable understanding of a more complex domain will be given a higher AIQ score than an agent on a less complex domain
Computing Multidimensional Persistence
The theory of multidimensional persistence captures the topology of a
multifiltration -- a multiparameter family of increasing spaces.
Multifiltrations arise naturally in the topological analysis of scientific
data. In this paper, we give a polynomial time algorithm for computing
multidimensional persistence. We recast this computation as a problem within
computational algebraic geometry and utilize algorithms from this area to solve
it. While the resulting problem is Expspace-complete and the standard
algorithms take doubly-exponential time, we exploit the structure inherent
withing multifiltrations to yield practical algorithms. We implement all
algorithms in the paper and provide statistical experiments to demonstrate
their feasibility.Comment: This paper has been withdrawn by the authors. Journal of
Computational Geometry, 1(1) 2010, pages 72-100.
http://jocg.org/index.php/jocg/article/view/1
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
An explosion of high-throughput DNA sequencing in the past decade has led to
a surge of interest in population-scale inference with whole-genome data.
Recent work in population genetics has centered on designing inference methods
for relatively simple model classes, and few scalable general-purpose inference
techniques exist for more realistic, complex models. To achieve this, two
inferential challenges need to be addressed: (1) population data are
exchangeable, calling for methods that efficiently exploit the symmetries of
the data, and (2) computing likelihoods is intractable as it requires
integrating over a set of correlated, extremely high-dimensional latent
variables. These challenges are traditionally tackled by likelihood-free
methods that use scientific simulators to generate datasets and reduce them to
hand-designed, permutation-invariant summary statistics, often leading to
inaccurate inference. In this work, we develop an exchangeable neural network
that performs summary statistic-free, likelihood-free inference. Our framework
can be applied in a black-box fashion across a variety of simulation-based
tasks, both within and outside biology. We demonstrate the power of our
approach on the recombination hotspot testing problem, outperforming the
state-of-the-art.Comment: 9 pages, 8 figure
Automatic Margin Computation for Risk-Limiting Audits
A risk-limiting audit is a statistical method to create confidence in the correctness of an election result by checking samples of paper ballots. In order to perform an audit, one usually needs to know what the election margin is, i.e., the number of votes that would need to be changed in order to change the election outcome.
In this paper, we present a fully automatic method for computing election margins. It is based on the program analysis technique of bounded model checking to analyse the implementation of the election function. The method can be applied to arbitrary election functions without understanding the actual computation of the election result or without even intuitively knowing how the election function works.
We have implemented our method based on the model checker CBMC; and we present a case study demonstrating that it can be applied to real-world elections
Querying the Guarded Fragment
Evaluating a Boolean conjunctive query Q against a guarded first-order theory
F is equivalent to checking whether "F and not Q" is unsatisfiable. This
problem is relevant to the areas of database theory and description logic.
Since Q may not be guarded, well known results about the decidability,
complexity, and finite-model property of the guarded fragment do not obviously
carry over to conjunctive query answering over guarded theories, and had been
left open in general. By investigating finite guarded bisimilar covers of
hypergraphs and relational structures, and by substantially generalising
Rosati's finite chase, we prove for guarded theories F and (unions of)
conjunctive queries Q that (i) Q is true in each model of F iff Q is true in
each finite model of F and (ii) determining whether F implies Q is
2EXPTIME-complete. We further show the following results: (iii) the existence
of polynomial-size conformal covers of arbitrary hypergraphs; (iv) a new proof
of the finite model property of the clique-guarded fragment; (v) the small
model property of the guarded fragment with optimal bounds; (vi) a
polynomial-time solution to the canonisation problem modulo guarded
bisimulation, which yields (vii) a capturing result for guarded bisimulation
invariant PTIME.Comment: This is an improved and extended version of the paper of the same
title presented at LICS 201
MV3: A new word based stream cipher using rapid mixing and revolving buffers
MV3 is a new word based stream cipher for encrypting long streams of data. A
direct adaptation of a byte based cipher such as RC4 into a 32- or 64-bit word
version will obviously need vast amounts of memory. This scaling issue
necessitates a look for new components and principles, as well as mathematical
analysis to justify their use. Our approach, like RC4's, is based on rapidly
mixing random walks on directed graphs (that is, walks which reach a random
state quickly, from any starting point). We begin with some well understood
walks, and then introduce nonlinearity in their steps in order to improve
security and show long term statistical correlations are negligible. To
minimize the short term correlations, as well as to deter attacks using
equations involving successive outputs, we provide a method for sequencing the
outputs derived from the walk using three revolving buffers. The cipher is fast
-- it runs at a speed of less than 5 cycles per byte on a Pentium IV processor.
A word based cipher needs to output more bits per step, which exposes more
correlations for attacks. Moreover we seek simplicity of construction and
transparent analysis. To meet these requirements, we use a larger state and
claim security corresponding to only a fraction of it. Our design is for an
adequately secure word-based cipher; our very preliminary estimate puts the
security close to exhaustive search for keys of size < 256 bits.Comment: 27 pages, shortened version will appear in "Topics in Cryptology -
CT-RSA 2007
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