118,218 research outputs found
CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling
In this paper, we present a compressed data structure for moving object
trajectories in a road network, which are represented as sequences of road
edges. Unlike existing compression methods for trajectories in a network, our
method supports pattern matching and decompression from an arbitrary position
while retaining a high compressibility with theoretical guarantees.
Specifically, our method is based on FM-index, a fast and compact data
structure for pattern matching. To enhance the compression, we incorporate the
sparsity of road networks into the data structure. In particular, we present
the novel concepts of relative movement labeling and PseudoRank, each
contributing to significant reductions in data size and query processing time.
Our theoretical analysis and experimental studies reveal the advantages of our
proposed method as compared to existing trajectory compression methods and
FM-index variants
The Hush Cryptosystem
In this paper we describe a new cryptosystem we call "The Hush Cryptosystem"
for hiding encrypted data in innocent Arabic sentences. The main purpose of
this cryptosystem is to fool observer-supporting software into thinking that
the encrypted data is not encrypted at all. We employ a modified Word
Substitution Method known as the Grammatical Substitution Method in our
cryptosystem. We also make use of Hidden Markov Models. We test our
cryptosystem using a computer program written in the Java Programming Language.
Finally, we test the output of our cryptosystem using statistical tests.Comment: 7 pages. 5 figures. Appeared in the 2nd International Conference on
Security of Information and Networks (SIN 2009), North Cyprus, Turkey;
Proceedings of the 2nd International Conference on Security of Information
and Networks (SIN 2009), North Cyprus, Turke
Resource Sharing and Coevolution in Evolving Cellular Automata
Evolving one-dimensional cellular automata (CAs) with genetic algorithms has
provided insight into how improved performance on a task requiring global
coordination emerges when only local interactions are possible. Two approaches
that can affect the search efficiency of the genetic algorithm are coevolution,
in which a population of problems---in our case, initial configurations of the
CA lattice---evolves along with the population of CAs; and resource sharing, in
which a greater proportion of a limited fitness resource is assigned to those
CAs which correctly solve problems that fewer other CAs in the population can
solve. Here we present evidence that, in contrast to what has been suggested
elsewhere, the improvements observed when both techniques are used together
depend largely on resource sharing alone.Comment: 8 pages, 1 figure; http://www.santafe.edu/~evca/rsc.ps.g
PDGA: The primal-dual genetic algorithm
Copyright @ 2003 IOS PressGenetic algorithms (GAs) are a class of search algorithms based on principles of natural evolution. Hence, incorporating mechanisms used in nature may improve the performance of GAs. In this paper inspired by the mechanisms of complementarity and dominance that broadly exist in nature, we present a new genetic algorithm — Primal-Dual Genetic Algorithm (PDGA). PDGA operates on a pair of chromosomes that are primal-dual to each other through the primal-dual mapping, which maps one to the other with a maximum distance away in a given distance space in genotype. The primal-dual mapping improves the exploration capacity of PDGA and thus its searching efficiency in the search space. To test the performance of PDGA, experiments were carried out to compare PDGA over traditional simple GA (SGA) and a peer GA, called Dual Genetic Algorithm (DGA), over a typical set of test problems. The experimental results demonstrate that PDGA outperforms both SGA and DGA on the test set. The results show that PDGA is a good candidate genetic algorithm
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