We introduce a novel Bayesian approach for deciphering complex substitution ciphers. Our method uses a decipherment model which combines information from letter n-gram language models as well as word dictionaries. Bayesian inference is performed on our model using an efﬁcient sampling technique. We evaluate the quality of the Bayesian decipherment output on simple and homophonic letter substitution ciphers and show that unlike a previous approach, our method consistently produces almost 100% accurate decipherments. ...
At the other extreme, each disk controller now has tens of mega-
bytes of storage and a very capable processor. It is quite feasible
to have intelligent disks that offer either database access (SQL or
some other non-procedural language) and even web service ac-
cess. Moving from a block-oriented disk interface to a file inter-
face, and then to a set or service interface has been the goal of
database machine advocates...
The turn of the millennium has been described as the dawn of a new scientific
revolution, which will have as great an impact on society as the industrial and
computer revolutions before. This revolution was heralded by a large-scale
DNA sequencing effort in July 1995, when the entire 1.8 million base pairs
of the genome of the bacterium Haemophilus influenzae was published – the
first of a free-living organism. Since then, the amount of DNA sequence data
in publicly accessible data bases has been growing exponentially, including a
working draft of the complete 3.
This paper examines how a new class of nonparametric Bayesian models can be effectively applied to an open-domain event coreference task. Designed with the purpose of clustering complex linguistic objects, these models consider a potentially inﬁnite number of features and categorical outcomes. The evaluation performed for solving both within- and cross-document event coreference shows signiﬁcant improvements of the models when compared against two baselines for this task.