The identification of a source given a sequence of random strings is discussed. Two modes of random string generation are analyzed. In the first mode, arbitrary strings are generated in which the individual symbols occur exactly once in each random string. The latter case corresponds to the situation in which the sources generate random permutations. In both cases, the best match to the distribution being used by each source can be obtained by maintaining an exponential number of statistics. This being infeasible, a simple parameterization of the distributions is proposed. For arbitrary strings, the simple unigram-based model (U-model) is proposed. For the case of permutations, a new model called the S-model is proposed, and it is used to analyze and/or approximate unknown distributions of permutations. The relevant estimation procedures, together with the applications to source recognition, are presented. The method presents a unique blend of syntactic and statistical pattern recognition.>

The authors consider the problem of assigning more than one symbol of a finite alphabet A to the same key on a keyboard. Since multiple symbols of the alphabet A reside on the same key, the representations of all the words in a finite dictionary H need not be unique. The problem is one of optimally assigning the symbols of the alphabet to the keys of a given keyboard with a view to minimize the total number of words that have ambiguous representation. The problem is proven to be NP-hard. After presenting the only reported solution to the problem, a fast learning-automaton-based solution to this problem is reported. Experimental results demonstrating the power of this solution are presented.>