Random generation of words of context-free languages according to the frequencies of letters

2000 ◽  
pp. 113-125 ◽  
Author(s):  
Alain Denise ◽  
Olivier Roques ◽  
Michel Termier
Keyword(s):  
Random Generation ◽  
Context Free

Efficient generation of random sentences

1996 ◽  
Vol 2 (1) ◽  
pp. 1-13 ◽  
Author(s):  
MARK-JAN NEDERHOF
Keyword(s):  
Static Analysis ◽  
Finite Domain ◽  
Random Generation ◽  
Efficient Generation ◽  
Parameter Values ◽  
Context Free ◽  
Context Free Grammars

We discuss the random generation of strings using the grammatical formalism AGFL. This formalism consists of context-free grammars extended with a parameter mechanism, where the parameters range over a finite domain. Our approach consists in static analysis of the combinations of parameter values with which derivations can be constructed. After this analysis, generation of sentences can be performed without backtracking.

scholarly journals Non-redundant random generation algorithms for weighted context-free grammars

2013 ◽  
Vol 502 ◽  
pp. 177-194 ◽  
Author(s):  
William Andrew Lorenz ◽  
Yann Ponty
Keyword(s):  
Random Generation ◽  
Context Free ◽  
Context Free Grammars

Uniform Random Generation of Strings in a Context-Free Language

1983 ◽  
Vol 12 (4) ◽  
pp. 645-655 ◽  
Author(s):  
Timothy Hickey ◽  
Jacques Cohen
Keyword(s):  
Random Generation ◽  
Context Free Language ◽  
Context Free ◽  
Free Language

scholarly journals Random Generation for Finitely Ambiguous Context-free Languages

2001 ◽  
Vol 35 (6) ◽  
pp. 499-512 ◽  
Author(s):  
Alberto Bertoni ◽  
Massimiliano Goldwurm ◽  
Massimo Santini
Keyword(s):  
Random Generation ◽  
Context Free

scholarly journals Multi-dimensional Boltzmann Sampling of Languages

2010 ◽  
Vol DMTCS Proceedings vol. AM,... (Proceedings) ◽  
Author(s):  
Olivier Bodini ◽  
Yann Ponty
Keyword(s):  
Frequency Of Occurrence ◽  
Random Generation ◽  
Tolerance Intervals ◽  
Strong Connectivity ◽  
Expected Time ◽  
Context Free Language ◽  
Different Types ◽  
International Audience ◽  
Context Free ◽  
Free Language

International audience We address the uniform random generation of words from a context-free language (over an alphabet of size $k$), while constraining every letter to a targeted frequency of occurrence. Our approach consists in a multidimensional extension of Boltzmann samplers. We show that, under mostly $\textit{strong-connectivity}$ hypotheses, our samplers return a word of size in $[(1- \epsilon)n, (1+ \epsilon)n]$ and exact frequency in $\mathcal{O}(n^{1+k/2})$ expected time. Moreover, if we accept tolerance intervals of width in $\Omega (\sqrt{n})$ for the number of occurrences of each letters, our samplers perform an approximate-size generation of words in expected $\mathcal{O}(n)$ time. We illustrate our approach on the generation of Tetris tessellations with uniform statistics in the different types of tetraminoes.

Framework for rare event detection using Artificial Neural Network based context free grammar

2020 ◽  
Vol 39 (6) ◽  
pp. 8463-8475
Author(s):  
Palanivel Srinivasan ◽  
Manivannan Doraipandian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Event Detection ◽  
Performance Metrics ◽  
Rare Events ◽  
Rare Event ◽  
Video Stream ◽  
Context Free Grammar ◽  
Artificial Neural ◽  
Context Free

Rare event detections are performed using spatial domain and frequency domain-based procedures. Omnipresent surveillance camera footages are increasing exponentially due course the time. Monitoring all the events manually is an insignificant and more time-consuming process. Therefore, an automated rare event detection contrivance is required to make this process manageable. In this work, a Context-Free Grammar (CFG) is developed for detecting rare events from a video stream and Artificial Neural Network (ANN) is used to train CFG. A set of dedicated algorithms are used to perform frame split process, edge detection, background subtraction and convert the processed data into CFG. The developed CFG is converted into nodes and edges to form a graph. The graph is given to the input layer of an ANN to classify normal and rare event classes. Graph derived from CFG using input video stream is used to train ANN Further the performance of developed Artificial Neural Network Based Context-Free Grammar – Rare Event Detection (ACFG-RED) is compared with other existing techniques and performance metrics such as accuracy, precision, sensitivity, recall, average processing time and average processing power are used for performance estimation and analyzed. Better performance metrics values have been observed for the ANN-CFG model compared with other techniques. The developed model will provide a better solution in detecting rare events using video streams.

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