Pumping lemma in context-free grammar theory based on complete residuated lattice-valued logic

2009 ◽  
Vol 160 (8) ◽  
pp. 1141-1151 ◽  
Author(s):  
Hongyan Xing ◽  
Daowen Qiu
Keyword(s):  
Residuated Lattice ◽  
Context Free Grammar ◽  
Complete Residuated Lattice ◽  
Pumping Lemma ◽  
Context Free

l-Valued multiset automata and l-valued multiset languages

2020 ◽  
pp. 1-15
Author(s):  
Vinay Gautam
Keyword(s):  
Finite Automaton ◽  
Residuated Lattice ◽  
Regular Languages ◽  
Automata Theory ◽  
Necessary And Sufficient Condition ◽  
Zero Divisors ◽  
Complete Residuated Lattice ◽  
Pumping Lemma ◽  
Deterministic Formula ◽  
Necessary And Sufficient

The reason for this work is to present and study deterministic multiset automata, multiset automata and their languages with membership values in complete residuated lattice without zero divisors. We build up the comparability of deterministic [Formula: see text]-valued multiset finite automaton and [Formula: see text]-valued multiset finite automaton in sense of recognizability of a [Formula: see text]-valued multiset language. Then, we relate multiset regular languages to a given [Formula: see text]-valued multiset regular languages and vice versa. At last, we present the concept of pumping lemma for [Formula: see text]-valued multiset automata theory, which we utilize to give a necessary and sufficient condition for a [Formula: see text]-valued multiset language to be non-constant.

Fuzzy Regular Languages Based on Residuated Lattice

2020 ◽  
Vol 16 (02) ◽  
pp. 363-376
Author(s):  
Anupam K. Singh ◽  
S. P. Tiwari
Keyword(s):  
Residuated Lattice ◽  
Finite Automata ◽  
Regular Languages ◽  
Sufficient Condition ◽  
Membership Functions ◽  
Necessary And Sufficient Condition ◽  
Complete Residuated Lattice ◽  
Pumping Lemma ◽  
Fuzzy Finite Automata ◽  
Necessary And Sufficient

The purpose of this work is to introduce the concept of fuzzy regular languages (FRL) accepted by fuzzy finite automata, and try to introduce the categorical look of fuzzy languages where the codomain of membership functions are taken as a complete residuated lattice, instead of [Formula: see text]. Also, we have introduced pumping lemma for FRL, which is used to establish a necessary and sufficient condition for a given fuzzy languages to be non-constant.

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.

scholarly journals Searching for universal model of amyloid signaling motifs using probabilistic context-free grammars

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Witold Dyrka ◽  
Marlena Gąsior-Głogowska ◽  
Monika Szefczyk ◽  
Natalia Szulc
Keyword(s):  
Functional Relationship ◽  
High Sensitivity ◽  
Alternative Methods ◽  
Discriminative Power ◽  
Context Free Grammar ◽  
Protein Motifs ◽  
Functional Features ◽  
Universal Grammars ◽  
Context Free ◽  
Probabilistic Context

Abstract Background Amyloid signaling motifs are a class of protein motifs which share basic structural and functional features despite the lack of clear sequence homology. They are hard to detect in large sequence databases either with the alignment-based profile methods (due to short length and diversity) or with generic amyloid- and prion-finding tools (due to insufficient discriminative power). We propose to address the challenge with a machine learning grammatical model capable of generalizing over diverse collections of unaligned yet related motifs. Results First, we introduce and test improvements to our probabilistic context-free grammar framework for protein sequences that allow for inferring more sophisticated models achieving high sensitivity at low false positive rates. Then, we infer universal grammars for a collection of recently identified bacterial amyloid signaling motifs and demonstrate that the method is capable of generalizing by successfully searching for related motifs in fungi. The results are compared to available alternative methods. Finally, we conduct spectroscopy and staining analyses of selected peptides to verify their structural and functional relationship. Conclusions While the profile HMMs remain the method of choice for modeling homologous sets of sequences, PCFGs seem more suitable for building meta-family descriptors and extrapolating beyond the seed sample.

A generalization of the concept of a context-free grammar

Cybernetics ◽  
1974 ◽  
Vol 8 (3) ◽  
pp. 349-351
Author(s):  
A. A. Letichevskii
Keyword(s):  
Context Free Grammar ◽  
Context Free

scholarly journals Knowledge Sources for Constituent Parsing of German, a Morphologically Rich and Less-Configurational Language

2013 ◽  
Vol 39 (1) ◽  
pp. 57-85 ◽  
Author(s):  
Alexander Fraser ◽  
Helmut Schmid ◽  
Richárd Farkas ◽  
Renjing Wang ◽  
Hinrich Schütze
Keyword(s):  
State Of The Art ◽  
Lessons Learned ◽  
Knowledge Sources ◽  
Lexical Knowledge ◽  
Context Free Grammar ◽  
The Impact ◽  
Context Free ◽  
Probabilistic Context

We study constituent parsing of German, a morphologically rich and less-configurational language. We use a probabilistic context-free grammar treebank grammar that has been adapted to the morphologically rich properties of German by markovization and special features added to its productions. We evaluate the impact of adding lexical knowledge. Then we examine both monolingual and bilingual approaches to parse reranking. Our reranking parser is the new state of the art in constituency parsing of the TIGER Treebank. We perform an analysis, concluding with lessons learned, which apply to parsing other morphologically rich and less-configurational languages.

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