Testing equivalence of morphisms on context-free languages

1994 ◽  
pp. 460-470 ◽  
Author(s):  
Wojciech Plandowski
Keyword(s):  
Context Free ◽  
Testing Equivalence

THE INCLUSION PROBLEM OF CONTEXT-FREE LANGUAGES: SOME TRACTABLE CASES

2011 ◽  
Vol 22 (02) ◽  
pp. 289-299 ◽  
Author(s):  
ALBERTO BERTONI ◽  
CHRISTIAN CHOFFRUT ◽  
ROBERTO RADICIONI
Keyword(s):  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Inclusion Problem ◽  
Context Free Language ◽  
Straight Line ◽  
Fixed Set ◽  
Previous Algorithm ◽  
Context Free ◽  
Testing Equivalence ◽  
Free Language

We study the problem of testing whether a context-free language is included in a fixed set L0, where L0 is the language of words reducing to the empty word in the monoid defined by a complete string rewrite system. We prove that, if the monoid is cancellative, then our inclusion problem is polynomially reducible to the problem of testing equivalence of straight-line programs in the same monoid. As an application, we obtain a polynomial time algorithm for testing if a context-free language is included in a Dyck language (the best previous algorithm for this problem was doubly exponential).

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.

Saussurelaisen kielikäsityksen kritiikki

Virittäjä ◽  
2020 ◽  
Vol 124 (1) ◽  
Author(s):  
Mikko Laasanen
Keyword(s):  
Communication System ◽  
Written Language ◽  
Strong Form ◽  
Continuous Speech ◽  
Language Bias ◽  
Context Free ◽  
Ontological Concept

Artikkeli käsittelee saussurelaista kielikäsitystä kohtaan esitettyä kritiikkiä. Artikkelin tavoitteena on puolustaa saussurelaista kielikäsitystä ja esittää Saussure moni-puolisempana ajattelijana kuin mitä Kurssin vahvasti strukturalisesta luennasta voisi päätellä. Artikkelissa tarkastellaan käsitystä kielestä järjestelmänä (Saussuren langue), kontekstivapaata merkitystä, kirjoitetun kielen vääristymää (written language bias), Roy Harrisin kielimyyttiä sekä kielen dynaamisuutta. Artikkelissa esitetään, että langue on sekä metodologinen että ontologinen käsite, joka viittaa sekä kielen järjestäytymättömiin sääntöihin että kielitieteilijän niistä luomaan järjestelmään. Kontekstivapaan merkityksen osalta korostetaan sitä, että jonkinlainen merkityksen pysyvyys on välttämätön osa kieltä kommunikaatiojärjestelmänä. Artikkelissa argumentoidaan kirjoitetun kielen vääristymän vahvaa muotoa vastaan, jonka mukaan esimerkiksi puheen analysoiminen diskreeteiksi yksiköiksi johtuu kirjoitetun kielen vaikutuksesta. Harrisin kielimyytin osalta esitetään, että kyse ei ole Saussuren näkemyksistä vaan Harrisin tulkinnoista. Artikkelissa esitetään myös, että dynaamisuus ei ole yhteensopimaton käsite saussurelaisen kielikäsityksen kanssa.   On the critique of the Saussurean concept of language: some perspectives and counter-arguments The article deals with the critique of the Saussurean concept of language. The purpose of the article is to defend the Saussurean concept of language and to present Saussure as a more versatile thinker than may be assumed from a purely structuralist reading of Course. The article discusses the concept of language as a system (Saussure’s langue), the notion of context-free meaning, the so-called written-language bias, Roy Harris’ language myth, and the notion of dynamicity in language in relation to the Saussurean concept of language. The article begins by arguing that langue is both a methodological and an ontological concept that refers both to the unorganised rules of language and to the system of language rules as organised by the linguist. Second, the author asserts that some kind of permanence of meaning is essential to the concept of language as a communication system. Third, an argument is presented against the strong form of written-language bias, according to which, for instance, the analysis and reduction of continuous speech into discrete units is based on the model of written language. Fourth, the author posits that the language myth, developed by Harris, is not based on Saussure’s views but on Harris’ interpretation of Saussure’s views. The article ends with the contention that the notion of dynamicity is not incompatible with the Saussurean concept of language.

PrePrint - Exploring Perceptions of Meaningful Relationships

2018 ◽  
Author(s):  
Nataly Beribisky ◽  
Heather Davidson ◽  
Rob Cribbie
Keyword(s):  
Effect Size ◽  
Mean Difference ◽  
Practical Significance ◽  
Equivalence Testing ◽  
Significant Variability ◽  
Meaningful Relationship ◽  
Meaningful Relationships ◽  
Context Free

Researchers often need to consider the practical significance of a relationship. For example, interpreting the magnitude of an effect size or establishing bounds in equivalence testing requires knowledge of the meaningfulness of a relationship. However, there has been little research exploring the degree of relationship among variables (e.g., correlation, mean difference) necessary for an association to be interpreted as meaningful or practically significant. In this study, we presented statistically trained and untrained participants with a collection of figures that displayed varying degrees of mean difference between groups or correlations among variables and participants indicated whether or not each relationship was meaningful. The results suggest that statistically trained and untrained participants differ in their qualification of a meaningful relationship, and that there is significant variability in how large a relationship must be before it is labeled meaningful. The results also shed some light on what degree of relationship is considered meaningful by individuals in a context-free setting.

Prior real-world experience influences non-linguistic statistical learning

2020 ◽  
Author(s):  
Stephen Charles Van Hedger ◽  
Ingrid Johnsrude ◽  
Laura Batterink
Keyword(s):  
Statistical Learning ◽  
Real World ◽  
Language Experience ◽  
Pitch Range ◽  
Test Items ◽  
Instrument Measuring ◽  
Statistical Regularities ◽  
Context Free ◽  
Specific Learning

Listeners are adept at extracting regularities from the environment, a process known as statistical learning (SL). SL has been generally assumed to be a form of “context-free” learning that occurs independently of prior knowledge, and SL experiments typically involve exposing participants to presumed novel regularities, such as repeating nonsense words. However, recent work has called this assumption into question, demonstrating that learners’ previous language experience can considerably influence SL performance. In the present experiment, we tested whether previous knowledge also shapes SL in a non-linguistic domain, using a paradigm that involves extracting regularities over tone sequences. Participants learned novel tone sequences, which consisted of pitch intervals not typically found in Western music. For one group of participants, the tone sequences used artificial, computerized instrument sounds. For the other group, the same tone sequences used familiar instrument sounds (piano or violin). Knowledge of the statistical regularities was assessed using both trained sounds (measuring specific learning) and sounds that differed in pitch range and/or instrument (measuring transfer learning). In a follow-up experiment, two additional testing sessions were administered to gauge retention of learning (one day and approximately one-week post-training). Compared to artificial instruments, training on sequences played by familiar instruments resulted in reduced correlations among test items, reflecting more idiosyncratic performance. Across all three testing sessions, learning of novel regularities presented with familiar instruments was worse compared to unfamiliar instruments, suggesting that prior exposure to music produced by familiar instruments interfered with new sequence learning. Overall, these results demonstrate that real-world experience influences SL in a non-linguistic domain, supporting the view that SL involves the continuous updating of existing representations, rather than the establishment of entirely novel ones.

scholarly journals Epsilon-reducible context-free languages and characterizations of indexed languages

2019 ◽  
Vol 269 ◽  
pp. 104444 ◽  
Author(s):  
Séverine Fratani ◽  
El Makki Voundy
Keyword(s):  
Context Free

Non-context-free probabilistic languages

1970 ◽  
pp. 9-14
Author(s):  
Clarence A. Ellis
Keyword(s):  
Probabilistic Languages ◽  
Context Free
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