scholarly journals Data type Modeling with DFA and NFA as a Lexical Analysis Generator

2019 ◽  
Vol 8 (4) ◽  
pp. 415
Author(s):  
Nisreen L. Abdulnabi ◽  
Hawar B. Ahmad
Keyword(s):  
Active Learning ◽  
Finite Automata ◽  
Data Type ◽  
Lexical Analysis ◽  
Lexical Analyzer

Lexical analysis helps the interactivity and visualization for active learning that can improve difficult concepts in automata. This study gives an implementation of two frequently used model, NFA for combination of Real and Integer data type and DFA for Double Data Type in Java this chosen model will be implemented using JFLAP. The model will also be tested using JFLAP that will accept at least FIVE (5) inputs and rejected FIVE (5) inputs. These two models are some of the different lexical analyzer generators that have been implemented for different purposes in finite automata.

scholarly journals A Survey about various Generations of Lexical Analyzer

2019 ◽  
Vol 8 (2) ◽  
pp. 50
Author(s):  
Zakiya Ali Nayef
Keyword(s):  
Active Learning ◽  
Regular Expression ◽  
Finite Automata ◽  
General Idea ◽  
Lexical Analysis ◽  
Advantages And Disadvantages ◽  
Lexical Analyzer

Lexical analysis helps the interactivity and visualization for active learning that can improve difficult concepts in automata. This study gives a view on different lexical analyzer generators that has been implemented for different purposes in finite automata. It also intends to give a general idea on the lexical analyzer process, which will cover the automata model that is used in the various reviews. Some concepts that will be described are finite automata model, regular expression and other related components. Also, the advantages and disadvantages of lexical analyzer will be discussed. 

Design and Development of a Parallel Lexical Analyzer for C Language

2018 ◽  
Vol 8 (1) ◽  
pp. 68-82
Author(s):  
Swagat Kumar Jena ◽  
Satyabrata Das ◽  
Satya Prakash Sahoo
Keyword(s):  
Experimental Result ◽  
Full Potential ◽  
Lexical Analysis ◽  
C Language ◽  
Analysis Process ◽  
Analysis Phase ◽  
Almost Everywhere ◽  
Compilation Process ◽  
Significant Performance ◽  
Lexical Analyzer

Future of computing is rapidly moving towards massively multi-core architecture because of its power and cost advantages. Almost everywhere Multi-core processors are being used now-a-days and number of cores per chip is also relatively increasing. To exploit full potential offered by multi-core architecture, the system software like compilers should be designed for parallelized execution. In the past, various significant works have been made to change the design of traditional compiler to take advantages of the future multi-core platform. This paper focuses on adapting parallelism in the lexical analysis phase of the compilation process. The main objective of our proposal is to do the lexical analysis i.e., finding the tokens in an input stream in parallel. We use the parallel constructs available in OpenMP to achieve parallelism in the lexical analysis process for multi-core machines. The experimental result of our proposal shows a significant performance improvement in the parallel lexical analysis phase as compared to sequential version in terms of time of execution.

Lexical Analysis in Content Management System Details

2019 ◽  
Vol 8 (2) ◽  
pp. 119-128
Author(s):  
Takudzwa Fadziso
Keyword(s):  
Cognitive Science ◽  
Semantic Content ◽  
Content Management ◽  
Structural Aspect ◽  
Computer Language ◽  
Content Management System ◽  
Lexical Analysis ◽  
Advantages And Disadvantages ◽  
Lexical Structure ◽  
Lexical Analyzer

In cognitive science, understanding language by humans starts with recognition. Without the phase, understanding languages become a very cumbersome task. The task of the lexical analyzer is to read the various input characters grouping them into lexemes and producing an output of a sequence of tokens. But before we discuss lexical analysis further, we should have an overview of this research. Lexical analysis is best described as tokenization that converts a sequence of characters (program) into tokens with identifiable meanings. This study aims to look at the various terms or words related to lexical structure, purpose, and how they are applied to get the required result. The lexical analysis offers researchers an idea of the structural aspect of computer language and its semantic content. The work also talks about the advantages and disadvantages of lexical analysis.

scholarly journals Learning Nondeterministic Real-Time Automata

2021 ◽  
Vol 20 (5s) ◽  
pp. 1-26
Author(s):  
Jie An ◽  
Bohua Zhan ◽  
Naijun Zhan ◽  
Miaomiao Zhang
Keyword(s):  
Active Learning ◽  
Integrated Circuits ◽  
Real Time ◽  
Learning Algorithm ◽  
Timed Automata ◽  
Finite Automata ◽  
Special Kind ◽  
Learning Problem ◽  
Equivalence Queries

We present an active learning algorithm named NRTALearning for nondeterministic real-time automata (NRTAs). Real-time automata (RTAs) are a subclass of timed automata with only one clock which resets at each transition. First, we prove the corresponding Myhill-Nerode theorem for real-time languages. Then we show that there exists a unique minimal deterministic real-time automaton (DRTA) recognizing a given real-time language, but the same does not hold for NRTAs. We thus define a special kind of NRTAs, named residual real-time automata (RRTAs), and prove that there exists a minimal RRTA to recognize any given real-time language. This transforms the learning problem of NRTAs to the learning problem of RRTAs. After describing the learning algorithm in detail, we prove its correctness and polynomial complexity. In addition, based on the corresponding Myhill-Nerode theorem, we extend the existing active learning algorithm NL* for nondeterministic finite automata to learn RRTAs. We evaluate and compare the two algorithms on two benchmarks consisting of randomly generated NRTAs and rational regular expressions. The results show that NRTALearning generally performs fewer membership queries and more equivalence queries than the extended NL* algorithm, and the learnt NRTAs have much fewer locations than the corresponding minimal DRTAs. We also conduct a case study using a model of scheduling of final testing of integrated circuits.

scholarly journals An Fuzzy Lexical Research

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1209-1211
Keyword(s):  
Finite Automata ◽  
String Matching ◽  
Regular Languages ◽  
Regular Expressions ◽  
Lexical Analysis ◽  
Design And Implementation ◽  
Comprehensive View ◽  
Analysis Phase ◽  
Source Program ◽  
Degree Of Membership

The objective of this paper is to analyse the design and implementation of the fuzzy lexical analyser and observe how it is different from the traditional lexical analyser. It is known that lexical analysis is an important phase of a compiler. It reads the source program character by character and uses regular expressions, finite automata methods for string matching. Unlike traditional lexical analysers, tokens in fuzzy analysers belong to more than one token type with varying degree of membership. The paper exchange views on the design and implementation of fuzzy lexical analysers. It observes algorithms that handle errors due to insertion, deletion etc. in the lexical analysis phase of a compiler. Several properties of fuzzy languages are also reviewed. Hence this paper gives a comprehensive view of fuzzy regular languages, models and algorithms

Active Learning Through Use of Learning and Study Strategies

1990 ◽  
Vol 35 (5) ◽  
pp. 471-472
Author(s):  
Herbert J. Klausmeier
Keyword(s):  
Active Learning ◽  
Study Strategies ◽  
Learning And Study Strategies

A randomized controlled trial of an online, modular, active learning training program for behavioral activation for depression.

2017 ◽  
Vol 85 (8) ◽  
pp. 814-825 ◽  
Author(s):  
Ajeng J. Puspitasari ◽  
Jonathan W. Kanter ◽  
Andrew M. Busch ◽  
Rachel Leonard ◽  
Shira Dunsiger ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Active Learning ◽  
Training Program ◽  
Behavioral Activation ◽  
Controlled Trial ◽  
Randomized Controlled
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