This document discusses context-free languages and grammars (CFLs and CFGs). Some key points:
- CFLs are a class of languages larger than regular languages that can be defined using context-free grammars (CFGs). CFGs use recursive rules called productions.
- An example CFG is given that defines the language of binary palindromes using the productions: A => 0A0 | 1A1 | 0 | 1 | ε. Strings can be generated or recognized using this grammar.
- Parse trees can represent derivations in a CFG. Derivations and parse trees are interchangeable representations. Leftmost and rightmost derivations may differ but define the same language.
This document provides information about the vision, mission, and course objectives of the Theory of Computation department. The vision is to become an excellent center for computer science education and produce competent engineers with strong ethics. The mission includes providing outcome-based education, industry interaction opportunities, lifelong learning platforms, and developing social responsibility. The course objectives are to examine finite automata, classify regular grammars, categorize context-free languages, and analyze Turing machines and language hierarchies. A table maps the course objectives to program outcomes. The document then provides details on context-free grammars, derivations, Backus-Naur form, and parse trees.
Regular expressions provide a declarative way to express patterns in strings and are equivalent to finite automata. They can be used to represent formal languages. Regular expressions can be converted to deterministic finite automata by tracing all distinct paths in the automata. Conversely, finite automata can be converted to regular expressions. Regular expressions follow algebraic laws and are widely used in tools like grep, sed and lexical analyzers.
The document provides information about a course on the theory of automata. It includes details such as the course title, prerequisites, duration, lectures, laboratories, and topics to be covered. The topics include finite automata, deterministic finite automata, non-deterministic finite automata, regular expressions, properties of regular languages, context-free grammars, pushdown automata, and Turing machines. It also lists reference books and textbooks, and the marking scheme for the course.
This document provides information about a course on the theory of automata, including:
- The course title is Theory of Computer Science [Automata] and is intended for students pursuing a BCS degree in their fifth semester.
- Key topics that will be covered include finite automata, regular expressions, context-free grammars, pushdown automata, and Turing machines.
- Reference textbooks and materials are listed to support student learning in the course over 18 weeks of lectures and labs.
1. The document contains questions from various computer science subjects including formal languages and automata, regular expressions and languages, pushdown automata, and context-free languages and Turing machines.
2. It includes definitions, examples, differences between models like DFAs and NFAs, properties of languages, and questions asking to construct automata or grammars for specific languages.
3. Several questions ask students to prove statements about language classes, pumping lemmas, ambiguity in grammars, and the equivalence of computational models like PDAs and Turing machines.
1. Regular expressions provide a declarative way to express patterns in strings and are commonly used in Unix environments and programming languages like Perl.
2. Regular expressions represent regular languages that can also be represented by finite state machines. There is a correspondence between regular expressions and finite state machines such that any regular expression can be converted to a finite state machine and vice versa.
3. Regular expressions are recursively defined based on operators like union, concatenation, and Kleene closure. The precedence of operators is also specified with Kleene closure having the highest precedence followed by concatenation and union.
The document discusses lexical analysis in compiler design. It covers the role of the lexical analyzer, tokenization, and representation of tokens using finite automata. Regular expressions are used to formally specify patterns for tokens. A lexical analyzer generator converts these specifications into a finite state machine (FSM) implementation to recognize tokens in the input stream. The FSM is typically a deterministic finite automaton (DFA) for efficiency, even though a nondeterministic finite automaton (NFA) may require fewer states.
This document provides information about the vision, mission, and course objectives of the Theory of Computation department. The vision is to become an excellent center for computer science education and produce competent engineers with strong ethics. The mission includes providing outcome-based education, industry interaction opportunities, lifelong learning platforms, and developing social responsibility. The course objectives are to examine finite automata, classify regular grammars, categorize context-free languages, and analyze Turing machines and language hierarchies. A table maps the course objectives to program outcomes. The document then provides details on context-free grammars, derivations, Backus-Naur form, and parse trees.
Regular expressions provide a declarative way to express patterns in strings and are equivalent to finite automata. They can be used to represent formal languages. Regular expressions can be converted to deterministic finite automata by tracing all distinct paths in the automata. Conversely, finite automata can be converted to regular expressions. Regular expressions follow algebraic laws and are widely used in tools like grep, sed and lexical analyzers.
The document provides information about a course on the theory of automata. It includes details such as the course title, prerequisites, duration, lectures, laboratories, and topics to be covered. The topics include finite automata, deterministic finite automata, non-deterministic finite automata, regular expressions, properties of regular languages, context-free grammars, pushdown automata, and Turing machines. It also lists reference books and textbooks, and the marking scheme for the course.
This document provides information about a course on the theory of automata, including:
- The course title is Theory of Computer Science [Automata] and is intended for students pursuing a BCS degree in their fifth semester.
- Key topics that will be covered include finite automata, regular expressions, context-free grammars, pushdown automata, and Turing machines.
- Reference textbooks and materials are listed to support student learning in the course over 18 weeks of lectures and labs.
1. The document contains questions from various computer science subjects including formal languages and automata, regular expressions and languages, pushdown automata, and context-free languages and Turing machines.
2. It includes definitions, examples, differences between models like DFAs and NFAs, properties of languages, and questions asking to construct automata or grammars for specific languages.
3. Several questions ask students to prove statements about language classes, pumping lemmas, ambiguity in grammars, and the equivalence of computational models like PDAs and Turing machines.
1. Regular expressions provide a declarative way to express patterns in strings and are commonly used in Unix environments and programming languages like Perl.
2. Regular expressions represent regular languages that can also be represented by finite state machines. There is a correspondence between regular expressions and finite state machines such that any regular expression can be converted to a finite state machine and vice versa.
3. Regular expressions are recursively defined based on operators like union, concatenation, and Kleene closure. The precedence of operators is also specified with Kleene closure having the highest precedence followed by concatenation and union.
The document discusses lexical analysis in compiler design. It covers the role of the lexical analyzer, tokenization, and representation of tokens using finite automata. Regular expressions are used to formally specify patterns for tokens. A lexical analyzer generator converts these specifications into a finite state machine (FSM) implementation to recognize tokens in the input stream. The FSM is typically a deterministic finite automaton (DFA) for efficiency, even though a nondeterministic finite automaton (NFA) may require fewer states.
1. A parse tree is a graphical representation of a derivation in a context-free grammar (CFG). It has nodes labeled with symbols and leaves labeled with terminals or epsilon.
2. An ambiguous CFG is one where a string can have two different leftmost derivations, rightmost derivations, or parse trees. Showing a string has two leftmost derivations proves a CFG is ambiguous.
3. The balanced parentheses language can have both ambiguous and unambiguous CFGs, showing ambiguity is a property of grammars, not languages.
Context-free grammars are a notation for describing languages using variables and productions. They are more powerful than regular expressions but still cannot define all possible languages. A context-free grammar consists of terminals, variables, a start symbol, and productions. Strings in the language are derived by replacing variables according to productions until only terminals remain. Leftmost and rightmost derivations restrict replacements to specific variables.
Context-free grammars are a notation for describing languages using variables and productions. They are more powerful than regular expressions but still cannot define all possible languages. A context-free grammar consists of terminals, variables, a start symbol, and productions. Strings in the language are derived by replacing variables according to the productions until a string of just terminals is reached. Leftmost and rightmost derivations restrict replacements to specific variables.
This document contains lecture notes on finite automata from the Theory of Computation unit 1 course. Some key points include:
1. Finite automata are defined as 5-tuples (Q, Σ, δ, q0, F) where Q is a set of states, Σ is an input alphabet, δ is a transition function, q0 is the initial state, and F is a set of final states.
2. Deterministic finite automata (DFAs) have a single transition between states for each input symbol, while non-deterministic finite automata (NFAs) can have multiple transitions for a single input.
3. Regular expressions are used to describe the languages
Regular languages can be described using regular grammars, regular expressions, or finite automata. A regular grammar contains productions of the form A->aB or A->a where A and B are nonterminals and a is a terminal. A language is regular if it can be generated by a regular grammar. Regular expressions describe languages using operators like concatenation, union, and Kleene star. Finite automata are machines that accept or reject strings using a finite number of states. The three models are equivalent in that they can generate the same regular languages.
Introduction:
A context-free grammar (CFG) is a term used in formal languages theory to describe a certain type of formal grammar. A context-free grammar is a set of production rules that describe all possible strings in a given formal language. Production rules are simple replacements. For example, the rule
A α
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
The document contains questions and answers related to compiler design topics such as parsing, grammars, syntax analysis, error handling, derivation, sentential forms, parse trees, ambiguity, left and right recursion elimination etc. Key points discussed are:
1. The role of parser is to verify the string of tokens generated by lexical analyzer according to the grammar rules and detect syntax errors. It outputs a parse tree.
2. Common parsing methods are top-down, bottom-up and universal. Top-down methods include LL, LR. Bottom-up methods include LR, LALR.
3. Errors can be lexical, syntactic, semantic and logical detected by different compiler phases. Error recovery strategies include panic mode
The presentation topic is on computational models. It will be presented by Md. Touhidur Rahman. The document discusses four computational models: finite automata (DFA and NFA), context free grammar, pushdown automata, and Turing machines. Finite automata are defined using states, symbols, transitions, a start state, and accepting states. Context free grammars are defined using variables, terminals, productions, and a start symbol. Pushdown automata extend NFAs with a stack. Turing machines are formally defined but also described at a higher level for understanding.
This document discusses automata theory and focuses on grammars, languages, and finite state machines. It defines key terminology like alphabets, strings, languages, and regular expressions. It explains Chomsky's hierarchy of formal languages from type-3 regular languages to type-0 recursively enumerable languages. The document also discusses finite state automata (FSA), deterministic finite automata (DFA), non-deterministic finite automata (NFA), context-free grammars, pushdown automata, and Turing machines. Examples of grammars, languages, and finite state machines are provided to illustrate these concepts.
Cs6660 compiler design may june 2016 Answer Keyappasami
The document describes the various phases of a compiler:
1. Lexical analysis breaks the source code into tokens.
2. Syntax analysis generates a parse tree from the tokens.
3. Semantic analysis checks for semantic correctness using the parse tree and symbol table.
4. Intermediate code generation produces machine-independent code.
5. Code optimization improves the intermediate code.
6. Code generation translates the optimized code into target machine code.
The document summarizes key concepts about context-free grammars and parsing from the book "Compiler Construction: Principles and Practice" by Kenneth C. Louden. It covers notations like EBNF and syntax diagrams for representing grammars, properties of context-free languages, and provides grammar rules and diagrams for a sample TINY language as an example.
Regular expressions are used to define the structure of tokens in a language. They are made up of symbols from a finite alphabet. A regular expression can be a single symbol, the empty string, alternation of two expressions, concatenation of two expressions, or Kleene closure of an expression. Deterministic finite automata (DFAs) are used to recognize languages defined by regular expressions. A DFA is defined by its states, input alphabet, start state, accepting states, and transition function between states based on input symbols. Examples show how to build DFAs to recognize languages defined by regular expressions.
Cs2303 theory of computation may june 2016appasami
This document contains a question paper for a Theory of Computation exam. It has two parts - Part A contains 10 short answer questions worth 2 marks each on topics like mathematical induction, finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, and complexity classes. Part B contains 5 long answer questions worth 16 marks each on topics like proofs by induction, Thompson's construction algorithm, regular expressions, context-free grammars, pushdown automata, Turing machines, undecidable problems, and recursively enumerable languages. Students are required to answer all questions in the paper.
This document provides an overview of context-free grammars, including their formal definition and notation. Key points include:
- A context-free grammar defines a language using variables, productions, terminals, and a start symbol. Productions recursively define variables in terms of one another and terminals.
- Strings in the language are derived by starting with the start symbol and repeatedly replacing variables according to productions until a string of terminals is reached.
- Common notations for grammars include Backus-Naur Form (BNF) and extensions like Kleene closure and optional/grouping elements.
This paper presents a novel SAT-based approach for the computation of extensions in abstract argumentation, with focus on preferred semantics, and an empirical evaluation of its performances. The approach is based on the idea of reducing the problem of computing complete extensions to a SAT problem and then using a depth-first search method to derive preferred extensions. The proposed approach has been tested using two distinct SAT solvers and compared with three state-of-the-art systems for preferred extension computation. It turns out that the proposed approach delivers significantly better performances in the large majority of the considered cases.
This document discusses regular languages and finite automata. It begins by defining regular languages and expressions, and describing the equivalence between non-deterministic finite automata (NFAs) and deterministic finite automata (DFAs). It then discusses converting between regular expressions, NFAs with epsilon transitions, NFAs without epsilon transitions, and DFAs. The document provides examples of regular expressions and conversions between different representations. It concludes by describing the state elimination, formula, and Arden's methods for converting a DFA to a regular expression.
The document discusses finite automata and theory of computation. It begins by defining finite automata as abstract machines that can have a finite number of states and read finite input strings, as well as the basic components of an automaton. It then explains the differences between deterministic finite automata (DFAs) and non-deterministic finite automata (NFAs), and provides examples of transition diagrams for visualizing state transitions in automata. The document aims to introduce the basics of finite automata as part of the theory of computation.
1. A parse tree is a graphical representation of a derivation in a context-free grammar (CFG). It has nodes labeled with symbols and leaves labeled with terminals or epsilon.
2. An ambiguous CFG is one where a string can have two different leftmost derivations, rightmost derivations, or parse trees. Showing a string has two leftmost derivations proves a CFG is ambiguous.
3. The balanced parentheses language can have both ambiguous and unambiguous CFGs, showing ambiguity is a property of grammars, not languages.
Context-free grammars are a notation for describing languages using variables and productions. They are more powerful than regular expressions but still cannot define all possible languages. A context-free grammar consists of terminals, variables, a start symbol, and productions. Strings in the language are derived by replacing variables according to productions until only terminals remain. Leftmost and rightmost derivations restrict replacements to specific variables.
Context-free grammars are a notation for describing languages using variables and productions. They are more powerful than regular expressions but still cannot define all possible languages. A context-free grammar consists of terminals, variables, a start symbol, and productions. Strings in the language are derived by replacing variables according to the productions until a string of just terminals is reached. Leftmost and rightmost derivations restrict replacements to specific variables.
This document contains lecture notes on finite automata from the Theory of Computation unit 1 course. Some key points include:
1. Finite automata are defined as 5-tuples (Q, Σ, δ, q0, F) where Q is a set of states, Σ is an input alphabet, δ is a transition function, q0 is the initial state, and F is a set of final states.
2. Deterministic finite automata (DFAs) have a single transition between states for each input symbol, while non-deterministic finite automata (NFAs) can have multiple transitions for a single input.
3. Regular expressions are used to describe the languages
Regular languages can be described using regular grammars, regular expressions, or finite automata. A regular grammar contains productions of the form A->aB or A->a where A and B are nonterminals and a is a terminal. A language is regular if it can be generated by a regular grammar. Regular expressions describe languages using operators like concatenation, union, and Kleene star. Finite automata are machines that accept or reject strings using a finite number of states. The three models are equivalent in that they can generate the same regular languages.
Introduction:
A context-free grammar (CFG) is a term used in formal languages theory to describe a certain type of formal grammar. A context-free grammar is a set of production rules that describe all possible strings in a given formal language. Production rules are simple replacements. For example, the rule
A α
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
This document contains information about a computer science examination from May 2017, including sections and questions. Section A contains 10 two-mark questions about topics like finite automata, regular expressions, pumping lemma, context-free grammars, pushdown automata, Turing machines, and Post correspondence problem. Section B has 5 five-mark questions. Section C contains 3 fifteen-mark questions. Section D has 1 ten-mark question. The document provides details about the exam format, sections, question types and marks for each question.
The document contains questions and answers related to compiler design topics such as parsing, grammars, syntax analysis, error handling, derivation, sentential forms, parse trees, ambiguity, left and right recursion elimination etc. Key points discussed are:
1. The role of parser is to verify the string of tokens generated by lexical analyzer according to the grammar rules and detect syntax errors. It outputs a parse tree.
2. Common parsing methods are top-down, bottom-up and universal. Top-down methods include LL, LR. Bottom-up methods include LR, LALR.
3. Errors can be lexical, syntactic, semantic and logical detected by different compiler phases. Error recovery strategies include panic mode
The presentation topic is on computational models. It will be presented by Md. Touhidur Rahman. The document discusses four computational models: finite automata (DFA and NFA), context free grammar, pushdown automata, and Turing machines. Finite automata are defined using states, symbols, transitions, a start state, and accepting states. Context free grammars are defined using variables, terminals, productions, and a start symbol. Pushdown automata extend NFAs with a stack. Turing machines are formally defined but also described at a higher level for understanding.
This document discusses automata theory and focuses on grammars, languages, and finite state machines. It defines key terminology like alphabets, strings, languages, and regular expressions. It explains Chomsky's hierarchy of formal languages from type-3 regular languages to type-0 recursively enumerable languages. The document also discusses finite state automata (FSA), deterministic finite automata (DFA), non-deterministic finite automata (NFA), context-free grammars, pushdown automata, and Turing machines. Examples of grammars, languages, and finite state machines are provided to illustrate these concepts.
Cs6660 compiler design may june 2016 Answer Keyappasami
The document describes the various phases of a compiler:
1. Lexical analysis breaks the source code into tokens.
2. Syntax analysis generates a parse tree from the tokens.
3. Semantic analysis checks for semantic correctness using the parse tree and symbol table.
4. Intermediate code generation produces machine-independent code.
5. Code optimization improves the intermediate code.
6. Code generation translates the optimized code into target machine code.
The document summarizes key concepts about context-free grammars and parsing from the book "Compiler Construction: Principles and Practice" by Kenneth C. Louden. It covers notations like EBNF and syntax diagrams for representing grammars, properties of context-free languages, and provides grammar rules and diagrams for a sample TINY language as an example.
Regular expressions are used to define the structure of tokens in a language. They are made up of symbols from a finite alphabet. A regular expression can be a single symbol, the empty string, alternation of two expressions, concatenation of two expressions, or Kleene closure of an expression. Deterministic finite automata (DFAs) are used to recognize languages defined by regular expressions. A DFA is defined by its states, input alphabet, start state, accepting states, and transition function between states based on input symbols. Examples show how to build DFAs to recognize languages defined by regular expressions.
Cs2303 theory of computation may june 2016appasami
This document contains a question paper for a Theory of Computation exam. It has two parts - Part A contains 10 short answer questions worth 2 marks each on topics like mathematical induction, finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, and complexity classes. Part B contains 5 long answer questions worth 16 marks each on topics like proofs by induction, Thompson's construction algorithm, regular expressions, context-free grammars, pushdown automata, Turing machines, undecidable problems, and recursively enumerable languages. Students are required to answer all questions in the paper.
This document provides an overview of context-free grammars, including their formal definition and notation. Key points include:
- A context-free grammar defines a language using variables, productions, terminals, and a start symbol. Productions recursively define variables in terms of one another and terminals.
- Strings in the language are derived by starting with the start symbol and repeatedly replacing variables according to productions until a string of terminals is reached.
- Common notations for grammars include Backus-Naur Form (BNF) and extensions like Kleene closure and optional/grouping elements.
This paper presents a novel SAT-based approach for the computation of extensions in abstract argumentation, with focus on preferred semantics, and an empirical evaluation of its performances. The approach is based on the idea of reducing the problem of computing complete extensions to a SAT problem and then using a depth-first search method to derive preferred extensions. The proposed approach has been tested using two distinct SAT solvers and compared with three state-of-the-art systems for preferred extension computation. It turns out that the proposed approach delivers significantly better performances in the large majority of the considered cases.
This document discusses regular languages and finite automata. It begins by defining regular languages and expressions, and describing the equivalence between non-deterministic finite automata (NFAs) and deterministic finite automata (DFAs). It then discusses converting between regular expressions, NFAs with epsilon transitions, NFAs without epsilon transitions, and DFAs. The document provides examples of regular expressions and conversions between different representations. It concludes by describing the state elimination, formula, and Arden's methods for converting a DFA to a regular expression.
The document discusses finite automata and theory of computation. It begins by defining finite automata as abstract machines that can have a finite number of states and read finite input strings, as well as the basic components of an automaton. It then explains the differences between deterministic finite automata (DFAs) and non-deterministic finite automata (NFAs), and provides examples of transition diagrams for visualizing state transitions in automata. The document aims to introduce the basics of finite automata as part of the theory of computation.
This document provides an introduction to Turing machines including their components, configuration, transitions, and languages. A Turing machine has a tape divided into cells that can be read from and written to. It has a finite set of states and uses its current state and the symbol being read to determine its next action. Transitions involve writing a symbol, moving the head, and changing state. The language of a Turing machine is the set of strings that cause it to enter an accepting state. Various examples are given to illustrate Turing machine definitions, components, and execution.
A pushdown automaton (PDA) is a nondeterministic finite automaton that uses a stack to process input symbols. It has a finite set of states, input symbols, stack symbols, and transition rules that can push symbols onto or pop symbols off the stack. The transition function defines the state and stack changes for each input symbol. A PDA accepts a language if any path through its transitions leads to an accepting state.
This document covers topics related to regular languages and finite automata including:
1. Writing regular expressions and recursive definitions of extended transition functions for DFAs and NFAs.
2. Comparing finite automata, NFAs, and NFA-Λ.
3. Converting between different types of finite automata like NFA to DFA.
4. Applying concepts like pumping lemma, closure properties, and Kleene's theorem to regular languages.
The document outlines topics in mathematical theory including: defining functions and relations; properties of functions such as composition, inverses, one-to-one and onto functions; properties of relations including reflexivity, symmetry and transitivity; using principles of mathematical induction to prove statements; proving statements using proof by contradiction; and closure properties of regular languages. Specific problems include proving square root of 2 is irrational, using induction to prove a summation formula, and analyzing relations.
This document is a model question paper for the Theory of Computation course for the 6th semester of BCA at Vijaya Degree College. It contains 4 sections with multiple choice and long answer questions. Section A contains 10 multiple choice questions worth 2 marks each. Section B contains 5 long answer questions worth 10 marks each. Section C contains 3 long answer questions worth 15 marks each. Section D contains 1 long answer question worth 10 marks. The questions cover topics related to finite automata, regular expressions, context-free grammars, pushdown automata, Turing machines, and properties of formal languages.
Microalgae have potential as a feedstock for biofuel production, but integrated production systems still need improvements to be economically viable at a large scale. Key research areas that could help microalgal biofuels attain economic reality include improving strain selection, cultivation techniques, and downstream processing methods. While microalgal biofuels show promise as a renewable alternative to controversial first generation biofuels, significant advances are still needed for the industry to develop and scale up successfully.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
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2. Not all languages are regular
So what happens to the languages
which are not regular?
Can we still come up with a language
recognizer?
i.e., something that will accept (or reject)
strings that belong (or do not belong) to the
language?
2
3. 3
Context-Free Languages
A language class larger than the class of regular
languages
Supports natural, recursive notation called “context-
free grammar”
Applications:
Parse trees, compilers
XML
Regular
(FA/RE)
Context-
free
(PDA/CFG)
4. 4
An Example
A palindrome is a word that reads identical from both
ends
E.g., madam, redivider, malayalam, 010010010
Let L = { w | w is a binary palindrome}
Is L regular?
No.
Proof:
Let w=0N10N (assuming N to be the p/l constant)
By Pumping lemma, w can be rewritten as xyz, such that xykz is also L
(for any k≥0)
But |xy|≤N and y≠
==> y=0+
==> xykz will NOT be in L for k=0
==> Contradiction
5. 5
But the language of
palindromes…
is a CFL, because it supports recursive
substitution (in the form of a CFG)
This is because we can construct a
“grammar” like this:
1. A ==>
2. A ==> 0
3. A ==> 1
4. A ==> 0A0
5. A ==> 1A1
Terminal
Productions
Variable or non-terminal
How does this grammar work?
Same as:
A => 0A0 | 1A1 | 0 | 1 |
6. How does the CFG for
palindromes work?
An input string belongs to the language (i.e.,
accepted) iff it can be generated by the CFG
Example: w=01110
G can generate w as follows:
1. A => 0A0
2. => 01A10
3. => 01110
6
G:
A => 0A0 | 1A1 | 0 | 1 |
Generating a string from a grammar:
1. Pick and choose a sequence
of productions that would
allow us to generate the
string.
2. At every step, substitute one variable
with one of its productions.
7. 7
Context-Free Grammar:
Definition
A context-free grammar G=(V,T,P,S), where:
V: set of variables or non-terminals
T: set of terminals (= alphabet U {})
P: set of productions, each of which is of the form
V ==> 1 | 2 | …
Where each i is an arbitrary string of variables and
terminals
S ==> start variable
CFG for the language of binary palindromes:
G=({A},{0,1},P,A)
P: A ==> 0 A 0 | 1 A 1 | 0 | 1 |
8. 8
More examples
Parenthesis matching in code
Syntax checking
In scenarios where there is a general need
for:
Matching a symbol with another symbol, or
Matching a count of one symbol with that of
another symbol, or
Recursively substituting one symbol with a string
of other symbols
9. 9
Example #2
Language of balanced paranthesis
e.g., ()(((())))((()))….
CFG?
G:
S => (S) | SS |
How would you “interpret” the string “(((()))()())” using this grammar?
10. 10
Example #3
A grammar for L = {0m1n | m≥n}
CFG? G:
S => 0S1 | A
A => 0A |
How would you interpret the string “00000111”
using this grammar?
11. 11
Example #4
A program containing if-then(-else) statements
if Condition then Statement else Statement
(Or)
if Condition then Statement
CFG?
12. More examples
L1 = {0n | n≥0 }
L2 = {0n | n≥1 }
L3={0i1j2k | i=j or j=k, where i,j,k≥0}
L4={0i1j2k | i=j or i=k, where i,j,k≥1}
12
13. 13
Applications of CFLs & CFGs
Compilers use parsers for syntactic checking
Parsers can be expressed as CFGs
1. Balancing paranthesis:
B ==> BB | (B) | Statement
Statement ==> …
2. If-then-else:
S ==> SS | if Condition then Statement else Statement | if Condition
then Statement | Statement
Condition ==> …
Statement ==> …
3. C paranthesis matching { … }
4. Pascal begin-end matching
5. YACC (Yet Another Compiler-Compiler)
14. 14
More applications
Markup languages
Nested Tag Matching
HTML
<html> …<p> … <a href=…> … </a> </p> … </html>
XML
<PC> … <MODEL> … </MODEL> .. <RAM> …
</RAM> … </PC>
15. 15
Tag-Markup Languages
Roll ==> <ROLL> Class Students </ROLL>
Class ==> <CLASS> Text </CLASS>
Text ==> Char Text | Char
Char ==> a | b | … | z | A | B | .. | Z
Students ==> Student Students |
Student ==> <STUD> Text </STUD>
Here, the left hand side of each production denotes one non-terminals
(e.g., “Roll”, “Class”, etc.)
Those symbols on the right hand side for which no productions (i.e.,
substitutions) are defined are terminals (e.g., ‘a’, ‘b’, ‘|’, ‘<‘, ‘>’, “ROLL”,
etc.)
16. 16
Structure of a production
A =======> 1 | 2 | … | k
head body
derivation
1. A ==> 1
2. A ==> 2
3. A ==> 3
…
K. A ==> k
The above is same as:
17. 17
CFG conventions
Terminal symbols <== a, b, c…
Non-terminal symbols <== A,B,C, …
Terminal or non-terminal symbols <== X,Y,Z
Terminal strings <== w, x, y, z
Arbitrary strings of terminals and non-
terminals <== , , , ..
18. 18
Syntactic Expressions in
Programming Languages
result = a*b + score + 10 * distance + c
Regular languages have only terminals
Reg expression = [a-z][a-z0-1]*
If we allow only letters a & b, and 0 & 1 for
constants (for simplification)
Regular expression = (a+b)(a+b+0+1)*
terminals variables Operators are also
terminals
19. 19
String membership
How to say if a string belong to the language
defined by a CFG?
1. Derivation
Head to body
2. Recursive inference
Body to head
Example:
w = 01110
Is w a palindrome?
Both are equivalent forms
G:
A => 0A0 | 1A1 | 0 | 1 |
A => 0A0
=> 01A10
=> 01110
20. 20
Simple Expressions…
We can write a CFG for accepting simple
expressions
G = (V,T,P,S)
V = {E,F}
T = {0,1,a,b,+,*,(,)}
S = {E}
P:
E ==> E+E | E*E | (E) | F
F ==> aF | bF | 0F | 1F | a | b | 0 | 1
21. 21
Generalization of derivation
Derivation is head ==> body
A==>X (A derives X in a single step)
A ==>*G X (A derives X in a multiple steps)
Transitivity:
IFA ==>*GB, and B ==>*GC, THEN A ==>*G C
22. 22
Context-Free Language
The language of a CFG, G=(V,T,P,S),
denoted by L(G), is the set of terminal
strings that have a derivation from the
start variable S.
L(G) = { w in T* | S ==>*G w }
23. 23
Left-most & Right-most
Derivation Styles
Derive the string a*(ab+10) from G:
E
==> E * E
==> F * E
==> aF * E
==> a * E
==> a * (E)
==> a * (E + E)
==> a * (F + E)
==> a * (aF + E)
==> a * (abF + E)
==> a * (ab + E)
==> a * (ab + F)
==> a * (ab + 1F)
==> a * (ab + 10F)
==> a * (ab + 10)
E =*=>G a*(ab+10)
Left-most
derivation:
E
==> E * E
==> E * (E)
==> E * (E + E)
==> E * (E + F)
==> E * (E + 1F)
==> E * (E + 10F)
==> E * (E + 10)
==> E * (F + 10)
==> E * (aF + 10)
==> E * (abF + 0)
==> E * (ab + 10)
==> F * (ab + 10)
==> aF * (ab + 10)
==> a * (ab + 10)
Right-most
derivation:
G:
E => E+E | E*E | (E) | F
F => aF | bF | 0F | 1F |
Always
substitute
leftmost
variable
Always
substitute
rightmost
variable
24. 24
Leftmost vs. Rightmost
derivations
Q1) For every leftmost derivation, there is a rightmost
derivation, and vice versa. True or False?
Q2) Does every word generated by a CFG have a
leftmost and a rightmost derivation?
Q3) Could there be words which have more than one
leftmost (or rightmost) derivation?
True - will use parse trees to prove this
Yes – easy to prove (reverse direction)
Yes – depending on the grammar
25. How to prove that your CFGs
are correct?
(using induction)
25
26. 26
CFG & CFL
Theorem: A string w in (0+1)* is in
L(Gpal), if and only if, w is a palindrome.
Proof:
Use induction
on string length for the IF part
On length of derivation for the ONLY IF part
Gpal:
A => 0A0 | 1A1 | 0 | 1 |
28. 28
Parse Trees
Each CFG can be represented using a parse tree:
Each internal node is labeled by a variable in V
Each leaf is terminal symbol
For a production, A==>X1X2…Xk, then any internal node
labeled A has k children which are labeled from X1,X2,…Xk
from left to right
A
X1 Xi Xk
… …
Parse tree for production and all other subsequent productions:
A ==> X1..Xi..Xk
29. 29
Examples
E
E + E
F F
a 1
Parse tree for a + 1
A
0 A 0
1 1
A
Parse tree for 0110
Recursive
inference
Derivation
G:
E => E+E | E*E | (E) | F
F => aF | bF | 0F | 1F | 0 | 1 | a | b
G:
A => 0A0 | 1A1 | 0 | 1 |
30. 30
Parse Trees, Derivations, and
Recursive Inferences
A
X1 Xi Xk
… …
Recursive
inference
Derivation
Production:
A ==> X1..Xi..Xk
Parse tree
Left-most
derivation
Right-most
derivation
Derivation
Recursive
inference
31. 31
Interchangeability of different
CFG representations
Parse tree ==> left-most derivation
DFS left to right
Parse tree ==> right-most derivation
DFS right to left
==> left-most derivation == right-most
derivation
Derivation ==> Recursive inference
Reverse the order of productions
Recursive inference ==> Parse trees
bottom-up traversal of parse tree
33. 33
CFLs & Regular Languages
A CFG is said to be right-linear if all the
productions are one of the following two
forms: A ==> wB (or) A ==> w
Theorem 1: Every right-linear CFG generates
a regular language
Theorem 2: Every regular language has a
right-linear grammar
Theorem 3: Left-linear CFGs also represent
RLs
Where:
• A & B are variables,
• w is a string of terminals
What kind of grammars result for regular languages?
34. Some Examples
34
A B C
0
1 0
1 0,1
A B C
0
1 0
1
1
0
A => 01B | C
B => 11B | 0C | 1A
C => 1A | 0 | 1
Right linear CFG? Right linear CFG? Finite Automaton?
36. 36
Ambiguity in CFGs
A CFG is said to be ambiguous if there
exists a string which has more than one
left-most derivation
LM derivation #1:
S => AS
=> 0A1S
=>0A11S
=> 00111S
=> 00111
Example:
S ==> AS |
A ==> A1 | 0A1 | 01
Input string: 00111
Can be derived in two ways
LM derivation #2:
S => AS
=> A1S
=> 0A11S
=> 00111S
=> 00111
37. 37
Why does ambiguity matter?
string = a * b + c
E ==> E + E | E * E | (E) | a | b | c | 0 | 1
• LM derivation #1:
•E => E + E => E * E + E
==>* a * b + c
• LM derivation #2
•E => E * E => a * E =>
a * E + E ==>* a * b + c
E
E + E
E * E
a b
c
(a*b)+c
E
E * E
E
+
E
a
b c
a*(b+c)
Values are
different !!!
The calculated value depends on which
of the two parse trees is actually used.
38. 38
Removing Ambiguity in
Expression Evaluations
It MAY be possible to remove ambiguity for
some CFLs
E.g.,, in a CFG for expression evaluation by
imposing rules & restrictions such as precedence
This would imply rewrite of the grammar
Precedence: (), * , +
How will this avoid ambiguity?
E => E + T | T
T => T * F | F
F => I | (E)
I => a | b | c | 0 | 1
Modified unambiguous version:
Ambiguous version:
E ==> E + E | E * E | (E) | a | b | c | 0 | 1
39. 39
Inherently Ambiguous CFLs
However, for some languages, it may not be
possible to remove ambiguity
A CFL is said to be inherently ambiguous if
every CFG that describes it is ambiguous
Example:
L = { anbncmdm | n,m≥ 1} U {anbmcmdn | n,m≥ 1}
L is inherently ambiguous
Why?
Input string: anbncndn