The document discusses different representations of intermediate code in compilers, including high-level and low-level intermediate languages. High-level representations like syntax trees and DAGs depict the structure of the source program, while low-level representations like three-address code are closer to the target machine. Common intermediate code representations discussed are postfix notation, three-address code using quadruples/triples, and syntax trees.
This document discusses M2M and IoT design methodologies. It begins with an overview of M2M architecture, including the key components of an M2M area network, M2M core network, M2M gateways, and M2M applications. It then contrasts M2M and IoT, noting differences in communication protocols, types of connected devices, emphasis on hardware vs software, how data is collected and analyzed, and applications. The document also introduces software-defined networking (SDN) and network function virtualization (NFV) as approaches to address limitations of conventional network architectures for IoT.
This produced by straight forward compiling algorithms made to run faster or less space or both. This improvement is achieved by program transformations that are traditionally called optimizations.compiler that apply-code improving transformation are called optimizing compilers.
The document discusses syntax analysis and parsing. It defines a syntax analyzer as creating the syntactic structure of a source program in the form of a parse tree. A syntax analyzer, also called a parser, checks if a program satisfies the rules of a context-free grammar and produces the parse tree if it does, or error messages otherwise. It describes top-down and bottom-up parsing methods and how parsers use grammars to analyze syntax.
This document discusses programming techniques for Turing machines (TMs), including storing data in states, using multiple tracks, and implementing subroutines. It also covers extensions to basic TMs, such as multitape and nondeterministic TMs. Restricted TMs like those with semi-infinite tapes, stack machines, and counter machines are also examined. Finally, the document informally argues that TMs and modern computers are equally powerful models of computation.
Secure Hash Algorithm (SHA) was developed by NIST and NSA to hash messages into fixed-length message digests. SHA has multiple versions including SHA-1, SHA-2, and SHA-3. SHA-1 produces a 160-bit message digest and works by padding the input message, appending the length, dividing into blocks, initializing variables, and processing blocks through 80 rounds of operations to output the digest. SHA-512 is closely modeled after SHA-1 but produces a 512-bit digest and uses 1024-bit blocks.
The document discusses the role and process of a lexical analyzer in compiler design. A lexical analyzer groups input characters into lexemes and produces a sequence of tokens as output for the syntactic analyzer. It strips out comments and whitespace, correlates line numbers with errors, and interacts with the symbol table. Lexical analysis improves compiler efficiency, portability, and allows for simpler parser design by separating lexical and syntactic analysis.
The document discusses code generation in compilers. It describes the main tasks of the code generator as instruction selection, register allocation and assignment, and instruction ordering. It then discusses various issues in designing a code generator such as the input and output formats, memory management, different instruction selection and register allocation approaches, and choice of evaluation order. The target machine used is a hypothetical machine with general purpose registers, different addressing modes, and fixed instruction costs. Examples of instruction selection and utilization of addressing modes are provided.
Lexical Analysis, Tokens, Patterns, Lexemes, Example pattern, Stages of a Lexical Analyzer, Regular expressions to the lexical analysis, Implementation of Lexical Analyzer, Lexical analyzer: use as generator.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
The document discusses code optimization techniques in compilers. It covers the following key points:
1. Code optimization aims to improve code performance by replacing high-level constructs with more efficient low-level code while preserving program semantics. It occurs at various compiler phases like source code, intermediate code, and target code.
2. Common optimization techniques include constant folding, propagation, algebraic simplification, strength reduction, copy propagation, and dead code elimination. Control and data flow analysis are required to perform many optimizations.
3. Optimizations can be local within basic blocks, global across blocks, or inter-procedural across procedures. Representations like flow graphs, basic blocks, and DAGs are used to apply optimizations at
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
LEX is a tool that allows users to specify a lexical analyzer by defining patterns for tokens using regular expressions. The LEX compiler transforms these patterns into a transition diagram and generates C code. It takes a LEX source program as input, compiles it to produce lex.yy.c, which is then compiled with a C compiler to generate an executable that takes an input stream and returns a sequence of tokens. LEX programs have declarations, translation rules that map patterns to actions, and optional auxiliary functions. The actions are fragments of C code that execute when a pattern is matched.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
This document discusses syntax-directed translation, which refers to a method of compiler implementation where the source language translation is completely driven by the parser. The parsing process and parse trees are used to direct semantic analysis and translation of the source program. Attributes and semantic rules are associated with the grammar symbols and productions to control semantic analysis and translation. There are two main representations of semantic rules: syntax-directed definitions and syntax-directed translation schemes. Syntax-directed translation schemes embed program fragments called semantic actions within production bodies and are more efficient than syntax-directed definitions as they indicate the order of evaluation of semantic actions. Attribute grammars can be used to represent syntax-directed translations.
This document discusses various techniques for optimizing computer code, including:
1. Local optimizations that improve performance within basic blocks, such as constant folding, propagation, and elimination of redundant computations.
2. Global optimizations that analyze control flow across basic blocks, such as common subexpression elimination.
3. Loop optimizations that improve performance of loops by removing invariant data and induction variables.
4. Machine-dependent optimizations like peephole optimizations that replace instructions with more efficient alternatives.
The goal of optimizations is to improve speed and efficiency while preserving program meaning and correctness. Optimizations can occur at multiple stages of development and compilation.
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
Introduction, Macro Definition and Call, Macro Expansion, Nested Macro Calls, Advanced Macro Facilities, Design Of a Macro Preprocessor, Design of a Macro Assembler, Functions of a Macro Processor, Basic Tasks of a Macro Processor, Design Issues of Macro Processors, Features, Macro Processor Design Options, Two-Pass Macro Processors, One-Pass Macro Processors
Intermediate code generation in Compiler DesignKuppusamy P
The document discusses intermediate code generation in compilers. It begins by explaining that intermediate code generation is the final phase of the compiler front-end and its goal is to translate the program into a format expected by the back-end. Common intermediate representations include three address code and static single assignment form. The document then discusses why intermediate representations are used, how to choose an appropriate representation, and common types of representations like graphical IRs and linear IRs.
Syntax analysis is the second phase of compiler design after lexical analysis. The parser checks if the input string follows the rules and structure of the formal grammar. It builds a parse tree to represent the syntactic structure. If the input string can be derived from the parse tree using the grammar, it is syntactically correct. Otherwise, an error is reported. Parsers use various techniques like panic-mode, phrase-level, and global correction to handle syntax errors and attempt to continue parsing. Context-free grammars are commonly used with productions defining the syntax rules. Derivations show the step-by-step application of productions to generate the input string from the start symbol.
Lexical analysis is the first phase of compilation. It reads source code characters and divides them into tokens by recognizing patterns using finite automata. It separates tokens, inserts them into a symbol table, and eliminates unnecessary characters. Tokens are passed to the parser along with line numbers for error handling. An input buffer is used to improve efficiency by reading source code in blocks into memory rather than character-by-character from secondary storage. Lexical analysis groups character sequences into lexemes, which are then classified as tokens based on patterns.
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
This presentation discusses peephole optimization. Peephole optimization is performed on small segments of generated code to replace sets of instructions with shorter or faster equivalents. It aims to improve performance, reduce code size, and reduce memory footprint. The working flow of peephole optimization involves scanning code for patterns that match predefined replacement rules. These rules include constant folding, strength reduction, removing null sequences, and combining operations. Peephole optimization functions by replacing slow instructions with faster ones, removing redundant code and stack instructions, and optimizing jumps.
This document discusses strings, languages, and regular expressions. It defines key terms like alphabet, string, language, and operations on strings and languages. It then introduces regular expressions as a notation for specifying patterns of strings. Regular expressions are defined over an alphabet and can combine symbols, concatenation, union, and Kleene closure to describe languages. Examples are provided to illustrate regular expression notation and properties. Limitations of regular expressions in describing certain languages are also noted.
The document discusses the role and process of lexical analysis in compilers. It can be summarized as:
1) Lexical analysis is the first phase of a compiler that reads source code characters and groups them into tokens. It produces a stream of tokens that are passed to the parser.
2) The lexical analyzer matches character sequences against patterns defined by regular expressions to identify lexemes and produce corresponding tokens.
3) Common tokens include keywords, identifiers, constants, and punctuation. The lexical analyzer may interact with the symbol table to handle identifiers.
The document discusses the role and process of a lexical analyzer in compiler design. A lexical analyzer groups input characters into lexemes and produces a sequence of tokens as output for the syntactic analyzer. It strips out comments and whitespace, correlates line numbers with errors, and interacts with the symbol table. Lexical analysis improves compiler efficiency, portability, and allows for simpler parser design by separating lexical and syntactic analysis.
The document discusses code generation in compilers. It describes the main tasks of the code generator as instruction selection, register allocation and assignment, and instruction ordering. It then discusses various issues in designing a code generator such as the input and output formats, memory management, different instruction selection and register allocation approaches, and choice of evaluation order. The target machine used is a hypothetical machine with general purpose registers, different addressing modes, and fixed instruction costs. Examples of instruction selection and utilization of addressing modes are provided.
Lexical Analysis, Tokens, Patterns, Lexemes, Example pattern, Stages of a Lexical Analyzer, Regular expressions to the lexical analysis, Implementation of Lexical Analyzer, Lexical analyzer: use as generator.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
The document discusses code optimization techniques in compilers. It covers the following key points:
1. Code optimization aims to improve code performance by replacing high-level constructs with more efficient low-level code while preserving program semantics. It occurs at various compiler phases like source code, intermediate code, and target code.
2. Common optimization techniques include constant folding, propagation, algebraic simplification, strength reduction, copy propagation, and dead code elimination. Control and data flow analysis are required to perform many optimizations.
3. Optimizations can be local within basic blocks, global across blocks, or inter-procedural across procedures. Representations like flow graphs, basic blocks, and DAGs are used to apply optimizations at
The purpose of types:
To define what the program should do.
e.g. read an array of integers and return a double
To guarantee that the program is meaningful.
that it does not add a string to an integer
that variables are declared before they are used
To document the programmer's intentions.
better than comments, which are not checked by the compiler
To optimize the use of hardware.
reserve the minimal amount of memory, but not more
use the most appropriate machine instructions.
LEX is a tool that allows users to specify a lexical analyzer by defining patterns for tokens using regular expressions. The LEX compiler transforms these patterns into a transition diagram and generates C code. It takes a LEX source program as input, compiles it to produce lex.yy.c, which is then compiled with a C compiler to generate an executable that takes an input stream and returns a sequence of tokens. LEX programs have declarations, translation rules that map patterns to actions, and optional auxiliary functions. The actions are fragments of C code that execute when a pattern is matched.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
This document discusses syntax-directed translation, which refers to a method of compiler implementation where the source language translation is completely driven by the parser. The parsing process and parse trees are used to direct semantic analysis and translation of the source program. Attributes and semantic rules are associated with the grammar symbols and productions to control semantic analysis and translation. There are two main representations of semantic rules: syntax-directed definitions and syntax-directed translation schemes. Syntax-directed translation schemes embed program fragments called semantic actions within production bodies and are more efficient than syntax-directed definitions as they indicate the order of evaluation of semantic actions. Attribute grammars can be used to represent syntax-directed translations.
This document discusses various techniques for optimizing computer code, including:
1. Local optimizations that improve performance within basic blocks, such as constant folding, propagation, and elimination of redundant computations.
2. Global optimizations that analyze control flow across basic blocks, such as common subexpression elimination.
3. Loop optimizations that improve performance of loops by removing invariant data and induction variables.
4. Machine-dependent optimizations like peephole optimizations that replace instructions with more efficient alternatives.
The goal of optimizations is to improve speed and efficiency while preserving program meaning and correctness. Optimizations can occur at multiple stages of development and compilation.
This document discusses parsing and context-free grammars. It defines parsing as verifying that tokens generated by a lexical analyzer follow syntactic rules of a language using a parser. Context-free grammars are defined using terminals, non-terminals, productions and a start symbol. Top-down and bottom-up parsing are introduced. Techniques for grammar analysis and improvement like left factoring, eliminating left recursion, calculating first and follow sets are explained with examples.
Introduction, Macro Definition and Call, Macro Expansion, Nested Macro Calls, Advanced Macro Facilities, Design Of a Macro Preprocessor, Design of a Macro Assembler, Functions of a Macro Processor, Basic Tasks of a Macro Processor, Design Issues of Macro Processors, Features, Macro Processor Design Options, Two-Pass Macro Processors, One-Pass Macro Processors
Intermediate code generation in Compiler DesignKuppusamy P
The document discusses intermediate code generation in compilers. It begins by explaining that intermediate code generation is the final phase of the compiler front-end and its goal is to translate the program into a format expected by the back-end. Common intermediate representations include three address code and static single assignment form. The document then discusses why intermediate representations are used, how to choose an appropriate representation, and common types of representations like graphical IRs and linear IRs.
Syntax analysis is the second phase of compiler design after lexical analysis. The parser checks if the input string follows the rules and structure of the formal grammar. It builds a parse tree to represent the syntactic structure. If the input string can be derived from the parse tree using the grammar, it is syntactically correct. Otherwise, an error is reported. Parsers use various techniques like panic-mode, phrase-level, and global correction to handle syntax errors and attempt to continue parsing. Context-free grammars are commonly used with productions defining the syntax rules. Derivations show the step-by-step application of productions to generate the input string from the start symbol.
Lexical analysis is the first phase of compilation. It reads source code characters and divides them into tokens by recognizing patterns using finite automata. It separates tokens, inserts them into a symbol table, and eliminates unnecessary characters. Tokens are passed to the parser along with line numbers for error handling. An input buffer is used to improve efficiency by reading source code in blocks into memory rather than character-by-character from secondary storage. Lexical analysis groups character sequences into lexemes, which are then classified as tokens based on patterns.
This document discusses various strategies for register allocation and assignment in compiler design. It notes that assigning values to specific registers simplifies compiler design but can result in inefficient register usage. Global register allocation aims to assign frequently used values to registers for the duration of a single block. Usage counts provide an estimate of how many loads/stores could be saved by assigning a value to a register. Graph coloring is presented as a technique where an interference graph is constructed and coloring aims to assign registers efficiently despite interference between values.
This presentation discusses peephole optimization. Peephole optimization is performed on small segments of generated code to replace sets of instructions with shorter or faster equivalents. It aims to improve performance, reduce code size, and reduce memory footprint. The working flow of peephole optimization involves scanning code for patterns that match predefined replacement rules. These rules include constant folding, strength reduction, removing null sequences, and combining operations. Peephole optimization functions by replacing slow instructions with faster ones, removing redundant code and stack instructions, and optimizing jumps.
This document discusses strings, languages, and regular expressions. It defines key terms like alphabet, string, language, and operations on strings and languages. It then introduces regular expressions as a notation for specifying patterns of strings. Regular expressions are defined over an alphabet and can combine symbols, concatenation, union, and Kleene closure to describe languages. Examples are provided to illustrate regular expression notation and properties. Limitations of regular expressions in describing certain languages are also noted.
The document discusses the role and process of lexical analysis in compilers. It can be summarized as:
1) Lexical analysis is the first phase of a compiler that reads source code characters and groups them into tokens. It produces a stream of tokens that are passed to the parser.
2) The lexical analyzer matches character sequences against patterns defined by regular expressions to identify lexemes and produce corresponding tokens.
3) Common tokens include keywords, identifiers, constants, and punctuation. The lexical analyzer may interact with the symbol table to handle identifiers.
Theory of automata and formal languageRabia Khalid
KleenE Star Closure, Plus operation, recursive definition of languages, INTEGER, EVEN, factorial, PALINDROME, languages of strings, cursive definition of RE, defining languages by RE,Examples
This document discusses lexical analysis and regular expressions. It begins by outlining topics related to lexical analysis including tokens, lexemes, patterns, regular expressions, transition diagrams, and generating lexical analyzers. It then discusses topics like finite automata, regular expressions to NFA conversion using Thompson's construction, NFA to DFA conversion using subset construction, and DFA optimization. The role of the lexical analyzer and its interaction with the parser is also covered. Examples of token specification and regular expressions are provided.
Regular expressions are used to describe regular languages and are composed of symbols and operators like union, concatenation, and closure. They can be used to define the syntax of identifiers in a language. Regular expressions denote the simplest type of language that can be accepted by finite automata. Common regular expression operations include union, concatenation, and Kleene closure to combine language elements and describe strings of varying lengths. Parentheses are often used but certain pairs can be omitted under conventions that define operator precedence and associativity. Regular expressions can also be used to provide regular definitions that assign names to expressions for reuse.
The document discusses concepts related to regular expressions and strings. It defines regular expressions as patterns used to match strings and describes how strings are sequences of characters that can represent text. It also discusses languages as sets of strings formed from alphabets and how operations like concatenation and union can combine languages.
This document provides an overview of the Theory of Computation course BCSE304L. The objectives are to gain a historical perspective of formal languages and automata theory, become familiar with Chomsky grammars and their hierarchy, explain automata theory, and discuss applications. The course covers languages, grammars, strings, operations on languages, Chomsky hierarchy, grammars, derivation trees, and regular, context-free, and context-sensitive languages. Evaluation includes quizzes, assignments, and exams. Module 1 topics are introduction to languages and grammars, proof techniques, and computational models.
This document discusses regular expressions and provides examples. It begins by defining regular expressions recursively. Key points include:
- Regular expressions can be used to concisely define languages. Common operations are concatenation, union, closure.
- Examples show how regular expressions can define languages with certain properties like having a single 1 or an even number of characters.
- Algebraic laws govern operations like distribution and idempotence for regular expressions. Concretization tests can verify proposed laws.
The document discusses scanning (lexical analysis) in compiler construction. It covers the scanning process, regular expressions, and finite automata. The scanning process identifies tokens from source code by categorizing characters as reserved words, special symbols, or other tokens. Regular expressions are used to represent patterns of character strings and define the language of tokens. Finite automata are mathematical models for describing scanning algorithms using states, transitions, and acceptance.
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.
The document discusses the importance and history of theory of computation. It introduces some key concepts including:
- What computation is and what we study in theory of computation such as what is computable and complexity theory.
- Common operations on languages like union, intersection, concatenation and closure.
- How problems can be represented as languages where the strings in the language correspond to instances with a "YES" answer.
- The history of theory of computation from Turing machines to complexity theory and applications in computer science.
This document discusses regular expressions, which are sequences of symbols used to describe patterns in text. Regular expressions are built recursively using operators like union, concatenation, and closure applied to the symbols of an alphabet. The notation defines regular expressions and the languages they denote. Key rules include: (1) r|s denotes the union of the languages of r and s; (2) rs denotes the concatenation of r and s; and (3) r* denotes the Kleene closure - zero or more repetitions - of r. Precedence rules simplify expressions by dropping unnecessary parentheses.
Lesson 02.ppt theory of automata including basics of itzainalvi552
ZA
description on theory of automata
Let me provide a comprehensive overview of the Theory of Automata.
The Theory of Automata is a fundamental branch of theoretical computer science that studies abstract machines and their computational capabilities. It is a critical area of study in computer science, mathematics, and computational theory, providing insights into the fundamental nature of computation and computational processes.
Key Components of Automata Theory:
Finite Automata (FA)
Simplest type of computational model
Can be deterministic (DFA) or non-deterministic (NFA)
Capable of recognizing regular languages
Used in pattern matching, text processing, and lexical analysis
Has a finite set of states and transitions between these states based on input symbols
Pushdown Automata (PDA)
More complex than finite automata
Includes a stack memory for additional computational power
Can recognize context-free languages
Fundamental to parsing programming languages and compiler design
Allows for more sophisticated state transitions using stack operations
Turing Machines
Most powerful computational model
Developed by Alan Turing in 1936
Can simulate any algorithm or computational process
Has an infinite memory tape
Can solve complex computational problems
Serves as a theoretical foundation for understanding computability and computational complexity
Fundamental Concepts:
Languages: Sets of strings that can be recognized by an automaton
State Transitions: Rules for moving between different states based on input
Acceptance and Rejection: Criteria for determining whether an input string belongs to a language
Computational Power: Different automata have varying levels of computational capabilities
Practical Applications:
Compiler Design
Text Processing
Pattern Matching
Network Protocol Design
Parsing and Syntax Analysis
Artificial Intelligence and Machine Learning
Theoretical Significance:
Provides mathematical foundation for understanding computation
Helps define the limits of what can be computed
Bridges computer science with mathematical logic
Explores fundamental questions about algorithmic processes and computational complexity
Research Areas:
Formal Language Theory
Computational Complexity
Algorithm Design
Computability Theory
The Theory of Automata continues to be a crucial field of study, helping researchers and computer scientists understand the fundamental principles of computation, design more efficient algorithms, and explore the theoretical limits of computational processes.
The document discusses lexical analysis and how it relates to parsing in compilers. It introduces basic terminology like tokens, patterns, lexemes, and attributes. It describes how a lexical analyzer works by scanning input, identifying tokens, and sending tokens to a parser. Regular expressions are used to specify patterns for token recognition. Finite automata like nondeterministic and deterministic finite automata are constructed from regular expressions to recognize tokens.
Regular expressions define patterns to match strings. A regular expression compares its pattern to a string and returns true or false. If true, it may return something depending on the function used. Strings are data types that represent text using characters. A language is a set of strings formed from a specific alphabet. Operations like concatenation and union can combine languages. Regular expressions, strings, and languages are fundamental concepts in compiler design.
The document discusses lexical analysis of computer programming languages. It introduces lexical analysis as the process of reading a string of characters and categorizing them into tokens based on their roles. This involves constructing regular expressions to define the patterns for different token classes like keywords, identifiers, and numbers. The document then explains how to specify the lexical structure of a language by defining regular expressions for each token class and using them to build a lexical analyzer that takes a string as input and outputs the sequence of tokens.
Building Security Systems in Architecture.pdfrabiaatif2
Building security systems are essential for protecting people, property, and assets within a structure. These systems include a range of technologies and strategies such as surveillance cameras (CCTV), access control systems, alarm systems, security lighting, and motion detectors. Modern security solutions often integrate smart technology, allowing remote monitoring and real-time alerts through mobile devices. Access control systems, like key cards or biometric scanners, ensure that only authorized individuals can enter certain areas, enhancing both safety and privacy. Alarm systems, whether triggered by unauthorized entry, fire, or environmental hazards, play a critical role in emergency response. Additionally, video surveillance acts as both a deterrent and a tool for investigating incidents. An effective building security system is carefully planned during the design phase, taking into account the building's size, purpose, and potential risks. Ultimately, robust security systems are vital for ensuring peace of mind, protecting lives, and preserving valuable assets.
Taking AI Welfare Seriously, In this report, we argue that there is a realist...MiguelMarques372250
In this report, we argue that there is a realistic possibility that some AI systems
will be conscious and/or robustly agentic in the near future. That means that the
prospect of AI welfare and moral patienthood — of AI systems with their own
interests and moral significance — is no longer an issue only for sci-fi or the
distant future. It is an issue for the near future, and AI companies and other actors
have a responsibility to start taking it seriously. We also recommend three early
steps that AI companies and other actors can take: They can (1) acknowledge that
AI welfare is an important and difficult issue (and ensure that language model
outputs do the same), (2) start assessing AI systems for evidence of consciousness
and robust agency, and (3) prepare policies and procedures for treating AI systems
with an appropriate level of moral concern. To be clear, our argument in this
report is not that AI systems definitely are — or will be — conscious, robustly
agentic, or otherwise morally significant. Instead, our argument is that there is
substantial uncertainty about these possibilities, and so we need to improve our
understanding of AI welfare and our ability to make wise decisions about this
issue. Otherwise there is a significant risk that we will mishandle decisions about
AI welfare, mistakenly harming AI systems that matter morally and/or mistakenly
caring for AI systems that do not.
The B.Tech in Computer Science and Engineering (CSE) at Lovely Professional University (LPU) is a four-year undergraduate program designed to equip students with strong theoretical and practical foundations in computing. The curriculum is industry-aligned and includes core subjects like programming, data structures, algorithms, operating systems, computer networks, databases, and software engineering. Students can also choose specializations such as Artificial Intelligence, Data Science, Cybersecurity, and Cloud Computing. LPU emphasizes hands-on learning through modern labs, live projects, and internships. The university has collaborations with tech giants like Google, Microsoft, and IBM, offering students excellent exposure and placement opportunities. With a vibrant campus life, international diversity, and a strong placement record, LPU's B.Tech CSE program prepares students to become future-ready professionals in the fast-evolving tech world.
The idea behind this session is to equip you with a practical, collaborative method to deeply understand your domain — not just from a technical perspective, but through a lens that aligns with how the business actually works.
By the end, you’ll walk away with a new mindset and tools you can take back to your team.
ELectronics Boards & Product Testing_Shiju.pdfShiju Jacob
This presentation provides a high level insight about DFT analysis and test coverage calculation, finalizing test strategy, and types of tests at different levels of the product.
Sorting Order and Stability in Sorting.
Concept of Internal and External Sorting.
Bubble Sort,
Insertion Sort,
Selection Sort,
Quick Sort and
Merge Sort,
Radix Sort, and
Shell Sort,
External Sorting, Time complexity analysis of Sorting Algorithms.
1. Specification of Tokens
• Definitions:
• The ALPHABET (often written ∑) is the set of legal input symbols
• A STRING over some alphabet ∑ is a finite sequence of symbols
from ∑
• The LENGTH of string s is written |s|
• The EMPTY STRING is a special 0-length string denoted ε
• REGULAR EXPRESSIONS (REs) are the most
common notation for pattern specification.
• Every pattern specifies a set of strings, so an RE
names a set of strings.
2. More definitions: strings and
substrings
• A PREFIX of s is formed by removing 0 or more
trailing symbols of s
• A SUFFIX of s is formed by removing 0 or more
leading symbols of s
• A SUBSTRING of s is formed by deleting a
prefix and a suffix from s
• A PROPER prefix, suffix, or substring is a
nonempty string x that is, respectively, a prefix,
suffix, or substring of s but with x ≠ s.
3. More definitions
• A LANGUAGE is a set of strings over a fixed
alphabet ∑.
• Example languages:
– Ø (the empty set)
– { ε }
– { a, aa, aaa, aaaa }
• The CONCATENATION of two strings x and y is
written xy
• String EXPONENTIATION is written si, where s0
= ε and si = si-1s for i>0.
4. Regular expressions
• REs let us precisely define a set of strings.
• For C identifiers, we might use
letter ( letter | digit )*
• Parentheses are for grouping, | means “OR”,
and * means zero or more instances.
• Every RE ‘r’ defines a language L(r).
5. Regular expressions
• Here are the rules for writing REs over an
alphabet ∑ :
1. ε is an RE denoting { ε }, the language containing
only the empty string.
2. If ‘a’ is in ∑, then a is a RE denoting { a }.
3. If r and s are REs denoting L(r) and L(s), then
1. (r)|(s) is a RE denoting L(r) ∪ L(s)
2. (r)(s) is a RE denoting L(r) L(s)
3. (r)* is a RE denoting (L(r))*
4. (r) is a RE denoting L(r)
6. Additional conventions
• To avoid too many parentheses, we assume:
1. * has the highest precedence, and is left
associative.
2. Concatenation has the 2nd highest precedence,
and is left associative.
3. | has the lowest precedence and is left
associative.
7. Example REs
1. a | b
2. ( a | b ) ( a | b )
3. a*
4. (a | b )*
5. a | a*b
8. Equivalence of REs
Axiom Description
r|s = s|r | is commutative
r|(s|t) = (r|s)t | is associative
(rs)t = r(st) Concatenation is associative
r(s|t) = rs|rt
(s|t)r = sr|tr
Concatenation distributes over |
ε r = r
r ε = r
ε Is the identity element for concatenation
r* = (r| ε)* Relation between * and ε
r** = r* * is idempotent
9. Regular definitions
• Example for identifiers in C:
letter -> A | B | … | Z | a | b | … | z
digit -> 0 | 1 | … | 9
id -> letter ( letter | digit )*
• Example for numbers in Pascal:
digit -> 0 | 1 | … | 9
digits -> digit digit*
optional_fraction -> . digits | ε
optional_exponent -> ( E ( + | - | ε ) digits ) | ε
num -> digits optional_fraction optional_exponent