This document discusses domain modeling and provides guidance on creating domain models using UML class diagrams. It defines a domain model as a visual representation of conceptual classes or real-world objects in a problem domain. It notes that identifying conceptual classes is key to object-oriented analysis. The document outlines best practices for developing a domain model, such as identifying classes, adding necessary associations and attributes, and applying analysis patterns. It warns against including irrelevant features or modeling classes as attributes.
PROCEDURAL ORIENTED PROGRAMMING VS OBJECT ORIENTED PROGRAMING Uttam Singh
This document compares procedure-oriented programming and object-oriented programming. Procedure-oriented programming divides programs into functions, uses global data, and does not support overloading or access specifiers. Object-oriented programming divides programs into objects, supports access specifiers like public and private, allows overloading, and hides data to provide more security. The document provides examples of how object-oriented programming makes it easier to add new classes and update existing classes compared to procedure-oriented programming.
The document discusses the limitations of procedural programming languages and how object-oriented programming (OOP) addresses these limitations. Specifically, it notes that procedural languages have issues with unrestricted access to global data and modeling real-world objects which have both attributes and behaviors. OOP combines data and functions that operate on that data into single units called objects, encapsulating the data and hiding it from direct access. This solves the problems of procedural languages by restricting access to data and more closely modeling real objects.
This document discusses locating strategies for elements in the Selenium IDE, including locating by ID, name, link text, and XPath. It explains that the command, target, and value fields in Selenium IDE function like arguments in a function. Target identifies elements uniquely using a locating strategy and format like "locatorType=locator". Common strategies covered are locating by ID, name, link text, and XPath using absolute paths, attributes, and functions like contains and starts-with. An exercise is provided to practice different locating strategies.
This document discusses syntax analysis in language processing. It covers lexical analysis, which breaks programs into tokens, and parsing, which analyzes syntax based on context-free grammars. Lexical analysis uses state machines or tables to recognize patterns in code. Parsing can be done top-down with recursive descent or bottom-up with LR parsers that shift and reduce based on lookahead. Well-formed grammars and separation of lexical and syntax analysis enable efficient parsing.
This document is a lecture introduction to object oriented analysis and design (OOA/D) at the University of Education Okara. It discusses key topics that will be covered in the course, including applying the Unified Modeling Language (UML), assigning responsibilities to objects, requirements analysis, use cases, the Unified Process (UP) methodology, and the differences between object oriented analysis, design and implementation. The goal is for students to learn skills in analyzing requirements and designing object-oriented systems using standard best practices.
This document provides an introduction to database design and applications (DBDA). It discusses the differences between file systems and database management systems (DBMS)/relational database management systems (RDBMS). It also covers the three schema architecture of a DBMS, including the conceptual, internal, and external schemas. Additionally, it discusses data independence and the advantages of using a DBMS compared to a file system. The document provides a brief history of DBMS and describes some popular DBMS software. It also outlines the characteristics, advantages, and disadvantages of using a DBMS.
This document discusses domain modeling and provides guidance on creating domain models using UML class diagrams. It defines a domain model as a visual representation of conceptual classes or real-world objects in a problem domain. It notes that identifying conceptual classes is key to object-oriented analysis. The document outlines best practices for developing a domain model, such as identifying classes, adding necessary associations and attributes, and applying analysis patterns. It warns against including irrelevant features or modeling classes as attributes.
PROCEDURAL ORIENTED PROGRAMMING VS OBJECT ORIENTED PROGRAMING Uttam Singh
This document compares procedure-oriented programming and object-oriented programming. Procedure-oriented programming divides programs into functions, uses global data, and does not support overloading or access specifiers. Object-oriented programming divides programs into objects, supports access specifiers like public and private, allows overloading, and hides data to provide more security. The document provides examples of how object-oriented programming makes it easier to add new classes and update existing classes compared to procedure-oriented programming.
The document discusses the limitations of procedural programming languages and how object-oriented programming (OOP) addresses these limitations. Specifically, it notes that procedural languages have issues with unrestricted access to global data and modeling real-world objects which have both attributes and behaviors. OOP combines data and functions that operate on that data into single units called objects, encapsulating the data and hiding it from direct access. This solves the problems of procedural languages by restricting access to data and more closely modeling real objects.
This document discusses locating strategies for elements in the Selenium IDE, including locating by ID, name, link text, and XPath. It explains that the command, target, and value fields in Selenium IDE function like arguments in a function. Target identifies elements uniquely using a locating strategy and format like "locatorType=locator". Common strategies covered are locating by ID, name, link text, and XPath using absolute paths, attributes, and functions like contains and starts-with. An exercise is provided to practice different locating strategies.
This document discusses syntax analysis in language processing. It covers lexical analysis, which breaks programs into tokens, and parsing, which analyzes syntax based on context-free grammars. Lexical analysis uses state machines or tables to recognize patterns in code. Parsing can be done top-down with recursive descent or bottom-up with LR parsers that shift and reduce based on lookahead. Well-formed grammars and separation of lexical and syntax analysis enable efficient parsing.
This document is a lecture introduction to object oriented analysis and design (OOA/D) at the University of Education Okara. It discusses key topics that will be covered in the course, including applying the Unified Modeling Language (UML), assigning responsibilities to objects, requirements analysis, use cases, the Unified Process (UP) methodology, and the differences between object oriented analysis, design and implementation. The goal is for students to learn skills in analyzing requirements and designing object-oriented systems using standard best practices.
This document provides an introduction to database design and applications (DBDA). It discusses the differences between file systems and database management systems (DBMS)/relational database management systems (RDBMS). It also covers the three schema architecture of a DBMS, including the conceptual, internal, and external schemas. Additionally, it discusses data independence and the advantages of using a DBMS compared to a file system. The document provides a brief history of DBMS and describes some popular DBMS software. It also outlines the characteristics, advantages, and disadvantages of using a DBMS.
This document discusses semantic analysis in compilers. It begins by defining semantics and semantic analysis, and provides an example of a syntactically valid but semantically invalid statement. It then discusses how semantic rules are associated with a context-free grammar to perform semantic analysis. It describes the annotated parse tree output of semantic analysis and how semantic rules are associated with grammar productions. The document discusses different ways to represent semantic rules like syntax-directed definitions and attribute grammars. It also covers different types of attributes like synthesized and inherited attributes. Finally, it discusses applications of semantic analysis like type checking and generating intermediate code.
UML is a graphical modeling language used to visualize software designs. It has two main types of diagrams - structural diagrams that show static relationships, and behavioral diagrams that show dynamic interactions. UML was created in the 1990s and standardized in 2005. It helps designers see the big picture of a project without code-level details through drawing classes, objects, and their relationships. Common diagrams include class, sequence, and use case diagrams.
This document discusses relational algebra and relational calculus operations used in relational database systems. It describes basic relational algebra operations like selection, projection, union, set difference, and cartesian product. It also covers additional operations like natural join, outer join, division, and aggregate functions. Finally, it provides an overview of tuple relational calculus and the notations used, such as tuple variables, predicates, quantifiers, and logical connectives.
This document provides an introduction to the C programming language. It discusses the history and characteristics of C, including that it was developed in the 1970s, is widely used for systems programming, and has influenced many other languages. The structures of a basic C program and functions are described. The document also covers various aspects of C programming such as data types, variables, constants, streams, and basic input/output functions like printf and scanf. Operators supported in C like arithmetic, relational, equality, logical, and bitwise operators are also summarized.
Syntax defines the grammatical rules of a programming language. There are three levels of syntax: lexical, concrete, and abstract. Lexical syntax defines tokens like literals and identifiers. Concrete syntax defines the actual representation using tokens. Abstract syntax describes a program's information without implementation details. Backus-Naur Form (BNF) uses rewriting rules to specify a grammar. BNF grammars can be ambiguous. Extended BNF simplifies recursive rules. Syntax analysis transforms a program into an abstract syntax tree used for semantic analysis and code generation.
This document discusses domain modeling and provides guidance on creating domain models using UML class diagrams. A domain model visually represents conceptual classes or real-world objects in a problem domain. It identifies classes without defining operations. Steps for creating a domain model include identifying classes, adding necessary associations and attributes, and applying analysis patterns. A common mistake is modeling classes as attributes when they should be separate classes associated through relationships. The document also provides examples of conceptual classes that describe items rather than being attributes of instances.
Oop lec 2(introduction to object oriented technology)Asfand Hassan
The document discusses high-level and low-level programming languages. It explains that high-level languages resemble human languages but must be translated into machine language that CPUs can understand using compilers. Low-level languages like assembly language are closer to machine language. The document also introduces object-oriented programming characteristics like encapsulation, inheritance and polymorphism. It provides examples of classes and objects in C++ and describes relationships between objects like attributes, associations and aggregations.
Java was originally created at Sun Microsystems in 1991 to program home appliances. It was designed to simplify compiler writing for different processors by using an intermediate bytecode language. This bytecode is the same for all processors and only requires a small program to translate to machine code for each specific processor. Java became widely used for internet applications when web browsers began incorporating Java applets in 1994-1995. Key aspects of Java include its object-oriented design with classes and methods, and its portability achieved through compiling to bytecode that runs on a virtual machine.
The document discusses object-oriented analysis and design concepts like objects, classes, class diagrams, and relationships between classes. It defines objects and classes, and notes that class diagrams describe the attributes and operations of classes and the relationships between them. The document also discusses different types of relationships between classes like association, generalization, aggregation, and their notation in class diagrams including association names, roles, and multiplicity.
In this presentation, the most important object oriented topics and features of C# is discussed. The session was presented in 42nd Session of CodeWeekend and it is the 3rd week of C# + CS50 Series of Training.
Project Lambda: To Multicore and BeyondDmitry Buzdin
This document outlines Project Lambda, which aims to add closures and related features to Java SE 8 to better support programming in a multicore environment. It discusses how hardware trends are leading to parallelism and how developers need simple parallel libraries. It proposes lambda expressions and method references to make parallel programming easier by allowing internal iteration idioms instead of serial for loops. Lambda expressions allow code to be passed as data in a concise way, avoiding boilerplate. Their type is a single-abstract-method (SAM) interface. Method references further simplify code by extracting data dependencies.
Csharp expressions, types, variables, control statements of both selection and loops, methods, namespaces, classes, inheritance and polymorphism topics are included in this presentation.
This document discusses console input and output in Java using methods like System.out.println, System.out.print, and System.out.printf. It covers formatting output with printf, specifying formats like field width and number of decimal places. It also covers reading input from the console using the Scanner class, its methods like nextInt() and nextLine(), and dealing with delimiters and line terminators. Multiple examples are provided to demonstrate console I/O techniques.
The document discusses data modeling and entity relationship diagrams. It defines data modeling as the process of defining and analyzing data requirements to support business processes. It describes the different types of data models including conceptual, logical, and physical models. It also explains the key components of entity relationship diagrams including entities, attributes, relationships, cardinality, and notation. The document provides an example of using an ERD to model a scenario involving departments, supervisors, employees, and projects.
Evaluate And Analysis of ALGOL, ADA ,PASCAL Programming Languages Charitha Gamage
This document will help you to understand the main features as well as contribution of ADA ALGOL PASCAL languages, major strengths and weakness of them clearly.
This document provides an overview of language design and translation issues. It covers several topics including programming language concepts, paradigms and models, programming environments, virtual computers and binding times, programming language syntax, stages in translation, formal transition models, elementary data types and properties of types and objects, and scalar and composite data types. The document is part of a syllabus for a course on programming language concepts.
This document is a lecture introduction to object oriented analysis and design (OOA/D). It discusses key topics that will be covered in the course, including the Unified Modeling Language (UML) for visualizing software design, assigning responsibilities to objects, requirements analysis, use cases, the Unified Process (UP) methodology, and the differences between object oriented analysis, design and implementation. The goal is for students to learn skills in analyzing requirements, modeling domains with objects, and designing software systems using an object oriented approach.
Tokens are the smallest individual units in a Java program. There are five types of tokens: keywords, identifiers, literals, operators, and separators. Keywords are reserved words that are essential for the Java language syntax. Identifiers are names given to classes, methods, variables and other program elements and have specific naming rules. Literals represent constant values like integers, floats, characters and strings. Operators perform operations on operands. Separators delineate different parts of code like parentheses, braces, brackets and semicolons.
Epsilon is a family of languages for managing and transforming models within the Eclipse Modeling Framework (EMF). It provides several domain-specific languages with consistent syntax for common model-driven engineering (MDE) tasks like validation, transformation, and code generation. Epsilon integrates tightly with EMF and the Eclipse Modeling Project to support building and editing domain-specific modeling languages.
This document provides an overview of programming concepts, including an introduction to programming logic, the components and purposes of programs, and different programming paradigms like structured and object-oriented programming. It discusses key object-oriented programming principles like inheritance, encapsulation, abstraction, and polymorphism. The document also briefly describes different architectural models for programs, including client-server and multi-tier architectures.
Designing A Syntax Based Retrieval System03Avelin Huo
The document proposes a syntax-based text retrieval system to support grammatical querying of tagged corpora for language learners and teachers. It describes building an index of part-of-speech tagged n-grams from a corpus, with a filter to select discriminative index terms. A regular expression query is rewritten and its positions in the index are used to find candidate matching text units efficiently. An evaluation compares the proposed index to a complete index in terms of size and query performance.
This document discusses semantic analysis in compilers. It begins by defining semantics and semantic analysis, and provides an example of a syntactically valid but semantically invalid statement. It then discusses how semantic rules are associated with a context-free grammar to perform semantic analysis. It describes the annotated parse tree output of semantic analysis and how semantic rules are associated with grammar productions. The document discusses different ways to represent semantic rules like syntax-directed definitions and attribute grammars. It also covers different types of attributes like synthesized and inherited attributes. Finally, it discusses applications of semantic analysis like type checking and generating intermediate code.
UML is a graphical modeling language used to visualize software designs. It has two main types of diagrams - structural diagrams that show static relationships, and behavioral diagrams that show dynamic interactions. UML was created in the 1990s and standardized in 2005. It helps designers see the big picture of a project without code-level details through drawing classes, objects, and their relationships. Common diagrams include class, sequence, and use case diagrams.
This document discusses relational algebra and relational calculus operations used in relational database systems. It describes basic relational algebra operations like selection, projection, union, set difference, and cartesian product. It also covers additional operations like natural join, outer join, division, and aggregate functions. Finally, it provides an overview of tuple relational calculus and the notations used, such as tuple variables, predicates, quantifiers, and logical connectives.
This document provides an introduction to the C programming language. It discusses the history and characteristics of C, including that it was developed in the 1970s, is widely used for systems programming, and has influenced many other languages. The structures of a basic C program and functions are described. The document also covers various aspects of C programming such as data types, variables, constants, streams, and basic input/output functions like printf and scanf. Operators supported in C like arithmetic, relational, equality, logical, and bitwise operators are also summarized.
Syntax defines the grammatical rules of a programming language. There are three levels of syntax: lexical, concrete, and abstract. Lexical syntax defines tokens like literals and identifiers. Concrete syntax defines the actual representation using tokens. Abstract syntax describes a program's information without implementation details. Backus-Naur Form (BNF) uses rewriting rules to specify a grammar. BNF grammars can be ambiguous. Extended BNF simplifies recursive rules. Syntax analysis transforms a program into an abstract syntax tree used for semantic analysis and code generation.
This document discusses domain modeling and provides guidance on creating domain models using UML class diagrams. A domain model visually represents conceptual classes or real-world objects in a problem domain. It identifies classes without defining operations. Steps for creating a domain model include identifying classes, adding necessary associations and attributes, and applying analysis patterns. A common mistake is modeling classes as attributes when they should be separate classes associated through relationships. The document also provides examples of conceptual classes that describe items rather than being attributes of instances.
Oop lec 2(introduction to object oriented technology)Asfand Hassan
The document discusses high-level and low-level programming languages. It explains that high-level languages resemble human languages but must be translated into machine language that CPUs can understand using compilers. Low-level languages like assembly language are closer to machine language. The document also introduces object-oriented programming characteristics like encapsulation, inheritance and polymorphism. It provides examples of classes and objects in C++ and describes relationships between objects like attributes, associations and aggregations.
Java was originally created at Sun Microsystems in 1991 to program home appliances. It was designed to simplify compiler writing for different processors by using an intermediate bytecode language. This bytecode is the same for all processors and only requires a small program to translate to machine code for each specific processor. Java became widely used for internet applications when web browsers began incorporating Java applets in 1994-1995. Key aspects of Java include its object-oriented design with classes and methods, and its portability achieved through compiling to bytecode that runs on a virtual machine.
The document discusses object-oriented analysis and design concepts like objects, classes, class diagrams, and relationships between classes. It defines objects and classes, and notes that class diagrams describe the attributes and operations of classes and the relationships between them. The document also discusses different types of relationships between classes like association, generalization, aggregation, and their notation in class diagrams including association names, roles, and multiplicity.
In this presentation, the most important object oriented topics and features of C# is discussed. The session was presented in 42nd Session of CodeWeekend and it is the 3rd week of C# + CS50 Series of Training.
Project Lambda: To Multicore and BeyondDmitry Buzdin
This document outlines Project Lambda, which aims to add closures and related features to Java SE 8 to better support programming in a multicore environment. It discusses how hardware trends are leading to parallelism and how developers need simple parallel libraries. It proposes lambda expressions and method references to make parallel programming easier by allowing internal iteration idioms instead of serial for loops. Lambda expressions allow code to be passed as data in a concise way, avoiding boilerplate. Their type is a single-abstract-method (SAM) interface. Method references further simplify code by extracting data dependencies.
Csharp expressions, types, variables, control statements of both selection and loops, methods, namespaces, classes, inheritance and polymorphism topics are included in this presentation.
This document discusses console input and output in Java using methods like System.out.println, System.out.print, and System.out.printf. It covers formatting output with printf, specifying formats like field width and number of decimal places. It also covers reading input from the console using the Scanner class, its methods like nextInt() and nextLine(), and dealing with delimiters and line terminators. Multiple examples are provided to demonstrate console I/O techniques.
The document discusses data modeling and entity relationship diagrams. It defines data modeling as the process of defining and analyzing data requirements to support business processes. It describes the different types of data models including conceptual, logical, and physical models. It also explains the key components of entity relationship diagrams including entities, attributes, relationships, cardinality, and notation. The document provides an example of using an ERD to model a scenario involving departments, supervisors, employees, and projects.
Evaluate And Analysis of ALGOL, ADA ,PASCAL Programming Languages Charitha Gamage
This document will help you to understand the main features as well as contribution of ADA ALGOL PASCAL languages, major strengths and weakness of them clearly.
This document provides an overview of language design and translation issues. It covers several topics including programming language concepts, paradigms and models, programming environments, virtual computers and binding times, programming language syntax, stages in translation, formal transition models, elementary data types and properties of types and objects, and scalar and composite data types. The document is part of a syllabus for a course on programming language concepts.
This document is a lecture introduction to object oriented analysis and design (OOA/D). It discusses key topics that will be covered in the course, including the Unified Modeling Language (UML) for visualizing software design, assigning responsibilities to objects, requirements analysis, use cases, the Unified Process (UP) methodology, and the differences between object oriented analysis, design and implementation. The goal is for students to learn skills in analyzing requirements, modeling domains with objects, and designing software systems using an object oriented approach.
Tokens are the smallest individual units in a Java program. There are five types of tokens: keywords, identifiers, literals, operators, and separators. Keywords are reserved words that are essential for the Java language syntax. Identifiers are names given to classes, methods, variables and other program elements and have specific naming rules. Literals represent constant values like integers, floats, characters and strings. Operators perform operations on operands. Separators delineate different parts of code like parentheses, braces, brackets and semicolons.
Epsilon is a family of languages for managing and transforming models within the Eclipse Modeling Framework (EMF). It provides several domain-specific languages with consistent syntax for common model-driven engineering (MDE) tasks like validation, transformation, and code generation. Epsilon integrates tightly with EMF and the Eclipse Modeling Project to support building and editing domain-specific modeling languages.
This document provides an overview of programming concepts, including an introduction to programming logic, the components and purposes of programs, and different programming paradigms like structured and object-oriented programming. It discusses key object-oriented programming principles like inheritance, encapsulation, abstraction, and polymorphism. The document also briefly describes different architectural models for programs, including client-server and multi-tier architectures.
Designing A Syntax Based Retrieval System03Avelin Huo
The document proposes a syntax-based text retrieval system to support grammatical querying of tagged corpora for language learners and teachers. It describes building an index of part-of-speech tagged n-grams from a corpus, with a filter to select discriminative index terms. A regular expression query is rewritten and its positions in the index are used to find candidate matching text units efficiently. An evaluation compares the proposed index to a complete index in terms of size and query performance.
This document summarizes a study group presentation on Apex basics for the Platform Developer 1 exam. It discusses what Apex is and how it fits into the exam topics. It covers working with sObjects, querying records using SOQL, manipulating records with loops, writing records using DML, and common mistakes like not bulkifying code. Resources like Trailhead and books are recommended for additional study. There is also information about an upcoming Trailblazer points competition within the Ladies Be Architects community group.
Explains Language Processors in deep, language processing activities are arises,what is program generation activities,fundamentals of lang. processors,Toy compiler,Grammar, LAPDTs Lex & Yacc
The document provides an overview of machine learning for natural language processing (NLP) tasks. It discusses framing NLP problems as supervised learning tasks, preprocessing text, feature extraction using the FEX tool, and examples of NLP tasks like part-of-speech tagging and named entity recognition that can be solved using these techniques. It also describes the typical components of a machine learning system for NLP, including preprocessing, feature extraction, classifiers, and evaluation.
The document provides an overview of machine learning for natural language processing (NLP) tasks. It discusses framing NLP problems as supervised learning tasks, preprocessing text, feature extraction using the FEX tool, and examples of NLP tasks like part-of-speech tagging and named entity recognition that can be solved using these techniques. It also describes the typical components of a machine learning system for NLP, including preprocessing, feature extraction, classifiers, and evaluation.
CS 112 PA #4Like the previous programming assignment, this assignm.docxannettsparrow
CS 112 PA #4
Like the previous programming assignment, this assignment builds off of a prior lab. In this assignment you will write a program that solves a maze by finding a connection between two locations within the maze. As was the case with the prior assignment, you will probably need the StringSplitter used in class and lab.
Maze Text File
Our maze will be read in via text file. The first line informs us of the number of spaces in the maze. Each line afterwards describes a connection between two points in the maze. For example:
2
1 S 2
2 N 1
The example above tells us that we have two spaces in our maze. Furthermore, we see that space 1 has a southern connection to space 2. Similarly, space 2 has a northward connection to space 1. The possible connections are N (north), E (east), S (south), and W (west).
MazeSpace
Each space in the maze will be represented by an object of type MazeSpace. Here's the UML class diagram:
Additional Functions
In addition to the MazeSpace class above, you must implement the following two functions.
string depthFirstSearch(MazeSpace *spaces, int num_spaces, int start, int end)
string breadthFirstSearch(MazeSpace *spaces, int num_spaces, int start, int end)
These functions perform the depth / breadth first searches and returns a string that represents the path taken by each search pattern (see screenshot in the next section). Note that depending on how you implement your searches, you may not get the exact same output as what I provide. This is okay!
Sample Output
Below is sample output from my program:
Header Comment, and Formatting
1. Be sure to modify the file header comment at the top of your script to indicate your name, student ID, completion time, and the names of any individuals that you collaborated with on the assignment.
2. Remember to follow the basic coding style guide. For a list of basic rules, see my website or examine my example files from previous assignments and labs.
Reflection Essay
In addition to the programming tasks listed above, your submission must include an essay that reflects on your experiences with this homework. This essay must be at least 350 words long. Note that the focus of this paper should be on your reflection, not on structure (e.g. introductory paragraph, conclusion, etc.). The essay is graded on content (i.e. it shows deep though) rather than syntax (e.g. spelling) and structure. Below are some prompts that can be used to get you thinking. Feel free to use these or to make up your own.
· Describe a particular struggle that you overcame when working on this programming assignment.
· Conversely, describe an issue with your assignment that you were unable to resolve.
· Provide advice to a future student on how he or she might succeed on this assignment.
1. Describe the most fun aspect of the assignment.
1. Describe the most challenging aspect of the assignment.
1. Describe the most difficult aspect of the assignment to understand.
1. Provide.
This document discusses language processors and their fundamentals. It begins by explaining the semantic gap between how software is designed and implemented, and how language processors help bridge this gap. It then covers different types of language processors like translators, interpreters, and preprocessors. The key activities of language processors - analysis and synthesis - are explained. Analysis includes lexical, syntax and semantic analysis, while synthesis includes memory allocation and code generation. Language specifications using grammars and different binding times are also covered. Finally, common language processing development tools like LEX and YACC are introduced.
Graph Databases in the Microsoft EcosystemMarco Parenzan
With SQL Server and Cosmos Db we now have graph databases broadly available, after being studied for decades in Db theory, or being a niche approach in Open Source with Neo4J. And then there are services like Microsoft Graph and Azure Digital Twins that give us vertical implementations of graph. So let's make a walkaround of graphs in the MIcrosoft ecosystem.
The document provides an overview of the relational data model and relational algebra. It discusses how the relational model represents data using tables of attribute-value pairs and allows standard logical operations. Key concepts covered include the relational operations of projection, selection, join, union, difference, and divide. SQL is introduced as the standard language for querying and manipulating relational data using these algebraic operations.
The document discusses combinator libraries and domain-specific languages (DSLs) in F#. It explains that combinator libraries allow defining DSLs that embed in a host language. It provides examples of using F# to define DSLs for expressions, HTML, and forms. The document also discusses how F# features like algebraic data types and quotations enable easily defining and compiling DSLs to other languages.
Designing Optimized Symbols for InduSoft Web Studio ProjectsAVEVA
Because InduSoft Web Studio allows you to easily develop applications for mobile devices and embedded systems it’s easy to get lost in the rich feature set and develop symbols that are not optimized for the entire range of systems the application will be deployed to. In this webinar, we’d like to give InduSoft Web Studio users a guide to developing symbols that can be easily resized or easily optimized for deployment to mobile devices and embedded computers.
The document analyzes natural language artifacts from software projects to extract useful information. It presents two text analysis methods: 1) Lexical analysis using TAPoR to identify keywords and trends, and 2) Syntactic and semantic analysis to annotate text and extract semantic relationships as RDF triples. The methods were applied to data from a project including wiki pages, SVN comments, tickets, emails and chats. The analysis identified developer expertise, responsibilities, contributions and relationships. Trends in team activities and communication over time were also found.
Task-oriented Conversational semantic parsingjie cao
The document discusses recent advances in task-oriented conversational semantic parsing. It outlines three papers: (1) SBTOP from Facebook introduces a hierarchical intent/slot representation and extensions for session-based dialog; (2) TreeDST from Apple uses hierarchical representations of intent/slot names and nested/conjunctive slots; (3) Dataflow synthesis from Microsoft models dialog as program prediction and dataflow graph construction rather than intent/slot frames. The document analyzes the contributions and limitations of each paper's approach to compositional representation, session-awareness and other challenges in conversational semantic parsing.
The Road to U-SQL: Experiences in Language Design (SQL Konferenz 2017 Keynote)Michael Rys
APL was an early language with high-dimensional arrays and nested data models. Pascal and C/C++ introduced procedural programming with structured control flow. Other influences included Lisp for functional programming and Prolog for logic programming. SQL introduced declarative expressions with procedural control flow for data processing. Modern languages combine aspects of declarative querying, imperative programming, and support for both structured and unstructured data models. Key considerations in language design include support for parallelism, distribution, extensibility, and optimization.
Monthly AI Tech Talks in Toronto 2019-08-28
https://www.meetup.com/aittg-toronto
The talk will cover the end-to-end details including contextual and linguistic feature extraction, vectorization, n-grams, topic modeling, named entity resolution which are based on concepts from mathematics, information retrieval and natural language processing. We will also be diving into more advanced feature engineering strategies such as word2vec, GloVe and fastText that leverage deep learning models.
In addition, attendees will learn how to combine NLP features with numeric and categorical features and analyze the feature importance from the resulting models.
The following libraries will be used to demonstrate the aforementioned feature engineering techniques: spaCy, Gensim, fasText and Keras in Python.
https://www.meetup.com/aittg-toronto/events/261940480/
Details
For September, DataScience Sg is starting a new series specially for the undergrads. The series aims to showcase undergrads and fresh grads project work.
The series is meant to encourage youths in joining the data science & artificial intelligence career. And for the employers to come in and recruit talents for your companies.
In this inaugural meetup for the series, we have the following youths to share about their work and project and how their projects helped them in their current career.
DSSG strongly encourage current undergrads and fresh grads to join us in this series. Its still open to the general community!
Details:
Ivan is currently a Data Scientist at Tech In Asia (TIA), with experience in developing recommender systems, customer churn prediction, network analysis and driving BI solutions through data visualization and analytics. He graduated with a Bachelor of Science (Informations Systems) and Major in Marketing Analytics from SMU in 2018.
Ivan will be sharing about his Final Year Project when he was an undergrad at SMU — KDDLabs, a web-based data mining application while explaining the team’s motivations, challenges and key takeaways. In addition, he will also be talking about his first data product at TIA, developing recommender systems to help better connect jobseekers with employers and vice versa.
LinkedIn: https://www.linkedin.com/in/yongsiang/
FYP: http://smu.sg/kddlabs
1. The document discusses using Azure Machine Learning (ML) capabilities for text classification, including binary and multiclass classification problems.
2. It provides examples of using ML models to detect spam and classify customer service issues from problem descriptions.
3. The document outlines the process for building ML text classification pipelines in Azure ML, including data preparation, feature extraction, model training and evaluation.
This document provides an introduction to fundamentals of programming with C#, including definitions of key concepts like algorithms, variables, data types, operators, and conditional statements. It explains that programming involves describing what you want the computer to do as a sequence of steps or algorithms. The stages of software development are outlined as gathering requirements, planning/design, implementation, testing, deployment, support, and documentation. An overview of C# programming language fundamentals is also provided, such as basic syntax structure, defining classes and methods, and using the console for input/output.
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Similarity computation exploiting the semantic and syntactic inherent structure among job titles
1. Similarity Computation Exploiting
the Semantic and Syntactic
Inherent Structure Among Job
Titles
Authors: Sarthak Ahuja1, Joydeep Mondal1, Sudhanhsu Shekhar Singh1 and
David Glenn George2
1 IBM Research Lab, India
2 IBM Talent Management Solutions, Portsmouth, UK
Presenter: Joydeep
3. List of Available Job Titles
• System Engineer
• Software Developer
• Senior Software Engineer
• Junior Network Engineer
• Junior Software Tester
Query Job Title
• Junior Software Engineer
No Other information (job descriptions or other
details except TITLE) is available corresponding to
these jobs
Similarity
Computation
Similarity
ComputationSimilarity
Computation
Similarity
Computation
Similarity
Computation
Best Match
5. • IBM Watson Recruitment (IWR) : https://www.ibm.com/talent-
management/hr-solutions/recruiting-software
Mapping requisition jobs to the available job
taxonomy without using computation intensive and
time consuming sate of the art document similarity
methods by narrow down the search space
7. Job Title Matching
Split Title keywords
into Three categories
(Domain, Functional,
Attribute)
Map each category of
one job title to those
of the other title
8. Example
• Title = “Junior Software Engineer”
• Domain keywords Set = [“Software”]
• Functional keywords Set = [“Engineer”]
• Attribute Keywords set = [“Junior”]
Title = “Junior Software Engineer”
Map Domain, Functional, Attribute keyword sets of one title to those of the
other title
9. Methods
• Objective: Any job title can be split into the attribute, functional and core descriptor/domain words.
• Input:
• Job Title (T)
• Output:
• 3 sets , Attribute words set (SA), functional words set (SF) and core descriptor/domain words set (SD)
• Resources/ Existing techniques used:
• Acronym dictionary (DictA ), Spell checker technique (TechS ), Classifier model (Mclass)
• Algorithm:
• Step 1: SWord = split the title T into separate words
• Step 2: for each word in Sword
• Step 2.1: word = resolve acronyms of word using DictA
• Step 2.2: word = resolve the spelling mistake using TechS
• Step 2.3: classify word using Mclass as either a Attribute (A) word or a functional word (F) or a core descriptor/domain word (D)
• Step2.4: Append word to the corresponding set (SA , SF , SD ) depending upon it’s class label (A, F, D)
• Feature vector used in Classifier model (Mclass):
• [POS (part of speech) of the word, position of the word in job title (T) (first word/last word/in between
word), POS of the root word for each word, word ends with “er”/”or”/”ar” or not]
10. • Why we used these features?
• POS (part of speech) of the word : We found most of the attribute-words are adjectives, e.g. Senior, Junior etc., most of the
functional-words are noun, e.g. developer, tester, teacher and most of the core descriptor/domain words are also noun, e.g.
Software, Network etc.
• position of the word in job title (T) (first word/last word/in between word) : We found that attribute-words are generally the first or
last words of the title e.g.: Senior software developer, Network administrator junior etc. Most of the functional-words appear as in-
between or last word of the title e.g.: Senior software developer, Network administrator junior etc. We also found that most of the
core descriptor/domain words appears as in-between or first word in a title e.g.: Senior software developer, Network administrator
junior etc.
• POS of the root word for each word : Our analysis showed that POS of the root word corresponding to the functional-words are verb,
e.g. : Senior software developer : root word for developer = “develop” which is a verb. We used
https://www.vocabulary.com/dictionary/ open source online dictionary to get the root words.
• word ends with “er”/”or”/”ar” or not: We also found that most of the functional words end with either of these three substrings
“er”/”or”/”ar”, e.g. : teacher, developer, engineer etc.
11. I’m the
Best!
Functional classifier o/p
-> input of Attribute
Classifier
Functional Classifier o/p
+ Attribute Classifier
o/p -> input of Domain
Classifier
12. Methods
Objective: mapping three category-set of words (Attribute, Functional and core descriptor/domain)
corresponding to the two titles among themselves using classical imbalanced assignment problem. Then the
mapping scores are combined based on weighted or hierarchical scoring scheme to generate job title similarity.
• Input:
• Job Title1 (T1), Job Titl2 (T2)
• Output:
• Similarity score (s) between T1 and T2
• Resources/ Existing techniques used:
• Wordnet Dictionary API (W), Hungarian method to solve imbalanced assignment problem (TH)
• Algorithm:
• Step 1: extract (SA1 , SF1 , SD1 ) from T1 and (SA2 , SF2 , SD2 ) from T2 by previous method
• Step 2: Get the mappings as MA(SA1 : SA2 ), MF(SF1 : SF2 ) and MD(SD1 : SD2 ) by TH
• Step 3: calculate the mapping similarity score simA , simF and simD for MA , MF and MD respectively.
• Step 4: S = simD (1+ simF (1 + simA ))/ (IndicatorD + IndicatorF + IndicatorA ) // importance order : D, F and A respectively.
• We used Wordnet Dictionary API (W) to calculate semantic similarity between two words. We built a
semantic similarity score matrix for each pair of sets (SA1 : SA2 ), (SF1 : SF2 ) and (SD1 : SD2 ) and provide this
matrix to TH as input. We also use the same matrix to calculate simA , simF and simD for MA , MF and MD.
16. Core Novelty
1 . Any job title can be split into three categories the attribute, functional and core
descriptor/domain words.
2. Job title similarity calculation involves mapping of these three categories of
words corresponding to the two titles among themselves using classical imbalanced
assignment problem. Then the mapping scores can be combined based on
weighted or hierarchical scoring scheme to generate job title similarity.
16