The document discusses different concepts related to graphs including definitions of graphs, representations of graphs using adjacency matrices and lists, graph traversal algorithms like depth-first search and breadth-first search, and applications of graphs. It provides examples of directed and undirected graphs and covers key graph concepts such as connectivity, paths, and connected components. Pseudocode and C++ code implementations are given for depth-first search and breadth-first search algorithms.
This document provides information about graphs and graph algorithms. It discusses different graph representations including adjacency matrices and adjacency lists. It also describes common graph traversal algorithms like depth-first search and breadth-first search. Finally, it covers minimum spanning trees and algorithms to find them, specifically mentioning Kruskal's algorithm.
This document provides an overview of graphs and graph algorithms. It begins with an introduction to graphs, including definitions of vertices, edges, directed/undirected graphs, and graph representations using adjacency matrices and lists. It then covers graph traversal algorithms like depth-first search and breadth-first search. Minimum spanning trees and algorithms for finding them like Kruskal's algorithm are also discussed. The document provides examples and pseudocode for the algorithms. It analyzes the time complexity of the graph algorithms. Overall, the document provides a comprehensive introduction to fundamental graph concepts and algorithms.
This document discusses graphs and graph data structures. It defines a graph as a pictorial representation of a set of objects connected by links, with the objects represented as vertices and the links as edges. It provides definitions and examples of basic graph terminology like vertices, edges, adjacency, and different types of graphs like directed vs undirected graphs. It also covers graph implementations using adjacency matrices and adjacency lists, as well as common graph algorithms like depth-first search and breadth-first search. Finally, it lists some applications of graphs like social networks, maps, and computer networks.
The document discusses depth-first search (DFS) and breadth-first search (BFS) algorithms for graph traversal. It explains that DFS uses a stack to systematically visit all vertices in a graph by exploring neighboring vertices before moving to the next level, while BFS uses a queue to explore neighboring vertices at the same level before moving to the next. Examples are provided to illustrate how DFS can be used to check for graph connectivity and cyclicity.
This Presentation Explains a very Important topic of Data Structure in Programming which is Graphs and Methods of Graph Traversing.
I think this presentation will help you in understanding the Data Structure Graph and its Traversing Methods.
This document provides definitions and concepts related to graphs. It defines a graph as a data structure consisting of vertices and edges linking vertices, which can represent objects and relationships. Graphs are a generalization of trees that allow any type of relationship between nodes. Common graph representations include adjacency lists and matrices. The document also discusses graph traversal methods, minimum spanning trees, and applications of graphs.
A graph G is composed of a set of vertices V connected by edges E. It can be represented using an adjacency matrix, with a 1 or 0 in position (i,j) indicating whether vertices i and j are connected, or an adjacency list storing the neighbors of each vertex. Graph search algorithms like depth-first search (DFS) and breadth-first search (BFS) are used to traverse the graph and find paths between vertices, with DFS prioritizing depth and BFS prioritizing breadth of exploration. DFS uses recursion to implicitly store paths while BFS uses queues and must store paths separately.
Graphs can be represented abstractly as a set of vertices and edges connecting pairs of vertices. They are used to model various real-world relationships and can be represented using adjacency lists or matrices. Breadth-first search (BFS) and depth-first search (DFS) are common graph traversal algorithms that systematically explore the edges of a graph to discover reachable vertices. BFS produces a breadth-first tree and finds the shortest paths from the source, while DFS explores edges deeper first before backtracking.
This document provides information about graphs and graph algorithms. It discusses different graph representations including adjacency matrices and adjacency lists. It also describes common graph traversal algorithms like depth-first search and breadth-first search. Finally, it covers minimum spanning trees and algorithms to find them, specifically mentioning Kruskal's algorithm.
This document provides an overview of graphs and graph algorithms. It begins with an introduction to graphs, including definitions of vertices, edges, directed/undirected graphs, and graph representations using adjacency matrices and lists. It then covers graph traversal algorithms like depth-first search and breadth-first search. Minimum spanning trees and algorithms for finding them like Kruskal's algorithm are also discussed. The document provides examples and pseudocode for the algorithms. It analyzes the time complexity of the graph algorithms. Overall, the document provides a comprehensive introduction to fundamental graph concepts and algorithms.
This document discusses graphs and graph data structures. It defines a graph as a pictorial representation of a set of objects connected by links, with the objects represented as vertices and the links as edges. It provides definitions and examples of basic graph terminology like vertices, edges, adjacency, and different types of graphs like directed vs undirected graphs. It also covers graph implementations using adjacency matrices and adjacency lists, as well as common graph algorithms like depth-first search and breadth-first search. Finally, it lists some applications of graphs like social networks, maps, and computer networks.
The document discusses depth-first search (DFS) and breadth-first search (BFS) algorithms for graph traversal. It explains that DFS uses a stack to systematically visit all vertices in a graph by exploring neighboring vertices before moving to the next level, while BFS uses a queue to explore neighboring vertices at the same level before moving to the next. Examples are provided to illustrate how DFS can be used to check for graph connectivity and cyclicity.
This Presentation Explains a very Important topic of Data Structure in Programming which is Graphs and Methods of Graph Traversing.
I think this presentation will help you in understanding the Data Structure Graph and its Traversing Methods.
This document provides definitions and concepts related to graphs. It defines a graph as a data structure consisting of vertices and edges linking vertices, which can represent objects and relationships. Graphs are a generalization of trees that allow any type of relationship between nodes. Common graph representations include adjacency lists and matrices. The document also discusses graph traversal methods, minimum spanning trees, and applications of graphs.
A graph G is composed of a set of vertices V connected by edges E. It can be represented using an adjacency matrix, with a 1 or 0 in position (i,j) indicating whether vertices i and j are connected, or an adjacency list storing the neighbors of each vertex. Graph search algorithms like depth-first search (DFS) and breadth-first search (BFS) are used to traverse the graph and find paths between vertices, with DFS prioritizing depth and BFS prioritizing breadth of exploration. DFS uses recursion to implicitly store paths while BFS uses queues and must store paths separately.
Graphs can be represented abstractly as a set of vertices and edges connecting pairs of vertices. They are used to model various real-world relationships and can be represented using adjacency lists or matrices. Breadth-first search (BFS) and depth-first search (DFS) are common graph traversal algorithms that systematically explore the edges of a graph to discover reachable vertices. BFS produces a breadth-first tree and finds the shortest paths from the source, while DFS explores edges deeper first before backtracking.
The document discusses different graph data structures including adjacency matrices and adjacency lists for representing graphs, and describes key graph terminology such as vertices, edges, paths, connected components, and directed vs undirected graphs. It also explains Dijkstra's algorithm for finding the shortest path between a starting node and all other nodes in a graph.
The document describes graphs and graph algorithms. It defines what a graph is composed of (vertices and edges) and different types of graphs like directed and undirected graphs. It provides terminology used with graphs like vertices, edges, paths, cycles, connectedness. It discusses ways of representing graphs through adjacency matrices and adjacency lists and provides examples. It also describes Dijkstra's algorithm, a graph algorithm to find the shortest path between vertices in a graph.
This document provides an overview of the topics covered in Unit 5, which include graphs, hashing, and collision resolution techniques. It defines graphs and their components such as vertices, edges, directed and undirected graphs. It also discusses different graph representations like adjacency matrix and adjacency list. Elementary graph operations like breadth-first search and depth-first search are explained along with examples. Hashing functions and collision resolution are also introduced.
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjtepournima055
The document discusses graphs and graph algorithms. It covers basic graph concepts like vertices, edges, paths, cycles. It describes different ways to represent graphs like adjacency matrix, adjacency list and their pros and cons. It also discusses operations on graphs like inserting and deleting vertices and edges. The document explains traversal algorithms like depth-first search and breadth-first search. It covers minimum spanning tree algorithms like Prim's and Kruskal's. It also briefly discusses shortest path algorithms like Dijkstra's and topological sorting.
Graph in data structure it gives you the information of the graph application. How to represent the Graph and also Graph Travesal is also there many terms are there related to garph
The document discusses graph theory and provides definitions and examples of various graph concepts. It defines what a graph is consisting of vertices and edges. It also defines different types of graphs such as simple graphs, multigraphs, digraphs and provides examples. It discusses graph terminology, models, degree of graphs, handshaking lemma, special graphs and applications. It also provides explanations of planar graphs, Euler's formula and graph coloring.
This document provides an overview of graphs and graph algorithms. It begins with definitions and data structures for representing graphs. It then covers graph traversal algorithms, including depth-first search (DFS) and breadth-first search (BFS). Topological sorting of directed acyclic graphs is also discussed. Exercises are provided to implement DFS, BFS, and topological sorting.
Graph Analytics - From the Whiteboard to Your Toolbox - Sam LermaPyData
This document provides an introduction to graph theory concepts and working with graph data in Python. It begins with basic graph definitions and real-world graph examples. Various graph concepts are then demonstrated visually, such as vertices, edges, paths, cycles, and graph properties. Finally, it discusses working with graph data structures and algorithms in the NetworkX library in Python, including graph generation, analysis, and visualization. The overall goal is to introduce readers to graph theory and spark their interest in further exploration.
The document discusses graphs and graph algorithms. It defines what a graph is - a non-linear data structure consisting of vertices connected by edges. It describes different graph representations like adjacency matrix, incidence matrix and adjacency list. It also explains graph traversal algorithms like depth-first search and breadth-first search. Finally, it covers minimum spanning tree algorithms like Kruskal's and Prim's algorithms for finding minimum cost spanning trees in graphs.
The document defines and provides examples of different types of graphs, including simple graphs, multigraphs, pseudographs, directed graphs, and directed multigraphs. It also defines key graph theory concepts such as vertices, edges, paths, degrees of vertices, adjacency matrices, subgraphs, unions of graphs, and connectivity. Examples are provided to illustrate these definitions and concepts, such as examples of graphs, paths, and how to construct the adjacency matrix of a graph.
beginning with a tree T containing a single vertex
repeatedly adding the least cost edge between T and the rest of the graph
terminating when T spans all vertices
- The document discusses graphs and graph algorithms including definitions of graph terminology, representations of graphs, graph traversal algorithms like depth-first search and breadth-first search, and algorithms for finding minimum spanning trees like Kruskal's algorithm and Prim's algorithm.
- It provides detailed descriptions and pseudocode for graph traversal algorithms, algorithms to find connected components and articulation points, and minimum cost spanning tree algorithms.
- The document is intended as a curriculum for teaching graph algorithms and data structures.
Graphs are data structures consisting of nodes and edges connecting nodes. They can be directed or undirected. Trees are special types of graphs. Common graph algorithms include depth-first search (DFS) and breadth-first search (BFS). DFS prioritizes exploring nodes along each branch as deeply as possible before backtracking, using a stack. BFS explores all nodes at the current depth before moving to the next depth, using a queue.
This document discusses issues related to unstructured mesh generation. It begins by introducing unstructured grids and their application of graph theory. It then discusses methods for generating unstructured meshes, including Delaunay triangulation, Voronoi diagrams, and non-triangulation methods. The document also covers data structures for storing unstructured mesh connectivity and algorithms for ordering and partitioning unstructured meshes.
Graph Traversal Algorithms - Depth First Search TraversalAmrinder Arora
This document discusses graph traversal techniques, specifically depth-first search (DFS) and breadth-first search (BFS). It provides pseudocode for DFS and explains key properties like edge classification, time complexity of O(V+E), and applications such as finding connected components and articulation points.
BFS is the most commonly used approach. BFS is a traversing algorithm where you should start traversing from a selected node (source or starting node) and traverse the graph layerwise thus exploring the neighbor nodes (nodes which are directly connected to the source node.
Graph Representation, DFS and BFS Presentation.pptxbashirabdullah789
The document describes the breadth-first search (BFS) algorithm for traversing graphs. BFS uses a queue to visit all neighboring nodes at the current level before moving to the next level out. It marks nodes as discovered, processes their neighbors, and marks them finished after processing. The algorithm initializes all nodes as undiscovered, enqueues the source node, and dequeues nodes to process their neighbors until the queue is empty.
This document provides instructions for a 150-minute mathematics scholarship test consisting of 45 multiple-choice questions across three sections: Algebra, Analysis, and Geometry. The instructions specify that candidates should answer each question in the provided answer booklet and not on the question paper. Various mathematical notations and concepts are defined for reference in answering the questions.
Graph terminology and algorithm and tree.pptxasimshahzad8611
This document provides an overview of key concepts in graph theory including graph terminology, representations, traversals, spanning trees, minimum spanning trees, and shortest path algorithms. It defines graphs, directed vs undirected graphs, connectedness, degrees, adjacency, paths, cycles, trees, and graph representations using adjacency matrices and lists. It also describes breadth-first and depth-first traversals, spanning trees, minimum spanning trees, and algorithms for finding minimum spanning trees and shortest paths like Kruskal's, Prim's, Dijkstra's, Bellman-Ford and A* algorithms.
This document discusses graph algorithms using depth-first search (DFS). It begins by defining graph terminology such as vertices, edges, directed and undirected graphs. It then explains DFS, including classifying edges based on the DFS spanning tree. It describes applications of DFS such as finding biconnected components, articulation points, and strongly connected components in directed graphs. The document provides pseudocode for DFS and algorithms for these applications that run in linear time with respect to the number of vertices and edges.
The document provides information about a faculty development program on discrete mathematics. It includes:
- An outline of the course content which covers topics like graph terminology, representations of graphs, connectivity, Euler and Hamilton paths, shortest path algorithms, planar graphs, and graph coloring.
- Details of learning resources including textbooks and reference books.
- A table listing the topics to be discussed in lectures, related self-learning tasks, reference materials and number of contact hours.
- Information on representation of graphs through adjacency matrix, incidence matrix and adjacency lists.
The program aims to teach key concepts in graph theory and related algorithms over 6 lecture hours through lectures, self-study and reference books.
The document discusses different graph data structures including adjacency matrices and adjacency lists for representing graphs, and describes key graph terminology such as vertices, edges, paths, connected components, and directed vs undirected graphs. It also explains Dijkstra's algorithm for finding the shortest path between a starting node and all other nodes in a graph.
The document describes graphs and graph algorithms. It defines what a graph is composed of (vertices and edges) and different types of graphs like directed and undirected graphs. It provides terminology used with graphs like vertices, edges, paths, cycles, connectedness. It discusses ways of representing graphs through adjacency matrices and adjacency lists and provides examples. It also describes Dijkstra's algorithm, a graph algorithm to find the shortest path between vertices in a graph.
This document provides an overview of the topics covered in Unit 5, which include graphs, hashing, and collision resolution techniques. It defines graphs and their components such as vertices, edges, directed and undirected graphs. It also discusses different graph representations like adjacency matrix and adjacency list. Elementary graph operations like breadth-first search and depth-first search are explained along with examples. Hashing functions and collision resolution are also introduced.
Unit II_Graph.pptxkgjrekjgiojtoiejhgnltegjtepournima055
The document discusses graphs and graph algorithms. It covers basic graph concepts like vertices, edges, paths, cycles. It describes different ways to represent graphs like adjacency matrix, adjacency list and their pros and cons. It also discusses operations on graphs like inserting and deleting vertices and edges. The document explains traversal algorithms like depth-first search and breadth-first search. It covers minimum spanning tree algorithms like Prim's and Kruskal's. It also briefly discusses shortest path algorithms like Dijkstra's and topological sorting.
Graph in data structure it gives you the information of the graph application. How to represent the Graph and also Graph Travesal is also there many terms are there related to garph
The document discusses graph theory and provides definitions and examples of various graph concepts. It defines what a graph is consisting of vertices and edges. It also defines different types of graphs such as simple graphs, multigraphs, digraphs and provides examples. It discusses graph terminology, models, degree of graphs, handshaking lemma, special graphs and applications. It also provides explanations of planar graphs, Euler's formula and graph coloring.
This document provides an overview of graphs and graph algorithms. It begins with definitions and data structures for representing graphs. It then covers graph traversal algorithms, including depth-first search (DFS) and breadth-first search (BFS). Topological sorting of directed acyclic graphs is also discussed. Exercises are provided to implement DFS, BFS, and topological sorting.
Graph Analytics - From the Whiteboard to Your Toolbox - Sam LermaPyData
This document provides an introduction to graph theory concepts and working with graph data in Python. It begins with basic graph definitions and real-world graph examples. Various graph concepts are then demonstrated visually, such as vertices, edges, paths, cycles, and graph properties. Finally, it discusses working with graph data structures and algorithms in the NetworkX library in Python, including graph generation, analysis, and visualization. The overall goal is to introduce readers to graph theory and spark their interest in further exploration.
The document discusses graphs and graph algorithms. It defines what a graph is - a non-linear data structure consisting of vertices connected by edges. It describes different graph representations like adjacency matrix, incidence matrix and adjacency list. It also explains graph traversal algorithms like depth-first search and breadth-first search. Finally, it covers minimum spanning tree algorithms like Kruskal's and Prim's algorithms for finding minimum cost spanning trees in graphs.
The document defines and provides examples of different types of graphs, including simple graphs, multigraphs, pseudographs, directed graphs, and directed multigraphs. It also defines key graph theory concepts such as vertices, edges, paths, degrees of vertices, adjacency matrices, subgraphs, unions of graphs, and connectivity. Examples are provided to illustrate these definitions and concepts, such as examples of graphs, paths, and how to construct the adjacency matrix of a graph.
beginning with a tree T containing a single vertex
repeatedly adding the least cost edge between T and the rest of the graph
terminating when T spans all vertices
- The document discusses graphs and graph algorithms including definitions of graph terminology, representations of graphs, graph traversal algorithms like depth-first search and breadth-first search, and algorithms for finding minimum spanning trees like Kruskal's algorithm and Prim's algorithm.
- It provides detailed descriptions and pseudocode for graph traversal algorithms, algorithms to find connected components and articulation points, and minimum cost spanning tree algorithms.
- The document is intended as a curriculum for teaching graph algorithms and data structures.
Graphs are data structures consisting of nodes and edges connecting nodes. They can be directed or undirected. Trees are special types of graphs. Common graph algorithms include depth-first search (DFS) and breadth-first search (BFS). DFS prioritizes exploring nodes along each branch as deeply as possible before backtracking, using a stack. BFS explores all nodes at the current depth before moving to the next depth, using a queue.
This document discusses issues related to unstructured mesh generation. It begins by introducing unstructured grids and their application of graph theory. It then discusses methods for generating unstructured meshes, including Delaunay triangulation, Voronoi diagrams, and non-triangulation methods. The document also covers data structures for storing unstructured mesh connectivity and algorithms for ordering and partitioning unstructured meshes.
Graph Traversal Algorithms - Depth First Search TraversalAmrinder Arora
This document discusses graph traversal techniques, specifically depth-first search (DFS) and breadth-first search (BFS). It provides pseudocode for DFS and explains key properties like edge classification, time complexity of O(V+E), and applications such as finding connected components and articulation points.
BFS is the most commonly used approach. BFS is a traversing algorithm where you should start traversing from a selected node (source or starting node) and traverse the graph layerwise thus exploring the neighbor nodes (nodes which are directly connected to the source node.
Graph Representation, DFS and BFS Presentation.pptxbashirabdullah789
The document describes the breadth-first search (BFS) algorithm for traversing graphs. BFS uses a queue to visit all neighboring nodes at the current level before moving to the next level out. It marks nodes as discovered, processes their neighbors, and marks them finished after processing. The algorithm initializes all nodes as undiscovered, enqueues the source node, and dequeues nodes to process their neighbors until the queue is empty.
This document provides instructions for a 150-minute mathematics scholarship test consisting of 45 multiple-choice questions across three sections: Algebra, Analysis, and Geometry. The instructions specify that candidates should answer each question in the provided answer booklet and not on the question paper. Various mathematical notations and concepts are defined for reference in answering the questions.
Graph terminology and algorithm and tree.pptxasimshahzad8611
This document provides an overview of key concepts in graph theory including graph terminology, representations, traversals, spanning trees, minimum spanning trees, and shortest path algorithms. It defines graphs, directed vs undirected graphs, connectedness, degrees, adjacency, paths, cycles, trees, and graph representations using adjacency matrices and lists. It also describes breadth-first and depth-first traversals, spanning trees, minimum spanning trees, and algorithms for finding minimum spanning trees and shortest paths like Kruskal's, Prim's, Dijkstra's, Bellman-Ford and A* algorithms.
This document discusses graph algorithms using depth-first search (DFS). It begins by defining graph terminology such as vertices, edges, directed and undirected graphs. It then explains DFS, including classifying edges based on the DFS spanning tree. It describes applications of DFS such as finding biconnected components, articulation points, and strongly connected components in directed graphs. The document provides pseudocode for DFS and algorithms for these applications that run in linear time with respect to the number of vertices and edges.
The document provides information about a faculty development program on discrete mathematics. It includes:
- An outline of the course content which covers topics like graph terminology, representations of graphs, connectivity, Euler and Hamilton paths, shortest path algorithms, planar graphs, and graph coloring.
- Details of learning resources including textbooks and reference books.
- A table listing the topics to be discussed in lectures, related self-learning tasks, reference materials and number of contact hours.
- Information on representation of graphs through adjacency matrix, incidence matrix and adjacency lists.
The program aims to teach key concepts in graph theory and related algorithms over 6 lecture hours through lectures, self-study and reference books.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Mind map of terminologies used in context of Generative AI
lecture11.ppt
1. CS 103 1
Graphs
• Definition of Graphs and Related
Concepts
• Representation of Graphs
• The Graph Class
• Graph Traversal
• Graph Applications
2. CS 103 2
Definition of Graphs
• A graph is a finite set of nodes with edges
between nodes
• Formally, a graph G is a structure (V,E)
consisting of
– a finite set V called the set of nodes, and
– a set E that is a subset of VxV. That is, E is a set of
pairs of the form (x,y) where x and y are nodes in
V
3. CS 103 3
Examples of Graphs
• V={0,1,2,3,4}
• E={(0,1), (1,2), (0,3), (3,0), (2,2), (4,3)}
0
1
4
2
3
When (x,y) is an edge,
we say that x is adjacent to
y, and y is adjacent from x.
0 is adjacent to 1.
1 is not adjacent to 0.
2 is adjacent from 1.
4. CS 103 4
A “Real-life” Example of a Graph
• V=set of 6 people: John, Mary, Joe, Helen,
Tom, and Paul, of ages 12, 15, 12, 15, 13,
and 13, respectively.
• E ={(x,y) | if x is younger than y}
John Joe
Mary Helen
Tom Paul
5. CS 103 5
Intuition Behind Graphs
• The nodes represent entities (such as people, cities,
computers, words, etc.)
• Edges (x,y) represent relationships between entities x and
y, such as:
– “x loves y”
– “x hates y”
– “x is a friend of y” (note that this not necessarily reciprocal)
– “x considers y a friend”
– “x is a child of y”
– “x is a half-sibling of y”
– “x is a full-sibling of y”
• In those examples, each relationship is a different graph
6. CS 103 6
Graph Representation
• For graphs to be computationally useful,
they have to be conveniently represented in
programs
• There are two computer representations of
graphs:
– Adjacency matrix representation
– Adjacency lists representation
7. CS 103 7
Adjacency Matrix Representation
• In this representation, each graph of n nodes
is represented by an n x n matrix A, that is,
a two-dimensional array A
• The nodes are (re)-labeled 1,2,…,n
• A[i][j] = 1 if (i,j) is an edge
• A[i][j] = 0 if (i,j) is not an edge
9. CS 103 9
Another Example of Adj. Matrix
• Re-label the nodes with numerical labels
John 2
Joe 3
Mary 0 Helen 1
Tom 4 Paul 5
0 0 0 0 0 0
0 0 0 0 0 0
1 1 0 0 1 1
1 1 0 0 1 1
1 1 0 0 0 0
1 1 0 0 0 0
A =
10. CS 103 10
Pros and Cons of Adjacency
Matrices
• Pros:
– Simple to implement
– Easy and fast to tell if a pair (i,j) is an edge:
simply check if A[i][j] is 1 or 0
• Cons:
– No matter how few edges the graph has, the
matrix takes O(n2) in memory
11. CS 103 11
Adjacency Lists Representation
• A graph of n nodes is represented by a one-
dimensional array L of linked lists, where
– L[i] is the linked list containing all the nodes
adjacent from node i.
– The nodes in the list L[i] are in no particular
order
12. CS 103 12
Example of Linked Representation
L[0]: empty
L[1]: empty
L[2]: 0, 1, 4, 5
L[3]: 0, 1, 4, 5
L[4]: 0, 1
L[5]: 0, 1
John 2
Joe
3
Mary 0 Helen 1
Tom 4 Paul 5
13. CS 103 13
Pros and Cons of Adjacency Lists
• Pros:
– Saves on space (memory): the representation
takes as many memory words as there are nodes
and edge.
• Cons:
– It can take up to O(n) time to determine if a pair
of nodes (i,j) is an edge: one would have to
search the linked list L[i], which takes time
proportional to the length of L[i].
14. CS 103 14
The Graph Class
class Graph {
public:
typedef int datatype;
typedef datatype * datatypeptr;
Graph( int n=0); // creates a graph of n nodes and no edges
bool isEdge( int i, int j);
void setEdge( int i, int j, datatype x);
int getNumberOfNodes(){return numberOfNodes;};
private:
datatypeptr *p; //a 2-D array, i.e., an adjacency matrix
int numberOfNodes;
};
15. CS 103 15
Graph Class Implementation
Graph::Graph( int n){
assert(n>=0);
numberOfNodes=n;
if (n==0) p=NULL;
else{
p = new datatypeptr[n];
for (int i=0;i<n;i++){
p[i] = new datatype[n];
for (int j=0;j<n;j++)
p[i][j]=0;
}
}
};
bool Graph::isEdge(int i, int j){
assert(i>=0 && j>=0);
return p[i][j] != 0;
};
void Graph:;setEdge(int i,
int j, datatype x){
assert(i>=0 && j>=0);
p[i][j]=x;
};
16. CS 103 16
Directed vs. Undirected Graphs
• If the directions of the edges matter, then we
show the edge directions, and the graph is
called a directed graph (or a digraph)
• The previous two examples are digraphs
• If the relationships represented by the edges
are symmetric (such as (x,y) is edge if and
only if x is a sibling of y), then we don’t
show the directions of the edges, and the
graph is called an undirected graph.
17. CS 103 17
Examples of Undirected Graphs
• V=set of 6 people: John, Mary, Joe, Helen,
Tom, and Paul, where the first 4 are siblings,
and the last two are siblings
• E ={(x,y) | x and y are siblings}
John Joe
Mary Helen
Tom Paul
if (x,y) is an edge:
we say that x is
adjacent to y, &
y adjacent to x.
We also say that
x and y are
neighbors
18. CS 103 18
Representations of Undirected
Graphs
• The same two representations for directed
graphs can be used for undirected graphs
• Adjacency matrix A:
– A[i][j]=1 if (i,j) is an edge; 0 otherwise
• Adjacency Lists:
– L[i] is the linked list containing all the
neighbors of i
19. CS 103 19
Example of Representations
Linked Lists:
L[0]: 1, 2, 3
L[1]: 0, 2, 3
L[2]: 0, 1, 3
L[3]: 0, 1, 2
L[4]: 5
L[5]: 4
John 2 Joe 3
Mary 0 Helen 1
Tom 4 Paul 5
0 1 1 1 0 0
1 0 1 1 0 0
1 1 0 1 0 0
1 1 1 0 0 0
0 0 0 0 0 1
0 0 0 0 1 0
A =
Adjacency Matrix:
20. CS 103 20
Definition of Some Graph Related
Concepts
• Let G be a directed graph
– The indegree of a node x in G is the number of
edges coming to x
– The outdegree of x is the number of edges
leaving x.
• Let G be an undirected graph
– The degree of a node x is the number of edges
that have x as one of their end nodes
– The neighbors of x are the nodes adjacent to x
21. CS 103 21
Things for You To Do
• Add a member function to the class graph,
called getIndegree( int x), which returns the
indegree of node x
• Add a member function to the class graph,
called getOutdegree( int x), which returns
the outdegree of node x
22. CS 103 22
Paths
• A path in a graph G is a sequence of nodes x1,
x2, …,xk, such that there is an edge from each
node the next one in the sequence
• For example, in the first example graph, the
sequence 3, 0, 1, 2 is a path, but the sequence
0, 3, 4 is not a path because (0,3) is not an edge
• In the “sibling-of” graph, the sequence John,
Mary, Joe, Helen is a path, but the
sequence Helen, Tom, Paul is not a path
23. CS 103 23
Graph Connectivity
• An undirected graph is said to be connected
if there is a path between every pair of
nodes. Otherwise, the graph is disconnected
• Informally, an undirected graph is
connected if it hangs in one piece
Disconnected Connected
24. CS 103 24
Connected Components
• If an undirected graph is not connected, then each
“piece” is called a connected component.
– A piece in itself is connected, but if you bring any other
node to it from the graph, it is no longer connected
• If the graph is connected, then the whole graph is
one single connected component
• Of Interest: Given any undirected graph G,
– Is G connected?
– If not, find its connected components.
25. CS 103 25
Graph Traversal Techniques
• The previous connectivity problem, as well
as many other graph problems, can be
solved using graph traversal techniques
• There are two standard graph traversal
techniques:
– Depth-First Search (DFS)
– Breadth-First Search (BFS)
26. CS 103 26
Graph Traversal (Contd.)
• In both DFS and BFS, the nodes of the
undirected graph are visited in a systematic
manner so that every node is visited exactly
one.
• Both BFS and DFS give rise to a tree:
– When a node x is visited, it is labeled as visited,
and it is added to the tree
– If the traversal got to node x from node y, y is
viewed as the parent of x, and x a child of y
27. CS 103 27
Depth-First Search
• DFS follows the following rules:
1. Select an unvisited node x, visit it, and treat as the
current node
2. Find an unvisited neighbor of the current node, visit it,
and make it the new current node;
3. If the current node has no unvisited neighbors,
backtrack to the its parent, and make that parent the
new current node;
4. Repeat steps 3 and 4 until no more nodes can be
visited.
5. If there are still unvisited nodes, repeat from step 1.
29. CS 103 29
Implementation of DFS
• Observations:
– the last node visited is the first node from
which to proceed.
– Also, the backtracking proceeds on the basis of
"last visited, first to backtrack too".
– This suggests that a stack is the proper data
structure to remember the current node and how
to backtrack.
30. CS 103 30
Illustrate DFS with a Stack
• We will redo the DFS on the previous
graph, but this time with stacks
• In Class
31. CS 103 31
DFS (Pseudo Code)
DFS(input: Graph G) {
Stack S; Integer x, t;
while (G has an unvisited node x){
visit(x); push(x,S);
while (S is not empty){
t := peek(S);
if (t has an unvisited neighbor y){
visit(y); push(y,S); }
else
pop(S);
}
}
}
32. CS 103 32
C++ Code for DFS
int * dfs(Graph G){ // returns a parent array representing the DFS tree
int n=G.getNumberOfNodes();
int * parent = new int[n];
Stack S(n); bool visited[n];
for ( int i=0; i<n; i++) visited[i]=false;
int x=0;// begin DFS from node 0
int numOfConnectedComponents=0;
while (x<n){ // begin a new DFS from x
numOfConnectedComponents++;
visited[x]=true; S.push(x); parent[x] = -1; // x is root
while(!S.isEmpty()) // traverse the current piece
// insert here the yellow box from the next slide
x= getNextUnvisited(visited,n,x);
}
cout<<“Graph has “<< numOfConnectedComponents<<
“ connected componentsn”;
return p;
}
33. CS 103 33
{
int t=S.peek( );
int y=getNextUnvisitedNeighbor(
t,G,visited,n);
if (y<n){
visited[y]=true;
S.push(y);
parent[y]=t;
}
else S.pop( );
}
//Put this before dfs(…). This returns the next unvisited node, or n otherwise
int getNextUnvisited(bool visited[],int n, int lastVisited){
int j=lastVisited+1;
while (visited[j] && j<n) j++;
return j;
}
// Put this before dfs(…)
// returns the leftmost unvisited
// neighbor of node t. If none
// remains, returns n.
int getNextUnvisitedNeighbor(int t,
graph G, bool visited[],int n){
for (int j=0;j<n;j++)
if (G.isEdge(t,j) && !visited[j])
return j;
// if no unvisited neighbors left:
return n;
}
34. CS 103 34
Breadth-First Search
• BFS follows the following rules:
1. Select an unvisited node x, visit it, have it be the root
in a BFS tree being formed. Its level is called the
current level.
2. From each node z in the current level, in the order in
which the level nodes were visited, visit all the
unvisited neighbors of z. The newly visited nodes from
this level form a new level that becomes the next
current level.
3. Repeat step 2 until no more nodes can be visited.
4. If there are still unvisited nodes, repeat from Step 1.
36. CS 103 36
Implementation of DFS
• Observations:
– the first node visited in each level is the first
node from which to proceed to visit new nodes.
• This suggests that a queue is the proper data
structure to remember the order of the steps.
37. CS 103 37
Illustrate BFS with a Queue
• We will redo the BFS on the previous
graph, but this time with queues
• In Class
38. CS 103 38
BFS (Pseudo Code)
BFS(input: graph G) {
Queue Q; Integer x, z, y;
while (G has an unvisited node x) {
visit(x); Enqueue(x,Q);
while (Q is not empty){
z := Dequeue(Q);
for all (unvisited neighbor y of z){
visit(y); Enqueue(y,Q);
}
}
}
}
39. CS 103 39
Things for you to Do
• Give a C++ implementation of BFS, using the
pseudo code as your guide
• Use BFS as a way to determine of the input graph
G is connected, and if not, to output the number of
connected components
• Modify BFS so that the level of each node in the
BFS tree is computed.
• Give another class for graph, this time using a
linked lists representation.