This document provides an introduction to an advanced algorithms analysis and design course taught by Dr. Nazir Ahmad Zafar. It outlines Dr. Zafar's educational background and the objectives of the course, which are to design and analyze modern algorithms, compare their efficiencies, and apply them to real-world problems. The course will cover topics like computational complexity, graph theory, string matching, and NP-completeness. The first lecture will introduce algorithms, design techniques, models of computation, and discuss the importance of algorithms.
This course introduces students to analyzing and designing computer algorithms. The course objectives are to analyze asymptotic performance, demonstrate familiarity with major algorithms and data structures, apply algorithm design paradigms and analysis methods, and synthesize efficient algorithms for engineering problems. Topics covered include foundations of algorithms, accuracy, efficiency, comparing efficiencies, and understanding various algorithm variants and their design and analysis.
Chapter1.1 Introduction to design and analysis of algorithm.pptTekle12
This document discusses the design and analysis of algorithms. It begins with defining what an algorithm is - a well-defined computational procedure that takes inputs and produces outputs. It describes analyzing algorithms to determine their efficiency and comparing different algorithms that solve the same problem. The document outlines steps for designing algorithms, including understanding the problem, deciding a solution approach, designing the algorithm, proving correctness, and analyzing and coding it. It discusses using mathematical techniques like asymptotic analysis and Big O notation to analyze algorithms independently of implementations or inputs. The importance of analysis is also covered.
This document discusses the design and analysis of algorithms. It begins with defining what an algorithm is - a well-defined computational procedure that takes inputs and produces outputs. It describes analyzing algorithms to determine their efficiency and comparing different algorithms that solve the same problem. The document outlines steps for designing algorithms, including understanding the problem, deciding a solution approach, designing the algorithm, proving correctness, and analyzing and coding it. It discusses using mathematical techniques like asymptotic analysis and Big O notation to analyze algorithms independently of implementations or data. The importance of analyzing algorithms and techniques like divide-and-conquer are also covered.
Data structures and algorithms Module-1.pdfDukeCalvin
This document provides an introduction to the course "Introduction to Data Structures and Algorithms". The course objectives are for students to learn basic static and dynamic data structures, analyze algorithms in terms of time and memory complexity, and understand the advantages and disadvantages of different algorithms and data structures. The document then discusses what algorithms are, their key characteristics, and why understanding algorithms and data structures is important for solving computational problems efficiently. It also defines what data structures are and why they are needed to organize large amounts of data.
Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic of how the program will work before developing the actual code for a program.
This document discusses algorithms, including what they are, common design techniques, and the problem solving process. It defines an algorithm as a step-by-step method for solving a problem using a computer. The document outlines what will be covered, such as algorithm design techniques like brute force, divide-and-conquer, and iterative improvement. It also describes the typical problem solving process of identifying a problem, devising a strategy, designing the algorithm, analyzing it, implementing it, and verifying the solution.
This document discusses algorithms and their analysis. It defines an algorithm as a finite sequence of unambiguous instructions that terminate in a finite amount of time. It discusses areas of study like algorithm design techniques, analysis of time and space complexity, testing and validation. Common algorithm complexities like constant, logarithmic, linear, quadratic and exponential are explained. Performance analysis techniques like asymptotic analysis and amortized analysis using aggregate analysis, accounting method and potential method are also summarized.
This document provides an introduction to the analysis of algorithms. It discusses algorithm specification, performance analysis frameworks, and asymptotic notations used to analyze algorithms. Key aspects covered include time complexity, space complexity, worst-case analysis, and average-case analysis. Common algorithms like sorting and searching are also mentioned. The document outlines algorithm design techniques such as greedy methods, divide and conquer, and dynamic programming. It distinguishes between recursive and non-recursive algorithms and provides examples of complexity analysis for non-recursive algorithms.
This course introduces students to analyzing and designing computer algorithms. The course objectives are to analyze asymptotic performance, demonstrate familiarity with major algorithms and data structures, apply algorithm design paradigms and analysis methods, and synthesize efficient algorithms for engineering problems. Topics covered include foundations of algorithms, accuracy, efficiency, comparing efficiencies, and understanding various algorithm variants and their design and analysis.
Chapter1.1 Introduction to design and analysis of algorithm.pptTekle12
This document discusses the design and analysis of algorithms. It begins with defining what an algorithm is - a well-defined computational procedure that takes inputs and produces outputs. It describes analyzing algorithms to determine their efficiency and comparing different algorithms that solve the same problem. The document outlines steps for designing algorithms, including understanding the problem, deciding a solution approach, designing the algorithm, proving correctness, and analyzing and coding it. It discusses using mathematical techniques like asymptotic analysis and Big O notation to analyze algorithms independently of implementations or inputs. The importance of analysis is also covered.
This document discusses the design and analysis of algorithms. It begins with defining what an algorithm is - a well-defined computational procedure that takes inputs and produces outputs. It describes analyzing algorithms to determine their efficiency and comparing different algorithms that solve the same problem. The document outlines steps for designing algorithms, including understanding the problem, deciding a solution approach, designing the algorithm, proving correctness, and analyzing and coding it. It discusses using mathematical techniques like asymptotic analysis and Big O notation to analyze algorithms independently of implementations or data. The importance of analyzing algorithms and techniques like divide-and-conquer are also covered.
Data structures and algorithms Module-1.pdfDukeCalvin
This document provides an introduction to the course "Introduction to Data Structures and Algorithms". The course objectives are for students to learn basic static and dynamic data structures, analyze algorithms in terms of time and memory complexity, and understand the advantages and disadvantages of different algorithms and data structures. The document then discusses what algorithms are, their key characteristics, and why understanding algorithms and data structures is important for solving computational problems efficiently. It also defines what data structures are and why they are needed to organize large amounts of data.
Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic of how the program will work before developing the actual code for a program.
This document discusses algorithms, including what they are, common design techniques, and the problem solving process. It defines an algorithm as a step-by-step method for solving a problem using a computer. The document outlines what will be covered, such as algorithm design techniques like brute force, divide-and-conquer, and iterative improvement. It also describes the typical problem solving process of identifying a problem, devising a strategy, designing the algorithm, analyzing it, implementing it, and verifying the solution.
This document discusses algorithms and their analysis. It defines an algorithm as a finite sequence of unambiguous instructions that terminate in a finite amount of time. It discusses areas of study like algorithm design techniques, analysis of time and space complexity, testing and validation. Common algorithm complexities like constant, logarithmic, linear, quadratic and exponential are explained. Performance analysis techniques like asymptotic analysis and amortized analysis using aggregate analysis, accounting method and potential method are also summarized.
This document provides an introduction to the analysis of algorithms. It discusses algorithm specification, performance analysis frameworks, and asymptotic notations used to analyze algorithms. Key aspects covered include time complexity, space complexity, worst-case analysis, and average-case analysis. Common algorithms like sorting and searching are also mentioned. The document outlines algorithm design techniques such as greedy methods, divide and conquer, and dynamic programming. It distinguishes between recursive and non-recursive algorithms and provides examples of complexity analysis for non-recursive algorithms.
This document provides an introduction to the CS-701 Advanced Analysis of Algorithms course. It includes the course objectives, which are to design and analyze modern algorithms and evaluate their efficiency. The instructor and contact information are provided. The document outlines the course contents, including topics like sorting algorithms, graph algorithms, and complexity theory. It also discusses what algorithms are, how to represent them, and examples of algorithm applications. Common algorithm design techniques like greedy algorithms and heuristics are introduced.
Lecture 2 role of algorithms in computingjayavignesh86
This document discusses algorithms and their role in computing. It defines an algorithm as a set of steps to solve a problem on a machine in a finite amount of time. Algorithms must be unambiguous, have defined inputs and outputs, and terminate. The document discusses designing algorithms, proving their correctness, and analyzing their performance and complexity. It provides examples of algorithm problems like sorting, searching, and graphs. The goal of analyzing algorithms is to evaluate and compare their performance as the problem size increases.
Models of Operational research, Advantages & disadvantages of Operational res...Sunny Mervyne Baa
This document discusses operational research models and their advantages and disadvantages. It describes several common OR models including linear programming, network flow programming, integer programming, nonlinear programming, dynamic programming, stochastic programming, combinatorial optimization, stochastic processes, discrete time Markov chains, continuous time Markov chains, queuing, and simulation. It notes advantages of OR in developing better systems, control, and decisions. However, it also lists limitations such as dependence on computers, inability to quantify all factors, distance between managers and researchers, costs of money and time, and challenges implementing OR solutions.
The document provides an overview of algorithms, including definitions, types, characteristics, and analysis. It begins with step-by-step algorithms to add two numbers and describes the difference between algorithms and pseudocode. It then covers algorithm design approaches, characteristics, classification based on implementation and logic, and analysis methods like a priori and posteriori. The document emphasizes that algorithm analysis estimates resource needs like time and space complexity based on input size.
The document discusses an algorithms analysis and design course. The major objectives are to design and analyze modern algorithms, compare their efficiencies, and solve real-world problems. Students will learn to prove algorithm correctness, analyze running times, and apply techniques like dynamic programming and graph algorithms. While algorithms can differ in efficiency, even on faster hardware, the computational model used makes reasonable assumptions for comparing algorithms asymptotically.
The document provides an introduction to the Unified Modeling Language (UML). It discusses that UML was developed as a standard language for writing software blueprints and was created by Grady Booch, James Rumbaugh, and Ivar Jacobson. Modeling with UML involves visualizing a system using standard diagrams and notations. It is used for specifying, constructing, and documenting systems using an iterative process that is use case driven and architecture-centric.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers various categories of DSS models including optimization, simulation, and predictive models. It also describes static and dynamic analysis, decision making under certainty, risk, and uncertainty. Different modeling approaches like mathematical modeling, simulation, and heuristics are explained.
Performance modeling provides important insights for capacity planning and system sizing without costly full-scale testing. While sophisticated mathematical modeling was common in the past, today's complex systems are difficult to model formally and existing tools are outdated. However, minimal modeling with common-sense approximations using metrics like resource usage per transaction and hardware capacity can still be useful. Keeping even informal models in mind helps performance engineers understand systems, but complex systems benefit from documenting models. Reviving the art of performance modeling can add value to modern continuous performance testing approaches.
Introduction to Algorithms And DataStructurePrasanna996462
This document provides an overview of key data structures and algorithms topics including recursion, dynamic programming, greedy algorithms, and solving coding interview problems. It discusses recursion techniques and applications like factorials, tree traversals, and maze solving. Dynamic programming is introduced as breaking problems into overlapping subproblems and storing solutions. Greedy algorithms make locally optimal choices at each step to hopefully find a global optimum. Example coding problems covered are generating all subsets, k-sum, and checking tree node values.
This document provides an introduction to the CSC112 Algorithms and Data Structures lecture. It discusses the need for data structures to organize data efficiently and enable more complex applications. Different types of data structures are presented, including linear structures like arrays, lists, queues and stacks, as well as non-linear structures like trees and graphs. Key data structure operations like traversing, searching, inserting and deleting records are also outlined. The document emphasizes that the choice of data structure and algorithm can significantly impact a program's efficiency and performance.
This document provides an overview of algorithms and their analysis. It defines an algorithm as a finite sequence of unambiguous instructions that will terminate in a finite amount of time. Key aspects that algorithms must have are being input-defined, having output, being definite, finite, and effective. The document then discusses steps for designing algorithms like understanding the problem, selecting data structures, and verifying correctness. It also covers analyzing algorithms through evaluating their time complexity, which can be worst-case, best-case, or average-case, and space complexity. Common asymptotic notations like Big-O, Omega, and Theta notation are explained for describing an algorithm's efficiency. Finally, basic complexity classes and their properties are summarized.
This document provides an introduction to computer simulation. It begins with defining key concepts like systems, models, simulation, and discrete event simulation. It discusses how simulation is used to imitate the operations of a system by developing a model and evaluating it numerically. The document then covers topics like the process of developing a simulation model, different types of simulation models, components and organization of discrete event simulation models, and time advance mechanisms used in simulation. Finally, it provides an example of simulating a single server queueing system to estimate performance measures like average delay in queue.
This document introduces algorithms and their basics. It defines an algorithm as a step-by-step procedure to solve a problem and get the desired output. Algorithms can be implemented in different programming languages. Common algorithm categories include search, sort, insert, update, and delete operations on data structures. An algorithm must be unambiguous, have well-defined inputs and outputs, terminate in a finite number of steps, and be feasible with available resources. The document also discusses how to write algorithms, analyze their complexity, and commonly used asymptotic notations like Big-O, Omega, and Theta.
The document provides an introduction to a course on Data Structures and Algorithms. It outlines the course topics which include arrays, linked lists, stacks, queues, trees, sorting algorithms, and graph algorithms. It introduces the lecturer, Syeda Nazia Ashraf, and provides her background and experience. The grading breakdown and textbooks are listed. The goals of the course are to learn commonly used data structures, design algorithms, and implement data structures and algorithms in C language code.
Algorithm and C code related to data structureSelf-Employed
Everything lies inside an algorithm in the world of coding and algorithm formation which is the basis of data structure and manipulation of the algorithm in computer science and information technology which is ultimately used to find a particular problems solution
The document provides an introduction to algorithms and their analysis. It defines an algorithm and lists its key criteria. It discusses different representations of algorithms including flowcharts and pseudocode. It also outlines the main areas of algorithm analysis: devising algorithms, validating them, analyzing performance, and testing programs. Finally, it provides examples of algorithms and their analysis including calculating time complexity based on counting operations.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers several topics: issues in DSS modeling like identifying problems and variables; categories of models like optimization, simulation, and predictive models; trends like using web tools for modeling; static vs dynamic analysis; decision making under certainty, risk, and uncertainty; and techniques like sensitivity analysis, what-if analysis, and goal analysis. Simulation is described as imitating reality to conduct experiments, and advantages include time compression while disadvantages include lack of optimal solutions.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
This document provides an introduction to the CS-701 Advanced Analysis of Algorithms course. It includes the course objectives, which are to design and analyze modern algorithms and evaluate their efficiency. The instructor and contact information are provided. The document outlines the course contents, including topics like sorting algorithms, graph algorithms, and complexity theory. It also discusses what algorithms are, how to represent them, and examples of algorithm applications. Common algorithm design techniques like greedy algorithms and heuristics are introduced.
Lecture 2 role of algorithms in computingjayavignesh86
This document discusses algorithms and their role in computing. It defines an algorithm as a set of steps to solve a problem on a machine in a finite amount of time. Algorithms must be unambiguous, have defined inputs and outputs, and terminate. The document discusses designing algorithms, proving their correctness, and analyzing their performance and complexity. It provides examples of algorithm problems like sorting, searching, and graphs. The goal of analyzing algorithms is to evaluate and compare their performance as the problem size increases.
Models of Operational research, Advantages & disadvantages of Operational res...Sunny Mervyne Baa
This document discusses operational research models and their advantages and disadvantages. It describes several common OR models including linear programming, network flow programming, integer programming, nonlinear programming, dynamic programming, stochastic programming, combinatorial optimization, stochastic processes, discrete time Markov chains, continuous time Markov chains, queuing, and simulation. It notes advantages of OR in developing better systems, control, and decisions. However, it also lists limitations such as dependence on computers, inability to quantify all factors, distance between managers and researchers, costs of money and time, and challenges implementing OR solutions.
The document provides an overview of algorithms, including definitions, types, characteristics, and analysis. It begins with step-by-step algorithms to add two numbers and describes the difference between algorithms and pseudocode. It then covers algorithm design approaches, characteristics, classification based on implementation and logic, and analysis methods like a priori and posteriori. The document emphasizes that algorithm analysis estimates resource needs like time and space complexity based on input size.
The document discusses an algorithms analysis and design course. The major objectives are to design and analyze modern algorithms, compare their efficiencies, and solve real-world problems. Students will learn to prove algorithm correctness, analyze running times, and apply techniques like dynamic programming and graph algorithms. While algorithms can differ in efficiency, even on faster hardware, the computational model used makes reasonable assumptions for comparing algorithms asymptotically.
The document provides an introduction to the Unified Modeling Language (UML). It discusses that UML was developed as a standard language for writing software blueprints and was created by Grady Booch, James Rumbaugh, and Ivar Jacobson. Modeling with UML involves visualizing a system using standard diagrams and notations. It is used for specifying, constructing, and documenting systems using an iterative process that is use case driven and architecture-centric.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers various categories of DSS models including optimization, simulation, and predictive models. It also describes static and dynamic analysis, decision making under certainty, risk, and uncertainty. Different modeling approaches like mathematical modeling, simulation, and heuristics are explained.
Performance modeling provides important insights for capacity planning and system sizing without costly full-scale testing. While sophisticated mathematical modeling was common in the past, today's complex systems are difficult to model formally and existing tools are outdated. However, minimal modeling with common-sense approximations using metrics like resource usage per transaction and hardware capacity can still be useful. Keeping even informal models in mind helps performance engineers understand systems, but complex systems benefit from documenting models. Reviving the art of performance modeling can add value to modern continuous performance testing approaches.
Introduction to Algorithms And DataStructurePrasanna996462
This document provides an overview of key data structures and algorithms topics including recursion, dynamic programming, greedy algorithms, and solving coding interview problems. It discusses recursion techniques and applications like factorials, tree traversals, and maze solving. Dynamic programming is introduced as breaking problems into overlapping subproblems and storing solutions. Greedy algorithms make locally optimal choices at each step to hopefully find a global optimum. Example coding problems covered are generating all subsets, k-sum, and checking tree node values.
This document provides an introduction to the CSC112 Algorithms and Data Structures lecture. It discusses the need for data structures to organize data efficiently and enable more complex applications. Different types of data structures are presented, including linear structures like arrays, lists, queues and stacks, as well as non-linear structures like trees and graphs. Key data structure operations like traversing, searching, inserting and deleting records are also outlined. The document emphasizes that the choice of data structure and algorithm can significantly impact a program's efficiency and performance.
This document provides an overview of algorithms and their analysis. It defines an algorithm as a finite sequence of unambiguous instructions that will terminate in a finite amount of time. Key aspects that algorithms must have are being input-defined, having output, being definite, finite, and effective. The document then discusses steps for designing algorithms like understanding the problem, selecting data structures, and verifying correctness. It also covers analyzing algorithms through evaluating their time complexity, which can be worst-case, best-case, or average-case, and space complexity. Common asymptotic notations like Big-O, Omega, and Theta notation are explained for describing an algorithm's efficiency. Finally, basic complexity classes and their properties are summarized.
This document provides an introduction to computer simulation. It begins with defining key concepts like systems, models, simulation, and discrete event simulation. It discusses how simulation is used to imitate the operations of a system by developing a model and evaluating it numerically. The document then covers topics like the process of developing a simulation model, different types of simulation models, components and organization of discrete event simulation models, and time advance mechanisms used in simulation. Finally, it provides an example of simulating a single server queueing system to estimate performance measures like average delay in queue.
This document introduces algorithms and their basics. It defines an algorithm as a step-by-step procedure to solve a problem and get the desired output. Algorithms can be implemented in different programming languages. Common algorithm categories include search, sort, insert, update, and delete operations on data structures. An algorithm must be unambiguous, have well-defined inputs and outputs, terminate in a finite number of steps, and be feasible with available resources. The document also discusses how to write algorithms, analyze their complexity, and commonly used asymptotic notations like Big-O, Omega, and Theta.
The document provides an introduction to a course on Data Structures and Algorithms. It outlines the course topics which include arrays, linked lists, stacks, queues, trees, sorting algorithms, and graph algorithms. It introduces the lecturer, Syeda Nazia Ashraf, and provides her background and experience. The grading breakdown and textbooks are listed. The goals of the course are to learn commonly used data structures, design algorithms, and implement data structures and algorithms in C language code.
Algorithm and C code related to data structureSelf-Employed
Everything lies inside an algorithm in the world of coding and algorithm formation which is the basis of data structure and manipulation of the algorithm in computer science and information technology which is ultimately used to find a particular problems solution
The document provides an introduction to algorithms and their analysis. It defines an algorithm and lists its key criteria. It discusses different representations of algorithms including flowcharts and pseudocode. It also outlines the main areas of algorithm analysis: devising algorithms, validating them, analyzing performance, and testing programs. Finally, it provides examples of algorithms and their analysis including calculating time complexity based on counting operations.
This document discusses modeling and analysis techniques used in decision support systems (DSS). It covers several topics: issues in DSS modeling like identifying problems and variables; categories of models like optimization, simulation, and predictive models; trends like using web tools for modeling; static vs dynamic analysis; decision making under certainty, risk, and uncertainty; and techniques like sensitivity analysis, what-if analysis, and goal analysis. Simulation is described as imitating reality to conduct experiments, and advantages include time compression while disadvantages include lack of optimal solutions.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Discover the Unseen: Tailored Recommendation of Unwatched ContentScyllaDB
The session shares how JioCinema approaches ""watch discounting."" This capability ensures that if a user watched a certain amount of a show/movie, the platform no longer recommends that particular content to the user. Flawless operation of this feature promotes the discover of new content, improving the overall user experience.
JioCinema is an Indian over-the-top media streaming service owned by Viacom18.
QA or the Highway - Component Testing: Bridging the gap between frontend appl...zjhamm304
These are the slides for the presentation, "Component Testing: Bridging the gap between frontend applications" that was presented at QA or the Highway 2024 in Columbus, OH by Zachary Hamm.
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...Jason Yip
The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation F...AlexanderRichford
QR Secure: A Hybrid Approach Using Machine Learning and Security Validation Functions to Prevent Interaction with Malicious QR Codes.
Aim of the Study: The goal of this research was to develop a robust hybrid approach for identifying malicious and insecure URLs derived from QR codes, ensuring safe interactions.
This is achieved through:
Machine Learning Model: Predicts the likelihood of a URL being malicious.
Security Validation Functions: Ensures the derived URL has a valid certificate and proper URL format.
This innovative blend of technology aims to enhance cybersecurity measures and protect users from potential threats hidden within QR codes 🖥 🔒
This study was my first introduction to using ML which has shown me the immense potential of ML in creating more secure digital environments!
Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdfleebarnesutopia
So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...Fwdays
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
What is an RPA CoE? Session 1 – CoE VisionDianaGray10
In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...GlobalLogic Ukraine
Під час доповіді відповімо на питання, навіщо потрібно підвищувати продуктивність аплікації і які є найефективніші способи для цього. А також поговоримо про те, що таке кеш, які його види бувають та, основне — як знайти performance bottleneck?
Відео та деталі заходу: https://bit.ly/45tILxj
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...
Lecture01.ppt
1. Advanced Algorithms Analysis and
Design
By
Dr. Nazir Ahmad Zafar
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
2. • M. Sc. Mathematics: 1989-91
Quaid-i-Azam University, Islamabad
• M. Phil Mathematics: 1991-93
Quaid-i-Azam University, Islamabad
• M. Sc. Nuclear Engineering: 1993-94
Quaid-i-Azam University, Islamabad
• PhD Computer Science: 2000-04
Kyushu University, Japan
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
3. Major objective of this course is:
• Design and analysis of modern algorithms
• Different variants
• Accuracy
• Efficiency
• Comparing efficiencies
• Motivation thinking new algorithms
• Advanced designing techniques
• Real world problems will be taken as examples
• To create feelings about usefulness of this course
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Objective of This Course
4. On successful completion, students will be able to
• Argue and prove correctness of algorithms
• Derive and solve mathematical models of problems
• Reasoning when an algorithm calls certain approach
• Analyze average and worst-case running times
• Integrating approaches in dynamic and greedy algos.
• Use of graph theory in problems solving
• Advanced topics such as
• Computational geometry, number theory etc.
• Several other algorithms such as
• String matching, NP completeness, approximate
algorithms etc.
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Expected Results
6. In this lecture we will cover the following
• What is Algorithm?
• Designing Techniques
• Model of Computation
• Algorithms as a technology
• Algorithms and other technologies
• Importance of algorithms
• Difference in Users and Developers
• Kinds of problems solved by algorithms
• Conclusion
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Today Covered
7. • A computer algorithm is a detailed step-by-step method
for solving a problem by using a computer.
• An algorithm is a sequence of unambiguous instructions
for solving a problem in a finite amount of time.
• An Algorithm is well defined computational procedure that
takes some value, or set of values, as input and produces
some value, or set of values as output.
• More generally, an Algorithm is any well defined
computational procedure that takes collection of elements
as input and produces a collection of elements as output.
Algorithm
Input output
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
What is Algorithm?
8. • Most basic and popular algorithms are
– Sorting algorithms
– Searching algorithms
Which algorithm is best?
• Mainly, it depends upon various factors, for
example in case of sorting
– The number of items to be sorted
– The extent to which the items are already sorted
– Possible restrictions on the item values
– The kind of storage device to be used etc.
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Popular Algorithms, Factors of Dependence
9. Problem
• The statement of the problem specifies, in general
terms, the desired input/output relationship.
Algorithm
• The algorithm describes a specific computational
procedure for achieving input/output relationship.
Example
• One might need to sort a sequence of numbers
into non-decreasing order.
Algorithms
• Various algorithms e.g. merge sort, quick sort,
heap sorts etc.
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
One Problem, Many Algorithms
10. • Brute Force
– Straightforward, naive approach
– Mostly expensive
• Divide-and-Conquer
– Divide into smaller sub-problems
• Iterative Improvement
– Improve one change at a time
• Decrease-and-Conquer
– Decrease instance size
• Transform-and-Conquer
– Modify problem first and then solve it
• Space and Time Tradeoffs
– Use more space now to save time later
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Important Designing Techniques
11. • Greedy Approach
– Locally optimal decisions, can not change once made.
– Efficient
– Easy to implement
– The solution is expected to be optimal
– Every problem may not have greedy solution
• Dynamic programming
– Decompose into sub-problems like divide and conquer
– Sub-problems are dependant
– Record results of smaller sub-problems
– Re-use it for further occurrence
– Mostly reduces complexity exponential to polynomial
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Some of the Important Designing Techniques
12. • Analysis
– How does system work?
– Breaking a system down to known components
– How components (processes) relate to each other
– Breaking a process down to known functions
• Synthesis
– Building tools
– Building functions with supporting tools
– Composing functions to form a process
– How components should be put together?
– Final solution
Problem Solving Phases
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
13. • Problem
• Strategy
• Algorithm
– Input
– Output
– Steps
• Analysis
– Correctness
– Time & Space
– Optimality
• Implementation
• Verification
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Problem Solving Process
14. • Design assumption
– Level of abstraction which meets our requirements
– Neither more nor less e.g. [0, 1] infinite continuous interval
• Analysis independent of the variations in
– Machine
– Operating system
– Programming languages
– Compiler etc.
• Low-level details will not be considered
• Our model will be an abstraction of a standard
generic single-processor machine, called a random
access machine or RAM.
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Model of Computation (Assumptions)
15. • A RAM is assumed to be an idealized machine
– Infinitely large random-access memory
– Instructions execute sequentially
• Every instruction is in fact a basic operation on two
values in the machines memory which takes unit time.
• These might be characters or integers.
• Example of basic operations include
– Assigning a value to a variable
– Arithmetic operation (+, - , × , /) on integers
– Performing any comparison e.g. a < b
– Boolean operations
– Accessing an element of an array.
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Model of Computation (Assumptions)
16. • In theoretical analysis, computational complexity
– Estimated in asymptotic sense, i.e.
– Estimating for large inputs
• Big O, Omega, Theta etc. notations are used to
compute the complexity
• Asymptotic notations are used because different
implementations of algorithm may differ in efficiency
• Efficiencies of two given algorithm are related
– By a constant multiplicative factor
– Called hidden constant.
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Model of Computation (Assumptions)
17. First poor assumption
• We assumed that each basic operation takes
constant time, i.e. model allows
– Adding
– Multiplying
– Comparing etc.
two numbers of any length in constant time
• Addition of two numbers takes a unit time!
– Not good because numbers may be arbitrarily
• Addition and multiplication both take unit time!
– Again very bad assumption
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Drawbacks in Model of Computation
18. Finally what about Our Model?
• But with all these weaknesses, our model is not so
bad because we have to give the
– Comparison not the absolute analysis of any algorithm.
– We have to deal with large inputs not with the small size
• Model seems to work well describing computational
power of modern nonparallel machines
Can we do Exact Measure of Efficiency ?
• Exact, not asymptotic, measure of efficiency can be
sometimes computed but it usually requires certain
assumptions concerning implementation
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Model of Computation not so Bad
19. • Analysis will be performed with respect to this
computational model for comparison of algorithms
• We will give asymptotic analysis not detailed
comparison i.e. for large inputs
• We will use generic uniprocessor random-access
machine (RAM) in analysis
– All memory equally expensive to access
– No concurrent operations
– All reasonable instructions take unit time,
except, of course, function calls
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Summary : Computational Model
20. • What, Why and Where Algorithms?
• Designing Techniques
• Problem solving Phases and Procedure
• Model of computations
– Major assumptions at design and analysis level
– Merits and demerits, justification of assumptions taken
• We proved that algorithm is a technology
• Compared algorithmic technology with others
• Discussed importance of algorithms
– In almost all areas of computer science and engineering
– Algorithms make difference in users and developers
Dr Nazir A. Zafar Advanced Algorithms Analysis and Design
Conclusion