Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
PPT on Analysis Of Algorithms.
The ppt includes Algorithms,notations,analysis,analysis of algorithms,theta notation, big oh notation, omega notation, notation graphs
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
PPT on Analysis Of Algorithms.
The ppt includes Algorithms,notations,analysis,analysis of algorithms,theta notation, big oh notation, omega notation, notation graphs
This slides contains assymptotic notations, recurrence relation like subtitution method, iteration method, master method and recursion tree method and sorting algorithms like merge sort, quick sort, heap sort, counting sort, radix sort and bucket sort.
Algorithmic Thinking_ Basics for Gen Z and Gen Alpha.pdfHoomale
Algorithmic Thinking: Basics for Gen Z and Gen Alpha
Home » Generation Alpha » Algorithm thinking for kids
Estimated reading time: 15 minutes
With the world becoming increasingly digital and automated, the younger generations need to develop algorithmic thinking skills.
Algorithmic thinking focuses on step-by-step processes or algorithms to solve problems. Artificial Intelligence (AI) is developing incredibly across the United States and other countries like India, China, and other European countries.
In this blog, we will discuss the meaning of algorithmic thinking and how it differs from critical thinking, why it is important for Generation Z and Alpha, and the base concepts of algorithmic thinking.
We will also explore examples of algorithmic thinking in everyday life and its relation to coding as a tool for problem-solving.
Help your children develop new age skills with our comprehensive guide to algorithmic thinking. Perfect for Generation Z and Alpha.
Photo by Jeffery Erhunse on Unsplash
What is Algorithmic Thinking?
Algorithmic thinking is looking at problems one step at a time, like following a recipe. It’s about breaking big problems into smaller parts and figuring out the best way to solve them. It also means looking for patterns and finding ways to do things faster and smarter.
Through algorithmic thinking, one can become a better problem solver and use creativity to derive cool solutions to everyday challenges in the digital world. It’s like having a superpower that helps us think logically and find the most efficient ways to get things done!
This process applies to various fields, including computer programming, mathematics, and science. When individuals develop algorithmic thinking skills, they become better at logical reasoning and critical thinking. It also helps them understand how technology works and how to create new solutions.
Understanding Algorithms
Algorithms are step-by-step instructions: Algorithms are like recipes that tell you exactly what to do in a specific order to solve a problem or complete a task.
They’re everywhere: Algorithms are not just for computers. They are in everyday life, like following instructions to bake a cake or solving a math problem using a specific method.
Algorithms make things efficient: By following a well-designed algorithm, you can solve problems faster and more efficiently. It helps you break down complex tasks into smaller, manageable steps.
Identifying patterns: Algorithms often involve recognizing patterns or similarities in the problem you’re trying to solve. By finding patterns, you can define a plan to solve the problem.
Algorithms in technology: Computers use algorithms to perform tasks. For example, search engines use algorithms to find relevant information when you type in a query.
Furthermore,
Different algorithms for different problems: There are many ways to solve a problem, and specific algorithms may work better for precise situations.
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.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
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
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.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
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.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
1. Algorithms
Chapter 10
What's The Pla
Algorithmic Thin
2. Problem Solving
Your roommate, who is taking Information
Technology II class, is in a panic. He is
worried that he might lose his financial aid if
his average goes under 6.0
How can he figure out what his current Average
is?
How can you build a process that any student
can use to tell them what average they have
based on grades provided thus far?
3. Algorithm Development Objectives
At the end of this unit the student will:
define the term algorithm
state 5 properties of a good algorithm
from a given problem and stated audience, create
an appropriate algorithm using the properties
stated above.
use the concept of abstraction and top-down
design in creating an algorithm.
begin to think about the kinds of problems that
have a computing solution.
4. Problem Solving
1. Understand the problem (and the audience)
1. Are you making a pie?
2. Needing directions?
3. Putting together a piece of equipment?
4. Trying to solve a mathematical puzzle?
2. Devise a plan
1. Is this similar to something else?
2. Who is the audience for the solution?
3. What are the required steps?
5. Problem Solving ( Cont’d )
3. Carry out the plan (implement)
1. Does it work?
2. Is each step correct? Necessary?
3. Is the solution accurate? (Correct)
1. Will it always lead to a solution
6. Algorithm Definition
A logical sequence of steps for solving a
problem, …
From http://Dictionary.msn.com
Dale and Lewis:
a plan of solution for a problem
Algorithm – An unambiguous (and precise) set of steps
for solving a problem (or sub-problem) in a finite
amount of time using a finite amount of data.
7. Algorithm Definition, cont
Shackelford, Russell L. in Introduction to
Computing and Algorithms –
“An algorithm is a specification of a behavioral
process. It consists of a finite set of instructions
that govern behavior step-by-step.”
8. Notice
Notice the term finite. Algorithms should
lead to an eventual solution.
Step by step process. Each step should do
one logical action.
9. Algorithms
Algorithms are addressed to some audience.
Consider:
A set of instructions for building a child’s bicycle.
A diagnostic checklist for a failure of some system on the
space shuttle.
The algorithm for what to do when a nuclear reactor
begins to overheat.
An algorithm that will run on a computer system to
calculate student GPA’s.
10. Audience
Each audience will have its own “rules” that
govern how we will address them, the
language that they speak.
Each audience will have certain assumptions
about what they know and don’t know.
An audience might include people or a
computer.
11. Good vs. Bad Algorithms
All algorithms will have input, perform a
process, and produce output.
A good algorithm should be:
Simple - relative
Complete – account for all inputs & cases
Correct (Right)
should have appropriate levels of Abstraction. –
grouping steps into a single module
Precise
Mnemonic - SCRAP
12. Precision
Precision means that there is only one way to
interpret the instruction. Unambiguous
Words like “maybe”, “sometimes” and
“occasionally” have no business in a well developed
algorithm.
Instead of “maybe”, we can specify the exact
circumstances in which an action will be carried out.
13. Simplicity
Simple can be defined as having no
unnecessary steps and no unnecessary
complexity. (You may lose points if your
algorithm contains unnecessary steps)
Each step of a well developed algorithm
should carry out one logical step of the
process.
Avoid something like: “Take 2nd right after you
exit at King Street”
14. It has Levels of Abstraction.
From the Oxford English Dictionary,
abstraction is defined as:
“The act or process of separating in thought, of
considering a thing independently of its
associations; or a substance independently of its
attributes; or an attribute or quality independently
of the substance to which it belongs.”
Example: Add all the scores then divide the sum
by the number of students to get the average.
15. Or in other words
The abstraction property lets us view an
algorithm as a series of high level aggregate
steps, with the detail hidden in a lower level.
16. Abstraction, cont.
Instead of approaching a problem and worrying
about each and every thing you must do to solve the
problem, you can begin to look at the major steps.
(Top down design)
After the major steps, you can begin to fill in how
you would accomplish the major step.
That fill in may lead to the need for additional levels
to fill in those details, etc.
Top down design.
17. Diagrammatically
Get directions Turn left out of your
Drive the car to
driveway
Start the car
At the next light, turn right.
school
Follow the directions
At the intersection with I-
Get parking pass 66, take the on-ramp for
I-66 West
Drive to the
destination …
Level 1 Find a place to park Level 3
Stop the car
Level 2
18. Other algorithm attributes
A good algorithm should be correct.
A good algorithm should be complete.
Shackelford again, “To be correct, an
algorithm must produce results that are
correct and complete given any and all sets
of appropriate data.”
And to be correct, an algorithm must proceed
through to a conclusion.
19. Steps from Schaum’s
Analyze the problem and develop the specification.
Design the solution
Test the solution as part of the design steps.
Implement the program (code the program)
Test the program
Validate the program (further extensive testing) to
insure it works under all circumstances.
20. For example:
For example, a student is taking 4 classes:
Spanish – 4 credits – 6
English – 1 credit – 9
Computer Science – 3 credits – 86
P.E. – 3 credits – 5
What is the student’s semester average?
How did you figure it out?
How can you describe that process for others in the
class?