The document discusses Haskell concepts including:
1) Pattern matching allows functions to handle different cases depending on the structure of input data, like matching empty and non-empty lists.
2) Guards allow selecting different code blocks based on Boolean conditions.
3) Combining pattern matching and guards allows for very expressive functions that concisely handle multiple cases.
4) Metasyntactic variables like (x:xs) follow conventions to bind pattern names and make code more readable.
5) Functions can match multiple patterns at once to handle different input structures.
Scala 3 enum for a terser Option Monad Algebraic Data TypePhilip Schwarz
(download for flawless slides)
* Explore a terser definition of the Option Monad that uses a Scala 3 enum as an Algebraic Data Type.
* In the process, have a tiny bit of fun with Scala 3 enums.
* Get a refresher on the Functor and Monad laws.
* See how easy it is to use Scala 3 extension methods, e.g. to add convenience methods and infix operators.
The diagrams for function composition and Kleisli composition were made using https://q.uiver.app/ by https://twitter.com/varkora.
Source code: https://github.com/philipschwarz/scala-3-enum-for-terser-option-monad-algebraic-data-type
Errata:
slide 14 is an unwanted leftover - it is the same as slide 15 minus a diagram.
on slide 19, the colons in the extension method declarations are not needed
N-Queens Combinatorial Problem - Polyglot FP for Fun and Profit - Haskell and...Philip Schwarz
See how feeding FP workhorses map and filter with monadic steroids turns them into the intriguing mapM and filterM.
Graduate to foldM by learning how it behaves with the help of three simple yet instructive examples of its usage.
Use the powers of foldM to generate all permutations of a collection with a simple one-liner.
Exploit what you learned about foldM to solve the N-Queens Combinatorial Problem with an iterative approach rather than a recursive one.
Scala 3 enum for a terser Option Monad Algebraic Data TypePhilip Schwarz
(download for flawless slides)
* Explore a terser definition of the Option Monad that uses a Scala 3 enum as an Algebraic Data Type.
* In the process, have a tiny bit of fun with Scala 3 enums.
* Get a refresher on the Functor and Monad laws.
* See how easy it is to use Scala 3 extension methods, e.g. to add convenience methods and infix operators.
The diagrams for function composition and Kleisli composition were made using https://q.uiver.app/ by https://twitter.com/varkora.
Source code: https://github.com/philipschwarz/scala-3-enum-for-terser-option-monad-algebraic-data-type
Errata:
slide 14 is an unwanted leftover - it is the same as slide 15 minus a diagram.
on slide 19, the colons in the extension method declarations are not needed
N-Queens Combinatorial Problem - Polyglot FP for Fun and Profit - Haskell and...Philip Schwarz
See how feeding FP workhorses map and filter with monadic steroids turns them into the intriguing mapM and filterM.
Graduate to foldM by learning how it behaves with the help of three simple yet instructive examples of its usage.
Use the powers of foldM to generate all permutations of a collection with a simple one-liner.
Exploit what you learned about foldM to solve the N-Queens Combinatorial Problem with an iterative approach rather than a recursive one.
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala - with ...Philip Schwarz
(download for perfect quality) - See how recursive functions and structural induction relate to recursive datatypes.
Follow along as the fold abstraction is introduced and explained.
Watch as folding is used to simplify the definition of recursive functions over recursive datatypes
Part 1 - through the work of Richard Bird and Graham Hutton.
This version corrects the following issues:
slide 7, 11 fib(0) is 0,rather than 1
slide 23: was supposed to be followed by 2-3 slides recapitulating definitions of factorial and fibonacci with and without foldr, plus translation to scala
slide 36: concat not invoked in concat example
slides 48 and 49: unwanted 'm' in definition of sum
throughout: a couple of typographical errors
throughout: several aesthetic imperfections (wrong font, wrong font colour)
Quicksort - a whistle-stop tour of the algorithm in five languages and four p...Philip Schwarz
Quicksort - a whistle-stop tour of the algorithm in five languages and four paradigms.
Programming Paradigms: Functional, Logic, Imperative, Imperative Functional
Languages: Haskell, Scala, Java, Clojure, Prolog
The Functional Programming Triad of Folding, Scanning and Iteration - a first...Philip Schwarz
This slide deck can work both as an aide mémoire (memory jogger), or as a first (not completely trivial) example of using left folds, left scans and iteration, to implement mathematical induction.
Errata: on almost half of the slides there is some minor typo or imperfection or in some cases a minor error and in one case, an omission. See a later version for corrections and some improvements.
(for best quality images, either download or view here: https://philipschwarz.dev/fpilluminated/?page_id=455)
Scala code for latest version: https://github.com/philipschwarz/fp-fold-scan-iterate-triad-a-first-example-scala
Slideshare hasn't imported my notes, so here's the link to the Google Presentation: https://goo.gl/Gl4Vhm
Haskell is a statically typed, non strict, pure functional programming language. It is often talked and blogged about, but rarely used commercially. This talk starts with a brief overview of the language, then explains how Haskell is evaluated and how it deals with non-determinism and side effects using only pure functions. The suitability of Haskell for real world data science is then discussed, along with some examples of its users, a small Haskell-powered visualization, and an overview of useful packages for data science. Finally, Accelerate is introduced, an embedded DSL for array computations on the GPU, and an ongoing attempt to use it as the basis for a deep learning package.
N-Queens Combinatorial Problem - Polyglot FP for Fun and Profit – Haskell and...Philip Schwarz
See how the guard function has migrated from MonadPlus to Alternative and learn something about the latter.
Learn how to write a Scala program that draws an N-Queens solution board using the Doodle graphics library.
See how to write the equivalent Haskell program using the Gloss graphics library.
Learn how to use Monoid and Foldable to compose images both in Haskell and in Scala.
Link to part 1: https://www.slideshare.net/pjschwarz/nqueens-combinatorial-problem-polyglot-fp-for-fun-and-profit-haskell-and-scala-part-1
Errata:
On slide 22, the last line of the showQueens function should of course be show(solution).draw(frame) rather than show(solution).draw
On slide 43, it would be better if the definitions of the beside, above and on Monoids were also shown.
Presented online for C++ on Sea (2020-07-17)
Video at https://www.youtube.com/watch?v=Bai1DTcCHVE
Lambdas. All the cool kid languages have them. But does lambda mean what C++ and other languages, from Java to Python, mean by lambda? Where did lambdas come from? What were they originally for? What is their relationship to data abstraction?
In this session we will into the history, the syntax, the uses and abuses of lambdas and the way in which lambda constructs in C++ and other languages do (or do not) match the original construct introduced in lambda calculus.
Left and Right Folds- Comparison of a mathematical definition and a programm...Philip Schwarz
We compare typical definitions of the left and right fold functions, with their mathematical definitions in Sergei Winitzki’s upcoming book: The Science of Functional Programming.
Errata:
Slide 13: "The way 𝑓𝑜𝑙𝑑𝑙 does it is by associating to the right" - should, of course ,end in "to the left".
The Functional Programming Triad of Map, Filter and FoldPhilip Schwarz
This slide deck is my homage to SICP, the book which first introduced me to the Functional Programming triad of map, filter and fold.
It was during my Computer Science degree that a fellow student gave me a copy of the first edition, not long after the book came out.
I have not yet come across a better introduction to these three functions.
The upcoming slides are closely based on the second edition of the book, a free online copy of which can be found here:
https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book.html.
Download for original image quality.
Errata:
slide 20: the Clojure map function is in fact the Scheme one repeated - see code below for correction.
Scheme code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-scheme
Clojure code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-clojure
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala - Part 5Philip Schwarz
(download for best quality slides) Gain a deeper understanding of why right folds over very large and infinite lists are sometimes possible in Haskell.
See how lazy evaluation and function strictness affect left and right folds in Haskell.
Learn when an ordinary left fold results in a space leak and how to avoid it using a strict left fold.
Errata:
slide 15: "as sharing is required" should be "as sharing is not required"
slide 43: 𝑠𝑓𝑜𝑙𝑑𝑙 (⊕) 𝑎 should be 𝑠𝑓𝑜𝑙𝑑𝑙 (⊕) 𝑒
An overview of the programming techniques available and some of the ongoing research in the Haskell community around concurrent and parallel programming.
The Functional Programming Triad of Folding, Scanning and Iteration - a first...Philip Schwarz
This slide deck can work both as an aide mémoire (memory jogger), or as a first (not completely trivial) example of using left folds, left scans and iteration, to implement mathematical induction.
This is just an updated version of the original slide deck which makes minor improvements and minor corrections in almost half of the slides.
(for best quality images, either download or view here: https://philipschwarz.dev/fpilluminated/?page_id=455)
You can see the Scala code here: https://github.com/philipschwarz/fp-fold-scan-iterate-triad-a-first-example-scala
download for better quality - Learn about the sequence and traverse functions through the work of Runar Bjarnason and Paul Chiusano, authors of Functional Programming in Scala https://www.manning.com/books/functional-programming-in-scala, and Sam Halliday, author of "Functional Programming for Mortals with Scalaz" https://leanpub.com/fpmortals
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala - Part ...Philip Schwarz
(download for flawless image quality) Can a left fold ever work over an infinite list? What about a right fold? Find out.
Learn about the other two functions used by functional programmers to implement mathematical induction: iterating and scanning.
Learn about the limitations of the accumulator technique and about tupling, a technique that is the dual of the accumulator trick.
Function Applicative for Great Good of Palindrome Checker Function - Polyglot...Philip Schwarz
Embark on an informative and fun journey through everything you need to know to understand how the Applicative instance for functions makes for a terse palindrome checker function definition in point-free style.
download for better quality - Learn about the sequence and traverse functions
through the work of Runar Bjarnason and Paul Chiusano, authors of Functional Programming in Scala https://www.manning.com/books/functional-programming-in-scala, and others (Martin Odersky, Derek Wyatt, Adelbert Chang)
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala - with ...Philip Schwarz
(download for perfect quality) - See how recursive functions and structural induction relate to recursive datatypes.
Follow along as the fold abstraction is introduced and explained.
Watch as folding is used to simplify the definition of recursive functions over recursive datatypes
Part 1 - through the work of Richard Bird and Graham Hutton.
This version corrects the following issues:
slide 7, 11 fib(0) is 0,rather than 1
slide 23: was supposed to be followed by 2-3 slides recapitulating definitions of factorial and fibonacci with and without foldr, plus translation to scala
slide 36: concat not invoked in concat example
slides 48 and 49: unwanted 'm' in definition of sum
throughout: a couple of typographical errors
throughout: several aesthetic imperfections (wrong font, wrong font colour)
Quicksort - a whistle-stop tour of the algorithm in five languages and four p...Philip Schwarz
Quicksort - a whistle-stop tour of the algorithm in five languages and four paradigms.
Programming Paradigms: Functional, Logic, Imperative, Imperative Functional
Languages: Haskell, Scala, Java, Clojure, Prolog
The Functional Programming Triad of Folding, Scanning and Iteration - a first...Philip Schwarz
This slide deck can work both as an aide mémoire (memory jogger), or as a first (not completely trivial) example of using left folds, left scans and iteration, to implement mathematical induction.
Errata: on almost half of the slides there is some minor typo or imperfection or in some cases a minor error and in one case, an omission. See a later version for corrections and some improvements.
(for best quality images, either download or view here: https://philipschwarz.dev/fpilluminated/?page_id=455)
Scala code for latest version: https://github.com/philipschwarz/fp-fold-scan-iterate-triad-a-first-example-scala
Slideshare hasn't imported my notes, so here's the link to the Google Presentation: https://goo.gl/Gl4Vhm
Haskell is a statically typed, non strict, pure functional programming language. It is often talked and blogged about, but rarely used commercially. This talk starts with a brief overview of the language, then explains how Haskell is evaluated and how it deals with non-determinism and side effects using only pure functions. The suitability of Haskell for real world data science is then discussed, along with some examples of its users, a small Haskell-powered visualization, and an overview of useful packages for data science. Finally, Accelerate is introduced, an embedded DSL for array computations on the GPU, and an ongoing attempt to use it as the basis for a deep learning package.
N-Queens Combinatorial Problem - Polyglot FP for Fun and Profit – Haskell and...Philip Schwarz
See how the guard function has migrated from MonadPlus to Alternative and learn something about the latter.
Learn how to write a Scala program that draws an N-Queens solution board using the Doodle graphics library.
See how to write the equivalent Haskell program using the Gloss graphics library.
Learn how to use Monoid and Foldable to compose images both in Haskell and in Scala.
Link to part 1: https://www.slideshare.net/pjschwarz/nqueens-combinatorial-problem-polyglot-fp-for-fun-and-profit-haskell-and-scala-part-1
Errata:
On slide 22, the last line of the showQueens function should of course be show(solution).draw(frame) rather than show(solution).draw
On slide 43, it would be better if the definitions of the beside, above and on Monoids were also shown.
Presented online for C++ on Sea (2020-07-17)
Video at https://www.youtube.com/watch?v=Bai1DTcCHVE
Lambdas. All the cool kid languages have them. But does lambda mean what C++ and other languages, from Java to Python, mean by lambda? Where did lambdas come from? What were they originally for? What is their relationship to data abstraction?
In this session we will into the history, the syntax, the uses and abuses of lambdas and the way in which lambda constructs in C++ and other languages do (or do not) match the original construct introduced in lambda calculus.
Left and Right Folds- Comparison of a mathematical definition and a programm...Philip Schwarz
We compare typical definitions of the left and right fold functions, with their mathematical definitions in Sergei Winitzki’s upcoming book: The Science of Functional Programming.
Errata:
Slide 13: "The way 𝑓𝑜𝑙𝑑𝑙 does it is by associating to the right" - should, of course ,end in "to the left".
The Functional Programming Triad of Map, Filter and FoldPhilip Schwarz
This slide deck is my homage to SICP, the book which first introduced me to the Functional Programming triad of map, filter and fold.
It was during my Computer Science degree that a fellow student gave me a copy of the first edition, not long after the book came out.
I have not yet come across a better introduction to these three functions.
The upcoming slides are closely based on the second edition of the book, a free online copy of which can be found here:
https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book.html.
Download for original image quality.
Errata:
slide 20: the Clojure map function is in fact the Scheme one repeated - see code below for correction.
Scheme code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-scheme
Clojure code: https://github.com/philipschwarz/the-fp-triad-of-map-filter-and-fold-clojure
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala - Part 5Philip Schwarz
(download for best quality slides) Gain a deeper understanding of why right folds over very large and infinite lists are sometimes possible in Haskell.
See how lazy evaluation and function strictness affect left and right folds in Haskell.
Learn when an ordinary left fold results in a space leak and how to avoid it using a strict left fold.
Errata:
slide 15: "as sharing is required" should be "as sharing is not required"
slide 43: 𝑠𝑓𝑜𝑙𝑑𝑙 (⊕) 𝑎 should be 𝑠𝑓𝑜𝑙𝑑𝑙 (⊕) 𝑒
An overview of the programming techniques available and some of the ongoing research in the Haskell community around concurrent and parallel programming.
The Functional Programming Triad of Folding, Scanning and Iteration - a first...Philip Schwarz
This slide deck can work both as an aide mémoire (memory jogger), or as a first (not completely trivial) example of using left folds, left scans and iteration, to implement mathematical induction.
This is just an updated version of the original slide deck which makes minor improvements and minor corrections in almost half of the slides.
(for best quality images, either download or view here: https://philipschwarz.dev/fpilluminated/?page_id=455)
You can see the Scala code here: https://github.com/philipschwarz/fp-fold-scan-iterate-triad-a-first-example-scala
download for better quality - Learn about the sequence and traverse functions through the work of Runar Bjarnason and Paul Chiusano, authors of Functional Programming in Scala https://www.manning.com/books/functional-programming-in-scala, and Sam Halliday, author of "Functional Programming for Mortals with Scalaz" https://leanpub.com/fpmortals
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and Scala - Part ...Philip Schwarz
(download for flawless image quality) Can a left fold ever work over an infinite list? What about a right fold? Find out.
Learn about the other two functions used by functional programmers to implement mathematical induction: iterating and scanning.
Learn about the limitations of the accumulator technique and about tupling, a technique that is the dual of the accumulator trick.
Function Applicative for Great Good of Palindrome Checker Function - Polyglot...Philip Schwarz
Embark on an informative and fun journey through everything you need to know to understand how the Applicative instance for functions makes for a terse palindrome checker function definition in point-free style.
download for better quality - Learn about the sequence and traverse functions
through the work of Runar Bjarnason and Paul Chiusano, authors of Functional Programming in Scala https://www.manning.com/books/functional-programming-in-scala, and others (Martin Odersky, Derek Wyatt, Adelbert Chang)
The Knowledge and Experience of Self-Referral Consciousness and the
Fulfillment of
Interdisciplinary Study
Samuel Y. Boothby
Maharishi University of Management
Fairfield, Iowa
Critical Mass Forrester 09: Marketing Vegas in a RecessionCritical Mass
Viva Las Vegas! Learn how Critical Mass and R&R Partners employed a 360 Degree Customer Experience strategy to drive travel to Las Vegas during these troubled economic times.
This is the second issue of Medical Self-Care magazine, created and published by Tom Ferguson, while he was a 4th year medical student at Yale, in 1976.
Jessica Bennett is currently studying towards a Master of Building Science at Victoria University of Wellington's School of Architecture, researching the affect that apartment design has on occupant health, comfort and well-being. Previously she has completed a Bachelor of Building Science with Honours researching apartment design, indoor air quality and New Zealand Building Code regulations. From 2006 she has worked part-time at BRANZ as a Building Performance Scientist, undertaking weathertightness assessments for Appraisal Certificates as well as simulation work on indoor environmental quality (IEQ), energy efficiency and climate change.
Jessica's interests include apartment design & refurbishment, occupant health, comfort & well-being, and the IEQ of buildings. Her role in The Plant Room (Wellington SHaC Team) involves providing technical information and advice as well as undertaking modelling and simulation of the proposed design.
Folding Unfolded - Polyglot FP for Fun and Profit - Haskell and ScalaPhilip Schwarz
(download for perfect quality) - See how recursive functions and structural induction relate to recursive datatypes.
Follow along as the fold abstraction is introduced and explained.
Watch as folding is used to simplify the definition of recursive functions over recursive datatypes
Part 1 - through the work of Richard Bird and Graham Hutton.
Errata:
slide 7, 11 fib(0) is 0,rather than 1
slide 23: was supposed to be followed by 2-3 slides recapitulating definitions of factorial and fibonacci with and without foldr, plus translation to scala
slide 36: concat not invoked in concat example
slides 48 and 49: unwanted 'm' in definition of sum
throughout: a couple of typographical errors
throughout: several aesthetic imperfections (wrong font, wrong font colour)
This is a friendly Lambda Calculus Introduction by Dustin Mulcahey. LISP has its syntactic roots in a formal system called the lambda calculus. After a brief discussion of formal systems and logic in general, Dustin will dive in to the lambda calculus and make enough constructions to convince you that it really is capable of expressing anything that is "computable". Dustin then talks about the simply typed lambda calculus and the Curry-Howard-Lambek correspondence, which asserts that programs and mathematical proofs are "the same thing".
Function Programming in Scala.
A lot of my examples here comes from the book
Functional programming in Scala By Paul Chiusano and Rúnar Bjarnason, It is a good book, buy it.
This 10 hours class is intended to give students the basis to empirically solve statistical problems. Talk 1 serves as an introduction to the statistical software R, and presents how to calculate basic measures such as mean, variance, correlation and gini index. Talk 2 shows how the central limit theorem and the law of the large numbers work empirically. Talk 3 presents the point estimate, the confidence interval and the hypothesis test for the most important parameters. Talk 4 introduces to the linear regression model and Talk 5 to the bootstrap world. Talk 5 also presents an easy example of a markov chains.
All the talks are supported by script codes, in R language.
Understanding the "Chain Rule" for Derivatives by Deriving Your Own VersionJames Smith
Because the Chain Rule can confuse students as much as it helps them solve real problems, we put ourselves in the shoes of the mathematicians who derived it, so that students may understand the motivation for the rule; its limitations; and why textbooks present it in its customary form. We begin by finding the derivative of sin2x without using the Chain Rule. That exercise, having shown that even a comparatively simple compound function can be bothersome to differentiate using the definition of the derivative as a limit, provides the motivation for developing our own formula for the derivative of the general compound function g[f(x)]. In the course of that development, we see why the function f must be continuous at any value of x to which the formula is applied. We finish by comparing our formula to that which is commonly given.
Presented online for javaBin (2020-04-14)
Video at https://www.youtube.com/watch?v=orcSUE0Jjdc
Lambdas. All the cool kid languages have them. But does ‘lambda’ mean what Java, JavaScript, etc. mean by ‘lambda’? Where did lambdas come from? What were they originally for? What is their relationship to data abstraction?
In this session we will look into the history, the syntax and the uses of lambdas and the way in which lambda constructs in Java and other languages do (or do not) match the original construct introduced in lambda calculus.
These are the slides of the talk I gave at the Dyla'14 workshop (http://conferences.inf.ed.ac.uk/pldi2014/). It's about monads for languages like Perl, Ruby and LiveScript.
The source code is available at
https://github.com/wimvanderbauwhede/Perl-Parser-Combinators
https://github.com/wimvanderbauwhede/parser-combinators-ls
Don't be put off by the word monad or the maths. This is basically a very practical way for doing tasks such as parsing.
Some helpful tips for programming the HP33s Scientific calculator, for members of our calculators for surveyors support group on Land Surveyors United http://landsurveyorsunited.com/group/hp48calculators/forum
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.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
2. My homework, using only concepts from last week
import Data . L i s t ( i s I n f i x O f )
pattern = ” toast ”
f o r m a t number l i n e = show number ++ ” : ” ++ l i n e
g r e p number i n p u t
= i f null input
then [ ]
e l s e i f i s I n f i x O f p a t t e r n ( head i n p u t )
then f o r m a t number ( head i n p u t )
: g r e p ( number + 1 ) ( t a i l i n p u t )
else g r e p ( number + 1 ) ( t a i l i n p u t )
grepFromOne i n p u t = u n l i n e s ( g r e p 1 ( l i n e s i n p u t ) )
main = i n t e r a c t grepFromOne
3. Wasn’t Haskell supposed to be “pretty”?
That grep function sure didn’t look pretty to me!
But what, specifically, is ugly about it?
We repeat ourselves, using head and tail twice.
There’s a mess of nested if /else badness going on.
4. Lists, revisited
There are two ways to construct a list:
An empty list
[]
A non-empty list
firstElement : restOfList
We refer to [] and : as list constructors, since they construct list
values.
5. Lists, constructed
Knowing about these constructors, how might we construct a
4-element list?
6. Lists, constructed
Knowing about these constructors, how might we construct a
4-element list?
1 : 2 : 3 : 4 : []
The bracketed notation we saw last week is syntactic sugar for the
form above.
In other words, any time you see this:
[1,2,3,4]
You can read it as this, and vice versa:
1 : 2 : 3 : 4 : []
7. Lists, misconstrued
Beginner mistake alert:
A list must end with an empty list. So a construction like this
makes no sense:
’a’ : ’b’ : ’c’
How would we fix it up?
’a’ : ’b’ : ’c’ : []
8. Back to our roots
Remember the fragment of square root code from last week?
oneRoot a b c = (−b + ( b ˆ2 + 4∗ a ∗ c ) ) / ( 2 ∗ a )
If we pass in a value of zero for a, the root is undefined, since we’d
be dividing by zero.
oneRoot a b c = i f a == 0
then (−b + ( b ˆ2 − 4∗ a ∗ c ) )
/ (2∗ a )
e l s e e r r o r ” d i v i d e by z e r o ! ”
9. But...
I don’t like that if , because how would we write this using
mathematical notation?
−b ± (b 2 − 4ac)
roots(a, b, c) = if a = 0
2a
= undefined otherwise
And . . . isn’t Haskell supposed to be mathematically inspired?
10. Introducing guards
A guard is a Boolean expression preceded by a vertical bar
character.
oneRoot a b c
| a /= 0 = (−b + ( b ˆ2 − 4∗ a ∗ c ) ) / ( 2 ∗ a )
| o t h e r w i s e = e r r o r ” d i v i d e by z e r o ”
Guards are evaluated in top-to-bottom order.
For the first one that evaluates to True, the expression on the
right of the = sign is used as the result of the function.
The name otherwise is simply another name for True.
11. Using guards
Here’s a second attempt at our grep function, this time using
guarded expressions:
g r e p number i n p u t
| null input
= []
| i s I n f i x O f p a t t e r n ( head i n p u t )
= f o r m a t number ( head i n p u t )
: g r e p ( number + 1 ) ( t a i l i n p u t )
| otherwise
= g r e p ( number + 1 ) ( t a i l i n p u t )
12. How did this help?
We got rid of the nested if expressions, and our “flatter” code is
easier to follow.
It’s still fugly and repetitive, though. What about head and tail ?
13. Pattern matching
When we construct a list, the Haskell runtime has to remember
what constructors we used.
It goes a step further, and makes this information available to us.
We can examine the structure of a piece of data at runtime using
pattern matching.
14. Pattern matching on an empty list
What’s the length of an empty list?
myLength [ ] = 0
This is a function of one argument.
If that argument matches the empty-list constructor, our function
returns the value 0.
15. Pattern matching on a non-empty list
What’s the length of a non-empty list?
myLength ( x : x s ) = 1 + myLength x s
If our argument matches the non-empty-list constructor “:”, then:
the head of the list is bound to the name x;
the tail to xs;
and the expression is returned with those bindings.
16. Aaaand it’s over to you
Now that we know how pattern matching works, let’s do some
super-simple exercises:
Write versions of the head and tail functions:
head [1 ,2 ,3]
== > 1
tail [ ’a’ , ’b’ , ’c ’]
== > [ ’b’ , ’c ’]
Give your versions different names, or you’ll have a hard time
trying them out in ghci.
17. Matching alternative patterns
We combine our two pattern matches into one function definition
by writing them one after the other:
myLength [ ] = 0
myLength ( x : x s ) = 1 + myLength x s
As with guards, pattern matching proceeds from top to bottom
and stops at the first success.
The RHS of the first pattern that succeeds is used as the
body of the function.
18. Matching alternative patterns
We combine our two pattern matches into one function definition
by writing them one after the other:
myLength [ ] = 0
myLength ( x : x s ) = 1 + myLength x s
As with guards, pattern matching proceeds from top to bottom
and stops at the first success.
The RHS of the first pattern that succeeds is used as the
body of the function.
Question: What do you suppose happens if no pattern matches?
19. Over to you, part two
And now that we know how to write function definitions that can
deal with multiple patterns, another exercise:
Write a version of the take function:
take 3 [100 ,200 ,300 ,400 ,500]
==> [100 ,200 ,300]
take 3 [ ’a’ , ’b ’]
==> [ ’a’ , ’b ’]
take 3 []
==> ???
20. Over to you, part two
And now that we know how to write function definitions that can
deal with multiple patterns, another exercise:
Write a version of the take function:
take 3 [100 ,200 ,300 ,400 ,500]
==> [100 ,200 ,300]
take 3 [ ’a’ , ’b ’]
==> [ ’a’ , ’b ’]
take 3 []
==> ???
Now use ghci to figure out what the drop function does, and
write a version of that.
21. Metasyntactic variables
Languages have their cultural habits, and Haskell is no exception.
You’ll very often see the names used when pattern matching a list
follow a naming convention like this:
(x: xs)
(y: ys)
(d:ds)
and so on.
Think of the “s” suffix as “pluralizing” a name, so “x” (ex) is the
head of the list, and “xs” (exes) is the rest.
22. Matching multiple patterns
We can match more than one pattern at a time.
Consider how we might add the elements of two vectors,
represented as lists:
sumVec ( x : x s ) ( y : y s ) = x + y : sumVec x s y s
sumVec [ ] [] = []
23. Combining pattern matching and guards
Things start to get seriously expressive when we combine language
features.
Remember that bloated grep definition from earlier? Let’s put our
new friends to work!
grep n [ ] = []
grep n ( x : xs )
| i s I n f i x O f pattern x = format n x
: g r e p ( n+1) x s
| otherwise = g r e p ( n+1) x s
24. What’s happening here?
When we define a function, a pattern binds names to values. Given
a list and a pattern (x: xs), if the list is non-empty, then x is bound
to its head, and xs to its tail.
Then each guard (if any) associated with that pattern is
evaluated in turn, with those bindings in effect, until a guard
succeeds.
Once a guard succeeds, its RHS is used as the result, with the
bindings from that pattern still in effect.
If the pattern match fails, or no guard succeeds, we fall
through to the next pattern and its guards.
25. What’s happening here?
When we define a function, a pattern binds names to values. Given
a list and a pattern (x: xs), if the list is non-empty, then x is bound
to its head, and xs to its tail.
Then each guard (if any) associated with that pattern is
evaluated in turn, with those bindings in effect, until a guard
succeeds.
Once a guard succeeds, its RHS is used as the result, with the
bindings from that pattern still in effect.
If the pattern match fails, or no guard succeeds, we fall
through to the next pattern and its guards.
Note: If all patterns and guards in a function definition were to fail
on some input, we’d get a runtime error. That would be bad.
26. And speaking of bad. . .
Remember our sumVec function?
sumVec ( x : x s ) ( y : y s ) = x + y : sumVec x s y s
sumVec [ ] [] = []
What happens if we apply this to lists of different lengths?
sumVec [1,2,3] [4,5,6,7,8]
27. And speaking of bad. . .
Remember our sumVec function?
sumVec ( x : x s ) ( y : y s ) = x + y : sumVec x s y s
sumVec [ ] [] = []
What happens if we apply this to lists of different lengths?
sumVec [1,2,3] [4,5,6,7,8]
So . . . what can we do about that exciting behaviour?
28. One possible response
Let’s declare that the sum of two vectors should end when we
reach the end of the shorter vector.
sumVec ( x : x s ) ( y : y s ) = x + y : sumVec x s y s
sumVec what ever = []
Whoa, dude. . . Why does this work?
29. One possible response
Let’s declare that the sum of two vectors should end when we
reach the end of the shorter vector.
sumVec ( x : x s ) ( y : y s ) = x + y : sumVec x s y s
sumVec what ever = []
Whoa, dude. . . Why does this work?
The names “what” and “ever” are patterns.
However, a plain name (with no constructors in sight) does
not inspect the structure of its argument.
So “what” and “ever” will each happily match either an
empty or a non-empty list.
30. An aside: strings are lists
In Haskell, we write characters surrounded by single quotes, and
strings in double quotes. Strings are lists, so:
” abc ”
is syntactic sugar for
[ ’ a ’ , ’b ’ , ’c ’ ]
and hence for
’a ’ : ’b ’ : ’c ’ : [ ]
Functions that can manipulate lists can thus manipulate strings.
Oh, and escape sequences such as ”rnt” work, too.
31. We are not limited to one constructor per pattern
Suppose we want to squish consecutive repeats of an element in a
list.
compress ” f o o o b a r r r r r r ”
== ” f o b a r ”
>
We can write a function to do this using an elegant combination of
pattern matching and guards:
compress ( x : y : ys )
| x == y = compress ( y : ys )
| otherwise = x : compress ( y : ys )
compress ys = ys
Notice that our pattern matches on two consecutive list
constructors!
32. Homework
Write a function that returns the nth element of a list,
counting from zero.
nth 2 ” squeak ”
== ’ u ’
>
Write a function that returns the element immdiately before
the last element of a list.
lastButOne [1 ,2 ,3 ,4 ,5]
== 4
>
Write a function that determines whether its input is a
palindrome.
isPalindrome ” foobar ”
== F a l s e
>
isPalindrome ” foobarraboof ”
== True
>