This document discusses Mendler-style recursion schemes for mixed-variant datatypes. It introduces Mendler-style iteration (mit) and iteration with a syntactic inverse (msfit). msfit allows formatting of untyped and simply-typed higher-order abstract syntax (HOAS) by evaluating at different kinds. A new recursion scheme is proposed to define a function for evaluating simply-typed HOAS into a user-defined value domain. The relationship between Mu and Mu' fixed point types is also discussed.
Linear types can make resource usage in Haskell safer by ensuring resources like memory and file handles are used exactly once. The proposed GHC extension adds linear types via the #-> arrow and a linear base library provides linear versions of Prelude functions and arrays. Examples show how linear types ensure file handles are closed exactly once and mutable arrays are destructively updated safely. Related work includes linear logic, uniqueness types in Clean and Mercury, and Rust's ownership and borrowing model.
Comonads are the category-theoretic dual of monads. While monads have been found to be a very useful design pattern for structuring programs in functional languages, such as Haskell, comonads have so far failed to gain much traction. In this talk we will look at what comonads are and how they are defined in Haskell, comparing them with the Haskell definition of monads. We will then look at some specific examples of comonads and how they can be used in practical programs.
We will assume basic knowledge of Haskell, including the Monad class and common instances. No knowledge of category theory will be required.
Zippers are a design pattern in functional programming languages, such as Haskell, which provides a focus point and methods for navigating around in a functional data structure. It turns out that for any algebraic data type with one parameter, the derivative of the type is a zipper for it.
Lectures from a Python workshop I taught in 2013 at the University of Pittsburgh. These are introductory slides to teach important aspects of the Python language.
Update: python limits the number of recursion calls, so computing the factorial as shown in the slides may not be generally usable.
Monoids - Part 1 - with examples using Scalaz and CatsPhilip Schwarz
A monoid is an algebraic structure consisting of a set and a binary operation that is associative and has an identity element. Some key properties of monoids include:
1) A monoid consists of a type A, a binary operation op that combines two values of type A, and a zero value that acts as an identity.
2) The binary operation must be associative, meaning op(op(x,y),z) = op(x,op(y,z)).
3) The zero value must satisfy the identity laws: op(x, zero) = x and op(zero, x) = x.
4) Common examples of monoids include string concatenation
Linear types can make resource usage in Haskell safer by ensuring resources like memory and file handles are used exactly once. The proposed GHC extension adds linear types via the #-> arrow and a linear base library provides linear versions of Prelude functions and arrays. Examples show how linear types ensure file handles are closed exactly once and mutable arrays are destructively updated safely. Related work includes linear logic, uniqueness types in Clean and Mercury, and Rust's ownership and borrowing model.
Comonads are the category-theoretic dual of monads. While monads have been found to be a very useful design pattern for structuring programs in functional languages, such as Haskell, comonads have so far failed to gain much traction. In this talk we will look at what comonads are and how they are defined in Haskell, comparing them with the Haskell definition of monads. We will then look at some specific examples of comonads and how they can be used in practical programs.
We will assume basic knowledge of Haskell, including the Monad class and common instances. No knowledge of category theory will be required.
Zippers are a design pattern in functional programming languages, such as Haskell, which provides a focus point and methods for navigating around in a functional data structure. It turns out that for any algebraic data type with one parameter, the derivative of the type is a zipper for it.
Lectures from a Python workshop I taught in 2013 at the University of Pittsburgh. These are introductory slides to teach important aspects of the Python language.
Update: python limits the number of recursion calls, so computing the factorial as shown in the slides may not be generally usable.
Monoids - Part 1 - with examples using Scalaz and CatsPhilip Schwarz
A monoid is an algebraic structure consisting of a set and a binary operation that is associative and has an identity element. Some key properties of monoids include:
1) A monoid consists of a type A, a binary operation op that combines two values of type A, and a zero value that acts as an identity.
2) The binary operation must be associative, meaning op(op(x,y),z) = op(x,op(y,z)).
3) The zero value must satisfy the identity laws: op(x, zero) = x and op(zero, x) = x.
4) Common examples of monoids include string concatenation
This document provides a summary of C# and .NET core concepts including:
1. Data types and their sizes in C#.
2. How to declare and perform operations on arrays such as sorting, clearing, and copying elements.
3. Methods for performing common string operations like comparison, indexing, replacing, and splitting.
The document discusses various topics in functions including:
- Types of relations such as one-to-one, one-to-many, and many-to-one.
- Ordered pairs, domains, codomains, and defining functions.
- Finding inverses of functions and identifying even and odd functions.
- Exponential and logarithmic functions including their properties and rules.
- Trigonometric and hyperbolic functions.
Monoids - Part 2 - with examples using Scalaz and CatsPhilip Schwarz
The document discusses monoids in Scalaz and Cats, specifically for the Option type. It explains that in Scalaz there are three Option monoids (optionFirst, optionLast, optionMonoid) that combine Options in different ways, while Cats only has one Option monoid (optionMonoid) that combines the contents. It also introduces MonoidK, which allows constructing a monoid that behaves like optionFirst by combining Options based on their orElse method. The document provides examples and explanations of how Semigroup, SemigroupK, Monoid, and MonoidK work and differ for types like List, Set, and Option. It also defines a monoid for endofunctions that composes
The derivative of a function is another function. We look at the interplay between the two. Also, new notations, higher derivatives, and some sweet wigs
The document discusses different types of functions including:
1. A function maps each input to a single output and can be represented by f(x) = y.
2. Types of functions include one-to-one, onto, and bijective functions.
3. Important concepts are the domain, range, and inverse of a function.
The document discusses top-down and bottom-up parsing techniques, with top-down parsing constructing a parse tree starting from the root node and working downward, while bottom-up parsing uses shift-reduce parsing to shift input symbols onto a stack and reduce the stack based on grammar rules. It also covers topics like recursive descent parsing, predictive parsing tables, LL(1) grammars, and error recovery techniques used in parsing.
The document discusses top-down parsing and predictive parsing. It defines key concepts like scanners, parsers, syntax analyzers, parse trees, top-down parsing, and predictive parsing. It also describes the process of constructing a predictive parsing table using the First and Follow sets of a grammar's productions.
Cyclone is a safer dialect of C that prevents common vulnerabilities like buffer overflows. It adds type qualifiers to pointers to enforce null checks, bounds checks, and memory region safety. The Cyclone compiler translates qualified C code and checks pointers against memory regions, arrays, and other constructs to catch errors. Cyclone aims to maintain C's performance while preventing unsafe accesses that could lead to crashes or security issues.
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
: String, List, Tuple, Dictionary
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
The document discusses functor composition and the functor laws. It explains that the composition of two functors F and G is itself a functor, where the map operation maps a function f by first applying F's map to lift f into F, and then applying G's map to lift the result into G. This satisfies the functor laws, where the mapping of a composition of functions is the composition of their individual mappings.
1) The document discusses parsing methods for context-free grammars including top-down and bottom-up approaches. Top-down parsing starts with the start symbol and works towards the leaves, while bottom-up parsing begins at the leaves and works towards the root.
2) Key aspects of parsing covered include left recursion elimination, left factoring, shift-reduce parsing which uses a stack and parsing table, and constructing parse trees from the parsing process.
3) The output of parsers can include parse sequences, parse trees, and abstract syntax trees which abstract away implementation details.
Why functional programming and category theory strongly mattersPiotr Paradziński
Abstractions of Category Theory to define abstractions (Functor, Applicative, Monad, Comonad, Coyoneda) commonly used in functional programming (FP). Using definitions from Category Theory to reason about modular and composable design. Examples based on Haskell papers: Functional pearls translated to Scala.
I am Wycliffe S. I am a Python Homework Expert at pythonhomeworkhelp.com. I hold a Master's in Computer Science from, the University of Aberdeen, UK. I have been helping students with their homework for the past 13 years. I solve homework related to Python.
Visit pythonhomeworkhelp.com or email support@pythonhomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Python Homework.
Slides originating from a talk I gave at ScalaMUC on 2013-12-17. The corresponding code can be found at https://github.com/afwlehmann/iteratee-tutorial .
This document contains notes from a calculus class section on continuity. Key points include:
- The definition of continuity requires that the limit of a function as x approaches a value exists and is equal to the value of the function at that point.
- Many common functions like polynomials, rational functions, trigonometric functions, exponentials and logarithms are continuous based on properties of limits.
- Functions can fail to be continuous if the limit does not exist or the function is not defined at a point. An example function is given that is not continuous at x=1.
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
I am Josh U. I am a Python Homework Expert at pythonhomeworkhelp.com. I hold a Master's in Computer Science from, St. Edward’s University, USA. I have been helping students with their homework for the past 10 years. I solve homework related to Python.
Visit pythonhomeworkhelp.com or email support@pythonhomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Python Homework.
This document provides an overview of Fourier series and the Fourier transform. It defines the Fourier transform and its inverse, and how they allow transforming between the time domain and frequency domain. It discusses how Fourier series can be used to decompose functions into sums of sinusoids. It also gives examples of Fourier transforms, such as for rectangle, triangle, exponential and Gaussian functions. It notes properties like symmetry and conditions for the existence of Fourier transforms.
This document describes an algebraic data type for representing mathematical expressions. The type, called Expression, can represent different kinds of expressions like numbers, variables, operators, fractions, roots, and more through discriminated union cases. It also discusses mapping over the union cases, pattern matching, and other techniques for working with Expression values programmatically.
This document provides a summary of C# and .NET core concepts including:
1. Data types and their sizes in C#.
2. How to declare and perform operations on arrays such as sorting, clearing, and copying elements.
3. Methods for performing common string operations like comparison, indexing, replacing, and splitting.
The document discusses various topics in functions including:
- Types of relations such as one-to-one, one-to-many, and many-to-one.
- Ordered pairs, domains, codomains, and defining functions.
- Finding inverses of functions and identifying even and odd functions.
- Exponential and logarithmic functions including their properties and rules.
- Trigonometric and hyperbolic functions.
Monoids - Part 2 - with examples using Scalaz and CatsPhilip Schwarz
The document discusses monoids in Scalaz and Cats, specifically for the Option type. It explains that in Scalaz there are three Option monoids (optionFirst, optionLast, optionMonoid) that combine Options in different ways, while Cats only has one Option monoid (optionMonoid) that combines the contents. It also introduces MonoidK, which allows constructing a monoid that behaves like optionFirst by combining Options based on their orElse method. The document provides examples and explanations of how Semigroup, SemigroupK, Monoid, and MonoidK work and differ for types like List, Set, and Option. It also defines a monoid for endofunctions that composes
The derivative of a function is another function. We look at the interplay between the two. Also, new notations, higher derivatives, and some sweet wigs
The document discusses different types of functions including:
1. A function maps each input to a single output and can be represented by f(x) = y.
2. Types of functions include one-to-one, onto, and bijective functions.
3. Important concepts are the domain, range, and inverse of a function.
The document discusses top-down and bottom-up parsing techniques, with top-down parsing constructing a parse tree starting from the root node and working downward, while bottom-up parsing uses shift-reduce parsing to shift input symbols onto a stack and reduce the stack based on grammar rules. It also covers topics like recursive descent parsing, predictive parsing tables, LL(1) grammars, and error recovery techniques used in parsing.
The document discusses top-down parsing and predictive parsing. It defines key concepts like scanners, parsers, syntax analyzers, parse trees, top-down parsing, and predictive parsing. It also describes the process of constructing a predictive parsing table using the First and Follow sets of a grammar's productions.
Cyclone is a safer dialect of C that prevents common vulnerabilities like buffer overflows. It adds type qualifiers to pointers to enforce null checks, bounds checks, and memory region safety. The Cyclone compiler translates qualified C code and checks pointers against memory regions, arrays, and other constructs to catch errors. Cyclone aims to maintain C's performance while preventing unsafe accesses that could lead to crashes or security issues.
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
: String, List, Tuple, Dictionary
• List is a collection, which is ordered and changeable. Allows duplicate members.
• Tuple is a collection, which is ordered and unchangeable. Allows duplicate members.
• Set is a collection, which is unordered and unindexed. No duplicate members.
• Dictionary is a collection, which is unordered, changeable and indexed. No duplicate members.
The document discusses functor composition and the functor laws. It explains that the composition of two functors F and G is itself a functor, where the map operation maps a function f by first applying F's map to lift f into F, and then applying G's map to lift the result into G. This satisfies the functor laws, where the mapping of a composition of functions is the composition of their individual mappings.
1) The document discusses parsing methods for context-free grammars including top-down and bottom-up approaches. Top-down parsing starts with the start symbol and works towards the leaves, while bottom-up parsing begins at the leaves and works towards the root.
2) Key aspects of parsing covered include left recursion elimination, left factoring, shift-reduce parsing which uses a stack and parsing table, and constructing parse trees from the parsing process.
3) The output of parsers can include parse sequences, parse trees, and abstract syntax trees which abstract away implementation details.
Why functional programming and category theory strongly mattersPiotr Paradziński
Abstractions of Category Theory to define abstractions (Functor, Applicative, Monad, Comonad, Coyoneda) commonly used in functional programming (FP). Using definitions from Category Theory to reason about modular and composable design. Examples based on Haskell papers: Functional pearls translated to Scala.
I am Wycliffe S. I am a Python Homework Expert at pythonhomeworkhelp.com. I hold a Master's in Computer Science from, the University of Aberdeen, UK. I have been helping students with their homework for the past 13 years. I solve homework related to Python.
Visit pythonhomeworkhelp.com or email support@pythonhomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Python Homework.
Slides originating from a talk I gave at ScalaMUC on 2013-12-17. The corresponding code can be found at https://github.com/afwlehmann/iteratee-tutorial .
This document contains notes from a calculus class section on continuity. Key points include:
- The definition of continuity requires that the limit of a function as x approaches a value exists and is equal to the value of the function at that point.
- Many common functions like polynomials, rational functions, trigonometric functions, exponentials and logarithms are continuous based on properties of limits.
- Functions can fail to be continuous if the limit does not exist or the function is not defined at a point. An example function is given that is not continuous at x=1.
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
I am Josh U. I am a Python Homework Expert at pythonhomeworkhelp.com. I hold a Master's in Computer Science from, St. Edward’s University, USA. I have been helping students with their homework for the past 10 years. I solve homework related to Python.
Visit pythonhomeworkhelp.com or email support@pythonhomeworkhelp.com. You can also call on +1 678 648 4277 for any assistance with Python Homework.
This document provides an overview of Fourier series and the Fourier transform. It defines the Fourier transform and its inverse, and how they allow transforming between the time domain and frequency domain. It discusses how Fourier series can be used to decompose functions into sums of sinusoids. It also gives examples of Fourier transforms, such as for rectangle, triangle, exponential and Gaussian functions. It notes properties like symmetry and conditions for the existence of Fourier transforms.
This document describes an algebraic data type for representing mathematical expressions. The type, called Expression, can represent different kinds of expressions like numbers, variables, operators, fractions, roots, and more through discriminated union cases. It also discusses mapping over the union cases, pattern matching, and other techniques for working with Expression values programmatically.
Numerical Fourier transform based on hyperfunction theoryHidenoriOgata
This is the PC slide of a contributed talk in the conference "ECMI2018 (The 20th European Conference on Mathematics for Industry)", 18-20 June 2018, Budapest, Hungary. In this talk, we propose a numerical method of Fourier transforms based on hyperfunction theory.
Abstract:
This paper deals with the vital role of primitive polynomials for designing PN sequence generators. The standard LFSR (linear feedback shift register) used for pattern generation may give repetitive patterns. Which are in certain cases is not efficient for complete test coverage. The programmable LFSR based on primitive polynomial generates maximum-length PRPG with maximum fault coverage.
This document discusses Fourier series and their applications. It contains the following key points:
1. Fourier introduced Fourier series to solve heat equations through metal plates, expressing functions as infinite sums of sines and cosines.
2. Sine and cosine functions are orthogonal and periodic, allowing any piecewise continuous periodic function to be represented by a Fourier series.
3. The Euler-Fourier formulas relate the Fourier coefficients to the function, allowing the coefficients to be determined.
4. Even functions only have cosine terms, odd only sine, and the Fourier series converges to the average at discontinuities for piecewise continuous functions.
This document provides an overview of Fourier series and Fourier transforms. It discusses the history of Fourier analysis and how Fourier introduced Fourier series to solve heat equations. It defines Fourier series and covers topics like odd and even functions, half-range Fourier series, and the complex form of Fourier series. The document also discusses the relationship between Fourier transforms and Laplace transforms. It concludes by listing some applications of Fourier analysis in fields like electrical engineering, acoustics, optics, and more.
An applicative value can be seen as a value with an added context. Functional programming uses functions instead of variables. Haskell is a pure functional programming language that uses lazy evaluation, pattern matching, list comprehensions, and type polymorphism. Typeclasses define properties of types like Eq, Ord, Show, Read, and Enum. Functions can be lifted to operate on applicative types like Maybe and [] to provide context.
This talk covers a novel approach to "name binding" in syntax trees for programming languages that makes it much easier to write compilers and interpreters with a higher degree of assurance.
Functional Programming by Examples using Haskellgoncharenko
The document discusses functional programming concepts in Haskell compared to traditional imperative languages like C++. It provides:
1) An example of quicksort implemented in both C++ and Haskell to illustrate the differences in approach and syntax between the two paradigms. The Haskell version is much more concise, using only 5 lines compared to 14 lines in C++.
2) Explanations of key functional programming concepts in Haskell including pure functions, recursion, pattern matching, and higher-order functions like map and fold.
3) Examples and definitions of commonly used Haskell functions and data types to summarize lists, sorting, and traversing elements - highlighting the more declarative style of functional programming.
Some properties of m sequences over finite field fpIAEME Publication
1) The document discusses properties of M-sequences over finite fields Fp when p is an odd prime.
2) Some key properties discussed are: the set of cyclic permutations of a non-zero period is not closed under addition; the matrix of these permutations is symmetric about the second diagonal; and the sum of any two rows with one translated by half the period is the zero sequence.
3) The document also presents theorems about the characteristics and representations of M-sequences over finite fields, including their relation to irreducible polynomials and representation as matrices.
Humble introduction to category theory in haskellJongsoo Lee
This document introduces category theory concepts in Haskell including functors, applicative functors, and monads. It provides examples of these concepts in Haskell like Maybe and lists being instances of functor and applicative. Functors allow mapping a function over a data type using fmap. Applicative functors allow chaining functions using pure and <*>. Monads generalize applicative functors and allow sequencing computations. The document recommends understanding these concepts as patterns found in Haskell code rather than focusing on their category theory origins.
The document discusses several key concepts regarding derivatives:
(1) It explains how to use the derivative to determine if a function is increasing, decreasing, or neither on an interval using the signs of the derivative.
(2) It provides theorems and rules for finding local extrema (maxima and minima) of functions using the first and second derivative tests.
(3) It also discusses absolute extrema, monotonic functions, and the Rolle's Theorem and Mean Value Theorem which relate the derivative of a function to values of the function.
A Language Designer’s Workbench. A one-stop shop for implementation and verif...Eelco Visser
The document describes a language designer's workbench that provides tools to support the implementation and verification of language designs. It allows language designers to define the syntax, name binding, type system, and semantics of a language and provides tools like parsers, type checkers, interpreters, and debuggers. The workbench integrates existing tools like SDF3, NaBL, TypeScript, and Stratego under a common framework. A prototype implementation of the workbench is demonstrated by defining the syntax, types, and semantics of a simple functional language called PCF.
I am Bing Jr. I am a Signal Processing Assignment Expert at matlabassignmentexperts.com. I hold a Master's in Matlab Deakin University, Australia. I have been helping students with their assignments for the past 9 years. I solve assignments related to Signal Processing.
Visit matlabassignmentexperts.com or email info@matlabassignmentexperts.com. You can also call on +1 678 648 4277 for any assistance with Signal Processing Assignments.
The document discusses iteration techniques for constructing collision resistant hash functions and universal one-way hash functions, including Merkle-Damgård's technique which maps inputs of variable length to fixed-length outputs using a compression function, and Shoup's technique which uses a universal one-way compression function along with masks to iteratively hash message blocks. The Merkle-Damgård and Shoup techniques are proven to construct universal one-way and collision resistant hash functions if the underlying compression function satisfies certain properties.
1) Fourier series and integrals are used to represent periodic functions as an infinite sum or integral of sines and cosines. They are useful for solving differential equations.
2) The Fourier series of a periodic function f(x) with period T is the sum of its coefficients multiplied by sines and cosines of integer multiples of x/T. The coefficients are calculated using integrals involving f(x).
3) Half range expansions like half range cosines (HRC) and half range sines (HRS) are used when f(x) is defined on a finite interval, by extending it to a periodic function on the entire real line.
The document defines the derivative of a function and discusses:
- The definition of the derivative as the limit of the slope between two points as they approach each other.
- Notation used to represent derivatives, including f'(x), dy/dx, and df/dx.
- How the graph of a function's derivative f' relates to the graph of the original function f - where f' is positive/negative/zero corresponds to parts of f that are increasing/decreasing/at an extremum.
- How to graph f given a graph of its derivative f' by sketching the curve that matches the behavior of f' at each point.
- One-sided derivatives at endpoints of functions defined
This document provides an overview of Math 5045: Advanced Algebra I (Module Theory). It begins with definitions of rings, including commutative and non-commutative rings, rings with unity, units, zero-divisors, integral domains, and fields. It then discusses modules, including left and right modules, submodules, module homomorphisms, kernels, images, and the module HomR(M,N). It also covers quotient modules and the composition of module homomorphisms.
Notes on exact and approximate bayesian implementationLeo Vivas
This document provides definitions and theorems related to exact and approximate Bayesian implementation of social choice functions. It introduces notation for agents, types, outcomes, utilities, and social choice functions. It then defines properties like incentive compatibility, Bayesian monotonicity, and Bayesian implementability. The main result is Theorem 1, which states that a social choice function is Bayesian implementable in mixed strategies if and only if there exists an equivalent function that satisfies incentive compatibility and mixed Bayesian monotonicity, under standard regularity conditions on the environment. A proposed direct mechanism is also described that would achieve exact Bayesian implementation.
Similar to Can two fixpoint types in Mendler style unite? (20)
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
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.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
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.
2. Outline
Mendler-style Recursion Schemes
mit (iteration)
msfit (iteration with syntactic Inverse)
Untyped HOAS (Regular Mixed-Variant Datatype)
Formatting Untyped HOAS (using msfit at kind *)
Simply-Typed HOAS (Indexed Mixed-Variant Datatype)
Formatting Untyped HOAS (using msfit at kind * → *)
The Nax language
Need for yet another Mendler-style Recursion Scheme
3. already inconsistent as a logic (via Curry--Howard correspondence)
without even having to introduce any term-level recursion
since we can already define general recursion using this
(iso-) Recursive Types
in Functional Languages
4. Having both unrestricted formation
and unrestricted elimination
of μ leads to inconsistency
data T a r = C (r → a)
w : μ (T a) → a -- encoding of (λx. x x) in the untyped λ-calc
w = λv. case (unIn v) of (C x) → f (C x)
-- w (C w) amounts to a well-known diverging term in λ-calculus
fix : (a → a) → a -- the Y combinator (λf.(λx.f(xx))(λx.f(xx))))
fix = λf. (λx. f (w x)) (In (C (λx. f (w x))))
To recover consistency, one could
either restrict formation (conventional approach)
or restrict elimination (Mendler-style approach)
5. Conventional Iteration over
Recursive Types
allow formation of only
positive recursive types
(i.e., when F has a map)
freely eliminate (i.e. use unIn) recursive values
7. Mendler-style Iteration
-- Mu at different kinds (there are many more, one at each kind)
data Mu0 (f :: * → *) = In0 (f (Mu0 f ))
data Mu1 (f :: (*→*)→(*→*)) i= In1 (f (Mu1 f ) i)
-- Mendler-style iterators at different kinds
mit0 :: Phi0 f a → Mu f → a
mit0 phi (In0 x) = phi (mit0 phi) x
mit1 :: Phi1 f a i → Mu f i → a i
mit1 phi (In1 x) = phi (mit1 phi) x
type Phi0 f = ∀r. (r → a ) → (f r → a )
type Phi1 f = ∀r. (∀i. r i → a i) → (∀i. f r i → a i)
✔ Uniformly defined at different
kinds (can handle non-regular
datatypes quite the same way
as handling regular datatypes)
✔ Terminates for ANY datatype
(can embed Mu and mit into Fw.
see Abel, Matthes and Uustalu TCS'04)
✔ However, not very useful for
mixed-variant datatypes
eliminating In0 or In1 by pattern matching
is only allowed in Mendler-style iterators
recursive call
8. Mendler-style Iteration
with a syntactic inverse
-- Mu' at different kinds (we only use two of them in this talk)
data Mu'0 (f :: * → *) a = In'0 (f (Mu'0 f a)) | Inverse0 a
data Mu'1 (f :: (*→*)→(*→*)) a i = In'1 (f (Mu'1 f a) i)| Inverse1 (a i)
-- msfit at different kinds
msfit0 :: Phi'0 f a → (∀a. Mu'0 f a) → a
msfit0 phi (In'0 x) = phi Inverse0 (msfit0 phi) x
msfit1 :: Phi'1 f a i → (∀a. Mu'1 f a i) → a i
msfit1 phi (In'1 x) = phi Inverse1 (msfit1 phi) x
type Phi'0 f a = ∀r. (a → r a ) → (r a → a ) → (f r a → a )
type Phi'1 f a = ∀r. (∀i. a i → r a i) → (∀i. r a i → a i) → (∀i. f (r a) i → a i)
✔ Terminates for ANY datatype
(can embed Mu and msfit into
Fw. see Ahn and Sheard ICFP'11)
✔ msfit is quite useful for
mixed-variant datatypes,
due to "inverse" operation in
addition to "recursive call"
eliminating In'0 or In'1 by pattern matching
is only allowed in Mendler-style iterators
recursive callinverse
9. -- using general recursion at type level
data Exp = Lam (Exp → Exp) | App Exp Exp
-- using fixpoint (Mu'0) over non-recursive base structure (ExpF)
data ExpF r = Lam (r → r) | App r r
type Exp' a = Mu'0 ExpF a -- (Exp' a) may contain Inverse
type Exp = ∀a . Exp' a -- Exp does not contain Inverse
lam :: (∀a. Exp' a → Exp' a) -> Exp -- it's not (Exp → Exp) → Exp
lam f = In'0 (Lam f ) -- f can handle Inverse containing values
-- but can never examine its content
app :: Exp → Exp → Exp
app e1 e2 = In'0 (App e1 e2)
Untyped HOAS
(a regular mixed-variant datatype)
10. Formatting Untyped HOAS
using msfit0 at kind *
showExp :: Exp → String
showExp e = msfit0 phi e vars where
phi :: Phi'0 ExpF ([String] → String)
phi inv showE (Lam z) =
λ(v:vs) → "(λ"++v++"→"++ showE (z (inv (const v))) vs ++")"
phi inv showE (App x y) =
λvs → "("++ showE x vs ++" "++ showE y vs ++")"
type Phi'0 f a = ∀ r. (a → r a) → (r a → a) → (f r a → a)
vars = [ "x"++show n | n<-[0..] ] :: [String]
recursive callinverse
11. Simply-Typed HOAS
-- using general recursion at type level
data Exp t where -- Exp :: * → *
Lam :: (Exp t1 → Exp t2) → Exp (t1 → t2)
App :: Exp (t1 → t2) → Exp t1 → Exp t2
-- using fixpoint (Mu'1) over non-recursive base structure (ExpF)
data ExpF r t where -- ExpF :: (* → *) → (* → *)
Lam :: (r t1 → r t2) → ExpF r (t1 → t2)
App :: r (t1 → t2) → r t1 → ExpF r t2
type Exp' a t = Mu'1 ExpF a t -- (Exp' a t) might contain Inverse
type Exp t = ∀a . Exp' a t -- (Exp t) does not contain Inverse
lam :: (∀a . Exp' a t1 → Exp' a t2) → Exp (t1 → t2)
lam f = In'1 (Lam f ) -- f can handle Inverse containing values
-- but can never examine its content
app :: Exp (t1 → t2) → Exp t1 → Exp t2
app e1 e2 = In'1 (App e1 e2)
(a non-regular mixed-variant datatype)
12. newtype Id a = MkId { unId :: a }
evalHOAS :: Exp t → Id t
evalHOAS e = msfit1 phi e where
phi :: Phi'1 ExpF Id {- v :: t1 -} {- f :: r Id t1 -> r Id t2 -}
phi inv ev (Lam f ) = MkId(λv → unId(ev (f (inv (MkId v)))))
phi inv ev (App e1 e2) = MkId(unId(ev e1) (unId(ev e2)))
type Phi'1 f a = ∀r. (∀i. a i → r a i) → (∀i. r a i → a i) → (∀i. f (r a) i → a i)
This is example is a super awesome cooooool discovery that
System Fw can express Simply-Typed HOAS evaluation!!!
Evaluating Simply-Typed HOAS
using msfit1 at kind *→*
recursive callinverse
13. The Nax language
✔Built upon the idea of Mendler-style recursion schemes
✔Supports an extension of Hindley--Milner type inference to make it
easier to use Mendler-style recursion schemes and indexed datatypes
✔Handling different notions of recursive type operators (Mu, Mu') still
needs more rigorous clarification in the theory, but intuitively,
Mu0 f = ∀a. Mu'0 f a
Mu1 f = ∀a. Mu'1 f a i
...
So, we hide Mu' from users as if there is one kind of Mu
and Mu' is only used during the computation of msfit
✔Supports term-indexed types as well as type-indexed types.
Our term indexed calculus, System Fi (to appear in TLCA 2013),
extends System Fw with erasable term indices.
14. Need for yet another
Mendler-style recursion scheme
There are more recursion schemes other than mit and msfit
E.g., Mendler-style primitive recursion (mpr) can cast from abstract
recursive type (r) to concrete recursive type (Mu f ).
Phi0 f a = ∀r. (r → Mu f ) → (r → a ) → (f r → a )
Phi1 f a = ∀r. (∀i. r i → Mu f i) → (∀i. r i → a i) → (∀i. f r i → a i)
With mpr, one can write constant time predecessor for natural
numbers and tail function for lists by using the cast operation.
Next example motivates an extension to mpr that can uncast from
concrete recursive type (Mu f i) to abstract recursive type (r i)
Phi1 f a = ∀r. (∀i. Mu f i → r i) →
(∀i. r i → Mu f i) → (∀i. r i → a i) → (∀i. f r i → a i)
Recursion scheme with above Phi1 type does not terminate for mixed-
variant datatypes -- needs some additional restriction on uncast.
15. data V r t where V :: (r t1 → r t2) → V r (t1 → t2)
type Val t = Mu V t
val f = In1 (V f )
evalHOAS :: Exp t → Val t
evalHOAS e = msfit1 phi e where
phi :: Phi'1 ExpF (Mu1 V) -- f :: r Val t1 -> r Val t2 , v :: Val t1
phi inv ev (Lam f ) = val(λv → ev (f (inv v)))
phi inv ev (App e1 e2) = unVal (ev e1) (ev e2)
Only if we had unVal :: Val (t1 → t2) → (Val t1 → Val t2) it would be
possible to write this, but unVal does not seem to be definable
using any known Mendler-style recursion scheme.
Evaluating Simply-Typed HOAS
into a user defined value domain
16. New recursion scheme to write unVal
data V r t where V :: (r t1 → r t2) → V r (t1 → t2)
type Val t = Mu V t
val f = In1 (V f )
unVal v = unId(mprsi phi v) where
phi :: Phi1 V Id
phi uncast cast (V f) = Id(λv. cast (f (uncast v)))
mprsi :: Phi1 f a → Mu f i → a i
mprsi phi (In1 x) = phi id id (mrpsi phi) x
-- size restriction over indices in both uncast and cast
type Phi1 f a = ∀ r j. (∀i. (i < j) ⇒ Mu f i → r i) → -- uncast
(∀i. (i < j) ⇒ r i → Mu f i) → -- cast
(∀i. r i → a i) → (r j → a j)
-- or, maybe without the size restriction over indices in cast
type Phi1 f a = ∀ r j. (∀i. (i < j) ⇒ Mu f i → r i) → -- uncast
(∀i. r i → Mu f i) → -- cast
(∀i. r i → a i) → (f r j → a j)
Preliminary idea that still need further
studies for the termination proof
17. How are Mu and Mu' related?
data Mu0 (f :: * → *) = In0 (f (Mu0 f ))
data Mu'0 (f :: * → *) a = In'0 (f (Mu'0 f a)) | Inverse0 a
type Phi0 f = ∀r. (r → a ) → (f r → a )
mit0 :: Phi0 f a → Mu f → a
mit0 phi (In0 x) = phi (mit0 phi) x
type Phi'0 f a = ∀ r. (a → r a) → (r a → a) → (f (r a) → a)
msfit0 :: Phi'0 f a → (∀a. Mu'0 f a) → a
msfit0 phi (In'0 x) = phi Inverse0 (msfit0 phi) x
Can we coerece from (∀a. Mu'0 f a) to Mu f ? Yes!!!
Can we coerece from Mu f to (∀a. Mu'0 f a) ? Not exactly...
18. Corecion from Mu' to Mu
data E r = Lam (r -> r) | App r r
type Expr = Mu0 E
type Exp' a = Mu'0 E a
type Exp = (forall a. Exp' a) -- .i.e (forall a. Rec0 E a)
exp2expr :: Exp -> Expr -- i.e., (forall a. Rec0 E a) -> Mu0 E
exp2expr = msfit0 phi where
phi inv p2r (Lam f) = In0(Lam((x -> p2r (f (inv x)))))
phi inv p2r (App e1 e2) = In0(App (p2r e1) (p2r e2))
19. Attempt from Mu to Mu'
msfit' :: (forall r. (a -> r a) -> (r a -> a) -> f (r a) -> a) -> Mu'0 f a -> a
msfit' phi (In'0 x) = phi Inverse0 (msfit' phi) x
msfit' phi (Inverse0 z) = z
exp'2expr :: Exp' Expr -> Expr -- i.e., Mu'0 E (Mu0 E) -> Mu0 E
exp'2expr = msfcata' phi where
phi inv p2r (Lam f) = In0(Lam((x -> p2r (f (inv x)))))
phi inv p2r (App e1 e2) = In0(App (p2r e1) (p2r e2))
-- using general recursion and pattern matching against In0
expr2exp' :: Expr -> Exp' Expr -- i.e., Mu0 E -> Mu'0 E (Mu0 E)
expr2exp' (In0(Lam f)) = In'0(Lam (x -> expr2exp' (f (exp'2expr x))))
expr2exp' (In0(App e1 e2)) = In'0(App (expr2exp' e1) (expr2exp' e2))
20. Why from Mu' to Mu possible
but not the other way?
data E r = Lam (r → r) | App r r
type Exp' a = Mu'0 ExpF a -- (Exp' a) may contain Inverse
type Exp = ∀a . Exp' a -- Exp does not contain Inverse
lam :: (∀a. Exp' a → Exp' a) -> Exp -- it's not (Exp → Exp) → Exp
lam f = In'0 (Lam f ) -- f can handle Inverse containing values
-- but can never examine its content
type Expr = Mu0 E
lam :: Expr → Expr
lam f = In0 (Lam f)
Assume that Expr = Exp = (∀a . Exp' a)
There are less functions with type
(∀a. Exp' a) → (∀a. Exp' a) than
functions with type (∀a. Exp' a → Exp' a)