An Authentic Introduction to the Functional Programming Language SML
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Little Languages for Little Robots
Little Languages for Little Robots Matthew C. Jadud Brooke N. Chenoweth Jacob Schleter University of Kent Canterbury Indiana University Gibson High School Canterbury, UK Bloomington Fort Branch, IN [email protected] Bloomington, IN [email protected] ABSTRACT where students learn by building personally meaningful With serendipity as our muse, we have created tools that artifacts in the world. allow students to author languages of their own design for robots of their own construction. In developing a Scheme 2. JACKLL: A NEW LANGUAGE compiler for the LEGO Mindstorm we realized that there In building up to the evolution of Jackll and the philoso- is great educational potential in the design and creation phies its creation embodies, we feel it is appropriate to of new languages for small robotics kits. As a side effect first introduce the LEGO Mindstorm, the target for our of bringing Scheme and the Mindstorm together in a cre- Scheme compiler, and situate Jackll with respect to other ative context, we have begun an exploration of teaching languages intended for beginner programmers. language design that is fundamentally different from the treatment of the subject in traditional literature. 2.1 What is the LEGO Mindstorm? The LEGO Mindstorm Robotics Invention System is a 1. INTRODUCTION commercial product from the LEGO Group that provides Jacob Schleter, a rising senior at Gibson High School, Fort an inexpensive, reconfigurable platform for exploring robotics. Branch, Indiana, took part in the Indiana University Col- It comes standard with two motors, two touch sensors, one lege of Arts and Sciences Summer Research Experience for light sensor, and hundreds of pieces for assembling all sorts six weeks during the summer of 2002. -
Causal Commutative Arrows Revisited
Causal Commutative Arrows Revisited Jeremy Yallop Hai Liu University of Cambridge, UK Intel Labs, USA [email protected] [email protected] Abstract init which construct terms of an overloaded type arr. Most of the Causal commutative arrows (CCA) extend arrows with additional code listings in this paper uses these combinators, which are more constructs and laws that make them suitable for modelling domains convenient for defining instances, in place of the notation; we refer such as functional reactive programming, differential equations and the reader to Paterson (2001) for the details of the desugaring. synchronous dataflow. Earlier work has revealed that a syntactic Unfortunately, speed does not always follow succinctness. Al- transformation of CCA computations into normal form can result in though arrows in poetry are a byword for swiftness, arrows in pro- significant performance improvements, sometimes increasing the grams can introduce significant overhead. Continuing with the ex- speed of programs by orders of magnitude. In this work we refor- ample above, in order to run exp, we must instantiate the abstract mulate the normalization as a type class instance and derive op- arrow with a concrete implementation, such as the causal stream timized observation functions via a specialization to stream trans- transformer SF (Liu et al. 2009) that forms the basis of signal func- formers to demonstrate that the same dramatic improvements can tions in the Yampa domain-specific language for functional reactive be achieved without leaving the language. programming (Hudak et al. 2003): newtype SF a b = SF {unSF :: a → (b, SF a b)} Categories and Subject Descriptors D.1.1 [Programming tech- niques]: Applicative (Functional) Programming (The accompanying instances for SF , which define the arrow operators, appear on page 6.) Keywords arrows, stream transformers, optimization, equational Instantiating exp with SF brings an unpleasant surprise: the reasoning, type classes program runs orders of magnitude slower than an equivalent pro- gram that does not use arrows. -
How to Design Co-Programs
JFP, 15 pages, 2021. c Cambridge University Press 2021 1 doi:10.1017/xxxxx EDUCATIONMATTERS How to Design Co-Programs JEREMY GIBBONS Department of Computer Science, University of Oxford e-mail: [email protected] Abstract The observation that program structure follows data structure is a key lesson in introductory pro- gramming: good hints for possible program designs can be found by considering the structure of the data concerned. In particular, this lesson is a core message of the influential textbook “How to Design Programs” by Felleisen, Findler, Flatt, and Krishnamurthi. However, that book discusses using only the structure of input data for guiding program design, typically leading towards structurally recur- sive programs. We argue that novice programmers should also be taught to consider the structure of output data, leading them also towards structurally corecursive programs. 1 Introduction Where do programs come from? This mystery can be an obstacle to novice programmers, who can become overwhelmed by the design choices presented by a blank sheet of paper, or an empty editor window— where does one start? A good place to start, we tell them, is by analyzing the structure of the data that the program is to consume. For example, if the program h is to process a list of values, one may start by analyzing the structure of that list. Either the list is empty ([]), or it is non-empty (a : x) with a head (a) and a tail (x). This provides a candidate program structure: h [ ] = ::: h (a : x) = ::: a ::: x ::: where for the empty list some result must simply be chosen, and for a non-empty list the result depends on the head a and tail x. -
Proceedings of the 8Th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (Ed.) Sponsors
Proceedings of the 8th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (ed.) Sponsors We gratefully acknowledge the support given to the 8th European Lisp Symposium by the following sponsors: WWWLISPWORKSCOM i Organization Programme Committee Julian Padget – University of Bath, UK (chair) Giuseppe Attardi — University of Pisa, Italy Sacha Chua — Toronto, Canada Stephen Eglen — University of Cambridge, UK Marc Feeley — University of Montreal, Canada Matthew Flatt — University of Utah, USA Rainer Joswig — Hamburg, Germany Nick Levine — RavenPack, Spain Henry Lieberman — MIT, USA Christian Queinnec — University Pierre et Marie Curie, Paris 6, France Robert Strandh — University of Bordeaux, France Edmund Weitz — University of Applied Sciences, Hamburg, Germany Local Organization Christophe Rhodes – Goldsmiths, University of London, UK (chair) Richard Lewis – Goldsmiths, University of London, UK Shivi Hotwani – Goldsmiths, University of London, UK Didier Verna – EPITA Research and Development Laboratory, France ii Contents Acknowledgments i Messages from the chairs v Invited contributions Quicklisp: On Beyond Beta 2 Zach Beane µKanren: Running the Little Things Backwards 3 Bodil Stokke Escaping the Heap 4 Ahmon Dancy Unwanted Memory Retention 5 Martin Cracauer Peer-reviewed papers Efficient Applicative Programming Environments for Computer Vision Applications 7 Benjamin Seppke and Leonie Dreschler-Fischer Keyboard? How quaint. Visual Dataflow Implemented in Lisp 15 Donald Fisk P2R: Implementation of -
An Exploration of the Effects of Enhanced Compiler Error Messages for Computer Programming Novices
Technological University Dublin ARROW@TU Dublin Theses LTTC Programme Outputs 2015-11 An Exploration Of The Effects Of Enhanced Compiler Error Messages For Computer Programming Novices Brett A. Becker Technological University Dublin Follow this and additional works at: https://arrow.tudublin.ie/ltcdis Part of the Computer and Systems Architecture Commons, and the Educational Methods Commons Recommended Citation Becker, B. (2015) An exploration of the effects of enhanced compiler error messages for computer programming novices. Thesis submitted to Technologicl University Dublin in part fulfilment of the requirements for the award of Masters (M.A.) in Higher Education, November 2015. This Theses, Masters is brought to you for free and open access by the LTTC Programme Outputs at ARROW@TU Dublin. It has been accepted for inclusion in Theses by an authorized administrator of ARROW@TU Dublin. For more information, please contact [email protected], [email protected]. This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License An Exploration of the Effects of Enhanced Compiler Error Messages for Computer Programming Novices A thesis submitted to Dublin Institute of Technology in part fulfilment of the requirements for the award of Masters (M.A.) in Higher Education by Brett A. Becker November 2015 Supervisor: Dr Claire McDonnell Learning Teaching and Technology Centre, Dublin Institute of Technology Declaration I certify that this thesis which I now submit for examination for the award of Masters (M.A.) in Higher Education is entirely my own work and has not been taken from the work of others, save and to the extent that such work has been cited and acknowledged within the text of my own work. -
Directing Javascript with Arrows
Directing JavaScript with Arrows Khoo Yit Phang Michael Hicks Jeffrey S. Foster Vibha Sazawal University of Maryland, College Park {khooyp,mwh,jfoster,vibha}@cs.umd.edu Abstract callback with a (short) timeout. Unfortunately, this style of event- JavaScript programmers make extensive use of event-driven pro- driven programming is tedious, error-prone, and hampers reuse. gramming to help build responsive web applications. However, The callback sequencing code is strewn throughout the program, standard approaches to sequencing events are messy, and often and very often each callback must hard-code the names of the next lead to code that is difficult to understand and maintain. We have events and callbacks in the chain. found that arrows, a generalization of monads, are an elegant solu- To combat this problem, many researchers and practitioners tion to this problem. Arrows allow us to easily write asynchronous have developed libraries to ease the construction of rich and highly programs in small, modular units of code, and flexibly compose interactive web applications. Examples include jQuery (jquery. them in many different ways, while nicely abstracting the details of com), Prototype (prototypejs.org), YUI (developer.yahoo. asynchronous program composition. In this paper, we present Ar- com/yui), MochiKit (mochikit.com), and Dojo (dojotoolkit. rowlets, a new JavaScript library that offers arrows to the everyday org). These libraries generally provide high-level APIs for com- JavaScript programmer. We show how to use Arrowlets to construct mon features, e.g., drag-and-drop, animation, and network resource a variety of state machines, including state machines that branch loading, as well as to handle API differences between browsers. -
The Glasgow Haskell Compiler User's Guide, Version 4.08
The Glasgow Haskell Compiler User's Guide, Version 4.08 The GHC Team The Glasgow Haskell Compiler User's Guide, Version 4.08 by The GHC Team Table of Contents The Glasgow Haskell Compiler License ........................................................................................... 9 1. Introduction to GHC ....................................................................................................................10 1.1. The (batch) compilation system components.....................................................................10 1.2. What really happens when I “compile” a Haskell program? .............................................11 1.3. Meta-information: Web sites, mailing lists, etc. ................................................................11 1.4. GHC version numbering policy .........................................................................................12 1.5. Release notes for version 4.08 (July 2000) ........................................................................13 1.5.1. User-visible compiler changes...............................................................................13 1.5.2. User-visible library changes ..................................................................................14 1.5.3. Internal changes.....................................................................................................14 2. Installing from binary distributions............................................................................................16 2.1. Installing on Unix-a-likes...................................................................................................16 -
SOME USEFUL STRUCTURES for CATEGORICAL APPROACH for PROGRAM BEHAVIOR in Computer Science, Where We Often Use More Complex Structures Not Expressible by Sets
JIOS, VOL. 35, NO. 1 (2011) SUBMITTED 02/11; ACCEPTED 03/11 UDC 004.423.45 [ SomeSome useful Useful structures Structures for categorical for Categorical approach Approach for program for Programbehavior Behavior Viliam Slodicákˇ [email protected] Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical university of Košice Letná 9, 042 00 Košice Slovak Republic Abstract Using of category theory in computer science has extremely grown in the last decade. Categories allow us to express mathematical structures in unified way. Algebras are used for constructing basic structures used in computer programs. A program can be considered as an element of the initial algebra arising from the used programming language. In our contribution we formulate two ways of expressing algebras in categories. We also construct the codomain functor from the arrow category of algebras into the base category of sets which objects are also the carrier-sets of the algebras. This functor expresses the relation between algebras and carrier-sets. Keywords: Algebra, arrow category, monad, Kleisli category, codomain functor 1. Introduction Knowing and proving of the expected behavior of complex program systems is very important and actual rôle. It carries the time and cost savings: in mathematics [8] or in practical applications of economical character. The aim of programming is to construct such correct programs and program systems that during their execution provide expected behavior [7]. A program can be considered as an element of the initial algebra arising from the used programming language [14]. Algebraic structures and number systems are widely used in computer science. -
The Bluej Environment Reference Manual
The BlueJ Environment Reference Manual Version 2.0 for BlueJ Version 2.0 Kasper Fisker Michael Kölling Mærsk Mc-Kinney Moller Institute University of Southern Denmark HOW DO I ... ? – INTRODUCTION ........................................................................................................ 1 ABOUT THIS DOCUMENT................................................................................................................................. 1 RELATED DOCUMENTS.................................................................................................................................... 1 REPORT AN ERROR.......................................................................................................................................... 1 USING THE CONTEXT MENU........................................................................................................................... 1 1 PROJECTS.......................................................................................................................................... 2 1.1 CREATE A NEW PROJECT................................................................................................................... 2 1.2 OPEN A PROJECT ............................................................................................................................... 2 1.3 FIND OUT WHAT A PROJECT DOES .................................................................................................... 2 1.4 COPY A PROJECT .............................................................................................................................. -
Essentials of Compilation an Incremental Approach
Essentials of Compilation An Incremental Approach Jeremy G. Siek, Ryan R. Newton Indiana University with contributions from: Carl Factora Andre Kuhlenschmidt Michael M. Vitousek Michael Vollmer Ryan Scott Cameron Swords April 2, 2019 ii This book is dedicated to the programming language wonks at Indiana University. iv Contents 1 Preliminaries 5 1.1 Abstract Syntax Trees and S-expressions . .5 1.2 Grammars . .7 1.3 Pattern Matching . .9 1.4 Recursion . 10 1.5 Interpreters . 12 1.6 Example Compiler: a Partial Evaluator . 14 2 Integers and Variables 17 2.1 The R1 Language . 17 2.2 The x86 Assembly Language . 20 2.3 Planning the trip to x86 via the C0 language . 24 2.3.1 The C0 Intermediate Language . 27 2.3.2 The dialects of x86 . 28 2.4 Uniquify Variables . 28 2.5 Remove Complex Operators and Operands . 30 2.6 Explicate Control . 31 2.7 Uncover Locals . 32 2.8 Select Instructions . 32 2.9 Assign Homes . 33 2.10 Patch Instructions . 34 2.11 Print x86 . 35 3 Register Allocation 37 3.1 Registers and Calling Conventions . 38 3.2 Liveness Analysis . 39 3.3 Building the Interference Graph . 40 3.4 Graph Coloring via Sudoku . 42 3.5 Print x86 and Conventions for Registers . 48 v vi CONTENTS 3.6 Challenge: Move Biasing∗ .................... 48 4 Booleans and Control Flow 53 4.1 The R2 Language . 54 4.2 Type Checking R2 Programs . 55 4.3 Shrink the R2 Language . 58 4.4 XOR, Comparisons, and Control Flow in x86 . 58 4.5 The C1 Intermediate Language . -
Efficient Applicative Programming Environments for Computer Vision
Efficient Applicative Programming Environments for Computer Vision Applications Integration and Use of the VIGRA Library in Racket Benjamin Seppke Leonie Dreschler-Fischer University of Hamburg University of Hamburg Dept. Informatics Dept. Informatics Vogt-Kölln-Str. 30 Vogt-Kölln-Str. 30 22527 Hamburg, Germany 22527 Hamburg, Germany [email protected] [email protected] ABSTRACT 1. INTRODUCTION Modern software development approaches, like agile soft- Although applicative programming languages have a long ware engineering, require adequate tools and languages to tradition, they still do not belong to the scrap heap. In- support the development in a clearly structured way. At stead, they have proven to support state-of-the-art devel- best, they shall provide a steep learning curve as well as opment approaches by means of an interactive development interactive development environments. In the field of com- cycle, genericity and simplicity. The influence of applicative puter vision, there is a major interest for both, general re- programming paradigms is even observable in modern lan- search and education e.g. of undergraduate students. Here, guages, like Python, Dart and Go. However, there are some one often has to choose between understandable but compa- research areas, which are computationally of high costs and rably slow applicative programming languages, like Racket are thus currently less supported by applicative program- and fast but unintuitive imperative languages, like C/C++. ming languages. In this paper we present a system, which combines the best of each approaches with respect to common tasks in com- In this paper, we select the research field of computer vision puter vision, the applicative language Racket and the VI- and show how to connect applicative languages to a generic GRA C++ library. -
Companion Object
1. Functional Programming Functional Programming What is functional programming? What is it good for? Why to learn it? Functional Programming Programs as a composition of pure functions. Pure function? Deterministic. Same result for the same inputs. Produces no observable side eects. fun mergeAccounts(acc1: Account, acc2: Account): Account = Account(acc1.balance + acc2.balance) val mergedAcc = mergeAccounts(Account(500), Account(1000)) Target platform: JVM Running on Arrow v. 0.11.0-SNAPSHOT Running on Kotlin v. 1.3.50 Referential Transparency Function can be replaced with its corresponding value without changing the program's behavior. Based on the substitution model. Referential Transparency Better reasoning about program behavior. Pure functions referentially transparent. Substitution model Replace a named reference (function) by its value. Based on equational reasoning. Unlocks local reasoning. fun add(a: Int, b: Int): Int = a + b val two = add(1, 1) // could be replaced by 2 everywhere in the program val equal = (two == 1 + 1) Target platform: JVM Running on Arrow v. 0.11.0-SNAPSHOT Running on Kotlin v. 1.3.50 Substitution model Same example, but with two Accounts. fun mergeAccounts(acc1: Account, acc2: Account): Account = Account(acc1.balance + acc2.balance) val mergedAcc = mergeAccounts(Account(100), Account(200)) // could be replaced by Account(300) everywhere // in the program val equal = (mergedAcc == Account(300)) Target platform: JVM Running on Arrow v. 0.11.0-SNAPSHOT Running on Kotlin v. 1.3.50 Side eect? Function tries to access or modify the external world. val accountDB = AccountDB() fun mergeAccounts(acc1: Account, acc2: Account): Account { val merged = Account(acc1.balance + acc2.balance) accountDB.remove(listOf(acc1, acc2)) // side effect! accountDB.save(merged) // side effect! return merged } val merged = mergeAccounts(Account(100), Account(400)) Target platform: JVM Running on Arrow v.