Homework 1: Overview and Picking Languages See Webcourses2 and the Syllabus for Due Dates
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Reflexive Interpreters 1 the Problem of Control
Reset reproduction of a Ph.D. thesis proposal submitted June 8, 1978 to the Massachusetts Institute of Technology Electrical Engineering and Computer Science Department. Reset in LaTeX by Jon Doyle, December 1995. c 1978, 1995 Jon Doyle. All rights reserved.. Current address: MIT Laboratory for Computer Science. Available via http://www.medg.lcs.mit.edu/doyle Reflexive Interpreters Jon Doyle [email protected] Massachusetts Institute of Technology, Artificial Intelligence Laboratory Cambridge, Massachusetts 02139, U.S.A. Abstract The goal of achieving powerful problem solving capabilities leads to the “advice taker” form of program and the associated problem of control. This proposal outlines an approach to this problem based on the construction of problem solvers with advanced self-knowledge and introspection capabilities. 1 The Problem of Control Self-reverence, self-knowledge, self-control, These three alone lead life to sovereign power. Alfred, Lord Tennyson, OEnone Know prudent cautious self-control is wisdom’s root. Robert Burns, A Bard’s Epitath The woman that deliberates is lost. Joseph Addison, Cato A major goal of Artificial Intelligence is to construct an “advice taker”,1 a program which can be told new knowledge and advised about how that knowledge may be useful. Many of the approaches towards this goal have proposed constructing additive formalisms for trans- mitting knowledge to the problem solver.2 In spite of considerable work along these lines, formalisms for advising problem solvers about the uses and properties of knowledge are rela- tively undeveloped. As a consequence, the nondeterminism resulting from the uncontrolled application of many independent pieces of knowledge leads to great inefficiencies. -
JCP at Javapolis 2007
Javapolis News ❙ 14 December 2007 ❙ Nr 5 ❙ Published by Minoc Business Press 54 www.nonillion.com Parleys Want to become a NONILLIONAIRE ? mail us at : [email protected] Building Rich Internet Applications with Flex and JavaFX “There’s a well thought out com- an online environment using Adobe AIR. “Even when you ponent model for Flex”, he said. are offl ine, you still can update data. When the connec- “And there’s a thriving market tion comes back on, the system synchronizes automati- for components out there, both cally.” Open Source and commercial. So there are literally hundreds JavaPolis founder Stephan Janssen was next to explain of components available to how he decided to have Parleys.com rewritten using Flex. use in Flex.” And no, Flex isn’t Parleys.com offers a massive amount of Java talks – from there for fun and games only. JavaPolis, JavaOne and other Java events from all over “There are already a great the world – combining video images with the actual pres- number of business applica- entation slides of the speakers. Janssen programmed the tions running today, all built application for fun at fi rst, but with over 10 TB of streamed with Flex.” Eckel backed up video in just under a year, it’s clear Parleys.com sort of his statement with an ex- started to lead its own life. “The decision to write a new ample of an interface for an version was made six months ago”, he said. “It was still intranet sales application. too early to use JavaFX. And Silverlight? No thanks.” “Some people think Flex Flex allowed him to leverage the Java code of the earlier isn’t the right choice to version of Parleys.com and to resolve the Web 2.0 and make for business applica- AJAX issues he had en- countered while programming tions, because the render- the fi rst version. -
The Machine That Builds Itself: How the Strengths of Lisp Family
Khomtchouk et al. OPINION NOTE The Machine that Builds Itself: How the Strengths of Lisp Family Languages Facilitate Building Complex and Flexible Bioinformatic Models Bohdan B. Khomtchouk1*, Edmund Weitz2 and Claes Wahlestedt1 *Correspondence: [email protected] Abstract 1Center for Therapeutic Innovation and Department of We address the need for expanding the presence of the Lisp family of Psychiatry and Behavioral programming languages in bioinformatics and computational biology research. Sciences, University of Miami Languages of this family, like Common Lisp, Scheme, or Clojure, facilitate the Miller School of Medicine, 1120 NW 14th ST, Miami, FL, USA creation of powerful and flexible software models that are required for complex 33136 and rapidly evolving domains like biology. We will point out several important key Full list of author information is features that distinguish languages of the Lisp family from other programming available at the end of the article languages and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSL): languages which are specialized to a particular area and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the “programmable programming language.” We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and AI research in bioinformatics and computational biology. -
Paul Graham's Revenge of the Nerds
Paul Graham’s Revenge of the Nerds What made Lisp different Symbols and S-Expression Trees 6. Programs composed of expressions. Lisp programs are trees of expressions, each of which returns a value. … 7. A symbol type. Symbols are effecBvely pointers to strings stored in a hash table. So you can test equality by comparing a pointer, instead of comparing each character. 8. A notation for code using trees of symbols and constants. CS251 Programming [Lyn adds: these trees are called symbolic expressions = s-expressions] Languages 9. The whole language there all the time. There is no real distinction Spring 2018, Lyn Turbak between read-time, compile-time, and runtime. … reading at runtime enables programs to communicate using s-expressions, an idea recently reinvented Department of Computer Science as XML. [Lyn adds: and JSON!] Wellesley College Symbols & S-expressions 2 Symbols TesBng Symbols for Equality: eq? Lisp was invented to do symbolic processing. (This was thought to be the core of ArBficial Intelligence, and disBnguished Lisp from Fortran (the other The key thing we do with symbols is test them for equality main language at the Bme), whose strength with numerical processing.) with eq? (pronounced “eek”). A symbol is eq? to itself and A key Racket value is the symbol. nothing else. The symbol cat is wriNen (quote cat) or 'cat. > (eq? 'cat 'cat) Symbols are values and so evaluate to themselves. #t > 'cat > (map (λ (s) (eq? s 'to)) 'cat (list 'to 'be 'or 'not 'to 'be)) '(#t #f #f #f #t #f) ; 'thing is just an abbreviation for (quote thing) > (quote cat) 'cat Symbols are similar to strings, except they’re atomic; we don’t do character manipulaBons on them. -
Teach Yourself Scheme in Fixnum Days"
http://xkcd.com/224/ CS 152: Programming Language Paradigms Prof. Tom Austin San José State University What are some programming languages? Taken from http://pypl.github.io/PYPL.html January 2016 Why are there so many? Different domains Different design choices • Flexibility • Type safety • Performance • Build time • Concurrency Which language is better? Good language features • Simplicity • Readability • Learn-ability • Safety • Machine independence • Efficiency These goals almost always conflict Conflict: Type Systems Stop "bad" programs … but ... restrict the programmer Why do we make you take a programming languages course? • You might use one of these languages. • Perhaps one of these languages is the language of the future (whatever that means). • You might see similar languages in your job. • Somebody made us take one, so now we want to make you suffer too. • But most of all… We want to warp your minds. Course goal: change the way that you think about programming. That will make you a better Java programmer. The "Blub" paradox Why do I need higher order functions? My language doesn't have them, and it works just fine!!! "As long as our hypothetical Blub programmer is looking down the power continuum, he knows he's looking down… [Blub programmers are] satisfied with whatever language they happen to use, because it dictates the way they think about programs." --Paul Graham http://www.paulgraham.com/avg.html Languages we will cover (subject to change) Administrative Details • Green sheet: http://www.cs.sjsu.edu/~austin/cs152- spring19/Greensheet.html. • Homework submitted through Canvas: https://sjsu.instructure.com/ • Academic integrity policy: http://info.sjsu.edu/static/catalog/integrity.html Schedule • The class schedule is available through Canvas. -
IP Log for Eclipse.Platform Release 4.0, July 2010 Licenses
IP Log for eclipse.platform Release 4.0, July 2010 Licenses • Eclipse Public License v1.0 Third-Party Code CQ Third-Party Code License Use ICU4J (core and extended ICU4J License (X License, 1065 function) and ICU4J MIT Style) Replacement plug-in Version: 3.6 ICU4J License (X License, 1116 ICU4J Version: 3.4.5.20061213 MIT Style) 1153 JSch 0.1.31 Version: 0.1.31 New BSD license Apache Lucene Version: 1.9.1 243 (Core+Contrib Analyzers Apache License, 2.0 Analysis Src) 257 APT Version: 1 New BSD license Mozilla Public License 1.1 (MPL), MIT Style with No 262 Cairo Version: 1.0.2 Endorsement Clause, Historical Permissive Notice & Disclaimer ICU4J License (X License, 280 ICU4J Version: 3.4 MIT Style) ICU4J License (X License, 281 ICU4J Version: 3.4.3 MIT Style) 293 jsch Version: 0.1.28 New BSD license 308 PNG unload Version: 1 MIT license 1232 Apache Ant Version: 1.7.0 Apache License, 2.0 ICU4J and ICU4J Replacement ICU4J License (X License, 1367 Version: 3.6.1 MIT Style) Olsen time zone data Version: 1368 Public Domain 2007e Work derived from IJG JPEG 1596 IJG License Version: Release 6b,337 unmodified 1826 JSch 0.1.35 New BSD license source & binary ICU4J and ICU4J replacement MIT License with "no unmodified 1919 Version: 3.8.1 edorsement" clause source & binary unmodified 2014 jsch Version: 0.1.37 New BSD license source & binary XHTML DTDs Version: unmodified 2044 W3C Document License Versions 1.0 and 1.1 (PB CQ331) source org.apache.ant Version: 1.6.5 2404 (ATO CQ1013) (using Orbit Apache License, 2.0 CQ2209) org.apache.lucene Version: 1.4.3 2405 (Core Source Only) (ATO Apache License, 2.0 CQ1014) (using Orbit CQ2210) Junit Version: 3.8.2 (ATO 2406 Common Public License 1.0 CQ299) (using Orbit CQ2206) Historical support for Java SSH modified 2410 Applet + Blowfish Version - v. -
A Critique of Abelson and Sussman Why Calculating Is Better Than
A critique of Abelson and Sussman - or - Why calculating is better than scheming Philip Wadler Programming Research Group 11 Keble Road Oxford, OX1 3QD Abelson and Sussman is taught [Abelson and Sussman 1985a, b]. Instead of emphasizing a particular programming language, they emphasize standard engineering techniques as they apply to programming. Still, their textbook is intimately tied to the Scheme dialect of Lisp. I believe that the same approach used in their text, if applied to a language such as KRC or Miranda, would result in an even better introduction to programming as an engineering discipline. My belief has strengthened as my experience in teaching with Scheme and with KRC has increased. - This paper contrasts teaching in Scheme to teaching in KRC and Miranda, particularly with reference to Abelson and Sussman's text. Scheme is a "~dly-scoped dialect of Lisp [Steele and Sussman 19781; languages in a similar style are T [Rees and Adams 19821 and Common Lisp [Steele 19821. KRC is a functional language in an equational style [Turner 19811; its successor is Miranda [Turner 1985k languages in a similar style are SASL [Turner 1976, Richards 19841 LML [Augustsson 19841, and Orwell [Wadler 1984bl. (Only readers who know that KRC stands for "Kent Recursive Calculator" will have understood the title of this There are four language features absent in Scheme and present in KRC/Miranda that are important: 1. Pattern-matching. 2. A syntax close to traditional mathematical notation. 3. A static type discipline and user-defined types. 4. Lazy evaluation. KRC and SASL do not have a type discipline, so point 3 applies only to Miranda, Philip Wadhr Why Calculating is Better than Scheming 2 b LML,and Orwell. -
The Evolution of Lisp
1 The Evolution of Lisp Guy L. Steele Jr. Richard P. Gabriel Thinking Machines Corporation Lucid, Inc. 245 First Street 707 Laurel Street Cambridge, Massachusetts 02142 Menlo Park, California 94025 Phone: (617) 234-2860 Phone: (415) 329-8400 FAX: (617) 243-4444 FAX: (415) 329-8480 E-mail: [email protected] E-mail: [email protected] Abstract Lisp is the world’s greatest programming language—or so its proponents think. The structure of Lisp makes it easy to extend the language or even to implement entirely new dialects without starting from scratch. Overall, the evolution of Lisp has been guided more by institutional rivalry, one-upsmanship, and the glee born of technical cleverness that is characteristic of the “hacker culture” than by sober assessments of technical requirements. Nevertheless this process has eventually produced both an industrial- strength programming language, messy but powerful, and a technically pure dialect, small but powerful, that is suitable for use by programming-language theoreticians. We pick up where McCarthy’s paper in the first HOPL conference left off. We trace the development chronologically from the era of the PDP-6, through the heyday of Interlisp and MacLisp, past the ascension and decline of special purpose Lisp machines, to the present era of standardization activities. We then examine the technical evolution of a few representative language features, including both some notable successes and some notable failures, that illuminate design issues that distinguish Lisp from other programming languages. We also discuss the use of Lisp as a laboratory for designing other programming languages. We conclude with some reflections on the forces that have driven the evolution of Lisp. -
Performance Engineering of Proof-Based Software Systems at Scale by Jason S
Performance Engineering of Proof-Based Software Systems at Scale by Jason S. Gross B.S., Massachusetts Institute of Technology (2013) S.M., Massachusetts Institute of Technology (2015) Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY February 2021 © Massachusetts Institute of Technology 2021. All rights reserved. Author............................................................. Department of Electrical Engineering and Computer Science January 27, 2021 Certified by . Adam Chlipala Associate Professor of Electrical Engineering and Computer Science Thesis Supervisor Accepted by . Leslie A. Kolodziejski Professor of Electrical Engineering and Computer Science Chair, Department Committee on Graduate Students 2 Performance Engineering of Proof-Based Software Systems at Scale by Jason S. Gross Submitted to the Department of Electrical Engineering and Computer Science on January 27, 2021, in partial fulfillment of the requirements for the degree of Doctor of Philosophy Abstract Formal verification is increasingly valuable as our world comes to rely more onsoft- ware for critical infrastructure. A significant and understudied cost of developing mechanized proofs, especially at scale, is the computer performance of proof gen- eration. This dissertation aims to be a partial guide to identifying and resolving performance bottlenecks in dependently typed tactic-driven proof assistants like Coq. We present a survey of the landscape of performance issues in Coq, with micro- and macro-benchmarks. We describe various metrics that allow prediction of performance, such as term size, goal size, and number of binders, and note the occasional surprising lack of a bottleneck for some factors, such as total proof term size. -
Evil Pickles: Dos Attacks Based on Object-Graph Engineering∗
Evil Pickles: DoS Attacks Based on Object-Graph Engineering∗ Jens Dietrich1, Kamil Jezek2, Shawn Rasheed3, Amjed Tahir4, and Alex Potanin5 1 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 2 NTIS – New Technologies for the Information Society Faculty of Applied Sciences, University of West Bohemia Pilsen, Czech Republic [email protected] 3 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 4 School of Engineering and Advanced Technology, Massey University Palmerston North, New Zealand [email protected] 5 School of Engineering and Computer Science Victoria University of Wellington, Wellington, New Zealand [email protected] Abstract In recent years, multiple vulnerabilities exploiting the serialisation APIs of various programming languages, including Java, have been discovered. These vulnerabilities can be used to devise in- jection attacks, exploiting the presence of dynamic programming language features like reflection or dynamic proxies. In this paper, we investigate a new type of serialisation-related vulnerabilit- ies for Java that exploit the topology of object graphs constructed from classes of the standard library in a way that deserialisation leads to resource exhaustion, facilitating denial of service attacks. We analyse three such vulnerabilities that can be exploited to exhaust stack memory, heap memory and CPU time. We discuss the language and library design features that enable these vulnerabilities, and investigate whether these vulnerabilities can be ported to C#, Java- Script and Ruby. We present two case studies that demonstrate how the vulnerabilities can be used in attacks on two widely used servers, Jenkins deployed on Tomcat and JBoss. -
Reading List
EECS 101 Introduction to Computer Science Dinda, Spring, 2009 An Introduction to Computer Science For Everyone Reading List Note: You do not need to read or buy all of these. The syllabus and/or class web page describes the required readings and what books to buy. For readings that are not in the required books, I will either provide pointers to web documents or hand out copies in class. Books David Harel, Computers Ltd: What They Really Can’t Do, Oxford University Press, 2003. Fred Brooks, The Mythical Man-month: Essays on Software Engineering, 20th Anniversary Edition, Addison-Wesley, 1995. Joel Spolsky, Joel on Software: And on Diverse and Occasionally Related Matters That Will Prove of Interest to Software Developers, Designers, and Managers, and to Those Who, Whether by Good Fortune or Ill Luck, Work with Them in Some Capacity, APress, 2004. Most content is available for free from Spolsky’s Blog (see http://www.joelonsoftware.com) Paul Graham, Hackers and Painters, O’Reilly, 2004. See also Graham’s site: http://www.paulgraham.com/ Martin Davis, The Universal Computer: The Road from Leibniz to Turing, W.W. Norton and Company, 2000. Ted Nelson, Computer Lib/Dream Machines, 1974. This book is now very rare and very expensive, which is sad given how visionary it was. Simon Singh, The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography, Anchor, 2000. Douglas Hofstadter, Goedel, Escher, Bach: The Eternal Golden Braid, 20th Anniversary Edition, Basic Books, 1999. Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 2nd Edition, Prentice Hall, 2003. -
Methoden Der Metaprogrammierung Zur Rekonfiguration Von Software
Lehrstuhl für Realzeit-Computersysteme Methoden der Metaprogrammierung zur Rekonfiguration von Software eingebetteter Systeme Thomas Maier-Komor Vollständiger Abdruck der von der Fakultät für Elektrotechnik und Informationstech- nik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktor-Ingenieurs (Dr.-Ing.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. sc. techn. (ETH) A. Herkersdorf Prüfer der Dissertation: 1. Univ.-Prof. Dr.-Ing. G. Färber 2. Univ.-Prof. Dr. rer. nat. Dr. rer. nat. habil. U. Baumgarten Die Dissertation wurde am 27.6.2006, bei der Technischen Universität München ein- gereicht und durch die Fakultät für Elektrotechnik und Informationstechnik am 8.12.2006 angenommen. ii Abstract Der Entwurf von Software für eingebettete Systeme wird sowohl durch die Systemum- gebung als auch durch das System selbst stark beeinflusst. Beide Faktoren reduzieren die Wiederverwendbarkeit und die Erweiterbarkeit der Software in erheblichem Maße. Insbesondere können wirtschaftliche Überlegungen mitunter hohe Anforderungen an das Design stellen. Eine Lösung dieser Problematik kann nur mit klar definierten Abstrakti- onsebenen und geeigneten Schnittstellen zur Integration ermöglicht werden. Mit MetaC wird in dieser Arbeit eine Spracherweiterung vorgestellt, die neue Konzepte bietet, um die speziellen Anforderungen querschneidender Strukturen von eingebetteter Software anzusprechen. Insbesondere werden Methoden zur Verbesserung der Wiederver- wendbarkeit, Erweiterbarkeit und Abstraktion von Software für eingebettete