Esoteric Programming Languages
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Radare2 Book
Table of Contents introduction 1.1 Introduction 1.2 History 1.2.1 Overview 1.2.2 Getting radare2 1.2.3 Compilation and Portability 1.2.4 Compilation on Windows 1.2.5 Command-line Flags 1.2.6 Basic Usage 1.2.7 Command Format 1.2.8 Expressions 1.2.9 Rax2 1.2.10 Basic Debugger Session 1.2.11 Contributing to radare2 1.2.12 Configuration 1.3 Colors 1.3.1 Common Configuration Variables 1.3.2 Basic Commands 1.4 Seeking 1.4.1 Block Size 1.4.2 Sections 1.4.3 Mapping Files 1.4.4 Print Modes 1.4.5 Flags 1.4.6 Write 1.4.7 Zoom 1.4.8 Yank/Paste 1.4.9 Comparing Bytes 1.4.10 Visual mode 1.5 Visual Disassembly 1.5.1 2 Searching bytes 1.6 Basic Searches 1.6.1 Configurating the Search 1.6.2 Pattern Search 1.6.3 Automation 1.6.4 Backward Search 1.6.5 Search in Assembly 1.6.6 Searching for AES Keys 1.6.7 Disassembling 1.7 Adding Metadata 1.7.1 ESIL 1.7.2 Scripting 1.8 Loops 1.8.1 Macros 1.8.2 R2pipe 1.8.3 Rabin2 1.9 File Identification 1.9.1 Entrypoint 1.9.2 Imports 1.9.3 Symbols (exports) 1.9.4 Libraries 1.9.5 Strings 1.9.6 Program Sections 1.9.7 Radiff2 1.10 Binary Diffing 1.10.1 Rasm2 1.11 Assemble 1.11.1 Disassemble 1.11.2 Ragg2 1.12 Analysis 1.13 Code Analysis 1.13.1 Rahash2 1.14 Rahash Tool 1.14.1 Debugger 1.15 3 Getting Started 1.15.1 Registers 1.15.2 Remote Access Capabilities 1.16 Remoting Capabilities 1.16.1 Plugins 1.17 Plugins 1.17.1 Crackmes 1.18 IOLI 1.18.1 IOLI 0x00 1.18.1.1 IOLI 0x01 1.18.1.2 Avatao 1.18.2 R3v3rs3 4 1.18.2.1 .intro 1.18.2.1.1 .radare2 1.18.2.1.2 .first_steps 1.18.2.1.3 .main 1.18.2.1.4 .vmloop 1.18.2.1.5 .instructionset 1.18.2.1.6 -
Programming Languages Shouldn't and Needn't Be Turing Complete
Programming languages shouldn't and needn't be Turing complete GABRIEL PICKARD Any algorithmic problem faced by application programmers in the wild1 can in principle be solved using a Turing incomplete programming language. [Rice 1953] suggests that Turing completeness bears a heavy price, fundamentally limiting the ability of automatic assistants to help programmers be more productive, create less bugs and write safer software. Nevertheless, no mainstream general purpose programming language is Turing incomplete, with the arguable exception of SQL. We identify historical causes for this discrepancy and argue that the current moment offers better conditions for introducing Turing incompleteness. We present an approach to eliminating Turing completeness during the language design process, with several examples suitable languages targeting application development. Designers of modern practical programming languages should strongly consider evading Turing completeness and enabling better verification, security, automated testing and distributed computing. 1 CEDING POWER TO GAIN CONTROL 1.1 Turing completeness considered harmful The history of programming language design2 has been a history of removing powers which were considered harmful (cf. [Dijkstra 1966]) and replacing them with tamer, more manageable and automated constructs, including: (1) Direct access to CPU registers and instructions ! higher level compilers (2) GOTO ! structured programming (3) Manual memory management ! garbage collection (and borrow checkers) (4) Arbitrary access ! information hiding (5) Side-effects and mutability ! pure functions and persistent data structures While not all application domains benefit from all the replacement steps above3, it appears as if removing some expressive power can often enable a new kind of programming, even a new kind of application altogether. -
Turing Machines
Basics Computability Complexity Turing Machines Bruce Merry University of Cape Town 10 May 2012 Bruce Merry Turing Machines Basics Computability Complexity Outline 1 Basics Definition Building programs Turing Completeness 2 Computability Universal Machines Languages The Halting Problem 3 Complexity Non-determinism Complexity classes Satisfiability Bruce Merry Turing Machines Basics Definition Computability Building programs Complexity Turing Completeness Outline 1 Basics Definition Building programs Turing Completeness 2 Computability Universal Machines Languages The Halting Problem 3 Complexity Non-determinism Complexity classes Satisfiability Bruce Merry Turing Machines Basics Definition Computability Building programs Complexity Turing Completeness What Are Turing Machines? Invented by Alan Turing Hypothetical machines Formalise “computation” Alan Turing, 1912–1954 Bruce Merry Turing Machines If in state si and tape contains qj , write qk then move left/right and change to state sm A finite set of states, including a start state A tape that is infinite in both directions, containing finitely many non-blank symbols A head which points at one position on the tape A set of transitions Basics Definition Computability Building programs Complexity Turing Completeness What Are Turing Machines? Each Turing machine consists of A finite set of symbols, including a special blank symbol () Bruce Merry Turing Machines If in state si and tape contains qj , write qk then move left/right and change to state sm A tape that is infinite in both directions, containing -
Magazin Rist 2014.Cdr
RISTian Annual Magazine 2014-15 REGIONAL INSTITUTE OF SCIENCE & TECHNOLOGY Techno City, Kling Road, 9th Mile, Ri-Bhoi, Meghalaya-793101 MAGAZINE COMMITTEE Patron : Mr. M. Hoque, Chairman, ERDF Advisors : Dr. P.C. Mahanta, Director, RIST Dr. U.K. Sharma, Executive Director, RIST Mr. N.I. Laskar, Director, Corporate Communication, RIST Mr. J.U. Choudhury, HR, ERDF EDITORIAL BOARD Co-ordinator: Mr. A.H. Barbhuiya, Registrar, RIST Faculty Editors: Mr. Nabendu Kumar Deb, Asst. Prof. (Physics), Deptt. Of Basic Science and Humanities. Ms. Nabamita Boral, Asst. Prof. (English), Deptt. Of Basic Science and Humanities. Ms. Mandira Baruah, Asst. Prof., Deptt. Of Electrical and Electronics Ms. Baishali Sharma, Administrative Assistant, RIST. Student Editors: Sagorika Raj Chakraborty, 4th sem, EEE Sauvik Dey, 6th sem, CE Gagandeep Kaur, 8th Sem, CSE. Rashmita Mudoi, 8th sem, CSE. Student Co-ordinators: Chinmoyuri Bora, 6th Sem, ECE. Abdul Ghani Kamson, 6th Sem, ECE. Sudeep Sinha, 6th sem, CSE. Saurav Jyoti Das, 6th sem, EEE Illustrator: Santoj Hazarika, 8th Sem, ME Layout & Designing: Giasuddin Ahmed, Graphic Designer, ERDF Jitu Mahanta, DTP Operator, ERDF Printed at : Aman Udyog Pvt. Ltd. Christian Basti, Guwahati-5 i From the faculty editors' desk... “The true sign of intelligence is not knowledge but imagination.” Albert Einstein RISTian is a forum in which the students, faculty members and staffs (RIST Community) can portray their literary talents. This volume, which is n the second, has contributions from the RIST Community in the form of essays, poems, facts, sketches, etc. We offer heartiest thanks to all the contributors. a Putting a magazine together is no cake-walk. -
Python C/C++ Java Perl Ruby
Programming Languages Big Ideas for CS 251 • What is a PL? Theory of Programming Languages • Why are new PLs created? Principles of Programming Languages – What are they used for? – Why are there so many? • Why are certain PLs popular? • What goes into the design of a PL? CS251 Programming Languages – What features must/should it contain? Spring 2018, Lyn Turbak – What are the design dimensions? – What are design decisions that must be made? Department of Computer Science Wellesley College • Why should you take this course? What will you learn? Big ideas 2 PL is my passion! General Purpose PLs • First PL project in 1982 as intern at Xerox PARC Java Perl • Created visual PL for 1986 MIT Python masters thesis • 1994 MIT PhD on PL feature Fortran (synchronized lazy aggregates) ML JavaScript • 1996 – 2006: worked on types Racket as member of Church project Haskell • 1988 – 2008: Design Concepts in Programming Languages C/C++ Ruby • 2011 – current: lead TinkerBlocks research team at Wellesley • 2012 – current: member of App Inventor development team CommonLisp Big ideas 3 Big ideas 4 Domain Specific PLs Programming Languages: Mechanical View HTML A computer is a machine. Our aim is to make Excel CSS the machine perform some specifieD acEons. With some machines we might express our intenEons by Depressing keys, pushing OpenGL R buIons, rotaEng knobs, etc. For a computer, Matlab we construct a sequence of instrucEons (this LaTeX is a ``program'') anD present this sequence to IDL the machine. Swift PostScript! – Laurence Atkinson, Pascal Programming Big ideas 5 Big ideas 6 Programming Languages: LinguisEc View Religious Views The use of COBOL cripples the minD; its teaching shoulD, therefore, be A computer language … is a novel formal regarDeD as a criminal offense. -
The Comprehensive LATEX Symbol List
The Comprehensive LATEX Symbol List Scott Pakin <[email protected]>∗ 22 September 2005 Abstract This document lists 3300 symbols and the corresponding LATEX commands that produce them. Some of these symbols are guaranteed to be available in every LATEX 2ε system; others require fonts and packages that may not accompany a given distribution and that therefore need to be installed. All of the fonts and packages used to prepare this document—as well as this document itself—are freely available from the Comprehensive TEX Archive Network (http://www.ctan.org/). Contents 1 Introduction 7 1.1 Document Usage . 7 1.2 Frequently Requested Symbols . 7 2 Body-text symbols 8 Table 1: LATEX 2ε Escapable “Special” Characters . 8 Table 2: Predefined LATEX 2ε Text-mode Commands . 8 Table 3: LATEX 2ε Commands Defined to Work in Both Math and Text Mode . 8 Table 4: AMS Commands Defined to Work in Both Math and Text Mode . 9 Table 5: Non-ASCII Letters (Excluding Accented Letters) . 9 Table 6: Letters Used to Typeset African Languages . 9 Table 7: Letters Used to Typeset Vietnamese . 9 Table 8: Punctuation Marks Not Found in OT1 . 9 Table 9: pifont Decorative Punctuation Marks . 10 Table 10: tipa Phonetic Symbols . 10 Table 11: tipx Phonetic Symbols . 11 Table 13: wsuipa Phonetic Symbols . 12 Table 14: wasysym Phonetic Symbols . 12 Table 15: phonetic Phonetic Symbols . 12 Table 16: t4phonet Phonetic Symbols . 13 Table 17: semtrans Transliteration Symbols . 13 Table 18: Text-mode Accents . 13 Table 19: tipa Text-mode Accents . 14 Table 20: extraipa Text-mode Accents . -
Image Processing with the Artificial Swarm Intelligence 86 Xiaodong Zhuang, Nikos E
Advances in Image Analysis - Nature Inspired Methodology Dr. Xiaodong Zhuang 1 Associate Professor, Qingdao University, China 2 WSEAS Research Department, Athens, Greece Prof. Dr. Nikos E. Mastorakis 1 Professor, Technical University of Sofia, Bulgaria 2 Professor, Military Institutes of University Education, Hellenic Naval Academy, Greece 3 WSEAS Headquarters, Athens, Greece Published by WSEAS Press ISBN: 978-960-474-290-5 www.wseas.org Advances in Image Analysis – Nature Inspired Methodology Published by WSEAS Press www.wseas.org Copyright © 2011, by WSEAS Press All the copyright of the present book belongs to the World Scientific and Engineering Academy and Society Press. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the Editor of World Scientific and Engineering Academy and Society Press. All papers of the present volume were peer reviewed by two independent reviewers. Acceptance was granted when both reviewers' recommendations were positive. See also: http://www.worldses.org/review/index.html ISBN: 978-960-474-290-5 World Scientific and Engineering Academy and Society Preface The development of human society relies on natural resources in every area (both material and spiritual). Nature has enormous power and intelligence behind its common daily appearance, and it is generous. We learn in it and from it, virtually as part of it. Nature-inspired systems and methods have a long history in human science and technology. For example, in the area of computer science, the recent well-known ones include the artificial neural network, genetic algorithm and swarm intelligence, which solve hard problems by imitating mechanisms in nature. -
Theorizing in Unfamiliar Contexts: New Directions in Translation Studies
Theorizing in Unfamiliar Contexts: New Directions in Translation Studies James Luke Hadley Submitted for the Degree of Doctor of Philosophy University of East Anglia School of Language and Communication Studies April 2014 This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that use of any information derived there from must be in accordance with current UK Copyright Law. In addition, any quotation or extract must include full attribution.” 1 ABSTRACT This thesis attempts to offer a reconceptualization of translation analysis. It argues that there is a growing interest in examining translations produced outside the discipline‟s historical field of focus. However, the tools of analysis employed may not have sufficient flexibility to examine translation if it is conceived more broadly. Advocating the use of abductive logic, the thesis infers translators‟ probable understandings of their own actions, and compares these with the reasoning provided by contemporary theories. It finds that it may not be possible to rely on common theories to analyse the work of translators who conceptualize their actions in radically different ways from that traditionally found in translation literature. The thesis exemplifies this issue through the dual examination of Geoffrey Chaucer‟s use of translation in the Canterbury Tales and that of Japanese storytellers in classical Kamigata rakugo. It compares the findings of the discipline‟s most pervasive theories with those gained through an abductive analysis of the same texts, finding that the results produced by the theories are invariably problematic. -
Socket Extensions for Esoteric Languages
Annals of the University of Craiova, Mathematics and Computer Science Series Volume 41(2), 2014, Pages 271{279 ISSN: 1223-6934 Socket extensions for esoteric languages Radu Dragos¸and Diana Halit¸a˘ Abstract. In this paper we have advanced the first interpreter for the brainfuck (BF) esoteric programming language, entirely written in awk, a fast text processing programming language. However, the main objective remained introducing socket communication to brainfuck pro- gramming language. In order to achieve that goal, we have improved the brainfuck language with a byte long instruction through which it is allowed socket communication. For that, we have advanced a series of procedures in order to test the compatibility of our brainfuck interpreter. Moreover, we have maintained brainfucks minimalism, which is one of the special char- acteristics of this programming language. In the end, we succeeded to map a minimalistic programming language to the client-server paradigm. 2010 Mathematics Subject Classification. 68N15 - Programming languages, 68N20 - Compilers and interpreters. Key words and phrases. brainfuck, socket, interpreter, esoteric languages. 1. Introduction This paper describes a simple interpreter for the brainfuck (BF) esoteric programming language, written in AWK. It is compatible with almost any version of AWK that is supplied within UNIX systems. The interpreter illustrates the use of AWK in implementing small programs using BF language, mainly for proof of concept reasons. 1.1. Motivation. Even if BF is known for its extreme minimalism and it is designed to challenge programmers, it is not usually suitable for practical use. However, the motivation of developing such an application comes from the usage of a simple and easy syntax which can help programmers to deeply understand other programming languages and besides that, other programming paradigms. -
The Intercal Programming Language Revised Reference Manual
THE INTERCAL PROGRAMMING LANGUAGE REVISED REFERENCE MANUAL Donald R. Woods and James M. Lyon C-INTERCAL revisions: Louis Howell and Eric S. Raymond Copyright (C) 1973 by Donald R. Woods and James M. Lyon Copyright (C) 1996 by Eric S. Raymond Redistribution encouragedunder GPL (This version distributed with C-INTERCAL 0.15) -1- 1. INTRODUCTION The names you are about to ignore are true. However, the story has been changed significantly.Any resemblance of the programming language portrayed here to other programming languages, living or dead, is purely coincidental. 1.1 Origin and Purpose The INTERCAL programming language was designed the morning of May 26, 1972 by Donald R. Woods and James M. Lyon, at Princeton University.Exactly when in the morning will become apparent in the course of this manual. Eighteen years later (give ortakeafew months) Eric S. Raymond perpetrated a UNIX-hosted INTERCAL compiler as a weekend hack. The C-INTERCAL implementation has since been maintained and extended by an international community of technomasochists, including Louis Howell, Steve Swales, Michael Ernst, and Brian Raiter. (There was evidently an Atari implementation sometime between these two; notes on it got appended to the INTERCAL-72 manual. The culprits have sensibly declined to identify themselves.) INTERCAL was inspired by one ambition: to have a compiler language which has nothing at all in common with anyother major language. By ’major’ was meant anything with which the authors were at all familiar,e.g., FORTRAN, BASIC, COBOL, ALGOL, SNOBOL, SPITBOL, FOCAL, SOLVE, TEACH, APL, LISP,and PL/I. For the most part, INTERCAL has remained true to this goal, sharing only the basic elements such as variables, arrays, and the ability to do I/O, and eschewing all conventional operations other than the assignment statement (FORTRAN "="). -
Trinary CPE Report
Ternary Computing Testbed 3!Trit Computer Architecture Je" Connelly Computer Engineering Department August 29th, 2008 with contributions from Chirag Patel and Antonio Chavez Advised by Professor Phillip Nico California Polytechnic State University of San Luis Obispo Table of Contents 1. Introduction 1 1.1. Method 1 1.2. Plan 2 1.3. Team and Individual Responsibilities 2 1.3.1. Jeff Connelly 2 1.3.2. Antonio Chavez 2 1.3.3. Chirag Patel 3 2. Background Theory 3 2.1. Base 3 3 2.1.1. Compared to Analog 3 2.1.2. Compared to Digital 4 2.1.3. Compared to Base e 5 2.1.4. Trits, Tribbles, and Trytes 7 2.1.5. Base 9 and 27 9 2.1.6. Text 10 2.2. Logic and Arithmetic 10 3. Application Description 10 3.1. Christmas Lights Game 10 3.2. Guessing Game 11 4. Architecture Description 11 4.1. Power Supply 12 4.2. Instruction Memory 13 4.2.1. Experimental Results 16 4.3. Program Counter 16 4.4. Clock Generator 17 4.5. Processor 18 4.5.1. Registers 18 4.5.2. Input and Output 19 4.5.3. 3-Trit Instruction Set 20 4.5.4. LWI Instruction Example (also known as TCA0) 21 4.5.5. CMP Instruction 25 4.5.5.1. ALU 26 4.5.5.2. CMP Instruction Example 29 4.5.6. BE Instruction 31 4.5.6.1. BE Instruction Example 32 4.5.7. Guessing Game Program 33 5. Evaluation 38 6. Conclusion and Future Directions 39 7. -
CS 488/688 Fall 2017 Stephen Mann CONTENTS 2 Contents
1 CS 488/688 Fall 2017 Stephen Mann CONTENTS 2 Contents 1 Administration 5 1.1 General Information . .5 1.2 Topics Covered . .6 1.3 Assignments . .6 2 Introduction 9 2.1 History . .9 2.2 Pipeline . 10 2.3 Primitives . 11 2.4 Algorithms . 12 2.5 APIs . 12 3 Devices and Device Independence 13 3.1 Calligraphic and Raster Devices . 13 3.2 How a Monitor Works . 13 3.3 Physical Devices . 14 4 Device Interfaces 17 4.1 Device Input Modes . 17 4.2 Application Structure . 17 4.3 Polling and Sampling . 18 4.4 Event Queues . 18 4.5 Toolkits and Callbacks . 19 4.6 Example for Discussion . 20 5 Geometries 23 5.1 Vector Spaces . 23 5.2 Affine Spaces . 23 5.3 Inner Product Spaces . 24 5.4 Euclidean Spaces . 24 5.5 Cartesian Space . 25 5.6 Why Vector Spaces Inadequate . 25 5.7 Summary of Geometric Spaces . 27 6 Affine Geometry and Transformations 29 6.1 Linear Combinations . 29 6.2 Affine Combinations . 29 6.3 Affine Transformations . 31 6.4 Matrix Representation of Transformations . 33 6.5 Geometric Transformations . 34 6.6 Compositions of Transformations . 35 6.7 Change of Basis . 36 6.8 Ambiguity . 39 6.9 3D Transformations . 40 CONTENTS 3 6.10 World and Viewing Frames . 42 6.11 Normals . 45 7 Windows, Viewports, NDC 47 7.1 Window to Viewport Mapping . 47 7.2 Normalized Device Coordinates . 49 8 Clipping 51 8.1 Clipping . 51 9 Projections and Projective Transformations 55 9.1 Projections . 55 9.2 Why Map Z? .