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												  Lecture 4. Higher-Order Functions Functional ProgrammingLecture 4. Higher-order functions Functional Programming [Faculty of Science Information and Computing Sciences] 0 I function call and return as only control-flow primitive I no loops, break, continue, goto I (almost) unique types I no inheritance hell I high-level declarative data-structures I no explicit reference-based data structures Goal of typed purely functional programming Keep programs easy to reason about by I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state [Faculty of Science Information and Computing Sciences] 1 I (almost) unique types I no inheritance hell I high-level declarative data-structures I no explicit reference-based data structures Goal of typed purely functional programming Keep programs easy to reason about by I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state I function call and return as only control-flow primitive I no loops, break, continue, goto [Faculty of Science Information and Computing Sciences] 1 I high-level declarative data-structures I no explicit reference-based data structures Goal of typed purely functional programming Keep programs easy to reason about by I data-flow only through function arguments and return values I no hidden data-flow through mutable variables/state I function call and return as only control-flow primitive I no loops, break, continue, goto I (almost) unique types I no inheritance hell [Faculty of Science Information and Computing Sciences] 1 Goal
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												  Higher-Order Functions 15-150: Principles of Functional Programming – Lecture 13Higher-order Functions 15-150: Principles of Functional Programming { Lecture 13 Giselle Reis By now you might feel like you have a pretty good idea of what is going on in functional program- ming, but in reality we have used only a fragment of the language. In this lecture we see what more we can do and what gives the name functional to this paradigm. Let's take a step back and look at ML's typing system: we have basic types (such as int, string, etc.), tuples of types (t*t' ) and functions of a type to a type (t ->t' ). In a grammar style (where α is a basic type): τ ::= α j τ ∗ τ j τ ! τ What types allowed by this grammar have we not used so far? Well, we could, for instance, have a function below a tuple. Or even a function within a function, couldn't we? The following are completely valid types: int*(int -> int) int ->(int -> int) (int -> int) -> int The first one is a pair in which the first element is an integer and the second one is a function from integers to integers. The second one is a function from integers to functions (which have type int -> int). The third type is a function from functions to integers. The two last types are examples of higher-order functions1, i.e., a function which: • receives a function as a parameter; or • returns a function. Functions can be used like any other value. They are first-class citizens. Maybe this seems strange at first, but I am sure you have used higher-order functions before without noticing it.
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												  The Machine That Builds Itself: How the Strengths of Lisp FamilyKhomtchouk 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.
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												  A Java Security Scanner for EclipseColby College Digital Commons @ Colby Honors Theses Student Research 2005 JeSS – a Java Security Scanner for Eclipse Russell Spitler Colby College Follow this and additional works at: https://digitalcommons.colby.edu/honorstheses Part of the Databases and Information Systems Commons, Other Computer Engineering Commons, Programming Languages and Compilers Commons, and the Systems Architecture Commons Colby College theses are protected by copyright. They may be viewed or downloaded from this site for the purposes of research and scholarship. Reproduction or distribution for commercial purposes is prohibited without written permission of the author. Recommended Citation Spitler, Russell, "JeSS – a Java Security Scanner for Eclipse" (2005). Honors Theses. Paper 567. https://digitalcommons.colby.edu/honorstheses/567 This Honors Thesis (Open Access) is brought to you for free and open access by the Student Research at Digital Commons @ Colby. It has been accepted for inclusion in Honors Theses by an authorized administrator of Digital Commons @ Colby. JeSS – a Java Security Scanner for Eclipse Russell Spitler Senior Honors Thesis Spring 2005 Colby College Department of Computer Science Advisor: Dale Skrien Contents Chapter 1 Introduction 1 Chapter 2 Secure Coding and Java Security 2.1 – Secure Coding 3 2.2 – Java Security 7 Chapter 3 Java Security Holes 3.1 – Don’t depend on initialization 13 3.2 – Make everything final 14 3.3 – Make your code unserializable and undeserializable 16 3.4 – Make your class non-Cloneable 19 3.5 – Don’t rely on
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												  Lecture 4 Resource Protection and Thread SafetyConcurrency and Correctness – Resource Protection and TS Lecture 4 Resource Protection and Thread Safety 1 Danilo Piparo – CERN, EP-SFT Concurrency and Correctness – Resource Protection and TS This Lecture The Goals: 1) Understand the problem of contention of resources within a parallel application 2) Become familiar with the design principles and techniques to cope with it 3) Appreciate the advantage of non-blocking techniques The outline: § Threads and data races: synchronisation issues § Useful design principles § Replication, atomics, transactions and locks § Higher level concrete solutions 2 Danilo Piparo – CERN, EP-SFT Concurrency and Correctness – Resource Protection and TS Threads and Data Races: Synchronisation Issues 3 Danilo Piparo – CERN, EP-SFT Concurrency and Correctness – Resource Protection and TS The Problem § Fastest way to share data: access the same memory region § One of the advantages of threads § Parallel memory access: delicate issue - race conditions § I.e. behaviour of the system depends on the sequence of events which are intrinsically asynchronous § Consequences, in order of increasing severity § Catastrophic terminations: segfaults, crashes § Non-reproducible, intermittent bugs § Apparently sane execution but data corruption: e.g. wrong value of a variable or of a result Operative definition: An entity which cannot run w/o issues linked to parallel execution is said to be thread-unsafe (the contrary is thread-safe) 4 Danilo Piparo – CERN, EP-SFT Concurrency and Correctness – Resource Protection and TS To Be Precise: Data Race Standard language rules, §1.10/4 and /21: • Two expression evaluations conflict if one of them modifies a memory location (1.7) and the other one accesses or modifies the same memory location.
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												  An Evaluation of Go and ClojureAn evaluation of Go and Clojure A thesis submitted in partial satisfaction of the requirements for the degree Bachelors of Science in Computer Science Fall 2010 Robert Stimpfling Department of Computer Science University of Colorado, Boulder Advisor: Kenneth M. Anderson, PhD Department of Computer Science University of Colorado, Boulder 1. Introduction Concurrent programming languages are not new, but they have been getting a lot of attention more recently due to their potential with multiple processors. Processors have gone from growing exponentially in terms of speed, to growing in terms of quantity. This means processes that are completely serial in execution will soon be seeing a plateau in performance gains since they can only rely on one processor. A popular approach to using these extra processors is to make programs multi-threaded. The threads can execute in parallel and use shared memory to speed up execution times. These multithreaded processes can significantly speed up performance, as long as the number of dependencies remains low. Amdahl‘s law states that these performance gains can only be relative to the amount of processing that can be parallelized [1]. However, the performance gains are significant enough to be looked into. These gains not only come from the processing being divvied up into sections that run in parallel, but from the inherent gains from sharing memory and data structures. Passing new threads a copy of a data structure can be demanding on the processor because it requires the processor to delve into memory and make an exact copy in a new location in memory. Indeed some studies have shown that the problem with optimizing concurrent threads is not in utilizing the processors optimally, but in the need for technical improvements in memory performance [2].
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												  Clojure, Given the Pun on Closure, Representing Anything Specificdynamic, functional programming for the JVM “It (the logo) was designed by my brother, Tom Hickey. “It I wanted to involve c (c#), l (lisp) and j (java). I don't think we ever really discussed the colors Once I came up with Clojure, given the pun on closure, representing anything specific. I always vaguely the available domains and vast emptiness of the thought of them as earth and sky.” - Rich Hickey googlespace, it was an easy decision..” - Rich Hickey Mark Volkmann [email protected] Functional Programming (FP) In the spirit of saying OO is is ... encapsulation, inheritance and polymorphism ... • Pure Functions • produce results that only depend on inputs, not any global state • do not have side effects such as Real applications need some changing global state, file I/O or database updates side effects, but they should be clearly identified and isolated. • First Class Functions • can be held in variables • can be passed to and returned from other functions • Higher Order Functions • functions that do one or both of these: • accept other functions as arguments and execute them zero or more times • return another function 2 ... FP is ... Closures • main use is to pass • special functions that retain access to variables a block of code that were in their scope when the closure was created to a function • Partial Application • ability to create new functions from existing ones that take fewer arguments • Currying • transforming a function of n arguments into a chain of n one argument functions • Continuations ability to save execution state and return to it later think browser • back button 3 ..
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												  Praise for Practical Common LispPraise for Practical Common Lisp “Finally, a Lisp book for the rest of us. If you want to learn how to write a factorial function, this is not your book. Seibel writes for the practical programmer, emphasizing the engineer/artist over the scientist and subtly and gracefully implying the power of the language while solving understandable real-world problems. “In most chapters, the reading of the chapter feels just like the experience of writing a program, starting with a little understanding and then having that understanding grow, like building the shoulders upon which you can then stand. When Seibel introduced macros as an aside while building a test frame- work, I was shocked at how such a simple example made me really ‘get’ them. Narrative context is extremely powerful, and the technical books that use it are a cut above. Congrats!” —Keith Irwin, Lisp programmer “While learning Lisp, one is often referred to the CL HyperSpec if they do not know what a particular function does; however, I found that I often did not ‘get it’ just by reading the HyperSpec. When I had a problem of this manner, I turned to Practical Common Lisp every single time—it is by far the most readable source on the subject that shows you how to program, not just tells you.” —Philip Haddad, Lisp programmer “With the IT world evolving at an ever-increasing pace, professionals need the most powerful tools available. This is why Common Lisp—the most powerful, flexible, and stable programming language ever—is seeing such a rise in popu- larity.
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												  Common Lisp - Viel Mehr Als Nur D¨Amliche KlammernEinf¨uhrung Geschichtliches Die Programmiersprache Abschluß Common Lisp - viel mehr als nur d¨amliche Klammern Alexander Schreiber <[email protected]> http://www.thangorodrim.de Chemnitzer Linux-Tage 2005 Greenspun’s Tenth Rule of Programming: “Any sufficiently-complicated C or Fortran program contains an ad-hoc, informally-specified bug-ridden slow implementation of half of Common Lisp.” Alexander Schreiber <[email protected]> Common Lisp - viel mehr als nur d¨amliche Klammern 1 / 30 Einf¨uhrung Geschichtliches Die Programmiersprache Abschluß Ubersicht¨ 1 Einf¨uhrung 2 Geschichtliches 3 Die Programmiersprache 4 Abschluß Alexander Schreiber <[email protected]> Common Lisp - viel mehr als nur d¨amliche Klammern 2 / 30 Einf¨uhrung Geschichtliches Die Programmiersprache Abschluß Lisp? Wof¨ur? NASA: Remote Agent (Deep Space 1), Planner (Mars Pathfinder), Viaweb, gekauft von Yahoo f¨ur50 Millionen $, ITA Software: Orbitz engine (Flugticket Planung), Square USA: Production tracking f¨ur“Final Fantasy”, Naughty Dog Software: Crash Bandicoot auf Sony Playstation, AMD & AMI: Chip-Design & Verifizierung, typischerweise komplexe Probleme: Wissensverarbeitung, Expertensysteme, Planungssysteme Alexander Schreiber <[email protected]> Common Lisp - viel mehr als nur d¨amliche Klammern 3 / 30 Einf¨uhrung Geschichtliches Die Programmiersprache Abschluß Lisp? Wof¨ur? NASA: Remote Agent (Deep Space 1), Planner (Mars Pathfinder), Viaweb, gekauft von Yahoo f¨ur50 Millionen $, ITA Software: Orbitz engine (Flugticket Planung), Square USA: Production tracking
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												  Eu-19-Zhang-New-Exploit-TechniqueNew Exploit Technique In Java Deserialization Attack • Yang Zhang • Yongtao Wang Keyi Li “在此键⼊引⽂。• ” • Kunzhe Chai –Johnny Appleseed New Exploit Technique In Java Deserialization Attack Back2Zero Team BCM Social Corp. BCM Social Group Who are we? Yang Zhang(Lucas) • Founder of Back2Zero Team & Leader of Security Research Department in BCM Social Corp. • Focus on Application Security, Cloud Security, Penetration Testing. • Spoke at various security conferences such as CanSecWest, POC, ZeroNights. Keyi Li(Kevin) • Master degree majoring in Cyber Security at Syracuse University. • Co-founder of Back2Zero team and core member of n0tr00t security team. • Internationally renowned security conference speaker. –Johnny Appleseed Who are we? Yongtao Wang • Co-founder of PegasusTeam and Leader of Red Team in BCM Social Corp. • Specializes in penetration testing and wireless security. • Blackhat, Codeblue, POC, Kcon, etc. Conference speaker. Kunzhe Chai(Anthony) • Founder of PegasusTeam and Chief Information Security Officer in BCM Social Corp. • Author of the well-known security tool MDK4. • Maker of China's first Wireless Security Defense Product Standard and he also is the world's first inventor of Fake Base Stations defense technology–Johnny Appleseed Agenda • Introduction to Java Deserialization • Well-Known Defense Solutions • Critical vulnerabilities in Java • URLConnection • JDBC • New exploit for Java Deserialization • Takeaways 2015: Chris Frohoff and Gabriel Lawrence presented their research into Java object deserialization vulnerabilities ultimately resulting in what can be readily described as the biggest wave of RCE bugs in Java history. Introduction to Java Deserialization Java Deserialization Serialization • The process of converting a Java object into stream of bytes. Databases Deserialization Serialization • A reverse process of creating a Java object from stream of bytes.
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												  Apache Harmony Project Tim Ellison Geir Magnusson JrThe Apache Harmony Project Tim Ellison Geir Magnusson Jr. Apache Harmony Project http://harmony.apache.org TS-7820 2007 JavaOneSM Conference | Session TS-7820 | Goal of This Talk In the next 45 minutes you will... Learn about the motivations, current status, and future plans of the Apache Harmony project 2007 JavaOneSM Conference | Session TS-7820 | 2 Agenda Project History Development Model Modularity VM Interface How Are We Doing? Relevance in the Age of OpenJDK Summary 2007 JavaOneSM Conference | Session TS-7820 | 3 Agenda Project History Development Model Modularity VM Interface How Are We Doing? Relevance in the Age of OpenJDK Summary 2007 JavaOneSM Conference | Session TS-7820 | 4 Apache Harmony In the Beginning May 2005—founded in the Apache Incubator Primary Goals 1. Compatible, independent implementation of Java™ Platform, Standard Edition (Java SE platform) under the Apache License 2. Community-developed, modular architecture allowing sharing and independent innovation 3. Protect IP rights of ecosystem 2007 JavaOneSM Conference | Session TS-7820 | 5 Apache Harmony Early history: 2005 Broad community discussion • Technical issues • Legal and IP issues • Project governance issues Goal: Consolidation and Consensus 2007 JavaOneSM Conference | Session TS-7820 | 6 Early History Early history: 2005/2006 Initial Code Contributions • Three Virtual machines ● JCHEVM, BootVM, DRLVM • Class Libraries ● Core classes, VM interface, test cases ● Security, beans, regex, Swing, AWT ● RMI and math 2007 JavaOneSM Conference | Session TS-7820 |
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												  Proceedings of the 8Th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (Ed.) SponsorsProceedings 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