Finite State Recognizer and String Similarity Based Spelling
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GNU Emacs GNU Emacs
GNU Emacs GNU Emacs Reference Card Reference Card Computing and Information Technology Computing and Information Technology Replacing Text Replacing Text Replace foo with bar M-x replace-string Replace foo with bar M-x replace-string foo bar foo bar Query replacement M-x query replace Query replacement M-x query replace then replace <Space> then replace <Space> then skip to next <Backspace> then skip to next <Backspace> then replace and exit <Period> then replace and exit <Period> then replace all ! then replace all ! then go back ^ then go back ^ then exit query <Esc> then exit query <Esc> Setting a Mark Setting a Mark Set mark here C-<Space> or C-@ Set mark here C-<Space> or C-@ Exchange point and mark C-x C-x Exchange point and mark C-x C-x Mark paragraph M-h Mark paragraph M-h Mark entire file C-x h Mark entire file C-x h Sorting Text Sorting Text Normal sort M-x sort-lines Normal sort M-x sort-lines Reverse sort <Esc>-1 M-x sort lines Reverse sort <Esc>-1 M-x sort lines Inserting Text Inserting Text Scan file fn M-x view-file fn Scan file fn M-x view-file fn Write buffer to file fn M-x write-file fn Write buffer to file fn M-x write-file fn Insert file fn into buffer M-x insert-file fn Insert file fn into buffer M-x insert-file fn Write region to file fn M-x write-region fn Write region to file fn M-x write-region fn Write region to end of file fn M-x append-to-file fn Write region to end of file fn M-x append-to-file fn Write region to beginning of Write region to beginning to specified buffer M-x prepend-to-buffer specified buffer -
Easy Slackware
1 Создание легкой системы на базе Slackware I - Введение Slackware пользуется заслуженной популярностью как классический linux дистрибутив, и поговорка "кто знает Red Hat тот знает только Red Hat, кто знает Slackware тот знает linux" несмотря на явный снобизм поклонников "бога Патре га" все же имеет под собой основания. Одним из преимуществ Slackware является возможность простого создания на ее основе практически любой системы, в том числе быстрой и легкой десктопной, о чем далее и пойдет речь. Есть дис трибутивы, клоны Slackware, созданные именно с этой целью, типа Аbsolute, но все же лучше создавать систему под себя, с максимальным учетом именно своих потребностей, и Slackware пожалуй как никакой другой дистрибутив подходит именно для этой цели. Легкость и быстрота системы определяется выбором WM (DM) , набором программ и оптимизацией программ и системы в целом. Первое исключает KDE, Gnome, даже новые версии XFCЕ, остается разве что LXDE, но набор программ в нем совершенно не устраивает. Оптимизация наиболее часто используемых про грамм и нескольких базовых системных пакетов осуществляется их сборкой из сорцов компилятором, оптимизированным именно под Ваш комп, причем каж дая программа конфигурируется исходя из Ваших потребностей к ее возможно стям. Оптимизация системы в целом осуществляется ее настройкой согласно спе цифическим требованиям к десктопу. Такой подход был выбран по банальной причине, возиться с gentoo нет ни какого желания, комп все таки создан для того чтобы им пользоваться, а не для компиляции программ, в тоже время у каждого есть минимальный набор из не большого количества наиболее часто используемых программ, на которые стоит потратить некоторое, не такое уж большое, время, чтобы довести их до ума. Кро ме того, такой подход позволяет иметь самые свежие версии наиболее часто ис пользуемых программ. -
Emacspeak — the Complete Audio Desktop User Manual
Emacspeak | The Complete Audio Desktop User Manual T. V. Raman Last Updated: 19 November 2016 Copyright c 1994{2016 T. V. Raman. All Rights Reserved. Permission is granted to make and distribute verbatim copies of this manual without charge provided the copyright notice and this permission notice are preserved on all copies. Short Contents Emacspeak :::::::::::::::::::::::::::::::::::::::::::::: 1 1 Copyright ::::::::::::::::::::::::::::::::::::::::::: 2 2 Announcing Emacspeak Manual 2nd Edition As An Open Source Project ::::::::::::::::::::::::::::::::::::::::::::: 3 3 Background :::::::::::::::::::::::::::::::::::::::::: 4 4 Introduction ::::::::::::::::::::::::::::::::::::::::: 6 5 Installation Instructions :::::::::::::::::::::::::::::::: 7 6 Basic Usage. ::::::::::::::::::::::::::::::::::::::::: 9 7 The Emacspeak Audio Desktop. :::::::::::::::::::::::: 19 8 Voice Lock :::::::::::::::::::::::::::::::::::::::::: 22 9 Using Online Help With Emacspeak. :::::::::::::::::::: 24 10 Emacs Packages. ::::::::::::::::::::::::::::::::::::: 26 11 Running Terminal Based Applications. ::::::::::::::::::: 45 12 Emacspeak Commands And Options::::::::::::::::::::: 49 13 Emacspeak Keyboard Commands. :::::::::::::::::::::: 361 14 TTS Servers ::::::::::::::::::::::::::::::::::::::: 362 15 Acknowledgments.::::::::::::::::::::::::::::::::::: 366 16 Concept Index :::::::::::::::::::::::::::::::::::::: 367 17 Key Index ::::::::::::::::::::::::::::::::::::::::: 368 Table of Contents Emacspeak :::::::::::::::::::::::::::::::::::::::::: 1 1 Copyright ::::::::::::::::::::::::::::::::::::::: -
Headprint: Detecting Anomalous Communications Through Header-Based Application Fingerprinting
HeadPrint: Detecting Anomalous Communications through Header-based Application Fingerprinting Riccardo Bortolameotti Thijs van Ede Andrea Continella University of Twente University of Twente UC Santa Barbara [email protected] [email protected] [email protected] Thomas Hupperich Maarten H. Everts Reza Rafati University of Muenster University of Twente Bitdefender [email protected] [email protected] [email protected] Willem Jonker Pieter Hartel Andreas Peter University of Twente Delft University of Technology University of Twente [email protected] [email protected] [email protected] ABSTRACT 1 INTRODUCTION Passive application fingerprinting is a technique to detect anoma- Data breaches are a major concern for enterprises across all indus- lous outgoing connections. By monitoring the network traffic, a tries due to the severe financial damage they cause [25]. Although security monitor passively learns the network characteristics of the many enterprises have full visibility of their network traffic (e.g., applications installed on each machine, and uses them to detect the using TLS-proxies) and they stop many cyber attacks by inspect- presence of new applications (e.g., malware infection). ing traffic with different security solutions (e.g., IDS, IPS, Next- In this work, we propose HeadPrint, a novel passive fingerprint- Generation Firewalls), attackers are still capable of successfully ing approach that relies only on two orthogonal network header exfiltrating data. Data exfiltration often occurs over network covert characteristics to distinguish applications, namely the order of the channels, which are communication channels established by attack- headers and their associated values. Our approach automatically ers to hide the transfer of data from a security monitor. -
Author Guidelines for 8
I.J. Information Technology and Computer Science, 2014, 01, 68-75 Published Online December 2013 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2014.01.08 FSM Circuits Design for Approximate String Matching in Hardware Based Network Intrusion Detection Systems Dejan Georgiev Faculty of Electrical Engineering and Information Technologies , Skopje, Macedonia E-mail: [email protected] Aristotel Tentov Faculty of Electrical Engineering and Information Technologies, Skopje, Macedonia E-mail: [email protected] Abstract— In this paper we present a logical circuits to perform a check to a possible variations of the design for approximate content matching implemented content because of so named "evasion" techniques. as finite state machines (FSM). As network speed Software systems have processing limitations in that increases the software based network intrusion regards. On the other hand hardware platforms, like detection and prevention systems (NIDPS) are lagging FPGA offer efficient implementation for high speed behind requirements in throughput of so called deep network traffic. package inspection - the most exhaustive process of Excluding the regular expressions as special case ,the finding a pattern in package payloads. Therefore, there problem of approximate content matching basically is a is a demand for hardware implementation. k-differentiate problem of finding a content Approximate content matching is a special case of content finding and variations detection used by C c1c2.....cm in a string T t1t2.......tn such as the "evasion" techniques. In this research we will enhance distance D(C,X) of content C and all the occurrences of the k-differentiate problem with "ability" to detect a a substring X is less or equal to k , where k m n . -
Behavior Based Software Theft Detection, CCS 2009
Behavior Based Software Theft Detection 1Xinran Wang, 1Yoon-Chan Jhi, 1,2Sencun Zhu, and 2Peng Liu 1Department of Computer Science and Engineering 2College of Information Sciences and Technology Pennsylvania State University, University Park, PA 16802 {xinrwang, szhu, jhi}@cse.psu.edu, [email protected] ABSTRACT (e.g., in SourceForge.net there were over 230,000 registered Along with the burst of open source projects, software open source projects as of Feb.2009), software theft has be- theft (or plagiarism) has become a very serious threat to the come a very serious concern to honest software companies healthiness of software industry. Software birthmark, which and open source communities. As one example, in 2005 it represents the unique characteristics of a program, can be was determined in a federal court trial that IBM should pay used for software theft detection. We propose a system call an independent software vendor Compuware $140 million dependence graph based software birthmark called SCDG to license its software and $260 million to purchase its ser- birthmark, and examine how well it reflects unique behav- vices [1] because it was discovered that certain IBM products ioral characteristics of a program. To our knowledge, our contained code from Compuware. detection system based on SCDG birthmark is the first one To protect software from theft, Collberg and Thoborson that is capable of detecting software component theft where [10] proposed software watermark techniques. Software wa- only partial code is stolen. We demonstrate the strength of termark is a unique identifier embedded in the protected our birthmark against various evasion techniques, including software, which is hard to remove but easy to verify. -
Comparison of Levenshtein Distance Algorithm and Needleman-Wunsch Distance Algorithm for String Matching
COMPARISON OF LEVENSHTEIN DISTANCE ALGORITHM AND NEEDLEMAN-WUNSCH DISTANCE ALGORITHM FOR STRING MATCHING KHIN MOE MYINT AUNG M.C.Sc. JANUARY2019 COMPARISON OF LEVENSHTEIN DISTANCE ALGORITHM AND NEEDLEMAN-WUNSCH DISTANCE ALGORITHM FOR STRING MATCHING BY KHIN MOE MYINT AUNG B.C.Sc. (Hons:) A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Computer Science (M.C.Sc.) University of Computer Studies, Yangon JANUARY2019 ii ACKNOWLEDGEMENTS I would like to take this opportunity to express my sincere thanks to those who helped me with various aspects of conducting research and writing this thesis. Firstly, I would like to express my gratitude and my sincere thanks to Prof. Dr. Mie Mie Thet Thwin, Rector of the University of Computer Studies, Yangon, for allowing me to develop this thesis. Secondly, my heartfelt thanks and respect go to Dr. Thi Thi Soe Nyunt, Professor and Head of Faculty of Computer Science, University of Computer Studies, Yangon, for her invaluable guidance and administrative support, throughout the development of the thesis. I am deeply thankful to my supervisor, Dr. Ah Nge Htwe, Associate Professor, Faculty of Computer Science, University of Computer Studies, Yangon, for her invaluable guidance, encouragement, superior suggestion and supervision on the accomplishment of this thesis. I would like to thank Daw Ni Ni San, Lecturer, Language Department of the University of Computer Studies, Yangon, for editing my thesis from the language point of view. I thank all my teachers who taught and helped me during the period of studies in the University of Computer Studies, Yangon. Finally, I am grateful for all my teachers who have kindly taught me and my friends from the University of Computer Studies, Yangon, for their advice, their co- operation and help for the completion of thesis. -
Emacspeak User's Guide
Emacspeak User's Guide Jennifer Jobst Revision History Revision 1.3 July 24,2002 Revised by: SDS Updated the maintainer of this document to Sharon Snider, corrected links, and converted to HTML Revision 1.2 December 3, 2001 Revised by: JEJ Changed license to GFDL Revision 1.1 November 12, 2001 Revised by: JEJ Revision 1.0 DRAFT October 19, 2001 Revised by: JEJ This document helps Emacspeak users become familiar with Emacs as an audio desktop and provides tutorials on many common tasks and the Emacs applications available to perform those tasks. Emacspeak User's Guide Table of Contents 1. Legal Notice.....................................................................................................................................................1 2. Introduction.....................................................................................................................................................2 2.1. What is Emacspeak?.........................................................................................................................2 2.2. About this tutorial.............................................................................................................................2 3. Before you begin..............................................................................................................................................3 3.1. Getting started with Emacs and Emacspeak.....................................................................................3 3.2. Emacs Command Conventions.........................................................................................................3 -
Spelling Correction: from Two-Level Morphology to Open Source
Spelling Correction: from two-level morphology to open source Iñaki Alegria, Klara Ceberio, Nerea Ezeiza, Aitor Soroa, Gregorio Hernandez Ixa group. University of the Basque Country / Eleka S.L. 649 P.K. 20080 Donostia. Basque Country. [email protected] Abstract Basque is a highly inflected and agglutinative language (Alegria et al., 1996). Two-level morphology has been applied successfully to this kind of languages and there are two-level based descriptions for very different languages. After doing the morphological description for a language, it is easy to develop a spelling checker/corrector for this language. However, what happens if we want to use the speller in the "free world" (OpenOffice, Mozilla, emacs, LaTeX, ...)? Ispell and similar tools (aspell, hunspell, myspell) are the usual mechanisms for these purposes, but they do not fit the two-level model. In the absence of two-level morphology based mechanisms, an automatic conversion from two-level description to hunspell is described in this paper. previous work and reuse the morphological information. 1. Introduction Two-level morphology (Koskenniemi, 1983; Beesley & Karttunen, 2003) has been applied successfully to the 2. Free software for spelling correction morphological description of highly inflected languages. Unfortunately there are not open source tools for spelling There are two-level based descriptions for very different correction with these features: languages (English, German, Swedish, French, Spanish, • It is standardized in the most of the applications Danish, Norwegian, Finnish, Basque, Russian, Turkish, (OpenOffice, Mozilla, emacs, LaTeX, ...). Arab, Aymara, Swahili, etc.). • It is based on the two-level morphology. After doing the morphological description, it is easy to The spell family of spell checkers (ispell, aspell, myspell) develop a spelling checker/corrector for the language fits the first condition but not the second. -
Automating Error Frequency Analysis Via the Phonemic Edit Distance Ratio
JSLHR Research Note Automating Error Frequency Analysis via the Phonemic Edit Distance Ratio Michael Smith,a Kevin T. Cunningham,a and Katarina L. Haleya Purpose: Many communication disorders result in speech corresponds to percent segments with these phonemic sound errors that listeners perceive as phonemic errors. errors. Unfortunately, manual methods for calculating phonemic Results: Convergent validity between the manually calculated error frequency are prohibitively time consuming to use in error frequency and the automated edit distance ratio was large-scale research and busy clinical settings. The purpose excellent, as demonstrated by nearly perfect correlations of this study was to validate an automated analysis based and negligible mean differences. The results were replicated on a string metric—the unweighted Levenshtein edit across 2 speech samples and 2 computation applications. distance—to express phonemic error frequency after left Conclusions: The phonemic edit distance ratio is well hemisphere stroke. suited to estimate phonemic error rate and proves itself Method: Audio-recorded speech samples from 65 speakers for immediate application to research and clinical practice. who repeated single words after a clinician were transcribed It can be calculated from any paired strings of transcription phonetically. By comparing these transcriptions to the symbols and requires no additional steps, algorithms, or target, we calculated the percent segments with a combination judgment concerning alignment between target and production. of phonemic substitutions, additions, and omissions and We recommend it as a valid, simple, and efficient substitute derived the phonemic edit distance ratio, which theoretically for manual calculation of error frequency. n the context of communication disorders that affect difference between a series of targets and a series of pro- speech production, sound error frequency is often ductions. -
Spell Checker
International Journal of Scientific and Research Publications, Volume 5, Issue 4, April 2015 1 ISSN 2250-3153 SPELL CHECKER Vibhakti V. Bhaire, Ashiki A. Jadhav, Pradnya A. Pashte, Mr. Magdum P.G ComputerEngineering, Rajendra Mane College of Engineering and Technology Abstract- Spell Checker project adds spell checking and For handling morphology language-dependent algorithm is an correction functionality to the windows based application by additional step. The spell-checker will need to consider different using autosuggestion technique. It helps the user to reduce typing forms of the same word, such as verbal forms, contractions, work, by identifying any spelling errors and making it easier to possessives, and plurals, even for a lightly inflected language like repeat searches .The main goal of the spell checker is to provide English. For many other languages, such as those featuring unified treatment of various spell correction. Firstly the spell agglutination and more complex declension and conjugation, this checking and correcting problem will be formally describe in part of the process is more complicated. order to provide a better understanding of these task. Spell checker and corrector is either stand-alone application capable of A spell checker carries out the following processes: processing a string of words or a text or as an embedded tool which is part of a larger application such as a word processor. • It scans the text and selects the words contained in it. Various search and replace algorithms are adopted to fit into the • domain of spell checker. Spell checking identifies the words that It then compares each word with a known list of are valid in the language as well as misspelled words in the correctly spelled words (i.e. -
Hunspell – the Free Spelling Checker
Hunspell – The free spelling checker About Hunspell Hunspell is a spell checker and morphological analyzer library and program designed for languages with rich morphology and complex word compounding or character encoding. Hunspell interfaces: Ispell-like terminal interface using Curses library, Ispell pipe interface, OpenOffice.org UNO module. Main features of Hunspell spell checker and morphological analyzer: - Unicode support (affix rules work only with the first 65535 Unicode characters) - Morphological analysis (in custom item and arrangement style) and stemming - Max. 65535 affix classes and twofold affix stripping (for agglutinative languages, like Azeri, Basque, Estonian, Finnish, Hungarian, Turkish, etc.) - Support complex compoundings (for example, Hungarian and German) - Support language specific features (for example, special casing of Azeri and Turkish dotted i, or German sharp s) - Handle conditional affixes, circumfixes, fogemorphemes, forbidden words, pseudoroots and homonyms. - Free software (LGPL, GPL, MPL tri-license) Usage The src/tools dictionary contains ten executables after compiling (or some of them are in the src/win_api): affixcompress: dictionary generation from large (millions of words) vocabularies analyze: example of spell checking, stemming and morphological analysis chmorph: example of automatic morphological generation and conversion example: example of spell checking and suggestion hunspell: main program for spell checking and others (see manual) hunzip: decompressor of hzip format hzip: compressor of