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Text Mechanic™ - Text Manipulation Tools

Basic Text Tools: Add/Remove Line Breaks - Add new line breaks and/or remove exisiting line breaks within your text's formatting. Merge Text (Line by Line) - Merge two sets of text line by line with the option of writing a prefix, divider or suffix into each merged line. Sort Text Lines - Sort your text's lines in alphabetical, length, random or reverse order. Obfuscation Tools: ASCII, Hex, Unicode, Base64, Binary, Octal Converter - Encode/decode ascii, hex, unicode, base64, binary, octal.

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R & Bioconductor - Manuals R & Bioconductor Manual R Basics Introduction SCIgen - An Automatic CS Paper Generator SCIgen - An Automatic CS Paper Generator About SCIgen is a program that generates random Computer Science research papers, including graphs, figures, and citations. It uses a hand-written context-free grammar to form all elements of the papers.

A Bioinformatician's UNIX Toolbox Most of bioinformaticians know how to analyze data with Perl or Python programming. However, not all of them realize that it is not always necessary to write programs. Sometimes, using UNIX commands is much more convenient and can save a lot of time spent on some trivial, yet tedious, routines. John Atkinson Grimshaw John Atkinson Grimshaw John Atkinson Grimshaw (6 September 1836 – 13 October 1893) was a Victorian-era artist, a "remarkable and imaginative painter"[1] known for his city night-scenes and landscapes.[2][3] His early paintings were signed "JAG," "J. A. How to Write Screenplays Using Microsoft Word: 9 steps Edit Article Edited by Moneybox35, Teresa, BW, Antarctica and 11 others Why should you pay hundreds of dollars for script writing software when you already own the most powerful program out there: Microsoft Word! We'll accomplish this through something called macros, which are programmable shortcut buttons.

Introduction to Feature selection for bioinformaticians using R, correlation matrix filters, PCA & backward selection Bioinformatics is becoming more and more a Data Mining field. Every passing day, Genomics and Proteomics yield bucketloads of multivariate data (genes, proteins, DNA, identified peptides, structures), and every one of these biological data units are described by a number of features: length, physicochemical properties, scores, etc. Careful consideration of which features to select when trying to reduce the dimensionality of a specific dataset is, therefore, critical if one wishes to analyze and understand their impact on a model, or to identify what attributes produce a specific biological effect. For instance, considering a predictive model C1A1 + C2A2 + C3A3 … CnAn = S, where Ci are constants, Ai are features or attributes and S is the predictor output (retention time, toxicity, score, etc). One of the simplest and most powerful filter approaches is the use of correlation matrix filters. Correlation Matrix :

Incorporated LICEcapsimple animated screen captures LICEcap can capture an area of your desktop and save it directly to .GIF (for viewing in web browsers, etc) or .LCF (see below). LICEcap is an intuitive but flexible application (for Windows and now OSX), that is designed to be lightweight and function with high performance. Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank Deeply Moving: Deep Learning for Sentiment Analysis This website provides a live demo for predicting the sentiment of movie reviews. Most sentiment prediction systems work just by looking at words in isolation, giving positive points for positive words and negative points for negative words and then summing up these points. That way, the order of words is ignored and important information is lost. In constrast, our new deep learning model actually builds up a representation of whole sentences based on the sentence structure. It computes the sentiment based on how words compose the meaning of longer phrases.

The 11 Best Psychology, Persuasion, and Usability Books You Should Read NOTE: This blog post was written in 2009. I have a new list of books at a newer blog post. I love to read. I read fiction and history and psychology… I’m an avid reader. Which means when I give talks on psychology, usability, user experience, or my book, Neuro Web Design, I often say, “Oh, there’s this great book…” and people then ask me for my “favorite books” list. I always tell them I’ll put one together, and then I never do. I WONDER “I Wonder offers crucial lessons in emotional intelligence, starting with being secure in the face of uncertainty. Annaka Harris has woven a beautiful tapestry of art, storytelling, and profound wisdom. Any young child – and parent – will benefit from sharing this wondrous book together.” –Daniel Goleman, author of the #1 bestseller Emotional Intelligence “What an enchanting children’s book – beautiful to look at, charming to read, and with a theme that wonderers of all ages should appreciate.” –Steven Pinker, Professor of Psychology, Harvard University, and author of How the Mind Works

INfoHesiveEP: Create & Publish ePublications In eBook, PDF, CHM Format If you are a software developer, you must know the importance of creating a help manual while moving through different phases of SDLC (Software Development Life Cycle). Without a detailed help manual, it becomes quite difficult for your audience to completely understand the functionality and the usage of the software. However, if you don’t want the hassle of creating help topics and compiling them into a single document, give InfoHesiveEP a shot. The application is developed for creating e-Publications such as help manuals, eBooks, support guides etc. The utility allows you to export files in PDF, EPUB, MOBI, LIT, CHM, HTML, RTF and TXT formats, tweak document layout, manage important keywords, tag topics, create table of contents and index page(s), insert timestamps, tables, links, anchors and cover page image.

rednoise Designed to support the creation of new works of computational literature, the RiTa† library provides tools for artists and writers working with natural language in programmable media. The library is designed to be simple while still enabling a range of powerful features, from grammar and Markov-based generation to text-mining, to feature-analysis (part-of-speech, phonemes, stresses, etc). All RiTa functions are heuristic and do not require training data, thus making the core library quite compact. RiTa can also be integrated with its own user-customisable lexicon, or with the WordNet database. RiTa is implemented in both Java and JavaScript, is free/libre and open-source, and runs in a number of popular programming environments including Android, Processing, Node, and p5.js.