Salsa is an experimental Haskell library and code generator that allows Haskell programs to host the .NET runtime and interact with .NET libraries. It uses type families extensively to provide a type-safe mapping of the .NET object model in the Haskell type system.



Introduction to ELENA Programming Language

Alexandre Bencz. CodeProject. 2016-09-14.
ELENA is a general-purpose, object-oriented, polymorphic language with late binding. It features message dispatching/manipulation, dynamic object mutation, a script engine / interpreter and group object support.
In this article I will present the basic concepts of how to code in ELENA and also will present the basic concepts of purely object-oriented programming.

[Introduction to ELENA Programming Language]

Image Processing

Sher Minn Chong. Code Words. Issue six. March 2016-03-01.
Image processing is the process of manipulating or performing operations on images to achieve a certain effect (making an image grayscale, for example), or of getting some information out of an image with a computer (like counting the number of circles in it).
Image processing is also very closely related to computer vision, and we do blur the line between them a lot. Don’t worry too much about that – you just need to remember that we are going to learn about methods of manipulating images, and how we can use those methods to collect information about them.
In this article, I will go through some basic building blocks of image processing, and share some code and approaches to basic how-tos. All code written is in Python and uses OpenCV, a powerful image processing and computer vision library.

[Image Processing]

Python: An introduction to functional programming

Mary Rose Cook. Code Words. Issue one. 2014-12-01
Many functional programming articles teach abstract functional techniques. That is, composition, pipelining, higher order functions. This one is different. It shows examples of imperative, unfunctional code that people write every day and translates these examples to a functional style.
The first section of the article takes short, data transforming loops and translates them into functional maps and reduces. The second section takes longer loops, breaks them up into units and makes each unit functional. The third section takes a loop that is a long series of successive data transformations and decomposes it into a functional pipeline.
The examples are in Python, because many people find Python easy to read. A number of the examples eschew pythonicity in order to demonstrate functional techniques common to many languages: map, reduce, pipeline.

[Python: An introduction to functional programming]