In the modern day, the ability to work through a problem and decipher it is essential to anyone who works in cybersecurity, partly because a lot of what they do involves working out what is going on with less than perfect knowledge.
The puzzles below have been drawn up with the help of the team behind the UK’s Cyber Security Challenge, which uses similar tests to find people who are good at problem solving who could be of use for attacking and defending computer networks.
Like records of land ownership. Creating and maintaining incorruptible registers of land titles is a huge – and mostly unsolved – problem for developing countries. So when the government of Honduras launched an investigation into whether a blockchain-based land registry could solve it, the non-geek world sat up and began to take notice. The unmistakable message was that this technology could be much more useful than merely securing cryptocurrencies. It might actually turn out to be one of the biggest IT inventions of our time.
These patterns explore the forces that encourage the emergence of a BIG BALL OF MUD, and the undeniable effectiveness of this approach to software architecture. What are the people who build them doing right? If more high-minded architectural approaches are to compete, we must understand what the forces that lead to a BIG BALL OF MUD are, and examine alternative ways to resolve them.
A number of additional patterns emerge out of the BIG BALL OF MUD. We discuss them in turn. Two principal questions underlie these patterns: Why are so many existing systems architecturally undistinguished, and what can we do to improve them?
Source: Big Ball of Mud
I get a lot of questions from aspiring programmers on what’s the best tool or languages to learn. It’s almost always a premature question to ask. I used to come up with answers like “depending on what you’re building” or “pick a beginner friendly community” or “invest in a growing language”. I think all of these are good answers, but it doesn’t really matter that early on in a programmer’s learning journey. It’s all the same when you’re essentially learning how to compute. Furthermore, these sort of answers enable the culture of tooling obsession.
As far as computer scientists know, the only general-purpose method to find the correct answer to a SAT problem is to try all possible settings of the variables one by one. The amount of time that this exhaustive or “brute-force” approach takes depends on how many variables there are in the formula. As the number of variables increases, the time it takes to search through all the possibilities increases exponentially. To complexity theorists and algorithm designers, this is bad. (Or, technically speaking, hard.)
SETH takes this situation from bad to worse. It implies that finding a better general-purpose algorithm for SAT — even one that only improves on brute-force searching by a small amount — is impossible.
A functional language (ideally) allows you to write a mathematical function, i.e. a function that takes n arguments and returns a value. If the program is executed, this function is evaluated.
A procedural language, on the other hand, performs a series of sequential steps, where the functional program would be nested. There’s a way of transforming sequential logic into functional logic called continuation passing style.
As a consequence, a purely functional program always yields the same value for an input, and the order of evaluation is not well-defined; which means that uncertain values like user input or random values are hard to model in purely functional languages.