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Pyflix is a small package written in Python that provides an easy entry point for getting up and running in the Netflix Prize competition. It combines an efficient storage scheme with an intuitive high-level API that allows contestants to focus on the real problem, the recommendation system algorithm. To get started with Pyflix, keep reading.

Attacks that exploit memory errors are still a serious problem. We present data randomization, a new technique that provides probabilistic protection against these attacks by xoring data with random masks. Data randomization uses static analysis to partition instruction operands into equivalence classes: it places two operands in the same class if they may refer to the same object in an execution that does not violate memory safety. Then it assigns a random mask to each class and it generates code instrumented to xor data read from or written to memory with the mask of the memory operand's class. Therefore, attacks that violate the results of the static analysis have unpredictable results. We implemented a data randomization prototype that compiles programs without modifications and can preventmany attacks with low overhead. Our prototype prevents all the attacks in our benchmarks while introducing an average runtime overhead of 11% (0%to 27%) and an average space overhead below 1%.

You really don't understand a concept until you know what it's made of. The taxonomy of marketing (filled with a bazillion tactics) is murky at best. The tactics are so numerous, expensive and sometimes emotional that we easily focus on the urgent instead of the important. Perhaps we could try a different approach:

It is important to understand how the human brain functions in the area attention, at the cognitive and neuronal levels, so we can attempt to replicate these functions in any decision support and business analytics system.