Integrating model development across computational neuroscience, cognitive science, and machine learning.

Neuroscience, cognitive science, and computer science are increasingly benefiting through their interactions. This could be accelerated by direct sharing of computational models across disparate modeling software used in each. We describe a Model Description Format designed to meet this challenge.

From Neuron Coverage to Steering Angle: Testing Autonomous Vehicles Effectively.

A Deep Neural Network (DNN) based system, such as the one used for autonomous vehicle operations, is a "black box" of complex interactions resulting in a classification or prediction. An important question for any such system is how to increase the reliability of, and consequently the trust in, the underlying model. To this end, researchers have largely resorted to adapting existing testing techniques.

Finding Bugs in Cryptographic Hash Function Implementations.

Cryptographic hash functions are security-critical algorithms with many practical applications, notably in digital signatures. Developing an approach to test them can be particularly difficult, and bugs can remain unnoticed for many years. We revisit the NIST hash function competition, which was used to develop the SHA-3 standard, and apply a new testing strategy to all available reference implementations.