A Comment on the Implementation of the Ziggurat Method

We show that the short period of the uniform random number generator in the published implementation of Marsaglia and Tsang's Ziggurat method for generating random deviates can lead to poor distributions. Changing the uniform random number generator used in its implementation fixes this issue.

gramEvol: Grammatical Evolution in R

We describe an R package which implements grammatical evolution (GE) for automatic program generation. By performing an unconstrained optimization over a population of R expressions generated via a user-defined grammar, programs which achieve a desired goal can be discovered. The package facilitates the coding and execution of GE programs, and supports parallel execution. In addition, three applications of GE in statistics and machine learning, including hyper-parameter optimization, classification and feature generation are studied

A monte-carlo floating-point unit for self-validating arithmetic

Monte-Carlo arithmetic is a form of self-validating arith-metic that accounts for the effect of rounding errors. We have implemented a floating point unit that can perform ei-ther IEEE 754 or Monte-Carlo floating point computation, allowing hardware accelerated validation of results during execution. Experiments show that our approach has a mod-est hardware overhead and allows the propagation of round-ing error to be accurately estimated

The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting

Wireless fingerprinting refers to a device identification method leveraging hardware imperfections and wireless channel variations as signatures. Beyond physical layer characteristics, recent studies demonstrated that user behaviours could be identified through network traffic, e.g., packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance. In contrast to previous works, by leveraging the recent multi-view machine learning paradigm, i.e., data with multiple forms, our method can cluster the device information shared among the multi-layer features without supervision. Our information-theoretic approach can be extended to supervised and semi-supervised settings with straightforward derivations. In solving the formulated problem, we obtain a tight surrogate bound using variational inference for efficient optimization. In extracting the shared device information, we develop an algorithm based on the Wyner common information method, enjoying reduced computation complexity as compared to existing approaches. The algorithm can be applied to data distributions belonging to the exponential family class. Empirically, we evaluate the algorithm in a synthetic dataset with real-world video traffic and simulated physical layer characteristics. Our empirical results show that the proposed method outperforms the state-of-the-art baselines in both supervised and unsupervised settings