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Zipf's power law is a ubiquitous empirical regularity found in many systems, thought to result from proportional growth. Here, we establish empirically the usually assumed ingredients of stochastic growth models that have been previously conjectured to be at the origin of Zipf's law. We use exceptionally detailed data on the evolution of open source(More)
Tracking changes in feature distributions is very important in the domain of network anomaly detection. Unfortunately, these distributions consist of thousands or even millions of data points. This makes tracking, storing and visualizing changes over time a difficult task. A standard technique for capturing and describing distributions in a compact form is(More)
In a variety of open source software projects, we document a superlinear growth of production intensity (R ~ c(β)) as a function of the number of active developers c, with a median value of the exponent β ≃ 4/3, with large dispersions of β from slightly less than 1 up to 3. For a typical project in this class, doubling of the group size multiplies typically(More)
Authenticating users of computer systems based on their brainwave signals is now a realistic possibility, made possible by the increasing availability of EEG (electroencephalography) sensors in wireless headsets and wearable devices. This possibility is especially interesting because brainwave-based authentication naturally meets the criteria for two-factor(More)
Every day, security engineers cope with a flow of cyber security incidents. While most incidents trigger routine reactions, others require orders of magnitude more effort to investigate and resolve. How security operation teams in organizations should tune their response to tame extreme events remains unclear. Analyzing the statistical properties of sixty(More)
From biotechnology to cyber-risks, most extreme technological risks cannot be reliably estimated from historical statistics. Engineers resort to probability safety analysis (PSA), which consists in developing models to simulate accidents, potential scenarios, their severity and frequency. However, even the best safety analysis struggles to account for(More)
This paper exhibits two methods for decreasing the time associated with training a machine learning classifier on biometric signals. Using electroencephalography (EEG) data obtained from a consumer-grade headset with a single electrode, we show that these methods produce significant gains in the computational performance and calibration time of a simple(More)
Bug bounty programs offer a modern platform for organizations to crowdsource their software security and for security researchers to be fairly rewarded for the vulnerabilities they find. Little is known however on the incentives set by bug bounty programs: How they drive new bug discoveries, and how they supposedly improve security through the progressive(More)
On December 19, 2015, Scholtes et al.'s work was published online in the Journal of Empirical Software Engineering [1], in which they challenged the exciting findings that we (with another co-author) presented in 2014, showing that open source software production exhibits superlinear productive bursts [2]. We presented our findings as the first(More)
A new generation of technologies allows firms to track online consumer behavior with increasing granularity, and to share this information with other firms. This promise of information sharing has driven considerable interest from firms; and its potential for mone-tization has allowed a large number of online and web services to be available free of charge(More)