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We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series. 

Gesture elicitation studies are a popular means of gaining valuable insights into how users interact with novel input devices. One of the problems elicitation faces is that of legacy bias, when elicited interactions are biased by prior technologies use. In response, methodologies have been introduced to reduce legacy bias. This is the first study that formally examines the production method of reducing legacy bias (i.e., repeated proposals for a single referent). This is done through a between-subject study that had 27 participants per group (control and production) with 17 referents placed in a virtual environment using a head-mounted display. This study found that over a range of referents, legacy bias was not significantly reduced over production trials. Instead, production reduced participant consensus on proposals. However, in the set of referents that elicited the most legacy biased proposals, production was an effective means of reducing legacy bias, with an overall reduction of 11.93% for the chance of eliciting a legacy bias proposal.