Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase. Many organizations have to carry out this phase, before they can utilize business process management and its benefits. Yet, research towards this is severely restrained by an apparent lack of large and high-quality datasets. This lack of data can be attributed to, among other things, an absence of proper tool assistance for dataset creation, resulting in high workloads and inferior data quality. We explore two assistance features to support dataset creation, a recommendation system for identifying process information in the text and visualization of the current state of already identified process information as a graphical business process model. A controlled user study with 31 participants shows that assisting dataset creators with recommendations lowers all aspects of workload, up to $-51.0\%$, and significantly improves annotation quality, up to $+38.9\%$. We make all data and code available to encourage further research on additional novel assistance strategies.

Along with robot butlers, billboard-sized TVs, and inadequately sanitized wearables being tried on by untold hordes, self-driving demonstrations have become a staple of CES. As the show takes over Las Vegas, the Strip, hotel parking lots, and side streets play host to robo-vehicles with spinning sensors on the roof, pods with splashy logos, and even autonomous Lyfts. Usually, these demos go the same way: You sit in the back and try to glean whatever you can from a carefully staged ride. So it was odd to find myself this week in the driver's seat of a Lincoln MKZ that looked like a full self-driver, sensors and bold logos included. And I was being told not just that I'd have to drive, but that I would be monitored--and graded--on my concentration, trust, and emotional state.

The marketing of driving assistance features such as Autopilot, ProPilot and others as "autonomous" is setting unrealistic expectations and causing dangerous driving, according to insurers and vehicle safety researchers. In a report, Thatcham Research and the Association of British Insurers (ABI) say that drivers are being lulled into a false sense of security by the marketing of new driver assistance features making their way into cars and costing upwards of £20,000. Features such as Tesla's Enhanced Autopilot and Nissan's ProPilot, as well as terms such as "full self-driving capability" and being "capable of driving autonomously" are giving the false impression of a level of autonomy not yet available. As such, drivers are not treating these features with the level of scrutiny and attention required resulting in crashes and dangerous driving. "We are starting to see real-life examples of the hazardous situations that occur when motorists expect the car to drive and function on its own," said Matthew Avery, the head of research at Thatcham Research. "Specifically, where the technology is taking ownership of more and more of the driving task, but the motorist may not be sufficiently aware that they are still required to take back control in problematic circumstances."