Extending persistent monitoring by combining ocean models and Markov decision processes

Ocean processes are complex and have high variability in both time and space. Thus, ocean scientists must collect data over long time periods to obtain a synoptic view of ocean processes and resolve their spatiotemporal variability. One way to perform these persistent observations is to utilise an autonomous vehicle that can remain on deployment for long time periods. However, such vehicles are generally underactuated and slow moving. A challenge for persistent monitoring with these vehicles is dealing with currents while executing a prescribed path or mission. Here we present a path planning method for persistent monitoring that exploits ocean currents to increase navigational accuracy and reduce energy consumption.

Representing USDL for humans and tools

This chapter deals with technical aspects of how USDL service descriptions can be read from and written to different representations for use by humans and tools. A combination of techniques for representing and exchanging USDL have been drawn from Model-Driven Engineering and Semantic Web technologies. The USDL language's structural definition is specified as a MOF meta-model, but some modules were originally defined using the OWL language from the Semantic Web community and translated to the meta-model format. We begin with the important topic of serializing USDL descriptions into XML, so that they can be exchanged beween editors, repositories, and other tools. The following topic is how USDL can be made available through the Semantic Web as a network of linked data, connected via URIs. Finally, consideration is given to human-readable representations of USDL descriptions, and how they can be generated, in large part, from the contents of a stored USDL model.

Self-identified experts lost on the interwebs

Security cues found in web browsers are meant to alert users to potential online threats, yet many studies demonstrate that security indicators are largely ineffective in this regard. Those studies have depended upon self-reporting of subjects' use or aggregate experimentation that correlate responses to sites with and without indicators. We report on a laboratory experiment using eye-tracking to follow the behavior of self-identified computer experts as they share information across popular social media websites. The use of eye-tracking equipment allows us to explore possible behavioral differences in the way experts perceive web browser security cues, as opposed to non-experts. Unfortunately, due to the use of self-identified experts, technological issues with the setup, and demographic anomalies, our results are inconclusive. We describe our initial experimental design, lessons learned in our experimentation, and provide a set of steps for others to follow in implementing experiments using unfamiliar technologies, eye-tracking specifically, subjects with different experience with the laboratory tasks, as well as individuals with varying security expertise. We also discuss recruitment and how our design will address the inherent uncertainties in recruitment, as opposed to design for an ideal population. Some of these modifications are generalizable, together they will allow us to run a larger 2x2 study, rather than a study of only experts using two different single sign-on systems.