The introduction of highly automated driving systems is expected to significantly change in-vehicle interactions, creating opportunities for the design of novel use cases and interactions for occupants. In this study, we sought to identify and extract these novel use cases and determine preliminary auditory display recommendations for these novel situations. We developed and generated use cases for level 4 automated vehicles through an expert workshop (N = 17) and online focus group interviews (N = 12). Most of the use cases we generated were then tested, apart from meditation, and user opinions were collected in a driving simulator study (N = 20). Results indicated participants were interested in functions that support their experience with both driving and non-driving related interactions in highly automated vehicles. Three categories of use cases for level 4 automated vehicles were developed: driving automation use cases, immersion use cases, and in-vehicle notification use cases. For the driving simulator study, we tested three display modalities for interaction with drivers: visual alert only, non-speech with visual, and speech with visual. In terms of situation awareness (SA), the non-speech with visual display was associated with significantly better SA for the use case consisting of a planned increase in automation level than the speech-with visual display. This study will provide guidance on sonification design to advance user experiences in highly automated vehicles.

Network attacks are becoming ever more sophisticated and are able to hide more easily in the increasing amount of traffic being generated by everyday activity. Administrators are placed in the unfortunate position of distinguishing between the two. The attack graph has been in use for some time because it provides a concise knowledge representation, and has had successful security metrics developed from it. Previous methods of attack plan recognition have relied on statistical inference to capture network attacks, however they are computationally expensive and can fail to capture obvious cause and effect relationships. In this paper, we use automated planning to capture new properties of attack graphs and use it for plan recognition. Experimental results demonstrate the efficacy of our approach.

Training animals is a process that requires a significant investment of time and energy on the part of the trainer. One of the most basic training tasks is to train dogs to perform postures on cue. While it might be easy for a human trainer to see when an animal has performed the desired posture, it is much more difficult for a computer to determine this. Most work in this area uses accelerometer and/or gyroscopic data to produce data from an animal's current state, but this has limitations. Take for example a normal standing posture. From an accelerometer's perspective, it closely resembles the "laying down" posture, but the posture can look very different if the animal is standing still, versus walking, versus running, and might look completely different from a "standing on incline" posture. A human trainer can instantly tell the difference between these postures and behaviors, but the process is much more difficult for a computer.

Ideally, software engineering courses should adequately reflect real-world software development so that students obtain a better understanding and experience with practices and techniques used in industry. Our objective is to improve software engineering courses by incorporating best practices for automated software engineering and facilitating rapid feedback for students using an open source continuous integration framework for evaluating student software development. The open source Jenkins Continuous Integration Server is the core of our framework, which provides a consistent environment for building student projects, executing automated test cases, calculating code coverage, executing static analysis, and generating reports for students. By using continuous integration, a common tool in real-world software development, we can incorporate software engineering best practices, introduce students to continuous integration in practice, and provide formative feedback to students throughout the software development lifecycle. We found that 76% or more of students in each of the classes that deploy our framework reported that using Jenkins increased their productivity, and that 84% or more of students in each of the classes reported that using Jenkins increased their code quality.