Multimodal systems offer their functionalities through multiple communication channels. A messenger application may take either keyboard or voice input, and present incoming messages as text or audio output. This allows the users to communicate with their devices using the modality that best suits their context and personal preference. Authentication is often the first interaction with an application. The users’ login behavior can thus be used to immediately adapt the communication channel to their preferences. Yet given the sensitive nature of authentication, this interaction may not be representative for the user’s inclination to use speech input in non-critical routine tasks. In this paper, we test whether the interactions during authentication differ from non-critical routine tasks in a smart home application. Our findings indicate that, even in such a private space, the authentication behavior does not correlate with the use, nor with the perceived usability of speech input during non-critical task. We further find that short interactions with the system are not indicative of the user’s attitude towards audio output, independent of whether authentication or non-critical tasks are performed. Since security concerns are minmized in the secure environment of private spaces, our findings can be generalized to other contexts where security threats are even more apparent.

Digitization is currently infiltrating all daily processes, forcing casual computer users to become acquainted with unfamiliar tools. In order to avoid overstraining these users, simplified interfaces that are reduced to the functionality and content which are relevant to the individual user are imperative. Gaze-contingent systems thus monitor viewing behavior during natural system interactions to predict relevant interface elements. The prediction performance is highly dependent on the underlying features and algorithm, especially when the interface consist of dynamic elements such as videos. In this paper, we conduct two studies with a total of 233 subjects in which we record the viewers' gaze while watching videos. We then compare the quality of preference predictions for video elements of majority voting to the performance of machine learning. Our results indicate that (1) majority voting can predict preferences with an accuracy of up to 73% (66%) for two (four) elements, (2) machine learning improves the performance to 82% (74%), (3) prediction accuracy depends on the strength of the user's preference for an element, and (4) we can rank preferences for individual elements.

Watching TV has become a side event rather than a deliberate pastime. Movie directors thus struggle to find new ways to sustain the attention of their audience. Interactive movies usually require the viewer to actively decide how the plot progresses, creating an experience more akin to video games than film. In this paper, we propose a system that analyses gaze data to personalize the plot of a video without the viewer’s active intervention. User preferences are inferred from their gaze allocation to different elements in a scene. The subsequent scene is then dynamically tailored towards the user’s predicted preference. In a user study (N = 175), we evaluate the effectiveness of the system with regard to user engagement. Our findings show that personalized videos have a positive effect on focused attention and involvement, whereas novelty perception is not significantly affected.

In the past decade, cloud computing has shown its potential to provide powerful and reliable resources at the core of the network. Many applications can benefit from the wide range of cloud services. However, as applications in the Internet of Things become more common, the computing environment faces new requirements and challenges that cloud computing cannot meet. Fog and edge computing paradigms can fill this gap by moving computation from the core to the edge of the network. While multiple solutions for edge- centric networks have been proposed, there is still confusion about the terminology and classification of edge-centric architectures. In this paper, we summarize the current discussion about fog and edge computing systems. Further, we identify application areas of these systems in the Internet of Things.