35 research outputs found
Exploring the Front Touch Interface for Virtual Reality Headsets
In this paper, we propose a new interface for virtual reality headset: a
touchpad in front of the headset. To demonstrate the feasibility of the front
touch interface, we built a prototype device, explored VR UI design space
expansion, and performed various user studies. We started with preliminary
tests to see how intuitively and accurately people can interact with the front
touchpad. Then, we further experimented various user interfaces such as a
binary selection, a typical menu layout, and a keyboard. Two-Finger and
Drag-n-Tap were also explored to find the appropriate selection technique. As a
low-cost, light-weight, and in low power budget technology, a touch sensor can
make an ideal interface for mobile headset. Also, front touch area can be large
enough to allow wide range of interaction types such as multi-finger
interactions. With this novel front touch interface, we paved a way to new
virtual reality interaction methods
Representing Information Collections for Visual Cognition
The importance of digital information collections is growing. Collections are
typically represented with text-only, in a linear list format, which turns out to be a
weak representation for cognition. We learned this from empirical research in cognitive
psychology, and by conducting a study to develop an understanding of current
practices and resulting breakdowns in human experiences of building and utilizing collections.
Because of limited human attention and memory, participants had trouble
finding specific elements in their collections, resulting in low levels of collection utilization.
To address these issues, this research develops new collection representations
for visual cognition. First, we present the image+text surrogate, a concise representation
for a document, or portion thereof, which is easy to understand and think
about. An information extraction algorithm is developed to automatically transform
a document into a small set of image+text surrogates. After refinement, the average
accuracy performance of the algorithm was 90%. Then, we introduce the composition
space to represent collections, which helps people connect elements visually in a
spatial format. To ensure diverse information from multiple sources to be presented
evenly in the composition space, we developed a new control structure, the ResultDis-
tributor. A user study has demonstrated that the participants were able to browse
more diverse information using the ResultDistributor-enhanced composition space.
Participants also found it easier and more entertaining to browse information in this
representation. This research is applicable to represent the information resources in contexts such as search engines or digital libraries. The better representation will enhance
the cognitive efficacy and enjoyment of people’s everyday tasks of information
searching, browsing, collecting, and discovering
PersonaSAGE: A Multi-Persona Graph Neural Network
Graph Neural Networks (GNNs) have become increasingly important in recent
years due to their state-of-the-art performance on many important downstream
applications. Existing GNNs have mostly focused on learning a single node
representation, despite that a node often exhibits polysemous behavior in
different contexts. In this work, we develop a persona-based graph neural
network framework called PersonaSAGE that learns multiple persona-based
embeddings for each node in the graph. Such disentangled representations are
more interpretable and useful than a single embedding. Furthermore, PersonaSAGE
learns the appropriate set of persona embeddings for each node in the graph,
and every node can have a different number of assigned persona embeddings. The
framework is flexible enough and the general design helps in the wide
applicability of the learned embeddings to suit the domain. We utilize publicly
available benchmark datasets to evaluate our approach and against a variety of
baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a
variety of important tasks including link prediction where we achieve an
average gain of 15% while remaining competitive for node classification.
Finally, we also demonstrate the utility of PersonaSAGE with a case study for
personalized recommendation of different entity types in a data management
platform.Comment: 10 pages, 6 figures, 7 table
ARShopping: In-Store Shopping Decision Support Through Augmented Reality and Immersive Visualization
Online shopping gives customers boundless options to choose from, backed by
extensive product details and customer reviews, all from the comfort of home;
yet, no amount of detailed, online information can outweigh the instant
gratification and hands-on understanding of a product that is provided by
physical stores. However, making purchasing decisions in physical stores can be
challenging due to a large number of similar alternatives and limited
accessibility of the relevant product information (e.g., features, ratings, and
reviews). In this work, we present ARShopping: a web-based prototype to
visually communicate detailed product information from an online setting on
portable smart devices (e.g., phones, tablets, glasses), within the physical
space at the point of purchase. This prototype uses augmented reality (AR) to
identify products and display detailed information to help consumers make
purchasing decisions that fulfill their needs while decreasing the
decision-making time. In particular, we use a data fusion algorithm to improve
the precision of the product detection; we then integrate AR visualizations
into the scene to facilitate comparisons across multiple products and features.
We designed our prototype based on interviews with 14 participants to better
understand the utility and ease of use of the prototype.Comment: VIS 2022 Short Paper; 5 page