58,829 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
Translating Video Recordings of Mobile App Usages into Replayable Scenarios
Screen recordings of mobile applications are easy to obtain and capture a
wealth of information pertinent to software developers (e.g., bugs or feature
requests), making them a popular mechanism for crowdsourced app feedback. Thus,
these videos are becoming a common artifact that developers must manage. In
light of unique mobile development constraints, including swift release cycles
and rapidly evolving platforms, automated techniques for analyzing all types of
rich software artifacts provide benefit to mobile developers. Unfortunately,
automatically analyzing screen recordings presents serious challenges, due to
their graphical nature, compared to other types of (textual) artifacts. To
address these challenges, this paper introduces V2S, a lightweight, automated
approach for translating video recordings of Android app usages into replayable
scenarios. V2S is based primarily on computer vision techniques and adapts
recent solutions for object detection and image classification to detect and
classify user actions captured in a video, and convert these into a replayable
test scenario. We performed an extensive evaluation of V2S involving 175 videos
depicting 3,534 GUI-based actions collected from users exercising features and
reproducing bugs from over 80 popular Android apps. Our results illustrate that
V2S can accurately replay scenarios from screen recordings, and is capable of
reproducing 89% of our collected videos with minimal overhead. A case
study with three industrial partners illustrates the potential usefulness of
V2S from the viewpoint of developers.Comment: In proceedings of the 42nd International Conference on Software
Engineering (ICSE'20), 13 page
Vision systems with the human in the loop
The emerging cognitive vision paradigm deals with vision systems that apply machine learning and automatic reasoning in order to learn from what they perceive. Cognitive vision systems can rate the relevance and consistency of newly acquired knowledge, they can adapt to their environment and thus will exhibit high robustness. This contribution presents vision systems that aim at flexibility and robustness. One is tailored for content-based image retrieval, the others are cognitive vision systems that constitute prototypes of visual active memories which evaluate, gather, and integrate contextual knowledge for visual analysis. All three systems are designed to interact with human users. After we will have discussed adaptive content-based image retrieval and object and action recognition in an office environment, the issue of assessing cognitive systems will be raised. Experiences from psychologically evaluated human-machine interactions will be reported and the promising potential of psychologically-based usability experiments will be stressed
Multi-touch 3D Exploratory Analysis of Ocean Flow Models
Modern ocean flow simulations are generating increasingly complex, multi-layer 3D ocean flow models. However, most researchers are still using traditional 2D visualizations to visualize these models one slice at a time. Properly designed 3D visualization tools can be highly effective for revealing the complex, dynamic flow patterns and structures present in these models. However, the transition from visualizing ocean flow patterns in 2D to 3D presents many challenges, including occlusion and depth ambiguity. Further complications arise from the interaction methods required to navigate, explore, and interact with these 3D datasets. We present a system that employs a combination of stereoscopic rendering, to best reveal and illustrate 3D structures and patterns, and multi-touch interaction, to allow for natural and efficient navigation and manipulation within the 3D environment. Exploratory visual analysis is facilitated through the use of a highly-interactive toolset which leverages a smart particle system. Multi-touch gestures allow users to quickly position dye emitting tools within the 3D model. Finally, we illustrate the potential applications of our system through examples of real world significance
Using Hover to Compromise the Confidentiality of User Input on Android
We show that the new hover (floating touch) technology, available in a number
of today's smartphone models, can be abused by any Android application running
with a common SYSTEM_ALERT_WINDOW permission to record all touchscreen input
into other applications. Leveraging this attack, a malicious application
running on the system is therefore able to profile user's behavior, capture
sensitive input such as passwords and PINs as well as record all user's social
interactions. To evaluate our attack we implemented Hoover, a proof-of-concept
malicious application that runs in the system background and records all input
to foreground applications. We evaluated Hoover with 40 users, across two
different Android devices and two input methods, stylus and finger. In the case
of touchscreen input by finger, Hoover estimated the positions of users' clicks
within an error of 100 pixels and keyboard input with an accuracy of 79%.
Hoover captured users' input by stylus even more accurately, estimating users'
clicks within 2 pixels and keyboard input with an accuracy of 98%. We discuss
ways of mitigating this attack and show that this cannot be done by simply
restricting access to permissions or imposing additional cognitive load on the
users since this would significantly constrain the intended use of the hover
technology.Comment: 11 page
Integrating 2D Mouse Emulation with 3D Manipulation for Visualizations on a Multi-Touch Table
We present the Rizzo, a multi-touch virtual mouse that has been designed to provide the fine grained interaction for information visualization on a multi-touch table. Our solution enables touch interaction for existing mouse-based visualizations. Previously, this transition to a multi-touch environment was difficult because the mouse emulation of touch surfaces is often insufficient to provide full information visualization functionality. We present a unified design, combining many Rizzos that have been designed not only to provide mouse capabilities but also to act as zoomable lenses that make precise information access feasible. The Rizzos and the information visualizations all exist within a touch-enabled 3D window management system. Our approach permits touch interaction with both the 3D windowing environment as well as with the contents of the individual windows contained therein. We describe an implementation of our technique that augments the VisLink 3D visualization environment to demonstrate how to enable multi-touch capabilities on all visualizations written with the popular prefuse visualization toolkit.
Animated virtual agents to cue user attention: comparison of static and dynamic deictic cues on gaze and touch responses
This paper describes an experiment developed to study the performance of virtual agent animated cues within digital interfaces. Increasingly, agents are used in virtual environments as part of the branding process and to guide user interaction. However, the level of agent detail required to establish and enhance efficient allocation of attention remains unclear. Although complex agent motion is now possible, it is costly to implement and so should only be routinely implemented if a clear benefit can be shown. Pevious methods of assessing the effect of gaze-cueing as a solution to scene complexity have relied principally on two-dimensional static scenes and manual peripheral inputs. Two experiments were run to address the question of agent cues on human-computer interfaces. Both experiments measured the efficiency of agent cues analyzing participant responses either by gaze or by touch respectively. In the first experiment, an eye-movement recorder was used to directly assess the immediate overt allocation of attention by capturing the participant’s eyefixations following presentation of a cueing stimulus. We found that a fully animated agent could speed up user interaction with the interface. When user attention was directed using a fully animated agent cue, users responded 35% faster when compared with stepped 2-image agent cues, and 42% faster when compared with a static 1-image cue. The second experiment recorded participant responses on a touch screen using same agent cues. Analysis of touch inputs confirmed the results of gaze-experiment, where fully animated agent made shortest time response with a slight decrease on the time difference comparisons. Responses to fully animated agent were 17% and 20% faster when compared with 2-image and 1-image cue severally. These results inform techniques aimed at engaging users’ attention in complex scenes such as computer games and digital transactions within public or social interaction contexts by demonstrating the benefits of dynamic gaze and head cueing directly on the users’ eye movements and touch responses
Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
We investigate whether a classifier can continuously authenticate users based
on the way they interact with the touchscreen of a smart phone. We propose a
set of 30 behavioral touch features that can be extracted from raw touchscreen
logs and demonstrate that different users populate distinct subspaces of this
feature space. In a systematic experiment designed to test how this behavioral
pattern exhibits consistency over time, we collected touch data from users
interacting with a smart phone using basic navigation maneuvers, i.e., up-down
and left-right scrolling. We propose a classification framework that learns the
touch behavior of a user during an enrollment phase and is able to accept or
reject the current user by monitoring interaction with the touch screen. The
classifier achieves a median equal error rate of 0% for intra-session
authentication, 2%-3% for inter-session authentication and below 4% when the
authentication test was carried out one week after the enrollment phase. While
our experimental findings disqualify this method as a standalone authentication
mechanism for long-term authentication, it could be implemented as a means to
extend screen-lock time or as a part of a multi-modal biometric authentication
system.Comment: to appear at IEEE Transactions on Information Forensics & Security;
Download data from http://www.mariofrank.net/touchalytics
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