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\n- (abb.1) Figure 1. lifeClipper3 equipment and walk situation
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\n- (abb.3) Figure 3. lifeClipper3 screenshots
\n- (abb.4) Figure 4. Studies for ground penetration and virtual sky
\n- (abb.5) Figure 5. Expert in the field (with interested passerby) and expert
snapshot (HMD screenshot) \n- DISCUSSION
\n- CONCLUSIONS
\n- ACKNOWLEDGMENTS
\n- REFERENCES
\n
",
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\n\n- ABSTRACT
\n- INTRODUCTION
\n- Present-day mobile AR applications
\n- (abb.1) Figure 1. Illustrations of AR interfaces;
\n- Research framework and our approach
\n- RELATED WORK
\n- METHODOLOGY
\n- Survey design and structure
\n- Data Collection and survey dissemination
\n- Analysis
\n- Respondents
\n- RESULTS
\n- (abb.2) Figure 2. Numbers of respondents having used different
applications, re-ordered for elucidatio \n- Statistics of application usage
\n- (abb.3) Table 1. Contexts of use inquired about with two different questions
\n- Application adoption and mobilisation grounds
\n- (abb.4) Table 2. Reasons for obtaining AR applications, reordered
according to frequency (N=90) \n- Strengths and weaknesses of the application
\n- (abb.5) Figure 3. Groupwise comparison of the user experience statements, with levels of significance of the difference between groups
\n- DISCUSSION AND IMPLICATIONS
\n- CONCLUDING REMARKS AND FUTURE WORK
\n- ACKNOWLEDGEMENTS
\n- REFERENCES
\n
",
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"note": "\n- ABSTRACT
\n- INTRODUCTION
\n- (abb.1) Table 1. Sutcliffe and Gault heuristics
\n- (abb.2) Figure 1. Citations per year and publication type
\n- RESULTS
\n- DISCUSSIONS ON LITERATURE REVIEW
\n- (abb.3) Figure 2. Patchwork of heuristics
\n- (abb.4) Figure 3. Patchwork of HE for Crisis Management Training
\n- HEURISTICS EVALUATION OF A TRAINING
SIMULATOR \n- (abb.5) Table 2. Tasks of two roles
\n- (abb.6) Table 3. A detailed description of an example heuristic
\n- Heuristics Evaluation Results
\n- (abb.7) Figure 4. Number of problems uncovered by participants
\n- (abb.8) Figure 5. Frequency of problems
\n- (abb.9) Figure 6. Heuristics instances
\n- Preliminary Findings of the HE Results with Respect to
a Patchwork \n- (abb.10) Figure 7. Comparison of heuristics used and the assessed utility
of heuristics \n- CONCLUSIONS AND FUTURE WORK
\n- ACKNOWLEDGMENTS
\n- REFERENCES
\n
",
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"note": "\n- ABSTRACT
\n- INTRODUCTION
\n- (abb.1) Figure 1: Augmented Reality view of a mobile game.
How to maintain the user experience when users move
away from the poster? \n- RELATED WORK
\n- INFORMATION ACCESS AT EVENT POSTERS
\n- Survey
\n- Discussion
\n- HYBRID INTERFACE FOR AUGMENTED PRINT MEDIA
\n- (abb.2)Figure 2: The representation of a physical print medium
can be preserved by turning it into a digital surface \n- (abb.3) Figure 3: To watch the augmented video users are
forced to keep the physical magazine in view [42]. \n- CASE STUDIES
\n- (abb.4) Figure 4: Transition from AR into zoomable view by
pointing the phone away from a poster. \n- (abb.5) Figure 5: Posters with depicted digital content. Left:
event poster with 2D media items like widgets (1), image
collection (2), trigger regions for showing / hiding
content (3) and videos (not visible). Right: game poster
with 3D (1) and 2D (2) animations. \n- (abb.6) Figure 6: The virtual representation of the AR view in Fig. 1.
\n- DISCUSSION AND RECOMMENDATIONS
\n- CONCLUSION
\n- ACKNOWLEGMENTS
\n- REFERENCES
\n
",
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\n- INTRODUCTION
\n- (abb.1) Figure 1. The 1931 Commission Internationale de l’ Éclairage
\n- BACKGROUND
\n- RELATED WORK
\n- (abb2) Figure 2. A formalization of color blending in oHMDs
\n- FORMALIZING COLOR BLENDING IN AR
\n- EXPERIMENTAL TESTBED
\n- (abb.3) Figure 3. (Above) A top view schematic of our experimental
testbed depicting an enclosure (green) containing two 15-watt lights \n- (abb.4) Figure 4. We color-matched paint to real objects to create
\n- ENGINEERING STUDY
\n- (abb.5) Figure 5. Using a colorimeter, we measured the actual and
\n- (abb.6) Figure 6. The 27 tested colors, defined by every combination of
\n- Collecting and Processing the Data
\n- (abb.7) Figure 7. The colored bars are the mean color distances
\n- RESULTS & CONCLUSIONS
\n- (abb.8) Figure 8. Chromatic changes between the no-background
\n- (abb.9) Figure 9. Chromatic changes between the foliage background
\n- (abb.10) Figure 10. Chromatic changes between the brick background
\n- (abb.11) Figure 11. Chromatic changes between the brick background
\n- FUTURE WORK
\n- REFERENCES
\n
",
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\n- Abstract
\n
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\n- Motivation
\n- Target Audience
\n- Outline of Tutorial
\n- Learning Objectives and Improvement Plans
\n- Forms of Presentation and Tutorial History
\n- Publicizing Tutorials
\n- Presenter Bios and Contact Information for Presenters
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\n
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"note": "\n- ABSTRACT
\n- INTRODUCTION
\n- FROM AR TO A²R: A THEORETICAL FRAMEWORK
\n- Visual Monitoring and Augmentation
\n- Acoustic Monitoring and Augmentation
\n- Affective Monitoring and Augmentation
\n- THE ADAPTIVE AR MUSEUM VISIT: SYSTEM ARCHITECTURE
\n- Architecture
\n- (abb.1) Figure 1: System Architecture and Collaborative Design
\n- Visual sensors and AR glasses
\n- Acoustic sensors
\n- Biosensors
\n- DESIGN CHALLENGES
\n- Project background
\n- Creating the 3rd space
\n- Data gathering for system requirements analysis
\n- (abb.2) Figure 2A, B, C and D: AR displays and gesture interaction tests
\n- IDENTIFYING MOTIVATIONS AND NEEDS
\n- MNAD: Museo Nacional de Artes Decorativas
\n- MAM: Le Musée des Arts et Métiers
\n- FACT: Foundation for Art and Creative
Technology \n- TOWARDS THE A2
R MUSEUM VISIT: SELECTING ARTIFACTS \n- MNAD: The 18th Century Valencian kitchen
\n- (abb.3) Figure 3A, B, C and D: Initial artifact selection and system mock-up
\n- MAM: The Lavoisier Laboratory (1743-1794)
\n- FACT: The VIP Signature Pillar
\n- COLLECTIVE VOICES
\n- CONCLUSIONS AND DIRECTIONS FOR FUTURE WORK
\n- AKNOWLEDGEMENTS
\n- REFERENCES
\n
",
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"note": "pARnorama:
360 Degree Interactive Video
for Augmented Reality Prototyping
\n
\n
\n
\n\n- Abstract
\n- General Terms
\n- Introduction
\n- (a) Recording video on site
\n- (b) Authoring the scenario
\n- (c) Playback on the device
\n- Figure 1: Simple three-step
workflow for video prototyping. \n- pARnorama
\n- Implementation
\n- Figure 2: Example still picture from a flattened 360
◦ video file. \n- Recording Procedure
\n- Editing and authoring tool
\n- Figure 3: The video preview and editing application.
\n- Viewer application
\n- Figure 4: Viewer application
running on a smartphone. \n- Discussion and Future Work
\n- References
\n
",
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"note": "Mobile Augmented Reality:
Video Prototyping
\n
\n
\n
\n
\n\n- Abstract
\n- General Terms
\n- figure 1. Participant viewing the
video prototype on an iPhone4 at
the first location – a busy and
crowded square. \n- Introduction
\n- Motivation and Related Work
\n- figure 2. Participant panning the
smart phone following the video at
the same location where it was
shot. \n- The Friend Radar App and the Design
approach \n- The Experiment
\n- figure 5. Post-test interview at the
Park. \n- figure 3. Video prototype. Each icon/avatar hovers above the
person it belongs to. Icons display availability and distance to
each person. The green arrow on the right points out friends
that are available to that direction. \n- Test
\n- Initial Results
\n- figure 4. Results from the questionnaires.
\n- Discussion and Future Work
\n- Pros
\n- Limitations
\n- Future work
\n- References
\n-
\n
\n
",
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"note": "Mobile Augmented Reality: Exploring Design and
Prototyping Techniques
\n
\n
\n\n- ABSTRACT
\n- INTRODUCTION
\n- BACKGROUND AND RELATED WORK
\n- Mobile Augmented Reality Channels and Trends
\n- Design Methodologies and Prototyping Techniques
\n- Challenges
\n- Figure 1. In addition to the AR interface, users can retrieve
detailed information on distance and friends’ activities. \n- THE FRIEND RADAR CONCEPT
\n- Design Approach
\n- PROTOYPES
\n- Low-fidelity mocks
\n- Figure 3. Video of the mixed-fi being played on a smart-phone.
\n- Figure 2. a) Low-fidelity mock. The see-through hole allows
users to see what is behind the device, simulating the camera. \n- Figure 4. High-fidelity prototype. The icons are overlaid on
the camera feed and move with the phone’s movement. \n- Mixed-Fidelity Videos
\n- High-Fidelity Prototype
\n- EXPERIMENT
\n- Table 1. Summary of requirements and development time for
each prototyping approach. \n- Evaluation Sessions
The experiment was \n- Figure 5. User testing the video (mixed-fidelity) prototype on a
busy urban location. \n- Post-experiment
\n- Figure 6. Results from the tests with the low-fidelity prototype.
Users had trouble understanding the concept. \n- RESULTS
\n- Low-Fidelity Mocks
\n- Figure 7. Results from the tests with the video prototype.
\n- Mixed-Fidelity Videos
\n- Figure 9. Results from the final question. The vast
majority of participants stated being very interested or
interested in using the Friend Radar application. \n- High-Fidelity Prototype
\n- DISCUSSION
\n- Figure 8. Results from the high-fidelity prototype tests.
\n- Interest and acceptance
\n- Table 2. Summary of benefits and drawbacks from the different approaches.
\n- Low-fidelity
\n- Mixed-fidelity
\n- CONCLUSIONS AND FUTURE WORK
\n- High-fidelity
\n- REFERENCES
\n
\n
",
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"note": "Heuristics for the Assessment of Interfaces of Mobile
Devices
\n
\n
\n
\n\n- ABSTRACT
\n- Categories and Subject Descriptors
\n- General Terms
\n- INTRODUCTION
\n- RELATEDWORK
\n- HEURISTICS FOR MOBILE DEVICES
\n- Inspection by Simulation
\n- Figure 1: Main screen of the MoViA multimedia
annotation tool. \n- VALIDATION OF THE HEURISTICS FOR
MOBILE DEVICES \n- Compilation
\n- Table 1: Association between the categories and Nielsen’s heuristics.
\n- Table 2: Heuristics for evaluating the usability of mobile device interfaces: second version.
\n- Table 3: Number of usability problems identified
\n- Acknowledgments
\n- REFERENCES
\n- FINAL REMARKS
\n
",
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"note": "\n- 1.INTRODUCTION
\n- 2.RELATED WORK
\n- 3. HIGH LEVEL LEARNING STUDY
\n- 3.1 Method
\n- Table 1. High level learning demographics. All are legally blind. Years is since onset, birth=since birth. Aides presented in order of use frequency. Cane=white cane; dog=guide dog; GPS=talking GPS; compass=tactile compass
\n- 3.2 Study Findings
\n- 3.2.1 High-level Information
\n- 3.2.2 Safety
\n- 3.2.3 Navigation
\n- 3.2.5 Factors Affecting Spatial Learning
\n- 3.3 Navigation Aide Opportunities
\n- 4. POINT OF INTEREST STUDY
\n- 4.1 Method
\n- 4.2 Study Findings
\n- Table 2. Information needs demographics. All are legally blind. Years is since onset, birth=since birth. Navigational aides presented in order of use frequency. Cane=white cane; dog=guide dog; GPS=talking GPS
\n- 4.2.1 Integrating Places into Existing Cognitive Map
\n- 4.2.2 Cognitive Map Creation Preferences
\n- 4.2.3 Responses to Push and Pull Information
\n- 5. DESIGN RECOMMENDATIONS
\n- 6. CONCLUSION
\n
",
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"note": "\n- 1. Introduction
\n- Figure 1. The experimental cloud-based 4G/LTE network
testbed \n- Figure 2. The standalone MAR application
\n- Table 1 System characteristics of 4G/LTE
network \n- 2. Architecture of SD-MAR on Cloud-based 4G/LTE Network
\n- Figure 3. Client-server (distributed) configuration for
4G/LTE network \n- Figure 5. The proposed overall system configuration of
SD-MAR \n- 2.2 Layer Architecture for SD-MAR System
\n- Figure 4. System layer architecture for SD-MAR with
cloud-based 4G/LTE \n- 2.3 Configuration for SD-MAR System
\n- 3. Applications Proposed for SD-MAR on
Cloud-based 4G/LTE Network \n- 3.1 Navigation/Tour Guidance Applications
on SD-MAR \n- Figure 6. Indoor AR-based navigation using computer vision
techniques [15] \n- Figure 7. Outdoor AR-based tour guidance using computer
vision techniques [16] \n- Figure 8.Cloud-based architecture of MAR for indoor
navigation application \n- Figure 9. Cloud-based architecture of MAR for outdoor
tourism application \n- 3.2 Collaborative Urban Design with
SD-MAR \n- Figure 10. The concept of urban design using a mobile
smart device \n- Figure 11. Multiuser collaborative urban design with
cloud-based using SD-MAR \n- 3.3 Multiuser Interactive Motion Learning
System with SD-MAR \n- Figure 12. Robot roles in a cloud-based 4G/LTE with
SD-MAR for an interactive motion learning
application \n- Figure 13. System architecture of robot learning system in
SD-MAR \n- Figure 14. Prototype system demo
\n- 4. System Performance Analysis of
QoS/QoE for SD-MAR \n- 5. Conclusion
\n
",
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"note": "\n- 1 Introduction
\n- 2 Related work
\n- 3 Mobile environmental monitoring
\n- Table 1 Classification of stakeholders based on their activities
\n- 3.2 Tasks
\n- 3.3 Challenges
\n- 3.3.1 Real-world conditions
\n- 3.3.2 Data (-source) heterogeneity
\n- 3.3.3 Data acquisition
\n- 3.3.4 Interpretation
\n- Fig. 1 Visualizations. Left combined plot comparing measurements of snow height and temperature over time. Middle left color coding of soil
moisture values. Middle right interpolations for surface skin temperature. Right color coding of land use \n- 4 Approach: in-context visualization of environmental
data \n- 4.1 The notions of context and situation
\n- 4.2 Visualization using mobile AR
\n- 4.3 Requirements
\n- 5 System infrastructure and workflow
\n- 5.1 Network layer
\n- Fig. 2 Overall system diagram.
Component blocks are
organized according to their
deployment (i.e., on-site,
remote). Icons at the top right of
each block indicate the (usual)
platform running the
components (i.e., dedicated
sensor station, distributed
systems, mainframe, laptop, and
handheld device) \n- 5.2 Data acquisition and management
\n- 5.3 Analysis and simulation
\n- 5.4 Deployment
\n- 5.5 Mobile client
\n- 5.5.1 Hardware platform
\n- 5.5.2 Software platform
\n- Fig. 3 Block diagram of the mobile client
\n- 6 Interactive tools for mobile visualization
\n- 6.1 Data exploration tools
\n- Fig. 4 Sensor visualization: different presentation modes. a Labels with station name. b Label with station name and one observed variable.
c Icons. d Icons with spatial filtering from station id \n- 6.2 View management tools
\n- Fig. 5 Terrain overlay visualization. Left terrain in fill mode with
transparency. Note how the transparency shows the real world
underneath while preserving the color coding of the overlay, and
isolines provide a sense of depth. Right overlay mapped on isolines.
The lines give a general idea of the color distribution while clearly
showing the video \n- 6.3 Collaboration tools
\n- 7 Validation and discussion
\n- 7.1 Methodology
\n- Table 2 Evaluation methods used in different phases of the development process
\n- 7.1.1 Prototyping cycle
\n- 7.1.2 Expert workshops
\n- Fig. 6 Prototypes overview. a The first prototype evaluated during
expert workshops displayed sensor label and a wireframe DEM.
b The second prototype, showcased in expert interviews, enabled
selection from a variety of sensors/simulations, showing line or fill
overlays. \n- 7.1.3 Showcase interviews
\n- 7.1.4 Formal evaluations
\n- 7.2 Case studies
\n- 7.2.1 Snow avalanches in Dorfberg, Davos
\n- 7.2.2 Watershed modeling in Kylma¨oja
\n- 7.3 Prototyping cycle
\n- 7.4 Expert workshops/showcase interviews
\n- 7.4.1 Usage scenarios and applications
\n- Fig. 7 Conceptual design for the visualization. Top sensor representation.
a Miniplots show data distribution of sensor over time.
b Labels present observations from all the stations. \n- Fig. 8 Scenario matrix. Sites, sensors, demos, and visualizations for two scenarios. Top row Dorfberg, Alps, Switzerland. Bottom row Kylma¨oja,
near Helsinki, Finland \n- 7.4.2 Visualization
\n- 7.4.3 Mobile device and user interface
\n- 7.4.4 Collaboration
\n- 7.5 Scenario evaluation
\n- 7.5.1 Dorfberg, Davos
\n- 7.5.1.2 Visualization pipeline
\n- 7.5.1.3 Workflow integration
\n- 7.5.1.4 Usability
\n- 7.5.2 Kylma¨oja, Finland
\n- 7.5.2.1 Formal evaluation
\n- 7.5.2.2 Platform components
\n- 7.5.2.3 Standards
\n- 7.5.2.4 Modeling
\n- 7.6 Discussion
\n- 7.6.1 Environmental monitoring
\n- 7.6.2 3D Visualization
\n- 7.6.3 Performance
\n- 7.6.4 Domain
\n- 7.6.5 Mobile sensing
\n- 7.6.6 Collaboration
\n- 8 Conclusion
\n
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"note": "\n- Introduction
\n- Issues to be addressed in ARET system evaluation
\n- Augmented Reality Exposure Therapy system
\n- Figure 1. Augmented Reality Exposure Therapy. The therapist observes the client’s viewpoint on the computer screen.
\n- Method
\n- Table 1. Mean ratings (SD) before and after the therapeutic session for : anxiety, belief, and avoidance on the 10-point Likert scale; and BAT on the 13-point Likert scale
\n- Figure 2. The mean frequencies of interactions in distributed therapeutic activity. The bold arrows correspond to the highest frequencies of interaction (per hour).
\n- Discussion and conclusions
\n
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"note": "\n- INTRODUCTION
\n- Figure 1. Example Scenario. From where you stand, you do not have a
view of the landmark. The traffic camera, however, does have a clear
view of the entire road containing the landmark, including the landmark.
A Virtual Mirror (VM) can be placed on the scene with the
contents of the camera overlaid onto it, such that it appears to reflect
the road containing the landmark. \n- SYSTEM OVERVIEW
\n- Figure 3. Diagram of System Modules. Three modules include Virtual
Mirror Mode, Video Mode, and Map Mode. Navigation between
Modules are facilitated by gestures. Pitch Sensor navigates between
Virtual Mirror Mode and Map Mode whereas screen touches navigates
between Virtual Mirror Mode and Video Mode. \n- Figure 4. Example of Map Mode. From where the user stands, the red
triangle is located at the same location on the map and is pointed in
the same direction that the user is facing. The red arrow with a plane
displays the location and orientation of a Virtual Mirror located within
a 1 kilometer radius of the user. The map can be zoomed in and out but
only virtual mirrors which fall in the range will be displayed. \n- Description of each module
\n- Figure 5. Example of difference in image quality. Picture on the left
shows the image quality of the video feed in Virtual Mirror Mode
whereas the picture on the right shows the image quality of the video
feed in Video Mode. Observe that the street sign is much more legible
in the higher quality image (right) \n- Figure 6. Diagram of triangulation. Using the longitude and latitude
shown as x-y coordinates of the virtual mirror, user, and the origin,
triangulation allows for the calculation of relative distances and angles
between the three points. The calculated angle VM represents the
Virtual Mirror angle relative to the user’s compass heading U. \n- Figure 7. Diagram of half-angle calculation of the virtual mirror (left)
and graphical manipulation of camera feed (right). Using the halfway
angle between the camera angle,user, and mirror position, the graphical
representation of the camera feed is mirrored, distorted and translated
as shown on the right. \n- Figure 8. Map of area surrounding McGill University with the locations
of the user, landmarks and virtual mirror indicated. \n- RESULTS
\n- DISCUSSION
\n- FUTURE WORKS
\n- Figure 9. Screen shot of the system in Map Mode in a real environment. The three images from top to bottom depicts the user, represented by a red
triangle, turning 180 degrees to face the Virtual Mirror which is represented by an arrow attached to a rectangle. \n- Figure 10. Screen shot of the system in Virtual Mirror Mode in a real environment. Overlayed on top of the video feed from the camera mounted
on the back on the phone is a Virtual Mirror Displaying the view of a street which is located around the corner from where the user is physically
standing. \n- Figure 11. Screen shot of the system in Video Mode in a real environment. A high quality video feed of the contents of the Virtual Mirror is displayed
which show detail not previously shown in Virtual Mirror Mode. Informational Overlay indicating the Arts Building and the Roddick Gates are
labeled in the image for clarity for the reader, and are not present in the system. \n- REFERENCES
\n
",
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"note": "\n- INTRODUCTION AND RELATED WORK
\n- SYSTEM OVERVIEW
\n- Figure 1. Description of the system concept and its different modes. The
grey arrows represent the device orientation while the red arrows represent
the direction in which POIs are sequenced. \n- Figure 2. Left: stop & listen mode with tested design for the slider gesture.
Right: alternative gesture tried spontaneously by some users. \n- TASK-BASED USABILITY STUDY
\n- Tasks
\n- Results and Discussion
\n- Figure 3. Sample trajectories taken by participants. The black line traces the logged position, and the size of the overlaid pink and red circles represent
the magnitude of GPS error as reported by the sensor itself. The red circles denote locations where the error exceeds 100 m. The positions on startup,
prior to self-calibration of the sensors, are often incorrect, as evident by the red circles and large spikes, as can be seen in the middle example. \n- LONG-TERM DEPLOYMENT: FIRST PHASE
\n- Results and Discussion
\n- Figure 4. Responses to the Likert scale questionnaire, represented as a net-stacked distribution. The scale represents the number of answers for each
question, with neutral responses omitted for both iterations of the deployment (6 and 2 participants). \n- Figure 5. NASA TLX questionnaire results for each iteration, averaged
over six participants for the first iteration and over two for the latter. \n- Suggested Improvements
\n- Changes Implemented
\n- Figure 6. Neck case design to ensure hands-free use of the application.
\n- LONG-TERM DEPLOYMENT: SECOND PHASE
\n- Results and Discussion
\n- Suggested Improvements
\n- Changes Implemented
\n- ONGOING CHALLENGES
\n- CONCLUSION
\n
",
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"note": "\n- 1. INTRODUCTION
\n- Figure 1. We support indoor navigation with a Mixed Reality World-in-Miniature (WIM) and sparse localization. As a user walks,
we use a Virtual Reality WIM to illustrate the next instruction (a). Once the user reaches an info point (a floor-mounted poster) we
show the whole path using an Augmented Reality WIM (b). After departing the info point, we resort again to Virtual Reality (c). \n- 2. RELATED WORK
\n- 3. INTERFACE DESIGN
\n- Figure 2. Conceptual design of info points for a shopping mall.
Left: a user accessing the info point. Right: view on the phone. \n- 3.1 Info points
\n- 3.2 Interface view
\n- 3.2.1 Instructions view
\n- Figure 3. World-in-Miniature views in our interface. To provide continuous navigation support with sparse localization, we transition
within Mixed Reality: we use Virtual Reality when there is no localization (a-c) and Augmented Reality otherwise (d-e). \n- 4. USER STUDY AND DISCUSSION
\n- Figure 4. Users walked the path from A to B. We divide the path in 10 path segments with one floor change (3), one skyway (4) and
various intersections. We asked participants to think aloud during navigation: keyword utterances are shown by the tag clouds. \n- 5. CONCLUSION
\n- 6. ACKNOWLEDGMENTS
\n- 7. REFERENCES
\n
",
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"note": "\n- 1. INTRODUCTION
\n- 2. CONTEXT
\n- 3. INDEXICALITY
\n- Fig. 1. The semiotic relation between object, representation, and interpretation.
\n- Fig. 2. Page from paper based timetable book: symbolic representations with no indexicality.$
\n- Fig. 3. Timetable poster at a train station: symbolic representation with spatial indexicality.
\n- 3.1 Reducing Information Representations by Increasing Indexicality
\n- Fig. 4. Electronic timetable on platform: spatial and temporal indexicality.
\n- 3.2 Indexical Interface Design
\n- 4. THREE PROTOTYPE SYSTEMS
\n- 4.1 TramMate II
\n- Fig. 5. TramMate II prototype system: route-planning information for the public transport system
in Melbourne, Australia, indexed to location, time and physical objects. \n- Fig. 6. MobileWARD prototype system: indexing patient information at a large regional hospital
in Denmark to patients in proximity, location and upcoming work activities. \n- 4.3 Just-for-Us
\n- Fig. 7. Just-for-Us: indexing to the user’s physical surroundings and history of visits.
\n- 5. THREE EVALUATIONS
\n- Fig. 8. Field evaluation of TramMate II on board a tram in Melbourne, Australia.
\n- 5.1 Evaluating on Public Transport: TramMate II
\n- 5.2 Evaluating at the Hospital: MobileWARD
\n- Fig. 9. Laboratory evaluation of MobileWARD in a usability lab emulating a hospital ward.
\n- 5.3 Evaluating in the City: Just-for-Us
\n- Fig. 10. Field evaluation of Just-for-Us at Federation Square, Melbourne, Australia.
\n- Fig. 11. Wireless camera on PDA and video recording of participants, interviewer, surroundings
and screen. \n- 6. DISCUSSION OF FINDINGS FROM EVALUATIONS
\n- 6.1 Indexing to Physical Context
\n- 6.1.1 Redundancy and the Sufficiency of Approximation. On a general
\n- 6.2 Indexing to Spatial Context
\n- 6.2.1 Trust and Control.
\n- 6.3 Indexing to Social Context
\n- 6.3.1 Subtle Context and Making the Implicit Explicit.
\n- 7. USING INDEXICALITY IN DESIGN
\n- 8. SUMMARY AND CONCLUSIONS
\n- 9. FURTHER WORK
\n
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"note": "\n- 1 Introduction
\n- Table 1 Comparison of the different approaches
\n- 2 Related work
\n- 3 System architecture
\n- Fig. 1 System architecture
\n- 3.1 User and device capabilities
\n- 4.1 Conditionals
\n- 4.2 Error handling
\n- 4.3 Parameterizing code
\n- 4 Preprocessor directives
\n- Fig. 2 Sample of ‘‘if…elif…else…endif’’ directives in Java, C# or
C?? \n- Fig. 3 Sample of error directive in VB or VB.NET
\n- Fig. 4 Sample of hidden directive in Python
\n- Fig. 5 Sample of fuzzy rules
\n- 4.4 Security
\n- 5 The fuzzy knowledge-eliciting reasoner
\n- 5.1 Initial data retrieval
\n- 5.2 The decay process
\n- 5.3 Automatic membership generation
\n- Fig. 6 Worldwide popularity calculation for all the resolutions of the
mobile devices in WURFL \n- Fig. 7 Calculation of the base
universe \n- Fig. 8 Calculation of the best
possible universe \n- Fig. 9 Calculation of the
membership functions \n- 6 Evaluation
\n- Table 2 Membership values for different devices using worldwide
data \n- Fig. 10 Generated membership functions for the screen resolution
\n- 6.1 Evaluating the source used for establishing mobile
devices relevance \n- 6.2 Evaluating the usability of Imhotep
\n- Fig. 11 Membership functions for the resolution size in different locations: a Worldwide, b Spain, c Japan
\n- Table 3 Membership values for different markets
\n- 7 Use case
\n- Table 4 Results in percent of the usability evaluation
\n- Fig. 12 Different user interfaces for: a Blind people and b People
without disabilities \n- Fig. 13 Snippet of the preprocessor directives used in the augmented
reality application developed using the Imhotep Framework \n- 8 Conclusions and further work
\n
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"note": "\n- 1. INTRODUCTION
\n- 1.1 Target scenario
\n- 1.2 Paper Outline
\n- 2. RELATED WORK
\n- Figure 1. Possible environment configuration (smart living
room). Tags highlight interactive entities present in the room \n- 3. MULTIMODALITY AND CONTEXT
\n- 4. HUMAN-SMART ENVIRONMENT
MULTIMODAL INTERACTION: CONCEPT \n- 4.1 Deictic Gesture
\n- 4.2 Symbolic Gesture Language
\n- Figure 2. System architecture
\n- Figure 3. Symbolic gesture vocabulary
\n- 4.3 Speech Recognition
\n- 4.4 Multimodality Management
\n- 4.5 Multimodal Feedback
\n- 4.6 Augmenting the Environment: Map and
Smart Entities \n- 4.7 Interaction Protocol
\n- Figure 5. Schematization of the interaction protocol.
\n- 5. PROTOTYPE IMPLEMENTATION
\n- 5.1 Set-ups
\n- 5.2 Deictic Gesture Implementation
\n- 5.2.1 Augmenting the Environment
\n- Figure 6. Handheld device: a Wii Remote with a camera
\n- Figure 7. Wearable configuration
\n- Figure 8. Object triangulation
\n- 5.2.2 Using PTAMM for pointing
\n- 5.2.3 Virtual Trigger’s types
\n- Figure 9. Environment set-up
\n- 5.3 Symbolic Gesture Recognition
Implementation \n- 5.4 Speech Recognition Implementation
\n- 5.5 Speech Recognition Implementation
\n- Table 1. Prototype Configuration
\n- 5.6 Multimodality
\n- 6. SYSTEM EVALUATION
\n- Figure 10. Error Rate aiming at targets from a distance of 4
meters \n- 6.1 Limitations and Future Work
\n- 7. CONCLUSION
\n
",
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"note": "\n- INTRODUCTION
\n- 1.1 User Experience and Learning in VT Systems
\n- 1.2 Measuring Human Aspects of VT Systems
\n- 2.1 Theoretical Model, Measurement Dimensions, and Factors
\n- Fig. 1. Conceptual model of the MUSTe method.
\n- 2.2 User-Centered Methodology
\n- 2.3 Measures
\n- 3.1 Participants
\n- 3.2 Apparatus
\n- Fig. 2. System architecture of the HAV training system.
\n- 3.3 Experimental Tasks and Training Design
\n- Fig. 3. A participant using the VT system.
\n- Fig. 4. Practice task.
\n- 3.4 Procedures
\n- Fig. 5. Flowchart of the experiment process.
\n- 3.5 Data Collection and Analysis
\n- 4.1 User Performance
\n- TABLE 1 Task Performance Results
\n- TABLE 2 Memory Test Outcomes
\n- 4.2 System Performance
\n- 4.2.1 SE Beliefs
\n- TABLE 3 SE Items and Factor Analysis Results
\n- 4.2.2 Perceived System Efficacy: Subjective Usability
\n- TABLE 4 Dimensionality of PVE (a) Factor Weight and Factor Score (b) Factor Result
\n- TABLE 5 Correlations within PVE Factors
\n- TABLE 6 User Satisfaction
\n- Fig. 6. System usability.
\n- 4.3 Synthesis of Human Performance Measures
\n- TABLE 7 Correlations of Performance and Perception Measures
\n- 4.4 Utilization of MUSTe
\n- 4.5 Formative User-Centered Evaluation
\n- CONCLUSIONS
\n
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"note": "\n- 1 INTRODUCTION
\n- Fig. 1. (Left) In multi-view AR, tapping on the green camera icons switches to their associated view, providing a zoom-in to remote
areas of a site. (Right) The variable perspective view deforms the datasets to overview the site. \n- 2 RELATED WORK
\n- 3 DESIGN CONSIDERATIONS
\n- 3.1 Application Scenario
\n- 3.2 Limitations
\n- Fig. 2. Multi-view AR used in a snow science scenario including an
AR view (A), a virtual top down view (B), a 3rd person virtual view
(C) and a second user’s view (D). \n- 4 MULTI-VIEW AR
\n- 4.1 Technique
\n- Fig. 3. Multi-view AR interface. (A) scrolling list of views. (B) iconographic
representation of views. (C) minimap from a top-down virtual
camera. (D) navigation controls for additional views, here for a
virtual 3D camera view. \n- 4.2 Implementation
\n- 5 VARIABLE PERSPECTIVE VIEW
\n- 5.1 Technique
\n- 5.2 Implementation
\n- Fig. 4. Variable perspective view. The video background is registered
with its 3D representation from two perspectives. \n- Fig. 5. Interactions with variable perspective view. (A) The secondary camera (sc) is rotated by α about an axis at distance d from the main
camera (mc). Vertices that fall within the transition area φ are interpolated between mc and sc depending on their distance from mc. (B) Effect
of changing the angle of rotation α to α’. The new camera sc’ has a better overview. (C) Effect of changing the distance d to the rotation. The
new camera sc’ (with a new distance d’) observes a portion of the environment further away from mc. (D) Effect of changing the size of the
transition area φ \n- 6 EVALUATION
\n- 6.1 Multi-View
\n- 6.1.1 Multi-View Evaluation
\n- 6.1.2 Multi-View Benefits
\n- Fig. 6. Unresolved occlusion. The remote view is behind the barn,
which does not exist in the 3D model. The system is unable to
compute the occlusion. \n- 6.2 Variable Perspective
\n- 6.2.1 Explorative Study
\n- 6.2.2 Comparative Study
\n- Fig. 7. Increasing overview. Image sequence for increasing overview by changing the angle of rotation α.
\n- 6.2.3 Variable Perspective Benefits
\n- Fig. 8. Unveiling occlusions. Spatial relationships between occluded objects can be discovered by changing the distance d to the rotation.
\n- 6.3 Combining Techniques
\n- 7 CONCLUSION AND FUTURE WORK
\n- Fig. 9. Detached variable perspective view. The view is detached from
the AR context to increase overview over the 3D model. \n- REFERENCES
\n
",
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