TouchPort collapses the multi-stage process of discovering, consenting to, and syncing mixed reality encounters into one embodied handshake-and-pull gesture.
Embracing technical debt, from a startup company perspective
5 Pith papers cite this work, alongside 53 external citations. Polarity classification is still indexing.
5
Pith papers citing it
53
external citations · external index
citation-role summary
background 1
citation-polarity summary
verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
SIDER-n achieves local consistency of order n+1 for smooth spherical curves via a degree-filtered formal expansion framework that cancels leading error terms recursively.
Introduces the Attention-Aware Pipeline (Capture-Record-Revisualize) to surface design tensions in XR attention feedback systems, illustrated via three prototypes and a musician eye-tracking study.
PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
A systematic literature review summarizing the shift in SATD detection from heuristic keyword methods to ML, DL, and Transformer models, along with performance trends and open challenges like dataset heterogeneity.
citing papers explorer
-
Allow Me Into Your Dream: A Handshake-and-Pull Protocol for Sharing Mixed Realities in Spontaneous Encounters
TouchPort collapses the multi-stage process of discovering, consenting to, and syncing mixed reality encounters into one embodied handshake-and-pull gesture.
-
Local Consistency and Higher-Order Structure of Spherical Interpolation
SIDER-n achieves local consistency of order n+1 for smooth spherical curves via a degree-filtered formal expansion framework that cancels leading error terms recursively.
-
The Attention-Aware Pipeline: Design Tensions from Making Attention Visible in XR
Introduces the Attention-Aware Pipeline (Capture-Record-Revisualize) to surface design tensions in XR attention feedback systems, illustrated via three prototypes and a musician eye-tracking study.
-
Floating-point autotuning with customized precisions
PROMISE tool automates mixed-precision tuning with user-defined floating-point formats, validated on linear solvers and Rodinia benchmarks showing many variables can use lower precision safely.
-
Self-Admitted Technical Debt Detection Approaches: A Decade Systematic Review
A systematic literature review summarizing the shift in SATD detection from heuristic keyword methods to ML, DL, and Transformer models, along with performance trends and open challenges like dataset heterogeneity.