scholarly journals Enhancing VGI application semantics by accounting for spatial bias

2019 ◽  
Vol 3 (3) ◽  
pp. 255-268 ◽  
Author(s):  
Guiming Zhang
Ecography ◽  
2000 ◽  
Vol 23 (5) ◽  
pp. 575-578 ◽  
Author(s):  
L. Scott Forbes ◽  
Martin Jajam ◽  
Gary W. Kaiser
Keyword(s):  

2020 ◽  
Vol 33 (4-5) ◽  
pp. 521-548
Author(s):  
Laura Cacciamani ◽  
Larisa Sheparovich ◽  
Molly Gibbons ◽  
Brooke Crowley ◽  
Kalynn E. Carpenter ◽  
...  

Abstract We often rely on our sense of vision for understanding the spatial location of objects around us. If vision cannot be used, one must rely on other senses, such as hearing and touch, in order to build spatial representations. Previous work has found evidence of a leftward spatial bias in visual and tactile tasks. In this study, we sought evidence of this leftward bias in a non-visual haptic object location memory task and assessed the influence of a task-irrelevant sound. In Experiment 1, blindfolded right-handed sighted participants used their non-dominant hand to haptically locate an object on the table, then used their dominant hand to place the object back in its original location. During placement, participants either heard nothing (no-sound condition) or a task-irrelevant repeating tone to the left, right, or front of the room. The results showed that participants exhibited a leftward placement bias on no-sound trials. On sound trials, this leftward bias was corrected; placements were faster and more accurate (regardless of the direction of the sound). One explanation for the leftward bias could be that participants were overcompensating their reach with the right hand during placement. Experiment 2 tested this explanation by switching the hands used for exploration and placement, but found similar results as Experiment 1. A third Experiment found evidence supporting the explanation that sound corrects the leftward bias by heightening attention. Together, these findings show that sound, even if task-irrelevant and semantically unrelated, can correct one’s tendency to place objects too far to the left.


Author(s):  
Pom Charras ◽  
Juan Lupianez ◽  
Paolo Bartolomeo
Keyword(s):  

2022 ◽  
Vol 27 (2) ◽  
pp. 1-33
Author(s):  
Liu Liu ◽  
Sibren Isaacman ◽  
Ulrich Kremer

Many embedded environments require applications to produce outcomes under different, potentially changing, resource constraints. Relaxing application semantics through approximations enables trading off resource usage for outcome quality. Although quality is a highly subjective notion, previous work assumes given, fixed low-level quality metrics that often lack a strong correlation to a user’s higher-level quality experience. Users may also change their minds with respect to their quality expectations depending on the resource budgets they are willing to dedicate to an execution. This motivates the need for an adaptive application framework where users provide execution budgets and a customized quality notion. This article presents a novel adaptive program graph representation that enables user-level, customizable quality based on basic quality aspects defined by application developers. Developers also define application configuration spaces, with possible customization to eliminate undesirable configurations. At runtime, the graph enables the dynamic selection of the configuration with maximal customized quality within the user-provided resource budget. An adaptive application framework based on our novel graph representation has been implemented on Android and Linux platforms and evaluated on eight benchmark programs, four with fully customizable quality. Using custom quality instead of the default quality, users may improve their subjective quality experience value by up to 3.59×, with 1.76× on average under different resource constraints. Developers are able to exploit their application structure knowledge to define configuration spaces that are on average 68.7% smaller as compared to existing, structure-oblivious approaches. The overhead of dynamic reconfiguration averages less than 1.84% of the overall application execution time.


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