33,726 research outputs found
Using software visualization technology to help genetic algorithm designers
This work is part of a three year PhD project to examine how Software Visualization(SV) can be applied to support the design and construction of Genetic Algorithms (GAs). A user survey carried out at the start of this project identified a set of key system features required by GA users. A visualization system embodying these features was then designed and a prototype built. This paper describes what genetic algorithms are and how they can be applied. It then reviews some of the survey results and their impact on the design of the visualization system. The paper concludes with an exploration of how the resulting prototype may be evaluated
A Glimpse Far into the Future: Understanding Long-term Crowd Worker Quality
Microtask crowdsourcing is increasingly critical to the creation of extremely
large datasets. As a result, crowd workers spend weeks or months repeating the
exact same tasks, making it necessary to understand their behavior over these
long periods of time. We utilize three large, longitudinal datasets of nine
million annotations collected from Amazon Mechanical Turk to examine claims
that workers fatigue or satisfice over these long periods, producing lower
quality work. We find that, contrary to these claims, workers are extremely
stable in their quality over the entire period. To understand whether workers
set their quality based on the task's requirements for acceptance, we then
perform an experiment where we vary the required quality for a large
crowdsourcing task. Workers did not adjust their quality based on the
acceptance threshold: workers who were above the threshold continued working at
their usual quality level, and workers below the threshold self-selected
themselves out of the task. Capitalizing on this consistency, we demonstrate
that it is possible to predict workers' long-term quality using just a glimpse
of their quality on the first five tasks.Comment: 10 pages, 11 figures, accepted CSCW 201
Efficient Image Gallery Representations at Scale Through Multi-Task Learning
Image galleries provide a rich source of diverse information about a product
which can be leveraged across many recommendation and retrieval applications.
We study the problem of building a universal image gallery encoder through
multi-task learning (MTL) approach and demonstrate that it is indeed a
practical way to achieve generalizability of learned representations to new
downstream tasks. Additionally, we analyze the relative predictive performance
of MTL-trained solutions against optimal and substantially more expensive
solutions, and find signals that MTL can be a useful mechanism to address
sparsity in low-resource binary tasks.Comment: Proceedings of the 43rd International ACM SIGIR Conference on
Research and Development in Information Retrieva
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