論文アブストラクト： We consider the challenge of motivating and coordinating large numbers of people to contribute to solving local, communal problems through their existing routines. In order to design such "on-the-go crowdsourcing" systems, there is a need for mechanisms that can effectively coordinate contributions to address problem solving needs in the physical world while leveraging people's existing mobility with minimal disruption. We thus introduce Hit-or-Wait, a general decision-theoretic mechanism that intelligently controls decisions over when to notify a person of a task, in ways that reason both about system needs across tasks and about a helper's changing patterns of mobility. Through simulations and a field study in the context of community-based lost-and-found, we demonstrate that using Hit-or-Wait enables a system to make efficient use of people's contributions with minimal disruptions to their routines without the need for explicit coordination. Interviews with field study participants further suggest that highlighting an individual's contribution to the global goal may help people value their contributions more.
論文アブストラクト： In this work, we propose two ensemble methods leveraging a crowd workforce to improve video annotation, with a focus on video object segmentation. Their shared principle is that while individual candidate results may likely be insufficient, they often complement each other so that they can be combined into something better than any of the individual results---the very spirit of collaborative working. For one, we extend a standard polygon-drawing interface to allow workers to annotate negative space, and combine the work of multiple workers instead of relying on a single best one as commonly done in crowdsourced image segmentation. For the other, we present a method to combine multiple automatic propagation algorithms with the help of the crowd. Such combination requires an understanding of where the algorithms fail, which we gather using a novel coarse scribble video annotation task. We evaluate our ensemble methods, discuss our design choices for them, and make our web-based crowdsourcing tools and results publicly available.
論文アブストラクト： Biologists often perform experiments whose results generate large quantities of data, such as interactions between molecules in a cell, that are best represented as networks (graphs). To visualize these networks and communicate them in publications, biologists must manually position the nodes and edges of each network to reflect their real-world physical structure. This process does not scale well, and graph layout algorithms lack the biological underpinnings to offer a viable alternative. In this paper, we present CrowdLayout, a crowdsourcing system that leverages human intelligence and creativity to design layouts of biological network visualizations. CrowdLayout provides design guidelines, abstractions, and editing tools to help novice workers perform like experts. We evaluated CrowdLayout in two experiments with paid crowd workers and real biological network data, finding that crowds could both create and evaluate meaningful, high-quality layouts. We also discuss implications for crowdsourced design and network visualizations in other domains.
論文アブストラクト： Users of complex software applications often rely on inefficient or suboptimal workflows because they are not aware that better methods exist. In this paper, we develop and validate a hierarchical approach combining topic modeling and frequent pattern mining to classify the workflows offered by an application, based on a corpus of community-generated videos and command logs. We then propose and evaluate a design space of four different workflow recommender algorithms, which can be used to recommend new workflows and their associated videos to software users. An expert validation of the task classification approach found that 82% of the time, experts agreed with the classifications. We also evaluate our workflow recommender algorithms, demonstrating their potential and suggesting avenues for future work.