Developing information technology to democratize scientific knowledge and support citizen empowerment is a challenging task. In our case, a local community suffered from air pollution caused by industrial activity. The residents lacked the technological fluency to gather and curate diverse scientific data to advocate for regulatory change. We collaborated with the community in developing an air quality monitoring system which integrated heterogeneous data over a large spatial and temporal scale. The system afforded strong scientific evidence by using animated smoke images, air quality data, crowdsourced smell reports, and wind data. In our evaluation, we report patterns of sharing smoke images among stakeholders. Our survey study shows that the scientific knowledge provided by the system encourages agonistic discussions with regulators, empowers the community to support policy making, and rebalances the power relationship between stakeholders.
There are tools which focus on understanding video data using crowdsourcing. SynTag @cite_21 is a real-time collaborative tagging system for labeling presentation videos. Audiences can tag "good", "question", or "disagree" during or after the presentation. These tags are shown on a line chart, which provides indices for video segments. Glance @cite_29 is a video coding tool which asks crowdworkers on Mechanical Turk to label small clips in parallel. Without searching the entire video, users can use the aggregated crowdsourcing labels to quickly identify events. Further extending this concept, Zensors @cite_9 is an image recognition tool using cameras on mobile devices, which combines crowdsourcing and computer vision to answer user defined questions. Initially, answers are provided by workers on Mechanical Turk. The tool then uses these answers as labels to train a computer vision classifier, which takes the image recognition task when its accuracy reaches a threshold for a certain period. These tools provide us with the concept that using other data sources to index archived videos can help users in making sense of video content efficiently.
@cite_9 @cite_29 @cite_21
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The promise of "smart" homes, workplaces, schools, and other environments has long been championed. Unattractive, however, has been the cost to run wires and install sensors. More critically, raw sensor data tends not to align with the types of questions humans wish to ask, e.g., do I need to restock my pantry? Although techniques like computer vision can answer some of these questions, it requires significant effort to build and train appropriate classifiers. Even then, these systems are often brittle, with limited ability to handle new or unexpected situations, including being repositioned and environmental changes (e.g., lighting, furniture, seasons). We propose Zensors, a new sensing approach that fuses real-time human intelligence from online crowd workers with automatic approaches to provide robust, adaptive, and readily deployable intelligent sensors. With Zensors, users can go from question to live sensor feed in less than 60 seconds. Through our API, Zensors can enable a variety of rich end-user applications and moves us closer to the vision of responsive, intelligent environments. Real-time video streaming has been widely used in multimedia learning environments. As production of online videos is increasing exponentially, it is becoming more difficult for users to reach relevant content. In this paper, we propose SynTag, a web-based platform that enables users to label three types of tags -- Good, Question, and Disagree -- and to make comments synchronously and asynchronously with visualization of time-stamp video previews on an interactive timeline. SynTag generates real-time thumbnails by using real-time tags for presenters to receive instant feedback and for other users to retrieve presentation videos. In a pilot study, we found our users' tagging behaviors significantly different when they were in lecture events or discussion events. We envision that enabling users to apply tags in real-time will help reduce the complexity of classification of videos.