Our paper on geolocating images using crowdsourcing and diagramming was accepted for the International Joint Conference on Artificial Intelligence (IJCAI 2018) in Stockholm, Sweden. Dr. Luther will give the presentation. Congratulations to co-authors and Crowd Lab alumni Rachel Kohler and John Purviance!
Our full paper on CrowdLayout, a system that uses crowdsourcing to design better layouts of biological network visualizations, was accepted for the CHI 2018 conference in Montreal, Canada. The acceptance rate for this top-tier HCI conference was 26% (of 2,590 submissions). Congrats to Crowd Lab alumni Divit Singh and Lee Lisle, and collaborator Dr. T.M. Murali, on this accomplishment.
Our paper on crowdsourced image geolocation and diagramming won the Notable Paper Award at HCOMP 2017. Congrats to Crowd Lab alums Rachel Kohler and John Purviance, co-authors of the paper, for this recognition. In the photo above, Dr. Luther receives the award certificate on behalf of his co-authors from Adam Kalai and Steven Dow (HCOMP 2017 co-chairs) and Jeff Nichols (Awards committee).
Networks have become ubiquitous in systems biology. Visualization is a crucial component in their analysis. However, collaborations within research teams in network biology are hampered by software systems that are either specific to a computational algorithm, create visualizations that are not biologically meaningful, or have limited features for sharing networks and visualizations. We present GraphSpace, a web-based platform that fosters team science by allowing collaborating research groups to easily store, interact with, layout and share networks.
Our paper on GroundTruth, a system that allows experts to collaborate with crowds on image geolocation tasks, was accepted for the second GroupSight workshop at HCOMP 2017. Congratulations to Crowd Lab alumni Rachel Kohler and John Purviance, co-authors on the accepted paper.
Here’s the abstract for the paper:
Geolocation, the process of identifying the specific location where a photo or video was taken, is an important task in verifying evidence for investigations in journalism, national security, human rights, and other domains. However, experts typically perform geolocation work as a time-consuming, manual process. This paper introduces GroundTruth, a web-based system that leverages the powerful vision system of crowd workers to support experts in image geolocation tasks. We describe the technical contributions of GroundTruth and present preliminary results from an evaluation with expert geolocators and novice crowds.
For details, please check out the paper and and the corresponding video.
Our full paper on using crowdsourcing and diagramming to support image and video geolocation was accepted for the HCOMP 2017 conference in Québec City, Canada. Only 29% of paper submissions were accepted for this competitive crowdsourcing conference. Congrats to MS Computer Science alumna Rachel Kohler and BS Computer Science alumnus John Purviance, the first and second authors of the paper, respectively.
Here’s the abstract for the paper:
Geolocation, the process of identifying the precise location in the world where a photo or video was taken, is central to many types of investigative work, from debunking fake news posted on social media to locating terrorist training camps. Professional geolocation is often a manual, time-consuming process that involves searching large areas of satellite imagery for potential matches. In this paper, we explore how crowdsourcing can be used to support expert image geolocation. We adapt an expert diagramming technique to overcome spatial reasoning limitations of novice crowds, allowing them to support an expert’s search. In two experiments (n=1080), we found that diagrams work significantly better than ground-level photos and allow crowds to reduce a search area by half before any expert intervention. We also discuss hybrid approaches to complex image analysis combining crowds, experts, and computer vision.
Our study of novice and expert image geolocation techniques was accepted for the GroupSight workshop on human computation for image and video analysis at HCOMP 2016 in Austin, Texas. Congrats to Ph.D. student Sneha Mehta, the lead author of the paper.
Ph.D. student Nai-Ching Wang was accepted to the HCOMP 2016 Doctoral Consortium, where he will present his dissertation research on crowdsourced analysis of historical documents, and receive feedback from experts in the field.
Congrats to Ph.D. student Nai-Ching Wang, who was a finalist in graduate student division of the CHI 2016 Student Research Competition. His paper is titled, “Crowdnection: Connecting High-level Concepts with Historical Documents via Crowdsourcing.” He traveled to San Jose, CA to attend the conference and present his research to a panel of expert judges.