Two members of the Crowd Lab each had a paper accepted for presentation at the upcoming IUI 2019 conference in Los Angeles, CA. The acceptance rate for this conference, which focuses on the intersection of human-computer interaction and artificial intelligence, was 25%.
Crowd Lab Ph.D. student Vikram Mohanty will present “Photo Sleuth: Combining Human Expertise and Face Recognition to Identify Historical Portraits“, co-authored with undergraduate David Thames and Ph.D. student Sneha Mehta. Here is the paper’s abstract:
Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this paper, we focus on identifying portraits of soldiers who participated in the American Civil War (1861- 65), the first widely-photographed conflict. Many thousands of these portraits survive, but only 10–20% are identified. We created Photo Sleuth, a web-based platform that combines crowdsourced human expertise and automated face recognition to support Civil War portrait identification. Our mixed-methods evaluation of Photo Sleuth one month after its public launch showed that it helped users successfully identify unknown portraits and provided a sustainable model for volunteer contribution. We also discuss implications for crowd-AI interaction and person identification pipelines.
Crowd Lab Ph.D. student Tianyi Li will present “What Data Should I Protect? A Recommender and Impact Analysis Design to Assist Decision Making“, co-authored with Informatica colleagues Gregorio Convertino, Ranjeet Kumar Tayi, and Shima Kazerooni. Here is the paper’s abstract:
Major breaches of sensitive company data, as for Facebook’s 50 million user accounts in 2018 or Equifax’s 143 million user accounts in 2017, are showing the limitations of reactive data security technologies. Companies and government organizations are turning to proactive data security technologies that secure sensitive data at source. However, data security analysts still face two fundamental challenges in data protection decisions: 1) the information overload from the growing number of data repositories and protection techniques to consider; 2) the optimization of protection plans given the current goals and available resources in the organization. In this work, we propose an intelligent user interface for security analysts that recommends what data to protect, visualizes simulated protection impact, and helps build protection plans. In a domain with limited access to expert users and practices, we elicited user requirements from security analysts in industry and modeled data risks based on architectural and conceptual attributes. Our preliminary evaluation suggests that the design improves the understanding and trust of the recommended protections and helps convert risk information in protection plans.
Congratulations to Vikram, David, Sneha, Tianyi, and their collaborators!
