Twitter, Facebook and Ten Red Balloons: Social Network Problem Solving and Homeland Security
The winner of the Center for Homeland Defense and Security (CHDS) Essay Contest in 2010, this essay looks at how homeland security could benefit from crowd-sourced applications accessed through social networking tools such as Twitter and Facebook. Christopher M. Ford looks at the apparent efficacy of two such endeavors: the Defense Advanced Research Projects Agency’s (DARPA) competition to find ten 8-foot balloons moored across the continental U.S. and Wired Magazine challenge to “find” author Evan Ratliff. Based on these and other crowd-sourced applications, Ford suggests that the U.S. government could utilize the internet and social networking sites to potentially solve an array of discrete problems through the active participation of interested citizens.
CrowdFound: A Mobile Crowdsourcing System to Find Lost Items On-the-Go
We present CrowdFound, a mobile crowdsourcing system to find lost items. CrowdFound allows users to input lost item descriptions on a map and then sends notifications to users passing near tagged areas. To assess the system's efficacy, we conducted interviews and user testing on CrowdFound. Our results show that users were able to find lost items when using a combination of the notification, map, and item description features. In addition, users were willing to deviate off path to look for lost items, particularly when exercising. Our findings also suggest socio-technical features to promote more effective on-the-go crowdsourced help on microtasks. This research builds our understanding of physical crowdsourcing as a tool for solving societal problems and suggests broader implications for utilizing mobile crowds.
Convolutional Neural Networks for Multidrug-resistant and Drug-sensitive Tuberculosis Distinction
Tuberculosis is a widespread disease and one of the top causes of death worldwide. Especially the distinction between drug-sensitive and multidrug-resistant tuberculosis is still problematic. The ImageCLEF 2017 Tuberculosis Task 1 aims at the development of a machine learning system able to decide whether a patient suffers from multidrugresistant tuberculosis or not, based solely on a CT scan of that person’s lungs. In this paper we describe our approach to solve this problem. This consists of a preprocessing step that denoises the scans and highlights certain parts of the lungs that we assume to be meaningful for the distinction. We then utilise a shallow convolutional neural network for the following classification.