Real-time crowd control of existing interfaces

Crowdsourcing has been shown to be an effective approach for solving difficult problems, but current crowdsourcing systems suffer two main limitations: (i) tasks must be repackaged for proper display to crowd workers, which generally requires substantial one-off programming effort and support infrastructure, and (ii) crowd workers generally lack a tight feedback loop with their task. In this paper, we introduce Legion, a system that allows end users to easily capture existing GUIs and outsource them for collaborative, real-time control by the crowd. We present mediation strategies for integrating the input of multiple crowd workers in real-time, evaluate these mediation strategies across several applications, and further validate Legion by exploring the space of novel applications that it enables

An introduction to crowdsourcing for language and multimedia technology research

Language and multimedia technology research often relies on large manually constructed datasets for training or evaluation of algorithms and systems. Constructing these datasets is often expensive with significant challenges in terms of recruitment of personnel to carry out the work. Crowdsourcing methods using scalable pools of workers available on-demand offers a flexible means of rapid low-cost construction of many of these datasets to support existing research requirements and potentially promote new research initiatives that would otherwise not be possible

StateLens: A Reverse Engineering Solution for Making Existing Dynamic Touchscreens Accessible

Blind people frequently encounter inaccessible dynamic touchscreens in their everyday lives that are difficult, frustrating, and often impossible to use independently. Touchscreens are often the only way to control everything from coffee machines and payment terminals, to subway ticket machines and in-flight entertainment systems. Interacting with dynamic touchscreens is difficult non-visually because the visual user interfaces change, interactions often occur over multiple different screens, and it is easy to accidentally trigger interface actions while exploring the screen. To solve these problems, we introduce StateLens - a three-part reverse engineering solution that makes existing dynamic touchscreens accessible. First, StateLens reverse engineers the underlying state diagrams of existing interfaces using point-of-view videos found online or taken by users using a hybrid crowd-computer vision pipeline. Second, using the state diagrams, StateLens automatically generates conversational agents to guide blind users through specifying the tasks that the interface can perform, allowing the StateLens iOS application to provide interactive guidance and feedback so that blind users can access the interface. Finally, a set of 3D-printed accessories enable blind people to explore capacitive touchscreens without the risk of triggering accidental touches on the interface. Our technical evaluation shows that StateLens can accurately reconstruct interfaces from stationary, hand-held, and web videos; and, a user study of the complete system demonstrates that StateLens successfully enables blind users to access otherwise inaccessible dynamic touchscreens.Comment: ACM UIST 201

Beyond safety drivers: Applying air traffic control principles to support the deployment of driverless vehicles.

By adopting and extending lessons from the air traffic control system, we argue that a nationwide remote monitoring system for driverless vehicles could increase safety dramatically, speed these vehicles' deployment, and provide employment. It is becoming clear that fully driverless vehicles will not be able to handle "edge" cases in the near future, suggesting that new methods are needed to monitor remotely driverless vehicles' safe deployment. While the remote operations concept is not new, a super-human driver is needed to handle sudden, critical events. We envision that the remote operators do not directly drive the vehicles, but provide input on high level tasks such as path-planning, object detection and classification. This can be achieved via input from multiple individuals, coordinated around a task at a moment's notice. Assuming a 10% penetration rate of driverless vehicles, we show that one remote driver can replace 14,840 human drivers. A comprehensive nationwide interoperability standard and procedure should be established for the remote monitoring and operation of driverless vehicles. The resulting system has potential to be an order of magnitude safer than today's ground transportation system. We articulate a research and policy roadmap to launch this nationwide system. Additionally, this hybrid human-AI system introduces a new job category, likely a source of employment nationwide

Great potential of ultrasound elastography for the assessment of the masseter muscle in patients with temporomandibular disorders. A systematic review

Objective: To summarize the available evidence on the use of elastography in the assessment of the masseter muscle in healthy individuals and patients with masseter muscle disorders.Methods: Systematic literature review has been performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines.Results: 16 of 142 studies identified were analyzed. Elastography was used in seven studies. Heterogeneity was observed in terms of study protocols, devices, patients, units of measure, and results. Elasticity values showed a correlation between the left and right masseter muscle side in healthy people, but not in patients with temporomandibular disorders (TMDs). Elasticity values increased in TMD and were correlated with the severity of TMD symptoms. Phantom studies proved the high reliability of elastography.Conclusion: Elastography is a promising tool for the assessment of the masseter muscle elasticity, but the evidence is insufficient. Studies on larger groups are needed to determine the accuracy of elastography to characterize masticatory muscle disorders.Radiolog