CrowdAR: a live video annotation tool for rapid mapping

Digital Humanitarians are a powerful and effective resource to analyse the vast amounts of data that disasters generate. Aerial vehicles are increasingly being used for gathering high resolution imagery of affected areas, but require a lot of effort to effectively analyse, typically taking days to complete. We introduce CrowdAR, a real-time crowdsourcing platform that tags live footage from aerial vehicles flown during disasters. CrowdAR enables the analysis of footage within minutes, can rapidly plot snippets of the video onto a map, and can reduce the cognitive load of pilots by augmenting their live video feed with crowd annotations

Out of hours workload management: Bayesian inference for decision support in secondary care

Objective: In this paper, we aim to evaluate the use of electronic technologies in Out of Hours (OoH) task-management for assisting the design of effective support systems in health care; targeting local facilities, wards or specific working groups. In addition, we seek to draw and validate conclusions with relevance to a frequently revised service, subject to increasing pressures. Methods and Material: We have analysed 4 years of digitised demand-data extracted from a recently deployed electronic task-management system, within the Hospital at Night setting in two jointly coordinated hospitals in the United Kingdom. The methodology employed relies on Bayesian inference methods and parameter-driven state-space models for multivariate series of count data. Results: Main results support claims relating to (i) the importance of data-driven staffing alternatives and (ii) demand forecasts serving as a basis to intelligent scheduling within working groups. We have displayed a split in workload patterns across groups of medical and surgical specialities, and sustained assertions regarding staff behaviour and work-need changes according to shifts or days of the week. Also, we have provided evidence regarding the relevance of day-to-day planning and prioritisation. Conclusions: The work exhibits potential contributions of electronic tasking alternatives for the purpose of data-driven support systems design; for scheduling, prioritisation and management of care delivery. Electronic tasking technologies provide means to design intelligent systems specific to a ward, speciality or task-type; hence, the paper emphasizes the importance of replacing traditional pager-based approaches to management for modern alternatives

Life-long Programming Implications of Exposure to Tobacco Smoking and Nicotine Before and Soon After Birth: Evidence for Altered Lung Development

Tobacco smoking during pregnancy remains common, especially in indigenous communities, and likely contributes to respiratory illness in exposed offspring. It is now well established that components of tobacco smoke, notably nicotine, can affect multiple organs in the fetus and newborn, potentially with life-long consequences. Recent studies have shown that nicotine can permanently affect the developing lung such that its final structure and function are adversely affected; these changes can increase the risk of respiratory illness and accelerate the decline in lung function with age. In this review we discuss the impact of maternal smoking on the lungs and consider the evidence that smoking can have life-long, programming consequences for exposed offspring. Exposure to maternal tobacco smoking and nicotine intake during pregnancy and lactation changes the genetic program that controls the development and aging of the lungs of the offspring. Changes in the conducting airways and alveoli reduce lung function in exposed offspring, rendering the lungs more susceptible to obstructive lung disease and accelerating lung aging. Although it is generally accepted that prevention of maternal smoking during pregnancy and lactation is essential, current knowledge of the effects of nicotine on lung development does not support the use of nicotine replacement therapy in this group

Prescribing for pain - how do nurses contribute? A national questionnaire survey

Aims and objectives. To provide information on the profile and practice of nurses in the UK who prescribe medication for pain. Background. Pain is widely under-reported and under-treated and can have negative consequences for health and psychosocial well-being. Indications are that nurses can improve treatment and access to pain medications when they prescribe. Whilst nurses working in many practice areas treat patients with pain, little is known about the profile, prescribing practice or training needs of these nurses. Design. A descriptive questionnaire survey. Method. An online questionnaire was used to survey 214 nurses who prescribed for pain in the UK between May and July 2010. Data were analysed using descriptive statistics and non-parametric tests. Results. Half the participants (50%) worked in primary care, 32% in secondary care and 14% worked across care settings. A range of services were provided, including general practice, palliative care, pain management, emergency care, walk-in-centres and out-of-hours. The majority (86%) independently prescribed 1-20 items per week. Non-opioid and weak opioids analgesics were prescribed by most (95%) nurses, whereas fewer (35%) prescribed strong opioids. Training in pain had been undertaken by 97% and 82% felt adequately trained, although 28% had problems accessing training. Those with specialist training prescribed a wider range of pain medications, were more likely to prescribe strong opioids and were more often in pain management roles. Conclusion. Nurses prescribe for pain in a range of settings with an emphasis on the treatment of minor ailments and acute pain. A range of medications are prescribed, and most nurses have access to training. Relevance to clinical practice. The nursing contribution to pain treatment must be acknowledged within initiatives to improve pain management. Access to ongoing training is required to support nurse development in this area of practice to maximise benefits. © 2012 Blackwell Publishing Ltd

Real-time opinion aggregation methods for crowd robotics

Unmanned Aerial Vehicles (UAVs) are increasingly becoming instrumental to many commercial applications, such as transportation and maintenance. However, these applications require flexibility, understanding of natural language, and comprehension of video streams that cannot currently be automated and instead require the intelligence of a skilled human pilot. While having one pilot individually supervising a UAV is not scalable, the machine intelligence, especially vision, required to operate a UAV is still inadequate. Hence, in this paper, we consider the use of crowd robotics to harness a real-time crowd to orientate a UAV in an unknown environment. In particular, we present two novel real-time crowd input aggregation methods. To evaluate these methods, we develop a new testbed for crowd robotics, called CrowdDrone, that allows us to evaluate crowd robotic systems in a variety of scenarios. Using this platform, we benchmark our real-time aggregation methods with crowds hired from Amazon Mechanical Turk and show that our techniques outperform the current state-of-the-art aggregation methods, enabling a robotic agent to travel faster across a fixed distance, and with more precision. Furthermore, our aggregation methods are shown to be significantly more effective in dynamic scenario

CrowdAR: augmenting live video with a real-time crowd

Finding and tracking targets and events in a live video feed is important formany commercial applications, from CCTV surveillance used by police and securityfirms, to the rapid mapping of events from aerial imagery.However, descriptions of targets are typically provided in natural language bythe end users, and interpreting these in the context of a live video stream is acomplex task. Due to current limitations in artificial intelligence, especiallyvision, this task cannot be automated and instead requires human supervision.Hence, in this paper, we consider the use of real-time crowdsourcing to identifyand track targets given by a natural language description. In particular wepresent a novel method for augmenting live video with a real-time crowd

Crowd robotics: real-time crowdsourcing for crowd controlled robotic agents

Major man-made and natural disasters have a significant and long-lasting economic and social impact on countries around the world. The response effort in the first few hours of the aftermath of the disaster is crucial to saving lives and minimising damage to infrastructure. In these conditions, emergency response organisations on the ground face a major challenge in trying to understand what is happening, and where the casualties are. Crowdsourcing is often used in disasters to analyse the masses of data generated, and report areas of importance to the first responders, but the results are to slow to inform immediate decision making. This thesis describes techniques for utilising real-time crowdsourcing to analyse the disaster data in real-time. We utilise this real-time analysis to influence or control robotic search agents, unmanned aerial vehicles, that are increasingly being used in disaster scenarios. We investigate methods for reliably and promptly aggregating real-time crowd input, for two different crowd robotic applications. First, direct control, used for directing a robotic search and rescue agent around a complicated and dynamic environment. Second, real-time locational sensing, used for rapidly mapping disasters and to augment a pilot's video feed, such that they can make more informed decisions on the fly, but could be used to inform a higher artificial intelligence process to direct a robotic agent. We describe two systems, CrowdDrone and CrowdAR, that use state-of-the-art methods for human-intelligent control and sensing for crowd robotics

Toward Scalable Social Alt Text: Conversational Crowdsourcing as a Tool for Refining Vision-to-Language Technology for the Blind

The access of visually impaired users to imagery in social media is constrained by the availability of suitable alt text. It is unknown how imperfections in emerging tools for automatic caption generation may help or hinder blind users' understanding of social media posts with embedded imagery. In this paper, we study how crowdsourcing can be used both for evaluating the value provided by existing automated approaches and for enabling workflows that provide scalable and useful alt text to blind users. Using real-time crowdsourcing, we designed experiences that varied the depth of interaction of the crowd in assisting visually impaired users at caption interpretation, and measured trade-offs in effectiveness, scalability, and reusability. We show that the shortcomings of existing AI image captioning systems frequently hinder a user's understanding of an image they cannot see to a degree that even clarifying conversations with sighted assistants cannot correct. Our detailed analysis of the set of clarifying conversations collected from our studies led to the design of experiences that can effectively assist users in a scalable way without the need for real-time interaction. They also provide lessons and guidelines that human captioners and the designers of future iterations of AI captioning systems can use to improve labeling of social media imagery for blind users