70,956 research outputs found

    The Formal, the Informal, and the Precarious: Making a Living in Urban Papua New Guinea

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    For many Papua New Guineans, the dominant accounts of 'the economy' � contained within development reports, government documents and the media � do not adequately reflect their experiences of making a living. Large-scale resource extraction, the private sector, export cash cropping and wage employment have dominated these accounts. Meanwhile, the broader economic picture has remained obscured, and the diversity of economic practices, including a flourishing 'informal' economy, has routinely been overlooked and undervalued. Addressing this gap, this paper provides some grounded examples of the diverse livelihood strategies people employ in Papua New Guinea's growing urban centres. We examine the strategies people employ to sustain themselves materially, and focus on how people acquire and recirculate money. We reveal the interconnections between a diverse range of economic activities, both formal and informal. In doing so, we complicate any clear narrative that might, for example, associate waged employment with economic security, or street selling with precarity and urban poverty. Our work is informed by observations of people's daily lives, and conversations with security guards (Stephanie Lusby), the salaried middle class (John Cox), women entrepreneurs (Ceridwen Spark), residents from the urban settlements (Michelle Rooney) and betel nut traders and vendors (Timothy Sharp). Collectively, our work takes an urban focus, yet the flows and connectivity between urban and rural, and our focus on livelihood strategies, means much of our discussion is also relevant to rural people and places. Our examples, drawn from urban centres throughout the country, each in their own way illustrate something of the diversity of economic activity in urban PNG. Our material captures the innovation and experimentation of people's responses to precarity in contemporary PNG.AusAI

    Learning Scheduling Algorithms for Data Processing Clusters

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    Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load

    Volunteering and Social Activism: Pathways for Participation in Human Development

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    This discussion paper explores the following questions, drawing on the above-mentioned background study: How is volunteering an d social activism understood?; How do volunteering and social activism foster people's participation?; What is the relationship between participation and development?; What is required to widen and sustain participation

    Medical data processing and analysis for remote health and activities monitoring

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    Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions
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