52,228 research outputs found

    Data mining based cyber-attack detection

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    LESSONS IN EVALUATING COMMUNICATIONS CAMPAIGNS

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    Builds on the findings of the first and second papers. It examines specifically how campaigns with different purposes (individual behavior change and policy change) have been evaluated, and how evaluators have tackled some of the associated evaluation challenges that the first three papers raised as important to address. It features fi ve brief case studies in which the main unit of analysis is not the campaign, but the campaign's evaluation. The case studies provide a brief snapshot of the real experiences of campaign evaluations. The paper also features cross-case lessons that highlight important findings and themes

    Predictive Analytics In Higher Education: Five Guiding Practices for Ethical Use

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    Without ethical practices, student data could be used to curtail academic success rather than help ensure it. For example, without a clear plan in place, an institution could use predictive analytics to justify using fewer resources to recruit low-income students because their chances of enrolling are less sure than for more affluent prospective students. In this report, New America lays out important questions to consider as administrators formulate how to use predictive analytics ethically

    The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey

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    The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected objects, such as Radio-frequency identification (RFIDs), sensors, actuators, as well as other instruments and smart appliances that are becoming an integral component of the future internet. Over the last decade, we have seen a large number of the IoT solutions developed by start-ups, small and medium enterprises, large corporations, academic research institutes (such as universities), and private and public research organisations making their way into the market. In this paper, we survey over one hundred IoT smart solutions in the marketplace and examine them closely in order to identify the technologies used, functionalities, and applications. More importantly, we identify the trends, opportunities and open challenges in the industry-based the IoT solutions. Based on the application domain, we classify and discuss these solutions under five different categories: smart wearable, smart home, smart, city, smart environment, and smart enterprise. This survey is intended to serve as a guideline and conceptual framework for future research in the IoT and to motivate and inspire further developments. It also provides a systematic exploration of existing research and suggests a number of potentially significant research directions.Comment: IEEE Transactions on Emerging Topics in Computing 201

    Toward a process theory of entrepreneurship: revisiting opportunity identification and entrepreneurial actions

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    This dissertation studies the early development of new ventures and small business and the entrepreneurship process from initial ideas to viable ventures. I unpack the micro-foundations of entrepreneurial actions and new ventures’ investor communications through quality signals to finance their growth path. This dissertation includes two qualitative papers and one quantitative study. The qualitative papers employ an inductive multiple-case approach and include seven medical equipment manufacturers (new ventures) in a nascent market context (the mobile health industry) across six U.S. states and a secondary data analysis to understand the emergence of opportunities and the early development of new ventures. The quantitative research chapter includes 770 IPOs in the manufacturing industries in the U.S. and investigates the legitimation strategies of young ventures to gain resources from targeted resource-holders.Open Acces

    Unmanned Aerial Systems for Wildland and Forest Fires

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    Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001
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