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Human falls are a global public health issue resulting in over 37.3 million severe injuries and 646,000 deaths yearly. Falls result in direct financial cost to health systems and indirectly to society productivity. Unsurprisingly, human fall detection and prevention are a major focus of health research. In this article, we consider deep learning for fall detection in an IoT and fog computing environment. We propose a Convolutional Neural Network composed of three convolutional layers, two maxpool, and three fully-connected layers as our deep learning model. We evaluate its performance using three open data sets and against extant research. Our approach for resolving dimensionality and modelling simplicity issues is outlined. Accuracy, precision, sensitivity, specificity, and the Matthews Correlation Coefficient are used to evaluate performance. The best results are achieved when using data augmentation during the training process. The paper concludes with a discussion of challenges and future directions for research in this domain.

Summary Microservices architectures are becoming the defacto standard for building continuously deployed systems. At the same time, there is a substantial growth in the demand for migrating on‐premise legacy applications to the cloud. In this context, organizations tend to migrate their traditional architectures into cloud‐native architectures using microservices. This article reports a set of migration and rearchitecting design patterns that we have empirically identified and collected from industrial‐scale software migration projects. These migration patterns can help information technology organizations plan their migration projects toward microservices more efficiently and effectively. In addition, the proposed patterns facilitate the definition of migration plans by pattern composition. Qualitative empirical research is used to evaluate the validity of the proposed patterns. Our findings suggest that the proposed patterns are evident in other architectural refactoring and migration projects and strong candidates for effective patterns in system migrations.

Extending the literature that has focused thus far on stock price impact, this study investigates the effect of data breach announcements on market activity, specifically through the response of the bid-ask spread and trading volume. We investigate data breach announcements as a potential source of asymmetric information and provide a new dimension to the ongoing debate on market efficiency. Adopting an event study methodology on a sample of 74 data breaches from 2005 to 2014, we find that data breach announcements have a positive short-term effect on both bid-ask spread and trading volume. The effect is only evidenced however on the day of the event, with market efficiency ensuring a quick return to normal market activity. No abnormal trading activity emerges before announcements, so there is no evidence in our study that these types of events are being exploited by informed market participants. The magnitude of event day effects is found to be more pronounced for large breaches, and when the breach involves lost devices.

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The fourth industrial revolution heralds a paradigm shift in how people, processes, things, data and networks communicate and connect with each other. Conventional computing infrastructures are struggling to satisfy dramatic growth in demand from a deluge of connected heterogeneous end points located at the edge of networks while, at the same time, meeting quality of service levels. The complexity of computing at the edge makes it increasingly difficult for infrastructure providers to plan for and provision resources to meet this demand. While simulation frameworks are used extensively in the modelling of cloud computing environments in order to test and validate technical solutions, they are at a nascent stage of development and adoption for fog and edge computing. This paper provides an overview of challenges posed by fog and edge computing in relation to simulation.

This study investigates the impact of cyber-security incidents on audit fees. Using a sample of 5,687 firms, we find that (i) breached firms are charged 12% higher audit fees, and (ii) firms operating in the same industry of a breached firm are charged 5% higher fees. Finally, using a difference-in-difference regression on a propensity score matched sample, we provide evidence suggesting that auditors do not revise their audit risk assessment following a breach. Overall, these results suggest that the increase in audit fees in the year of a breach is only temporary, and that auditors include cyber-security risk in their audit risk assessment even before an incident occurs. Higher cyber-security risk is ultimately reflected in higher audit fees paid by auditees.