Journalists in the 21st century are expected to work for different platforms, gather online information, become multi‐media professionals, and learn how to deal with amateur contributions. The business model of gathering, producing and distributing news changed rapidly. Producing content is not enough; moderation and curation are at least as important when it comes to working for digital platforms. There is a growing pressure on news organizations to produce more inexpensive content for digital platforms, resulting in new models of low‐cost or even free content production. Aggregation, either by humans or machines ‘finding’ news and re‐publishing it, is gaining importance. At so‐called ‘content farms’ freelancers, part‐timers and amateurs produce articles that are expected to end up high in web searches. Apart from this low‐pay model a no‐pay model emerged were bloggers write for no compensation at all. At the Huffington Post thousands of bloggers actually work for free. Other websites use similar models, sometimes offering writers a fixed price depending on the number of clicks a page gets. We analyse the background, the consequences for journalists and journalism and the implications for online news organizations. We investigate aggregation services and content farms and no‐pay or low‐pay news websites that mainly use bloggers for input.
Journalists in the 21st century are expected to work for different platforms, gather online information, become multi‐media professionals, and learn how to deal with amateur contributions. The business model of gathering, producing and distributing news changed rapidly. Producing content is not enough; moderation and curation are at least as important when it comes to working for digital platforms. There is a growing pressure on news organizations to produce more inexpensive content for digital platforms, resulting in new models of low‐cost or even free content production. Aggregation, either by humans or machines ‘finding’ news and re‐publishing it, is gaining importance. At so‐called ‘content farms’ freelancers, part‐timers and amateurs produce articles that are expected to end up high in web searches. Apart from this low‐pay model a no‐pay model emerged were bloggers write for no compensation at all. At the Huffington Post thousands of bloggers actually work for free. Other websites use similar models, sometimes offering writers a fixed price depending on the number of clicks a page gets. We analyse the background, the consequences for journalists and journalism and the implications for online news organizations. We investigate aggregation services and content farms and no‐pay or low‐pay news websites that mainly use bloggers for input.
Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Mod- ern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary tech- niques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We vali- date this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two ob- jectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.
