Encoding multimedia streams of video calls is a very compute-intense task that significantly decreases battery lifetime of mobile phones. This paper introduces an approach to reduce power consumption of mobile phones by offloading video encoding efforts from mobile devices to external services. These services are hosted on servers co-located with cellular base stations. The paper describes how these services are integrated into the existing mobile network architecture and presents a communication protocol for negotiating offloading settings. First measurement results indicate that power consumption of mobile devices is reduced by approximately 13%.

Background Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for each patient accurately and individually. However, physicians can reduce their workload by using recommender systems, still having every decision under control. In this way, they also get an insight into how other physicians make treatment decisions in each situation. In this work, we report the development of a novel recommender system that uses predicted outcomes based on continuous-valued logic and multi-criteria decision operators. The advantage of this methodology is that it is transparent, since the model outcomes emulate logical decision processes based on the hierarchy of relevant physiological parameters, and second, it is safer against adversarial attacks than conventional deep learning methods since it drastically reduces the number of trainable parameters. Methods We test our methodology in a patient population with diabetes and heart insufficiency that becomes a therapy (beta-blockers, ACE or Aspirin). The original database (Pakistan database) is publicly available and accessible via the internet. However, to explore methods to protect the patient's identity and guarantee data privacy we implemented a methodology on a variable-by-variable basis by fitting a sequence of regression models and drawing synthetic values from the corresponding predictive distributions using linear regressions and norm rank. Furthermore, we implemented a deep-learning model based on logical gates modeled by perceptrons with fixed weights and biases. While a first trainable layer automatically recognizes a meaningful parameter hierarchy, the implemented Logic-Operator Neuronal Network (LONN) simulates cognitive processes like a rational, logical thinking process, considering that this logic is joined by fuzziness, i.e., logical operations are not exact but essentially fuzzy due to the implemented continuous-valued operators. The predicted outcomes of the model (kind of therapy-ACE, Aspirin or beta-blocker- and expected therapy time of the patient) are then implemented in a recommender system that compares two different models: model 1 trained on a population excluding negative outcomes (patient group 1, with no patient dead and long therapy times) and a model 2 trained on the whole patient population (patient group 2). In this way, we provide a recommendation of the best possible therapy based on the outcome of the model and the confidence of this recommendation when the outcome of model 1 is compared with the outcome of model 2. Results With the applied method for data synthetization, we obtained an error of about 1% for all the relevant parameters. Furthermore, we demonstrate that the LONN models reach an accuracy of about 75%. After comparing the LONN models against conventional deep-learning models we observe that our implemented models are less accurate (accuracy loss of about 8%). However, the loss of accuracy is compensated by the fact that LONN models are transparent and safe because the freezing of training parameters makes them less prone to adversarial attacks. Finally, we predict the best therapy as well as the expected therapy time. We were able to predict individualized therapies, which were classified as optimal (binary value) when the prediction fully matched predictions made with models 1 and 2. The results provided by the recommender system are displayed using a graphical interface. The current is a proof of concept to improve the quality of the disease management, while the methods are continuously visualized to preserve transparency for the customers. Conclusions This work contributes to simplify administrative functions and boost the quality of management of patients improving the quality of healthcare with models that are both transparent and safe. Our methodology can be extended to different clinical scenarios where recommender systems can be applied. The acceptance and further development of the app is one of the next important steps and still requires further development depending on specific requirements of the health management, the physicians or health professionals, and the patent population.

Background Out of the pressure of Digital Transformation, the major industrial domains are using advanced and efficient digital technologies to implement processes that are applied on a daily basis. Unfortunately, this still does not happen in the same way in the medical domain. For this reason, doctors usually do not have the time or knowledge to evaluate all alternative treatment options for an individual patient accurately and individually. However, physicians can reduce their workload by using Recommender Systems, still having every decision under control. In this way, they also get an insight into how other physicians make treatment decisions in each situation. In this work, we report the development of a novel recommender system that uses predicted outcomes based on continuous-valued logic and multi-criteria decision operators. The advantage of this methodology is that it is transparent, since the model outcomes emulate logical decision processes based on the hierarchy of relevant physiological parameters, and second, it is safer against adversarial attacks than conventional deep learning methods since it drastically reduces the number of trainable parameters.Methods We test our methodology in a patient population with diabetes and heart insufficiency that becomes a therapy (beta-blockers, ACE or Aspirin). The original database (Pakistan database) is publicly available and accessible via the internet. However, to explore methods to protect the patient`s identity and guarantee data privacy we implemented a methodology on a variable-by-variable basis by fitting a sequence of regression models and drawing synthetic values from the corresponding predictive distributions using linear regressions and norm rank.Furthermore, we implemented a deep-learning model based on logical gates modeled by perceptrons with fixed weights and biases. While a first trainable layer automatically recognizes a meaningful parameter hierarchy, the implemented Logic-Operator Neuronal Network (LONN) simulates cognitive processes like a rational, logical thinking process, considering that this logic is joined by fuzziness, i.e., logical operations are not exact but essentially fuzzy due to the implemented continuous-valued operators.The predicted outcomes of the model (kind of therapy -ACE, Aspirin or beta-blocker- and expected therapy time of the patient) are then implemented in a recommender system that compares two different models: model 1 trained on a population excluding negative outcomes (patient group 1, with no patient dead and long therapy times) and a model 2 trained on the whole patient population (patient group 2). In this way, we provide a recommendation of the best possible therapy based on the outcome of model 1and the confidence of this recommendation when the outcome of model 1 is compared with the outcome of model 2. Results With the applied method for data synthetization, we obtained an error of about 1% for all the relevant parameters. Furthermore, we demonstrate that the LONN models reach an accuracy of about 75%. After comparing the LONN models against conventional deep-learning models we observe that our implemented models are less accurate (accuracy loss of about 8%). However, the loss of accuracy is compensated by the fact that LONN models are transparent and safe because the freezing of training parameters makes them less prone to adversarial attacks. Finally, we predict the best therapy as well as the expected therapy time. We were able to predict individualized therapies, which were classified as optimal (binary value) when the prediction fully matched predictions made with models 1 and 2. The results provided by the recommender system are displayed using a graphical interface. The current is a proof of concept to improve the quality of the disease management, while the methods are continuously visualized to preserve transparency for the customers. Conclusions This work contributes to boost administrative functions and quality of management of patients and improve the quality of healthcare with models that are both transparent and safe. Our methodology can be extended to different clinical scenarios where recommender systems can be applied. The acceptance and further development of the app is one of the next important steps and still requires further development depending on specific requirements of the health management, the physicians or health professionals, and the patent population.

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