Abstract

Abstract
device to increase energy autonomy of moored oceanographic monitoring stations. Oscillations and currents through the sea or river are used to produce energy when the whole system is submerged to a depth between 3 to 10 meters. In order to have an inexpensive system, a buoy containing a Linear Electromagnetic Generator (LEG), is fabricated in a 3D printer, using PLA (polylactic acid) filament. Inside of the buoy, one cylinder shaped LEG (98mm length and 25mm of diameter) produces a maximum output power of 20 mW with a 4 Hz movement. To increase power output in larger systems, more LEGs can be added.

Abstract

Abstract

Abstract
Exergames require obtaining or computing information regarding the players’ physical activity and context. Additionally, ensuring that the players are assigned challenges that are adequate to their physical ability, safe and adapted for the current context (both physical and spatial) is also important, as it can improve both the gaming experience and the outcomes of the exercise. However, the impact adaptivity has in the specific case of virtual reality exergames still has not been researched in depth. In this paper, we present a virtual reality exergame and an experimental design aiming to compare the players’ experience when playing both adaptive and regular versions of the game. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
Ensemble methods have been successfully used in the past to improve recommender systems; however, they have never been studied with incremental recommendation algorithms. Many online recommender systems deal with continuous, potentially fast, and unbounded flows of databig data streamsand often need to be responsive to fresh user feedback, adjusting recommendations accordingly. This is clear in tasks such as social network feeds, news recommender systems, automatic playlist completion, and other similar applications. Batch ensemble approaches are not suitable to perform continuous learning, given the complexity of retraining new models on demand. In this paper, we adapt a general purpose online bagging algorithm for top-N recommendation tasks and propose two novel online bagging methods specifically tailored for recommender systems. We evaluate the three approaches, using an incremental matrix factorization algorithm for top-N recommendation with positive-only user feedback data as the base model. Our results show that online bagging is able to improve accuracy up to 55% over the baseline, with manageable computational overhead.