Abstract
Nowadays tourists rely on technology for inspiration, research, booking, experiencing and sharing. Not only it provides access to endless sources of information, but has become an unbounded source of tourist-related data. In such crowd-sourced data-intensive scenario, we argue that new approaches are required to enrich current and new travelling experiences. This work, which supports the "dreaming stage", proposes the automatic recommendation of personalised destinations based on textual reviews, i.e.,a semantic content-based filter of crowd-sourced information. Our approach relies on Topic Modelling - to extract meaningful information from textual reviews - and Semantic Similarity to identify relevant recommendations. Our main contribution is the processing of crowd-sourced tourism information employing data mining techniques in order to automatically discover untapped destinations on behalf of tourists.

Abstract
Recommender systems try to predict which items a user will prefer. Traditional models for recommendation only take into account the user-item interaction, usually expressed by explicit ratings. However, in these days, web services continuously generate auxiliary data from users and items that can be incorporated into the recommendation model to improve recommendations. In this work, we propose an incremental Matrix Co-factorization model with implicit user feedback, considering a real-world data-stream scenario. This model can be seen as an extension of the conventional Matrix Factorization that includes additional dimensions to be decomposed in the common latent factor space. We test our proposal against a baseline algorithm that relies exclusively on interaction data, using prequential evaluation. Our experimental results show a significant improvement in the accuracy of recommendations, after incorporating an additional dimension in three music domain datasets. © 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.

Abstract
The widespread usage of smart devices and sensors together with the ubiquity of the Internet access is behind the exponential growth of data streams. Nowadays, there are hundreds of machine learning algorithms able to process high-speed data streams. However, these algorithms rely on human expertise to perform complex processing tasks like hyper-parameter tuning. This paper addresses the problem of data variability modelling in data streams. Specifically, we propose and evaluate a new parameter tuning algorithm called Self Parameter Tuning (SPT). SPT consists of an online adaptation of the Nelder & Mead optimisation algorithm for hyper-parameter tuning. The method explores a dynamic size sample method to evaluate the current solution, and uses the Nelder & Mead operators to update the current set of parameters. The main contribution is the adaptation of the Nelder-Mead algorithm to automatically tune regression hyper-parameters for data streams. Additionally, whenever concept drifts occur in the data stream, it re-initiates the search for new hyper-parameters. The proposed method has been evaluated on regression scenario. Experiments with well known time-evolving data streams show that the proposed SPT hyper-parameter optimisation outperforms the results of previous expert hyper-parameter tuning efforts. © 2018, Springer Nature Switzerland AG.

Abstract
Electric vehicles will certainly play an important and increasing role in the transport sector over the next years. As their number grows, they will affect the behavior of the electricity demand seen not only by distribution but also by transmission networks and so changes will also occur in the operation and expansion planning of the power systems. In this sense, this paper addresses the impact of large fleets of Plug-in-Electric Vehicles (PEVs) in transmission equipment investments. The developed model uses evolutionary particle swarm optimization (EPSO) to handle the planning problem over different scenarios regarding the evolution of PEVs and their impact on the demand. These scenarios consider the PEVs penetration level, the availability of charging and the related charging policies. The paper includes a Case Study based on the IEEE 24 busbar power system model for a 10-year period. The model uses an AC Optimal Power Flow to analyse the operation of the system for different investment paths over the years and the results show that coordinating the charging of PEVs can be an interesting solution to postpone the investments in transmission equipment thus reducing the associated costs.

Abstract
Contrary to outdoor positioning and navigation systems, there isn't a counterpart global solution for indoor environments. Usually, the deployment of an indoor positioning system must be adapted case by case, according to the infrastructure and the objective of the localization. A particularly delicate case is related with persons who are blind or visually impaired. A robust and easy to use indoor navigation solution would be extremely useful, but this would also be particularly difficult to develop, given the special requirements of the system that would have to be more accurate and user friendly than a general solution. This paper presents a contribute to this subject, by proposing a hybrid indoor positioning system adaptable to the surrounding indoor structure, and dealing with different types of signals to increase accuracy. This would permit lower the deployment costs, since it could be done gradually, beginning with the likely existing Wi-Fi infrastructure to get a fairy accuracy up to a high accuracy using visual tags and NFC tags when necessary and possible. © 2018 AISTI.

Abstract
This paper addresses the wind farm contribution in frequency control during the integration in the power grid. In the proposed model, the wind farm utilizes inertia control and droop control techniques with the purpose of improving the frequency regulation. In order to achieve optimal results, all the parameters of the controllers for the different units in the power grid are obtained by using a particle swarm optimization algorithm (PSO) and by introducing a modified objective function instead of a conventional objective function e.g., Integral Time-weighted Absolute Error (ITAE). Also, different constraints such as reheat turbine, time delay, governor dead band and generation rate constraint (GRC) are considered for thermal and hydro units with the aim of studying a more realistic power system, which is the main contribution of this paper when compared to the other works in this field. It is shown that, in case of a perturbation in power demand, the system frequency will recover quickly and effectively in comparison with the traditional approaches. In addition, a sensitivity test is carried out in a single power grid area in order to examine the effectiveness of the proposed approach. Then, the system is extended to a multi-area power system using a multi-terminal HVDC for further investigation of the suggested strategy. Simulation results are presented in order to assess the performance of the proposed approach in the power system.