Abstract
Stochastic search and optimization techniques are used in a vast number of areas, ranging from refining the design of vehicles, determining the effectiveness of new drugs, developing efficient strategies in games, or learning proper behaviors in robotics. However, they specialize for the specific problem they are solving, and if the problem's context slightly changes, they cannot adapt properly. In fact, they require complete re-leaning in order to perform correctly in new unseen scenarios, regardless of how similar they are to previous learned environments. Contextual algorithms have recently emerged as solutions to this problem. They learn the policy for a task that depends on a given context, such that widely different contexts belonging to the same task are learned simultaneously. That being said, the state-of-the-art proposals of this class of algorithms prematurely converge, and simply cannot compete with algorithms that learn a policy for a single context. We describe the Contextual Relative Entropy Policy Search (CREPS) algorithm, which belongs to the before-mentioned class of contextual algorithms. We extend it with a technique that allows the algorithm to severely increase its performance, and we call it Contextual Relative Entropy Policy Search with Covariance Matrix Adaptation (CREPS-CMA). We propose two variants, and demonstrate their behavior in a set of classic contextual optimization problems, and on complex simulator robot tasks.

Abstract
Real time classification of objects using computer vision techniques are becoming relevant with emergence of advanced perceptions systems required by, surveillance systems, industry 4.0 robotics and agricultural robots. Conventional video surveillance basically detects and tracks moving object whereas there is no indication of whether the object is approaching or receding the camera (looming). Looming detection and classification of object movements aids in knowing the position of the object and plays a crucial role in military, vehicle traffic management, robotics, etc. To accomplish real-time object trajectory classification, a contour tracking algorithm is necessary. In this paper, an application is made to perform looming detection and to detect imminent collision on a system-on-chip field-programmable gate array (SoC-FPGA) hardware. The work presented in this paper was designed for running in Robotic platforms, Unmanned Aerial Vehicles, Advanced Driver Assistance System, etc. Due to several advantages of SoC-FPGA the proposed work is performed on the hardware. The proposed work focusses on capturing images, processing, classifying the movements of the object and issues an imminent collision warning on-the-fly. This paper details the proposed software algorithm used for the classification of the movement of the object, simulation of the results and future work.

Abstract
A new wearable data capture system for gait analysis is being developed. It consists of a pantyhose with embedded conductive yarns interconnecting customized sensing electronic devices that capture inertial and electromyographic signals and send aggregated information to a personal computer through a wireless link. The use of conductive yarns to build the myoelectric electrodes and the interconnections of the wired sensors network as well as the topology and functionality of the sensor modules are presented. © The Institution of Engineering and Technology 2017.

Abstract
Soil erosion constitute a major threat to human lives and assets worldwide, as well as a major environmental disturbance. The Revised Universal Soil Loss Equation (RUSLE) integrated with Geographical Information System (GIS) has been the most widely used model in predicting and mapping soil erosion loss. Remote sensing has particular utility for soil loss model applications, providing observations on several key aspects of Land use and Land cover (LULC) linked to the cover-management factor C of the RUSLE, over wide areas and in consistent and repeatable measurements. A free and open source GIS application coupled with remote sensing data was developed under QGIS software allowing to improve the C factor functionality: (i) automatically download satellite images; (ii) clip with the study case and; (ii) perform a supervised or unsupervised classification, in order to obtain the land cover classification and produce the final C map. One of the most efficient supervised classification algorithms is the Support Vector Machine (SVM). Random Forest (RF) is also an easy-to-use machine learning algorithm for supervised classification. The automation of this functionality was based in the R and SAGA software, both integrated in QGIS. To perform the supervised classification, SVM and RF methods were incorporated. The overall accuracy and Kappa values are also automatically obtained by the R script and GRASS algorithms, which allows to evaluate the result obtained. To perform the unsupervised classification K-means algorithm from SAGA was used. This updating in RUSLE application improve the results obtained for C factor and help us to obtain a most accurate estimation of RUSLE erosion risk map. The application was tested using Sentinel 2A images in two different periods, after and before the forest fire event in Coimbra region, Portugal. In the end, the three resulted maps from SVM, RF and K-means classification were compared.

Abstract
The present paper sets a milestone on incremental recommender systems approaches by comparing several state-of-the-art algorithms with two different mathematical foundations - matrix and tensor factorization. Traditional Pairwise Interaction Tensor Factorization is revisited and converted into a scalable and incremental option that yields the best predictive power. A novel tensor inspired approach is described. Finally, experiments compare contextless vs context-aware scenarios, the impact of noise on the algorithms, discrepancies between time complexity and execution times, and are run on five different datasets from three different recommendation areas - music, gross retail and garment. Relevant conclusions are drawn that aim to help choosing the most appropriate algorithm to use when faced with a novel recommender tasks.

Abstract
Tourism is an increasingly important global economic activity. The proliferation of technology-based mechanisms applied to this activity, has been prompted by a growing number of more demanding consumers, well informed and receptive to new tools to access information and also by the fact that tourism is an information-intensive activity. However, in what concerns peripheral rural tourism destinations, which are to a large extend made up of micro and small enterprises, there is a lack of evidence that the maturity of data that is captured, processed and maintained, by tourism organizations, has a sufficient level of maturity to support the application of Big Data Analytics techniques. This paper, which intends to examine peripheral and mainly rural tourism destinations, analyses the key issues about technology on tourism and proposes a matrix so that we can gauge if the data currently available, and its maturity level, are sufficient to support the use of Big Data Analytics, with all the inherent benefits that rural tourism destinations could arise from its use. Copyright © 2018 International Business Information Management Association (IBIMA).