Abstract
Human Activity Recognition has been primarily investigated as a machine learning classification task forcing it to handle with two main limitations. First, it must assume that the testing data has an equal distribution with the training sample. However, the inherent structure of an activity recognition systems is fertile in distribution changes over time, for instance, a specific person can perform physical activities differently from others, and even sensors are prone to misfunction. Secondly, to model the pattern of activities carried out by each user, a significant amount of data is needed. This is impractical especially in the actual era of Big Data with effortless access to public repositories. In order to deal with these problems, this paper investigates the use of Transfer Learning, specifically Unsupervised Domain Adaptation, within human activity recognition systems. The yielded experiment results reveal a useful transfer of knowledge and more importantly the convenience of transfer learning within human activity recognition. Apart from the delineated experiments, our work also contributes to the field of transfer learning in general through an exhaustive survey on transfer learning for human activity recognition based on wearables. © 2018, Springer Nature Switzerland AG.

Abstract
We present a method for improving the prediction accuracy using multiple predictive algorithms. Several techniques have been developed to tackle this issue such as bagging, boosting and stacking. In contrary to the first two that, usually, generate homogeneous ensembles of classifiers, stacking techniques have demonstrated success using heterogeneous ensembles. In our method, we adopt the stacking mechanism. Several models are generated using different learning algorithms. Forward stepwise selection is implemented to link each instance to its appropriate learning model. Experiments with three datasets benchmarked with four learning schemes show that this novel method improves prediction accuracy and can serve as a bridge to transfer knowledge between tasks given the same feature space but different data distributions. © 2018, Springer Nature Switzerland AG.

Abstract

Abstract
An efficient traffic-network is an essential demand for any smart city. Usually, city traffic forms a huge network with millions of locations and trips. Traffic flow prediction using such large data is a classical problem in intelligent transportation system (ITS). Many existing models such as ARIMA, SVR, ANN etc, are deployed to retrieve important characteristics of traffic-network and for forecasting mobility. However, these methods suffer from the inability to handle higher data dimensionality. The tensor-based approach has recently gained success over the existing methods due to its ability to decompose high dimension data into factor components. We present a modified Tucker decomposition method which predicts traffic mobility by approximating very large networks so as to handle the dimensionality problem. Our experiments on two big-city traffic-networks show that our method reduces the forecasting error, for up to 7 days, by around 80% as compared to the existing state of the art methods. Further, our method also efficiently handles the data dimensionality problem as compared to the existing methods. © 2018, Springer Nature Switzerland AG.

Abstract

Abstract
This work proposes a multi-criteria decision making approach to help assessing and selecting suppliers in the olive oil sector. Olive oil is a protected agricultural product, by region and origin certificate. Therefore to select a supplier, it is of utter importance to inspect and test (taste, colour, smell, density, among others) the olive oil in addition to the supplying company. The identification of possible suppliers was done in two stages: firstly, the region of origin from which to choose possible suppliers was identified and then potential suppliers were evaluated on a set of characteristics for which minimum threshold values were set. From this study, which is not part of the research reported here, we were able to identify the suppliers of interest. Due to the several characteristics and characteristic dimensions used to choose a supplier we resort to the Analytic Hierarchy Process to rank them, this way allowing for a better choice. The rank obtained is robust as the top ranked supplier remains the same for any reasonable change in the criteria weighs and in the evaluation of the suppliers on each criterion. The involved company found the results of value, as well as the lessons learned by addressing the supplier evaluation problem using a more systematic approach.