Abstract
In this paper, we advance an enhanced method for computing Harte et al.’s (2006) Harmonic Change Detection Function (HCDF), which aims to detect harmonic transitions in musical audio signals. Each of the HCDF component blocks is revisited in light of recent advances in harmonic description and transformation. To evaluate our proposal, we compute an exhaustive grid search to compare the multiple proposed algorithms and a large set of parameterizations across four large style-specific musical datasets. Our results show that the newly proposed methods and parameter optimization improve the detection of harmonic changes by 5.57% (f-score) with respect to previous methods. Furthermore, while guaranteeing recall values at >99%, our other method improves precision by 6.28%.

Abstract
This paper tackles problems where attribute selection is not only able to choose a few features but also to achieve a low performance classification in terms of accuracy compared to the full attribute set. Correlation-based feature selection (CFS) has been set as the baseline attribute subset selector due to its popularity and high performance. Around hundred data sets have been collected and submitted to CFS; then the problems fulfilling simultaneously the conditions: (a) a number of selected attributes lower than six and (b) a percentage of selected attributes lower than a forty per cent, have been tested onto two directions. Firstly, in the scope of data selection at the feature level, an advanced contemporary approach have been conducted as well as some options proposed in a prior work. Secondly, the pre-processed and initial problems have been tested with some sturdy classifiers. Moreover, this work introduces a new taxonomy of feature selection according to the solution type and the followed way to compute it. The test bed comprises seven problems featured by a low dimensionality after the CFS application, three out of them report a single selected attribute, another one with two extracted features and the three remaining data sets with four or five retained attributes; additionally, the initial feature set is between six and twenty-nine and the complexity of the problems, in terms of classes, fluctuates between two and twenty-one, throwing averages of sixteen and around five for both aforementioned properties. The contribution concluded that the advanced procedure (extended CFS) is suitable for problems where only one or two attributes are selected by CFS; for data sets with more than two selected features the baseline method is preferable to the advanced one, although the considered feature ranking method achieved intermediate results.

Abstract
HPC Shelf is a proposal of a cloud computing platform to provide component-oriented services for High Performance Computing (HPC) applications. This paper presents a Verification-as-a-Service (VaaS) framework for component certification on HPC Shelf. Certification is aimed at providing higher confidence that components of parallel computing systems of HPC Shelf behave as expected according to one or more requirements expressed in their contracts. To this end, new abstractions are introduced, starting with certifier components. They are designed to inspect other components and verify them for different types of functional, non-functional and behavioral requirements. The certification framework is naturally based on parallel computing techniques to speed up verification tasks.

Abstract
Energy management systems (EMSs) play an important role in the optimal operation of prosumers. As an essential segment of each EMS, the load forecasting (LF) block enhances the optimal utilization of renewable energy sources (RESs) and battery energy storage systems (BESSs). In this paper, a new optimal day-ahead scheduling and operation of the prosumer is proposed based on the two-level corrective LF. The proposed two-level corrective LF actions are developed through a very precise short-term LF. In the first level, a time-series LF is applied using multi-layer perceptron artificial neural networks (MLP-ANNs). In order to improve the accuracy of the forecasted load data at the first level, the second level corrective LF is applied using feed-forward (FF) ANNs. The second stage prediction is initiated when the LF results violate the pre-defined criteria. The proposed method is applied to a prosumer under different cases (based on the consideration of BESS operation behaviors and cost) and various scenarios (based on the accuracy of the load data). The obtained optimal day-ahead operation results illustrate the advantages of the proposed method and its corrective forecasting process. The comparison of the obtained results and those of other available ones show the effectiveness of the proposed optimal operation of the prosumers. The advantages of the proposed method are highlighted while the BESS costs are considered.

Abstract
Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot's motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution-called AgRoBPP-bridge-to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.

Abstract
Nowadays demand response (DR) is known as one of the main parts of the power system especially in the smart grid infrastructure. Furthermore, to enhance the participation of the consumers in the DR programs, the Independent System Operators (ISOs) have introduced a new entity, i.e. Demand Response Aggregator (DRA). The main contribution of this paper is to investigate a novel framework to increase the profits of the DRA participating in the day-ahead electricity market, i.e. employment of an axillary generation system in the DRA entity. It is supposed that the DRA in this paper has an axillary generation system and it would lead to an increase in the profit of the DRA through avoiding the economic loss in the process of trading DR obtained by the active participation of prosumers in the electricity market. The fuel cell is introduced as the axillary generation unit to the DRA unit. In the framework proposed in this paper, the DR is acquired from end-users during peak periods and will be offered to the day-ahead electricity market. The power flow during the off-peak hours is in another direction, i.e. from the grid to the consumers. In this model, the information-gap decision theory (IGDT) is chosen as the risk measure. The uncertain parameter is the day-ahead electricity market price. The optimization problem's objective is to maximize the profit of the DRA. The behavior of the risk-seeker decision-maker is analyzed and investigated. The feasibility of the program is demonstrated by applying it to realistic data.