Abstract
We extend hybrid dynamic logic with binders (for state variables) by distinguishing between observable and silent transitions. This differentiation gives rise to two kinds of observational interpretations: The first one relies on observational abstraction from the ordinary model class of a specification Sp by considering its closure under weak bisimulation. The second one uses an observational satisfaction relation for the axioms of the specification Sp, which relaxes the interpretation of state variables and the satisfaction of modal formulae by abstracting from silent transitions. We establish a formal relationship between both approaches and show that they are equivalent under mild conditions. For the proof we instantiate the previously introduced concept of a behaviour-abstractor framework to the case of dynamic logic with binders and silent transitions. As a particular outcome we provide an invariance theorem and show the Hennessy-Milner property for weakly bisimilar labelled transition systems and observational satisfaction. In the second part of the paper we integrate our results in a development methodology for reactive systems leading to two versions of observational refinement. We provide conditions under which both kinds of refinement are semantically equivalent, involving implementation constructors for relabelling, hiding, and parallel composition.

Abstract
As a key technology to help bridging the gap between deaf and hearing people, sign language recognition (SLR) has become one of the most active research topics in the human-computer interaction field. Although several SLR methodologies have been proposed, the development of a real-world SLR system is still a very challenging task. One of the main challenges is related to the large intersigner variability that exists in the manual signing process of sign languages. To address this problem, we propose a novel end-to-end deep neural network that explicitly models highly discriminative signer-independent latent representations from the input data. The key idea of our model is to learn a distribution over latent representations, conditionally independent of signer identity. Accordingly, the learned latent representations will preserve as much information as possible about the signs, and discard signer-specific traits that are irrelevant for recognition. By imposing such regularization in the representation space, the result is a truly signer-independent model which is robust to different and new test signers. The experimental results demonstrate the effectiveness of the proposed model in several SLR databases.

Abstract
Enhancing tourist visits to cultural heritage sites by making use of mobile augmented reality has been a tendency in the last few years, presenting mainly audiovisual experiences. However, these explorations using only visuals and sounds, or narratives, do not allow users to be presented with, for example, a particular smell that can be important to feel engaged or to better understand the history of the site. This article pursues the goal of creating an experience that puts the user in a scene planned to evoke several stimuli with SensiMAR prototype - a Multisensory Augmented Reality system that aims to be used in cultural heritage outdoors. When using SensiMAR, the user will be involved with visual reconstructions, surrounded by the soundscape of ancient times, and is exposed to a particular smell very common that time. Given the novelty of this proposal, ascertaining the usability of such a system was raised as a foremost demand. Thus, in addition to its development and implementation specifications, an experimental study was conducted to evaluate the usability of the system in end-users' perspective. The results obtained from random visitors of an archaeological site were analysed according to their sex, age, previous experience with augmented reality technology, and provided condition - audiovisual condition, and multisensory condition, with visual, audio, and smell stimuli. Results were collected from a total of 67 participants and show that this multisensory prototype achieved good usability results across all groups. No statistically differences were found, demonstrating good usability of the SensiMAR system regardless of their sex, age, previous experience with the technology or provided condition.

Abstract
Solar irradiance forecasting is a major priority for the power transmission systems in order to generate and incorporate the performance of massive photovoltaic plants efficiently. As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to meet the output demands in specific situations with dynamic variability. Therefore, in this study, we propose an efficient novel hybrid solar irradiance forecasting based on three steps. In step I, we employ a powerful variable input selection strategy named as partial mutual information (PMI) to calculate the linear and non-linear correlations of the original solar irradiance data. In step II, unlike the traditional deep learning models designing their architectures manually, we utilize several deep convolutional neural network (CNN) models optimized by a novel modified whale optimization algorithm in order to compute the forecasting results of the solar irradiance datasets. Finally in step III, we deploy a deep Q-learning reinforcement learning strategy for selecting the best subsets of the combined deep optimized CNN models. Through analysing the forecasting results over two USA solar irradiance stations, it can be inferred that the proposed optimized deep RL-ensemble framework (ODERLEN) outperforms other powerful benchmarked algorithms in different time-step horizons.

Abstract
Due to the huge mortality rate of lung cancer, there is a strong need for developing solutions that help with the early diagnosis and the definition of the most appropriate treatment. In the particular case of target therapy, effective genotyping of the tumor is fundamental since this treatment uses targeted drugs that can induce death in cancer cells. The biopsy is the traditional method to assess the genotype information but it is extremely invasive and painful. Medical imaging is a valuable alternative to biopsies, considering the potential to extract imaging features correlated with specific genomic alterations. Regarding the limitations of single model approaches for gene mutation status predictions, ensemble strategies might bring valuable benefits by combining the strengths and weaknesses of the aggregated methods. This preliminary work aims to provide further advances in the radiogenomics field by studying the use of ensemble methods to predict the Epidermal Growth Factor Receptor (EGFR) mutation status in lung cancer. The best result obtained for the proposed ensemble approach was an AUC of 0.706 (± 0.122). However, the ensemble did not outperform the single models with AUC values of 0.712 (± 0.119) for Logistic Regression, 0.711 (± 0.119) for Support Vector Machine and 0.712 (± 0.120) for Elastic Net. The high correlation found on the decisions of each single model might be a plausible explanation for this behavior, which caused the ensemble to misclassify the same examples as the single models.

Abstract
This article investigates three families of symmetric single-phase multilevel converters with two dc links for inverter applications. The proposed configurations are based on cascaded cells formed by two-level legs and injection transformers that are connected to a basic cell formed by two half H-bridgeswith shared-legs. Three types of cells are proposed: cellU, composed of three legs and two transformers; cell V, composed of four legs and three transformers; and cellW, composed of six legs and four transformers. The topologies operate in the nonisolated condition (with one less transformer). The generalized system model equations of proposed topologies are discussed. The transformer turns ratios and the dc-link voltages are determined in order to improve the system symmetry and generate the output voltage with low harmonic contents. An unidimensional pulsewidth modulation technique is presented to control the switches of the converters. Harmonic distortion analysis, number of components, rating of the semiconductor devices, power semiconductor losses, IGBTand diode temperature analysis, and transformer ratings are used to compare the proposed configurations with a modular symmetric multilevel inverter formed by three-legmodules cascaded thought injection transformers. The proposed converters present advantages as higher quality of output voltages, and lower semiconductor power losses using the same or lower number of circuit components. Experimental results are shown to demonstrate the feasibility of the systems.