Abstract
This paper proposes the first mutation-based technique for the repair of Alloy 6 first-order temporal logic specifications. This technique was developed with the educational context in mind, in particular, to repair submissions for specification challenges, as allowed, for example, in the Alloy4Fun web-platform. Given an oracle and an incorrect submission, the proposed technique searches for syntactic mutations that lead to a correct specification, using previous counterexamples to quickly prune the search space, thus enabling timely feedback to students. Evaluation shows that, not only is the technique feasible for repairing temporal logic specifications, but also outperforms existing techniques for non-temporal Alloy specifications in the context of educational challenges.

Abstract

Abstract
The analysis of ambient sounds can be very useful when developing sound base intelligent systems. Acoustic scene classification (ASC) is defined as identifying the area of a recorded sound or clip among some predefined scenes. ASC has huge potential to be used in urban sound event classification systems. This research presents a hybrid method that includes a novel mathematical fusion step which aims to tackle the challenges of ASC accuracy and adaptability of current state-of-the-art models. The proposed method uses a stereo signal, two ensemble classifiers (random subspace), and a novel mathematical fusion step. In the proposed method, a stable, invariant signal representation of the stereo signal is built using Wavelet Scattering Transform (WST). For each mono, i.e., left and right, channel, a different random subspace classifier is trained using WST. A novel mathematical formula for fusion step was developed, its parameters being found using a Genetic algorithm. The results on the DCASE 2017 dataset showed that the proposed method has higher classification accuracy (about 95%), pushing the boundaries of existing methods.

Abstract
The decrease of short-circuit current in islanded mode due to the existence of resources with low inertial, topology changing, multi-directional current, and telecommunication are the most critical issues encountered by micro-grid protection. The central adaptive protection schemes are widely used for micro-grids, but due to the lack of reliability of this schemes, they are not perfect methods. In this paper, a hybrid scheme of adaptive and multi-agent protection for micro-grid is discussed, which will be able to provide safety protection at several layers and levels, using the equipment in the micro-grid with distributed generation including renewable and nonrenewable energy resources. The proposed scheme calculates relays pickup current with a formula that uses the superposition theorem. To demonstrate the proposed scheme performance, it is implemented and simulated on a sample micro-grid in MATLAB/Simulink, and its results have been analyzed and discussed. Simulations and numerical results show that for 96% of simulated topologies in single and multi-event faults, the relay settings are updated correctly and detect subsequent faults at the right time. Also, due to the use of offline calculations in the equipment layer, the time delay due to sending information to higher layers is minimized for single-event faults in the micro-grid. (C)& nbsp;2022 Published by Elsevier Ltd.

Abstract
The coronavirus disease 2019 (COVID-19) evolved into a global pandemic, responsible for a significant number of infections and deaths. In this scenario, point-of-care ultrasound (POCUS) has emerged as a viable and safe imaging modality. Computer vision (CV) solutions have been proposed to aid clinicians in POCUS image interpretation, namely detection/segmentation of structures and image/patient classification but relevant challenges still remain. As such, the aim of this study is to develop CV algorithms, using Deep Learning techniques, to create tools that can aid doctors in the diagnosis of viral and bacterial pneumonia (VP and BP) through POCUS exams. To do so, convolutional neural networks were designed to perform in classification tasks. The architectures chosen to build these models were the VGG16, ResNet50, DenseNet169 e MobileNetV2. Patients images were divided in three classes: healthy (HE), BP and VP (which includes COVID-19). Through a comparative study, which was based on several performance metrics, the model based on the DenseNet169 architecture was designated as the best performing model, achieving 78% average accuracy value of the five iterations of 5- Fold Cross-Validation. Given that the currently available POCUS datasets for COVID-19 are still limited, the training of the models was negatively affected by such and the models were not tested in an independent dataset. Furthermore, it was also not possible to perform lesion detection tasks. Nonetheless, in order to provide explainability and understanding of the models, Gradient-weighted Class Activation Mapping (GradCAM) were used as a tool to highlight the most relevant classification regions. Clinical relevance - Reveals the potential of POCUS to support COVID-19 screening. The results are very promising although the dataset is limite

Abstract
In this paper we examine the use of social media as a marketing channel by Higher Education Institutions (HEI) and its impact on the institution's brand, attracting top professionals and students. HEIs are annually evaluated globally based on various success parameters to be published in rankings. Specifically, we analyze the Twitter publishing strategies of the selected HEIs, and we compare the results with their overall ranking positions. Our study shows that there are no significant differences between the well-known university rankings based on Kendall t and RBO metrics. However, our data retrieval indicates a tendency for the top-ranked universities to adopt specific strategies, which are further emphasized when analyzing emotions and topics. Conversely, some universities have less prominent strategies that do not align with their ranking positions. This study provides insights into the role of social media in the marketing strategies of HEIs and its impact on their global rankings.