Abstract
Phasor measurement unit (PMU) provides beneficial information for dynamic power system stability, analysis, and control. One main application of such useful information is data-driven analysis and control. This article presents an approach for optimal signal selection and controller structure determination in PMU-based power system stabilizer (PSS) design. An algorithm is suggested for selecting the optimal input and output signals for PSS, in which a combination of system clustering, modal analysis, and principal component analysis techniques is used. The solution for the optimal PSS input-output selection is determined to increase the observability and damping of the power system. The approach can efficiently reduce the number of input-output signals, while the overall performance is not deteriorated. Then, a linear matrix inequality-based technique is elaborated to design the PMU-based PSS parameters. The stabilizer design approach is formulated as a convex optimization problem and the appropriate stabilizer for pole allocation of the closed-loop model is designed. This method is simulated on two sample power systems. Also, to compare the results with the previous methods, the system is simulated and the results of two previously developed algorithms are compared with the proposed approach. The results show the benefit of the suggested method in reducing the required signals, which decreases the number of required PMUs, while the system damping is not affected.

Abstract

Abstract
There have been several research works proposing new Explainable AI (XAI) methods designed to generate model explanations having specific properties, or desiderata, such as fidelity, robustness, or human-interpretability. However, explanations are seldom evaluated based on their true practical impact on decision-making tasks. Without that assessment, explanations might be chosen that, in fact, hurt the overall performance of the combined system of ML model + end-users. This study aims to bridge this gap by proposing XAI Test, an application-grounded evaluation methodology tailored to isolate the impact of providing the end-user with different levels of information. We conducted an experiment following XAI Test to evaluate three popular XAI methods - LIME, SHAP, and TreeInterpreter - on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts. During the experiment, we gradually increased the information provided to the fraud analysts in three stages: Data Only, i.e., just transaction data without access to model score nor explanations, Data + ML Model Score, and Data + ML Model Score + Explanations. Using strong statistical analysis, we show that, in general, these popular explainers have a worse impact than desired. Some of the conclusion highlights include: i) showing Data Only results in the highest decision accuracy and the slowest decision time among all variants tested, ii) all the explainers improve accuracy over the Data + ML Model Score variant but still result in lower accuracy when compared with Data Only; iii) LIME was the least preferred by users, probably due to its substantially lower variability of explanations from case to case. © 2021 ACM.

Abstract
For the regularly medicated population, the management of the posology is of utmost importance. With increasing average life expectancy, people tend to become older and more likely to have chronic medical disorders, consequently taking more medicines. This is predominant in the older population, but it’s not exclusive to this generation. It’s a common problem for all those suffering from chronic diseases, regardless of age group. Performing a correct management of the medicines stock, as well as, taking them at the ideal time, is not always easy and, in some cases, the diversity of medicines needed to treat a particular medical disorder is a proof of that. Knowing what to take, how much to take, and ensuring compliance with the medication intervals, for each medication in use, becomes a serious problem for those who experience this reality. The situation is aggravated when the posology admits variable amounts, intervals, and combinations depending on the patient’s health condition. This paper presents a solution that optimizes the management of medication of users who use the services of institutions that provide health care to the elderly (e.g., day care centers or nursing homes). Making use of the NB-IoT network, artificial intelligence algorithms, a set of sensors and an Arduino MKR NB 1500, this solution, in addition to the functionalities already described, eHealthCare also has mechanisms that allow identifying the non-adherence to medication by the elderly. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract
Virtual Reality could be useful for heritage management and preservation by complementing or, even, by replacing the ‘real’ visitation to more threatened destinations. The objective of this study was to empirically test the level of similarity perceived by a group of students between VR experience and the ‘real’ visit in a UNESCO World Heritage Cultural attraction in order to assess the capacity of VR to act as a substitute of the ‘real’ visit. Ridit analysis was conducted in order to rank the level of agreement perceived by respondents concerning to similarity between the VR experience and the ‘real’ visit. Results revealed that VR experience could act as a complement, rather than a substitute of the ‘real’ visitation. This is, the feelings and emotions derived from the ‘real’ visit could not be replaced by the VR experience. VR could be an effective marketing tool to encourage sustainable tourism behaviors, rather than to substitute the ‘real’ visit. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Only recently has the software engineering community started conducting research on developing energy efficient software, or green software. This is shadowed when compared to the research already produced in the computer hardware community. While research in green software is rapidly increasing, several recent studies with software engineers show that they still miss techniques, knowledge, and tools to develop greener software. Indeed, all such studies suggest that green software should be part of a modern Computer Science Curriculum. In this paper, we present survey results from both researchers' and educators' perspective on green software education. These surveys confirm the lack of courses and educational material for teaching green software in current higher education. Additionally, we highlight three key pedagogical challenges in bringing green software to computer science curriculum and discussed existing solutions to address these key challenges. We firmly believe that 'green thinking"and the broad adoption of green software in computer science curriculum can greatly benefit our environment, society, and students in an era where software is everywhere and evolves in an unprecedented speed. © 2021 Owner/Author.