Abstract
This article presents Kadabra, a Java source-to-source compiler that allows users to make code queries, code analysis and code transformations, all user-programmable using the domain-specific language LARA. We show how Kadabra can be used as the basis for developing a runtime autotuning and adaptivity framework, able to adapt existing source Java code in order to take advantage of runtime autotuning. Specifically, this article presents the framework, consisting of Kadabra and an API for runtime adaptivity. We show the use of the framework to extend Java applications with autotuning and runtime adaptivity mechanisms to target performance improvement and/or energy saving goals.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Abstract
In every critical infrastructure system, unexpected events and outages have the potential to cause massive impacts, affecting people and the economy, such as in the power power grid blackouts. To avoid similar incidents in the future, extensive research is necessary to improve resilience and reliability of power grids. This work presents a Transmission Expansion Planning (TEP) model that confronts the largely adopted deterministic security criteria N-1 versus an AC-Cascade Failure Model (AC-CFM) analysis. The main goal is to highlight the importance of cascade failure analysis to increase power system resilience. Tests over the NREL-118 system verify the AC-CFM coupling in TEP models, demonstrating its benefits for assuming a risky proneness behavior for reaching long-term power grid resilience.

Abstract

Abstract
The recent situation of the COVID-19 pandemic has stimulated both the discussion on the use of IT-related teaching tools and the exposure of the student population to vulnerabilities linked to cybersecurity literacy as an integral part of the educational projects of educational institutions and a component of the exercise of citizenship and social sustainability of educational communities. The study presented is based on the assumption that the use of gamification as an element or tool that promotes learning within digital environments may be feasible, and more specifically may function as a teaching element on issues related to cybersecurity for students, especially for higher education students. In order to quantify the openness of students to such a tool path, quantitative methodology was used, and a survey was carried out in two Polytechnic Institutions (PI), achieving a sample of 95 students, and seeking perceptions on positive impacts resulting from the creation of a game scenario for better learning. Results show that students, regardless of their higher education course, clearly understand what gamification is and its goals, and also that students adopt good cybersecurity practices according to their higher education course. This last result goes accordingly with the supposition that gamification can and should be used in cybersecurity literacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract
Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. When the objects are prone to entanglement, having an estimation of their pose and shape is highly valuable for more reliable grasp and motion planning. This paper focuses on modeling entangled tubes with varying degrees of curvature. An unconventional machine learning technique, Inductive Logic Programming (ILP), is used to construct sets of rules (theories) capable of modeling multiple tubes when given the cylinders that constitute them. Datasets of entangled tubes are created via simulation in Gazebo. Experiments using Aleph and SWI-Prolog illustrate how ILP can build explainable theories with a high performance, using a relatively small dataset and low amount of time for training. Therefore, this work serves as a proof-of-concept that ILP is a valuable method to acquire knowledge and validate heuristics for pose and shape estimation in complex bin picking scenarios.

Abstract
Nowadays, cities face severe problems related to traffic management and mobility in general. Therefore, technologies have been developed that can handle these situations and somehow mitigate the caused impact, such as CCTV cameras. However, the techniques for analyzing the images collected by these cameras are increasingly complex and have numerous applications, being dispersed in the literature. Therefore, this article fills an important research gap by presenting a systematic review of the literature on the possible applications of data collected from CCTV cameras and the image analysis and processing techniques that have been developed and proposed in recent years. This systematic review followed the PRISMA statement guidelines and checklist, and three databases were searched, namely Scopus, Web of Science, and Inspec. From the analysis performed, the following applications were identified: Image/video analysis and traffic estimation, pedestrian detection, traffic data analysis, and forecasting, and traffic management. Regarding the image analysis and processing techniques YOLO (only look once), GMM (Gaussian mixture method), morphological methods, fuzzy logic, and other proprietary methods stand out. After a thorough analysis of traffic data, most works still implemented relatively trivial traffic management systems to generate a series of actions to be eventually applied to traffic controllers. Additionally, it was realized that these techniques could be implemented in industrial products from a future perspective. © 2023, Aracne Editrice. All rights reserved.