Abstract
This work presents an interrogator system based on an erbium-doped fiber ring cavity for refractive-index measurements. This fiber ring cavity is assisted by a fiber Bragg grating and a phase-shift fiber Bragg grating, both with a similar central emission wavelength to increase the output power levels.

Abstract
Background: modern robots employ artificial intelligence algorithms in a broad ange of applications. These robots acquire information about their surroundings and use these highly-specialized algorithms to reason about their next actions. Despite their effectiveness, artificial intelligence algorithms are highly susceptible to adversarial attacks. This work focuses on mitigating attacks aimed at tampering with the communication channel between nodes running micro-ROS, which is an adaptation of the Robot Operating System (ROS) for extremely resource-constrained devices (usually assigned to collect information), and more robust nodes running ROS2, typically in charge of executing computationally costly tasks, like processing artificial intelligence algorithms. Methods: we followed the instructions described in the Data Distribution Service for Extremely Resource Constrained Environments (DDS-XRCE) specification on how to secure the communication between micro-ROS and ROS2 nodes and developed a custom communication transport that combines the application programming interface (API) provided by eProsima and the implementation of the Transport Security Layer version 1.3 (TLS 1.3) protocol developed by wolfSSL. Results: first, we present the first open-source transport layer based on TLS 1.3 to secure the communication between micro-ROS and ROS2 nodes, providing initial benchmarks that measure its temporal overhead. Second, we demystify how the DDS-XRCE and DDS Security specifications interact from a cybersecurity point of view. Conclusions: by providing a custom encrypted transport for micro-ROS and ROS2 applications to communicate, extremely resource-constrained devices can now participate in DDS environments without compromising the security, privacy, and authenticity of their message exchanges with ROS2 nodes. Initial benchmarks show that encrypted single-value messages present around 20% time overhead compared to the default non-encrypted micro-ROS transport. Finally, we presented an analysis of how the DDS-XRCE and DDS Security specifications relate to each other, providing insights not present in the literature that are crucial for further investigating the security characteristics of combining these specifications.

Abstract
Metaheuristic optimization algorithms are increasingly used to reach near-optimal solutions for complex and large-scale problems that cannot be solved in due time by exact methods. Metaheuristics’ performance is, however, deeply dependent on their effective configuration and fine-tuning to align the algorithm’s search process with the specific characteristics of the problem that is being solved. Although the literature already offers some solutions for automatic algorithm configuration, these are usually either algorithm-specific or problem-specific, thus lacking the capability of being used for diverse metaheuristic models or diverse optimization problems. This work proposes a new approach for the automatic optimization of metaheuristic algorithms’ parameters based on a multi-agent system approach. The proposed model includes an automated fine-tuning process, which is used to optimize a given function in an algorithm- and problem-agnostic manner. Results show that the proposed model is able to achieve better optimization results than standard metaheuristic algorithms, with a negligible increase in the required execution time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms' ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology's performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

Abstract
The assignment of appropriate reviewers to academic articles, known as the reviewer assignment problem (RAP), has become a crucial issue in academia. While there has been much research on RAP, there has not yet been a systematic literature review (SLR) examining the various approaches, techniques, algorithms and discoveries related to this topic. To conduct the SLR, we identified and evaluated relevant articles from four databases using defined inclusion and exclusion criteria. We analysed the selected articles and extracted information, and assessed their quality. Our review identified 67 articles on RAP published in conferences and journals up to mid-2022. As one of the main challenges in RAP is acquiring open data, we have studied the data sources used by researchers and found that most studies use real data from conferences, bibliographic databases and online academic search engines. RAP is divided into two main phases: (1) finding/recommending expert reviewers and (2) assigning reviewers to submitted manuscripts. In Phase 1, we have identified that decision support systems, recommendation systems, and machine learning-oriented approaches are more commonly used due to better results. In Phase 2, heuristics and metaheuristics are the approaches that present better results and are consequently more commonly used by researchers. Based on the analysed studies, we have identified potential areas for future research that could lead to improved results. Specifically, we suggest exploring the application of deep neural networks for calculating the degree of correspondence and using the Boolean satisfiability problem to optimise the attribution process.

Abstract
It is important to properly test developed software because this may contribute to fewer bugs going unreported in deployed software. Often, little attention is spent on the topic of software testing in curricula, yielding graduate students without adequate preparation to deal with the quality standards required by the industry. This problem could be tackled by introducing bite-sized software testing education capsules that allow teachers to introduce software testing to their students in a less time-consuming manner and with a hands-on component that will facilitate learning. In order to design appropriate software testing educational tools, it is necessary to consider both the software testing needs of the industry and the cognitive models of students. This work-in-progress paper proposes an experimental design to gain an understanding of the cognitive strategies used by students during test case design based on real-life cases. Ultimately, the results of the experiment will be used to develop educational support for teaching software testing.