Abstract
This study aims to develop a methodology to assist Small and Medium Enterprises (SMEs) in effectively adopting Business Process Automation (BPA). Despite its growing importance in streamlining routine tasks and enabling employees to focus on more creative activities, numerous organizations face challenges in implementing BPA due to unclear procedures, insufficient knowledge of eligible processes, and uncertainty regarding the necessary technology. In response to these challenges, we introduce the Methodology for Business Process Automation (M4BPA), an artifact designed to guide SMEs through a structured BPA implementation process. The research follows the Design Science Research Methodology (DSRM). The requirements for the artifact came from the results of a previous Systematic Literature Review (SLR). M4BPA was demonstrated within real SME environments, providing solid evidence of its efficacy. The findings suggest that M4BPA significantly enhances SMEs' ability to implement BPA efficiently, offering a practical toolkit that facilitates the process. The novelty of this work lies in the development of a BPA methodology specifically tailored for SMEs, addressing existing gaps in current frameworks and providing a best-practice model for similar organizations. This research contributes to the intermediate results of a doctoral project, offering valuable insights for both practitioners and researchers in the field of BPA. © 2025 by the authors.

Abstract
The pervasiveness of mobile devices has fostered a multitude of services and applications, but also raised serious privacy concerns. In order to avoid users' tracking and/or users' fingerprinting, smartphones have been tightening the access to unique identifiers. Nevertheless, smartphone applications can still collect diverse data from available sensors and smartphone resources. Using real-world data from a field study we performed, this paper demonstrates the possibility of fingerprinting users from Wi-Fi data in mobile devices and the consequent privacy impact. From the performed analysis, we concluded that a single snapshot of a set of scanned Wi-Fi BSSIDs (MAC addresses) per user is enough to uniquely identify about 99% of the users. In addition, the most frequent Wi-Fi BSSID is sufficient to re-identify more than 90% of the users, a percentage that goes up to 97% of the users with the top-2 scanned BSSIDs. The Wi-Fi SSID (network name) also leads to a re-identification risk of about 83% and 97% with 1 and 2 of the strongest Wi-Fi Access Points (APs), respectively.

Abstract
This paper addresses the persistent rise in motorcycle-related fatalities, even as overall road deaths decline, by introducing an adaptive Fuzzy System based on the Takagi-Sugeno model. The system evaluates parameters such as acceleration and lean angle to classify rider behavior into categories such as normal, aggressive, or dangerous, providing timely feedback aimed at promoting safer driving practices. A key component of this approach is the Local Outlier Factor (LOF) algorithm, which identifies hazardous behaviors by quantifying deviations from standard riding patterns, thereby allowing the establishment of adaptive safety thresholds. By integrating fuzzy logic, the system offers refined decision-making capabilities in complex riding conditions, enhancing active safety systems such as traction and braking controls. This work emphasizes the critical role of behavior-based insights in mitigating accidents, particularly since rider actions are a major contributing factor to motorcycle incidents.

Abstract
The effectiveness of optimizing complex problems is closely linked to the configuration of parameters in search algorithms, especially when considering metaheuristic optimization models. Although various automated methods for algorithm configuration have been proposed to alleviate users from manually tuning parameters, there is still unexplored potential in dynamically adjusting certain algorithm parameters during execution, which can lead to enhanced performance. The main objective is to comparatively analyze the effectiveness of manual parameter tuning compared to a dynamic online configuration approach based on reinforcement learning. To this end, the State-Action-Reward-State-Action (SARSA) algorithm is adapted to adjust the parameters of a genetic algorithm, namely population size, crossover rate, mutation rate, and number of generations. Tests are conducted with these two methods on benchmark functions commonly used in the literature. Additionally, the proposed model has been evaluated in a practical problem of optimizing energy trading portfolios in the electricity market. Results indicate that the reinforcement learning-based algorithm tends to achieve seemingly better results than manual configuration, while maintaining very similar execution times. This result suggests that online parameter tuning approaches may be more effective and offer a viable alternative for optimization in metaheuristic algorithms.

Abstract
Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

Abstract