Abstract
We present a novel solution for automatic task allocation in multidevice environments, where configured robots compete for task assignment when announcing tasks, minimizing manual intervention. To this end, we propose the specification of a task assignment system and a task-oriented programming method aimed at automating processes and optimizing resource utilization in multiple controller environments. The proposed solution with its market-based algorithm and developed architecture improves the adaptability, scalability and overall efficiency of the system. The research discussion extends to broader implications that are consistent with the overall goal of improving robot capabilities in various deployment scenarios.

Abstract
This paper proposes an Artificial Intelligence (AI) driven solution, Chatto, designed for emotional support among older adults. It integrates emotion recognition, Natural Language Processing (NLP), and human-computer interaction (HCI) to facilitate meaningful interactions and aid in self-emotion regulation while providing caregivers with tools to monitor and support the elder's emotional state remotely. The proposal includes an infrastructure to personalize the system through a human labeling approach and retraining of the deep learning models. The findings revealed the solution's impact on the emotional well-being of the elderly and identified potential improvements in emotion detection, conversational features, and user interface. These improvements were based on feedback from feasibility and usability tests conducted with caregivers and older adults subject to the influence of demographic variables, such as age, cultural background, and technological literacy.

Abstract
We live in an era where robotics and IoT represent a significant transition towards a unified and automated world. Nonetheless, this convergence faces challenges, including system compatibility and device interoperability. The lack of flexibility of conventional robotic architectures amplifies these obstacles, highlighting the urgency for solutions. Furthermore, the complexity of adopting new technologies can be overwhelming. To address these challenges, this article features a Robot Operating System (ROS2)-centered middleware, referred to as Gateway since it applies the concept of a gateway, designed to ease the robot integration. Focusing on the payload module and fostering several types of external communication, it enhances modularity and interoperability. Developers can select payloads and communication modes through a console, which the middleware subsequently configures, guaranteeing flexibility. The goal is to highlight this middleware's potential to overcome robotics limitations, allowing a flexible integration of robots. This work contributes to the Internet of Robotic Things (IoRT) matter, underscoring the importance of modular payload engineering and interoperable communication in robotics and IoT.

Abstract
Professional training presents a significant challenge for organizations, particularly in captivating and engaging employees in these learning initiatives. With the ever-evolving landscape of workplace education, various learning modes have emerged within organizations, and e-learning stands out as a prominent choice. This increasingly cost-effective and adaptable solution has revolutionized training by facilitating numerous learning activities, including the seamless integration of educational games driven by cutting-edge technologies. However, incorporating serious games into educational and professional settings introduces its own set of challenges, particularly in quantifying their tangible impact on learning and assessing their adaptability across diverse contexts. Organizations require a consistent framework to guide best practices in implementing e-learning combined with serious games in professional training. The primary objective of this research is to bridge this gap. Rooted in the methodology of Design Science Research, it aims to provide a comprehensive framework for creating and assessing adaptive serious games that achieve desired learning and engagement outcomes. The overarching goal is to enhance the teaching-learning process in professional training, ultimately elevating student engagement and boosting learning outcomes to new heights. The proposal is grounded in a review of literature, expert insights, and user experiences with Serious Games in professional training, considering learning outcomes and forms of adaptation as essential characteristics for developing or evaluating Serious Games. The result is a framework designed to guide learners toward improved learning outcomes and increased engagement. The proposal underwent evaluation through triangulation, involving focus groups and expert interviews. Additionally, it was utilized in the development and assessment of a Serious Game, offering new insights and application suggestions. This experiment provided an evaluation of the framework based on real courses. In summary, this investigation contributes to the development of evidence-based approaches for the effective use of Serious Games in professional training.

Abstract
As sports analytics evolve to include a broad spectrum of data from diverse sources, the challenge of integrating heterogeneous data becomes pronounced. Current methods struggle with flexibility and rapid adaptation to new data formats, risking data integrity and accuracy. This paper introduces PlayField, a framework designed to robustly handle diverse sports data through adaptable configuration and an automated API. PlayField ensures precise data integration and supports manual interventions for data integrity, making it essential for accurate and comprehensive sports analysis. A case study with ZeroZero demonstrates the framework's capability to improve data integration efficiency significantly, showcasing its potential for advanced analytics in sports.

Abstract
Mutation testing has evolved beyond academic research, is deployed in industrial and open-source settings, and is increasingly part of universities' software engineering curricula. While many mutation testing tools exist, each with different strengths and weaknesses, integrating them into educational activities and exercises remains challenging due to the tools' complexity and the need to integrate them into a development environment. Additionally, it may be desirable to use different tools so that students can explore differences, e.g.. in the types or numbers of generated mutants. Asking students to install and learn multiple tools would only compound technical complexity and likely result in unwanted differences in how and what students learn. This paper presents FRAFOL, a framework for learning mutation testing. FRAME provides a common environment for using different mutation testing tools in an educational setting.