As automation shifts towards human-centric paradigms under Industry 5.0, the ability to objectively assess human physiological and psychological states becomes vital for safe and effective human-machine cooperation. This paper presents the development and integration of two embedded measurement systems: the Human Measurement Device (HMD) and the Stress Inducing Device (SID). The HMD is a modular, real-time physiological data acquisition platform designed for continuous monitoring of key human indicators such as heart rate variability, electrodermal activity, respiration, and foot pressure. Complementing this, the SID elicits controlled stress responses via gamified cognitive tasks, enabling systematic data generation. Together, these tools facilitate synchronized, high-fidelity analysis of human reactions in dynamic, automated environments. Initially applied within automated driving scenarios to assess trust and comfort, the systems are extensible to broader Industry 5.0 applications, including human-robot collaboration and user interface design. The work contributes a scalable, objective methodology for real-time human state assessment, supporting improved safety, performance, and acceptance in human-in-the-loop systems.
Estimation is an important challenge in software engineering, determining cost, effort, and resource planning. This paper presents an adapted Multivocal Literature Review (MLR) synthesising formal and grey literature on four main estimation approaches identified in literature: algorithmic, expert-based, machine learning, and ensemble-based hybrid approaches. This review discusses the strengths and limitations of each approach, identifying the emerging role of ML models, while also examining non-ML based agile approaches such as planning poker and story points. The findings indicate that estimation is both complex and non-deterministic, where no single approach universally applies across all contexts. Ensemble-based hybrid models which employ a variety of estimation techniques in parallel report promising results in terms of accuracy and adaptability.
This study conducts a Multivocal Literature Review (MLR) to explore the strengths and weaknesses of Artificial Intelligence (AI) in software engineering, focusing on four key areas: reliability, sustainability, security and convenience. AI tools improve efficiency and automation, but they also have challenges such as bias, non-determinism and model transparency. There are threats to the sustainability of AI in software engineering, such as high computational energy demands and legal constraints on proprietary and copyrighted data. Security risks can also emerge from AI-generated code, as models may produce insecure implementations that require human oversight to ensure robustness. Even with these challenges, AI seems to be gaining in popularity due to its convenience, enabling developers to streamline workflows and enhance their productivity. This study explores these strengths and weaknesses and provides insights into the future of AI in software engineering.
Process Debt (PD), a concept derived from the Technical Debt, consists of a sub-optimal activity that might have short-term benefits but generates a negative impact in the medium-long term. PD identification is a key phase of PD management as it aims to determine the type of PD, where it is located, and how to estimate its impact. However, PD identification is the most challenging phase, as practitioners find it the most effort-intensive, mainly due to the immaterial nature of the process. In this paper, to mitigate the difficulties of PD identification, we propose a methodology that relies on frameworks compliant with the ISO/IEC 33000 family standard, the reference standard for process assessment. We also provide an exemplar application using the data from a process assessment performed using the Automotive SPICE, a framework compliant with the ISO/IEC 33000 requirements. An excerpt of the case study results is presented, showing the potential for a systematic and effective approach to PD identification.
Cultural Heritage has moved into focus of the European Union as engine for employment, economic drive and development. According to the report of the Horizon 2020 Expert Group on Cultural Heritage “Cultural heritage must be seen as a special, but integral, component in the production of European GDP and innovation, […]”. To guarantee longevity usability of European heritage it is inevitable to look at all options. The role of Cultural Heritage for climate protection and the climate impact on Cultural Heritage are also in the focus of the OMC report “Strengthening Cultural Heritage Resilience for Climate Change”. Ensuring and increasing biodiversity will also be a major task for Cultural heritage site owners/managers. The INTERREG project “Cities. Gardens. Diversity” is working on reducing heat hotspots in historic sites, like the Imperial Palace Vienna district by identifying appropriate measures to reduce the solar entry and increase the biodiversity in the district. All these experiences will be integrated into a Vocational Education and Training programme which will be developed by Burghauptmannschaft Oesterreich (BHOe), who is responsible for the district. These course modules will be integrated in the training offers by The European Heritage Academy (EHA), which was established in 2017, and are accessible online and in person. All further education offers by EHA provide certification by ECQA, participants can obtain their certificate through validation of prior learning, eLearning and participation in course modules before passing an exam.
