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The fast advancement of artificial intelligence presents ethical challenges that exceed the scope of traditional moral theories. This paper proposes a value-centered framework for AI ethics grounded in axiology, which distinguishes intrinsic values like dignity and fairness from instrumental ones such as accuracy and efficiency. This distinction supports ethical pluralism and contextual sensitivity. Using Multi-Criteria Decision Analysis (MCDA), the framework translates values into structured evaluations, enabling transparent trade-offs. A healthcare case study illustrates how ethical outcomes vary across physician, patient, and public health perspectives. The results highlight the limitations of single-theory approaches and emphasize the need for adaptable models that reflect diverse stakeholder values. By linking philosophical inquiry with governance initiatives like Responsible Artificial Intelligence (AI) and Digital Humanism, the framework offers actionable design criteria for inclusive and context-aware AI development.

Digital Humanism aims to influence the complex interplay between technology and humankind, striving for a better societywhile fully respecting universal human rights. While software is thecrucial technological component in this interplay, current mainstreamapproaches in software engineering research and education are largelyagnostic about the goals of Digital Humanism. Scientists and engineerstend to view, design, and study software systems from a purely technical perspective, often disregarding human and societal implications.In this position paper, we argue that research and education should beprofoundly revisited by broadening the disciplinary landscape throughcross-disciplinary collaborations, particularly with the humanities andsocial sciences, to ensure that the human and societal implications ofnew designs, as well as the principles of Digital Humanism, are properlyconsidered. We provide an initial roadmap for this effort, mainly addressing software engineering. The roadmap is intended as a basis for furtherdiscussion and refinement, and serves as a call for increased communityaction in this area.

As Artificial Intelligence (AI) becomes increasingly influential, ethical concerns surrounding its impact on society and people grow. Current AI ethics frameworks often prioritize efficiency and optimization, neglecting core humanistic values such as dignity, autonomy, and social justice. Digital Humanism offers an essential philosophical foundation to ensure that AI aligns with human values and societal well-being. This paper explores the integration of Responsible AI within the philosophical framework of Digital humanism. It argues that Digital Humanism principles, such as Human-Centered Design, inclusivity, transparency, and ethical universalism, offer critical guidelines for developing and deploying AI that serves humanity. In doing so, this work advocates a humanistic approach to AI development that transcends the limitations of technocentric paradigms.

The Slot-Shifting algorithm presents a solution to combine the benefits of offline and online scheduling in time-triggered systems. It dynamically adjusts the allocation of time slots to tasks in the scheduling tables to accommodate aperiodic tasks at runtime. In this note, we revisit an extension to Slot-Shifting that enables it to handle sporadic task sets. In particular, we clarify the assumptions required for the correct application of its offline acceptance test, identify sources of pessimism within it, and address its schedulability analysis interval.

Many embedded systems have complex timing constraints and, at the same time, have flexibility requirements which prohibit offline planning of the entire system. To support a mixture of time-triggered and event-triggered tasks, some industrial systems deploy a table-driven dispatcher for time-triggered tasks complemented with a preemptive scheduler to allocate the free time slots to event-driven tasks. Contrary to fully preemptive scheduling, limiting the preemptions of tasks to fixed preemptions points may reduce memory requirements and it alleviates the preemption costs in the system. We revisit slotshifting, which at run time mixes time-triggered and event-triggered tasks in a preemptive EDF schedule. In this paper, we extend slotshifting with limited-preemptive execution of event-triggered tasks. We present a synchronization protocol to arbitrate the executions of non-preemptive regions, so that time-triggered tasks keep meeting their timing constraints implicitly. Furthermore, we investigate how to disable preemptions of event-triggered tasks during the execution of the offline-scheduled time-triggered tasks, while keeping the feature of slotshifting to reallocate their slots of execution dynamically at run time.

Mälardalen University has a long history of a successful cooperation and coproduction with the industry and public sector in Sweden. This has eventually led it to become one of the leading higher education institutes in Sweden for excellent coproduction with different societal actors, both internationally and nationally. The university has through its coproduction activities become convinced of its value and of the wide range of opportunities it can bring to all parties involved. In this paper, we share our experience through some good examples both from research and education and discuss what is needed for successful and sustainable coproduction with industry and public sector.

Nelder-Mead method is a powerful mathematical technique for multidimensional unconstrained optimization. The other merit of the Nelder-Mead method is that it does not require any derivative information, which makes it suitable for exploitation in non-smooth regions. This paper proposes a new memetic algorithm incorporating Nelder-Mead method for local search. Nelder-Mead method coupling with GA will lead to utilization of more information derived from mathematical principle in the search process, thereby increasing the efficiency of search. The effectiveness of the proposed algorithm has been demonstrated in a case study of optimization of a fuzzy control system.

Many embedded systems have complex timing constraints and, at the same time, have flexibility requirements which prohibit offline planning of the entire system. To support a mixture of time-triggered and event-triggered tasks, some industrial systems deploy an RTOS with a table-driven dispatcher complemented with a preemptive scheduler to allocate the free time slots to event-driven tasks. Rather than allocating dedicated time-slots to time-triggered tasks, in this work we provide RTOS support to dynamically re-allocate time-slots of time triggered tasks within a pre-computed time range to maximize the available processing capacity for event-triggered tasks. Although the concept - called slotshifting - is not new, we are the first to extend a commercial RTOS with such support. In addition, we extend slot shifting with a run-time mechanism to reclaim resources of time-triggered tasks when their reserved capacities are unused. This mechanism eliminates over-provisioning of capacities to (event-triggered) tasks that have been converted to periodic tasks to resolve interdependencies during off-line synthesis, e.g. allowing for a resource-efficient implementation of a polling task. After implementing our unique RTOS extensions, we investigate the run-time overheads for the corresponding scheduling mechanisms. Measurements show that the increase in terms of absolute run-time overhead is minor compared to an off-the-shelf micro-kernel with a fixed-priority scheduler

Many embedded systems have complex timing constraints and, at the same time, have flexibility requirements which prohibit offline planning of the entire system. To support a mixture of time-triggered and event-triggered tasks, some industrial systems deploy a real-time operating system (RTOS) with a table-driven dispatcher complemented with a preemptive scheduler to allocate free time slots to event-driven tasks. Rather than allocating dedicated time-slots to time-triggered tasks, we propose to dynamically re-allocate time-slots of time-triggered tasks within a pre-computed time range to maximize the available processing capacity for event-triggered tasks. Although the concept - called slotshifting - is not new, we are unaware of a commercial RTOS with such support. After identifying the mechanisms for an RTOS implementation of slotshifting, we discuss the run-time overheads for admitting aperiodic requests into the system1.

In this paper, we present a combined structural and inter-frame skipping method for quality aware processing of MPEG-2 video upon overload situations. The method selects video frames both based on the structural properties, such as frame types, sizes and position in the video stream, as well as on the internal, sub-frame characteristics, i.e., the number of relevant macroblocks within a frame.