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Abstract: Developing distributed real-time systems is a complex task that has historically entailed specialized handcraft. In this paper, we propose a retrospective on the (r)evolutionary changes that led to the transition from low-level programming to industrial full-fledged model-based development embodied by the Rubus Component Model and its tool-ecosystem. We focus on the needs, challenges, and solution...
Abstract: Research shows that the global society as organized today, with our current technological and economic system, is impossible to sustain. We are living in an era in which human activities in highly industrialized countries are responsible for overshooting several planetary boundaries, with poorer communities contributing the least to the problems but being impacted the most. At the same time, techn...
Abstract: Good data quality is crucial for any data-driven system’s effective and safe operation. For critical safety systems, the significance of data quality is even higher since incorrect or low-quality data may cause fatal faults. However, there are challenges in identifying and managing data quality. In particular, there is no accepted process to define and continuously test data quality concerning wha...
Abstract: Castor ( Ricinus communis L.) is an oilseed crop, that is cultivated in arid and semi-arid region. Seed yield per plant of castor significantly effective by different environmental factors, but planting density also influence on seed yield. A field experiments were conducted in 2016 and 2017 to investigate the effects of in-row plant spacing (30, 40, 50 and 60 cm) on seed yield and weight in diffe...
Abstract: The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware...
Abstract: Technical debt (TD) requires the management of several technical and nontechnical aspects. Based on 10 years of research and practice, we have created an effective approach, TD Pulse, to assess TD management in large software companies and find critical improvement areas....
Abstract: This edition of the “Practitioner’s Digest” features recent papers on artificial intelligence (AI) and machine learning (ML), along with papers on tech debt, energy consumption, and collaboration between industry and academia....
Abstract: Diversity-based techniques (DBT) have been cost-effective by prioritizing the most dissimilar test cases to detect faults at earlier stages of test execution. Diversity is measured on test specifications to convey how different test cases are from one another. However, there is little research on the trade-off of diversity measures based on different types of text-based specification (lexicographi...
Abstract: Probabilistic approaches have gained attention over the past decade, providing a modeling framework that enables less pessimistic analysis of real-time systems. Among the different proposed approaches, Markov chains have been shown effective for analyzing real-time systems, particularly in estimating the pending workload distribution and deadline miss probability. However, the state-of-the-art mai...
Abstract: The edge computing paradigm brings the capabilities of the cloud such as on-demand resource availability to the edge for applications with low-latency and real-time requirements. While cloud-native load balancing and scheduling algorithms strive to improve performance metrics like mean response times, real-time systems, that govern physical systems, must satisfy deadline requirements. This paper e...
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