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Abstract: The increasing demand for autonomous machines in construction environments necessitates the development of robust object detection algorithms that can perform effectively across various weather and environmental conditions. This paper introduces a new semantic segmentation dataset specifically tailored for construction sites, taking into account the diverse challenges posed by adverse weather and ...
Abstract: The objective of this study is to produce multi-criteria model for the dry weight prediction of Wedelia trilobata under flooding and nitrogen conditions. Plants of W. trilobata were grown in a greenhouse, and treatments were given for two months . Growth parameters of 60 plants were used to build a numerical model. The neural network model was built using Quasi-Newton approaches that containing Br...
Abstract: Plant height (PH) plays a crucial role in determining per-plant growth and biomass production. Various characteristics of PH, along with the formulation of mathematical growth models, can provide a theoretical yield or biomass production based on water quality, fruit quality, and yields. The aim of this study was to investigate the relationship between PH and biomass per-plant production of two fo...
Abstract: CAMP proposes a hierarchical cache subsystem for multi-core mixed criticality processors, focusing on ensuring worst-case execution time (WCET) predictability in automotive applications. It incorporates criticality-aware locked L1 and L2 caches, reconfigurable at mode change intervals, along with criticality-aware last level cache partitioning. Evaluation using CACOSIM, Moola Multicore simulator, ...
Abstract: Applications are becoming increasingly data-intensive, requiring significant computational resources to meet their demand. Cloud-based services are insufficient to meet such demand, leading to a shift of the computation towards the devices closer to the edge of the network, leading to the emergence of an Edge-to-Cloud computing Continuum (E2C). An application can offload part of its computation to...
Abstract: Multi-agent systems can be prone to failures during the execution of a mission, depending on different circumstances, such as the harshness of the environment they are deployed in. As a result, initially devised plans for completing a mission may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. There are two main approaches to solve the re-plan...
Abstract: Numerous industrial sectors employ Programmable Logic Controllers (PLC) software to control safety-critical systems. These systems necessitate extensive testing and stringent coverage measurements, which can be facilitated by automated test-generation techniques. Existing such techniques have not been applied to PLC programs, and therefore do not directly support the latter regarding automated tes...
Abstract: Machine Learning Operations (MLOps) play a crucial role in the success of data science projects in companies. However, despite its obvious benefits, several companies struggle to adopt MLOps practices and face difficulty in deciding how to deploy and evolve ML models. To gain a deeper understanding of these challenges, we conduct a multi-case study involving nine practitioners from seven companies...
Abstract: Locating features is one of the most common software development activities. It is typically done during maintenance and evolution, when developers need to identify the exact places in a codebase where specific features are implemented. Unfortunately, locating features is laborious and error-prone, since feature knowledge fades, projects are developed by different developers, and features are ofte...
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