The Computing First Network (CFN) is a novel network paradigm that efficiently allocates and schedules computing force, storage resources, and network capacity across cloud, edge, and device domains. Leveraging the co...
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Rich web-based applications are complex systems with multiple application elements running on diverse platforms distributed over different tiers. There are no UML-based modelling languages or tools catering for the sp...
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Open Source software (OSS) is software whose source code is accessible to the public, allowing its modification and distribution at no cost. This feature encourages the active collaboration of the community of develop...
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ISBN:
(纸本)9783031612800;9783031612817
Open Source software (OSS) is software whose source code is accessible to the public, allowing its modification and distribution at no cost. This feature encourages the active collaboration of the community of developers, who come together to learn and contribute to the project. The objective of this research work has been to validate the incorporation of the adapted State Transition Diagrams technique in the Open Source software project "Sports Tactics Board" to improve its usability. The research results reveal that implementing this technique facilitated the understanding of the requirements and functionalities of the software, as well as the identification and efficient resolution of errors. In addition, using the State Transition Diagrams technique increased user satisfaction and adoption of the software. Despite the challenges and difficulties encountered in applying this technique, such as the need for closer collaboration between developers and usability experts, the findings indicate that implementing techniques during the OSS development process can significantly improve its usability.
As one of the most popular technologies nowadays, cloud computing has a big demand in the distributed software space. It is highly difficult for CSPs to work together in a multi-cloud context, and contemporary literat...
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This research examines the integration of sustainability principles within Agile software Development Life Cycle (SDLC) methodologies. While Agile frameworks such as GLUX emphasize user experience and adaptability, th...
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Scene coordinate regression has significantly enhanced visual localization by segmenting landmark patches and aggregating votes for landmark points within these patches. However, this approach neglects dynamic semanti...
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Combining with the register-transfer-level description, we aim to develop a generic hardware interface architecture for SPI devices using high-level synthesis that automatically converts software into hardware. Device...
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Photovoltaic systems are being used more and more in the production of electricity because of the high cost of fossil fuel-based energy. The main issue that happens between grid and end-user transmission lines is powe...
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The increasing availability of quantum devices via the cloud led to a multitude of commercial and scientific tools for developing quantum applications. However, since quantum applications are typically hybrid, compris...
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ISBN:
(纸本)9783031623615;9783031623622
The increasing availability of quantum devices via the cloud led to a multitude of commercial and scientific tools for developing quantum applications. However, since quantum applications are typically hybrid, comprising both quantum and classical parts, these tools are very heterogeneous. Therefore, combining them within a single application is complicated by incompatible programming languages, data formats, and interfaces. Hence, to enable the development of portable and interoperable quantum applications a standards-based toolchain is required. In this demonstration, we present a holistic toolchain for developing quantum applications utilizing well-established standards for defining workflows, deployment topologies, application interfaces, and provenance data. To demonstrate the practical feasibility of our toolchain, we showcase it for two use cases from the cryptography and machine learning domains.
Deep Neural Network (DNN) testing is one of the most widely-used techniques to guarantee the quality of DNNs. However, DNN testing typically requires the ground truth of test inputs, which is time-consuming and labor-...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
Deep Neural Network (DNN) testing is one of the most widely-used techniques to guarantee the quality of DNNs. However, DNN testing typically requires the ground truth of test inputs, which is time-consuming and labor-intensive to obtain. To relieve the labeling-cost problem of DNN testing, we propose TDPR, a test input prioritization technique for DNNs based on training dynamics. The key insight of TDPR is that bug-revealing samples exhibit different learning trajectories compared to normal ones. Based on this, TDPR constructs a learning trajectory for each test input, which characterizes the evolving learning behavior of DNNs. Then, TDPR extracts features from these learning trajectories and applies learning-to-rank techniques to build a ranking model, which can intelligently utilize the generated features to prioritize test inputs. To evaluate TDPR, we conduct extensive experiments on 8 diverse subjects, considering various domains of test inputs, different DNN architectures, and diverse types of test inputs. The evaluation results demonstrate that TDPR outperforms 7 baseline approaches in both prioritizing test inputs and guiding the retraining of DNNs.
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