The problem of effective Big Data processing in the Industrial Internet of Things was investigated in the work. The method of Federated Machine Learning as a tool for the analysis of large volumes of information in II...
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In order to study the damage characteristics of typical armored vehicle track under shock wave load, the finite element software LS-DYNA was used to simulate the process of shock wave damage to typical armored vehicle...
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Lately, online issue tracking systems like Jira are used extensively for monitoring open-source software projects. Using these systems, different contributors can collaborate towards planning features and resolving is...
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Artificial Intelligence (AI) built-in Consumer Electronics is popular, but it is hard to test and evaluate AI-based system with the existing performance metrics. Even though AI-based systems are implemented in softwar...
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Mobile sensing applications are software programs that are written for mobile devices, such as smartphones and tablets, whose collective purpose is to turn the user and the device into a sensor for data collection. Th...
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User Experience (UX) is a concept based on the human-product interaction. An increase of UX studies in the Human-computer interaction (HCI) field was observed in the last decade. Empirical studies based their experime...
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ISBN:
(纸本)9783030912345;9783030912338
User Experience (UX) is a concept based on the human-product interaction. An increase of UX studies in the Human-computer interaction (HCI) field was observed in the last decade. Empirical studies based their experimental activity on HCI products, which are characterized by two components: software and intangible (digital interfaces and web apps) and Hardware and physical (devices). Trough an explorative study, the authors propose a research direction to compares UX studies targeting software and hardware components of HCI products. A preliminary sample of papers was considered. The authors collected contributions where UX in HCI design is investigated through case studies involving devices with software and hardware components. Objectives, methods, and tools of each case study were compared. It emerged that complex systems require both quantitative and qualitative analysis approaches, as the wide variety of tools for data acquisition and processing show. Since Hardware components are more closely related to products such as consumer goods and engineering products, it is possible that methods and tools used to study hardware components could also be applicable to other physical and tangible products, i.e., the main reference for product, engineering, and mechanical design.
Modern softwaresystems are becoming more and more complicated, which calls for sophisticated techniques to ensure code quality and dependability. The subtleties of complex softwaresystems are difficult for tradition...
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ISBN:
(数字)9798331541217
ISBN:
(纸本)9798331541224
Modern softwaresystems are becoming more and more complicated, which calls for sophisticated techniques to ensure code quality and dependability. The subtleties of complex softwaresystems are difficult for traditional techniques to problem discovery and fixing to handle as they frequently rely on manual code reviews and static analysis tools. With a new focus on context-aware bug identification, this research explores the use of deep learning models to automate the detection and correction of problems in software code. The suggested models are made to find issues in the context of the software system by using a huge dataset of code annotated with bug information and associated remedies, together with metadata like project architecture, dependencies, and runtime circumstances. Various neural network architectures, including transformers and graph neural networks (GNNs), are explored to capture both the syntactic and semantic aspects of code while considering the contextual interactions between different code modules. This context-aware approach enhances the model’s ability to detect bugs that may arise from the interaction between components, runtime behaviors, or specific usage scenarios often missed by traditional methods. Additionally, the integration of these models into continuous integration/continuous deployment (CI/CD) pipelines is examined, enabling real-time bug detection and automated patch generation. The proposed system aims to improve code quality, reduce debugging time, and minimize human intervention, accelerating the software development lifecycle. Preliminary results demonstrate that the inclusion of contextual information significantly improves the accuracy and relevance of bug detection and fixing, paving the way for more robust and self-healing softwaresystems.
Our publication in the software and systems Modeling Journal 2022 started by observing that relations of views, like requirements documents, are scarcely considered in requirements traceability, despite being a key fa...
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Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new ...
For online ride-hailing platforms, choosing the right time to match idle vehicles with passengers is one of the most important factors affecting the platform’s profit. On one hand, vehicles and passengers arrive dyna...
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