Based on the DEFORM software platform, a three-dimensional thermal-mechanical coupled finite element model of continuous grain flow forming process for stationary swashplate with 7A09 high strength aluminum alloy was ...
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Crack segmentation is a critical and demanding task in pavement engineering. Recent studies using Convolutional Neural Networks (CNNs) or vision Transformer (ViT) for crack segmentation have shown promising results, b...
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Undergraduate softwareengineering courses typically require students to work on team-based projects that reinforce disciplinary content and soft skills. At the same time, these students, particularly those at liberal...
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ISBN:
(纸本)9781665495929
Undergraduate softwareengineering courses typically require students to work on team-based projects that reinforce disciplinary content and soft skills. At the same time, these students, particularly those at liberal arts institutions, are required to take courses that focus on civic issues, including on racial and ethnic inequality. Often, students perceive these courses to be outside their disciplinary areas, and may not comprehend how these topics are applicable to computer science and softwareengineering. This paper reports on the experience of Pulimood and Leigey as they and their students grappled with issues of racial injustice in the criminal justice system, and drew upon their own disciplinary backgrounds to apply computational thinking and softwareengineering principles to help the community better understand these issues and advocate for reform. The paper also describes the experience of teaching courses from different disciplines in a collaborative model, working closely with a local community partner to support its work on an identified social issue, and the learning outcomes, as well as the benefits and challenges of this approach. Recommendations and future directions are also discussed.
The idea of a MIMO antenna with novel decoupling scheme is presented in this study. Two symmetric inverted L-shaped antenna elements make up this MIMO antenna setup. The compact dimension of the suggested 2 × 1 M...
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Automated program repair (APR) aims to fix software bugs without manual debugging efforts and plays a crucial role in software development and maintenance. Template-based APR has been widely investigated and shown pro...
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ISBN:
(纸本)9798350329964
Automated program repair (APR) aims to fix software bugs without manual debugging efforts and plays a crucial role in software development and maintenance. Template-based APR has been widely investigated and shown promising results. However, it is challenging for template-based APR to select the appropriate donor code, which is an important repair ingredient for generating candidate patches. Inappropriate donor code may cause plausible but incorrect patch generation even with correct fix patterns, limiting the repair performance. In this paper, we aim to revisit template-based APR, and propose GAMMA, to directly leverage large pre-trained language models for donor code generation. Our main insight is that instead of retrieving donor code in the local buggy file, we can directly predict the correct code tokens based on the context code snippets and repair patterns by a cloze task. Specifically, (1) GAMMA revises a variety of fix templates from state-of-the-art template-based APR techniques (i.e., TBar) and transforms them into mask patterns. (2) GAMMA adopts a pre-trained language model to predict the correct code for masked code as a fill-in-the-blank task. Although our idea is general and can be built on various existing pre-trained language models, we have implemented GAMMA as a practical APR tool based on the recent UniXcoder model. The experimental results demonstrate that GAMMA correctly repairs 82 bugs on Defects4J-v1.2, which achieves 20.59% (14 bugs) and 26.15% (17 bugs) improvement over the previous state-of-the-art template-based approach TBar and learning-based one Recoder. Furthermore, GAMMA repairs 45 bugs and 22 bugs from the additional Defects4J-v2.0 and QuixBugs, indicating the generalizability of GAMMA in addressing the dataset overfitting issue. We also prove that adopting other pre-trained language models can provide substantial advancement, e.g., CodeBERT-based and ChatGPT-based GAMMA is able to fix 80 and 67 bugs on Defects4J-v1.2, indicating th
software testing is a crucial aspect of the software development lifecycle, ensuring the delivery of high-quality, reliable, and secure software systems. With the advancements in Artificial Intelligence (AI) and Natur...
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With the rapid development of software industry, a variety of new technologies are produced. Research and development in cloud computing, big data, artificial intelligence, the Internet of Things, blockchain, and othe...
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In this paper, a multi-objective optimization design method for permanent magnet synchronous motor based on differential evolution algorithm is proposed. Taking the motor quality and motor efficiency as the optimizati...
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This paper explores the structural dynamic behavior of electric vehicle (EV) batteries and its impact on driver vibration comfort, especially at low frequencies. Being an additional bulky element of the EVs, the batte...
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In response to the problems of massive heterogeneous data, complex structural hierarchy, and inefficient functional implementation in the design of the ground PHM principled sample machine for airborne fuel pump, base...
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