This paper presents a six-node system comprising two transmitter-receiver pairs, an eavesdropper, and a relay. It evaluates the security of the primary pair while the secondary uses jamming to protect against eavesdro...
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In today's society, people increasingly need information acquisition due to the rapid development of science and technology and the consequent increase in available data. However, finding the information users nee...
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The goal of the proposed system is to identify whether a video has endured software manipulation or not. This system specially deals with identifying deepfake videos from real ones. As new techniques emerged to make d...
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It is mostly known health is wealth, as if health is good, we can obtain more wealth but if health is not good, we cannot get wealth. The use of mobile devices by health care professionals (HCPs) has altered several a...
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Some universal segmentation models have already shown good performance on public datasets, but they perform poorly in detecting cracks in earth dams. This deficiency stems from two aspects: one is the shortage of the ...
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ISBN:
(纸本)9798350389166;9798350389173
Some universal segmentation models have already shown good performance on public datasets, but they perform poorly in detecting cracks in earth dams. This deficiency stems from two aspects: one is the shortage of the dataset about cracks on earth dams, and the other is the complex and unstable appearance influenced by material and environmental conditions, making crack segmentation more susceptible to background noise. In this paper, we put forward a crack segmentation model based on transfer learning for earth dams with higher accuracy in few-shot conditions, named Dual Attention and Multi-scale Feature Enhancement Network (DAMFE-Net). It introduces a Spatial and Channel Squeeze and Excitation module (SCSE) to electively enhance key features for better extraction, and it incorporates the Multi-scale Feature Enhancement Module (MFEM) suppresses irrelevant features, and enhances crack feature extraction in earth dam images, reducing high background noise. The MFEM learns the weight of each scale feature map and fuses the semantic information of features at different levels, so it improves the representation of effective features. The F1-score for crack segmentation on earth dams with our model is 86.29%, surpassing DeepLab v3+ by 16.4%, which indicates that the proposed model can accurately segment cracks on earth dams.
In large-scale engineering experiments, such as hydrodynamics and aerodynamics experiments, computer aided software (CAE) is always used to manage a large number of parameters and experimental data to simulate the phy...
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In behavior recognition, the accuracy of recognition results is deeply affected by input video processing, which is crucial. Therefore, in order to improve the efficiency and accuracy of SlowFast behavior recognition ...
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Teaming is a key aspect of most professional softwareengineering positions, and consequently, team-based learning (TBL) features heavily in many undergraduate computerscience (CS) and softwareengineering programs. ...
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ISBN:
(纸本)9798350322590
Teaming is a key aspect of most professional softwareengineering positions, and consequently, team-based learning (TBL) features heavily in many undergraduate computerscience (CS) and softwareengineering programs. However, while TBL offers many pedagogical benefits, it is not without challenges. One such challenge is assessment, as the course teaching staff must be able to accurately identify individual students' contributions to both encourage and reward participation. In this paper, we study improvements to grading practises in the context of a CS1.5 introductory softwareengineering course, where assessing individual students' contributions to weekly lab assignments is done manually by teaching assistants (TAs). We explore the impact of presenting TAs with automated summaries of individual student contributions to their team's GitHub repository. To do so, we propose a novel algorithm, and implement a tool based off of it, AutoVCS. We measure the impact on grading metrics in terms of grading speed, grading consistency, and TA satisfaction. We evaluate our algorithm, as implemented in AutoVCS, in a controlled experimental study on Java-based lab assignments from a recent offering of NC State University's CS1.5 course. We find our automated summaries help TAs grade more consistently and provides students with more actionable feedback. Although TAs grade no faster using automated summaries, they nonetheless strongly prefer grading with the support of them than without. We conclude with recommendations for future work to explore improving consistency in contribution grading for student softwareengineering teams.
In order to meet the maintenance requirements of harsh environment and unattended equipment sites, it is proposed to use an embedded Web server as the core in the embedded target turntable system and use HTML web page...
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As the software industry embraces more and more DevOps practices, issue tracking systems and Continuous Integration / Continuous Delivery tools have become of utmost importance. However, as software projects’ complex...
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