This paper motivates and describes a methodology for annotating SLAM benchmark data sets where GPS or motion capture equipment is not viable. Ground truth camera pose measurements are obtained at the camera frame rate...
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This study explores automation and prompt engineering to enhance productivity by leveraging both emerging and existing technologies. It covers topics such as automated bug fixing, AI-driven office tasks, web data extr...
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The extensive incorporation of machine vision into the fields of robotics and automation in a variety of different ways. The various uses of machine vision and the revolutionary impact it has on the capabilities of ro...
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Cervical cancer is a female-specific malignancy that poses a substantial threat worldwide, especially in less privileged and underdeveloped regions. Despite continuous progress and advances in medical science research...
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Social media platforms are facing a significant challenge with cyberbullying, which is inflicting severe mental health consequences on individuals. To help tackle this, our project is focused on creating computer prog...
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Deep learning models have revolutionized various domains but have also raised concerns regarding their security and reliability. Adversarial attacks and coverage-based testing have been extensively studied to assess a...
ISBN:
(纸本)9798350384581;9798350384574
Deep learning models have revolutionized various domains but have also raised concerns regarding their security and reliability. Adversarial attacks and coverage-based testing have been extensively studied to assess and enhance the dependability of deep neural networks. However, current research in this area has reached a state of stagnation. Adversarial attacks focus on exploiting vulnerabilities in models, while coverage-based testing aims to achieve comprehensive testing but overlooks application scenarios. Moreover, evaluating test cases solely based on their fault-revealing capability is insufficient. To address these limitations, we propose an innovative interdisciplinary framework that incorporates human-computer interaction methods in deep learning security testing. By considering the attributes of model application scenarios, we can design more effective test suites that intend to reveal the model's behavior across various scenarios, aiding in the identification of potential defects. Consequently, the test suite plays a crucial role in the testing process of deep learning models, contributing to the assurance of model robustness and reliability. Additionally, we establish a comprehensive evaluation metric for test suite quality, considering factors such as diversity and naturalness. This framework promotes reliable and secure deployment of deep learning models, fostering interdisciplinary collaboration between artificial intelligence and human-computer interaction.
This paper intends to propose a new robot which overcomes the hurdles of an AED(Automated External Defibrillator) at the nearest Ambubot: A robot, ambulance robot designed and developed that carries an AED for assisti...
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In this paper, we propose a methodology for measuring figures of merit relevant to microfluidics practitioners. We also present a benchmark suite for microfluidics design automation (MFDA). The suite is composed of ge...
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A system for smart vehicle emissions monitoring and analysis using cloud computing and neural networks is presented in this paper. As global environmental concerns develop, monitoring and reducing vehicle emissions is...
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Currently, cybercrimes are a serious threat to computer networks. Researchers have come to view Network Intrusion Detection Systems as a critical defensive component and have worked to develop new methods to detect ho...
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