In object detection tasks, the quality of downsampling significantly impacts model performance. Conventional downsampling methods, such as max pooling which retains the maximum value within a window, often lead to the...
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This research presents a novel system that combines MobileNetV2 structure, attention systems, and a sophisticated feature pyramid network to categorize breast cancer X-ray images into three distinct groups: normal, be...
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The emergence of new network technologies, such as software-Defined Networking (SDN), has introduced many features to traditional networks, but SDN also presents its own challenges and limitations. Hybrid SDN combines...
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Managing the complexity of microservices and ensuring that they work together effectively is a challenge. To effectively solve this problem, the API Gateway design pattern was derived during implementation. This study...
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ISBN:
(纸本)9798350376975;9798350376968
Managing the complexity of microservices and ensuring that they work together effectively is a challenge. To effectively solve this problem, the API Gateway design pattern was derived during implementation. This study proposed a high-performance PHP API Gateway called Anser-Gateway based on microservices architecture with event loop and coroutine mechanisms. Compared to existing solutions, our API gateway framework leverages the cooperative scheduling provided by the coroutines and event loop, making the overall framework more efficient in handling each request, resulting in high throughput performance.
Adversarial code examples are important to investigate the robustness of deep code models. Existing work on adversarial code example generation has shown promising results yet still falls short in practical applicatio...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
Adversarial code examples are important to investigate the robustness of deep code models. Existing work on adversarial code example generation has shown promising results yet still falls short in practical applications due to either the high number of model invocations or the limited naturalness of generated examples. In this paper, we propose AACEGEN, an attention-guided adversarial code example generation method for deep code models. The key idea of AACEGEN is to utilize the attention distributions behind deep code models to guide the generation of adversarial code examples. As such, the code elements critical for model predictions could be prioritized for exploration, enhancing the effectiveness and efficiency of adversarial code example generation. In addition, AACEGEN implements a code transformation library providing diverse semantic-preserving code transformations for various code elements, and further conducts a search under the constraint of a maximum number of allowable code transformations to generate adversarial code examples with subtlety and stealth. Our extensive experiments on 9 diverse subjects, taking into account different softwareengineering tasks and varied deep code models, demonstrate that AACEGEN outperforms 3 baseline approaches under comprehensive evaluation.
The creation of software that is capable of performing OCR in multiple languages is the goal of this project. The primary objective of optical character recognition (OCR) software is to convert pre-existing paper docu...
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Existing CI/CD tools require users to define the entire process of software build and release, manage dependencies between projects, and determine the build sequence. The complexity and difficulty of project build and...
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software piracy is a major problem faced in the software industry. Despite the several techniques proposed in previous studies, software piracy and intellectual property theft have continued to pose a threat in the so...
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Deep learning (DL) allows for the creation of computer models that have many processing layers and are capable of learning data representations at different levels of abstraction. DL algorithms have also significantly...
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