Trace analysis is a powerful tool for troubleshooting with information from the production environment, especially with the rising complexity of software applications. Recent trends in favor of microservices and High-...
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In industrial environments, predictinghuman actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to unde...
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IoT devices enable a massive amount of data to be aggregated and analyzed for anomaly detection. The nature of heterogeneous devices introduces the challenge of collecting and handling these massive datasets to perfor...
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Vehicular network services in the smart cities generate enormous data by vehicular road users, which is a critical challenge. Network traffic leads to a negative impact on safety applications. AI techniques are a prom...
Vehicular network services in the smart cities generate enormous data by vehicular road users, which is a critical challenge. Network traffic leads to a negative impact on safety applications. AI techniques are a promising solution to address network traffic in VANETs with V2X data. In this paper, we propose a soft voting classification model, which consists of hybrid supervised machine learning algorithms to predict traffic in the network. We evaluate the prediction performance of five well-known machine learning models and the proposed model based on various classification evaluation metrics. The simulation results show that the proposed network traffic prediction model performs better than other considered machine learning models in terms of accuracy (0.94%), time consumption (12.25 seconds) and AUROC (0.907) that proves its stability.
We will present different-type InAs/InP quantum dot (QD) coherent comb lasers (CCLs) and semiconductor optical amplifiers (SOAs) around 1550 nm with their detailed technical specifications. By using those QD-CCLs and ...
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It is broadly accepted that requirements engineering is one of the most important phases of a software project, and requires tools to be effective. For a variety of reasons, paper as a tool has lasted for millennia an...
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Poly(d,l-lactide) is a biocompatible and biodegradable polymer with applications in the biomedical field (drug delivery, implants) and packaging. Conventional synthesis with stannous octoate is slow (>4 h) and can ...
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This paper proposes a framework integrating Rate Splitting Multiple Access (RSMA), semantic communications, and generative AI for optimizing next-generation wireless networks. We present a unified model that combines ...
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Image segmentation is a fundamental component of either image processing or computer vision, finding its applications in medical image analysis, augmented reality, and video surveillance, among others. However, the cu...
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Image segmentation is a fundamental component of either image processing or computer vision, finding its applications in medical image analysis, augmented reality, and video surveillance, among others. However, the current research is paying too little attention to the robustness of such models, which is actually a factor that easily predisposes the model to adversarial perturbations caused by slight, imperceptible distortions added to the input images. In this work, we leverage Metamorphic Testing (MT) to evaluate and boost Segmentation models robustness. Our key innovation lies in using GA to intelligently evolve and optimize transformation sequences, systematically discovering the most effective combinations of spatial and spectral distortions while maintaining image fidelity. Our segmentation robustness metamorphic testing approach (SegRMT) generates adversarial examples that maintain the visual coherence of images while adhering to a predefined Peak Signal-to-Noise Ratio (PSNR) threshold, ensuring genuine disruptions. We use the Cityscapes dataset for our experiments, which consists of 5,000 images from diverse stereo video sequences in urban environments across 50 cities. Our findings show that by combining metamorphic testing and a genetic algorithm (GA), our approach can significantly reduce the mean Intersection over Union (mIoU) produced by the DeepLabV3 segmentation model to 6.4%, while other baseline adversaries decrease mIoU values between 21.7% and 8.5%. Other findings indicate that SegRMT and other baseline adversarial training achieve higher performance if training and testing occurred on their separate specific adversarial datasets, with mIoU values up to 73%. Other findings indicate that SegRMT adversarial training increases the mIoU of a segmentation model to 53.8% in cross-adversarial testings, while other baseline adversaries only increase mIoU values to between 2% and 10% on the SegRMT adversarial testing. This demonstrates that SegRMT effectiv
In recent years, cryptocurrencies have received much attention due to their recent price surge and crash. In fact, their prices have been volatile, making them very difficult to predict. Accordingly, various machine l...
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