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NVIDIA has as generative art system that uses AI to turn words into visually spectacular works of art. This isn't the first time this sort of concept has been
Network traffic classification (NTC) plays an important role in cyber security and network performance, for example in intrusion detection and facilitating a higher quality of service. However, due to the unbalanced nature of traffic datasets, NTC can be extremely challenging and poor management can degrade classification performance. While existing NTC methods seek to re-balance data distribution through resampling strategies, such approaches are known to suffer from information loss, overfitting, and increased model complexity. To address these challenges, we propose a new cost-sensitive deep learning approach to increase the robustness of deep learning classifiers against the imbalanced class problem in NTC. First, the dataset is divided into different partitions, and a cost matrix is created for each partition by considering the data distribution. Then, the costs are applied to the cost function layer to penalize classification errors. In our approach, costs are diverse in each typ
The data generated by Generative Adversarial Network (GAN) inevitably contains noise, which can be reduced by searching and optimizing the architecture of GAN. To search for generative adversarial networks architectures stably, a neural architecture search (NAS) method, StableAutoGAN, is proposed based on the existing algorithm, AutoGAN. The stability of conventional reinforcement learning (RL)-based NAS methods for GAN is adversely influenced by the uncertainty of direction, where the controller will go forward once receiving inaccurate rewards. In StableAutoGAN, a multi-controller model is employed to mitigate this problem via comparing the performance of controllers after receiving rewards. During the search process, each controller independently learns the sampling policy. Meanwhile, the learning effect is measured by the credibility score, which further determines the usage of controllers. Our experiments show that the standard deviation of Frchet Inception Distance (FID) scores o
Ottawa, ON (PRWEB) November 03, 2021 Kongsberg Geospatial and SFL Scientific, a Boston-based data science consulting company announced that they will be