527 research outputs found
A Black-box Attack on Neural Networks Based on Swarm Evolutionary Algorithm
Neural networks play an increasingly important role in the field of machine
learning and are included in many applications in society. Unfortunately,
neural networks suffer from adversarial samples generated to attack them.
However, most of the generation approaches either assume that the attacker has
full knowledge of the neural network model or are limited by the type of
attacked model. In this paper, we propose a new approach that generates a
black-box attack to neural networks based on the swarm evolutionary algorithm.
Benefiting from the improvements in the technology and theoretical
characteristics of evolutionary algorithms, our approach has the advantages of
effectiveness, black-box attack, generality, and randomness. Our experimental
results show that both the MNIST images and the CIFAR-10 images can be
perturbed to successful generate a black-box attack with 100\% probability on
average. In addition, the proposed attack, which is successful on distilled
neural networks with almost 100\% probability, is resistant to defensive
distillation. The experimental results also indicate that the robustness of the
artificial intelligence algorithm is related to the complexity of the model and
the data set. In addition, we find that the adversarial samples to some extent
reproduce the characteristics of the sample data learned by the neural network
model
Prediction of traffic flow based on deep learning
Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. Although existing DNN models can provide better performance than shallow models, it is still an open question to make full use of the spatio-temporal characteristics of traffic flows to improve performance. We propose a novel deep architecture combining CNN and LSTM for traffic flow (RCF) predictio. The model uses CNN to explore temporal correlation and LSTM to explore spatial correlation . Factors such as weather and historical period data are also added to the feature. Its advantage lies in making full use of the spatial-temporal correlation of traffic data and more comprehensively considered the impact of multiple related factors. Aiming at the difficult problem of obtaining spatial features, a feature selection method based on Random Forests is proposed. We use the gini score to represent the spatial connection between intersections to form a network graph constructed based on data. The experimental results show that based on the random forest feature selection and RCF model, the accuracy of traffic prediction reaches 90%
Superconducting properties of novel BiSe-based layered LaOFBiSe single crystals
F-doped LaOBiSe superconducting single crystals with typical size of
240.2 mm are successfully grown by flux method and the
superconducting properties are studied. Both the superconducting transition
temperature and the shielding volume fraction are effectively improved with
fluorine doping. The LaOFBiSe sample exhibits
zero-resistivity at 3.7 K, which is higher than that of the
LaOFBiSe polycrystalline sample (2.4K). Bulk
superconductivity is confirmed by a clear specific-heat jump at the associated
temperature. The samples exhibit strong anisotropy and the anisotropy parameter
is about 30, as estimated by the upper critical field and effective mass modelComment: 5 pages, 5 figures, 2 tables, accepted for publication in Europhysics
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