SinGAN-Based Asteroid Surface Image Generation

Guo, Yundong; Pan, Jeng‐Shyang; Qiu, Chengbo; Xie, Fang; Luo, Hao; Shang, Huiqiang; Liu, Zhenyu; Tan, Jianrong

doi:10.4018/jdm.2021100103

Cited by 3 publications

(2 citation statements)

References 41 publications

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“…At the present era, the role of artificial intelligence (AI) and its subfields is increasing rapidly in different applications areas (Y. Guo et al, 2021;Jiao, Wu, Bie, Umek, & Kos, 2018;Lyu & Liu, 2021;Xue, Jiang, Zhang, & Hu, 2021). The majority of the frameworks underlying IoE-enabled technologies are built on powerful Artificial Intelligence algorithms (AI).…”

Section: Introductionmentioning

confidence: 99%

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Bukhari

Yasmin

Gillani

et al. 2023

Journal of Database Management

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Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.

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Section: Introductionmentioning

confidence: 99%

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Bukhari

Yasmin

Gillani

et al. 2023

Journal of Database Management

View full text Add to dashboard Cite

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“…Xue et al proposed an attention-based bidirectional LSTM network ontology matching technique to address this problem (Xue et al, 2021). Guo et al suggested a method based on SIGNAN, which generated various high-fidelity images with only one input image (Guo et al, 2021). The method achieved the operations of shape change, illumination direction changes and super-resolution generation, and improved the sampling efficiency.…”

Section: Introductionmentioning

confidence: 99%

Improved Equilibrium Optimizer for Short-Term Traffic Flow Prediction

Pan

Pan³

et al. 2023

Journal of Database Management

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Meta-heuristic algorithms have been widely used in deep learning. A hybrid algorithm EO-GWO is proposed to train the parameters of long short-term memory (LSTM), which greatly balances the abilities of exploration and exploitation. It utilizes the grey wolf optimizer (GWO) to further search the optimal solutions acquired by equilibrium optimizer (EO) and does not add extra evaluation of objective function. The short-term prediction of traffic flow has the characteristics of high non-linearity and uncertainty and has a strong correlation with time. This paper adopts the structure of LSTM and EO-GWO to implement the prediction, and the hyper parameters of the LSTM are optimized by EO-GWO to transcend the problems of backpropagation. Experiments show that the algorithm has achieved wonderful results in the accuracy and computation time of the three prediction models in the highway intersection.

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