Online advertising, which depends on consumers’ click, creates revenue for media sites, publishers, and advertisers. However, click fraud by criminals, i.e., the ad is clicked either by malicious machines or hiring people, threatens this advertising system. To solve the problem, many schemes are proposed which are mainly based on machine learning or statistical analysis. Although these schemes mitigate the problem of click fraud, several problems still exist. For example, some fraudulent clicks are still in the wild since their schemes only discover the fraudulent clicks with a probability approaching but not 100%. Also, the process of detecting a click fraud is executed by a single publisher, which makes a chance for the publisher to obtain illegal income by deceiving advertisers and media sites. Besides, the identity privacy of consumers is also exposed because the schemes deal with the plain text of consumers’ real identity. Therefore, in this paper, a blockchain-based click fraud detection and prevention scheme (BCFDPS) for online advertising is proposed to deal with the above problems. Specifically, the BCFDPS mainly introduces bilinear pairing to implicitly verify whether a consumer’s real digital identity is contained in a click message to significantly avoid click fraud and employs a consortium blockchain to ensure the transparency of the detection and prevention process. In our scheme, the clicks by machines or fraud ones by a human can be accurately detected and prevented by media sites, publishers, and advertisers. Furthermore, ciphertext-policy attribute-based encryption is adopted to protect the identity privacy of consumers. The implementation and evaluation results show that compared with the existing click fraud detection and prevention schemes based on machine learning and statistical analysis, BCFDPS achieves detection of each fraudulent click with a probability of 100% and consumes lower computation cost; furthermore, BCFDPS adds functions of consumers’ privacy protection and click fraud detection and prevention, compared to the existing blockchain-based online advertising scheme, by introducing limited communication cost ( bytes) at lower storage cost.

In distributed real-time machine learning of smart sensing equipment, training speed and training accuracy are two hard-to-choose trade-off performance measures directly influenced by the design of distributed machine learning algorithms. And it will influence effort of smart sensing equipment directly. We take the model aggregation method of distributed machine learning as a starting point. Due to the loss of accuracy caused by the direct averaging of the parameter average method, we developed the loss function weight reorder stochastic gradient descent method (LR-SGD). LR-SGD uses the loss function value to determine the weight of the work nodes when aggregating the model parameters, and it improves the performance of the parameter average method for nonconvex problems. As shown in the experiment results, our algorithm can improve the training accuracy by a maximum of approximately 0.57% for the Bulk Synchronous Parallel (BSP) model and approximately 6.30% for the Stale Synchronous Parallel (SSP) model.

Information diffusion and diffusion prediction have attracted a great deal of research attention over the past decades. Existing approaches usually make predictions based on the order of the activated users, while recently, some studies have taken the social network into consideration and begun to analyze the influence of neighbors via some graph neural networks. However, they ignore the fact that the interests of users and their neighbors may dynamically change along with the growth of the cascade, and thus fail to model the potential impact of activated users. To address the above shortcomings, we proposed in this paper a deep learning model that combines the Mode of cascades Growth and potential Impact of users (MGI). It leverages GCNs to represent users from the social network to model their static features. Besides, we designed an attention mechanism on the cascade sequence to compute features of activated users, and added the popularity variable to model features of users in cascades. Finally, we combined the growth of cascades and impact of users in our model for diffusion prediction. We conducted extensive experiments on several real-world datasets, and the experimental results demonstrate that our model significantly outperforms the state-of-the-art methods in diffusion prediction.

With the widespread use of automatic identification systems (AISs), some ships use deceptive information or intentionally close their AISs to conceal their illegal activities or evade the supervision of maritime departments. Although radar measurements can be effectively utilized to evaluate the credibility of received AIS data, the propagation of non-line-of-sight (NLOS) signal conditions is an important factor that affects location accuracy. This study addresses the NLOS problem in a special geometric dilution of precision (GDOP) scenario on a coast and several base stations. We employed data augmentation and a deep residual shrinkage network in order to alleviate the adverse effects of NLOS errors. The results of our simulations demonstrate that the proposed method outperforms other range-based localization algorithms in a mixed LOS/NLOS environment. For a special GDOP scenario with four radars, our algorithm’s root-mean-square error (RMSE) was lower than 180 m.