Nowadays, emoji image is widely used in social networks. To achieve covert communication in emoji images, this paper proposes a distortion function for emoji images steganography. The profile of image content, the intra-and inter-frame correlation are taken into account in the proposed distortion function to fit the unique properties of emoji image. The three parts are combined together to measure the risks of detection due to the modification on the cover data. With the popular syndrome trellis coding (STC), the distortion of stego emoji image is minimized using the proposed distortion function. As a result, less detectable artifacts could be found in the stego images. Experimental results show that the proposed distortion function performs much higher undetectability than current state-of-the-art distortion function HILL which is designed for natural image.

This study contrasts the call and continuous auction methods using Taiwan Stock Exchange data. Volatility under the call market method is approximately one-half of that under the continuous auction method. The call market method is more effective in reducing the volatility of high-volume stocks than low-volume stocks. This contradicts conventional wisdom which suggests that the call market method is superior for thinly traded stocks, while the continuous auction method is preferred for heavily traded stocks. The call market method does not impair liquidity and price discovery. The call market appears more efficient than in the continuous auction market. Copyright Blackwell Publishers Ltd 1999.

Aim To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone‐beam computed tomography. Materials and Methods Six hundred and three patients who underwent implant surgery (279 high‐risk patients who did and 324 low‐risk patients who did not experience implant loss within 5 years) between January 2012 and January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a DL model based on radiography features, and an integrated model (IM) developed by combining CM with DL, were developed to predict the 5‐year implant loss risk. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. Time to implant loss was considered for both groups, and Kaplan–Meier curves were created and compared by the log‐rank test. Results The IM exhibited the best performance in predicting implant loss risk (AUC = 0.90, 95% confidence interval [CI] 0.84–0.95), followed by the DL model (AUC = 0.87, 95% CI 0.80–0.92) and the CM (AUC = 0.72, 95% CI 0.63–0.79). Conclusions Our study offers preliminary evidence that both the DL model and the IM performed well in predicting implant fate within 5 years and thus may greatly facilitate implant practitioners in assessing preoperative risks.

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