In reversible data hiding (RDH) community, researchers often embed bits by shifting prediction-error histogram based on the mean-square error metric (MSEM). This will cause more pixel distortion in the smooth areas. Considering that the human eye is more sensitive to the distortion in the smooth areas, in this letter, we propose a new histogram shifting strategy for RDH by referring to the general distortion metric (GDM). With the GDM, data can be embedded by first modifying those pixels in the texture areas. In both theoretical analysis and experimental testing, we have shown that the use of the proposed GDM-based histogram shifting strategy for RDH can further improve the visual quality of marked images in higher SSIM values by comparing with typical MSEM-based histogram shifting methods.

Existing reversible audio watermarking (RAW) techniques are often vulnerable to intentional or even unintentional attacks on the cover object. This paper proposes a robust RAW scheme based on lattices, which is referred to as Meet-in-the-Middle Embedding (MME). In MME, the lattice quantization errors are properly scaled and added back to the quantized host signals such that the receiver can estimate the cover. Scaling factor serves as a key factor to the reversibility of MME, whose feasible range is rigorously justified. Both theoretically and experimentally, we demonstrate the superiority of MME to improved quantization index modulation (IQIM) in terms of signal-to-watermark ratio (SWR) and generalized signal-to-noise ratio (GSNR). Moreover, simulations show that MME also outperforms other state-of-the-arts in SWR, objective difference grade (ODG), and bit error rate (BER).

Community group-buying is an emerging shopping model in which individuals within the same community collectively purchase daily necessities at lower prices. The logistics transportation in this scenario requires higher turnover rates and timeliness compared to traditional online shopping. Therefore, making efficient vehicle dispatch decisions is necessary to improve delivery efficiency and reduce transportation costs. However, uncertainties in orders and complex dependencies between multi-stage decisions in the community group-buying scenario challenge the scheduling of delivery vehicles. In this paper, we propose a vehicle dispatch system for community group-buying logistics based on a predict-then-optimize process, which incorporates a demand prediction module and a vehicle dispatch module. In the prediction module, we categorize goods and predict the total quantity of each category using a multi-layer perceptron (MLP). In the vehicle dispatch module, we model the vehicle dispatch process as a reinforcement learning problem and use an action masking mechanism to prune the policy search space. Experimental results demonstrate that the vehicle dispatch system achieves state-of-the-art performance compared to several popular baselines across three different datasets. Our system reduces transportation costs by at least 8.89% and improves decision efficiency by at least 10%. Moreover, it behaves close to the approximate optimal solution in most cases.

In reversible data hiding (RDH), the use of general distortion metric for improvement of image visual quality is a new issue. In this letter, a new RDH method of grayscale images using general distortion metric is proposed. The proposed method can adaptively compute different distortions for different pixels by considering the influence of surrounding pixels. Due to the fact that the pixel distortion in smooth areas is more obvious than that in texture areas under the same modification, the modifications on pixels are adaptively adjusted by combining a minimum distortion coding to achieve better visual quality. Experimental results show that the proposed method can reduce modifications on smooth areas and achieve better embedding performance for grayscale images in comparison with several classical and advanced RDH methods

The analysis of express trajectory is essential for delivery service optimization and courier management. Express trajectory consists of a large amount of community paths which is different from the trajectory of transportation tools such as taxis which run in the main road of a city. Due to the complexity of the community road networks and the energy constraint of devices, trajectories are collected under a low sampling rate and accuracy. These trajectories need to be recovered and mapped to the road networks for further analysis. Current recovery approaches utilize two-stage or end-to-end models to generate the upsampled trajectories. These methods fail to fully exploit the periodicity relation in historical data, which leads to accuracy losses for complex community road networks. In this paper, we design PATR, a periodicity-aware Seq2Seq model, to recover the couriers' trajectories by leveraging the periodical patterns of the historical data. We conduct experimental evaluations based on real-world GPS data of JD logistics consisting of 836,959 points and 25,726 road segments, and our model outperforms the state-of-the-art baselines by reducing recovery error by 2.4%-21.9%.