Human motion generation is a critical task with a wide spectrum of applications. Achieving high realism in generated motions requires naturalness, smoothness, and plausibility. However, current evaluation metrics often rely on simple heuristics or distribution distances and do not align well with human perceptions. In this work, we propose a data-driven approach to bridge this gap by introducing a large-scale human perceptual evaluation dataset, MotionPercept, and a human motion critic model, MotionCritic, that capture human perceptual preferences. Our critic model offers a more accurate metric for assessing motion quality and could be readily integrated into the motion generation pipeline to enhance generation quality. Extensive experiments demonstrate the effectiveness of our approach in both evaluating and improving the quality of generated human motions by aligning with human perceptions. Code and data are publicly available at https://motioncritic.github.io/.

Abstract Group shilling attacks are more threatening than individual shilling attacks due to the collusive behaviours among group members, which pose a great challenge to the credibility of recommender systems. Detection of group shilling attacks can reduce the risk caused by such attacks and ensure the credibility of recommendations. The existing methods for detecting group shilling attacks mainly extract features from the rating patterns of users at group level to measure the shilling behaviours of groups. However, they may become ineffective with the change of attack strategy, resulting in a decrease in detection performance. Aiming at this problem, a new solution based on user multi-dimensional features and collusive behaviour analysis is presented for detecting group shilling attacks. First, we employ the information entropy and latent semantic analysis to analyse the user behavioural patterns from dimensions of item, rating, time and interest, and propose a suite of indicators to measure the anomaly behaviours of users. Second, we propose a measure based on the multi-dimensional features of users to capture the collusion of group members from the perspective of their synchronized behaviours and abnormal behaviours, and treat the groups with high collusion as candidate groups. Finally, based on the multi-dimensional features of users, we construct the user behaviour similarity matrix using Gaussian radial basis function (Gaussian-RBF) and adopt the spectral clustering algorithm to spot group shilling attackers in the candidate groups. Experiments show that the detection performance (F1-measure) of the proposed method can achieve 0.965, 0.964, 0.991 and 0.868 on the Netflix, CiaoDVD, Epinions and Amazon datasets, respectively, which is better than that of state-of-the-art methods.

Improving the calibration accuracy of infrared hyperspectral interferometers is a prerequisite for their quantitative application. Updating satellite in-orbit calibration parameters is an important way to keep calibration accuracy. However, how to separate the deviation of one or more calibration parameters from the coupling error of observation data is a key issue that needs to be solved. Therefore, we propose a variational-based calibration parameter optimization algorithm (VarCalPOA), which uses only observation data and reference data to optimize the key calibration parameters based on variational assimilation theory to obtain the optimized values of the calibration parameters, thereby improving the calibration accuracy. Based on the observation and calibration simulation model, we choose$T_{\mathrm {ict}}$,$e_{\mathrm {ict}}$, and$a_{2}$as the key calibration parameters. Using the VarCalPOA method, the simulation results show that the optimized calibration parameters have a certain positive effect on the improvement of calibration accuracy. This study proves that the VarCalPOA method is potentially an effective method for monitoring the in-orbit state of infrared hyperspectral interferometers.