Practical constraints in rater-mediated assessments limit the availability of complete data. Instead, most scoring procedures include one or two ratings for each performance, with overlapping performances across raters or linking sets of multiple-choice items to facilitate model estimation. These incomplete scoring designs present challenges for detecting rater biases, or differential rater functioning (DRF). The purpose of this study is to illustrate and explore the sensitivity of DRF indices in realistic sparse rating designs that have been documented in the literature that include different types and levels of connectivity among raters and students. The results indicated that it is possible to detect DRF in sparse rating designs, but the sensitivity of DRF indices varies across designs. We consider the implications of our findings for practice related to monitoring raters in performance assessments.

With the rapid development of smart TV industry, a large number of TV programs have been available for meeting various user interests, which consequently raise a great demand of building personalized TV recommender systems. Indeed, a personalized TV recommender system can greatly help users to obtain their preferred programs and assist TV and channel providers to attract more audiences. While different methods have been proposed for TV recommendations, most of them neglect the mixture of watching groups behind an individual TV. In other words, there may be different groups of audiences at different times in front of a TV. For instance, watching groups of a TV may consist of children, wife and husband, husband, wife, etc in many US household. To this end, in this paper, we propose a Mixture Probabilistic Matrix Factorization (mPMF) model to learn the program preferences of televisions, which assumes that the preference of a given television can be regarded as the mixed preference of different watching groups. Specifically, the latent vector of a television is drawn from a mixture of Gaussian and the mixture number is the estimated number of watching groups behind the television. To evaluate the proposed mPMF model, we conduct extensive experiments with many state-of-the-art baseline methods and evaluation metrics on a real-world data set. The experimental results clearly demonstrate the effectiveness of our model.

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Polycrystalline samples of Y0.4Pr0.6Ba2Cu3O7- delta have been prepared and characterized by X-ray powder diffraction, differential thermal analysis and temperature dependence of resistivity and AC susceptibility. We observed that both superconductivity and metallic conductivity were revived at x>or=0.75, accompanied by an orthorhombic-to-tetragonal transition. The superconducting transition temperature could be further enhanced by increasing the Sr content, suggesting that there is an ion-size effect on Tc at the Ba site. The results were discussed in terms of the Pr-O covalent bonding effect.

When the quadruped robot run high-speed movement, Frequent or large impact force from the ground on the robot system cause significant damage to stability and reliability of the hydraulic system and robot itself. Through decomposition the high-speed running process, the touchdown, slippage, and stationary state dynamic model of robot mechanism are established. By analyzing the running state, the influence of the ground impact force on the structure of the body and the characteristic curve of the hip joint and knee joint movement are obtained. Through the establishment of impact dynamics equation, using the Matlab and Adams dynamic simulation software, carried out simulation experimental research on the impact force on the ground when the robot running. The results can provide a reliable basis and theory of control for the design of zero impact gait planning and high speed gait planning.