Information about college students’ social networks plays a pivotal role in college students’ mental health monitoring and student management. While there have been many studies to infer social networks by data mining, the mining of college students’ social networks lacks consideration of homophily. College students’ social behaviors show significant homophily in the aspect of major and grade. Consequently, the inferred inter-major and inter-grade social ties will be erroneously omitted without considering such an effect. In this work, we aimed to increase the fidelity of the extracted networks by alleviating the homophily effect. To achieve this goal, we propose a method that combines the sliding time-window method with the hierarchical encounter model based on association rules. Specifically, we first calculated the counts of spatial–temporal co-occurrences of each student pair. The co-occurrences were acquired by the sliding time-window method, which takes advantage of the symmetry of the social ties. We then applied the hierarchical encounter model based on association rules to extract social networks by layer. Furthermore, we propose an adaptive method to set co-occurrence thresholds. Results suggested that our model infers the social networks of students with better fidelity, with the proportion of extracted inter-major social ties in entire social ties increasing from 0.89% to 5.45% and the proportion of inter-grade social ties rising from 0.92% to 4.65%.
The display colorimetric accuracy is a key factor to ensure printing quality. The main mission for display’s color management is to compute its display color to get accurate colorimetric characterization, namely to ascertain the converting relationship between the RGB color data and their corresponding CIE X Y Z values. The related color values of EIZO, Mac and common PC displays were measured in the experiment, fitted the parameter, and computed the displayed color with gain-offset-gamma(GOG)model.Two-Primary Crosstalk (TPC) model, Piecewise Partition (PP)model. The three models were analyzed and compared with the measurement values. The model performance was assessed using 729 color samples uniformly distributed on the RGB display gamut. Compare the measured values and computed values and then find the more precise way to determine the display’s colorimetric characterization.
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