Research on government service quality can help ensure the success of digital government services and has been the focus of numerous studies that proposed different frameworks and approaches. Most of the existing studies are based on traditional researcher-led methods, which struggle to capture the needs of citizens. In this paper, a citizen-feedback-based analysis framework was proposed to explore citizen demands and analyze the service quality of digital government. Citizen feedback data are a direct expression of citizens’ demands, so the citizen-feedback-based framework can help to obtain more targeted management insights and improve citizen satisfaction. Efficient machine learning methods used in the framework make data collection and processing more efficient, especially for large-scale internet data. With the crawled user feedback data from the Q&A e-government portal of Luzhou, Sichuan Province, China, we conducted experiments on the proposed framework to verify its feasibility. From citizens’ online feedback on Q&A services, we extracted five service quality factors: efficiency, quality, attitude, compliance, and execution of response. The analysis of five service quality factors provides some management insights, which can provide a guide for improvements in Q&A services.

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.

Faculty mobility is one of the most important research issues in the field of higher education. Reasonable faculty mobility can actively promote the fair, coordinated, balanced, healthy, and sustainable development of higher education. Scientific impact is the best proof of faculty members’ research abilities and is often represented by the quality of their articles. In particular, the gradual increase in citations of high-quality papers is undoubtedly an important reflection of healthy development in the academic field. This paper aims to explore the influence of faculty mobility on scientific impact, while comparative analysis is used to investigate whether there are disciplinary differences in the relationship between faculty mobility and scientific impact. Four major disciplines—sociology, mathematics, mechanical engineering, and philosophy—are selected as the scope of this study. Articles in these four major disciplines from 2000 to 2020 are obtained from the Web of Science, and Spearman’s rank correlation coefficient and the Wilcoxon signed-rank test are used to analyze the collected data. The results indicate the following: (1) faculty mobility has increased, with differences across disciplines; (2) mobility leads to a decrease in the number of citations, which decreases significantly with increased mobility frequency; and (3) the impact of mobility has disciplinary differences, with a relatively obvious decrease in mechanical engineering.

The window size of texture feature extraction has a significant impact on the accuracy of tree species classification. The forms of all texture features share an optimal extraction window, and different types of texture features use their independent optimal extraction windows, which is conducive to tree species classification. In this study, we used a RedEdge-MX image as the data source and a random forest to determine two forms of the best texture extraction windows and construct their own best texture feature set. Then, we combined the best texture feature sets with spectral bands and the digital surface model (DSM) to analyze the difference between the two best texture extraction forms in tree species classification. The results show that the classification accuracy of the best texture feature set was significantly different between the two extraction forms. The overall accuracy of the first extraction form was 79.6365% and that of the second extraction form was 81.8915%. When they are combined with a spectral band and the DSM, the classification accuracy of the latter was higher than that of the former (between 0.4295% and 2.2248%). Hence, in the classification of tree species, the construction of the best texture feature set should be determined by the best extraction window for each feature type.

Many scholars have conducted in‐depth research on the evaluation and prediction of scholars’ scientific impact and meanwhile discovered various factors that affect the success of scholars. Among all these relevant factors, scholars’ ages have been universally acknowledged as one of the most important factors for it can shed light on many practical issues, e.g., finding supervisors, discovering rising stars, and research funding or award applications. However, due to the inaccessibility or the privacy issues of acquiring scholars’ personal data, there is little research to explore the true ages of scholars currently. Alternatively, scholars’ publications’ information can be obtained through various digital libraries. Inspired by this fact, we propose a novel scholar’s age prediction method based on their articles’ information. Our method first classifies factors that affect scholars’ ages into intuitive and complex types according to their computational complexity and then apply machine learning algorithms to predict the ages of scholars based on these factors. The experimental results on the real dataset demonstrate that our method can effectively predict the true ages of scholars. Given that there is no completely accurate dataset because of the continuous publication of academic papers, we then apply our method on the incomplete dataset. Nevertheless, our method still has high prediction accuracy in such situations.