努力培养适应农村工作的大学生村官

实现大学生村官和农村工作的无缝对接,需要从源头上提高大学生对新农村建设的适应性,也就是要重视高校母体对大学生村官素质的培育工作。为此,要加强思想教育,培养大学生立志建设农村的情感;设置就业指导课,使大学生通过课堂了解党和国家的农业农村政策;通过暑期社会实践活动,使大学生实地感受农村生活;建立大学生与农村农民联系制度,促进大学生岗前转型;将农民代表请进课堂,使大学生不断加深对农村的了解。     

Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

Sex estimation standards are population specific however, we argue that machine learning techniques (ML) may enhance the biological sex determination on trans-population application. Linear discriminant analysis (LDA) versus nine ML including quadratic discriminant analysis (QDA), support vector machine (SVM), Decision Tree (DT), Gaussian process (GPC), Naïve Bayesian (NBC), K-Nearest Neighbor (KNN), Random Forest (RFM) and Adaptive boosting (Adaboost) were compared. The experiments involve two contemporary populations: Turkish (n = 300) and Egyptian populations (n = 100) for training and validation, respectively. Base models were calibrated using isotonic and sigmoid calibration schemes. Results were analyzed at posterior probabilities (pp) thresholds >0.95 and >0.80. At pp = 0.5, ML algorithms yielded comparable accuracies in the training (90% to 97%) and test sets (81% to 88%) which are not modified after employing the calibration techniques. At pp >0.95, the raw RFM, LDA, QDA, and SVM models have shown the best performance however, calibration techniques improved the performance of various classifier especially NBC and Adaboost. By contrast, the performance of GPC, KNN, QDA models worsened by calibration. RFM has shown the best performance among all models at both thresholds whereas LDA benefited the best from using both calibration methods at pp >0.80. Complex ML models are not necessarily achieving better performance metrics. LDA and QDA remain the fastest and simplest classifiers. We demonstrated the capability of enhancing sex estimation using ML on an independent population sample however, differences in the underlying probability distribution generated by models were detected which warranted more cautious application by forensic practitioners.     

Why are the affluent better represented around the world?

Scholars have discovered remarkable inequalities in who gets represented in electoral democracies. Around the world, the preferences of the rich tend to be better represented than those of the less well-off. In this paper, we use the most comprehensive comparative dataset of unequal representation available to answer why the poor are underrepresented. By leveraging variation over time and across countries, we study which factors explain why representation is more unequal in some places than in others. We compile a number of covariates examined in previous studies and use machine learning to describe which mechanisms best explain the data. Globally, we find that economic conditions and good governance are most important in determining the extent of unequal representation, and we find little support for hypotheses related to political institutions, interest groups or political behaviour, such as turnout. These results provide the first broadly comparative explanations for unequal representation.     

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.     

"四情"与高校思想政治工作"三观"教育

新形势下,用什么样的世界观、人生观和价值观来培养什么样的青年大学生和怎样培养今天的青年大学生,日益成为高校思想政治工作的重点和主要对象.应该从当前的世情(世界形势变化)、国情(国内发展状况)、党情(中国共产党执政地位和方式)和社情(当前青年大学生思想行为状况)的角度,做好这项工作.     

论公安院校学生优良学风的养成

尊学问为神圣之风气要靠养成,而养成良好学风,有赖于学校作为学术殿堂的浓厚氛围。公安院校的学生一定要在任课教师和管理干部的正确引导下努力营造出这种具有自身鲜明特色的浓厚氛围。     

浅谈语文教学中审美情趣的培养

对青年学生审美情趣的培养,可以根据他们的心理特征,借助语文中美的因素,采用多种教育手段和现代教育技术,让学生在美的文化氛围中熏陶.由感知美、鉴赏美到创造美,提高自身的整体素质.     

Ideology,Learning, and Policy Diffusion: Experimental Evidence

We introduce experimental research design to the study of policy diffusion in order to better understand how political ideology affects policymakers’ willingness to learn from one another's experiences. Our two experiments–embedded in national surveys of U.S. municipal officials–expose local policymakers to vignettes describing the zoning and home foreclosure policies of other cities, offering opportunities to learn more. We find that: (1) policymakers who are ideologically predisposed against the described policy are relatively unwilling to learn from others, but (2) such ideological biases can be overcome with an emphasis on the policy's success or on its adoption by co‐partisans in other communities. We also find a similar partisan‐based bias among traditional ideological supporters, who are less willing to learn from those in the opposing party. The experimental approach offered here provides numerous new opportunities for scholars of policy diffusion.     

大学英语教学改革背景下的公安院校英语教学

公安院校英语教学是全国大学英语教学的一个组成部分。目前全国大学英语教学正处于新一轮的改革之中。置身于这样的改革潮流之中，我们有必要研究、了解并把握这场改革的实质精神，并以此为依据研究适合我国公安院校英语教学的途径。     

我国高校学生权利探讨

在我国高等教育实行成本补偿政策后，高校内部管理行为开始进入司法审查的视野。坚持依法治校、依法执教、完善高校管理、保护学生合法权利已成为社会关注的热点，也成为高校亟待解决的问题。高校学生的权利应包括：公法上的权利和私法上的权利。