The Influence of Class Teachers’ Leadership Behavior and Personal Characteristics on Student Academic Performance: A Study of Fuzzy Set Qualitative Comparative Analysis Citing H Middle School as a Case Study

Leadership behavior of classroom teachers has a significant impact on students’ academic success. However, little empirical research on this subject has been undertaken in China. By developing truth tables and doing necessary computations, this study examines the influence of differential combinations of parameters connected to class teachers’ qualities and leadership behavior on student academic performance. It has been discovered that the democratic leadership behavior displayed by class teachers is the most popular among students and is helpful to student academic achievement; the numerous combinations of linked characteristics have varying effects on student academic performance. The study compensates for a dearth of empirical research on the subject and sheds light on the process by which class teacher leadership influences student academic progress

Fabric defect detection algorithm based on PHOG and SVM

In order to effectively improve the detection probabilityfor different types of fabrics and defects, a fabric defectdetection method based on pyramid histogram of edge orientationgradients (PHOG) and support vector machine (SVM) has beenproposed. The algorithm combines fabric texture statisticalmethod and machine learning method. It has two main parts,namely the feature extraction and classification. The detectionprocess mainly includes image segmentation, PHOG featureextraction, SVM model training and detection classification. Thesimulation results show that, based on the detection rate and thefalse alarm rate, the algorithm has a good detection andclassification effect, has a certain robustness, and can be appliedto the actual production department

Fabric defect detection algorithm based on PHOG and SVM

123-126In order to effectively improve the detection probability for different types of fabrics and defects, a fabric defect detection method based on pyramid histogram of edge orientation gradients (PHOG) and support vector machine (SVM) has been proposed. The algorithm combines fabric texture statistical method and machine learning method. It has two main parts, namely the feature extraction and classification. The detection process mainly includes image segmentation, PHOG feature extraction, SVM model training and detection classification. The simulation results show that, based on the detection rate and the false alarm rate, the algorithm has a good detection and classification effect, has a certain robustness, and can be applied to the actual production department

A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives

A multitude of publicly-available driving datasets and data platforms have been raised for autonomous vehicles (AV). However, the heterogeneities of databases in size, structure and driving context make existing datasets practically ineffective due to a lack of uniform frameworks and searchable indexes. In order to overcome these limitations on existing public datasets, this paper proposes a data unification framework based on traffic primitives with ability to automatically unify and label heterogeneous traffic data. This is achieved by two steps: 1) Carefully arrange raw multidimensional time series driving data into a relational database and then 2) automatically extract labeled and indexed traffic primitives from traffic data through a Bayesian nonparametric learning method. Finally, we evaluate the effectiveness of our developed framework using the collected real vehicle data.Comment: 6 pages, 7 figures, 1 table, ITSC 201

A General Framework of Learning Multi-Vehicle Interaction Patterns from Videos

Semantic learning and understanding of multi-vehicle interaction patterns in a cluttered driving environment are essential but challenging for autonomous vehicles to make proper decisions. This paper presents a general framework to gain insights into intricate multi-vehicle interaction patterns from bird's-eye view traffic videos. We adopt a Gaussian velocity field to describe the time-varying multi-vehicle interaction behaviors and then use deep autoencoders to learn associated latent representations for each temporal frame. Then, we utilize a hidden semi-Markov model with a hierarchical Dirichlet process as a prior to segment these sequential representations into granular components, also called traffic primitives, corresponding to interaction patterns. Experimental results demonstrate that our proposed framework can extract traffic primitives from videos, thus providing a semantic way to analyze multi-vehicle interaction patterns, even for cluttered driving scenarios that are far messier than human beings can cope with.Comment: 2019 IEEE Intelligent Transportation Systems Conference (ITSC

Political embeddedness in public–private partnership for nature conservation: A land trust reserve case from China

Private sector plays an increasingly vital role in nature conservation globally. This study explores the concept of political embeddedness, which suggests that governments and environmental nongovernmental organizations (ENGOs) can leverage each other’s strengths to achieve both formal and informal goals. Using the case of Laohegou Nature Reserve in China, this study illustrated how the complementary advantages of the government and ENGOs form the foundation of a land trust reserve. Within the case, the study found that power and interest balance between the government and ENGOs during project implementation supported their formal cooperation in nature conservation. This study proposed a political perspective to elaborate power and interest in the formal and informal dimensions of nature conservation public–private partnership (PPP) project. Moreover, it noted that a balance of power between the government and ENGOs is essential in building partnership networks with inclusive interests.Peer Reviewe

Assumption-lean and Data-adaptive Post-Prediction Inference

A primary challenge facing modern scientific research is the limited availability of gold-standard data which can be both costly and labor-intensive to obtain. With the rapid development of machine learning (ML), scientists have relied on ML algorithms to predict these gold-standard outcomes with easily obtained covariates. However, these predicted outcomes are often used directly in subsequent statistical analyses, ignoring imprecision and heterogeneity introduced by the prediction procedure. This will likely result in false positive findings and invalid scientific conclusions. In this work, we introduce an assumption-lean and data-adaptive Post-Prediction Inference (POP-Inf) procedure that allows valid and powerful inference based on ML-predicted outcomes. Its "assumption-lean" property guarantees reliable statistical inference without assumptions on the ML-prediction, for a wide range of statistical quantities. Its "data-adaptive'" feature guarantees an efficiency gain over existing post-prediction inference methods, regardless of the accuracy of ML-prediction. We demonstrate the superiority and applicability of our method through simulations and large-scale genomic data

RA-ICM: A Novel Independent Cascade Model Incorporating User Relationships and Attitudes

The rapid development of social networks has a wide range of social effects, which facilitates the study of social issues. Accurately forecasting the information propagation process within social networks is crucial for promptly understanding the event direction and effectively addressing social problems in a scientific manner. The relationships between non-adjacent users and the attitudes of users significantly influence the information propagation process within social networks. However, existing research has ignored these two elements, which poses challenges for accurately predicting the information propagation process. This limitation significantly hinders the study of emotional contagion and influence maximization in social networks. To address these issues, by considering the relationships between non-adjacent users and the influence of user attitudes, we propose a new information propagation model based on the independent cascade model. Experimental results obtained from six real Weibo datasets validate the effectiveness of the proposed model, which is reflected in increased prediction accuracy and reduced time complexity. Furthermore, the information dissemination trend in social networks predicted by the proposed model closely resembles the actual information propagation process, which demonstrates the superiority of the proposed model