What Shapes Undergraduate Students’ Satisfaction in Unstable Learning Contexts?

This paper investigates what determinants, and to what extent, they influence students’ satisfaction in unstable learning contexts. Using a national-scaled sample of Vietnamese HEIs with a sound theoretical background, we find that regardless of instabilities from external shocks, the key factors that shape students’ satisfaction are fixed by traditional norms (self-efficacy, infrastructure, lecturer) rather than occasional factors occurring from each event. We find in particular that self-efficacy is the most influential factor for students’ satisfaction and friendship is the most prominent element that enhances students’ self- efficacy. Overall, this paper enriched the literature on student satisfaction, especially during unstable contexts. Thus, it has important implications for educators and HEIs stakeholders in management planning in the time to come

Heterogeneous ensemble selection for evolving data streams.

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model. In contrast, by combining several types of different learning models, a heterogeneous ensemble system can achieve greater diversity among its members, which helps to improve its performance. Although heterogeneous ensemble systems have achieved many successes in the batch classification setting, it is not trivial to extend them directly to the data stream setting. In this study, we propose a novel HEterogeneous Ensemble Selection (HEES) method, which dynamically selects an appropriate subset of base classifiers to predict data under the stream setting. We are inspired by the observation that a well-chosen subset of good base classifiers may outperform the whole ensemble system. Here, we define a good candidate as one that expresses not only high predictive performance but also high confidence in its prediction. Our selection process is thus divided into two sub-processes: accurate-candidate selection and confident-candidate selection. We define an accurate candidate in the stream context as a base classifier with high accuracy over the current concept, while a confident candidate as one with a confidence score higher than a certain threshold. In the first sub-process, we employ the prequential accuracy to estimate the performance of a base classifier at a specific time, while in the latter sub-process, we propose a new measure to quantify the predictive confidence and provide a method to learn the threshold incrementally. The final ensemble is formed by taking the intersection of the sets of confident classifiers and accurate classifiers. Experiments on a wide range of data streams show that the proposed method achieves competitive performance with lower running time in comparison to the state-of-the-art online ensemble methods

DEFEG: deep ensemble with weighted feature generation.

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiers’ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimisation method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms

Kinematic and dynamic modelling for a class of hybrid robots composed of m local closed-loop linkages appended to an n-link serial manipulator

Recently, more and more hybrid robots have been designed to meet the increasing demand for a wide spectrum of applications. However, development of a general and systematic method for kinematic design and dynamic analysis for hybrid robots is rare. Most publications deal with the kinematic and dynamic issues for individual hybrid robots rather than any generalization. Hence, in this paper, we present a novel method for kinematic and dynamic modelling for a class of hybrid robots. First, a generic scheme for the kinematic design of a general hybrid robot mechanism is proposed. In this manner, the kinematic equation and the constraint equations for the robot class are derived in a generalized case. Second, in order to simplify the dynamic modelling and analysis of the complex hybrid robots, a Lemma about the analytical relationship among the generalized velocities of a hybrid robot system is proven in a generalized case as well. Last, examples of the kinematic and dynamic modelling of a newly designed hybrid robot are presented to demonstrate and validate the proposed method