Reviewerly/exHarmony huggingface dataset:
A Dimension Group-Based Comprehensive Elite Learning Swarm Optimizer for Large-Scale Optimization

High-dimensional optimization problems are more and more common in the era of big data and the Internet of things (IoT), which seriously challenge the optimization performance of existing optimizers. To solve these kinds of problems effectively, this paper devises a dimension group-based comprehensive elite learning swarm optimizer (DGCELSO) by integrating valuable evolutionary information in different elite particles in the swarm to guide the updating of inferior ones. Specifically, the swarm is first separated into two exclusive sets, namely the elite set (ES) containing the top best individuals, and the non-elite set (NES), consisting of the remaining individuals. Then, the dimensions of each particle in NES are randomly divided into several groups with equal sizes. Subsequently, each dimension group of each non-elite particle is guided by two different elites randomly selected from ES. In this way, each non-elite particle in NES is comprehensively guided by multiple elite particles in ES. Therefore, not only could high diversity be maintained, but fast convergence is also likely guaranteed. To alleviate the sensitivity of DGCELSO to the associated parameters, we further devise dynamic adjustment strategies to change the parameter settings during the evolution. With the above mechanisms, DGCELSO is expected to explore and exploit the solution space properly to find the optimum solutions for optimization problems. Extensive experiments conducted on two commonly used large-scale benchmark problem sets demonstrate that DGCELSO achieves highly competitive or even much better performance than several state-of-the-art large-scale optimizers.
Teaching and Curriculum of the Preschool Physical Education Major Direction in Colleges and Universities under Virtual Reality Technology

The present work aims to study the influence of virtual reality (VR) technology on the teaching and curriculum of preschool physical education in colleges and universities and establish a virtual teaching model suitable for the college teaching system. The classroom teaching situation of using VR technology in physical training of preschool education major direction in colleges and universities is investigated using the questionnaire survey and teaching experiment. Firstly, the feasibility of applying VR technology to teaching is proved by analyzing the relevant education theories. Secondly, the experimental research method is designed to verify the application effect of VR technology in teaching behavior. Finally, the collected data is sorted out to judge the method’s feasibility. The experimental results demonstrate that 88.0% of the respondents are curious about the application of VR, and 88.6% of the respondents can accept the application of VR in sports dance teaching. Besides, 89.1% claimed that VR technology could enhance students’ understanding of knowledge, and 93.0% thought that VR applications would not interfere with teachers’ explanations. In addition, 80.0% thought that virtual teaching could stimulate students’ interest in learning, 75.0% said that VR application could attract students’ attention, and 63.0% believed that VR application could improve learning efficiency. The preliminary investigation suggests that introducing VR technology can reduce the work intensity of teachers, and the students participating in the survey are optimistic about the application of VR technology in preschool physical education. The formal test based on a t-test indicates that the average score of the experimental group is 3.18 points higher than that of the control group, and there are significant differences in technology between the control group and the experimental group. VR technology can improve students’ technical level, enhance self-confidence, and improve students’ grades. To sum up, this study provides a reference for developing virtual teaching mode and applying VR technology to physical education.
An Object-Based Genetic Programming Approach for Cropland Field Extraction

Cropland fields are the basic spatial units for agricultural management, and information about their distribution is critical for analyzing agricultural investments and management. However, the extraction of cropland fields of smallholder farms is a challenging task because of their irregular shapes and diverse spectrum. In this paper, we proposed a new object-based Genetic Programming (GP) approach to extract cropland fields. The proposed approach used the multiresolution segmentation (MRS) method to acquire objects from a very high resolution (VHR) image, and extracted spectral, shape and texture features as inputs for GP. Then GP was used to automatically evolve the optimal classifier to extract cropland fields. The results show that the proposed approach has obtained high accuracy in two areas with different landscape complexities. Further analysis show that the GP approach significantly outperforms five commonly used classifiers, including K-Nearest Neighbor (KNN), Decision Tree (DT), Naïve Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF). By using different numbers of training samples, GP can maintain high accuracy with any volume of samples compared to other classifiers.
Solving the class imbalance problem using ensemble algorithm: application of screening for aortic dissection

Imbalance between positive and negative outcomes, a so-called class imbalance, is a problem generally found in medical data. Despite various studies, class imbalance has always been a difficult issue. The main objective of this study was to find an effective integrated approach to address the problems posed by class imbalance and to validate the method in an early screening model for a rare cardiovascular disease aortic dissection (AD).Different data-level methods, cost-sensitive learning, and the bagging method were combined to solve the problem of low sensitivity caused by the imbalance of two classes of data. First, feature selection was applied to select the most relevant features using statistical analysis, including significance test and logistic regression. Then, we assigned two different misclassification cost values for two classes, constructed weak classifiers based on the support vector machine (SVM) model, and integrated the weak classifiers with undersampling and bagging methods to build the final strong classifier. Due to the rarity of AD, the data imbalance was particularly prominent. Therefore, we applied our method to the construction of an early screening model for AD disease. Clinical data of 523,213 patients from the Institute of Hypertension, Xiangya Hospital, Central South University were used to verify the validity of this method. In these data, the sample ratio of AD patients to non-AD patients was 1:65, and each sample contained 71 features.The proposed ensemble model achieved the highest sensitivity of 82.8%, with training time and specificity reaching 56.4 s and 71.9% respectively. Additionally, it obtained a small variance of sensitivity of 19.58 × 10-3 in the seven-fold cross validation experiment. The results outperformed the common ensemble algorithms of AdaBoost, EasyEnsemble, and Random Forest (RF) as well as the single machine learning (ML) methods of logistic regression, decision tree, k nearest neighbors (KNN), back propagation neural network (BP) and SVM. Among the five single ML algorithms, the SVM model after cost-sensitive learning method performed best with a sensitivity of 79.5% and a specificity of 73.4%.In this study, we demonstrate that the integration of feature selection, undersampling, cost-sensitive learning and bagging methods can overcome the challenge of class imbalance in a medical dataset and develop a practical screening model for AD, which could lead to a decision support for screening for AD at an early stage.
Dense Residual Transformer for Image Denoising

Image denoising is an important low-level computer vision task, which aims to reconstruct a noise-free and high-quality image from a noisy image. With the development of deep learning, convolutional neural network (CNN) has been gradually applied and achieved great success in image denoising, image compression, image enhancement, etc. Recently, Transformer has been a hot technique, which is widely used to tackle computer vision tasks. However, few Transformer-based methods have been proposed for low-level vision tasks. In this paper, we proposed an image denoising network structure based on Transformer, which is named DenSformer. DenSformer consists of three modules, including a preprocessing module, a local-global feature extraction module, and a reconstruction module. Specifically, the local-global feature extraction module consists of several Sformer groups, each of which has several ETransformer layers and a convolution layer, together with a residual connection. These Sformer groups are densely skip-connected to fuse the feature of different layers, and they jointly capture the local and global information from the given noisy images. We conduct our model on comprehensive experiments. In synthetic noise removal, DenSformer outperforms other state-of-the-art methods by up to 0.06–0.28 dB in gray-scale images and 0.57–1.19 dB in color images. In real noise removal, DenSformer can achieve comparable performance, while the number of parameters can be reduced by up to 40%. Experimental results prove that our DenSformer achieves improvement compared to some state-of-the-art methods, both for the synthetic noise data and real noise data, in the objective and subjective evaluations.
Mortality prediction of patients in intensive care units using machine learning algorithms based on electronic health records

Abstract Improving predictive models for intensive care unit (ICU) inpatients requires a new strategy that periodically includes the latest clinical data and can be updated to reflect local characteristics. We extracted data from all adult patients admitted to the ICUs of two university hospitals with different characteristics from 2006 to 2020, and a total of 85,146 patients were included in this study. Machine learning algorithms were trained to predict in-hospital mortality. The predictive performance of conventional scoring models and machine learning algorithms was assessed by the area under the receiver operating characteristic curve (AUROC). The conventional scoring models had various predictive powers, with the SAPS III (AUROC 0.773 [0.766–0.779] for hospital S) and APACHE III (AUROC 0.803 [0.795–0.810] for hospital G) showing the highest AUROC among them. The best performing machine learning models achieved an AUROC of 0.977 (0.973–0.980) in hospital S and 0.955 (0.950–0.961) in hospital G. The use of ML models in conjunction with conventional scoring systems can provide more useful information for predicting the prognosis of critically ill patients. In this study, we suggest that the predictive model can be made more robust by training with the individual data of each hospital.
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning

Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance in various graph learning tasks. However, existing endeavors predominately focus on the conventional semi-supervised setting where relatively abundant gold-labeled nodes are provided. While it is often impractical due to the fact that data labeling is unbearably laborious and requires intensive domain knowledge, especially when considering the heterogeneity of graph-structured data. Under the few-shot semi-supervised setting, the performance of most of the existing GNNs is inevitably undermined by the overfitting and oversmoothing issues, largely owing to the shortage of labeled data. In this paper, we propose a decoupled network architecture equipped with a novel meta-learning algorithm to solve this problem. In essence, our framework Meta-PN infers high-quality pseudo labels on unlabeled nodes via a meta-learned label propagation strategy, which effectively augments the scarce labeled data while enabling large receptive fields during training. Extensive experiments demonstrate that our approach offers easy and substantial performance gains compared to existing techniques on various benchmark datasets. The implementation and extended manuscript of this work are publicly available at https://github.com/kaize0409/Meta-PN.
Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.
Amazon cloud computing platform EC2 and VANET simulations

Network simulations are resource and time intensive tasks due to the involvement of a number of factors attributable to scalability with respect to computation time, cost, and energy. Academic clouds are employed for large-scale network simulations, which require extensive capacity and intelligent resource management. This paper explores the performance efficiency of cloud solution to facilitate network researchers by proposing the utilisation of network simulator configured virtual machines. The prototype framework is executed using Amazon elastic computing cloud (EC2) Windows' instances configured with OPNET simulator. Simulation results show three significant benefits i.e., i) for scalable network simulations, consumption of considerably fewer resources in terms of simulation elapsed time, hardware resources and usage costs; ii) reduction of carbon emission leading towards sustainable IT; iii) promotion of network research by making complicated issues like simulation scalability and setup (installation or configuration) of network simulators to facilitate researchers and students.
Motion Planning for Highly Automated Road Vehicles with a Hybrid Approach Using Nonlinear Optimization and Artificial Neural Networks

Over the last decade, many different algorithms were developed for the motion planning of road vehicles due to the increasing interest in the automation of road transportation.To be able to ensure dynamical feasibility of the planned trajectories, nonholonomic dynamics of wheeled vehicles must be considered.Nonlinear optimization based trajectory planners are proven to satisfy this need, however this happens at the expense of increasing computational effort, which jeopardizes the real-time applicability of these methods.This paper presents an algorithm which offers a solution to this problematic with a hybrid approach using artificial neural networks (ANNs).First, a nonlinear optimization based trajectory planner is presented which ensures the dynamical feasibility with the model-based prediction of the vehicle's motion.Next, an artificial neural network is trained to reproduce the behavior of the optimization based planning algorithm with the method of supervised learning.The generation of training data happens off-line, which eliminates the concerns about the computational requirements of the optimization-based method.The trained neural network then replaces the original motion planner in on-line planning tasks which significantly reduces computational effort and thus run-time.Furthermore, the output of the network is supervised by the model based motion prediction layer of the original optimization-based algorithm and can thus always be trusted.Finally, the performance of the hybrid method is benchmarked with computer simulations in terms of dynamical feasibility and run-time and the results are investigated.Examinations show that the computation time can be significantly reduced while maintaining the feasibility of resulting vehicle motions.
Hybrid Feature Selection and Ensemble Learning Method for Spam Email Classification

The data mining techniques produce good work in many domains.The spam emails are becoming a serious dilemma and an important matter to have different solutions, and enhanced methods and algorithms.Using Ensemble methods which are well-established classifiers.In this paper data mining techniques used to classify spam email using the UCI spam base dataset.The results achieved by the machine learning tools and techniques, and the Ensemble learning methods, after applying feature selection methods on the data set; which gave better result, and better classification accuracy.For the evaluation method used the cross-validation for testing and training option, and the confusion matrix to show the accuracy and the performance result of the chosen classifiers; which are Naïve Bayes, decision tree, ensemble boosting and ensemble hybrid boosting classifiers.
Efficient Way to Detect Bone Cancer using Image Segmentation

Malignant growth is a wild division of irregular cells, which is spread over the parts of the body. Bone disease is one of the sorts of malignancy. Bone malignancy is a pernicious and threatening illness, caused because of uncontrolled division of cells in the bone. The most compromising and usually happened malignancy is bone disease. Prior the location of bone malignant growth is most testing issue. A definitive objective of this paper is to play out an examination on the bone disease pictures to discover the tumor. In this exploration we are looking at K-implies and fluffy C-Means grouping procedures to recognize the presize accuracy tumor part in the bone. In this exploration at first picture experiences into the division procedure and k-implies and Fuzzy C-Means calculations are connected to distinguish the exact tumor part in the bone. In this exploration is completely utilized MATLAB as a programming instrument for the way toward stacking a picture and to perform picture division. For clear comprehension of this exploration the outline and the outcomes will be shown in the sessions of this paper
An Examination of Students Online Learning Satisfaction, Interaction, Self-efficacy and Self-regulated Learning

Despite constantly growing, many educational institutions have not been prepared to shift from traditional to online learning environments until the pandemic.Current research aims to examine online students' satisfaction, interactions, internet self-efficacy and self-regulated learning among 210 high school students.The questionnaire has been used to collect the data from the participants.The findings suggested that the participants feel confident while using the Internet and are quite self-directed and do not lack interactions or satisfaction with online learning.Furthermore, the findings indicated that while grade level and GPA insignificantly influence students' satisfaction, time spent online and gender influence it significantly, with males reporting higher levels of satisfaction.While GPA and grade level significantly affect online interaction, gender and time spent online have no impact.Morever, students who invest extra efforts into learning and obtain high grades feel significantly more satisfied with online learning than those with lower grades.The time spent online significantly affect internet self-efficacy and self-regulated learning, while the influence of grade level, GPA and gender is insignificant.This study findings may help instructors create an online classroom environment conducive to improving online students' satisfaction, interaction, internet self-efficacy and self-regulated learning and, as a result, improve the effects of online education.
Joint Segmentation and Identification Feature Learning for Occlusion Face Recognition

The existing occlusion face recognition algorithms almost tend to pay more attention to the visible facial components. However, these models are limited because they heavily rely on existing face segmentation approaches to locate occlusions, which is extremely sensitive to the performance of mask learning. To tackle this issue, we propose a joint segmentation and identification feature learning framework for end-to-end occlusion face recognition. More particularly, unlike employing an external face segmentation model to locate the occlusion, we design an occlusion prediction module supervised by known mask labels to be aware of the mask. It shares underlying convolutional feature maps with the identification network and can be collaboratively optimized with each other. Furthermore, we propose a novel channel refinement network to cast the predicted single-channel occlusion mask into a multi-channel mask matrix with each channel owing a distinct mask map. Occlusion-free feature maps are then generated by projecting multi-channel mask probability maps onto original feature maps. Thus, it can suppress the representation of occlusion elements in both the spatial and channel dimensions under the guidance of the mask matrix. Moreover, in order to avoid misleading aggressively predicted mask maps and meanwhile actively exploit usable occlusion-robust features, we aggregate the original and occlusion-free feature maps to distill the final candidate embeddings by our proposed feature purification module. Lastly, to alleviate the scarcity of real-world occlusion face recognition datasets, we build large-scale synthetic occlusion face datasets, totaling up to 980193 face images of 10574 subjects for the training dataset and 36721 face images of 6817 subjects for the testing dataset, respectively. Extensive experimental results on the synthetic and real-world occlusion face datasets show that our approach significantly outperforms the state-of-the-art in both 1:1 face verification and 1:N face identification.
ProQA: Structural Prompt-based Pre-training for Unified Question Answering

Question Answering (QA) is a longstanding challenge in natural language processing.Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skills.The specialty in QA research hinders systems from modeling commonalities between tasks and generalization for wider applications.To address this issue, we present ProQA, a unified QA paradigm that solves various tasks through a single model.ProQA takes a unified structural prompt as the bridge and improves the QA-centric ability by structural promptbased pre-training.Through a structurally designed prompt-based input schema, ProQA concurrently models the knowledge generalization for all QA tasks while keeping the knowledge customization for every specific QA task.Furthermore, ProQA is pre-trained with structural prompt-formatted large-scale synthesized corpus, which empowers the model with the commonly-required QA ability.Experimental results on 11 QA benchmarks demonstrate that ProQA consistently boosts performance on both full data fine-tuning, few-shot learning, and zero-shot testing scenarios.Furthermore, ProQA exhibits strong ability in both continual learning and transfer learning by taking the advantages of the structural prompt.