Reviewerly/exHarmony huggingface dataset:
Overcoming challenges to make e-learning a panacea for present and future crises

Purpose This study identified salient challenges related to future preference for e-learning of undergraduates during closure of institutions of higher learning due to the Coronavirus disease 2019 (COVID-19) and put forth suggestions to overcome challenges of e-learning for present and future crises. Design/methodology/approach Undergraduates were drawn from two universities in Malaysia who normally attend conventional classroom learning. Underpinned by the literature, the challenges and future preference for e-learning were operationalised into statements. Data were collected via a self-administered questionnaire hosted on Google Forms and were analysed with Statistical Package for the Social Sciences and STATA statistical software. Findings The hierarchical multiple linear regression results showed that the salient challenges related to undergraduates' future preference for e-learning, in descending order, were learning outcomes, followed by disadvantages (negatively) and discipline. Moreover, there were nuanced gender differences on the relative importance of salient challenges related to future preference for e-learning. Research limitations/implications Because the literature on e-learning during the closure of institutions of learning due to the COVID-19 pandemic is evolving, some salient challenges may not be captured in this study. Next, this survey was carried out in July 2020 when all courses were conducted using one hundred per cent e-learning for the first time. Hence, future replication studies may produce different results. Lastly, findings are not generalisable to other contexts as the salient challenges may be unique to the Malaysian context. Practical implications Concerted efforts by all stakeholders to address these salient challenges will shift e-learning in higher education closer to a panacea during present and future crises. Originality/value The sudden and involuntary switch from face-to-face learning to e-learning during the closure of institutions of higher learning due to the COVID-19 pandemic provided opportunity to research the salient challenges encountered by undergraduates and to overcome these salient challenges.
When is deep learning better and when is shallow learning better: qualitative analysis

In many practical situations, deep neural networks work better than the traditional ‘shallow’ ones; however, in some cases, the shallow neural networks lead to better results. At present, deciding which type of neural networks will work better is mostly done by trial and error. It is therefore desirable to come up with some criterion of when deep learning is better and when shallow is better. In this paper, we argue that this depends on whether the corresponding situation has natural symmetries: if it does, we expect deep learning to work better, otherwise we expect shallow learning to be more effective. Our general qualitative arguments are strengthened by the fact that in the simplest case, the connection between symmetries and effectiveness of deep learning can be theoretically proven.
Design of Education Information Platform on Education Big Data Visualization

Big data visualization in education plays an essential role in understanding and uncovering complex educational laws and is increasingly becoming an essential topic in the current research fields of educational information science and educational management. In the context of China’s new curriculum reform, research on big data in education is in the process of deepening. A series of high-tech information technologies represented by big data technology is widely used in education, thus promoting intelligent campus construction. Based on this, the researcher combed through the relevant literature and found that there is still much room for improvement in the current research on the visualization of education big data, especially the research on the design of education informatization platforms based on the visualization of education big data. This study first summarizes the current and practical use of educational visualization research and finds that big educational data generated in the educational process plays an essential role for students, teachers, and teaching administrators. Visualizing big educational data provides an optimized way for students, teachers, and teaching administrators. Therefore, this paper designs the framework design process, platform selection, related technology introduction, platform implementation, and functional analysis of the educational information platform visualized by big data and conducts system testing. Through the research of 250 customers, it is concluded that the platform satisfaction of educational information visualized by big data is 92.4%, the satisfaction of individual function is 95.6%, system ease of use is 93.2%, and the satisfaction of system compatibility is 93.6%.
Learning 6-DoF Object Poses to Grasp Category-Level Objects by Language Instructions

This paper studies the task of any objects grasping from the known categories by free-form language instructions. This task demands the technique in computer vision, natural language processing, and robotics. We bring these disciplines together on this open challenge, which is essential to human-robot interaction. Critically, the key challenge lies in inferring the category of objects from linguistic instructions and accurately estimating the 6-DoF information of unseen objects from the known classes. In contrast, previous works focus on inferring the pose of object candidates at the instance level. This significantly limits its applications in real-world scenarios. In this paper, we propose a language-guided 6-DoF category-level object localization model to achieve robotic grasping by comprehending human intention. To this end, we propose a novel two-stage method. Particularly, the first stage grounds the target in the RGB image through language description of names, attributes, and spatial relations of objects. The second stage extracts and segments point clouds from the cropped depth image and estimates the full 6-DoF object pose at category-level. Under such a manner, our approach can locate the specific object by following human instructions, and estimate the full 6-DoF pose of a category-known but unseen instance which is not utilized for training the model. Extensive experimental results show that our method is competitive with the state-of-the-art language-conditioned grasp method. Importantly, we deploy our approach on a physical robot to validate the usability of our framework in real-world applications. Please refer to the supplementary for the demo videos of our robot experiments.
TMRN-GLU: A Transformer-Based Automatic Classification Recognition Network Improved by Gate Linear Unit

Automaticmodulation recognition (AMR) has been a long-standing hot topic among scholars, and it has obvious performance advantages over traditional algorithms. However, CNN and RNN, which are commonly used in serial classification tasks, suffer from the problems of not being able to make good use of global information and slow running speed due to serial operations, respectively. In this paper, to solve the above problems, a Transformer-based automatic classification recognition network improved by Gate Linear Unit (TMRN-GLU) is proposed, which combines the advantages of CNN with a high efficiency of parallel operations and RNN with a sufficient extraction of global information of the temporal signal context. Relevant experiments on the RML2016.10b public dataset show that the proposed algorithm not only has a significant advantage in the number of parameters compared with the existing algorithms, but also has improved recognition accuracy under various signal-to-noise ratios.In particular, the accuracy of the proposed algorithm improves significantly compared with other algorithms under low signal-to-noise ratio conditions. The accuracy is improved by at least 9% at low signal-to-noise ratio (6 dB) and about 3% at high signal-to-noise ratio (&gt;2 dB).
Kendall transformation brings a robust categorical representation of ordinal data

Kendall transformation is a conversion of an ordered feature into a vector of pairwise order relations between individual values. This way, it preserves ranking of observations and represents it in a categorical form. Such transformation allows for generalisation of methods requiring strictly categorical input, especially in the limit of small number of observations, when discretisation becomes problematic. In particular, many approaches of information theory can be directly applied to Kendall-transformed continuous data without relying on differential entropy or any additional parameters. Moreover, by filtering information to this contained in ranking, Kendall transformation leads to a better robustness at a reasonable cost of dropping sophisticated interactions which are anyhow unlikely to be correctly estimated. In bivariate analysis, Kendall transformation can be related to popular non-parametric methods, showing the soundness of the approach. The paper also demonstrates its efficiency in multivariate problems, as well as provides an example analysis of a real-world data.
Gradient-supervised person re-identification based on dense feature pyramid network

Abstract In the monitoring scene, parameters of different cameras are vary greatly, which makes Person re-identification (Re-ID) tasks extremely susceptible to factors such as scale, blur, and occlusion. To alleviate the these problems, this paper proposes a Dense Feature Pyramid Network (DFPN), which can converge to a better performance without pretraining. To be more specific, DFPN is composed of three main parts. First, a new Residual Convolutional Block (RCB) is designed by referring to the construction method of ResBlock. Taking RCB as a basic unit and combining it with the convolution layer structure of VGGNet, we construct the backbone RVNet (Residual VGGNet) to realize the rapid convergence of the network and solve the disappearance of the gradient. Second, based on Feature Pyramid Network, we design the Dense Pyramid Fusion Module by integrating the connection mode of DenseNet, which aims at the improvement of the richness and scale diversity of feature maps by taking semantic information and detail information into account. Finally, to increase the receptive field of the feature map, we introduce an improved retinal receptive field structure Improved RFB (IRFB) on the basis of Receptive Field Block (RFB), which can effectively solve the problem of pedestrian occlusion. In experiments on the public datasets Market1501, DukeMTMC-reID and Occluded-Duke, the Rank-1 accuracy can reach 94.12%, 87.25% and 51.72% with pretraining, respectively. A series of ablation experiments and comparative experiments have proved the effectiveness of our modules and overall scheme.
Application of Knowledge Map Based on BiLSTM-CRF Algorithm Model in Ideological and Political Education Question Answering System

Ideological and political education plays an important role in university education and is an important way to realize the function of educating people. It is of great significance to establish a perfect automatic question answering system for ideological and political education. Traditional automatic question answering methods usually rely on predicates and other prior information to achieve knowledge base question answering, which requires a lot of manpower and poor generalization ability. In order to solve this problem, this paper designs a question answering system for ideological and political education based on BiLSTM-CRF algorithm model (BiLSTM: Bidirectional Long Short-Term Memory and CRF: Conditional Random Fields). For the knowledge base question answering method with weak-dependent information, this paper combines BERT (Bidirectional Encoder Representation from Transformers) and BiLSTM-CRF network to extract the named entity in the questions and locate the triplet information related to the entity in the knowledge base. Through the answer matching network, the similarity score is marked for the answers in the triplet set, and the threshold selection strategy is used to select the answers that meet the requirements. And according to the similarity score from high to paper, it is presented to the user. The experimental results show that the method weakens the dependence on prior information, reduces manual intervention and ensures the quality of question answering, and completes the validity verification of the question answering system of ideological and political education.
An LSTM with Differential Structure and Its Application in Action Recognition

Because of the broad application of human action recognition technology, action recognition has always been a hot spot in computer vision research. The Long Short-Term Memory (LSTM) network is a classic action recognition algorithm, and many effective hybrid algorithms have been proposed based on basic LSTM infrastructure. Although some progress has been made in accuracy, most of those hybrid algorithms have to have more and more complex structures and deeper network levels. After analyzing the structure of the classic LSTM from the perspective of control theory, we determined that the classic LSTM could strengthen the differential characteristics of human action recognition technology to reflect the change of speed. Thus, an improved LSTM structure with an input differential characteristic module is proposed. Furthermore, in this article, we considered the influence of first-order and second-order differential on the extraction of movement pose information, that is, the influence of movement speed and acceleration on action recognition. We designed four different LSTM units with first-order and second-order differential. Moreover, the experiments were performed for the four units on three common datasets repeatedly. We found that the LSTM network with the input differential feature module proposed in this article can effectively improve action recognition accuracy and stability without deepening the complexity of the network and can be used as a new basic LSTM network architecture.
Federated learning based multi‐task feature fusion framework for code expressive semantic extraction

Abstract Using multi‐task learning to extract code features can effectively increase the information of the features. However, the existing multi‐task learning methods mainly have two limitations: (1) They cannot extract enough code‐related information or only extract similar semantic features. Similar multi‐task makes the information in the features increased insufficiently. However, the high difference multi‐task is challenging to converge. (2) They cannot train multi‐task on heterogeneous datasets. In standard multi‐task training, we need to label all tasks for all data, which consumes enormous labor. To solve the above limitations, we select two high difference tasks, the cross‐language code completion task and variable misuse task, to extract expressive semantic code features. We propose an attention‐based feature fusion module to merge information among high difference tasks, avoiding the convergence dilemma of standard multi‐task learning. We propose a federated learning framework, extracting semantic information and using the feature fusion module to integrate multi‐task information among single labeled datasets. We experiment on C# and Python datasets for the code completion and variable misuse tasks. The results show that the performance of fusion features by FedMTFF improved by up to 22.6% and 15.1% compared to single tasks. We use FedMTFF to perform four cross‐language multi‐task features fusion, exceeding the current best baseline by 24.1%.
Discriminative transfer feature learning based on robust-centers

Most feature-based Unsupervised Domain Adaptation (UDA) methods aligned distributions of the source and target domains by minimizing Maximum Mean Discrepancy (MMD) between the two domains. However, MMD using mean values may misalign distributions of the two domains due to outliers. Besides, to enhance the identifiability of learned features, some feature-based UDA methods adopted samples-based distances to measure the intra-class compactness and inter-class separability. But, the number of samples-based distances is a quadratic function of the sample size, feature-based UDA methods using samples-based distances may lead to the inefficient computation of measuring the intra-class compactness and inter-class separability. To overcome the two problems, we propose Discriminative Transfer Feature Learning based on Robust-Centers (DTFLRC) for UDA. First, we design robust-class-centers and robust-domain-centers for decreasing the influence of outliers and establish MMD with robust-centers to align distributions of the two domains. Second, noticing that the number of centers is far smaller than the sample size, we construct three robust-centers-based distances to effectively reduce the number of distances in measuring the intra-class compactness and inter-class separability. Specifically, three robust-centers-based distances include Sample-Class-center, Class-center-Domain-center, and Class-center-Nearest-neighbor-class-center distances, where Sample-Class-center distance measures the intra-class compactness, and Class-center-Domain-center and Class-center-Nearest-neighbor-class-center distances jointly reflect the inter-class separability. Then, the optimization objective of DTFLRC is established to minimize the MMD with robust-centers and Sample-Class-center distance, and maximize Class-center-Domain-center and Class-center-Nearest-neighbor-class-center distances. Finally, experimental results demonstrate that DTFLRC outperforms state-of-the-art methods, where the accuracies of DTFLRC on datasets CMU-PIE, Office-Caltech, ImageCLEF-DA, and VisDA-2017 are 81.8%, 94.6%, 90.2%, and 80.5%.
Collaborative Decision-Reinforced Self-Supervision for Attributed Graph Clustering

Attributed graph clustering aims to partition nodes of a graph structure into different groups. Recent works usually use variational graph autoencoder (VGAE) to make the node representations obey a specific distribution. Although they have shown promising results, how to introduce supervised information to guide the representation learning of graph nodes and improve clustering performance is still an open problem. In this article, we propose a Collaborative Decision-Reinforced Self-Supervision (CDRS) method to solve the problem, in which a pseudo node classification task collaborates with the clustering task to enhance the representation learning of graph nodes. First, a transformation module is used to enable end-to-end training of existing methods based on VGAE. Second, the pseudo node classification task is introduced into the network through multitask learning to make classification decisions for graph nodes. The graph nodes that have consistent decisions on clustering and pseudo node classification are added to a pseudo-label set, which can provide fruitful self-supervision for subsequent training. This pseudo-label set is gradually augmented during training, thus reinforcing the generalization capability of the network. Finally, we investigate different sorting strategies to further improve the quality of the pseudo-label set. Extensive experiments on multiple datasets show that the proposed method achieves outstanding performance compared with state-of-the-art methods. Our code is available at https://github.com/Jillian555/TNNLS_CDRS.
Resnet based Deep Gated Recurrent Unit for Image Captioning on Smartphone

Image captioning aims at generating grammatically and semantically acceptable natural language sentences for visual contents.Gated recurrent units (GRU) based approaches have recently attracted much attention due to their performance in caption generation.Challenges with GRU are to deal with vanishing gradient problems and modulation of the most relevant information flow in deep networks.In this paper, we propose a resnet-based deep GRU approach to overcome the vanishing gradient problem with residual connections while the most relevant information is ensured to flow using multiple layers of GRU.Residual connections are employed between consecutive layers of deep GRU, which improves the gradient flow from lower to upper layers.Experimental investigations on the publicly available MSCOCO dataset prove that the proposed approach achieves comparable performance with some state-ofthe-art approaches.Moreover, the approach is embedded into our custom-designed Android application, CaptionEye, which offers the ability to generate captions without an internet connection under a voice user interface.
Effectiveness of Artificial Intelligence Multimedia Courseware in Classroom Teaching Application

In the information age, traditional teaching methods can no longer meet the needs of modern students, and the emergence of multimedia courseware teaching mode just solves this problem. Under the trend of education integration, multimedia courseware is becoming more and more important in classroom teaching. Aiming at the characteristics of AI multimedia courseware classroom teaching mode, we discuss the practical application of AI multimedia courseware in classroom teaching and study the classroom teaching mode. Building a distance education platform through network technology can promote the education level. At the same time, the interactive distance education platform serves as a new network platform for system users in the website. In order to speed up the process of education informationization and improve the quality of education, this paper uses .NET technology and video transmission technology to design and implement a multiperson video conversation interactive distance education platform. Through the comparative experiment of two classes in Chengguan Middle School, using this platform for teaching, the average grade of students and the average grade of each course are better than the average grade of nonplatform classes, and the student's cognitive level has improved and is higher than the same level class. The results show that this platform can support a large number of online users and is very good for students of different majors. Through flexible and interactive course interaction, students are more convenient and more independent to learn but also reduce the teacher's time cost and greatly play the advantages of distance education, and the English teaching mode of artificial intelligence multimedia courseware is very popular with students. More than 60% of teachers are satisfied with the teaching mode and above.