Reviewerly/exHarmony huggingface dataset:
Predicting the quality of answers with less bias in online health question answering communities

• We establish an automatic answer quality prediction model that combing semantic features with textual and non-textual features for online health question answering (HQA) communities. • We extend the knowledge adoption model (KAM) theory to obtain textual and non-textual features of answers. • We employ the Bidirectional Encoder Representations from Transformers (BERT) model to get the semantic feature for answer quality prediction. • Experimental results on a real-world dataset demonstrate that the proposed model significantly outperforms state-of-the-art methods on answer quality prediction. Existing approaches in online health question answering (HQA) communities to identify the quality of answers either address it subjectively by human assessment or mainly using textual features. This process may be time-consuming and lose the semantic information of answers. We present an automatic approach for predicting answer quality that combines sentence-level semantics with textual and non-textual features in the context of online healthcare. First, we extend the knowledge adoption model (KAM) theory to obtain the six dimensions of quality measures for textual and non-textual features. Then we apply the Bidirectional Encoder Representations from Transformers (BERT) model for extracting semantic features. Next, the multi-dimensional features are processed for dimensionality reduction using linear discriminant analysis (LDA). Finally, we incorporate the preprocessed features into the proposed BK-XGBoost method to automatically predict the answer quality. The proposed method is validated on a real-world dataset with 48121 question-answer pairs crawled from the most popular online HQA communities in China. The experimental results indicate that our method competes against the baseline models on various evaluation metrics. We found up to 2.9% and 5.7% improvement in AUC value in comparison with BERT and XGBoost models respectively.
Competence in Digital Interaction and Communication—A Study of First-Year Preservice Teachers’ Competence in Digital Interaction and Communication at the Start of Their Teacher Education

This article explores the variation in first-year preservice teachers' professional competence in digital sharing and communication tools (software) by applying social constructivist learning theory and relevant concepts.The data were obtained from questionnaires distributed to 395 preservice teachers at two Norwegian universities in the first semester of 2019.Correlation and multiple regression are used for the analysis.The results reveal that the students' attitudes toward and experiences with virtual communication solutions were the main factors contributing to their competence in digital interaction and communication.The students' mastery of and emotional engagement with virtual collaboration solutions also played a definite but smaller role in their digital competence.
Towards Robust Neural Graph Collaborative Filtering via Structure Denoising and Embedding Perturbation

Neural graph collaborative filtering has received great recent attention due to its power of encoding the high-order neighborhood via the backbone graph neural networks. However, their robustness against noisy user-item interactions remains largely unexplored. Existing work on robust collaborative filtering mainly improves the robustness by denoising the graph structure, while recent progress in other fields has shown that directly adding adversarial perturbations in the embedding space can significantly improve the model robustness. In this work, we propose to improve the robustness of neural graph collaborative filtering via both denoising in the structure space and perturbing in the embedding space. Specifically, in the structure space, we measure the reliability of interactions and further use it to affect the message propagation process of the backbone graph neural networks; in the embedding space, we add in-distribution perturbations by mimicking the behavior of adversarial attacks and further combine it with contrastive learning to improve the performance. Extensive experiments have been conducted on four benchmark datasets to evaluate the effectiveness and efficiency of the proposed approach. The results demonstrate that the proposed approach outperforms the recent neural graph collaborative filtering methods especially when there are injected noisy interactions in the training data.
New feature analysis-based elastic net algorithm with clustering objective function

Cluster analysis, as one of the core methods of data mining, is critical in discovering the natural structure of data to obtain useful information from massive amounts of data. However, many existing clustering algorithms have problems such as poor clustering accuracy and high sensitivity to noise points. These problems are particularly prominent when solving high-dimensional and large-data clustering problems. To overcome these problems, a new feature analysis-based elastic net algorithm with a clustering objective function (FAENC) is proposed in this paper. The new algorithm redefines a cost function based on the goal of clustering, and a new energy function of the clustering elastic net is presented based on the cost function and maximum entropy principle. The proposed model is an unsupervised optimization method. By minimizing the energy function, clustering problems can be solved through self-learning, without manual training or intervention. Additionally, a method for calculating the dispersion degree of the feature attributes is proposed, and the noise attributes can be identified. Each feature attribute is weighted automatically according to the weighting strategy, which can eliminate the influence of noise variables and improve the clustering quality and efficiency. The proposed FAENC algorithm can significantly reduce the impact of the internal structure of the dataset, identify clusters of different sizes, shapes, and densities, and obtain higher clustering quality. Compared with several classical and state-of-the-art clustering methods, FAENC substantially improves the accuracy of clustering results on a large number of synthetic and real-world datasets.
A novel small-scale pedestrian detection method base on residual block group of CenterNet

Pedestrian detection is widely used in intelligent supervision and assisted driving. With the development of deep learning, the accuracy of pedestrian detection has been greatly improved. In actual scenes, there are often pedestrians who are far away from the camera. Such pedestrians usually have small image sizes, while existing algorithms still have defects such as missed detection for similar small-scale pedestrian detection, which will reduce the accuracy of operation. Therefore, this paper designs a Three ResNet Blocks based on CenterNet detection model. Aiming at the limited ability of a single feature extraction block to extract semantic information at different levels in the network, this paper proposes Three ResNet Blocks, which is a simple and effective multi-block group. This block group integrates three different basic blocks, each of which extracts pedestrian information separately to enhance information flow in the network structure and make detection results more accurate. In addition, combined with the advantages of activation function in the model expression, the relu6 activation function is introduced to improve the performance of the detector by preventing numerical explosion being sensitive to decimal. Comprehensive experiments on pedestrian detection datasets (Caltech and ETH) show that the proposed method exhibits excellent accuracy and detection speed, especially for small-scale pedestrian detection.
Three-dimensional reconstruction using SFM for actual pedestrian classification

In recent years, the popularity of intelligent and autonomous vehicles has grown notably. In fact, there already exist commercial models with a high degree of autonomy as regards self-driving capabilities. A key feature for this kind of vehicle is object detection, which is commonly performed in 2D space. This has some inherent issues as an object and the depiction of such an object would be classified as the actual object, which is inadequate since urban environments are full of billboards, printed adverts and posters that would likely make these systems fail. In order to overcome this problem, a 3D sensor could be leveraged, although this would make the platform more expensive, energy inefficient and computationally complex. Thus, we propose the use of structure from motion to reconstruct the three-dimensional information of the scene from a set of images, and merge the 2D and 3D data to differentiate actual objects from depictions. As expected, our approach is able to work with a regular color camera. No 3D sensors whatsoever are required. As the experiments confirm, our approach is able to distinguish between actual pedestrians and depictions of them more than 87% of times in synthetic and real-world tests in the worst scenarios, while the accuracy is of almost 98% in the best case.
Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications

Deep reinforcement learning has shown useful in the field of robotics but the black-box nature of deep neural networks impedes the applicability of deep reinforcement learning agents for real-world tasks. This is addressed in the field of explainable artificial intelligence, by developing explanation methods that aim to explain such agents to humans. Model trees as surrogate models have proven useful for producing explanations for black-box models used in real-world robotic applications, in particular, due to their capability of providing explanations in real time. In this paper, we provide an overview and analysis of available methods for building model trees for explaining deep reinforcement learning agents solving robotics tasks. We find that multiple outputs are important for the model to be able to grasp the dependencies of coupled output features, i.e. actions. Additionally, our results indicate that introducing domain knowledge via a hierarchy among the input features during the building process results in higher accuracies and a faster building process.
The impact of preprocessing on word embedding quality: a comparative study

Data preprocessing is among the principal stages in virtually all text-based tasks. In this light, recent approaches have employed word embeddings in the majority of text-based tasks, wherein word co-occurrences are used as the basis of word vector generation processes in the approaches thereof. Word embedding techniques are primarily trained on large corpora. The preprocessing of said corpora is quite crucial in that it can alter the co-occurrence counts of words, and hence the quality of generated word vectors. This highlights the significance of selecting the right preprocessing approach when working with word embeddings. The present study proceeds to scrutinize the effects of preprocessing on the quality of word embeddings on Persian and English corpora, with a focus on preprocessing approaches capable of altering the co-occurrence counts of words by virtue of procedures such as the elimination of stopwords. The quality of the word vectors generated employing different types of preprocessing are intrinsically evaluated by word similarity and word analogy. Text classification and sentiment analysis are also used as extrinsic evaluations. The study also presents four word similarity datasets for Persian, namely PER-RG-65, PER-RC-30, PER-simlex-999, and PER-Mturk-287. Results obtained using GloVe, Word2vec (CBOW), and Fasttext word embeddings show that in certain tasks, such as in the case of text classification or solving semantic questions in word analogy, the removal of punctuations and stopwords will lead to higher performance, as in the case of sentiment analysis and syntactic questions regarding word analogy, where withholding stopwords increases overall performance.
Blind Deblurring of Remote-Sensing Single Images Based on Feature Alignment

Motion blur recovery is a common method in the field of remote sensing image processing that can effectively improve the accuracy of detection and recognition. Among the existing motion blur recovery methods, the algorithms based on deep learning do not rely on a priori knowledge and, thus, have better generalizability. However, the existing deep learning algorithms usually suffer from feature misalignment, resulting in a high probability of missing details or errors in the recovered images. This paper proposes an end-to-end generative adversarial network (SDD-GAN) for single-image motion deblurring to address this problem and to optimize the recovery of blurred remote sensing images. Firstly, this paper applies a feature alignment module (FAFM) in the generator to learn the offset between feature maps to adjust the position of each sample in the convolution kernel and to align the feature maps according to the context; secondly, a feature importance selection module is introduced in the generator to adaptively filter the feature maps in the spatial and channel domains, preserving reliable details in the feature maps and improving the performance of the algorithm. In addition, this paper constructs a self-constructed remote sensing dataset (RSDATA) based on the mechanism of image blurring caused by the high-speed orbital motion of satellites. Comparative experiments are conducted on self-built remote sensing datasets and public datasets as well as on real remote sensing blurred images taken by an in-orbit satellite (CX-6(02)). The results show that the algorithm in this paper outperforms the comparison algorithm in terms of both quantitative evaluation and visual effects.
Implementing Blockchain-Based Traceable Certificates as Sustainable Technology in Democratic Elections

A democratic election is a crucial event in any country. Therefore, the government of the country is concerned with creating more competitive and fairer elections. This paper discusses the survey and scope of Blockchain technology adoptions in conducting elections. A distributed digital ledger is used in the Blockchain technology that is utilized for recording transactions happening between two parties. Ledger conducts this processing in an efficient and effective manner with latest secure mechanism of encryption algorithms. Therefore, the data stored in several blocks in each transaction is secure, transparent, and tamper-proof, which ultimately improves the transparency and voter confidentiality. This paper demonstrates how the benefits of the Blockchain technology such as immutability, transparency and end-to-end verifiability can be utilized by the national governments around the world to ensure fair democratic elections. In short, we aim to present a rigorous mechanism of a Blockchain based e-voting system, its efficiency based on different consensus algorithms and the overall progress and analysis based on some critical parameters to anticipate the feasibility of the successful implementation of the proposed e-voting system.
Advantageous comparison: using Twitter responses to understand similarities between cybercriminals (“Yahoo Boys”) and politicians (“Yahoo men”)

This article is about the manifestations of similarities between two seemingly distinct groups of Nigerians: cybercriminals and politicians. Which linguistic strategies do Twitter users use to express their opinions on cybercriminals and politicians? The study undertakes a qualitative analysis of ‘engaged’ tweets of an elite law enforcement agency in West Africa. We analyzed and coded over 100,000 ‘engaged’ tweets based on a component of mechanisms of moral disengagement (i.e., advantageous comparison), a linguistic device. The results reveal how respondents defend the actions of online fraudsters (“the deviant group”) by strategically comparing them to the wrongful acts of corrupt politicians (“the respectable group”). Similarly, the results show how respondents positioned this linguistic strategy to compare “the powerless group” (online fraudsters) and “the powerful group” (politicians) in society. Indeed, tweet responses suggest that the Economic and Financial Crimes Commission (EFCC) generally looks downwards for culprits (i.e., online fraudsters) while ignoring fraudulent politicians. We conclude that the process by which some actions are interpreted as a crime compared to others is a moral enterprise.
Supply Chain Finance and Blockchain in Operations Management: A Literature Review

The emerging blockchain technology is believed to be a disruptive innovation in the fields of both supply chain management and financial management. Yet, little is known on the interaction of the two domains. In this paper, we conducted a thematic literature review in the novel field of blockchain and supply chain finance (SCF), which is based on 52 papers published from 2017 to 2021 in academic journals, proceedings and books. Based on thematic analysis, the current status of this field is concluded and presented in this research, including the challenges in traditional SCF, factors influencing blockchain adoption in SCF, blockchain-based SCF solutions, and the blockchain adoption mechanism and system design in SCF. Furthermore, a conceptual framework of blockchain adoption in SCF is developed, which combines the emerged themes. Finally, three future research directions are proposed for further research, including cost optimization of blockchain adoption in SCF, risk management of blockchain operations in SCF, and blockchain and sustainable SCF. This research presents a timely and useful summary on existing research and points out the future research directions on blockchain and SCF.
Getting More Out of Large Databases and EHRs with Natural Language Processing and Artificial Intelligence

Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR data are stored in unstructured form (e.g., clinical notes, surgical notes, and medication instructions), and researchers need data to be in computable form (structured) to extract meaningful relationships involving variables that can influence patient outcomes. Clinical natural language processing (NLP) is the field of extracting structured data from unstructured text documents in EHRs. Clinical text has several characteristics that mandate the use of special techniques to extract structured information from them compared with generic NLP methods. In this article, we define clinical NLP models, introduce different methods of information extraction from unstructured data using NLP, and describe the basic technical aspects of how deep learning-based NLP models work. We conclude by noting the challenges of working with clinical NLP models and summarizing the general steps needed to launch an NLP project.
Optimizing Echo State Networks for Enhancing Large Prediction Horizons of Chaotic Time Series

Reservoir computing has shown promising results in predicting chaotic time series. However, the main challenges of time-series predictions are associated with reducing computational costs and increasing the prediction horizon. In this sense, we propose the optimization of Echo State Networks (ESN), where the main goal is to increase the prediction horizon using a lower count number of neurons compared with state-of-the-art models. In addition, we show that the application of the decimation technique allows us to emulate an increase in the prediction of up to 10,000 steps ahead. The optimization is performed by applying particle swarm optimization and considering two chaotic systems as case studies, namely the chaotic Hindmarsh–Rose neuron with slow dynamic behavior and the well-known Lorenz system. The results show that although similar works used from 200 to 5000 neurons in the reservoir of the ESN to predict from 120 to 700 steps ahead, our optimized ESN including decimation used 100 neurons in the reservoir, with a capability of predicting up to 10,000 steps ahead. The main conclusion is that we ensured larger prediction horizons compared to recent works, achieving an improvement of more than one order of magnitude, and the computational costs were greatly reduced.
Real-time masked face classification and head pose estimation for RGB facial image via knowledge distillation

Recently, human head pose estimation and masked face classification are two essential problems in facial analysis. It is necessary to design a compact model to resolve both tasks in order to reduce the computational cost when deploying face recognition-based applications such as camera surveillance systems, AI cameras while maintaining accuracy. In this work, we proposed a lightweight multi-task model called MHPNet that simultaneously addresses both head pose estimation and masked face classification problems. Because of the lack of datasets with available labels for both tasks, we first train teacher models independently on two labelled datasets 300 W-LPA and MAFA to extract the head pose and masked soft label. After that, we design architecture with ResNet18 backbone and two branches for two tasks and train our proposed model with the predictions of teacher models on joint datasets via the knowledge distillation process. To evaluate the effectiveness of our model, we use AFLW2000 and BIWI datasets for head pose estimation problems and MAFA datasets for masked face classification problems. Experiment results show that our proposed model significantly improves the accuracy compared to the state-of-the-art head pose estimation methods and achieves remarkable performance on masked face dataset. Furthermore, our model has the real-time speed of ∼400 FPS when inferring on a Tesla V100 GPU device. Source codes and datasets are available at https://github.com/chientv99/maskpose.
Vehicle Logo Detection Method Based on Improved YOLOv4

A vehicle logo occupies a small proportion of a car and has different shapes. These characteristics bring difficulties to machine-vision-based vehicle logo detection. To improve the accuracy of vehicle logo detection in complex backgrounds, an improved YOLOv4 model was presented. Firstly, the CSPDenseNet was introduced to improve the backbone feature extraction network, and a shallow output layer was added to replenish the shallow information of small target. Then, the deformable convolution residual block was employed to reconstruct the neck structure to capture the various and irregular shape features. Finally, a new detection head based on a convolutional transformer block was proposed to reduce the influence of complex backgrounds on vehicle logo detection. Experimental results showed that the average accuracy of all categories in the VLD-45 dataset was 62.94%, which was 5.72% higher than the original model. It indicated that the improved model could perform well in vehicle logo detection.