Reviewerly/exHarmony huggingface dataset:
A study on the relationships between playfulness, physical self-efficacy, and school happiness among middle school students participating in “0th-period physical education class” in South Korea

The purpose of this study is to create a scientific basis for the establishment of "0th-period physical education class" activities in schools in the future, with the expectation that the associations of morning exercise can be activated in the Korean educational community. To achieve this goal, the present study aimed to determine the relationship between the playfulness experienced during the early morning exercise of middle school students and their physical self-efficacy and education for happiness. To examine the model, questionnaires were collected from 296 middle school students located in Seoul and Gyeonggi-do, South Korea. Correlation analysis and standard multiple regression analysis were performed to analyze the data using the SPSS 21.0. The findings were as follows: First, the playfulness of the middle school "0th-period physical education class" had a significant effect on physical self-efficacy. Second, playfulness had a significant effect on education for happiness. Thirdly, physical self-efficacy was found to have a significant effect on education for happiness. Based on the results of this study, we suggest that a "0th-period physical education class" with various activities should be held during the legally required time in South Korea.
Transformer-based contrastive prototypical clustering for multimodal remote sensing data

Given the increasing diversity of available remote sensing data sources, multimodal fusion land cover classification has emerged as a promising direction in the community of Earth observation. However, modern supervised multimodal deep learning methods heavily rely on extensive amounts of human-annotated training data. To address this issue, we propose a novel unsupervised method for multimodal remote sensing land-cover type clustering: Transformer-based Multimodal Prototypical Contrastive Clustering (TMPCC). It is based on three core designs. First, we design a multimodal Transformer that learns a shared space through adaptive interactions between and within modalities. Second, we introduce an online clustering mechanism based on unified prototype learning that is scalable to large-scale multimodal datasets. Third, we employ a self-supervised training strategy that combines instance contrastive loss and clustering loss to enable efficient and effective model training. Coupling these three designs allows for training an end-to-end online clustering network that achieves state-of-the-art performance in multimodal RS data clustering, e.g., the highest clustering accuracy (92.28%) among existing methods on the hyperspectral-LiDAR Trento dataset. Our method demonstrates promising scalability in terms of modality and sample size, and it can also generalize to out-of-sample data. The code for this method is openly available at https://github.com/AngryCai/TMPCC.
The Use of a Large Language Model for Cyberbullying Detection

The dominance of social media has added to the channels of bullying for perpetrators. Unfortunately, cyberbullying (CB) is the most prevalent phenomenon in todays cyber world, and is a severe threat to the mental and physical health of citizens. This opens the need to develop a robust system to prevent bullying content from online forums, blogs, and social media platforms to manage the impact in our society. Several machine learning (ML) algorithms have been proposed for this purpose. However, their performances are not consistent due to high class imbalance and generalisation issues. In recent years, large language models (LLMs) like BERT and RoBERTa have achieved state-of-the-art (SOTA) results in several natural language processing (NLP) tasks. Unfortunately, the LLMs have not been applied extensively for CB detection. In our paper, we explored the use of these models for cyberbullying (CB) detection. We have prepared a new dataset (D2) from existing studies (Formspring and Twitter). Our experimental results for dataset D1 and D2 showed that RoBERTa outperformed other models.
Data adjusting strategy and optimized XGBoost algorithm for novel insider threat detection model

With the growth of access to internal system resources, the insider threat problem is emerging and can bring immeasurable losses to enterprises. In order to detect the hidden threats and guide the formulation of enterprise management strategy, it is important to analyze and understand the employee behavior. Thus, we suggest a user behavior analysis system framework, which mainly includes data processing, user behavior modeling and results analysis. Data Adjusting (DA) strategy and optimized eXtreme Gradient Boosting (XGBoost) model are utilized with the aim of full analysis small amount of feature information. The strategy for detecting suspicious behavior can be the following. Firstly, select initial suspicious data, misclassification data retention and combination sampling. Secondly, for further behavior model construction, an improved Particle Swarm Optimization algorithm based on ethnic randomized particles (ERPSO), which introduces Gaussian white noise with adjustable intensity into acceleration coefficients is given for searching the optimal XGBoost parameters. In addition, based on the designed DA strategy and the proposed ERPSO algorithm, we have also compared the results of the proposed methods with the current state-of-the-art methods. Experimental results show that the XGBoost optimized by the ERPSO (ERPSO-XGBoost) model has comprehensive performance, which proves the rationality and effectiveness of the insider behavior analysis system framework. Through a comprehensive understanding of insider behavior, the obvious characteristic behavior is found to adjust the management strategy and guide the behavior modeling, so as to prevent more losses in time.
Hybrid Dilated Convolution with Attention Mechanisms for Image Denoising

In the field of image denoising, convolutional neural networks (CNNs) have become increasingly popular due to their ability to learn effective feature representations from large amounts of data. In the field of image denoising, CNNs are widely used to improve performance. However, increasing network depth can weaken the influence of shallow layers on deep layers, especially for complex denoising tasks such as real denoising and blind denoising, where conventional networks fail to achieve high-quality results. To address this issue, this paper proposes a hybrid dilated convolution-based denoising network (AMDNet) that incorporates attention mechanisms. In specific, AMDNet consists of four modules: the sparse module (SM), the feature fusion module (FFM), the attention guidance module (AGM), and the image residual module (IRM). The SM employs hybrid dilated convolution to extract local features, while the FFM is used to integrate global and local features. The AGM accurately extracts noise information hidden in complex backgrounds. Finally, the IRM reconstructs images in a residual manner to obtain high-quality results after denoising. AMDNet has the following features: (1) The sparse mechanism in hybrid dilated convolution enables better extraction of local features, enhancing the network’s ability to capture noise information. (2) The feature fusion module, through long-range connections, fully integrates global and local features, improving the performance of the model; (3) the attention module is ingeniously designed to precisely extract features in complex backgrounds. The experimental results demonstrate that AMDNet achieves outstanding performance on three tasks (Gaussian noise, real noise, and blind denoising).
Contour attention network for cerebrovascular segmentation from TOF‐MRA volumetric images

Cerebrovascular segmentation is a crucial step in the computer-assisted diagnosis of cerebrovascular pathologies. However, accurate extraction of cerebral vessels from time-of-flight magnetic resonance angiography (TOF-MRA) data is still challenging due to the complex topology and slender shape.The existing deep learning-based approaches pay more attention to the skeleton and ignore the contour, which limits the segmentation performance of the cerebrovascular structure. We aim to weight the contour of brain vessels in shallow features when concatenating with deep features. It helps to obtain more accurate cerebrovascular details and narrows the semantic gap between multilevel features.This work proposes a novel framework for priority extraction of contours in cerebrovascular structures. We first design a neighborhood-based algorithm to generate the ground truth of the cerebrovascular contour from original annotations, which can introduce useful shape information for the segmentation network. Moreover, We propose an encoder-dual decoder-based contour attention network (CA-Net), which consists of the dilated asymmetry convolution block (DACB) and the Contour Attention Module (CAM). The ancillary decoder uses the DACB to obtain cerebrovascular contour features under the supervision of contour annotations. The CAM transforms these features into a spatial attention map to increase the weight of the contour voxels in main decoder to better restored the vessel contour details.The CA-Net is thoroughly validated using two publicly available datasets, and the experimental results demonstrate that our network outperforms the competitors for cerebrovascular segmentation. We achieved the average dice similarity coefficient ( DSC$DSC$ ) of 68.15 and 99.92% in natural and synthetic datasets. Our method segments cerebrovascular structures with better completeness.We propose a new framework containing contour annotation generation and cerebrovascular segmentation network that better captures the tiny vessels and improve vessel connectivity.
Contextual Explanations for Decision Support in Predictive Maintenance

Explainable artificial intelligence (XAI) methods aim to explain to the user on what basis the model makes decisions. Unfortunately, general-purpose approaches that are independent of the types of data, model used and the level of sophistication of the user are not always able to make model decisions more comprehensible. An example of such a problem, which is considered in this paper, is a predictive maintenance task where a model identifying outliers in time series is applied. Typical explanations of the model’s decisions, which present the importance of the attributes, are not sufficient to support the user for such a task. Within the framework of this work, a visualisation and analysis of the context of local explanations presenting attribute importance are proposed. Two types of context for explanations are considered: local and global. They extend the information provided by typical explanations and offer the user greater insight into the validity of the alarms triggered by the model. Evaluation of the proposed context was performed on two time series representations: basic and extended. For the extended representation, an aggregation of explanations was used to make them more intuitive for the user. The results show the usefulness of the proposed context, particularly for the basic data representation. However, for the extended representation, the aggregation of explanations used is sometimes insufficient to provide a clear explanatory context. Therefore, the explanation using simplification with a surrogate model on basic data representation was proposed as a solution. The obtained results can be valuable for developers of decision support systems for predictive maintenance.
Path Planning for Autonomous Vehicles in Unknown Dynamic Environment Based on Deep Reinforcement Learning

Autonomous vehicles can reduce labor power during cargo transportation, and then improve transportation efficiency, for example, the automated guided vehicle (AGV) in the warehouse can improve the operation efficiency. To overcome the limitations of traditional path planning algorithms in unknown environments, such as reliance on high-precision maps, lack of generalization ability, and obstacle avoidance capability, this study focuses on investigating the Deep Q-Network and its derivative algorithm to enhance network and algorithm structures. A new algorithm named APF-D3QNPER is proposed, which combines the action output method of artificial potential field (APF) with the Dueling Double Deep Q Network algorithm, and experience sample rewards are considered in the experience playback portion of the traditional Deep Reinforcement Learning (DRL) algorithm, which enhances the convergence ability of the traditional DRL algorithm. A long short-term memory (LSTM) network is added to the state feature extraction network part to improve its adaptability in unknown environments and enhance its spatiotemporal sensitivity to the environment. The APF-D3QNPER algorithm is compared with mainstream deep reinforcement learning algorithms and traditional path planning algorithms using a robot operating system and the Gazebo simulation platform by conducting experiments. The results demonstrate that the APF-D3QNPER algorithm exhibits excellent generalization abilities in the simulation environment, and the convergence speed, the loss value, the path planning time, and the path planning length of the APF-D3QNPER algorithm are all less than for other algorithms in diverse scenarios.
Recent Deep Learning Techniques for Sentiment Analysis

Sentiment analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study emotional states and subjective information. Because of its practicality, this technology has become more and more popular in recent years, attracting the attention of many scholars. With the development of hardware devices in recent years, deep learning has also become a popular research discipline. Some researchers have also begun to try to apply the ideas of deep learning to sentiment analysis tasks. Although great progress has been made in the research work around the task, there are very few reviews on the task, and a comprehensive review of the development of the task in recent years is lacking. Therefore, this article will provide an overview of sentiment analysis tasks. This paper introduces what methods have been used to improve the performance of the task in recent years, and also analyses the potential problems in the task.
The application of data encryption technology in computer network security

People rely more and more on network security. Therefore, this paper proposes a research on the application of computer network communication security based on data encryption technology. Use data encryption technology to design distributed data access, optimize multi-authorization center attribute data encryption technology; establish multi-authorization center attribute data encryption model, establish security initialization function, and optimize multi-authorization center attribute data encryption technology. Find various application forms and application fields of data encryption technology in computer network security, and conduct research and analysis.
Entity Relationship Extraction Method Based on Multi-head Attention and Graph Convolutional Network

Extracting entities and relations from text is crucial in the field of natural language processing. Current methods for relation extraction rely on training sets labeled using remote supervision techniques. However, these methods have limitations as they do not consider the connection between entity and relation extraction and cannot extract overlapping entities and relations. Therefore, accurate joint entity-relation extraction remains challenging. Our paper introduces a model for entity relation extraction based on multi-head attention and graph convolutional networks. We utilize the multi-head attention approach to extract entity features, building on the text features extracted by the graph convolutional network. Utilizing the New York Times (NYT) dataset, we evaluated the performance of our model. The experimentation revealed that our model effectively captures the semantic correlation between entity and relation extraction and minimizes the impact of unrelated entity pairings, resulting in improved recognition accuracy even in scenarios with overlapping entities.
FastSpanNER: Speeding up SpanNER by Named Entity Head Prediction

Named Entity Recognition (NER) is one of the most fundamental tasks in natural language processing (NLP). Different from the widely-used sequence labeling framework in NER, span prediction based methods are more naturally suitable for the nested NER problem and have received a lot of attention recently. However, classifying the samples generated by traversing all sub-sequences is computational expensive during training and very ineffective at inference. In this paper, we propose the FastSpanNER approach to reduce the computation of both training and inferring. We introduce a task of Named Entity Head (NEH) prediction for each word in given sequence, and perform multi-task learning together with the task of span classification, which uses no more than half of the samples in SpanNER. In the inference phase, only the words predicted as NEHs are used to generate candidate spans for named entity classification. Experimental results on the four standard benchmark datasets (CoNLL2003, MSRA, CNERTA and GENIA) show that our FastSpanNER method not only greatly reduces the computation of training and inferring but also achieves better F1 scores compared with the SpanNER method.
On the implementation of the algorithm for representation of discontinuity in natural language

The present paper is a demonstration of the algorithm to integrate the representational principles of three grammar formalisms: constituency by Phrase Structure Grammar (PSG), head-dependency relations by Dependency Grammar (DG) and functor-argument relations by Categorial Grammar (CG) for achieving a unified representation. This algorithm is written for analyzing both continuous and discontinuous sentences in natural language and thereby provides a unique solution towards discontinuity in natural language. For mustrative purposes, a discontinuous relative clause from Salish has been taken to show the implementation of the algorithm. A discussion on the significance of this algorithm and the unified representation is present towards the end of the paper, followed by a conclusion.
Detection of distracted driving via edge artificial intelligence

Real-time detection and alert systems for distracted driving are pivotal areas of research. With Edge AI, it is feasible to process data in real-time without relying on Internet connectivity, thereby safeguarding user privacy. However, the computational and storage constraints of edge devices can hamper the deployment of deep learning models, such as VGG-16. In response, we introduce a framework tailored for distracted driving detection using an ensemble model. This model taps into the potential of lightweight pre-trained networks like Inception, Xception, DenseNet, and MobileNet. Our objective is to amalgamate these models to achieve high accuracy, low latency, and fewer parameter set. Experiments on the State Farm dataset reveal a remarkable 99% accuracy rate, underlining its viability for devices with limited resources, such as the Nvidia Jetson Nano. These results underscore the efficacy and feasibility of our framework in the realm of edge computing.
Autonomous complex knowledge mining and graph representation through natural language processing and transfer learning

Regulatory documents play a significant role in securing engineering project quality, standard process management and long-term sustainable developments. With the digitisation of knowledge in the AEC industry, the demand for automated knowledge mining has emerged when confronted with substantial regulations. However, the current interpretation approaches for regulatory documents are still mostly labour-intensive and flawed in complex knowledge. Based on transfer learning (BERT) and natural language processing (e.g., NLP-Syntactic Parsing), this paper proposes a fully automated knowledge mining framework to convert complex knowledge in textual regulations to graph-based knowledge representations. The framework uses a BERT-based engine to extract clauses from regulation documents through fine-tuning with the self-developed domain dataset. A constituent extractor is developed to process the provisions with complex knowledge and extract constituents. A knowledge modelling engine integrates the extracted constituents into a graph-based regulation knowledge model, which can be queried, visualised, and directly applied to downstream applications. The outcome has demonstrated promising performance in complex knowledge mining and knowledge graph modelling based on ISO 19650 case study. This research can effectively convert textual regulation documents to their counterpart regulatory knowledge base, contributing to automated knowledge acquisition and multi-domain knowledge fusion toward regulation digitalization.
SCGformer: Smart contract vulnerability detection based on control flow graph and transformer

Abstract The security of smart contract has always been one of the significant problems in blockchain. As shown in previous studies, vulnerabilities in smart contracts can lead to unpredictable losses. With the rapid growth of the number of smart contracts, more and more data driven detection technologies based on machine learning have been proposed. However, some state‐of‐the‐art approaches mainly rely on the source code of smart contract. These methods are limited by the openness of the source code and the version of the programming language. To address this problem, we propose a novel vulnerability detection method based on transformer by constructing the control flow graph (CFG) of smart contracts operation codes (opcodes), which shields the difference of various versions of program language. Extensive experiments are conducted to evaluate the effectiveness of the proposed method on the authors' own collected dataset. The experimental results show that the proposed method achieves 94.36% accuracy in vulnerability detection, which performs better than other state‐of‐the‐art methods.