Reviewerly/exHarmony huggingface dataset:
Application and Prospect Analysis of Artificial Intelligence in the Field of Physical Education

The development of AI technology has a significant impact on every sector of business. Artificial intelligence uses this technology to reduce the amount of work required, duplicate work, and increase the accuracy of work by modelling human behaviour and thought. On the basis of a thorough analysis of artificial intelligence technology, the technology is applied to teaching, specialised training, and specialised testing in response to the current issues in the field of physical education and the business that can be improved through auxiliary tools. The paper discusses how artificial intelligence technology can be used in physical education. After that, it examines how physical education is currently taught in classrooms and discusses how artificial intelligence is affecting this field. Following a thorough demonstration of the aforementioned material, the commonly used artificial intelligence technologies are introduced in turn, along with the methodology used in physical education. The use of computer models in physical education is then explained, and future analysis is conducted on the basis of this information.
Automatic microseismic events detection using morphological multiscale top-hat transformation

The occurrence of microseismic is not random but is related to the physical properties of the underground medium. Due to the low intensity and the influence of noise, microseismic eventually lead to poor signal-to-noise ratio. We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation. The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section. The microseismic data are decomposed into different scales by multiscale morphology top-hat transformation firstly. Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section, and the characteristic profile obtains the start point with a specific threshold value. Finally, the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions, and the filed data example also shows its reliability and adaptability.
An Improved NAMLab Image Segmentation Algorithm Based on the Earth Moving Distance and the CIEDE2000 Color Difference Formula

How to effectively segment an image into the non-overlapping sub-regions and make the segmentation results conform to the perception of the human vision have always been a key issue in the field of computer vision. Extensive studies have proved that the CIEDE2000 formula is the most consistent method with the color space distribution for objects recognized by human perspective. The representation system of the NAMLab algorithm ignored the differences of the pixels at different positions of a region, and produced information loss in the process of dimensionality reduction. Therefore, in this paper, we propose an improved NAMLab algorithm based on the Earth Moving Distance (EMD) and the CIEDE2000 color difference formula. First, a K-means clustering is performed on the Lab features of all the pixels in the region in order to obtain the region color feature histogram. Then, the CIEDE2000 formula is used to calculate the differences between the cluster centers of the adjacent region color histograms. Finally, the EMD algorithm is used to calculate the color similarity among regions. When compared with the state-of-the-art algorithms, the experimental results presented in this paper demonstrate that the proposed algorithm has better segmentation performance, and the obtained segmentation results are more in line with the perception of the human vision.
Illumination Invariant Face Recognition Using Directional Gradient Maps

Face recognition under varying illumination is a difficult problem in many real-life applications. Various methods have been developed in the literature to deal with this problem. In this paper, we propose a new method for face recognition under varying lighting conditions. Our method calculates the difference of pixel intensity values along a direction and then divides the difference by the mean intensity value of a small region centered at the pixel to generate a map for this direction. It can be shown that this directional gradient map is approximately illumination invariant. Our experiments show that our proposed method compares favourably to existing methods especially under the noisy environments. For the noise-free Extended Yale Face Database B, we obtained 95.4% correct recognition rate, and for the noise-free CMU-PIE face database, we achieved 100% correct recognition rate.
Graph-Based Anomaly Detection via Attention Mechanism

Graph-based anomaly detection aims to spot outliers and anomalies from big data, with numerous high-impact applications in areas such as security, industry, and data auditing. Deep learning-based methods could implicitly identify patterns from data. Recently, graph representation learning based on Deep Neural Network (DNN) have made significant progress. How to use deep learning methods to detect graph anomalies and assist audit work has received extensive attention from academia and industry. Related works have explored the graph-based anomaly detection, but they lack attention to DNN and auditing techniques. This paper investigates the DNN-based graph representation learning and sorts out the graph anomaly detection algorithm based on deep learning and proposes a graph-based anomaly detection algorithm via attention mechanism to assist the audit process.
Understanding Barriers to Network Exploration With Visualization: A Report from the Trenches

Barriers (red) that may be encountered during visual network exploration process, while translating domain concepts (ideas) into network structures and visualizations (forms) and back into findings.
Multi-Branch Attention-Based Grouped Convolution Network for Human Activity Recognition Using Inertial Sensors

Recently, deep neural networks have become a widely used technology in the field of sensor-based human activity recognition and they have achieved good results. However, some convolutional neural networks lack further selection for the extracted features, or the networks cannot process the sensor data from different locations of the body independently and in parallel. Therefore, the accuracy of existing networks is not ideal. In particular, similar activities are easy to be confused, which limits the application of sensor-based HAR. In this paper, we propose a multi-branch neural network based on attention-based convolution. Each branch of the network consists of two layers of attention-based grouped convolution submodules. We introduce a dual attention mechanism that consists of channel attention and spatial attention to select the most important features. Sensor data collected at different positions of the human body are separated and fed into different network branches for training and testing independently. Finally, the multi-branch features are fused. We test the proposed network on three large datasets: PAMAP2, UT, and OPPORTUNITY. The experiment results show that our method outperforms the existing state-of-the-art methods.
Double C-NOT Attack on a Single-State Semi-Quantum Key Distribution Protocol and Its Improvement

Recently, Zhang et al. proposed a single-state semi-quantum key distribution protocol to help a quantum participant share a secret key with a classical participant. However, this study shows that an eavesdropper can use a double C-NOT attack to obtain parts of the final shared key without being detected by the participants. To avoid this problem, a modification is proposed here.
Digital reform of higher education: implementation experience

The study examines the existing experience in implementing digital reform in higher educational system, and outlines the prospects for its further development. The philosophical and pedagogical issues of different approaches to implementing educational trajectories and customized lifelong educational routes are tackled and substantiated. The main aim of underpinning the existing steps of implementing the reform is proved to be the advancement of student self-development. The practical significance of the research lies in the attempts of designing a matrix of competencies underpinning the possibility of building up individual educational routes. The main results comprise the introduction of 16 main industries of higher education for the future. Further development of this topic is seen in the study of the interdisciplinary and cultural aspects of interaction in the educational process, implemented in digital management of the stated industries, such as Industry of Cognition, Industry of Thinking and Design, Industry of Health Preservation, Industry of Knowledge, Neuro Industry, Industry of Management, Industry of Law, Industry of Culture, Industry of Hospitality, Industry of Territories, Industry of High Technologies, Industry of Logistics, Industry of Production Systems, Industry of Resource Conservation and Efficiency, Industry of Research, Industry of Transfer, Industry of Social Initiatives.
FCNet: Stereo 3D Object Detection with Feature Correlation Networks

Deep-learning techniques have significantly improved object detection performance, especially with binocular images in 3D scenarios. To supervise the depth information in stereo 3D object detection, reconstructing the 3D dense depth of LiDAR point clouds causes higher computational costs and lower inference speed. After exploring the intrinsic relationship between the implicit depth information and semantic texture features of the binocular images, we propose an efficient and accurate 3D object detection algorithm, FCNet, in stereo images. First, we construct a multi-scale cost-volume containing implicit depth information using the normalized dot-product by generating multi-scale feature maps from the input stereo images. Secondly, the variant attention model enhances its global and local description, and the sparse region monitors the depth loss deep regression. Thirdly, for balancing the channel information preservation of the re-fused left-right feature maps and computational burden, a reweighting strategy is employed to enhance the feature correlation in merging the last-layer features of binocular images. Extensive experiment results on the challenging KITTI benchmark demonstrate that the proposed algorithm achieves better performance, including a lower computational cost and higher inference speed in 3D object detection.
A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME

Predictive maintenance is a crucial strategy in smart industries and plays an important role in small and medium-sized enterprises (SMEs) to reduce the unexpected breakdown. Machine failures are due to unexpected events or anomalies in the system. Different anomaly detection methods are available in the literature for the shop floor. However, the current research lacks SME-specific results with respect to comparison between and investment in different available predictive maintenance (PdM) techniques. This applies specifically to the task of anomaly detection, which is the crucial first step in the PdM workflow. In this paper, we compared and analyzed multiple anomaly detection methods for predictive maintenance in the SME domain. The main focus of the current study is to provide an overview of different unsupervised anomaly detection algorithms which will enable researchers and developers to select appropriate algorithms for SME solutions. Different Anomaly detection algorithms are applied to a data set to compare the performance of each algorithm. Currently, the study is limited to unsupervised algorithms due to limited resources and data availability. Multiple metrics are applied to evaluate these algorithms. The experimental results show that Local Outlier Factor and One-Class SVM performed better than the rest of the algorithms.
Blockchain for Cybersecure Healthcare

The medical care industry is especially at risk and focused on cyberattacks on the grounds that they have a huge amount of data of high financial and knowledge worth to hackers. Thus, there is an urgent need for the implementation of countermeasures to the consequences of such crimes in priority areas like law, technology, and education. This article discusses the recent cyber threats and the utilization of blockchain to secure electronic healthcare technologies. Further, this article talks about various blockchain-empowered digital secure answers for medical care zeroing in on added regard, including immutability, auditability, and accountability in processes, for example, user-device binding, registration, maintenance, and alerting. We have likewise proposed a blockchain design that executes a smart contract (SC) to convey safer administrations in the medical service industry.
Security Aspects of Behavioral Biometrics for Strong User Authentication

Efficient user identification and authentication are fundamental for securing access to systems processing sensitive data. This paper provides an analysis of current research in the field of user identification and identity verification with a focus on the behavioral biometrics supported by Machine Learning. It identifies the methods for user modeling with the potential of application in real-world scenarios such as strong authentication and fraud detection domain. This paper further elaborates on the current state-of-the-art approaches, feature extraction, and classification methods. We describe our experimental setup and provide an evaluation of our method in the selected deployment. We focus on user interactions in a controlled web environment. We performed classification experiments with the machine learning models on various datasets showing promising results in the robustness and proving relevance as a modern non-intrusive security measure.
The Relationship between Technology Leadership and Technology-enhanced Teaching and Learning Engagement in Higher Education

A significant challenge for current education is developing online teaching and learning, demanding today's leadership that is not only centered on traditional common practices but also on technological skills. Although the topic of technology leadership has been attracting growing focuses in the field of educational technology, very few have been discussed regarding the influence of technology leadership on members’ engagement in technology-enabled activities. The purpose of this study is to investigate the relationship between technology leadership (TL) and technology-enhanced teaching (TET) or technology-enhanced learning (TEL) engagement. This study applied a quantitative survey design and randomly selected 198 teachers and 367 students from two Chinese public universities as participants. Descriptive analysis was carried out to evaluate the current condition of TL, TET, and TEL, and inferential analysis was conducted to explain their interactions. Results found the significant connections of technology leaders’ practices regarding advocating equity and citizenship, planning vision, empowering practices, designing systems, and connecting professional learning to TET and TEL. Technology leadership thus significantly influenced the engagement of technology-enhanced teaching and learning. This article sheds light on how various benefits occur in teaching and learning when leaders successfully practice technology leadership in higher education. The contributions of this article are especially valuable for university policymakers, leaders, and instructors in the digital era. Implications and suggestions for future studies in this field are discussed.
A study on the components of a smart education system

The development of modern information technology has brought innovative ideas to education and promoted the rapid development of smart education. This paper analyses the differences between traditional and smart education through the changes in teaching and learning styles and the teaching environment. The relevant interpretation of smart education puts forward a conceptual framework for a smart education system. Three core elements for building a smart education system are summarized: smart learning, smart teaching, and a smart learning environment, and these three elements are studied and analyzed.
Fed-MT-ISAC: Federated Multi-task Inverse Soft Actor-Critic for Human-Like NPCs in the Metaverse Games

Imitation Learning (IL) is a powerful approach for constructing human-like NPCs in games. Unlike general games, metaverse games tend to build up the more complex, and more diverse game characters. Data between distinct roles is not interoperable, which leads to the challenge of sparse samples. We consider transferring the information from different tasks to solve the problem of sparse samples. Unfortunately, this approach also faces two thorny issues of privacy protection and knowledge transfer across tasks. To solve a series of problems, we propose a practical approach termed Federated Multi-Task Inverse Soft Actor-Critic (Fed-MT-ISAC), which extends the Inverse Soft Actor-Critic (ISAC) to federated and multi-task cases. We compare the performance of all algorithms in multi-style tasks and multi-skill tasks. Empirical results show that the proposed algorithm successfully achieves performance improvement in all experimental settings.
Single‐element Anomaly Mapping from Stream Sediment Geochemical Landscapes Aided by Digital Terrain Analysis

Abstract The identification of anomalies within stream sediment geochemical data is one of the fastest developing areas in mineral exploration. The various means used to achieve this objective make use of either continuous or discrete field models of stream sediment geochemical data. To map anomalies in a discrete field model of such data, two corrections are required: background correction and downstream dilution correction. Topography and geomorphology are important factors in variations of element content in stream sediments. However, few studies have considered, through the use of digital terrain analysis, the influence of geomorphic features in downstream dilution correction of stream sediment geochemical data. This study proposes and demonstrates an improvement to the traditional downstream dilution correction equation, based on the use of digital terrain analysis to map single‐element anomalies in stream sediment geochemical landscapes. Moreover, this study compares the results of analyses using discrete and continuous field models of stream sediment geochemical data from the Xincang area, Tibet. The efficiency of the proposed methodology was validated against known mineral occurrences. The results indicate that catchment‐based analysis outperforms interpolation‐based analysis of stream sediment geochemical data for anomaly mapping. Meanwhile, the proposed modified downstream dilution correction equation proved more effective than the original equation. However, further testing of this modified downstream dilution correction is needed in other areas, in order to investigate its efficiency further.
Principled pattern curation to guide data-driven learning design

Insights from corpus linguistics (CL) have informed language learning and materials design, among many other areas.An important nexus between CL and language learning is the use of Data-Driven Learning (DDL), which draws on the use of corpus data in the classroom and which brings opportunities for inductive language discovery.Within the ethos of DDL, learners are encouraged to discover patterns of language and, in so doing, foster more complex cognitive processes such as making inferences.While many studies on DDL concur on the success of this approach, it is still perceived as a marginal practice.Its success so far has been largely limited to intermediate to advanced level learners in higher education settings (Boulton and Cobb 2017).This paper aims to offer guiding principles for how DDL might have wider application across all levels (not just at Intermediate and above) and to set out exemplars for their application at different levels of proficiency.Based on insights from second language acquisition (SLA) and learner corpus research (LCR), the focus of this paper will be on identifying principles for the curation of language patterns that are differentiated for stage of learning.In particular, we are keen to build on recent and important work which looks at SLA through the lens of the usage-based (UB) models (that is, models that view language as being acquired through the use of and exposure to language).
A Scalable Graph-Based Framework for Multi-Organ Histology Image Classification

Graph-based approaches are successful for histology image classification tasks but still face many challenges, such as: 1) the lack of nuclei-level labels and the significant variations between histology images make it extremely difficult to extract discriminative high-level nuclei features like nuclei type, texture and micro-environment; 2) graph-based approaches cannot handle large-scale cell graph nodes typically contained in histology images; and 3) graph neural networks (GNNs) struggle to learn the long-range dependency of cell graphs. To address the above challenges, we propose a scalable graph-based framework for multi-organ histology image classification. We develop a two-step masked nuclei patches supervised training approach to extract discriminative high-level nuclei features for histology images without nuclei-level labels. Additionally, we introduce a nuclei sampling strategy to make our graph-based framework scalable for large-scale cell graphs. Furthermore, we propose <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>H</b></u> ier <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>A</b></u> rchical <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>T</b></u> ransformer Graph Neural <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>Net</b></u> work (HAT-Net+) for cell graph classi- fications. HAT-Net+ adopts Transformer to model the long-range dependency of cell graphs and a parameter-free approach to adaptively fuse different hierarchical graph representations of each layer. We achieved the state-of-the-art results on four public histology image classification datasets: CRC dataset (100%), Extended CRC dataset (98%), UZH dataset (96.9%) and BACH dataset (88%). Unlike other methods, our approach can be used in various histology image classification tasks, even for images without nuclei-level labels, indicating its potential in cancer diagnosis. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/suyouooooo/HAT-Net</uri> .