Reviewerly/exHarmony huggingface dataset:
Application of information technologies in the educational process of higher educational institutions under the martial law

The aim is to study the features of the use of information technology for the education of students (n=184) with different social and living conditions under martial law.Methods: questionnaire survey, mathematical methods of statistics.Results.The choice of technical means and information technology for education under martial law depends on the social and living conditions of students.The frequency of using applications that allow teaching and learning has no statistically significant differences between students who have different social and living conditions.Education with the use of information technology under martial law allows students to continue their studies, to be confident at least in education.At the same time, students' anxiety still remains an influential factor in assessing the use of technology.According to the results of one-factor analysis, the frequency of use of information technology for education does not affect the attitude of students to the use of technology in education under martial law.Prospects.Further research on the use of information technology for students with strong signs of stress and its consequences, the direct consequences of war, and other factors.
Development and validation of the information security attitude questionnaire (<scp>ISA‐Q</scp>) for nurses

Abstract Aim The aim of the study was to understand nurses' information security attitudes towards patient information. This study developed the Information Security Attitude Questionnaire (ISA‐Q) to measure the physical, technical and administrative aspects of information security for nurses and assessed its validity and reliability. Design Cross‐sectional study and scale development. Methods Exploratory and confirmatory factor analyses and correlation analyses were performed to assess construct, discriminant and convergent validity; Cronbach's α and test–retest reliability were examined. Results Exploratory and confirmatory factor analyses yielded a 6‐factor, 30‐item solution. Six factors accounted for 60.19% of the total variance. The confirmatory factor analysis was achieved through structural equation modelling. Discriminant and convergent validity were confirmed. The internal consistency of the ISA‐Q was 0.94, and the test–retest reliability was 0.74. The ISA‐Q is an appropriate questionnaire for identifying information security attitudes of nurses, making it useful for developing systematic performance methods to enhance nurses' information security levels.
Data and its role in reducing the risk of disasters in the built environment

Abstract In accordance with the Sendai framework, we need to protect our global neighbours from the risk of future disasters. This short paper calls for the disaster risk reduction (DRR) community to make investments in standardising and collecting the muchneeded vulnerability-relevant exposure data to help quantify, and therefore reduce risk.
PointGAT: Graph attention networks for 3D object detection

3D object detection is a critical technology in many applications, and among the various detection methods, pointcloud-based methods have been the most popular research topic in recent years. Since Graph Neural Network (GNN) is considered to be effective in dealing with pointclouds, in this work, we combined it with the attention mechanism and proposed a 3D object detection method named PointGAT. Our proposed PointGAT outperforms previous approaches on the KITTI test dataset. Experiments in real campus scenarios also demonstrate the potential of our method for further applications.
A General-Purpose and Configurable Planar Data Processor for Energy-Efficient Pooling Computation

Convolutional Neural Networks (CNN) have been widely used in artificial intelligence applications. A typical CNN contains both convolution and pooling layer, in which the convolution is to detect local conjunctions of features and the pooling is to merge similar patterns into one. It is necessary to make pooling operation, which plays a great role in CNN. Up to now, there have been numerous researches on CNN accelerators, however, most of the previous works are only focused on the acceleration of convolution layers, and the specific studies on pooling units are still lacking. Besides, the existing pooling designs are usually constrained by either the poor flexibility or the low energy/area efficiency. In this work, we propose a general purpose and energy-efficient planar data processor to support the pooling operation from different CNN structure. By using the configurable data path control method, the processor is able to support universal pooling operation with arbitrary input feature shape and arbitrary pooling kernel/stride/padding size. Besides, the processor exhibits high efficiency with hardware utilization ratio near 100% during operation, indicating good performance of the design. Most importantly, it is energy-efficient that exhibits 86%-off on power consumption and 62%-off on area utilization when compared with the separate pooling module of NVDLA (NVIDIA Deep Learning Accelerator), thus is particularly suitable for the resource-limited edge intelligent devices.
Research on UAV access deployment algorithm based on improved virtual force model

In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station.First, the UAV-adapted Harris Hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot.Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users.Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs).The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period.Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average.Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.
CutPaste-Based Anomaly Detection Model using Multi Scale Feature Extraction in Time Series Streaming Data

The aging society increases emergency situations of the elderly living alone and a variety of social crimes.In order to prevent them, techniques to detect emergency situations through voice are actively researched.This study proposes CutPaste-based anomaly detection model using multi-scale feature extraction in time series streaming data.In the proposed method, an audio file is converted into a spectrogram.In this way, it is possible to use an algorithm for image data, such as CNN.After that, mutli-scale feature extraction is applied.Three images drawn from Adaptive Pooling layer that has different-sized kernels are merged.In consideration of various types of anomaly, including point anomaly, contextual anomaly, and collective anomaly, the limitations of a conventional anomaly model are improved.Finally, CutPaste-based anomaly detection is conducted.Since the model is trained through selfsupervised learning, it is possible to detect a diversity of emergency situations as anomaly without labeling.Therefore, the proposed model overcomes the limitations of a conventional model that classifies only labelled emergency situations.Also, the proposed model is evaluated to have better performance than a conventional anomaly detection model.
Exploration and Prospect of Future Science Teaching Mode in the Field of Metaverse

In the context of the recent new crown pneumonia epidemic, long-duration online teaching has become a new trend in teaching development.With the continuous emergence of new information technologies, such as AR, MR, VR technologies, virtual reality resources, and network environments, the metaverse field has become an important environment for future teaching development.Based on the current demand towards virtual teaching and the recent development and improvement of the metaverse field, this paper explores the impact of the metaverse technology on science teaching, and accordingly puts forward the prospect of the science teaching model in the metaverse field, and discusses the impact of the metaverse technology on science teaching, including the teaching goals of strengthening scientific concepts, cultivating scientific thinking, encouraging inquiry practice, and clarifying attitude and responsibility; it involves the technical, environmental, and ethical conditions that need to be realized for science teaching to enter the metaverse field; the actual operation procedures that can be divided into three levels and and seven modules; the efficient, continuous, and diverse evaluation of the entire teaching activity, which provides new perspectives for solving the common problems in the current online science teaching and realizing the innovation of science teaching mode, enabling teachers and learners to accept the influence of the progress of science and technology itself in science teaching activities, and to better develop their own scientific literacy through independent inquiry and practice.
Research on the connection and application of the administrative management system and the wechat public platform

With the continuous development of China's economic and social construction, China's administrative management system has gradually shown a higher level and a high degree of intelligence, and the perfection of information between colleges and universities has formed its new characteristics and advantages in college management. The introduction of various public platforms, including wechat public accounts, into the administrative management system can improve the upgrade and conditions for the establishment of the administrative management system, and is an important attempt to build a constructive relationship between the college and students. The introduction of wechat assisted teaching provides a new opportunity for the innovation of educational technology in colleges and universities. In the process of using wechat platform, the use of its information dissemination, exchange and interaction, knowledge sharing and other features is a kind of technological innovation committed to expanding the scope of professional learning knowledge in colleges and universities and promoting the in-depth understanding of knowledge. It can effectively break the constraints of time, space and content in professional teaching, and innovate the professional teaching mode of colleges and universities, and improve the level and efficiency of professional personnel training through the mutual cooperation of application function modules and flexible switch between task-based application mechanism and interest-based application mechanism. This paper takes administrative management as an example to explore the application of wechat auxiliary technology in professional teaching in colleges and universities, and makes relevant analysis and explanation according to the current status of administrative management system, aiming to strengthen the application of administrative management system and speed up the connection with wechat public platform.
An Unsupervised Sentence Embedding Method by Maximizing the Mutual Information of Augmented Text Representations

For natural language processing tasks with unlabeled or partially labeled datasets, it is vital to learn sentence representations in an unsupervised manner. However, unsupervised methods pale by comparison to supervised ones on many tasks. Recently, some unsupervised methods propose to learn sentence representations by maximizing the mutual information between text representations of different levels, such as global MI maximization: global and global representations, local MI maximization: local and global representations. Among these methods, local MI maximization encourages the global representations to capture useful information that shared across the local contexts. Despite this advantage, this method suffers from the inherent gap of semantic information contained in the global representations and the local representations. Consequently, the performance is inferior to models using global MI maximization as well as supervised ones. In this paper, we propose an unsupervised sentence embedding method by maximizing the mutual information of augmented text representations. Experimental results show that our model achieves an average of 73.36% Spearman’s correlation on a series of semantic text similarity tasks, a 7 points improvement compared to the previous best model using local MI maximization. Furthermore, our model outperforms models using global MI maximization and close the gap to supervised methods to 1.5 points.
Middle school students' interactional behaviors and perception of English speaking activities in metaverse

The present study explores the educational use of metaverse for middle school students’ English speaking activities. As an online learning space, we chose Gather.town, one of the most popular metaverse platforms used in Korea, to investigate student perceptions of their learning experience and examine how they interacted with the teachers and peers on metaverse by analyzing the screen recording of the activities. The results showed that the students (N=36) were highly satisfied with the experience and actively initiated opportunities for interaction during the English speaking sessions. Several factors may have contributed to their active engagement during the sessions. First, the affordances of metaverse enabled the learners to have control over his/her own avatar and gave them the freedom to choose who to talk to and when to approach them. Second, providing them with less controlled activities seemed to have positively affected their levels of interest and motivation for learning English speaking. Third, designing speaking tasks that make them move around different locations on the metaverse made learning more dynamic and engaging. Pedagogical implications are discussed based on the findings of the study.
Construction of Teaching Management Platform for Universities Based on Big Data

The construction of the teaching management platform for colleges and universities needs to actively embrace information technology, change the problems existing in traditional offline teaching through technical innovation, make full use of massive educational resources, and improve teaching efficiency. However, no research has yet discussed how to handle the increasing scale of online teaching resources, leading to the hindrance of building teaching management platforms in universities, which requires the use of big data technologies and thinking to build new platforms. We propose a dynamic compression method to compress the data file backup in the database based on the dynamic adjustment of the system workload, and optimize the data file backup model of the database to reduce the total cost of ownership, which provides a basis for managing massive online education resources and building a teaching management platform for universities.
F-Measure Optimization for Multi-class, Imbalanced Emotion Classification Tasks

Recent NLP breakthroughs have significantly advanced the state of emotion classification (EC) over text data. However, current treatments guide learning by traditional performance metrics, such as classification error rate, which are not suitable for the highly-imbalanced EC problems; in fact, EC models are predominantly evaluated by variations of the F-measure, recognizing the data imbalance. This paper addresses the dissonance between the learning objective and the performance evaluation for EC with moderate to severe data imbalance. We propose a series of increasingly powerful algorithms for F-measure improvement. An ablation study demonstrates the superiority of learning an optimal class decision threshold. Increased performance is demonstrated when joint learning is carried out over both the representation and the class decision thresholds. Thorough empirical evaluation on benchmark EC datasets that span the spectrum of number of classes and class imbalance shows clear F-measure improvements over baseline models, with good improvements over pre-trained deep models and higher improvements over untrained deep architectures.
Challenges and Optimization Paths of Guzheng Professional Education in Colleges under Big Data Era

As a treasure among Chinese national musical instruments, guzheng is an important part of traditional Chinese music. As the art of national music goes to the world, the art of guzheng has been widely promoted. As the best form to carry forward the art of guzheng, the teaching of guzheng majors in colleges is significant in teaching and continuously improves guzheng art accomplishment. Oral teaching and step-by-step music theory and technique teaching are typical ways of teaching musical instrument performance in colleges. However, under big data, Chinese education is undergoing a profound change, and the combination of big data and education has become a new contemporary education method. This work studies the guzheng professional education in colleges under big data. First, this work aims at the existing outstanding issues of guzheng teaching in colleges and studies the challenges and optimization paths of guzheng professional education in colleges under big data. Second, this work proposes a multiscale residual attention fusion network (MSRAFNET) to evaluate the teaching quality of guzheng majors in colleges in the era of big data. The feature extraction of the network model is mainly completed by the residual module, which is composed of several multiscale residual learning units. Adding an attention mechanism to the multiscale residual learning unit can enhance the feature extraction of key information by the network and reduce the interference of redundant information, which is more conducive to the learning of data features. It adopts the design of GAP and Dropout to reduce spatial parameters in network training, and the effect of antioverfitting is better. Third, this work systematically evaluates the optimization path of Guzheng education and MSRAFNET, and the systematic experiments verify the superiority of the designed method.
The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study

Many contact tracing solutions developed by countries around the globe in containing the Covid-19 pandemic are in the area of location-based tracing, which does not enable them to identify close contacts accurately. As location-based tracing implementations continuous on, the results have not been as effective as intended. Thus, in providing some closure, this study will dissect the need for close contact tracing solutions for the pandemic by providing a comprehensive contact tracing characteristic framework (CCTCF) for Covid-19, which will help authorities toward better pandemic management. In this study, CCTCF for Covid-19 was constructed by applying several methods. Using Problem, Intervention, Comparison, Outcome (PICO) as the framework, methods conducted were: (1) Case study to analyze the contact tracing systems in 30 countries; (2) Systematic literature review (n=2056) regarding solutions’ elements, (3) Thematic analysis for characteristics framework development. A total of 25 items were obtained for CCTCF, along with valuable insights that necessitate close contact tracing for the pandemic. Results from CCTCF have also shown that the best contact tracing solution for Covid-19 is bi-directional human-to-human close contact tracing, which uses a retrospective approach and is able to identify the source as well as groups of infection using a personal area network (PAN). Doi: 10.28991/esj-2022-SPER-019 Full Text: PDF
Artlution: Architecture Design of Mobile Apps for Artblock Problems

Art Block problems occur until today in the design world, where designers struggle to find ideas. Sometimes, designers open social media to solve art block problems. But the solution they have are not effective enough to increase their creativity and start their work, because the search engine could not help them enough, and the lack of others’ opinions about each other’s work. The researcher came out with the idea to create an application that can solve art block problems by increasing the work of the search engine, widening the category, and adding the Art Helper feature where designers can discuss their work and can help others who needed help. By using design thinking methods this research proposed Artlution mobile apps design concept, meaning a solution for your art problems. Hopefully, this research can be used as a recommendation for further development to make the designer relatively more creative.
PC-LSTM: Ontology-based Long Short-Term Memory State Model for Data Incompleteness Prediction

Medical practices are engaged and motivated by new technologies and methods to enhance patient care as efficiently as possible. These new methods and technologies give way for medical practices and clinicians to have the insight, comprehension, and projections to develop better decisions and overall levels of care. In this paper, we propose a model, PatientCentered-LSTM (or PC-LSTM), using the states of the LSTM model to produce a novel, ontology-based state system for data incompleteness. The overall architecture and system design are based around utilizing the hidden and cell states of the LSTM model to produce a network of states for each of the corresponding hierarchies in an Electronic Health Record (EHR) system. The resulting methodology allows for an accurate and precise approach to predicting data incompleteness in electronic health records. Clinical relevance— The method presented uses the hierarchical nature of electronic health record systems to positively influence the analysis of its data completeness; thereby, increasing the possibility of improved healthcare outcomes.