Reviewerly/exHarmony huggingface dataset:
GTT-Bert: Pre-training of Graph-To-Tree Model for Math Word Problems

Math word problem (MWP) is an important problem in the field of intelligent education and natural language processing.The existing models for solving MWP problems mainly include sequence to sequence (Seq2Seq), sequence to tree (Seq2Tree), graph to tree (Graph2Tree) and other methods.Graph2Tree model can well capture the relationship and order representation between quantities.However, the existing Graph2Tree model usually uses the embedded layer to represent the input text sequence as a word vector, which does not obtain the representation without paying attention to numerical attributes and context representation interpretation information.We propose a pre-train-model based on Graph2Tree structure.The experimental results show that the performance of Graph2Tree model with our pre-training model is significantly better than the existing Graph2Tree model.
EC-GAN: Emotion-Controllable GAN for Face Image Completion

Image completion methods based on deep learning, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have succeeded in producing semantically plausible results. However, existing facial image completion methods can either produce only one result or, although they can provide multiple results, cannot attribute particular emotions to the results. We propose EC-GAN, a novel facial Emotion-Controllable GAN-based image completion model that can infer and customize generative facial emotions. We propose an emotion inference module that infers the emotions of faces based on the unmasked regions of the faces. The emotion inference module is trained in a supervised manner and enforces the encoder to disentangle the emotion semantics from the native latent space. We also developed an emotion control module to modify the latent codes of emotions, moving the latent codes of the initial emotion toward the desired one while maintaining the remaining facial features. Extensive experiments were conducted on two facial datasets, CelebA-HQ and CFEED. Quantitative and qualitative results indicate that EC-GAN produces images with diverse desired expressions even when the main features of the faces are masked. On the other hand, EC-GAN promotes semantic inference capability with irregularly masked holes, resulting in more natural facial expressions.
An ANOVA-Based Study on the Factors Influencing College Students’ English Learning Attitudes in an Intelligent Environment

With the acceleration of the deep integration of information technology and education teaching, the blended intelligent teaching based on intelligent learning environment will become the new normal of school teaching.This paper selects a typical teaching class of non-English majors in Jiaying University in eastern Guangdong as the sample source and uses ANOVA analysis of variance model to analyze the influencing factors of college students' English learning attitudes in the intelligent learning environment, in order to provide reference for improving the quality of college English teaching in the intelligent environment.
Intended Human Arm Movement Direction Prediction using Eye Tracking

Collaborative robots are becoming increasingly popular in industries, providing flexibility and increased productivity for complex tasks. However, the robots are still not interactive enough since they cannot yet interpret humans and adapt to their behaviour, mainly due to limited sensory input. Prediction of human movement intentions could be one way to improve these robots. This paper presents a system that uses a recurrent neural network to predict the intended human arm movement direction, solely based on eye gaze, utilizing the notion of uncertainty to determine whether to trust a prediction or not. The network was trained with eye tracking data gathered using a virtual reality environment. The presented deep learning solution makes predictions on continuously incoming data and reaches an accuracy of 70.7%, for predictions with high certainty, and correctly classifies 67.89% of the movements at least once. The movements are, in 99% of the cases, correctly predicted the first time, before the hand reaches the target and more than 24% ahead of time in 75% of the cases. This means that a robot could receive warnings regarding in which direction an operator is likely to move and adjust its behaviour accordingly.
Digital Twin Models Supporting Cognitive Buildings for Ambient Assisted Living

Abstract The rapid and global aging of population is outlining the need for environments that can provide support for these individuals during their daily activities. The challenge of an aging society is being addressed through the incorporation of new technologies into the home environment, which is nothing less than Ambient Assisted Living (AAL). To date, some of the AAL solutions exploit AI models to recognize the elderly’s behaviors through data collected by sensors. In recent times, Digital Twins (DTs) at building level have begun to appear on the construction domain. These are still under development but through the integration of users into assessments, they improve efficiency, prevention, and prediction of likely events through real-time AI computing. The integration of DT and AAL defines cognitive buildings which aim to learn at scale, reason with a purpose, and co-operate with users in a natural way. This research aims to develop DT models to achieve scenario awareness to provide support to elderly people living alone and suffering from cognitive disorders. The proposed multi-agent architecture is based on a five-layer system that autonomously develops high-level knowledge to detect anomalies in the home environment scenarios and therefore support the user. Bayesian networks (BNs) are exploited to perform high-level deductive reasoning on low-level multi-modal information, thus recognizing senseless or dangerous behaviors, environmental disruptions, changes in behavioral patterns, and serious medical events. Bi-directional user-system interaction provides user support by leveraging Speech-To-Text and Text-To-Speech AI agents. Three main functions were tested: real-time data integration, anomaly detection, and two-way interaction.
Virtual Reality Technology in the Context of 5G to Improve the Quality of Mental Health Education in Universities

With the vigorous and efficient development of 5G Internet era, virtual reality technology provides a new channel for the innovation of mental health education model in colleges and universities.Based on 5G technology, virtual reality technology is combined with cloud rendering technology, causal brain network, and EEG analysis method to provide virtual reality learning scenes, establish VR deep learning models, and solve the problem of delay in online classes through cloud rendering technology for mental health education in colleges and universities.This makes college mental health education enter a new era, and the teaching quality and management level are significantly improved.
A Survey of Cybersecurity Awareness Among Undergraduate Students at Yunnan University of Finance and Economics in China

In 2022, more than 55% of internet users have encountered cybersecurity problems, which makes it have negative impact on public security.The group under the age of 20 is mainly students at school or young people just entering society.Because of their low financial resources and lack of security awareness, they are particularly vulnerable to fraud.We want to know what the current cybersecurity awareness of college students is.This study conducted a questionnaire survey on undergraduates of Yunnan University of Finance and Economics.According to the four aspects of cybersecurity awareness, their awareness was evaluated, namely, cybersecurity knowledge, privacy, password management and trust.We use quantitative research to verify the hypothesis through Spearman correlation.Finally, according to the collected data, we describe and analyze it, and provide some suggestions for cybersecurity education.
Teaching Exploration of Integrated Circuits Under the Background of Informatization

This paper explores the teaching mode in digitalization and informatization, taking integrated circuit major as an example.By introducing practical engineering projects into curriculum practice and taking discipline competitions as the guide, it is explored new method to improve the ability to solve complex problems.It is built a self-circulating, self-iteration, scientific and technological innovation platform for students, building online and offline mode by cloud service to meet their requirements.After years of practice, this method can effectively stimulate students' internal driving force, improve their comprehensive qualities such as knowledge mastery, professional practice and application ability.It is proved that this way is effective to cultivate high-quality compound new engineering talents with strong practical ability, strong innovation ability and international competitiveness.
Establishing a Pattern of Mobile Assisted Language Learning Under the Big Data Age: To Exemplify by the University English Practical Teaching Beneath the Frame of MALL

With the continuous deepening of college English teaching reform and the rapid popularization of wireless communication technology, the new English teaching model based on big data technology and Internet technology provides unlimited development space for the cultivation of college students' English level.This paper analyzes the construction ideas and architecture design of college English education platform based on big data and "Internet +", and carries out learning behavior analysis and research combined with the platform.As can be seen from the effect analysis chart, the system designed in this paper can meet the needs of users.Meanwhile, the author will continue to transform and optimize the platform system, focusing on the improvement of platform functions and performance.The big data age definitely provides the reformation and renovation for teaching and learning methods.Establishing a Pattern of Mobile Assisted Language Learning is expected to be adopted as a major aided way of teaching and learning and a valid approach of self-learning as well.Generally, in the process of English teaching and learning in college, a frame of MALL is built reasonably under the theory of mobile assisted learning, by which the students' enthusiasm, enlistment, ability of self-learning and teamwork learning can be enhanced from all aspects.
The Influence of Academic Tutors’ Mentoring Behavior on University Students’ Professional Commitment: Based on Universities with the Full Credit System Using SPSS and Mplus Software

Based on self-regulation theory, SPSS and Mplus were used to explore the relationship mechanism between academic tutors' mentoring behavior and university students' professional commitment through hierarchical regression and moderated mediation model.Hierarchical regression analysis results showed that academic tutors' mentoring behavior stimulates university students' professional commitment, both university students' feedback inquiry and feedback monitoring mediate the relationship between academic tutors' mentoring behavior and university students' professional commitment.Moreover, university students' perceived university learning climate moderates the aforesaid indirect effects of university students' feedback inquiry and feedback monitoring.The moderated mediation model analysis showed that university students' perceived university learning climate positively moderates the indirect effects of feedback inquiry and feedback monitoring on the relationship between academic tutors' mentoring behavior and university students' professional commitment.These conclusions provide theoretical and practical implications for universities with the full credit system.
Research on a Hybrid Teaching Method Based on Big Data Analysis and Informatization 2.0

Internet + vocational education" under the background of informatization 2.0 has become the mainstream path of professional curriculum construction in higher vocational colleges.From the perspective of specialty system construction in higher vocational education, the core courses of specialty series are determined on the basis of sorting out the target positions of talent training.Under the guidance of relevant theories, taking a course as an example, this paper explains in detail the implementation process of the determination of teaching objectives, the setting of teaching projects and the integration of 1 + x course certificates, and gives the path of course resource construction and the design, implementation and assessment scheme of hybrid course teaching.
Graph-Enhanced Biomedical Abstractive Summarization Via Factual Evidence Extraction

Abstract Infusing structured semantic representations into language models is a rising research trend underpinning many natural language processing tasks that require understanding and reasoning capabilities. Decoupling factual non-ambiguous concept units from the lexical surface holds great potential in abstractive summarization, especially in the biomedical domain, where fact selection and rephrasing are made more difficult by specialized jargon and hard factuality constraints. Nevertheless, current graph-augmented contributions rely on extractive binary relations, failing to model real-world n-ary and nested biomedical interactions mentioned in the text. To alleviate this issue, we present EASumm, the first framework for biomedical abstractive summarization empowered by event extraction, namely graph-based representations of relevant medical evidence derived from the source scientific document. By relying on dual text-graph encoders, we prove the promising role of explicit event structures, achieving better or comparable performance than previous state-of-the-art models on the CDSR dataset. We conduct extensive ablation studies, including a wide experimentation of graph representation learning techniques. Finally, we offer some hints to guide future research in the field.
Improving the Effect of Education and Teaching with VR Technology

Virtual reality (VR) technology utilizes computers to generate all kinds of realistic scenes, immersing users in virtualized and simulacra experiences.Advance in technology requires an equivalent breakthrough in practical education and teaching.Current practice education faces many kinds of problems no limited to lack of practice equipment and instructors, hidden security dangers, hard for students to preview and review.In this paper, we apply VR technology in practice education to explore a new area of teaching effect.First, we analyze the problems current practice education faces, and the advantages VR technology holds in practice education.Then, we refer to former VR practice education failures and come up with corresponding solutions to improve traditional education.Finally, we utilize VR technology in practice teaching, and the feedback of students verifies the improvement of effect in education and teaching.
A Study of Factors Influencing College Students’ Engagement in Blended Learning

With the continuous development of information technology, college education is also in the constant process of reform and innovation.Owing to the great improvement of Internet technology, blended teaching has been rapidly developed in colleges and universities.This paper measures college students' engagement in blended learning and the factors influencing it based on the theoretical model of learning engagement.By the use of SPSSAU software analyzing the data, it was evident that college students' engagement in blended learning was inefficient, with cognitive engagement lower than affective engagement and behavioral engagement.It is found that teaching design, teaching organization, teaching interaction, and academic self-efficacy all significantly influenced college students' engagement in learning.
Efficient and privacy-preserving multi-party skyline queries in online medical primary diagnosis

Wireless body area networks enable data collection from wearable devices, thereby allowing online medical primary diagnosis via cloud computing. Data security and diagnosis accuracy are two main concerns in the online medical primary diagnosis system. While traditional solutions can ensure the confidentiality of online data, their incapacity to integrate data from multiple users restricts the development of accurate diagnostic models and leads to low accuracy. Recently, a medical preliminary diagnosis scheme with improved accuracy was proposed, which employs skyline computation to construct a precise diagnostic model using multiple medical datasets. However, their scheme requires a trusted third party and experiences excessive query time. To address this issue, we present an Effective and Privacy-preserving Multi-party Skyline diagnosis scheme (EPMS) that offers even higher accuracy and extremely fast diagnosis without trusted third parties. Specifically, we devise several sub-protocols to support secure skyline computation. By integrating our protocols with privacy matrix techniques, the cloud server can generate a comprehensive diagnostic model from multiple data sources, offering accurate diagnosis services without disclosing any users’ personal information. We implement our scheme and conduct extensive experiments, which showed that our approach achieves a speedup of approximately 200× in query time and nearly 20% improvement in accuracy.
Design and Development of College English Online Teaching System Based on J2EE

In order to improve the effect of college English teaching and cultivate professional English talents who meet the needs of the society, this paper takes college English teaching as the research object and designs a set of online college English teaching system based on J2EE with the help of network information, database management, computer application and other technologies.Under the j2EE specification, the system adopts B/S structure to simplify the development process, the web server is written in Java language, the Springboot framework is introduced to complete the MVC three-tier design of the system, and MySQL is selected to realize the data support of a huge resource pool.The client is written in HTML, CSS and Javascript, and two subsystems, the student subsystem and the teacher subsystem, are set up.It has created a knowledge platform for English teaching and learning for teachers and students of college English majors, and provided a new model and new ideas for the development of college English majors and the training of national English talents.
A Comparative Study on Class Characteristics of Devanãgri Script Writers from Three Different States of India

India is a multilingual country. Hindi is the national language of India and Devanãgri script is used to write Hindi language. Various documents with legal value are made in Devanãgri script, which may be questioned for their authenticity and authorship. Sufficient research has been done and reported about different scripts. However, researches based on Devanãgri script are limited. This study focuses on finding the significant class characteristics of writers from three different states of India, namely, Bihar, Madhya Pradesh and Punjab. The data was also statistically analysed using Pearson’s Chi-Square test for its significance. Handwriting samples from 300 subjects were collected from three different states of India to analyze various class characteristics in Devanãgri script. The samples were examined qualitatively and statistically. Various general characteristics were selected for the analysis and characteristic features were tabulated after qualitative examination. Statistical analysis showed that the data was statistically significant. The general characteristics selected for the analysis and comparison of the handwriting samples in Devanãgri script were found to be significant. The impact of regional scripts on the Devanãgri script should be performed, as the influence of regional language could be seen in the samples collected from Punjab.
Design and Implementation of Interactive Multimedia Teaching System for Oral English Under the Background of Internet Plus

In order to solve many problems in college oral English teaching, such as the limitation of teaching resources and the lack of practice opportunities, this paper takes oral English teaching as the research object, and develops an interactive multimedia teaching system for oral English with the help of network information technology, database management technology and computer application technology.By adopting B/S architecture to simplify the development process, the server side follows the MVC three-tier architecture design idea, and the JAVA language is used for programming.MySQL database is used to realize data support, and an "online + offline" oral English teaching mode is constructed, which creates an applied oral English learning environment for students and contributes to the cultivation of oral English talents in China.
Exploring the Motivation and Satisfaction of Students International Organization Talent Cultivation

Compared to Western nations, China's involvement in international organizations is still lacking due to a scarcity of international organization talent.This study examines the motivation and satisfaction levels of students aspiring to work in an international organization, utilizing the Global Engagement Program (GEP) at Zhejiang University as an exemplar.Drawing on the Uses and Gratifications (U&G) theory, this study explores the motives and satisfaction of GEP students using both qualitative and quantitative methods.The research reveals that factors such as training programs, curriculum design, and a sense of accomplishment are crucial for GEP students.Additionally, the course content, overseas exchange, and extracurricular activities were particularly well-received by students.Consequently, this article recommends granting more autonomy to students interested in pursuing careers in international organizations, enabling them to manage their work independently and enhance their satisfaction and performance in talent development programs.
Investigation on the Current Situation of Information Technology Education in Rural Primary Schools in Hainan

The paper is to explore the development status of rural primary schools in Hainan Province under the background of information education.The significance of this study is to understand the existing problems in the information education of rural primary schools in Hainan Province, analyze the causes of the problems, and then provide solutions and ways, and provide experience reference for the future information education of rural primary schools, and help the better development of information education of rural primary schools.This study adopts questionnaire survey, literature method and in-depth observation method, and analyzes the whole process of information education.The results show that students' unreasonable media usage habits, the absence of media literacy education and the lack of interaction and feedback mechanism in rural primary school information education led to less interaction and communication between students and teachers, which affects teachers' lack of understanding of feedback on information education results.