Reviewerly/exHarmony huggingface dataset:
Advance QR Code-Based Attendance Management System

In this day and age technology is growing very fast and becoming a crucial part of our life and due to this advancement in the technology. There is a need to replace the traditional attendance-taking system with the advance one in the college and universities to save both time and effort. Knowing that smartphones are also becoming an integral part of student life nowadays, so as to speed up the process of attendance taken by professors in the college and save lecturing time, and increase productivity. In this paper, a QR code-based attendance system is developed. The Qr code is displayed by every teacher at the beginning or end of the lecture and students have to scan the Qr code through their mobile application to mark their presence in the lecture. The material here discusses the way the system functions in detail.
CQUPT-FL: Cross-Domain Sharing and Security Awareness and Early Warning Platform of Health Science Big Data

Federated learning (FL) is a rapidly growing research area in machine learning, but it is problematic. It has been questioned whether or not existing FL libraries are practical in the area of medical privacy. To address these issues, we developed the CQUPT-FL system. The system focuses on resolving the conflict between data integrity and medical data privacy protection in cross-domain and cross-institution collaborative analysis. CQUPT-FL supports distributed computing and stand-alone simulation computing methods. To deal with the problems of heterogeneity, data domain diversity, and effective data scarcity, we adopted key technologies such as multi-party secure computing and holistic information representation and studied user identification, privacy protection, and heterogeneous user alignment to achieve sustainable Cross-domain and cross-platform data fusion of letters. The goal of introducing the CQUPT-FL system is to improve the level of data privacy protection and enhance the data privacy protection mechanism, solve the machine learning dilemma in the field of medical privacy, and provide a reliable solution for cross-domain collaborative analysis.
A Method and Practice for Menopausal Disease Prediction Based on Knowledge Graph

As the population of women entering menopause grows, so does the potential for chronic conditions like os-teoporosis, cardiovascular disease, and metabolic syndrome, which underscores the the critical need of early intervention and treatment during menopause. Meanwhile the Electronic Healthcare Records (EHR) is becoming a valuable resource for health event predictions, including diagnosis, mortality, length-of-stay, and readmission. However, effectively utilizing diagnosis features and co-occurrence relationships among medical concepts in EHR data remains a challenge. To address this issue, we propose a new knowledge graph-based approach, which can integrate co-occurrence relationships among medical concepts and numerical information in EHR data. Our approach leverages a concurrency-aware graph learning method and a feature-wise fusion block to enhance knowledge representations and fully exploit the information in patients' EHR data. Experiments using actual EHR data showcases a superior efficacy of our model in forecasting menopausal diseases compared to existing approaches.
A Knowledge-Enhanced Medical Named Entity Recognition Method that Integrates Pre-Trained Language Models

Medical Named Entity Recognition (NER) is a critical task in medical text processing. But medical documents exhibit high variability in terms of language usage, abbreviations, synonyms, misspellings, and typographical errors, so the precise extraction of named entities is challenging. Although large language models (LLMs) have shown good performance in medical knowledge extraction tasks in few-shot settings, their performance is difficult to fully leverage in supervised medical named entity recognition (NER) tasks. This is because NER is a sequence labeling task, while LLMs are more suitable for tasks such as text generation. Furthermore, the structured output of NER tasks leads to a performance loss when LLMs convert it into generative text. Therefore, it is a challenging problem to utilize LLMs to improve the accuracy of medical named entity recognition tasks. On this paper, we propose a method that integrates LLM knowledge to enhance the performance of medical NER models. Firstly, we improve the structure of the LLM model to make it more adaptable to NER tasks. Secondly, we adopt the LoRA method and incorporate Chinese vocabulary information into the model training. Finally, to fully utilize the fine-tuned LLM to enhance the medical NER model, we convert the output of the LLM into a knowledge concentration matrix and inject it into the NER model. We have verify the effectiveness of our new method on the CMeEE dataset. The results demonstrate that our method can efficiently fine-tune the LLM and improve its performance. Moreover, our method can also leverage the prior knowledge of the fine-tuned LLM to enhance the BERT-based medical NER model. In addition, our method demonstrates good generalization and can tackle entity recognition tasks in other domains. We validated the superiority of our approach on the resume-zh dataset.
SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.
Migration: An Efficient Explorer Operator to Guide Swarm Evolution

Particle Swarm Optimization with Migration (MPSO) is proposed to solve the issue that PSO will encounter unbearable time cost problems when dealing with High-dimension, Expensive and Black-box objective function tasks. The Migration operator is inspired by the migration of Salmon. Salmon will start a dangerous journey from the ocean to the home rivers for reproduction. The process of the entire behavior is similar to the reduction and recovery of dimension. Therefore, we design the Migration operator where a pretrained Wasserstein Autoencoders (WAE) is applied to simulate the migration behavior to accelerate the process of evolution in PSO, and we use Least-Squares Regression in lower space to produce better generation. In comparison with famous baseline methods in some benchmark functions, MPSO converges faster and more accurately, which shows the great potential of migration operations.
VAC-UNet: Visual Attention Convolution U-Net for 3D Medical Image Segmentation

Deep learning-based methods have achieved significant success in medical image segmentation. However, the intrinsic locality of convolutional operations limits the learning of global semantic information. In this paper, we propose a novel method, namely VAC-UNet that is a convolutional transformer U-Net architecture for enhanced global representation. Specially, we introduce a novel Visual Attention Convolution (VAC) block integrating transformers and CNNs for joint local-global feature learning. VAC blocks replace standard convolutions in the encoder-decoder backbone. Asymmetric application on encoders improves localization. Additionally, we design Visual Attention Gates using learned convolution filters to suppress irrelevant regions in skip connections. Finally, we evaluated VAC-UNet on two medical image segmentation tasks against state-of-the-art methods. The results demonstrate VAC blocks effectively capture global context while maintaining local precision. The visual attention improves feature propagation for better segmentation.
Double-Rule Causal Feature Selection in Risk Factor Mining for Disease Diagnosis

In healthcare, high-dimensional medical observation data brings challenges to disease diagnosis. In order to reduce the data dimension, feature selection becomes a key step in processing medical observation data. Causal feature selection, i.e., Markov Blanket (MB) discovering, discovers local causal relationships that exist around the target feature, and studies have shown that causal feature selection improves the interpretability and robustness of disease diagnosis models. However, in practical applications, the causal feature selection algorithm based on conditional independence (CI) tests always suffers from two types of errors. These errors include false positives where some incorrect MB features are selected, and false negatives where some true MB features are discarded. Existing algorithms cannot efficiently handle both types of errors at the same time. To address this issue, this paper presented a simultaneous-fusion-of-double-rules MB learning (SF-DRMB) algorithm for risk factor mining of disease. The algorithm is structured into three phases: in the first phase, SF-DRMB identifies both parent-child and spouse sets of the target feature; in the second phase, OR rule is used to solve false negatives error; in the third phase, an improved AND rule is used to solve false positives error. Through the three phases of SF-DRMB, the MB of the target feature is discovered. Finally, SF-DRMB was compared with existing causal feature selection algorithms with benchmark BN datasets and medical datasets, and the experimental results demonstrated that SF-DRMB is competitive with the fastest MB discovery algorithm in terms of computational efficiency and achieves good accuracy among the compared algorithms.
Application research on data analysis and knowledge discovery of video lesson based on teaching and research

Human beings have no understanding of the problem itself and cannot recognize its nature, but they can learn through scientific research. In the current context of big data, learning this behavior can generate a large amount of information and data, which may become a potential threat if not effectively managed, processed, and utilized. For the education industry of lifelong learning and development of teachers, in addition to practical experience in the classroom, it is inseparable from the development of teacher training activities. In previous teacher training activities, schools usually adopted traditional face-to-face communication and learning. With the application and popularization of information technology, teaching and research activities based on online platforms have gradually become the center of teacher training projects. However, through actual research, the author found that the educational data generated during the teaching and research process has not been deeply explored and effectively utilized. Based on this, this article briefly analyzes the current situation of video lesson data analysis and knowledge discovery and proposes new application design ideas for teacher training and development based on the design of teaching and research platforms, in order to provide technical guidance for teacher lifelong learning and development.
Research hotspots and enlightenment of teachers' digital competency under the background of education digital transformation

It is of great significance to study teachers' digital competency under the background of education digital transformation. This study takes the literatures related to teachers' digital competency in the core database of wos as the sample, analyzes the correlation between the literatures and the publishing trend based on bibliometrics, and tracks the distribution characteristics, high-frequency keywords, and highly cited articles of the literatures related to teachers' digital competency with the help of the bibliometric software CiteSpace (version 6.4 R4), and makes an in-depth analysis of the contents of the relevant literatures. The study found that the number of papers was on the rise, and the research results mainly focused on the connotation, standards, level evaluation and training path of teachers' digital competency and influencing factors. Based on the review and analysis of the existing literature, this paper puts forward three enlightenments for the research of teachers' digital competency.
Research on Clustering Recommendation Based on analysis of students of College English Source Data in Chinese Mainland

Due to the current shortage of educational resources, it is difficult to teach in line with the students' ability. This paper aims to use data mining and artificial intelligence technology to explore the clustering recommendation research based on the analysis of learning situation of college English source data in Chinese Mainland. For one thing, the source data is analyzed to find the differences in English proficiency among students followed by the application of KMeans clustering. For another, based on user and article association rules, the collaborative filtering recommendation algorithm is combined to cluster recommendation providing personalized English learning resources and learning paths for students in the "second classroom". This study is expected to provide reference for personalized recommendation and optimization of college English education and improve the quality of education.
Research on the application mode of knowledge graph in education

With the emergence of AI technology, the application of artificial intelligence in education has been a popular topic in educational technology. Knowledge maps, the latest form of knowledge engineering in the field of AI, have significant research value in education. This paper introduces fundamental research methods in knowledge maps and analyzes classic cases in the education field, using discipline knowledge graphs. Based on existing analysis and further study, three research models for knowledge maps in the education field have been developed. These models include intellectualized teaching resources, personalized teaching, and optimized educational decision-making. They are designed for research, teaching, and management levels respectively. Finally, this paper proposes specific suggestions and expectations for the reasonable implementation of knowledge map technology in the education field.
Development and Verification of the Evaluation Scale of MOOC Learners'Online Deep Learning Ability

It appears to be quite important and urgent to discuss deep learning in an online learning environment in this "Internet + Education" era. Online deep learning ability is the source of power to solve the problems related to current MOOC learning, such as low quality and high dropout. Research and evaluation of learners' online deep learning ability are important to improve the quality of MOOC learning. However, currently, there are few researches on the evaluation of online deep learning ability, and by far no online deep learning measurement tool based on ability perspective is available. In order to provide a powerful measurement tool to evaluate learners' online deep learning ability from the perspective of ability, according to scientific scale programming, based on the related achievements done by both domestic and foreign researchers and the framework of deep learning ability compatibility, this research has developed an evaluation scale of MOOC learners' online deep learning ability. This scale has covered 25 measurement items, including six dimensions such as knowledge controlling ability, problem solving ability, online collaborating ability, effectively communicating ability, self-management ability, and online develop academic mindsets. Verification has proved that the revised evaluation scale is of good reliability and validity.
A study on students' satisfaction with Blended Learning methods: A case study at FPT Education

The innovation and progress in technology have brought about changes in the approach to education at universities, leading to the emergence of blended learning models. The impact of the Covid-19 pandemic in Vietnam during 2020-2021 has accelerated the adoption of blended learning in some educational institutions, with FPT education system being at the forefront, including FPT University, FPT Polytechnic College, FPT BTEC College, and others. In this study, the authors adopt the Technology Acceptance Model (TAM) proposed by [7] and the SERVQUAL model derived from the research conducted by [25] to propose a research model analyzing the factors influencing students' satisfaction with the blended learning method. The research surveyed 264 students at FPT educational institutions who participated in classes using the blended teaching method. Quantitative research methods, including reliability testing of measurement scales, exploratory factor analysis, and linear regression models, were employed. The results identified that three out of four factors have an impact on student satisfaction with the blended learning method, including: (1) Perceived usefulness, (2) Interaction, (3) Learning Environment. Additionally, some suggested solutions are proposed to help FPT educational institutions effectively implement blended learning, thereby enhancing student satisfaction with this approach in the future
Research on Autonomous Learning of Chinese as a Second Language Through Content Analysis of the Social Q&amp;A Website Quora

Social question-and-answer (Q&A) websites offer an interactive platform that extends to a broad and diverse array of learners, facilitating Q&A sessions and promoting learning. This study collected data and conducted a qualitative analysis of posts related to CSL learning methods on the social Q&A platform Quora. By striving to comprehensively and authentically capture the subjective viewpoints and learning journeys of international learners of Chinese as a second language (CSL), this study offered approaches and materials that could be readily embraced by autonomous learners beyond the traditional classroom setting. Moreover, it served as a catalyst for educational institutions and educators to deepen their understanding of their learners and develop more impactful instructional strategies.
Analysis of information technology classroom teaching behavior in smart classroom environment

With the continuous development of education informatization, more and more primary and secondary schools in our country introduce smart classrooms for information technology teaching. In order to have a deeper understanding of the current situation of information technology classroom teaching behaviors in the context of smart classrooms, this study selects 10 high-quality information technology teaching videos in high schools as the research objects, and relies on the improved smart classroom teaching behavior coding system. Nvivo 11 qualitative analysis software was used to label and analyze the teaching behavior of 10 classes. It is found that teachers and students mainly use teaching, resource presentation and question and answer in the teaching process, in which teachers' activities are more frequent than students' activities, and teachers guide students to think and participate in learning through questioning and technology application. The teaching process of teachers' questions and students' responses has been normalized, but it mainly focuses on closed questions. At the same time, in the information technology classroom in the smart classroom environment, the proportion of teachers and students applying technology has been significantly improved, and the teaching atmosphere has also shown a good state. However, only the use of smart classroom equipment has not improved the teaching mode, and it is difficult to achieve the "student-centered" teaching concept of information technology courses, and it is unable to effectively guide students to think. To this end, we should introduce advanced teaching concepts, improve the quality of classroom question-answering, stimulate students' autonomy and creativity, strengthen teacher-student interaction, and create an open, equal and creative teaching environment.
Research on the educational application of generative artificial intelligence images in the design of semiotics learning models

Generative Artificial Intelligence (AI) has emerged as a novel technology with profound implications for education and deep learning, particularly due to its advancements in image generation. This progress has had a disruptive impact on the design industry, where designers are increasingly embracing generative AI images as innovative tools and techniques to enhance design ideation and creative expression. Integrating generative AI images into design education has become an inevitable trend, as it guides students in developing a deeper understanding of design aesthetics. However, the current application of generative AI images in design education lacks innovative use grounded in design theory. Therefore, the incorporation of generative AI images within the framework of design theory becomes crucial to interpret and refine design processes and design thinking. This research aims to explore the application of generative AI images in design semiotics, utilizing design theory as its foundation. By integrating design processes and incorporating design case studies, the study seeks to analyze how generative AI images can be effectively applied in design semiotics. Ultimately, the research strives to establish a pedagogical approach for the application of generative AI image-based design semiotics.
The Empowerment and Impact of ChatGPT Technology on Foreign Language Education in Colleges and Universities

OpenAI, an American artificial intelligence laboratory, launched ChatGPT, an intelligent chatting tool at the end of late 2022, which has caused great repercussions all over the world. ChatGPT technology has already had an important impact on education in today's world, so what kind of empowerment and impact will it have on the teaching and learning of foreign languages in Chinese colleges and universities? This article combines the author's experience of using ChatGPT, firstly describes the new teacher-student relationship it generates, then discusses how ChatGPT empowers foreign language students and teachers in colleges and universities and summarises several aspects of how ChatGPT can be applied to foreign language students and teachers in colleges and universities. At the end, the article reveals the potential risks, limitations and implications of ChatGPT on foreign language education in colleges and universities in the future and deepens the knowledge and understanding of the utility of artificial intelligence in foreign language education in colleges and universities.