This systematic review aims to examine the role of modern technologies in supporting quality in teaching and learning processes.Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guideline...This systematic review aims to examine the role of modern technologies in supporting quality in teaching and learning processes.Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,a comprehensive analysis of literature published between 2020-2025 was conducted to identify current trends,challenges,and advances in technology-enhanced education.The study analyzed 15 peer-reviewed articles focusing on the implementation of modern technologies such as technology-enhanced learning platforms,professional development systems,electronic educational resources,information technology applications,and next-generation learning systems in educational settings.The findings reveal that modern technologies significantly enhance learning outcomes,student engagement,and teaching effectiveness when properly implemented.Key benefits include improved 21st century skills development(up to 38%improvement),enhanced teaching competency(37%improvement),increased learning efficiency(41%enhancement),and better classroom performance(35%improvement in efficiency).However,challenges such as systematic implementation requirements,pedagogical integration needs,infrastructure limitations,technical investment requirements,and institutional coordination barriers remain significant obstacles to widespread adoption.The review identifies emerging technologies including next-generation learning systems,interactive digital platforms,and innovative teaching technologies as promising solutions for future educational enhancement.The study concludes that successful integration of modern technologies requires systematic implementation approaches,comprehensive teacher training,robust institutional support,pedagogical alignment,and continuous evaluation processes.Critical success factors include strategic planning,quality assurance frameworks,sustainability planning,and evidence-based decision making.These findings provide valuable insights for educators,policymakers,and technology developers working to improve educational quality through technological innovation.The research contributes to the growing body of knowledge on technology-enhanced education and offers practical recommendations for implementing sustainable and effective digital learning solutions that prioritize pedagogical effectiveness while leveraging technological capabilities for comprehensive educational enhancement.展开更多
Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission sche...Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is proposed.To model CSI uncertainty,an expectation-based error model is utilized.The main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model aggregation.The problem is formulated as a combinatorial optimization problem and is solved in two steps.First,the priority order of devices is determined by a sparsity-inducing procedure.Then,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met.An alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex subproblems.Numerical results illustrate the effectiveness and robustness of the proposed scheme.展开更多
Research in L2 writing assessment has overwhelmingly focused on helping students enhance their writing quality and global development in language proficiency by various means and approaches of assessment.However,studi...Research in L2 writing assessment has overwhelmingly focused on helping students enhance their writing quality and global development in language proficiency by various means and approaches of assessment.However,studies of the learning on the part of EFL writing teachers,especially when engaging in collaborative assessment with students,are few and far between.This qualitative case study therefore fills this void of foregoing research by examining the learning and development of a Chinese EFL writing teacher who employed teacher-student collaborative assessment(TSCA)(Wen,2016)in an L2 academic writing course.Drawing upon multiple types of data,three themes emerged with regard to the learning of the L2 writing teacher:1)becoming more assessment literate and capable of providing constructive feedback;2)gaining more efficacy in instructional tactics,student engagement and classroom management;and 3)developing a better understanding of students’evaluation focus as well as their needs and expertise in writing.This study offers a robust picture of how TSCA can foster multidimensional teacher learning-cognitively,affectively,and relationally-affirming its value not only as an assessment tool but as a transformative pedagogical practice.展开更多
The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the ...The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the policies and teaching demands that formed the basis of this model were analyzed.The study shows the importance of the implementation of the teaching model“promoting teaching and learning through competitions.”This model puts emphasis on the curriculum and teaching resources,while also integrating the teaching process and evaluation with competition.These efforts aim to drive education reform in order to better align with the objectives of vocational education personnel training,while also acting as a reference for similar courses.展开更多
Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients m...Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.展开更多
In cooperative multiagent systems, to learn the optimal policies of multiagents is very difficult. As the numbers of states and actions increase exponentially with the number of agents, their action policies become mo...In cooperative multiagent systems, to learn the optimal policies of multiagents is very difficult. As the numbers of states and actions increase exponentially with the number of agents, their action policies become more intractable. By learning these value functions, an agent can learn its optimal action policies for a task. If a task can be decomposed into several subtasks and the agents have learned the optimal value functions for each subtask, this knowledge can be helpful for the agents in learning the optimal action policies for the whole task when they are acting simultaneously. When merging the agents’ independently learned optimal value functions, a novel multiagent online reinforcement learning algorithm LU-Q is proposed. By applying a transformation to the individually learned value functions, the constraints on the optimal value functions of each subtask are loosened. In each learning iteration process in algorithm LU-Q, the agents’ joint action set in a state is processed. Some actions of that state are pruned from the available action set according to the defined multiagent value function in LU-Q. As the items of the available action set of each state are reduced gradually in the iteration process of LU-Q, the convergence of the value functions is accelerated. LU-Q’s effectiveness, soundness and convergence are analyzed, and the experimental results show that the learning performance of LU-Q is better than the performance of standard Q learning.展开更多
This study investigates the learning curve of commercial banks regarding the efficiency of credit and value creation.However,current empirical methods for accessing the learning curve in organizations are not suitable...This study investigates the learning curve of commercial banks regarding the efficiency of credit and value creation.However,current empirical methods for accessing the learning curve in organizations are not suitable for use in financial institutions.Considering bank-specific characteristics,we introduce a dynamic learning curve using a cost function adjusted to capture learning-by-doing in banks.Using the model,we test several hypotheses on the impact of bank intermediary experience(learning)on the efficiency of credit and value creation in Japanese commercial banks.The findings show that bank intermediary learning significantly improves the cost efficiency gain in the gross value created,total credit created,and investment.However,bank intermediary experience has no significant effect on the efficiency of the economic value created for all the banks analyzed.These findings have practical implications for evaluating cost dynamics in bank credit and value creation,risk management,lending to the real sector,and shareholder value creation.展开更多
Experiential learning is the opportunity to meld teaching with experience; ＂to do＂ the things students learn about in the classroom, yet outside the classroom walls. I am an instructor that embraces experiential lea...Experiential learning is the opportunity to meld teaching with experience; ＂to do＂ the things students learn about in the classroom, yet outside the classroom walls. I am an instructor that embraces experiential learning. Every other year, I lead a Chican@ Literature class which, after the semester is finished, culminates with a two-week excursion to New Mexico. While on-campus I highlight specific themes within Chicano narrative and poetry. Discussions focus on several key aspects regarding Chican@ Literature whose purpose is to create a voice for those whom have been marginalized within mainstream American culture. Chican@ Literature emphasizes a concept of origin which is reiterated in New Mexico through a sense of place in nature. A second topic often addressed in Chican@ Literature is the idea of aprendizaje. This is a journey of knowledge. In each episode experienced, the narrative voice gains a broader understanding of identity. This aprendizaje is also shared by my students as they gain a sense of self as defined by their own community in juxtaposition with their New Mexican fieldwork and the bilingual poetry they write. Lastly, Chican@ Literature often reveals an author or poet＇s personal culture clash or cultural fusion within the creative work itself. Once again, my students write about their own perspectives in a poetry workshop and presented their pieces during a poetry slam. Some of these pieces are included in this manuscript.展开更多
Improving Non-English major students＇ autonomous learning is a focus for the teachers in the high vocational and technical college. Furthermore, promoting students＇ autonomous learning through metcacognitive strateg...Improving Non-English major students＇ autonomous learning is a focus for the teachers in the high vocational and technical college. Furthermore, promoting students＇ autonomous learning through metcacognitive strategies training has been received increasing attention. The research reports an empirical study on the relationship between metacognitive strategies and English autonomous learning. Finally, the teaching experiment was proved to be feasible and valid.展开更多
This study used quantitative methods to assess students'Chinese language learning attitudes and learning habits on Kahoot,a game-based learning platform.Kahoot enables teachers to transform bland Chinese vocabular...This study used quantitative methods to assess students'Chinese language learning attitudes and learning habits on Kahoot,a game-based learning platform.Kahoot enables teachers to transform bland Chinese vocabulary memorization into exciting,game-like situations.It makes Chinese language learning fun and interactive.The study aims to compare Kahoot team play mode with individual play mode.Sixty-four fifth graders participated.In the experimental group,students grouped by themselves or the teacher to compete with one another.They enjoyed working together to share what they knew and learned from each other.Students were tested prior to the course(pretest)and following the course(post-test).Observation notes,lesson plans,and surveys were also included.Analysis of the multiple types of data strengthens the conclusion that Kahoot can be an effective tool for teaching Chinese vocabulary,sentences,and culture.展开更多
The need for evidence-based practice has been recognized by physiotherapy organizations over the past decades. Earlier studies have documented facilitators and barriers that affect the use and implementation of eviden...The need for evidence-based practice has been recognized by physiotherapy organizations over the past decades. Earlier studies have documented facilitators and barriers that affect the use and implementation of evidence-based practice. Less is known about what kind of interventions might be useful to implement evidence-based practice. This study explores what physiotherapists learn through participation in a research project relevant to their professional development towards achieving a more evidence-based physiotherapy practice. To what extent this learning was transferred to colleagues for organizational learning is also examined. This study was set in Sweden, where health care is publicly funded. Patients do not need a referral from a physician to consult a physiotherapist. Eleven interviews were conducted with physiotherapists who had participated in a randomized, controlled, multicenter, physiotherapy intervention investigating neck-specific exercise for patients with whiplash disorder. Gadamer’s hermeneutics was used to analyze the data. The physiotherapists described a range of learning experiences from their project participation, including instrumental learning (the concrete application of knowledge to achieve changes in practice) and conceptual learning (changes in knowledge, understanding or attitudes). The research project enabled the physiotherapists to develop new treatment techniques for broader application and extend their competence in techniques already known (instrumental learning). The physiotherapists believed that project participation enhanced their overall competence as physiotherapists, increased their job motivation and strengthened their self-confidence and self-efficacy (conceptual learning). Physiotherapists’ participation in the research project yielded many individual learning experiences, fostered positive attitudes to research and was conducive to achieving a more research-informed physiotherapy practice. Participation was associated with a deeper understanding of the challenges involved in conducting research. The transfer from individual learning to the wider organization in terms of organizational learning was limited.展开更多
This study examined the overall effectiveness of reading proficiency especially the extensive reading in ESL.In order to investi-gate the process of acquisition more efficiently,author used reading-to-write approach t...This study examined the overall effectiveness of reading proficiency especially the extensive reading in ESL.In order to investi-gate the process of acquisition more efficiently,author used reading-to-write approach to investigate the connection between readingand foreign language learning.The research also shows that learners need to be provided with plenty of interesting and comprehensiblebooks and they are supposed to use strategies that they will acquire anyway as they read.展开更多
In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,a...In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,and innovative thinking.Traditional teaching models often fail to bridge the gap between theoretical knowledge and practical application,resulting in passive learning and limited problem-solving capabilities.This paper proposes a three-dimensional integrated teaching model centered on“Information Technology-Domain Knowledge-Outcome Production”(the“2+2+2”credit framework)to address these challenges.Drawing on constructivist theories,Bloom’s Taxonomy,and the CDIO model,the framework uses real projects to drive learning,facilitating the seamless integration of theoretical teaching and practical innovation.The model emphasizes tiered teaching objectives and interdisciplinary pathways,supported by dynamic assessment systems that track students’growth in knowledge,skills,and abilities.Applied in smart health and financial technology domains,this approach enhances students’comprehensive capabilities,aligning educational outcomes with industry demands.This study offers replicable strategies for educational reform in new engineering disciplines,aiming to transform students into proactive innovators and versatile talents.展开更多
Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility ar...Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.展开更多
With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loose...With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loosening faults are difficult to identify through conventional spectrum analysis,and the extreme scarcity of fault data leads to limited training datasets,making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity.This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution.This method proposes specific data processing approaches and a configuration of Bayesian Convolutional Neural Network(BCNN).It can effectively improve the model’s generalization ability.Experimental results demonstrate high detection accuracy and alignment with real-world applications,offering practical significance and reference value for industrial fan bolt loosening detection under data-limited conditions.展开更多
To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervis...To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.展开更多
With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows...With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.展开更多
This study examines how foreign language education in the artificial intelligence(AI)era could assist the cultivation of national consciousness through a technology-enhanced pedagogy of film appreciation.Using The Wil...This study examines how foreign language education in the artificial intelligence(AI)era could assist the cultivation of national consciousness through a technology-enhanced pedagogy of film appreciation.Using The Wild Robot as a case study,we argue that cinematic narratives serve as cultural mirrors,offering immersive,reflective,and affective sites for intercultural learning.We propose a three-layered pedagogical framework-progressing from semiotic decoding,through narrative and value comparison,to creative identity construction-that integrates intelligent tools to develop both communicative competence and an agentive sense of belonging.The approach exemplifies a humanistic turn in language teaching,aiming to form“rooted global communicators”who can engage in cross-civilization dialogue with cultural confidence and critical awareness.展开更多
文摘This systematic review aims to examine the role of modern technologies in supporting quality in teaching and learning processes.Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines,a comprehensive analysis of literature published between 2020-2025 was conducted to identify current trends,challenges,and advances in technology-enhanced education.The study analyzed 15 peer-reviewed articles focusing on the implementation of modern technologies such as technology-enhanced learning platforms,professional development systems,electronic educational resources,information technology applications,and next-generation learning systems in educational settings.The findings reveal that modern technologies significantly enhance learning outcomes,student engagement,and teaching effectiveness when properly implemented.Key benefits include improved 21st century skills development(up to 38%improvement),enhanced teaching competency(37%improvement),increased learning efficiency(41%enhancement),and better classroom performance(35%improvement in efficiency).However,challenges such as systematic implementation requirements,pedagogical integration needs,infrastructure limitations,technical investment requirements,and institutional coordination barriers remain significant obstacles to widespread adoption.The review identifies emerging technologies including next-generation learning systems,interactive digital platforms,and innovative teaching technologies as promising solutions for future educational enhancement.The study concludes that successful integration of modern technologies requires systematic implementation approaches,comprehensive teacher training,robust institutional support,pedagogical alignment,and continuous evaluation processes.Critical success factors include strategic planning,quality assurance frameworks,sustainability planning,and evidence-based decision making.These findings provide valuable insights for educators,policymakers,and technology developers working to improve educational quality through technological innovation.The research contributes to the growing body of knowledge on technology-enhanced education and offers practical recommendations for implementing sustainable and effective digital learning solutions that prioritize pedagogical effectiveness while leveraging technological capabilities for comprehensive educational enhancement.
文摘Over-the-air computation(AirComp)enables federated learning(FL)to rapidly aggregate local models at the central server using waveform superposition property of wireless channel.In this paper,a robust transmission scheme for an AirCompbased FL system with imperfect channel state information(CSI)is proposed.To model CSI uncertainty,an expectation-based error model is utilized.The main objective is to maximize the number of selected devices that meet mean-squared error(MSE)requirements for model broadcast and model aggregation.The problem is formulated as a combinatorial optimization problem and is solved in two steps.First,the priority order of devices is determined by a sparsity-inducing procedure.Then,a feasibility detection scheme is used to select the maximum number of devices to guarantee that the MSE requirements are met.An alternating optimization(AO)scheme is used to transform the resulting nonconvex problem into two convex subproblems.Numerical results illustrate the effectiveness and robustness of the proposed scheme.
文摘Research in L2 writing assessment has overwhelmingly focused on helping students enhance their writing quality and global development in language proficiency by various means and approaches of assessment.However,studies of the learning on the part of EFL writing teachers,especially when engaging in collaborative assessment with students,are few and far between.This qualitative case study therefore fills this void of foregoing research by examining the learning and development of a Chinese EFL writing teacher who employed teacher-student collaborative assessment(TSCA)(Wen,2016)in an L2 academic writing course.Drawing upon multiple types of data,three themes emerged with regard to the learning of the L2 writing teacher:1)becoming more assessment literate and capable of providing constructive feedback;2)gaining more efficacy in instructional tactics,student engagement and classroom management;and 3)developing a better understanding of students’evaluation focus as well as their needs and expertise in writing.This study offers a robust picture of how TSCA can foster multidimensional teacher learning-cognitively,affectively,and relationally-affirming its value not only as an assessment tool but as a transformative pedagogical practice.
文摘The inherent teaching approach can no longer meet the demands of society.In this paper,current issues within the teaching landscape of architectural engineering technology in higher vocational colleges as well as the policies and teaching demands that formed the basis of this model were analyzed.The study shows the importance of the implementation of the teaching model“promoting teaching and learning through competitions.”This model puts emphasis on the curriculum and teaching resources,while also integrating the teaching process and evaluation with competition.These efforts aim to drive education reform in order to better align with the objectives of vocational education personnel training,while also acting as a reference for similar courses.
基金supported by Key Research and Development Program of China (No.2022YFC3005401)Key Research and Development Program of Yunnan Province,China (Nos.202203AA080009,202202AF080003)+1 种基金Science and Technology Achievement Transformation Program of Jiangsu Province,China (BA2021002)Fundamental Research Funds for the Central Universities (Nos.B220203006,B210203024).
文摘Data sharing and privacy protection are made possible by federated learning,which allows for continuous model parameter sharing between several clients and a central server.Multiple reliable and high-quality clients must participate in practical applications for the federated learning global model to be accurate,but because the clients are independent,the central server cannot fully control their behavior.The central server has no way of knowing the correctness of the model parameters provided by each client in this round,so clients may purposefully or unwittingly submit anomalous data,leading to abnormal behavior,such as becoming malicious attackers or defective clients.To reduce their negative consequences,it is crucial to quickly detect these abnormalities and incentivize them.In this paper,we propose a Federated Learning framework for Detecting and Incentivizing Abnormal Clients(FL-DIAC)to accomplish efficient and security federated learning.We build a detector that introduces an auto-encoder for anomaly detection and use it to perform anomaly identification and prevent the involvement of abnormal clients,in particular for the anomaly client detection problem.Among them,before the model parameters are input to the detector,we propose a Fourier transform-based anomaly data detectionmethod for dimensionality reduction in order to reduce the computational complexity.Additionally,we create a credit scorebased incentive structure to encourage clients to participate in training in order tomake clients actively participate.Three training models(CNN,MLP,and ResNet-18)and three datasets(MNIST,Fashion MNIST,and CIFAR-10)have been used in experiments.According to theoretical analysis and experimental findings,the FL-DIAC is superior to other federated learning schemes of the same type in terms of effectiveness.
文摘In cooperative multiagent systems, to learn the optimal policies of multiagents is very difficult. As the numbers of states and actions increase exponentially with the number of agents, their action policies become more intractable. By learning these value functions, an agent can learn its optimal action policies for a task. If a task can be decomposed into several subtasks and the agents have learned the optimal value functions for each subtask, this knowledge can be helpful for the agents in learning the optimal action policies for the whole task when they are acting simultaneously. When merging the agents’ independently learned optimal value functions, a novel multiagent online reinforcement learning algorithm LU-Q is proposed. By applying a transformation to the individually learned value functions, the constraints on the optimal value functions of each subtask are loosened. In each learning iteration process in algorithm LU-Q, the agents’ joint action set in a state is processed. Some actions of that state are pruned from the available action set according to the defined multiagent value function in LU-Q. As the items of the available action set of each state are reduced gradually in the iteration process of LU-Q, the convergence of the value functions is accelerated. LU-Q’s effectiveness, soundness and convergence are analyzed, and the experimental results show that the learning performance of LU-Q is better than the performance of standard Q learning.
基金supported by JSPS KAKENHI Grant Number 19J10715.
文摘This study investigates the learning curve of commercial banks regarding the efficiency of credit and value creation.However,current empirical methods for accessing the learning curve in organizations are not suitable for use in financial institutions.Considering bank-specific characteristics,we introduce a dynamic learning curve using a cost function adjusted to capture learning-by-doing in banks.Using the model,we test several hypotheses on the impact of bank intermediary experience(learning)on the efficiency of credit and value creation in Japanese commercial banks.The findings show that bank intermediary learning significantly improves the cost efficiency gain in the gross value created,total credit created,and investment.However,bank intermediary experience has no significant effect on the efficiency of the economic value created for all the banks analyzed.These findings have practical implications for evaluating cost dynamics in bank credit and value creation,risk management,lending to the real sector,and shareholder value creation.
文摘Experiential learning is the opportunity to meld teaching with experience; ＂to do＂ the things students learn about in the classroom, yet outside the classroom walls. I am an instructor that embraces experiential learning. Every other year, I lead a Chican@ Literature class which, after the semester is finished, culminates with a two-week excursion to New Mexico. While on-campus I highlight specific themes within Chicano narrative and poetry. Discussions focus on several key aspects regarding Chican@ Literature whose purpose is to create a voice for those whom have been marginalized within mainstream American culture. Chican@ Literature emphasizes a concept of origin which is reiterated in New Mexico through a sense of place in nature. A second topic often addressed in Chican@ Literature is the idea of aprendizaje. This is a journey of knowledge. In each episode experienced, the narrative voice gains a broader understanding of identity. This aprendizaje is also shared by my students as they gain a sense of self as defined by their own community in juxtaposition with their New Mexican fieldwork and the bilingual poetry they write. Lastly, Chican@ Literature often reveals an author or poet＇s personal culture clash or cultural fusion within the creative work itself. Once again, my students write about their own perspectives in a poetry workshop and presented their pieces during a poetry slam. Some of these pieces are included in this manuscript.
文摘Improving Non-English major students＇ autonomous learning is a focus for the teachers in the high vocational and technical college. Furthermore, promoting students＇ autonomous learning through metcacognitive strategies training has been received increasing attention. The research reports an empirical study on the relationship between metacognitive strategies and English autonomous learning. Finally, the teaching experiment was proved to be feasible and valid.
文摘This study used quantitative methods to assess students'Chinese language learning attitudes and learning habits on Kahoot,a game-based learning platform.Kahoot enables teachers to transform bland Chinese vocabulary memorization into exciting,game-like situations.It makes Chinese language learning fun and interactive.The study aims to compare Kahoot team play mode with individual play mode.Sixty-four fifth graders participated.In the experimental group,students grouped by themselves or the teacher to compete with one another.They enjoyed working together to share what they knew and learned from each other.Students were tested prior to the course(pretest)and following the course(post-test).Observation notes,lesson plans,and surveys were also included.Analysis of the multiple types of data strengthens the conclusion that Kahoot can be an effective tool for teaching Chinese vocabulary,sentences,and culture.
文摘The need for evidence-based practice has been recognized by physiotherapy organizations over the past decades. Earlier studies have documented facilitators and barriers that affect the use and implementation of evidence-based practice. Less is known about what kind of interventions might be useful to implement evidence-based practice. This study explores what physiotherapists learn through participation in a research project relevant to their professional development towards achieving a more evidence-based physiotherapy practice. To what extent this learning was transferred to colleagues for organizational learning is also examined. This study was set in Sweden, where health care is publicly funded. Patients do not need a referral from a physician to consult a physiotherapist. Eleven interviews were conducted with physiotherapists who had participated in a randomized, controlled, multicenter, physiotherapy intervention investigating neck-specific exercise for patients with whiplash disorder. Gadamer’s hermeneutics was used to analyze the data. The physiotherapists described a range of learning experiences from their project participation, including instrumental learning (the concrete application of knowledge to achieve changes in practice) and conceptual learning (changes in knowledge, understanding or attitudes). The research project enabled the physiotherapists to develop new treatment techniques for broader application and extend their competence in techniques already known (instrumental learning). The physiotherapists believed that project participation enhanced their overall competence as physiotherapists, increased their job motivation and strengthened their self-confidence and self-efficacy (conceptual learning). Physiotherapists’ participation in the research project yielded many individual learning experiences, fostered positive attitudes to research and was conducive to achieving a more research-informed physiotherapy practice. Participation was associated with a deeper understanding of the challenges involved in conducting research. The transfer from individual learning to the wider organization in terms of organizational learning was limited.
文摘This study examined the overall effectiveness of reading proficiency especially the extensive reading in ESL.In order to investi-gate the process of acquisition more efficiently,author used reading-to-write approach to investigate the connection between readingand foreign language learning.The research also shows that learners need to be provided with plenty of interesting and comprehensiblebooks and they are supposed to use strategies that they will acquire anyway as they read.
文摘In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,and innovative thinking.Traditional teaching models often fail to bridge the gap between theoretical knowledge and practical application,resulting in passive learning and limited problem-solving capabilities.This paper proposes a three-dimensional integrated teaching model centered on“Information Technology-Domain Knowledge-Outcome Production”(the“2+2+2”credit framework)to address these challenges.Drawing on constructivist theories,Bloom’s Taxonomy,and the CDIO model,the framework uses real projects to drive learning,facilitating the seamless integration of theoretical teaching and practical innovation.The model emphasizes tiered teaching objectives and interdisciplinary pathways,supported by dynamic assessment systems that track students’growth in knowledge,skills,and abilities.Applied in smart health and financial technology domains,this approach enhances students’comprehensive capabilities,aligning educational outcomes with industry demands.This study offers replicable strategies for educational reform in new engineering disciplines,aiming to transform students into proactive innovators and versatile talents.
基金The National Key Research and Development Program of China,No.2023YFC3206601。
文摘Landslides pose a formidable natural hazard across the Qinghai-Tibet Plateau(QTP),endangering both ecosystems and human life.Identifying the driving factors behind landslides and accurately assessing susceptibility are key to mitigating disaster risk.This study integrated multi-source historical landslide data with 15 predictive factors and used several machine learning models—Random Forest(RF),Gradient Boosting Regression Trees(GBRT),Extreme Gradient Boosting(XGBoost),and Categorical Boosting(CatBoost)—to generate susceptibility maps.The Shapley additive explanation(SHAP)method was applied to quantify factor importance and explore their nonlinear effects.The results showed that:(1)CatBoost was the best-performing model(CA=0.938,AUC=0.980)in assessing landslide susceptibility,with altitude emerging as the most significant factor,followed by distance to roads and earthquake sites,precipitation,and slope;(2)the SHAP method revealed critical nonlinear thresholds,demonstrating that historical landslides were concentrated at mid-altitudes(1400-4000 m)and decreased markedly above 4000 m,with a parallel reduction in probability beyond 700 m from roads;and(3)landslide-prone areas,comprising 13%of the QTP,were concentrated in the southeastern and northeastern parts of the plateau.By integrating machine learning and SHAP analysis,this study revealed landslide hazard-prone areas and their driving factors,providing insights to support disaster management strategies and sustainable regional planning.
基金funded by the Zhejiang Provincial Key Science and Technology“LingYan”Project Foundation,grant number 2023C01145Zhejiang Gongshang University Higher Education Research Projects,grant number Xgy22028.
文摘With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loosening faults are difficult to identify through conventional spectrum analysis,and the extreme scarcity of fault data leads to limited training datasets,making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity.This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution.This method proposes specific data processing approaches and a configuration of Bayesian Convolutional Neural Network(BCNN).It can effectively improve the model’s generalization ability.Experimental results demonstrate high detection accuracy and alignment with real-world applications,offering practical significance and reference value for industrial fan bolt loosening detection under data-limited conditions.
基金supported by the National Natural Science Foundation of China Funded Project(Project Name:Research on Robust Adaptive Allocation Mechanism of Human Machine Co-Driving System Based on NMS Features,Project Approval Number:52172381).
文摘To address the issue of scarce labeled samples and operational condition variations that degrade the accuracy of fault diagnosis models in variable-condition gearbox fault diagnosis,this paper proposes a semi-supervised masked contrastive learning and domain adaptation(SSMCL-DA)method for gearbox fault diagnosis under variable conditions.Initially,during the unsupervised pre-training phase,a dual signal augmentation strategy is devised,which simultaneously applies random masking in the time domain and random scaling in the frequency domain to unlabeled samples,thereby constructing more challenging positive sample pairs to guide the encoder in learning intrinsic features robust to condition variations.Subsequently,a ConvNeXt-Transformer hybrid architecture is employed,integrating the superior local detail modeling capacity of ConvNeXt with the robust global perception capability of Transformer to enhance feature extraction in complex scenarios.Thereafter,a contrastive learning model is constructed with the optimization objective of maximizing feature similarity across different masked instances of the same sample,enabling the extraction of consistent features from multiple masked perspectives and reducing reliance on labeled data.In the final supervised fine-tuning phase,a multi-scale attention mechanism is incorporated for feature rectification,and a domain adaptation module combining Local Maximum Mean Discrepancy(LMMD)with adversarial learning is proposed.This module embodies a dual mechanism:LMMD facilitates fine-grained class-conditional alignment,compelling features of identical fault classes to converge across varying conditions,while the domain discriminator utilizes adversarial training to guide the feature extractor toward learning domain-invariant features.Working in concert,they markedly diminish feature distribution discrepancies induced by changes in load,rotational speed,and other factors,thereby boosting the model’s adaptability to cross-condition scenarios.Experimental evaluations on the WT planetary gearbox dataset and the Case Western Reserve University(CWRU)bearing dataset demonstrate that the SSMCL-DA model effectively identifies multiple fault classes in gearboxes,with diagnostic performance substantially surpassing that of conventional methods.Under cross-condition scenarios,the model attains fault diagnosis accuracies of 99.21%for the WT planetary gearbox and 99.86%for the bearings,respectively.Furthermore,the model exhibits stable generalization capability in cross-device settings.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2025S1A5A2A01005171)by the BK21 programat Chungbuk National University(2025).
文摘With an increase in internet-connected devices and a dependency on online services,the threat of Distributed Denial of Service(DDoS)attacks has become a significant concern in cybersecurity.The proposed system follows a multi-step process,beginning with the collection of datasets from different edge devices and network nodes.To verify its effectiveness,experiments were conducted using the CICDoS2017,NSL-KDD,and CICIDS benchmark datasets alongside other existing models.Recursive feature elimination(RFE)with random forest is used to select features from the CICDDoS2019 dataset,on which a BiLSTM model is trained on local nodes.Local models are trained until convergence or stability criteria are met while simultaneously sharing the updates globally for collaborative learning.A centralised server evaluates real-time traffic using the global BiLSTM model,which triggers alerts for potential DDoS attacks.Furthermore,blockchain technology is employed to secure model updates and to provide an immutable audit trail,thereby ensuring trust and accountability among network nodes.This research introduces a novel decentralized method called Federated Random Forest Bidirectional Long Short-Term Memory(FRF-BiLSTM)for detecting DDoS attacks,utilizing the advanced Bidirectional Long Short-Term Memory Networks(BiLSTMs)to analyze sequences in both forward and backward directions.The outcome shows the proposed model achieves a mean accuracy of 97.1%with an average training delay of 88.7 s and testing delay of 21.4 s.The model demonstrates scalability and the best detection performance in large-scale attack scenarios.
基金supported by the project:Hunan Provincial Educational Science Research Project“Research on Cultivating National Consciousness in College Foreign Language Courses(XJT23CGD001)”.
文摘This study examines how foreign language education in the artificial intelligence(AI)era could assist the cultivation of national consciousness through a technology-enhanced pedagogy of film appreciation.Using The Wild Robot as a case study,we argue that cinematic narratives serve as cultural mirrors,offering immersive,reflective,and affective sites for intercultural learning.We propose a three-layered pedagogical framework-progressing from semiotic decoding,through narrative and value comparison,to creative identity construction-that integrates intelligent tools to develop both communicative competence and an agentive sense of belonging.The approach exemplifies a humanistic turn in language teaching,aiming to form“rooted global communicators”who can engage in cross-civilization dialogue with cultural confidence and critical awareness.
