In recent years,machine learning has made great progress in intrusion detection,network protection,anomaly detection,and other issues in cyberspace.However,these traditional machine learning algorithms usually require...In recent years,machine learning has made great progress in intrusion detection,network protection,anomaly detection,and other issues in cyberspace.However,these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown attacks.Among them,“one-shot learning”,“few-shot learning”,and“zero-shot learning”are challenges that cannot be ignored for traditional machine learning.The more intractable problem in cyberspace security is the changeable attack mode.When a new attack mode appears,there are few or even zero samples that can be learned.Meta-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when learning.Its purpose is to quickly obtain a model with high accuracy and strong generalization through less data training.This article first divides the meta-learning model into five research directions based on different principles of use.They are model-based,metric-based,optimization-based,online-learning-based,or stacked ensemble-based.Then,the current problems in the field of cyberspace security are categorized into three branches:cyber security,information security,and artificial intelligence security according to different perspectives.Then,the application research results of various meta-learning models on these three branches are reviewed.At the same time,based on the characteristics of strong generalization,evolution,and scalability of meta-learning,we contrast and summarize its advantages in solving problems.Finally,the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.展开更多
The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep le...The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.展开更多
Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and...Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and pest detection is necessary to address the mentioned problem.Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses,this article proposes a Model-Agnostic Meta-Learning(MAML)attention model based on the meta-learning paradigm.The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention(ECA)mod-ule.The module follows the local cross-channel interactive strategy of non-dimensional reduction to strengthen the weight parameters corresponding to certain disease characteristics.The proposed meta-learning-based algorithm has the advantage of strong generalization capability and,by integrating the ECA module in the original model,can achieve more efficient detection in new tasks.The proposed model is verified by experiments,and the experimental results show that compared with the original MAML model,the proposed improved MAML-Attention model has a better performance by 1.8–9.31 percentage points in different classification tasks;the maximum accuracy is increased by 1.15–8.2 percentage points.The experimental results verify the strong generalization ability and good robustness of the proposed MAML-Attention model.Compared to the other few-shot methods,the proposed MAML-Attention performs better.展开更多
The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA...The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.展开更多
Machine learning,especially deep learning,has been highly successful in data-intensive applications;however,the performance of these models will drop significantly when the amount of the training data amount does not ...Machine learning,especially deep learning,has been highly successful in data-intensive applications;however,the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement.This leads to the so-called few-shot learning(FSL)problem,which requires the model rapidly generalize to new tasks that containing only a few labeled samples.In this paper,we proposed a new deep model,called deep convolutional meta-learning networks,to address the low performance of generalization under limited data for bearing fault diagnosis.The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data.The proposed method was compared to several FSL methods,including methods with and without pre-training the embedding mapping,and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain.The comparisons are carried out on 1-shot and 10-shot tasks using the Case Western Reserve University bearing dataset and a cylindrical roller bearing dataset.The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions.In addition,we found that the pretraining process does not always improve the prediction accuracy.展开更多
Efficiently solving partial differential equations(PDEs)is a long-standing challenge in mathematics and physics research.In recent years,the rapid development of artificial intelligence technology has brought deep lea...Efficiently solving partial differential equations(PDEs)is a long-standing challenge in mathematics and physics research.In recent years,the rapid development of artificial intelligence technology has brought deep learning-based methods to the forefront of research on numerical methods for partial differential equations.Among them,physics-informed neural networks(PINNs)are a new class of deep learning methods that show great potential in solving PDEs and predicting complex physical phenomena.In the field of nonlinear science,solitary waves and rogue waves have been important research topics.In this paper,we propose an improved PINN that enhances the physical constraints of the neural network model by adding gradient information constraints.In addition,we employ meta-learning optimization to speed up the training process.We apply the improved PINNs to the numerical simulation and prediction of solitary and rogue waves.We evaluate the accuracy of the prediction results by error analysis.The experimental results show that the improved PINNs can make more accurate predictions in less time than that of the original PINNs.展开更多
capacity and creativity tendencies among Chinese baccalaureate nursing students.Design:Cross-sectional study design.Methods:A convenient sample of 445 baccalaureate nursing students were surveyed in two universities i...capacity and creativity tendencies among Chinese baccalaureate nursing students.Design:Cross-sectional study design.Methods:A convenient sample of 445 baccalaureate nursing students were surveyed in two universities in Tianjin,China.Students completed a questionnaire that included their demographic information,Achievement Motivation Scale,Meta-Learning Capacity Questionnaire,and Creativity Tendencies Scale.Pearson correlation was performed to test the correlation among achievement motivation,meta-learning capacity and creativity tendencies.Hierarchical linear regression analyses were performed to explore the mediating role of meta-learning capacity.Results:The participants had moderate levels of achievement motivation(mean score 2.383±0.240)and meta-learning capacity(mean score 1.505±0.241)and a medium-high level of creativity tendency(mean score 1.841±0.288).Creativity tendencies was significantly associated with both achievement motivation and meta-learning capacity(both P<0.01).Furthermore,meta-learning capacity mediated the relationship between achievement motivation and high creativity tendencies.Conclusion:The study hypotheses were supported.Higher achievement motivation,and meta-learning capacity can increase creativity tendencies of baccalaureate nursing students,and meta-learning capacity was found to mediate the relationship between achievement motivation and creativity tendencies.Nursing educators should pay attention to the positive role of meta-learning capacity in nursing students’learning,and make them more confident when they finish their studies.展开更多
Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain d...Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain differences among them.These factors often result in poor generalization in existing meta-learning models.In this work,a novel smoother manifold for graph meta-learning(SGML)is proposed,which derives the similarity parameters of node features from the relationship between nodes and edges in the graph structure,and then utilizes the similarity parameters to yield smoother manifold through embedded propagation module.Smoother manifold can naturally filter out noise from the most important components when generalizing the local mapping relationship to the global.Besides suiting for generalizing on unseen low data issues,the framework is capable to easily perform transductive inference.Experimental results on MiniImageNet and TieredImageNet consistently show that applying SGML to supervised and semi-supervised classification can improve the performance in reducing the noise of domain shift representation.展开更多
Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional m...Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional machine learning to areas where there are small windows of time or data available. One such area is stock trading, where the relevance of data decreases as time passes, requiring fast results on fewer data points to respond to fast-changing market trends. We, to the best of our knowledge, are the first to apply meta-learning algorithms to an evolutionary strategy for stock trading to decrease learning time by using fewer iterations and to achieve higher trading profits with fewer data points. We found that our meta-learning approach to stock trading earns profits similar to a purely evolutionary algorithm. However, it only requires 50 iterations during test, versus thousands that are typically required without meta-learning, or 50% of the training data during test.展开更多
In this paper,we propose a Rough Set assisted Meta-Learning method on how to select the most-suited machine-learning algorithms with minimal effort for a new given dataset. A k-Nearest Neighbor (k-NN) algorithm is use...In this paper,we propose a Rough Set assisted Meta-Learning method on how to select the most-suited machine-learning algorithms with minimal effort for a new given dataset. A k-Nearest Neighbor (k-NN) algorithm is used to recognize the most similar datasets that have been performed by all of the candidate algorithms.By matching the most similar datasets we found,the corresponding performance of the candidate algorithms is used to generate recommendation to the user.The performance derives from a multi-criteria evaluation measure-ARR,which contains both accuracy and time.Furthermore,after applying Rough Set theory,we can find the redundant properties of the dataset.Thus,we can speed up the ranking process and increase the accuracy by using the reduct of the meta attributes.展开更多
基金supported by Hebei Province Natural Science Fund for Distinguished Young Scholars (NO.E2020209082).
文摘In recent years,machine learning has made great progress in intrusion detection,network protection,anomaly detection,and other issues in cyberspace.However,these traditional machine learning algorithms usually require a lot of data to learn and have a low recognition rate for unknown attacks.Among them,“one-shot learning”,“few-shot learning”,and“zero-shot learning”are challenges that cannot be ignored for traditional machine learning.The more intractable problem in cyberspace security is the changeable attack mode.When a new attack mode appears,there are few or even zero samples that can be learned.Meta-learning comes from imitating human problem-solving methods as humans can quickly learn unknown things based on their existing knowledge when learning.Its purpose is to quickly obtain a model with high accuracy and strong generalization through less data training.This article first divides the meta-learning model into five research directions based on different principles of use.They are model-based,metric-based,optimization-based,online-learning-based,or stacked ensemble-based.Then,the current problems in the field of cyberspace security are categorized into three branches:cyber security,information security,and artificial intelligence security according to different perspectives.Then,the application research results of various meta-learning models on these three branches are reviewed.At the same time,based on the characteristics of strong generalization,evolution,and scalability of meta-learning,we contrast and summarize its advantages in solving problems.Finally,the prospect of future deep application of meta-learning in the field of cyberspace security is summarized.
基金supported in part by the National Natural Science Foundation of China under Grant U1908212,62203432 and 92067205in part by the State Key Laboratory of Robotics of China under Grant 2023-Z03 and 2023-Z15in part by the Natural Science Foundation of Liaoning Province under Grant 2020-KF-11-02.
文摘The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.
基金supported by the Science and Technology Project of Jilin Provincial Department of Education[JJKH20210346KJ]the Capital Construction funds within the budget of Jilin Province in 2021(Innovation capacity construction)[2021C044-4]the Jilin Province Science and Technology Development Plan Project[20210101185JC].
文摘Currently,one of the most severe problems in the agricultural industry is the effect of diseases and pests on global crop production and economic development.Therefore,further research in the field of crop disease and pest detection is necessary to address the mentioned problem.Aiming to identify the diseased crops and insect pests timely and accurately and perform appropriate prevention measures to reduce the associated losses,this article proposes a Model-Agnostic Meta-Learning(MAML)attention model based on the meta-learning paradigm.The proposed model combines meta-learning with basic learning and adopts an Efficient Channel Attention(ECA)mod-ule.The module follows the local cross-channel interactive strategy of non-dimensional reduction to strengthen the weight parameters corresponding to certain disease characteristics.The proposed meta-learning-based algorithm has the advantage of strong generalization capability and,by integrating the ECA module in the original model,can achieve more efficient detection in new tasks.The proposed model is verified by experiments,and the experimental results show that compared with the original MAML model,the proposed improved MAML-Attention model has a better performance by 1.8–9.31 percentage points in different classification tasks;the maximum accuracy is increased by 1.15–8.2 percentage points.The experimental results verify the strong generalization ability and good robustness of the proposed MAML-Attention model.Compared to the other few-shot methods,the proposed MAML-Attention performs better.
文摘The aviation industry has seen significant advancements in safety procedures over the past few decades, resulting in a steady decline in aviation deaths worldwide. However, the safety standards in General Aviation (GA) are still lower compared to those in commercial aviation. With the anticipated growth in air travel, there is an imminent need to improve operational safety in GA. One way to improve aircraft and operational safety is through trajectory prediction. Trajectory prediction plays a key role in optimizing air traffic control and improving overall flight safety. This paper proposes a meta-learning approach to predict short- to mid-term trajectories of aircraft using historical real flight data collected from multiple GA aircraft. The proposed solution brings together multiple models to improve prediction accuracy. In this paper, we are combining two models, Random Forest Regression (RFR) and Long Short-term Memory (LSTM), using k-Nearest Neighbors (k-NN), to output the final prediction based on the combined output of the individual models. This approach gives our model an edge over single-model predictions. We present the results of our meta-learner and evaluate its performance against individual models using the Mean Absolute Error (MAE), Absolute Altitude Error (AAE), and Root Mean Squared Error (RMSE) evaluation metrics. The proposed methodology for aircraft trajectory forecasting is discussed in detail, accompanied by a literature review and an overview of the data preprocessing techniques used. The results demonstrate that the proposed meta-learner outperforms individual models in terms of accuracy, providing a more robust and proactive approach to improve operational safety in GA.
基金This research was funded by RECLAIM project“Remanufacturing and Refurbishment of Large Industrial Equipment”and received funding from the European Commission Horizon 2020 research and innovation program under Grant Agreement No.869884The authors also acknowledge the support of The Efficiency and Performance Engineering Network International Collaboration Fund Award 2022(TEPEN-ICF 2022)project“Intelligent Fault Diagnosis Method and System with Few-Shot Learning Technique under Small Sample Data Condition”.
文摘Machine learning,especially deep learning,has been highly successful in data-intensive applications;however,the performance of these models will drop significantly when the amount of the training data amount does not meet the requirement.This leads to the so-called few-shot learning(FSL)problem,which requires the model rapidly generalize to new tasks that containing only a few labeled samples.In this paper,we proposed a new deep model,called deep convolutional meta-learning networks,to address the low performance of generalization under limited data for bearing fault diagnosis.The essential of our approach is to learn a base model from the multiple learning tasks using a support dataset and finetune the learnt parameters using few-shot tasks before it can adapt to the new learning task based on limited training data.The proposed method was compared to several FSL methods,including methods with and without pre-training the embedding mapping,and methods with finetuning the classifier or the whole model by utilizing the few-shot data from the target domain.The comparisons are carried out on 1-shot and 10-shot tasks using the Case Western Reserve University bearing dataset and a cylindrical roller bearing dataset.The experimental result illustrates that our method has good performance on the bearing fault diagnosis across various few-shot conditions.In addition,we found that the pretraining process does not always improve the prediction accuracy.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.42005003 and 41475094).
文摘Efficiently solving partial differential equations(PDEs)is a long-standing challenge in mathematics and physics research.In recent years,the rapid development of artificial intelligence technology has brought deep learning-based methods to the forefront of research on numerical methods for partial differential equations.Among them,physics-informed neural networks(PINNs)are a new class of deep learning methods that show great potential in solving PDEs and predicting complex physical phenomena.In the field of nonlinear science,solitary waves and rogue waves have been important research topics.In this paper,we propose an improved PINN that enhances the physical constraints of the neural network model by adding gradient information constraints.In addition,we employ meta-learning optimization to speed up the training process.We apply the improved PINNs to the numerical simulation and prediction of solitary and rogue waves.We evaluate the accuracy of the prediction results by error analysis.The experimental results show that the improved PINNs can make more accurate predictions in less time than that of the original PINNs.
文摘capacity and creativity tendencies among Chinese baccalaureate nursing students.Design:Cross-sectional study design.Methods:A convenient sample of 445 baccalaureate nursing students were surveyed in two universities in Tianjin,China.Students completed a questionnaire that included their demographic information,Achievement Motivation Scale,Meta-Learning Capacity Questionnaire,and Creativity Tendencies Scale.Pearson correlation was performed to test the correlation among achievement motivation,meta-learning capacity and creativity tendencies.Hierarchical linear regression analyses were performed to explore the mediating role of meta-learning capacity.Results:The participants had moderate levels of achievement motivation(mean score 2.383±0.240)and meta-learning capacity(mean score 1.505±0.241)and a medium-high level of creativity tendency(mean score 1.841±0.288).Creativity tendencies was significantly associated with both achievement motivation and meta-learning capacity(both P<0.01).Furthermore,meta-learning capacity mediated the relationship between achievement motivation and high creativity tendencies.Conclusion:The study hypotheses were supported.Higher achievement motivation,and meta-learning capacity can increase creativity tendencies of baccalaureate nursing students,and meta-learning capacity was found to mediate the relationship between achievement motivation and creativity tendencies.Nursing educators should pay attention to the positive role of meta-learning capacity in nursing students’learning,and make them more confident when they finish their studies.
基金Supported by the National Natural Science Foundation of China(No.61171131)the Key R&D Program of Shandong Province(No.YD01033)the China Scholarship Council Project(No.021608370049).
文摘Meta-learning provides a framework for the possibility of mimicking artificial intelligence.How-ever,data distribution of the training set fails to be consistent with the one of the testing set as the limited domain differences among them.These factors often result in poor generalization in existing meta-learning models.In this work,a novel smoother manifold for graph meta-learning(SGML)is proposed,which derives the similarity parameters of node features from the relationship between nodes and edges in the graph structure,and then utilizes the similarity parameters to yield smoother manifold through embedded propagation module.Smoother manifold can naturally filter out noise from the most important components when generalizing the local mapping relationship to the global.Besides suiting for generalizing on unseen low data issues,the framework is capable to easily perform transductive inference.Experimental results on MiniImageNet and TieredImageNet consistently show that applying SGML to supervised and semi-supervised classification can improve the performance in reducing the noise of domain shift representation.
文摘Meta-learning algorithms learn about the learning process itself so it can speed up subsequent similar learning tasks with fewer data and iterations. If achieved, these benefits expand the flexibility of traditional machine learning to areas where there are small windows of time or data available. One such area is stock trading, where the relevance of data decreases as time passes, requiring fast results on fewer data points to respond to fast-changing market trends. We, to the best of our knowledge, are the first to apply meta-learning algorithms to an evolutionary strategy for stock trading to decrease learning time by using fewer iterations and to achieve higher trading profits with fewer data points. We found that our meta-learning approach to stock trading earns profits similar to a purely evolutionary algorithm. However, it only requires 50 iterations during test, versus thousands that are typically required without meta-learning, or 50% of the training data during test.
文摘In this paper,we propose a Rough Set assisted Meta-Learning method on how to select the most-suited machine-learning algorithms with minimal effort for a new given dataset. A k-Nearest Neighbor (k-NN) algorithm is used to recognize the most similar datasets that have been performed by all of the candidate algorithms.By matching the most similar datasets we found,the corresponding performance of the candidate algorithms is used to generate recommendation to the user.The performance derives from a multi-criteria evaluation measure-ARR,which contains both accuracy and time.Furthermore,after applying Rough Set theory,we can find the redundant properties of the dataset.Thus,we can speed up the ranking process and increase the accuracy by using the reduct of the meta attributes.
