The following sections of this article are the background of the experiences described in the book Creative Journals in a Bottle.Out-of-the-Box Activities That Help Teenagers Become Sensitive and Self-Confident Adults...The following sections of this article are the background of the experiences described in the book Creative Journals in a Bottle.Out-of-the-Box Activities That Help Teenagers Become Sensitive and Self-Confident Adults(Cuccu,2024).Being a teacher in a classroom of young people involves more than just being able to tell them about a topic they have to study,they are also educators and play an important role in their development in a critical period of their lives.The following sections deal with things to do and not to do in order to create an ideal environment characterized by empathy,motivation,and learning together.展开更多
Subretinal injection is a complicated task for retinal surgeons to operate manually.In this paper we demonstrate a robust framework for needle detection and localisation in robotassisted subretinal injection using mic...Subretinal injection is a complicated task for retinal surgeons to operate manually.In this paper we demonstrate a robust framework for needle detection and localisation in robotassisted subretinal injection using microscope-integrated Optical Coherence Tomography with deep learning.Five convolutional neural networks with different architectures were evaluated.The main differences between the architectures are the amount of information they receive at the input layer.When evaluated on ex-vivo pig eyes,the top performing network successfully detected all needles in the dataset and localised them with an Intersection over Union value of 0.55.The algorithm was evaluated by comparing the depth of the top and bottom edge of the predicted bounding box to the ground truth.This analysis showed that the top edge can be used to predict the depth of the needle with a maximum error of 8.5μm.展开更多
This study evaluates the performance of advanced machine learning(ML)models in predicting the mechanical properties of eco-friendly self-compacting concrete(SCC),with a focus on compressive strength,V-funnel time,Lbox...This study evaluates the performance of advanced machine learning(ML)models in predicting the mechanical properties of eco-friendly self-compacting concrete(SCC),with a focus on compressive strength,V-funnel time,Lbox ratio,and slump flow.The motivation for this study stems from the increasing need to optimize concrete mix designs while minimizing environmental impact and reducing the reliance on costly physical testing.Six ML models-backpropagation neural network(BPNN),random forest regression(RFR),K-nearest neighbors(KNN),stacking,bagging,and eXtreme gradient boosting(XGBoost)-were trained and validated using a comprehensive dataset of 239 mix design parameters.The models'predictive accuracies were assessed using the coefficient of determination,mean squared error,root mean squared error,and mean absolute error.XGBoost consistently outperformed other models,achieving the coefficient of determination values of 0.999,0.933,and 0.935 for compressive strength in the training,validation,and testing datasets,respectively.Sensitivity analysis revealed that cement,silica fume,coarse aggregate,and superplasticizer positively influenced compressive strength,while water content had a negative impact.These findings highlight the potential of ML models,particularly XGBoost and RFR,in optimizing SCC mix designs,reducing reliance on physical testing,and enhancing sustainability in construction.The application of these models can lead to more efficient and eco-friendly concrete mix designs,benefiting real-world construction projects by improving quality control and reducing costs.展开更多
The purpose of this research is to analyze the causal mechanisms of learning difficulties of middle school students and use them to propose strategies to help them.This research is particularly valuable for its focus ...The purpose of this research is to analyze the causal mechanisms of learning difficulties of middle school students and use them to propose strategies to help them.This research is particularly valuable for its focus on middle school students.Research on this critical transition period is often lacking compared to primary and high school.Therefore,this research establishes a structured equation model and analyzes the data from the survey using the partial least squares method.The data were obtained from a 13,900 Wenzhou City,China students’questionnaire.The research found that learning strategies were the most significant influences on learning effectiveness,followed by learning motivation and learning relationships.Meanwhile,learning relationships had a significant impact on learning pressure.Therefore,this research proposes targeted support strategies.It aims to enhance learning motivation(Set achievable learning goals for each student with learning difficulties based on their actual situation),optimize learning strategies(Encourage students with learning difficulties to learn self-regulatory strategies such as goal setting,time management,and self-reflection),and improve learning relationships(Establish a good social network to promote positive interaction between students with learning difficulties and their peers).At the same time,it reduces students’learning pressure.Ultimately,the learning effectiveness of students with learning difficulties is improved.展开更多
This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the traini...This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.展开更多
Studies have shown that vascular dysfunction is closely related to the pathogenesis of Alzheimer's disease.The middle temporal gyrus region of the brain is susceptible to pronounced impairment in Alzheimer's d...Studies have shown that vascular dysfunction is closely related to the pathogenesis of Alzheimer's disease.The middle temporal gyrus region of the brain is susceptible to pronounced impairment in Alzheimer's disease.Identification of the molecules involved in vascular aberrance of the middle temporal gyrus would support elucidation of the mechanisms underlying Alzheimer's disease and discove ry of novel targets for intervention.We carried out single-cell transcriptomic analysis of the middle temporal gyrus in the brains of patients with Alzheimer's disease and healthy controls,revealing obvious changes in vascular function.CellChat analysis of intercellular communication in the middle temporal gyrus showed that the number of cell interactions in this region was decreased in Alzheimer's disease patients,with altered intercellular communication of endothelial cells and pericytes being the most prominent.Differentially expressed genes were also identified.Using the CellChat results,AUCell evaluation of the pathway activity of specific cells showed that the obvious changes in vascular function in the middle temporal gyrus in Alzheimer's disease were directly related to changes in the vascular endothelial growth factor(VEGF)A-VEGF receptor(VEGFR)2 pathway.AUCell analysis identified subtypes of endothelial cells and pericytes directly related to VEGFA-VEGFR2 pathway activity.Two subtypes of middle temporal gyrus cells showed significant alteration in AD:endothelial cells with high expression of Erb-B2 receptor tyrosine kinase 4(ERBB4^(high))and pericytes with high expression of angiopoietin-like 4(ANGPTL4^(high)).Finally,combining bulk RNA sequencing data and two machine learning algorithms(least absolute shrinkage and selection operator and random forest),four characteristic Alzheimer's disease feature genes were identified:somatostatin(SST),protein tyrosine phosphatase non-receptor type 3(PTPN3),glutinase(GL3),and tropomyosin 3(PTM3).These genes were downregulated in the middle temporal gyrus of patients with Alzheimer's disease and may be used to target the VEGF pathway.Alzheimer's disease mouse models demonstrated consistent altered expression of these genes in the middle temporal gyrus.In conclusion,this study detected changes in intercellular communication between endothelial cells and pericytes in the middle temporal gyrus and identified four novel feature genes related to middle temporal gyrus and vascular functioning in patients with Alzheimer's disease.These findings contribute to a deeper understanding of the molecular mechanisms underlying Alzheimer's disease and present novel treatment targets.展开更多
The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the s...The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.展开更多
Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are...Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.展开更多
With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices ge...With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.展开更多
In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach...In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.展开更多
Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the pre...Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident severity.Deep learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and classification.Despite prior success,there is a need for an efficient system recognizing ASL in diverse road conditions.To address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent.The framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved generalization.Sensitivity and factor contribution analyses identify influential predictors.Evaluated on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB dataset.The proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.展开更多
文摘The following sections of this article are the background of the experiences described in the book Creative Journals in a Bottle.Out-of-the-Box Activities That Help Teenagers Become Sensitive and Self-Confident Adults(Cuccu,2024).Being a teacher in a classroom of young people involves more than just being able to tell them about a topic they have to study,they are also educators and play an important role in their development in a critical period of their lives.The following sections deal with things to do and not to do in order to create an ideal environment characterized by empathy,motivation,and learning together.
基金ZJU 100 Young Talent ProgramKey Program for Robot-assisted Subretinal Injection Research Center in Zhejiang Province,Grant/Award Number:2023ZY1061。
文摘Subretinal injection is a complicated task for retinal surgeons to operate manually.In this paper we demonstrate a robust framework for needle detection and localisation in robotassisted subretinal injection using microscope-integrated Optical Coherence Tomography with deep learning.Five convolutional neural networks with different architectures were evaluated.The main differences between the architectures are the amount of information they receive at the input layer.When evaluated on ex-vivo pig eyes,the top performing network successfully detected all needles in the dataset and localised them with an Intersection over Union value of 0.55.The algorithm was evaluated by comparing the depth of the top and bottom edge of the predicted bounding box to the ground truth.This analysis showed that the top edge can be used to predict the depth of the needle with a maximum error of 8.5μm.
文摘This study evaluates the performance of advanced machine learning(ML)models in predicting the mechanical properties of eco-friendly self-compacting concrete(SCC),with a focus on compressive strength,V-funnel time,Lbox ratio,and slump flow.The motivation for this study stems from the increasing need to optimize concrete mix designs while minimizing environmental impact and reducing the reliance on costly physical testing.Six ML models-backpropagation neural network(BPNN),random forest regression(RFR),K-nearest neighbors(KNN),stacking,bagging,and eXtreme gradient boosting(XGBoost)-were trained and validated using a comprehensive dataset of 239 mix design parameters.The models'predictive accuracies were assessed using the coefficient of determination,mean squared error,root mean squared error,and mean absolute error.XGBoost consistently outperformed other models,achieving the coefficient of determination values of 0.999,0.933,and 0.935 for compressive strength in the training,validation,and testing datasets,respectively.Sensitivity analysis revealed that cement,silica fume,coarse aggregate,and superplasticizer positively influenced compressive strength,while water content had a negative impact.These findings highlight the potential of ML models,particularly XGBoost and RFR,in optimizing SCC mix designs,reducing reliance on physical testing,and enhancing sustainability in construction.The application of these models can lead to more efficient and eco-friendly concrete mix designs,benefiting real-world construction projects by improving quality control and reducing costs.
基金2025 Wenzhou Key Research Base of Philosophy and Social Science(Wenzhou University Learning Science and Technology Research Centre)Research Project:Investigation and Strategy Research on the Causes of Middle School Students’Learning Difficulties in the Context of the Leading Country in Education.
文摘The purpose of this research is to analyze the causal mechanisms of learning difficulties of middle school students and use them to propose strategies to help them.This research is particularly valuable for its focus on middle school students.Research on this critical transition period is often lacking compared to primary and high school.Therefore,this research establishes a structured equation model and analyzes the data from the survey using the partial least squares method.The data were obtained from a 13,900 Wenzhou City,China students’questionnaire.The research found that learning strategies were the most significant influences on learning effectiveness,followed by learning motivation and learning relationships.Meanwhile,learning relationships had a significant impact on learning pressure.Therefore,this research proposes targeted support strategies.It aims to enhance learning motivation(Set achievable learning goals for each student with learning difficulties based on their actual situation),optimize learning strategies(Encourage students with learning difficulties to learn self-regulatory strategies such as goal setting,time management,and self-reflection),and improve learning relationships(Establish a good social network to promote positive interaction between students with learning difficulties and their peers).At the same time,it reduces students’learning pressure.Ultimately,the learning effectiveness of students with learning difficulties is improved.
基金supported by the Chinese Academy of Science"Light of West China"Program(2022-XBQNXZ-015)the National Natural Science Foundation of China(11903071)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China and administered by the Chinese Academy of Sciences。
文摘This paper addresses the performance degradation issue in a fast radio burst search pipeline based on deep learning.This issue is caused by the class imbalance of the radio frequency interference samples in the training dataset,and one solution is applied to improve the distribution of the training data by augmenting minority class samples using a deep convolutional generative adversarial network.Experi.mental results demonstrate that retraining the deep learning model with the newly generated dataset leads to a new fast radio burst classifier,which effectively reduces false positives caused by periodic wide-band impulsive radio frequency interference,thereby enhancing the performance of the search pipeline.
基金supported by the Natural Science Foundation of Shanxi Province,No.20210302123299The Belt and Road Program of Shanxi Province,No.110000261420228002(both to CZ)。
文摘Studies have shown that vascular dysfunction is closely related to the pathogenesis of Alzheimer's disease.The middle temporal gyrus region of the brain is susceptible to pronounced impairment in Alzheimer's disease.Identification of the molecules involved in vascular aberrance of the middle temporal gyrus would support elucidation of the mechanisms underlying Alzheimer's disease and discove ry of novel targets for intervention.We carried out single-cell transcriptomic analysis of the middle temporal gyrus in the brains of patients with Alzheimer's disease and healthy controls,revealing obvious changes in vascular function.CellChat analysis of intercellular communication in the middle temporal gyrus showed that the number of cell interactions in this region was decreased in Alzheimer's disease patients,with altered intercellular communication of endothelial cells and pericytes being the most prominent.Differentially expressed genes were also identified.Using the CellChat results,AUCell evaluation of the pathway activity of specific cells showed that the obvious changes in vascular function in the middle temporal gyrus in Alzheimer's disease were directly related to changes in the vascular endothelial growth factor(VEGF)A-VEGF receptor(VEGFR)2 pathway.AUCell analysis identified subtypes of endothelial cells and pericytes directly related to VEGFA-VEGFR2 pathway activity.Two subtypes of middle temporal gyrus cells showed significant alteration in AD:endothelial cells with high expression of Erb-B2 receptor tyrosine kinase 4(ERBB4^(high))and pericytes with high expression of angiopoietin-like 4(ANGPTL4^(high)).Finally,combining bulk RNA sequencing data and two machine learning algorithms(least absolute shrinkage and selection operator and random forest),four characteristic Alzheimer's disease feature genes were identified:somatostatin(SST),protein tyrosine phosphatase non-receptor type 3(PTPN3),glutinase(GL3),and tropomyosin 3(PTM3).These genes were downregulated in the middle temporal gyrus of patients with Alzheimer's disease and may be used to target the VEGF pathway.Alzheimer's disease mouse models demonstrated consistent altered expression of these genes in the middle temporal gyrus.In conclusion,this study detected changes in intercellular communication between endothelial cells and pericytes in the middle temporal gyrus and identified four novel feature genes related to middle temporal gyrus and vascular functioning in patients with Alzheimer's disease.These findings contribute to a deeper understanding of the molecular mechanisms underlying Alzheimer's disease and present novel treatment targets.
基金the National Key Research and Development Program of China(No.2020YFB1713500)the Natural Science Basic Research Program of Shaanxi(Grant No.2023JCYB289)+1 种基金the National Natural Science Foundation of China(Grant No.52175112)the Fundamental Research Funds for the Central Universities(Grant No.ZYTS23102).
文摘The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the toolwill generate significant noise and vibration, negatively impacting the accuracy of the forming and the surfaceintegrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wearstate andpromptly replace anyheavilyworn tools toguarantee thequality of the cutting.The conventional tool wearmonitoring models, which are based on machine learning, are specifically built for the intended cutting conditions.However, these models require retraining when the cutting conditions undergo any changes. This method has noapplication value if the cutting conditions frequently change. This manuscript proposes a method for monitoringtool wear basedonunsuperviseddeep transfer learning. Due to the similarity of the tool wear process under varyingworking conditions, a tool wear recognitionmodel that can adapt to both current and previous working conditionshas been developed by utilizing cutting monitoring data from history. To extract and classify cutting vibrationsignals, the unsupervised deep transfer learning network comprises a one-dimensional (1D) convolutional neuralnetwork (CNN) with a multi-layer perceptron (MLP). To achieve distribution alignment of deep features throughthe maximum mean discrepancy algorithm, a domain adaptive layer is embedded in the penultimate layer of thenetwork. A platformformonitoring tool wear during endmilling has been constructed. The proposedmethod wasverified through the execution of a full life test of end milling under multiple working conditions with a Cr12MoVsteel workpiece. Our experiments demonstrate that the transfer learning model maintains a classification accuracyof over 80%. In comparisonwith the most advanced tool wearmonitoring methods, the presentedmodel guaranteessuperior performance in the target domains.
基金supported by the Ministry of Science and Technology of China,No.2020AAA0109605(to XL)Meizhou Major Scientific and Technological Innovation PlatformsProjects of Guangdong Provincial Science & Technology Plan Projects,No.2019A0102005(to HW).
文摘Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.
基金supported by the Shandong Province Science and Technology Project(2023TSGC0509,2022TSGC2234)Qingdao Science and Technology Plan Project(23-1-5-yqpy-2-qy)Open Topic Grants of Anhui Province Key Laboratory of Intelligent Building&Building Energy Saving,Anhui Jianzhu University(IBES2024KF08).
文摘With the rapid development of artificial intelligence,the Internet of Things(IoT)can deploy various machine learning algorithms for network and application management.In the IoT environment,many sensors and devices generatemassive data,but data security and privacy protection have become a serious challenge.Federated learning(FL)can achieve many intelligent IoT applications by training models on local devices and allowing AI training on distributed IoT devices without data sharing.This review aims to deeply explore the combination of FL and the IoT,and analyze the application of federated learning in the IoT from the aspects of security and privacy protection.In this paper,we first describe the potential advantages of FL and the challenges faced by current IoT systems in the fields of network burden and privacy security.Next,we focus on exploring and analyzing the advantages of the combination of FL on the Internet,including privacy security,attack detection,efficient communication of the IoT,and enhanced learning quality.We also list various application scenarios of FL on the IoT.Finally,we propose several open research challenges and possible solutions.
基金supported by Systematic Major Project of Shuohuang Railway Development Co.,Ltd.,National Energy Group(Grant Number:SHTL-23-31)Beijing Natural Science Foundation(U22B2027).
文摘In the realm of Intelligent Railway Transportation Systems,effective multi-party collaboration is crucial due to concerns over privacy and data silos.Vertical Federated Learning(VFL)has emerged as a promising approach to facilitate such collaboration,allowing diverse entities to collectively enhance machine learning models without the need to share sensitive training data.However,existing works have highlighted VFL’s susceptibility to privacy inference attacks,where an honest but curious server could potentially reconstruct a client’s raw data from embeddings uploaded by the client.This vulnerability poses a significant threat to VFL-based intelligent railway transportation systems.In this paper,we introduce SensFL,a novel privacy-enhancing method to against privacy inference attacks in VFL.Specifically,SensFL integrates regularization of the sensitivity of embeddings to the original data into the model training process,effectively limiting the information contained in shared embeddings.By reducing the sensitivity of embeddings to the original data,SensFL can effectively resist reverse privacy attacks and prevent the reconstruction of the original data from the embeddings.Extensive experiments were conducted on four distinct datasets and three different models to demonstrate the efficacy of SensFL.Experiment results show that SensFL can effectively mitigate privacy inference attacks while maintaining the accuracy of the primary learning task.These results underscore SensFL’s potential to advance privacy protection technologies within VFL-based intelligent railway systems,addressing critical security concerns in collaborative learning environments.
文摘Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident severity.Deep learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and classification.Despite prior success,there is a need for an efficient system recognizing ASL in diverse road conditions.To address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent.The framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved generalization.Sensitivity and factor contribution analyses identify influential predictors.Evaluated on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB dataset.The proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.
