Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer i...Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.展开更多
In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different f...In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.展开更多
Generative Models have been shown to be extremely useful in learning features from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, w...Generative Models have been shown to be extremely useful in learning features from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, while extracting natural and human-understandable features without labels. In this paper we combine two highly useful classes of models, variational ladder autoencoders, and MMD variational autoencoders, to model face images. In particular, we show that we can disentangle highly meaningful and interpretable features. Furthermore, we are able to perform arithmetic operations on faces and modify faces to add or remove high level features.展开更多
In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have b...In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.展开更多
Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method ...Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.展开更多
Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of f...Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of face reconstruction,face reenactment and talking head et al..However,due to the sparsity of landmarks and the lack of accurate labels for the factors,it is hard to learn the disentangled representation of landmarks.To address these problem,we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations,which is based on a Variational Autoencoder framework.Besides,we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training stage.Moreover,we implement an identity preservation loss to further enhance the representation ability of identity factor.To the best of our knowledge,this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark.展开更多
Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classif...Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.展开更多
Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the co...Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.展开更多
Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the deve...Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the development of the machine learning and neuroimaging technology,extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging(s-MRI).However,most studies involve with datasets where participants'age are above 5 and lack interpretability.In this paper,we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years,based on s-MRI features extracted using Contrastive Variational AutoEncoder(CVAE).78 s-MRIs,collected from Shenzhen Children's Hospital,are used for training CVAE,which consists of both ASD-specific feature channel and common-shared feature channel.The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control(TC)participants represented by the common-shared features.In case of degraded predictive accuracy when data size is extremely small,a transfer learning strategy is proposed here as a potential solution.Finally,we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions,which discloses potential biomarkers that could help target treatments of ASD in the future.展开更多
Anomaly detection is one of the many challenging areas in cybersecurity.The anomaly can occur in many forms,such as fraudulent credit card transactions,network intrusions,and anomalous imageries or documents.One of th...Anomaly detection is one of the many challenging areas in cybersecurity.The anomaly can occur in many forms,such as fraudulent credit card transactions,network intrusions,and anomalous imageries or documents.One of the most common challenges in anomaly detection is the obscurity of the normal state and the lack of anomalous samples.Traditionally,this problem is tackled by using resampling techniques or choosing models that approximate the distribution of the normal states.Variational AutoEncoder(VAE)has been studied in anomaly detections despite being more suitable in generative tasks.This study aims to explore the usage of VAE in credit card anomaly detection and evaluate latent space sampling techniques.In this study,we evaluate the usage of the convolutional network-based VAE model on a credit card transaction dataset.We train two VAE models,one with a large number of normal data and one with a small number of anomalous data.We compare the performance of both VAE models and evaluate the latent space of both VAE models by rescaling them with reconstruction error vectors.We also compare the effectiveness of the VAE model with other anomaly detection models when they are trained on imbalanced dataset.展开更多
The generation and optimization of simulation data for electrical machines remain challenging,largely due to the complexities of magneto-staticfinite element analysis.Traditional methodologies are not only resource-in...The generation and optimization of simulation data for electrical machines remain challenging,largely due to the complexities of magneto-staticfinite element analysis.Traditional methodologies are not only resource-intensive,but also time-consuming.Deep learning models can be used to shortcut these calculations.However,challenges arise when considering the unique parameter sets specific to each machine topology.Building on two recent studies(Parekh et al.in IEEE Trans.Magn.58(9):1-4,2022;Parekh et al.,Deep learning based meta-modeling for multi-objective technology optimization of electrical machines,2023,arXiv:2306.09087),that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization,this paper proposes a refined architecture and optimization workflow.Our modifications aim to streamline and enhance the robustness of both the training and optimization processes,and compare the results with the variational autoencoder architecture proposed recently.展开更多
Federated Learning(FL) heralds a paradigm shift in the training of artificial intelligence(AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity...Federated Learning(FL) heralds a paradigm shift in the training of artificial intelligence(AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of AI technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional FL methods are susceptible to Deep Leakage from Gradients(DLG) attacks, and typical defensive strategies in current research, such as secure multi-party computation and diferential privacy, often lead to excessive computational costs or significant decreases in model accuracy. To address DLG attacks in FL, this study introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders(VAEs) to enhance privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, ofering a secure and efficient FL approach for the sensitive application of FL in the financial domain.展开更多
目的生成式和传统人工智能模型是信息时代的关键工具。在这些技术的助力下,眼动过程的样本生成与识别显得尤为关键,它已成为深入研究认知机制的重要手段。为了推动生成式人工智能在眼动技术领域的应用发展,解决眼动样本生成及因网络深...目的生成式和传统人工智能模型是信息时代的关键工具。在这些技术的助力下,眼动过程的样本生成与识别显得尤为关键,它已成为深入研究认知机制的重要手段。为了推动生成式人工智能在眼动技术领域的应用发展,解决眼动样本生成及因网络深度增加而导致的不透明性和不可解释性问题,并深入挖掘与幼儿语言发展相关的眼动数据,方法采集4~6岁幼儿理解不同焦点结构的眼动数据,采用生成式人工智能模型-变分自编码器(variational autoencoder,VAE)和传统模型-多层感知器(multi-layer perceptron,MLP)识别眼动模式的发展差异并尝试生成新样本,基于灰色关联分析和混淆矩阵对生成式数据集进行解释。结果结果表明:(1)VAE生成的4岁组、5岁组和6岁组幼儿眼动数据集精度高于MINIST数据集(mixed National Institute of Standards and Technology database),且与MLP分析结果一致,具有准确性、多样性和一定的可解释性;(2)生成式眼动数据及混淆矩阵结果表明,在无焦点结构句式中,幼儿在4~5岁、5~6岁两个阶段理解水平均有提升,而宾语焦点结构和主语焦点结构的眼动特征在4~5岁变化较小,5~6岁变化较大,说明幼儿对焦点结构的理解在5岁是一个关键期,这符合幼儿焦点结构理解发展规律。结论提出的人工智能耦合分析方法,具备有效识别眼动特征发展模式的能力,并能据此生成可靠的新样本。这一方法不仅为生成式人工智能与眼动技术的融合开辟了新的途径,而且为复杂语言理解问题提供了全新的思考方向。展开更多
基金National Natural Science Foundation of China(61976209,62020106015,U21A20388)in part by the CAS International Collaboration Key Project(173211KYSB20190024)in part by the Strategic Priority Research Program of CAS(XDB32040000)。
文摘Traditional electroencephalograph(EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject,which restricts the application of the affective brain computer interface(BCI)in practice.We attempt to use the multi-modal data from the past session to realize emotion recognition in the case of a small amount of calibration samples.To solve this problem,we propose a multimodal domain adaptive variational autoencoder(MMDA-VAE)method,which learns shared cross-domain latent representations of the multi-modal data.Our method builds a multi-modal variational autoencoder(MVAE)to project the data of multiple modalities into a common space.Through adversarial learning and cycle-consistency regularization,our method can reduce the distribution difference of each domain on the shared latent representation layer and realize the transfer of knowledge.Extensive experiments are conducted on two public datasets,SEED and SEED-IV,and the results show the superiority of our proposed method.Our work can effectively improve the performance of emotion recognition with a small amount of labelled multi-modal data.
文摘In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)models.These models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure metrics.While the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through Variational AutoEncoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our models.While combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature Elimination.As the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of 0.675.Although the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_own.When all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock features.It was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.
文摘Generative Models have been shown to be extremely useful in learning features from unlabeled data. In particular, variational autoencoders are capable of modeling highly complex natural distributions such as images, while extracting natural and human-understandable features without labels. In this paper we combine two highly useful classes of models, variational ladder autoencoders, and MMD variational autoencoders, to model face images. In particular, we show that we can disentangle highly meaningful and interpretable features. Furthermore, we are able to perform arithmetic operations on faces and modify faces to add or remove high level features.
文摘In modern industry,process monitoring plays a significant role in improving the quality of process conduct.With the higher dimensional of the industrial data,the monitoring methods based on the latent variables have been widely applied in order to decrease the wasting of the industrial database.Nevertheless,these latent variables do not usually follow the Gaussian distribution and thus perform unsuitable when applying some statistics indices,especially the T^(2) on them.Variational AutoEncoders(VAE),an unsupervised deep learning algorithm using the hierarchy study method,has the ability to make the latent variables follow the Gaussian distribution.The partial least squares(PLS)are used to obtain the information between the dependent variables and independent variables.In this paper,we will integrate these two methods and make a comparison with other methods.The superiority of this proposed method will be verified by the simulation and the Trimethylchlorosilane purification process in terms of the multivariate control charts.
基金Supported by National Natural Science Foundation of China(41804126,41604107).
文摘Supervised machine learning algorithms have been widely used in seismic exploration processing,but the lack of labeled examples complicates its application.Therefore,we propose a seismic labeled data expansion method based on deep variational Autoencoders(VAE),which are made of neural networks and contains two partsEncoder and Decoder.Lack of training samples leads to overfitting of the network.We training the VAE with whole seismic data,which is a data-driven process and greatly alleviates the risk of overfitting.The Encoder captures the ability to map the seismic waveform Y to latent deep features z,and the Decoder captures the ability to reconstruct high-dimensional waveform Yb from latent deep features z.Later,we put the labeled seismic data into Encoders and get the latent deep features.We can easily use gaussian mixture model to fit the deep feature distribution of each class labeled data.We resample a mass of expansion deep features z* according to the Gaussian mixture model,and put the expansion deep features into the decoder to generate expansion seismic data.The experiments in synthetic and real data show that our method alleviates the problem of lacking labeled seismic data for supervised seismic facies analysis.
基金Supported by the National Natural Science Foundation of China(61210007).
文摘Learning disentangled representation of data is a key problem in deep learning.Specifically,disentangling 2D facial landmarks into different factors(e.g.,identity and expression)is widely used in the applications of face reconstruction,face reenactment and talking head et al..However,due to the sparsity of landmarks and the lack of accurate labels for the factors,it is hard to learn the disentangled representation of landmarks.To address these problem,we propose a simple and effective model named FLD-VAE to disentangle arbitrary facial landmarks into identity and expression latent representations,which is based on a Variational Autoencoder framework.Besides,we propose three invariant loss functions in both latent and data levels to constrain the invariance of representations during training stage.Moreover,we implement an identity preservation loss to further enhance the representation ability of identity factor.To the best of our knowledge,this is the first work to end-to-end disentangle identity and expression factors simultaneously from one single facial landmark.
基金Project supported by the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Grant No.SKLACSS-202108)the National Natural Science Foundation of China(Grant No.U162271070)Scientific Research Fund of Zaozhuang University(Grant No.102061901).
文摘Label propagation is an essential semi-supervised learning method based on graphs,which has a broad spectrum of applications in pattern recognition and data mining.This paper proposes a quantum semi-supervised classifier based on label propagation.Considering the difficulty of graph construction,we develop a variational quantum label propagation(VQLP)method.In this method,a locally parameterized quantum circuit is created to reduce the parameters required in the optimization.Furthermore,we design a quantum semi-supervised binary classifier based on hybrid Bell and Z bases measurement,which has a shallower circuit depth and is more suitable for implementation on near-term quantum devices.We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set,and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier.This work opens a new path to quantum machine learning based on graphs.
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2021R1A2C2011391)was supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-01806Development of security by design and security management technology in smart factory).
文摘Contemporary attackers,mainly motivated by financial gain,consistently devise sophisticated penetration techniques to access important information or data.The growing use of Internet of Things(IoT)technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation,as it facilitates multiple new attack vectors to emerge effortlessly.As such,existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems.To address this problem,we designed a blended threat detection approach,considering the possible impact and dimensionality of new attack surfaces due to the aforementioned convergence.We collectively refer to the convergence of different technology sectors as the internet of blended environment.The proposed approach encompasses an ensemble of heterogeneous probabilistic autoencoders that leverage the corresponding advantages of a convolutional variational autoencoder and long short-term memory variational autoencoder.An extensive experimental analysis conducted on the TON_IoT dataset demonstrated 96.02%detection accuracy.Furthermore,performance of the proposed approach was compared with various single model(autoencoder)-based network intrusion detection approaches:autoencoder,variational autoencoder,convolutional variational autoencoder,and long short-term memory variational autoencoder.The proposed model outperformed all compared models,demonstrating F1-score improvements of 4.99%,2.25%,1.92%,and 3.69%,respectively.
基金supported by the Shenzhen Science and Technology Program(Nos.KQTD20200820113106007 and SGDX20201103095603009)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB38050100)+4 种基金the Shenzhen Key Laboratory of Intelligent Bioinformatics(No.ZDSYS20220422103800001)the Shenzhen Basic Research Fund(No.RCYX20200714114734194)the Key Research and Development Project of Guangdong Province(No.2021B0101310002)the National Natural Science Foundation of China(Nos.U22A2041 and 62272449)the Youth Innovation Promotion Association(No.Y2021101).
文摘Autism Spectrum Disorder(ASD)is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients,making early screening and intervention of ASD critical.With the development of the machine learning and neuroimaging technology,extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging(s-MRI).However,most studies involve with datasets where participants'age are above 5 and lack interpretability.In this paper,we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years,based on s-MRI features extracted using Contrastive Variational AutoEncoder(CVAE).78 s-MRIs,collected from Shenzhen Children's Hospital,are used for training CVAE,which consists of both ASD-specific feature channel and common-shared feature channel.The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control(TC)participants represented by the common-shared features.In case of degraded predictive accuracy when data size is extremely small,a transfer learning strategy is proposed here as a potential solution.Finally,we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions,which discloses potential biomarkers that could help target treatments of ASD in the future.
文摘Anomaly detection is one of the many challenging areas in cybersecurity.The anomaly can occur in many forms,such as fraudulent credit card transactions,network intrusions,and anomalous imageries or documents.One of the most common challenges in anomaly detection is the obscurity of the normal state and the lack of anomalous samples.Traditionally,this problem is tackled by using resampling techniques or choosing models that approximate the distribution of the normal states.Variational AutoEncoder(VAE)has been studied in anomaly detections despite being more suitable in generative tasks.This study aims to explore the usage of VAE in credit card anomaly detection and evaluate latent space sampling techniques.In this study,we evaluate the usage of the convolutional network-based VAE model on a credit card transaction dataset.We train two VAE models,one with a large number of normal data and one with a small number of anomalous data.We compare the performance of both VAE models and evaluate the latent space of both VAE models by rescaling them with reconstruction error vectors.We also compare the effectiveness of the VAE model with other anomaly detection models when they are trained on imbalanced dataset.
文摘The generation and optimization of simulation data for electrical machines remain challenging,largely due to the complexities of magneto-staticfinite element analysis.Traditional methodologies are not only resource-intensive,but also time-consuming.Deep learning models can be used to shortcut these calculations.However,challenges arise when considering the unique parameter sets specific to each machine topology.Building on two recent studies(Parekh et al.in IEEE Trans.Magn.58(9):1-4,2022;Parekh et al.,Deep learning based meta-modeling for multi-objective technology optimization of electrical machines,2023,arXiv:2306.09087),that utilized a variational autoencoder to cohesively map diverse topologies into a singular latent space for subsequent optimization,this paper proposes a refined architecture and optimization workflow.Our modifications aim to streamline and enhance the robustness of both the training and optimization processes,and compare the results with the variational autoencoder architecture proposed recently.
基金supported by the Yangtze River Delta Science and Technology Innovation Community Joint Research Project (2022CSJGG0800)the Shanghai Science and Technology Project (22510761000)
文摘Federated Learning(FL) heralds a paradigm shift in the training of artificial intelligence(AI) models by fostering collaborative model training while safeguarding client data privacy. In sectors where data sensitivity and AI model security are of paramount importance, such as fintech and biomedicine, maintaining the utility of models without compromising privacy is crucial with the growing application of AI technologies. Therefore, the adoption of FL is attracting significant attention. However, traditional FL methods are susceptible to Deep Leakage from Gradients(DLG) attacks, and typical defensive strategies in current research, such as secure multi-party computation and diferential privacy, often lead to excessive computational costs or significant decreases in model accuracy. To address DLG attacks in FL, this study introduces VAEFL, an innovative FL framework that incorporates Variational Autoencoders(VAEs) to enhance privacy protection without undermining the predictive prowess of the models. VAEFL strategically partitions the model into a private encoder and a public decoder. The private encoder, remaining local, transmutes sensitive data into a latent space fortified for privacy, while the public decoder and classifier, through collaborative training across clients, learn to derive precise predictions from the encoded data. This bifurcation ensures that sensitive data attributes are not disclosed, circumventing gradient leakage attacks and simultaneously allowing the global model to benefit from the diverse knowledge of client datasets. Comprehensive experiments demonstrate that VAEFL not only surpasses standard FL benchmarks in privacy preservation but also maintains competitive performance in predictive tasks. VAEFL thus establishes a novel equilibrium between data privacy and model utility, ofering a secure and efficient FL approach for the sensitive application of FL in the financial domain.
文摘目的生成式和传统人工智能模型是信息时代的关键工具。在这些技术的助力下,眼动过程的样本生成与识别显得尤为关键,它已成为深入研究认知机制的重要手段。为了推动生成式人工智能在眼动技术领域的应用发展,解决眼动样本生成及因网络深度增加而导致的不透明性和不可解释性问题,并深入挖掘与幼儿语言发展相关的眼动数据,方法采集4~6岁幼儿理解不同焦点结构的眼动数据,采用生成式人工智能模型-变分自编码器(variational autoencoder,VAE)和传统模型-多层感知器(multi-layer perceptron,MLP)识别眼动模式的发展差异并尝试生成新样本,基于灰色关联分析和混淆矩阵对生成式数据集进行解释。结果结果表明:(1)VAE生成的4岁组、5岁组和6岁组幼儿眼动数据集精度高于MINIST数据集(mixed National Institute of Standards and Technology database),且与MLP分析结果一致,具有准确性、多样性和一定的可解释性;(2)生成式眼动数据及混淆矩阵结果表明,在无焦点结构句式中,幼儿在4~5岁、5~6岁两个阶段理解水平均有提升,而宾语焦点结构和主语焦点结构的眼动特征在4~5岁变化较小,5~6岁变化较大,说明幼儿对焦点结构的理解在5岁是一个关键期,这符合幼儿焦点结构理解发展规律。结论提出的人工智能耦合分析方法,具备有效识别眼动特征发展模式的能力,并能据此生成可靠的新样本。这一方法不仅为生成式人工智能与眼动技术的融合开辟了新的途径,而且为复杂语言理解问题提供了全新的思考方向。
