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公开(公告)号：US11675641B2
公开(公告)日：2023-06-13
申请号：US16458687
申请日：2019-07-01
发明人： Masanao Natsumeda , Wei Cheng , Haifeng Chen
IPC分类号： G06F11/00
CPC分类号： G06F11/008
摘要： A failure prediction system is provided. The system includes a model-based signature generator generating feature vectors from individual attributes of multi-variate time series data based on sequence importance and attribute importance. The system further includes a knowledge database storing feature vectors corresponding to a set of different failure types. The system also includes a set of similarity detectors. Each detect any of the feature vectors generated by the model-based signature generator that are similar to any of the feature vectors corresponding to a respective one of the different failure types stored in the knowledge database based on a similarity threshold and output the respective one of the different failure types and a likely time period when the respective one of the different failure types will occur.
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12.发明申请公开(公告)号：US20230109729A1
公开(公告)日：2023-04-13
申请号：US17958597
申请日：2022-10-03
发明人： Yuncong Chen , Zhengzhang Chen , Xuchao Zhang , Wenchao Yu , Haifeng Chen , LuAn Tang , Zexue He
摘要： A computer-implemented method for multi-model representation learning is provided. The method includes encoding, by a trained time series (TS) encoder, an input TS segment into a TS-shared latent representation and a TS-private latent representation. The method further includes generating, by a trained text generator, a natural language text that explains the input TS segment, responsive to the TS-shared latent representation, the TS-private latent representation, and a text-private latent representation.
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公开(公告)号：US11620518B2
公开(公告)日：2023-04-04
申请号：US16866885
申请日：2020-05-05
发明人： Cristian Lumezanu , Haifeng Chen , Dongjin Song , Wei Cheng , Takehiko Mizoguchi , Xiaoyuan Liang , Yuncong Chen
摘要： Systems and methods for updating a classification model of a neural network. The methods include selecting, as a set of landmarks, a limited number of data from a set of historical data used to train a classification model. Additionally, the methods generate new training data from recently collected data. Further, the methods update the classification model with the new training data and the set of landmarks to obtain an updated classification model having a loss function configured to capture similarities in the new training data and remember similarities in the historical data represented by the set of landmarks within a predefined tolerance.
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公开(公告)号：US11604969B2
公开(公告)日：2023-03-14
申请号：US16553465
申请日：2019-08-28
发明人： Wei Cheng , LuAn Tang , Dongjin Song , Bo Zong , Haifeng Chen , Jingchao Ni , Wenchao Yu
摘要： Systems and methods for predicting system device failure are provided. The method includes representing device failure related data associated with the devices from a predetermined domain by temporal graphs for each of the devices. The method also includes extracting vector representations based on temporal graph features from the temporal graphs that capture both temporal and structural correlation in the device failure related data. The method further includes predicting, based on the vector representations and device failure related metrics in the predetermined domain, one or more of the devices that is expected to fail within a predetermined time.
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公开(公告)号：US20230076127A1
公开(公告)日：2023-03-09
申请号：US17883040
申请日：2022-08-08
发明人： Xiao Yu , Yanchi Liu , Haifeng Chen , Yufei Li
IPC分类号： G06F40/295 , G06F40/40 , G06F21/57
摘要： Systems and methods are provided for adapting a pretrained language model to perform cybersecurity-specific named entity recognition and relation extraction. The method includes introducing a pretrained language model and a corpus of security text to a model adaptor, and generating a fine-tuned language model through unsupervised training utilizing the security text corpus. The method further includes combining a joint extraction model from a head for joint extraction with the fine-tuned language model to form an adapted joint extraction model that can perform entity and relation label prediction. The method further includes applying distant labels to security text in the corpus of security text to produce security text with distant labels, and performing Distant Supervision Training for joint extraction on the adapted joint extraction model using the security text to transform the adapted joint extraction model into a Security Language Model for name-entity recognition (NER) and relation extraction (RE).
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公开(公告)号：US20230074002A1
公开(公告)日：2023-03-09
申请号：US17892192
申请日：2022-08-22
发明人： Xuchao Zhang , Yuncong Chen , Haifeng Chen , Wenchao Yu , Wei Cheng , Xujiang Zhao
摘要： Systems and methods for Evidence-based Sound Event Early Detection is provided. The system/method includes parsing collected labeled audio corpus data and real time audio streaming data utilizing mel-spectrogram, encoding features of the parsed mel-spectrograms using a trained neural network, and generating a final predicted result for a sound event based on the belief, disbelief and uncertainty outputs from the encoded mel-spectrograms.
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公开(公告)号：US20230070443A1
公开(公告)日：2023-03-09
申请号：US17896590
申请日：2022-08-26
发明人： Wei Cheng , Haifeng Chen , Jingchao Ni , Wenchao Yu , Yuncong Chen , Dongsheng Luo
IPC分类号： G06K9/62
摘要： A computer-implemented method for meta-learning is provided. The method includes receiving a training time series and labels corresponding to some of the training time series. The method further includes optimizing time series augmentations of the training time series using a time series augmentation selection process performed by a meta learner to obtain a selected augmentation from a plurality of candidate augmentations. The method also includes training a time series encoder with contrastive loss using the selected augmentation to obtain a learned time series encoder. The method additionally includes learning, by the learned time series encoder, a vector representation of another time series. The method further includes performing, by the learned time series encoder, a downstream task of label classification for at least a portion of the other time series.
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公开(公告)号：US11521255B2
公开(公告)日：2022-12-06
申请号：US16995052
申请日：2020-08-17
发明人： Jingchao Ni , Haifeng Chen , Bo Zong , Xin Dong , Wei Cheng
摘要： A method for implementing a recommendation system using an asymmetrically hierarchical network includes, for a user and an item corresponding to a user-item pair, aggregating, using asymmetrically designed sentence aggregators, respective ones of a set of item sentence embeddings and a set of user sentence embeddings to generate a set of item review embeddings based on first item attention weights and a set of user review embeddings based on first user attention weights, respectively, aggregating, using asymmetrically designed review aggregators, respective ones of the set of item review embeddings and the set of user review embeddings to generate an item embedding based on a second item attention weights and a user embedding based on second user attention weights, respectively, and predicting a rating of the user-item pair based on the item embedding and the user embedding.
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公开(公告)号：US20220382614A1
公开(公告)日：2022-12-01
申请号：US17745134
申请日：2022-05-16
发明人： Zhengzhang Chen , Haifeng Chen , Yuncong Chen
摘要： Methods and systems for detecting and responding to an anomaly include determining a first system-level performance prediction using system-level statistics. A second system-level performance prediction is determined using system-level statistics and service-level statistics. The first prediction to the second prediction are compared to identify a discrepancy. It is determined that a service corresponding to the service-level statistics is a cause of a detected failure in a distributed computing system. An action directed to the service is performed responsive to the detected failure.
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公开(公告)号：US11468262B2
公开(公告)日：2022-10-11
申请号：US16169184
申请日：2018-10-24
发明人： Wei Cheng , Haifeng Chen , Kenji Yoshihira , Wenchao Yu
摘要： Methods and systems for embedding a network in a latent space include generating a representation of an input network graph in the latent space using an autoencoder model and generating a representation of a set of noise samples in the latent space using a generator model. A discriminator model discriminates between the representation of the input network graph and the representation of the set of noise samples. The autoencoder model, the generator model, and the discriminator model are jointly trained by minimizing a joint loss function that includes parameters for each model. A final representation of the input network graph is generated using the trained autoencoder model.
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