公开(公告)号：US11783181B2

公开(公告)日：2023-10-10

申请号：US16987789

申请日：2020-08-07

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Jingchao Ni ,  Dongkuan Xu ,  Wenchao Yu

IPC: G06N3/08 ,  G06F17/18 ,  G06N5/04 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06F17/18 ,  G06F18/214 ,  G06N3/044 ,  G06N3/045 ,  G06N5/04

Abstract: A method for executing a multi-task deep learning model for learning trends in multivariate time series is presented. The method includes collecting multi-variate time series data from a plurality of sensors, jointly learning both local and global contextual features for predicting a trend of the multivariate time series by employing a tensorized long short-term memory (LSTM) with adaptive shared memory (TLASM) to learn historical dependency of historical trends, and employing a multi-task one-dimensional convolutional neural network (1dCNN) to extract salient features from local raw time series data to model a short-term dependency between local time series data and subsequent trends.

公开(公告)号：US20230252302A1

公开(公告)日：2023-08-10

申请号：US18152238

申请日：2023-01-10

Applicant: NEC Laboratories America, Inc.

Inventor： Liang Tong ,  Takehiko Mizoguchi ,  Zhengzhang Chen ,  Wei Cheng ,  Haifeng Chen ,  Nauman Ahad

IPC: G06N3/0895 ,  G06N3/0442

CPC classification number: G06N3/0895 ,  G06N3/0442

Abstract: A computer-implemented method for ordinal prediction is provided. The method includes encoding time series data with a temporal encoder to obtain latent space representations. The method includes optimizing the temporal encoder using semi-supervised learning to distinguish different classes in the labeled space using labeled data, and augment the latent space representations using unlabeled training data, to obtain semi-supervised representations. The method further includes discarding a linear layer after the temporal encoder and fixing the temporal encoder. The method also includes training k−1 binary classifiers on top of the semi-supervised representations to obtain k−1 binary predictions. The method additionally includes identifying and correcting inconsistent ones of the k−1 binary predictions by matching the inconsistent ones to consistent ones of the k−1 binary predictions. The method further includes aggregating the k−1 binary predictions to obtain an ordinal prediction.

公开(公告)号：US20230236927A1

公开(公告)日：2023-07-27

申请号：US18152546

申请日：2023-01-10

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： LuAn Tang ,  Haifeng Chen ,  Yuncong Chen ,  Wei Cheng ,  Zhengzhang Chen ,  Yuji Kobayashi

IPC: G06F11/07 ,  G06N3/0455

CPC classification number: G06F11/0793 ,  G06F11/0721 ,  G06N3/0455

Abstract: Methods and systems for anomaly detection include determining whether a system is in a stable state or a dynamic state based on input data from one or more sensors in the system, using reconstruction errors from a respective stable model and dynamic model. It is determined that the input data represents anomalous operation of the system, responsive to a determination that the system is in a stable state, using the reconstruction errors. A corrective operation is performed on the system responsive to a determination that the input data represents anomalous operation of the system.

公开(公告)号：US20230094623A1

公开(公告)日：2023-03-30

申请号：US17950203

申请日：2022-09-22

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen

IPC: G06N3/08 ,  G06N7/00

Abstract: A computer-implemented method for model building is provided. The method includes receiving a training set of medical records and model hyperparameters. The method further includes initializing an encoder as a Dual-Channel Combiner Network (DCNN) and initialize distribution related parameters. The method also includes performing, by a hardware processor, a forward computation to (1) the DCNN to obtain the embeddings of the medical records, and (2) the distribution related parameters to obtain class probabilities. The method additionally includes checking by a convergence evaluator if the iterative optimization has converged. The method further includes performing model personalization responsive to model convergence by encoding the support data of a new patient and using the embeddings and event subtype labels to train a personalized classifier.

公开(公告)号：US11606393B2

公开(公告)日：2023-03-14

申请号：US17004547

申请日：2020-08-27

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Haifeng Chen ,  Bo Zong ,  LuAn Tang ,  Wei Cheng

IPC: H04L9/40 ,  G06N3/049 ,  H04L41/16 ,  G06N3/08

Abstract: Methods and systems for detecting and responding to anomalous nodes in a network include inferring temporal factors, using a computer-implemented neural network, that represent changes in a network graph across time steps, with a temporal factor for each time step depending on a temporal factor for a previous time step. An invariant factor is inferred that represents information about the network graph that does not change across the time steps. The temporal factors and the invariant factor are combined into a combined temporal-invariant representation. It is determined that an unlabeled node is anomalous, based on the combined temporal-invariant representation. A security action is performed responsive to the determination that unlabeled node is anomalous.

公开(公告)号：US11604934B2

公开(公告)日：2023-03-14

申请号：US16816752

申请日：2020-03-12

Applicant: NEC Laboratories America, Inc.

Inventor： Masanao Natsumeda ,  Wei Cheng ,  Haifeng Chen ,  Yuncong Chen

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/084 ,  G06N3/04 ,  G06N3/063

Abstract: Methods and systems for predicting failure in a cyber-physical system include determining a prediction index based on a comparison of input time series, from respective sensors in a cyber-physical system, to failure precursors. A failure precursor is detected in the input time series, responsive to a comparison of the prediction index to a threshold. A subset of the sensors associated with the failure precursor is determined, based on a gradient of the prediction index. A corrective action is performed responsive to the determined subset of sensors.

公开(公告)号：US20230076575A1

公开(公告)日：2023-03-09

申请号：US17883729

申请日：2022-08-09

Applicant: NEC Laboratories America, Inc.

Inventor： Jingchao Ni ,  Wei Cheng ,  Haifeng Chen

IPC: G16H50/30 ,  G16H50/70 ,  G16H50/50

Abstract: A method for making prognostic prediction scores during a pre-dialysis period on an incidence of events in future dialysis includes learning a meta-training model that simultaneously classifies dialysis in-distribution events and detects out-of-distribution (OOD) events during model personalization by employing a data preprocessing component to extract different parts of data from historical medical records of patients to generate a meta-training dataset, a meta-training component to analyze the meta-training dataset, the meta-training component including a class pool generator, a task generator, a prototype network, an attention component, and a model training component, the class pool generator splitting training classes into a first class pool and a second class pool for generating a distribution statistics dictionary, a storage component to store the meta-training model for distribution to local machines, and a personalization component including a local data collection component, and a class and OOD detector component.

公开(公告)号：US20220374600A1

公开(公告)日：2022-11-24

申请号：US17723994

申请日：2022-04-19

Applicant: NEC Laboratories America, Inc.

Inventor： Wei Cheng ,  Haifeng Chen ,  Xuchao Zhang ,  Dongsheng Luo

IPC: G06F40/289 ,  G06F40/169 ,  G06F40/284 ,  G06F16/34 ,  G06N3/08

Abstract: A computer-implemented method is provided for keyphrase generation. The method includes pretraining, by a processor device, a policy neural network on training documents using a sequence-to-sequence model. The training documents are each associated with a list of keyphrases included therein. The method further includes training, by the processor device, the policy neural network using reinforcement learning with a summarization reward on present annotated keyphrases in an input training document and absent annotated keyphrase from the input training document that semantically describe a concept of the input training document. The method also includes predicting, by the processor device, new keyphrases using the trained policy neural network.

公开(公告)号：US20220374232A1

公开(公告)日：2022-11-24

申请号：US17739727

申请日：2022-05-09

Applicant: NEC Laboratories America, Inc.

Inventor： Xuchao Zhang ,  Haifeng Chen ,  Wei Cheng

IPC: G06F8/72 ,  G06F8/30

Abstract: Systems and methods are provided for automated computer code editing. The method includes training a code-editing neural network model using a corpus of code editing data samples, including the pre-editing samples and post-editing samples, and parsing the pre-editing samples and post-editing samples into an Abstract Syntax Tree (AST). The method further includes using a grammar specification to transform the AST tree into a unified Abstract Syntax Description Language (ASDL) graph for different programming languages, and using a gated graph neural network (GGNN) to compute a vector representation for each node in the unified Abstract Syntax Description Language (ASDL) graph. The method further includes selecting and aggregating support samples based on a query code with a multi-extent ensemble method, and altering the query code iteratively using the pattern learned from the pre- and post-editing samples.

公开(公告)号：US20220318593A1

公开(公告)日：2022-10-06

申请号：US17711553

申请日：2022-04-01

Applicant: NEC Laboratories America, Inc.

Inventor： Yuncong Chen ,  Cristian Lumezanu ,  Wei Cheng ,  Takehiko Mizoguchi ,  Masanao Natsumeda ,  Haifeng Chen

IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F40/284

Abstract: A method for explaining sensor time series data in natural language is presented. The method includes training a neural network model with text-annotated time series data, the neural network model including a time series encoder and a text generator, allowing a human operator to select a time series segment from the text-annotated time series data, the time series segment processed by the time series encoder, outputting, from the time series encoder, a sequence of hidden state vectors, one for each timestep, and generating readable explanatory texts for the human operator based on the selected time series segment, the readable explanatory texts being a set of comment texts explaining and interpreting the selected time series segment in a plurality of different ways.