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    Learning representations of generalized cross-modal entailment tasks
    92.
    发明授权
    Learning representations of generalized cross-modal entailment tasks 有权

    公开(公告)号：US11250299B2

    公开(公告)日：2022-02-15

    申请号：US16668680

    申请日：2019-10-30

    Applicant: NEC Laboratories America, Inc.

    Inventor： Farley Lai ,  Asim Kadav ,  Ning Xie

    IPC: G06K9/00 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08 ,  G06K9/46 ,  G06K9/72 ,  G06K9/32

    Abstract: A method is provided for determining entailment between an input premise and an input hypothesis of different modalities. The method includes extracting features from the input hypothesis and an entirety of and regions of interest in the input premise. The method further includes deriving intra-modal relevant information while suppressing intra-modal irrelevant information, based on intra-modal interactions between elementary ones of the features of the input hypothesis and between elementary ones of the features of the input premise. The method also includes attaching cross-modal relevant information to the features from the input premise to the features from the input hypothesis to form a cross-modal representation, based on cross-modal interactions between pairs of different elementary features from different modalities. The method additionally includes classifying a relationship between the input premise and the input hypothesis using a label selected from the group consisting of entailment, neutral, and contradiction based on the cross-modal representation.

    Deep Q-network reinforcement learning for testing case selection and prioritization
    93.
    发明授权
    Deep Q-network reinforcement learning for testing case selection and prioritization 有权

    公开(公告)号：US11249887B2

    公开(公告)日：2022-02-15

    申请号：US16998224

    申请日：2020-08-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Jianwu Xu ,  Haifeng Chen ,  Yuchen Bian

    IPC: G06F9/44 ,  G06F11/36 ,  G06N20/00

    Abstract: Systems and methods for automated software test design and implementation. The system and method being able to establish an initial pool of test cases for testing computer code; apply the initial pool of test cases to the computer code in a testing environment to generate test results; preprocess the test results into a predetermined format; extract metadata from the test results; generate a training sequence; calculate a reward value for the pool of test cases; input the training sequence and reward value into a reinforcement learning agent; utilizing the value output from the reinforcement learning agent to produce a ranking list; prioritizing the initial pool of test cases and one or more new test cases based on the ranking list; and applying the prioritized initial pool of test cases and one or more new test cases to the computer code in a testing environment to generate test results.

    Early anomaly prediction on multi-variate time series data
    96.
    发明授权
    Early anomaly prediction on multi-variate time series data 有权

    公开(公告)号：US11204602B2

    公开(公告)日：2021-12-21

    申请号：US16433206

    申请日：2019-06-06

    Applicant: NEC Laboratories America, Inc.

    Inventor： Wei Cheng ,  Haifeng Chen ,  Masanao Natsumeda

    IPC: G05B23/02 ,  G06N3/04 ,  G06F17/16 ,  G06K9/62 ,  G06N3/08

    Abstract: Systems and methods for early anomaly prediction on multi-variate time series data are provided. The method includes identifying a user labeled abnormal time period that includes at least one anomaly event. The method also includes determining a multi-variate time series segment of multivariate time series data that occurs before the user labeled abnormal time period, and treating, by a processor device, the multi-variate time series segment to include precursor symptoms of the at least one anomaly event. The method includes determining instance sections from the multi-variate time series segment and determining at least one precursor feature vector associated with the at least one anomaly event for at least one of the instance sections based on applying long short-term memory (LSTM). The method further includes dispatching predictive maintenance based on the at least one precursor feature vector.

    FREE FLOW FEVER SCREENING
    97.
    发明申请
    FREE FLOW FEVER SCREENING 有权

    公开(公告)号：US20210378520A1

    公开(公告)日：2021-12-09

    申请号：US17325613

    申请日：2021-05-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kunal Rao ,  Giuseppe Coviello ,  Min Feng ,  Biplob Debnath ,  Wang-pin Hsiung ,  Murugan Sankaradas ,  Srimat Chakradhar ,  Yi Yang ,  Oliver Po ,  Utsav Drolia

    IPC: A61B5/01 ,  A61B5/00

    Abstract: A method for free flow fever screening is presented. The method includes capturing a plurality of frames from thermal data streams and visual data streams related to a same scene to define thermal data frames and visual data frames, detecting and tracking a plurality of individuals moving in a free-flow setting within the visual data frames, and generating a tracking identification for each individual of the plurality of individuals present in a field-of-view of the one or more cameras across several frames of the plurality of frames. The method further includes fusing the thermal data frames and the visual data frames, measuring, by a fever-screener, a temperature of each individual of the plurality of individuals within and across the plurality of frames derived from the thermal data streams and the visual data streams, and generating a notification when a temperature of an individual exceeds a predetermined threshold temperature.

    Teaching syntax by adversarial distraction
    98.
    发明授权
    Teaching syntax by adversarial distraction 有权

    公开(公告)号：US11194974B2

    公开(公告)日：2021-12-07

    申请号：US16522742

    申请日：2019-07-26

    Applicant: NEC Laboratories America, Inc.

    Inventor： Christopher Malon ,  Asim Kadav ,  Juho Kim

    IPC: G10L15/00 ,  G10L15/28 ,  G06F17/00 ,  G06F40/40 ,  G06N3/08

    Abstract: A computer-implemented method and system are provided for teaching syntax for training a neural network based natural language inference model. The method includes selectively performing, by the hardware processor, person reversal on a set of hypothesis sentences, based on person reversal prevention criteria, to obtain a first training data set. The method further includes enhancing, by the hardware processor, a robustness of the neural network based natural language inference model to syntax changes by training the neural network based natural language inference model on original training data combined with the first data set.

    INTERACTIVE BEAM ALIGNMENT WITH DELAYED FEEDBACK
    99.
    发明申请
    INTERACTIVE BEAM ALIGNMENT WITH DELAYED FEEDBACK 有权

    公开(公告)号：US20210376906A1

    公开(公告)日：2021-12-02

    申请号：US17331078

    申请日：2021-05-26

    Applicant: NEC Laboratories America, Inc.

    Inventor： Mohammad Khojastepour ,  Shahram Shahsavari

    IPC: H04B7/06 ,  H04W72/04 ,  H04B7/0408

    Abstract: Methods for transmitting data include sending a first probing packet using a first scanning beam, selected from a set of scanning beams. Feedback about the first probing packet is determined. A second probing packet is sent using a second scanning beam, selected from the set of scanning beams based on the determined feedback about the first probing packet. Feedback about the second probing packet is determined. A data transmission beam is determined based on the set of scanning beams and the received feedback about the first probing packet and the second probing packet. Data is transmitted using an antenna that is configured according to the determined transmission beam.

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