公开(公告)号：US20210111794A1

公开(公告)日：2021-04-15

申请号：US17106143

申请日：2020-11-29

Applicant: NEC Laboratories America, Inc.

Inventor： Yue-Kai HUANG ,  Shaoliang ZHANG ,  Ezra IP ,  Jiakai YU

IPC: H04B10/079 ,  G06N3/04

Abstract: Aspects of the present disclosure describe systems, methods. and structures in which a hybrid neural network combining a CNN and several ANNs are shown useful for predicting G-ONSR for Ps-256QAM raw data in deployed SSMF metro networks with 0.27 dB RMSE. As demonstrated, the CNN classifier is trained with 80.96% testing accuracy to identify channel shaping factor. Several ANN regression models are trained to estimate G-OSNR with 0.2 dB for channels with various constellation shaping. Further aspects include the tuning of existing optical networks and the characterization of retrofit/upgraded optical networks to estimate capacity—both aspects employing our inventive hybrid neural network methodology.

公开(公告)号：US20210110210A1

公开(公告)日：2021-04-15

申请号：US17128535

申请日：2020-12-21

Applicant: NEC Laboratories America, Inc.

Inventor： Yi-Hsuan Tsai ,  Kihyuk Sohn ,  Buyu Liu ,  Manmohan Chandraker ,  Jong-Chyi Su

IPC: G06K9/62 ,  G06K9/00 ,  G06K9/32 ,  B60W30/095 ,  B60W30/09 ,  B60W10/20 ,  B60W10/18 ,  B60W50/00 ,  G08G1/16 ,  G06N3/08

Abstract: Systems and methods for lane marking and road sign recognition are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes one or more road scenes having lane markings and road signs. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

公开(公告)号：US20210103706A1

公开(公告)日：2021-04-08

申请号：US17060850

申请日：2020-10-01

Applicant: NEC Laboratories America, Inc.

Inventor： Wenchao Yu ,  Bo Zong ,  Wei Cheng ,  Haifeng Chen ,  Xiusi Chen

IPC: G06F40/58 ,  G06F40/44 ,  G06N5/02 ,  G06N5/04 ,  G06N3/04

Abstract: Methods and systems for performing a knowledge graph task include aligning multiple knowledge graphs and performing a knowledge graph task using the aligned multiple knowledge graphs. Aligning the multiple knowledge graphs includes updating entity representations based on representations of neighboring entities within each knowledge graph, updating entity representations based on representations of entities from different knowledge graphs, and learning machine learning model parameters to align the multiple knowledge graphs, based on the updated entity representations.

公开(公告)号：US10943419B2

公开(公告)日：2021-03-09

申请号：US16689663

申请日：2019-11-20

Applicant: NEC Laboratories America, Inc.

Inventor： Mohammad Khojastepour ,  Mustafa Arslan ,  Sampath Rangarajan

IPC: G07C9/00 ,  G07C9/28 ,  H01Q1/12 ,  E06B11/02 ,  G06K7/10 ,  H04W4/80 ,  E06B11/08 ,  G07C9/15 ,  G07C9/27

Abstract: A walk-though gate (WTG) is presented. The WTG includes a WTG structure including a first wall and a second wall, the first and second walls defining a walk though pass way between an entrance and exit, at least one sensor located at the entrance and the exit of a cavity defined by the walk though pass way, at least one first antenna facing toward an inside region of the WTG structure, at least one second antenna facing away from the inside region of the WTG structure, an RFID reader connected to the at least one first and second antennas, and a judgement module to judge if an RFID tag is located inside or outside the walk though gate structure.

公开(公告)号：US20210067558A1

公开(公告)日：2021-03-04

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: H04L29/06 ,  G06N3/04

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：US20210067549A1

公开(公告)日：2021-03-04

申请号：US17004752

申请日：2020-08-27

Applicant: NEC Laboratories America, Inc.

Inventor： Zhengzhang Chen ,  Jiaping Gui ,  Haifeng Chen ,  Junghwan Rhee ,  Shen Wang

IPC: H04L29/06 ,  G06N3/08

Abstract: Methods and systems for detecting and responding to an intrusion in a computer network include generating an adversarial training data set that includes original samples and adversarial samples, by perturbing one or more of the original samples with an integrated gradient attack to generate the adversarial samples. The original and adversarial samples are encoded to generate respective original and adversarial graph representations, based on node neighborhood aggregation. A graph-based neural network is trained to detect anomalous activity in a computer network, using the adversarial training data set. A security action is performed responsive to the detected anomalous activity.

公开(公告)号：US20210065735A1

公开(公告)日：2021-03-04

申请号：US16997314

申请日：2020-08-19

Applicant: NEC Laboratories America, Inc.

Inventor： Cristian Lumezanu ,  Yuncong Chen ,  Dongjin Song ,  Takehiko Mizuguchi ,  Haifeng Chen ,  Bo Dong

IPC: G10L25/51 ,  G10L25/24

Abstract: A method is provided. Intermediate audio features are generated from an input acoustic sequence. Using a nearest neighbor search, segments of the input acoustic sequence are classified based on the intermediate audio features to generate a final intermediate feature as a classification for the input acoustic sequence. Each segment corresponds to a respective different acoustic window. The generating step includes learning the intermediate audio features from Multi-Frequency Cepstral Component (MFCC) features extracted from the input acoustic sequence. The generating step includes dividing the same scene into the different acoustic windows having varying MFCC features. The generating step includes feeding the MFCC features of each of the different acoustic windows into respective LSTM units such that a hidden state of each respective LSTM unit is passed through an attention layer to identify feature correlations between hidden states at different time steps corresponding to different ones of the different acoustic windows.

公开(公告)号：US20210065059A1

公开(公告)日：2021-03-04

申请号：US17002960

申请日：2020-08-26

Applicant: NEC Laboratories America, Inc.

Inventor： Dongjin Song ,  Yuncong Chen ,  Cristian Lumezanu ,  Takehiko Mizoguchi ,  Haifeng Chen ,  Dixian Zhu

IPC: G06N20/00 ,  G06F11/30 ,  G06F11/34

Abstract: A computer-implemented method for monitoring computing system status by implementing a deep unsupervised binary coding network includes receiving multivariate time series data from one or more sensors associated with a system, implementing a long short-term memory (LSTM) encoder-decoder framework to capture temporal information of different time steps within the multivariate time series data and perform binary coding, the LSTM encoder-decoder framework including a temporal encoding mechanism, a clustering loss and an adversarial loss, computing a minimal distance from the binary code to historical data, and obtaining a status determination of the system based on a similar pattern analysis using the minimal distance.

公开(公告)号：US20210064959A1

公开(公告)日：2021-03-04

申请号：US16998280

申请日：2020-08-20

Applicant: NEC Laboratories America, Inc.

Inventor： Jiaping Gui ,  Zhengzhang Chen ,  Junghwan Rhee ,  Haifeng Chen ,  Pengyang Wang

IPC: G06N3/04

Abstract: Systems and methods for predicting road conditions and traffic volume is provided. The method includes generating a graph of one or more road regions including a plurality of road intersections and a plurality of road segments, wherein the road intersections are represented as nodes and the road segments are represented as edges. The method can also include embedding the nodes from the graph into a node space, translating the edges of the graph into nodes of a line graph, and embedding the nodes of the line graph into the node space. The method can also include aligning the nodes from the line graph with the nodes from the graph, and optimizing the alignment, outputting a set of node and edge representations that predicts the traffic flow for each of the road segments and road intersections based on the optimized alignment of the nodes.

公开(公告)号：US10931635B2

公开(公告)日：2021-02-23

申请号：US16146166

申请日：2018-09-28

Applicant: NEC Laboratories America, Inc. ,  NEC Corporation

Inventor： Junghwan Rhee ,  Hongyu Li ,  Shuai Hao ,  Chung Hwan Kim ,  Zhenyu Wu ,  Zhichun Li ,  Kangkook Jee ,  Lauri Korts-Parn

IPC: H04L29/06 ,  G06N20/00 ,  H04W4/44 ,  H04W4/40 ,  H04W4/48 ,  H04L29/08

Abstract: Systems and methods for an automotive security gateway include an in-gateway security system that monitors local host behaviors in vehicle devices to identify anomalous local host behaviors using a blueprint model trained to recognize secure local host behaviors. An out-of-gateway security system monitors network traffic across remote hosts, local devices, hotspot network, and in-car network to identify anomalous behaviors using deep packet inspection to inspect packets of the network. A threat mitigation system issues threat mitigation instructions corresponding to the identified anomalous local host behaviors and the anomalous remote host behaviors to secure the vehicle devices by removing the identified anomalous local host behaviors and the anomalous remote host behaviors. Automotive security gateway services and vehicle electronic control units operate the vehicle devices according to the threat mitigation instructions.