ANOMALY DETECTION WITH GRAPH ADVERSARIAL TRAINING IN COMPUTER SYSTEMS

    公开(公告)号:US20210067549A1

    公开(公告)日:2021-03-04

    申请号:US17004752

    申请日:2020-08-27

    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.

    SEQUENCE MODELS FOR AUDIO SCENE RECOGNITION

    公开(公告)号:US20210065735A1

    公开(公告)日:2021-03-04

    申请号:US16997314

    申请日:2020-08-19

    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.

    FLEXIBLE EDGE-EMPOWERED GRAPH CONVOLUTIONAL NETWORKS WITH NODE-EDGE ENHANCEMENT

    公开(公告)号:US20210064959A1

    公开(公告)日:2021-03-04

    申请号:US16998280

    申请日:2020-08-20

    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.

    Efficient beam search and data communication in millimeter-wave wireless networks

    公开(公告)号:US10931352B2

    公开(公告)日:2021-02-23

    申请号:US16591070

    申请日:2019-10-02

    Abstract: A method for establishing communication links in a millimeter wave network is presented. The method includes determining an active communication link between first and second devices, the first device transmitting probing packets to the second device, employing a beam search technique to locate narrow beams by triggering the first device to adjust its transmission pattern to cover a fraction of an angular uncertainty region (AUR) at a beginning of a time-slot, and adjusting transmission coefficients of the first device based on a response received from the second device such that if the second device receives a probing packet, the second device sends an acknowledgment packet to the first device and the first device updates the AUR such that the AUR is set to a probed angular interval, and if the second device does not receive the probing packet, the first device updates the AUR to a complementary part of the probed interval.

    Sequence models for audio scene recognition

    公开(公告)号:US10930301B1

    公开(公告)日:2021-02-23

    申请号:US16997314

    申请日:2020-08-19

    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.

    Recommender system for heterogeneous log pattern editing operation

    公开(公告)号:US10929763B2

    公开(公告)日:2021-02-23

    申请号:US15684293

    申请日:2017-08-23

    Abstract: A heterogeneous log pattern editing recommendation system and computer-implemented method are provided. The system has a processor configured to identify, from heterogeneous logs, patterns including variable fields and constant fields. The processor is also configured to extract a category feature, a cardinality feature, and a before-after n-gram feature by tokenizing the variable fields in the identified patterns. The processor is additionally configured to generate target similarity scores between target fields to be potentially edited and other fields from among the variable fields in the heterogeneous logs using pattern editing operations based on the extracted category feature, the extracted cardinality feature, and the extracted before-after n-gram feature. The processor is further configured to recommend, to a user, log pattern edits for at least one of the target fields based on the target similarity scores between the target fields in the heterogeneous logs.

    SELF-SUPERVISED VISUAL ODOMETRY FRAMEWORK USING LONG-TERM MODELING AND INCREMENTAL LEARNING

    公开(公告)号:US20210042937A1

    公开(公告)日:2021-02-11

    申请号:US16939604

    申请日:2020-07-27

    Abstract: A computer-implemented method for implementing a self-supervised visual odometry framework using long-term modeling includes, within a pose network of the self-supervised visual odometry framework including a plurality of pose encoders, a convolution long short-term memory (ConvLSTM) module having a first-layer ConvLSTM and a second-layer ConvLSTM, and a pose prediction layer, performing a first stage of training over a first image sequence using photometric loss, depth smoothness loss and pose cycle consistency loss, and performing a second stage of training to finetune the second-layer ConvLSTM over a second image sequence longer than the first image sequence.

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