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    Computational framework for modeling adversarial activities
    1.
    发明授权
    Computational framework for modeling adversarial activities 有权

    公开(公告)号：US11671436B1

    公开(公告)日：2023-06-06

    申请号：US17021542

    申请日：2020-09-15

    Applicant: HRL Laboratories, LLC

    Inventor： Jiejun Xu ,  Kang-Yu Ni ,  Alexei Kopylov ,  Shane M. Roach ,  Tsai-Ching Lu

    IPC: H04L9/40 ,  G06F16/2457

    CPC classification number: H04L63/1425 ,  G06F16/24578

    Abstract: Described is a system for producing indicators and warnings of adversarial activities. The system receives multiple networks of transactional data from different sources. Each node of a network of transactional data represents an entity, and each edge represents a relation between entities. A worldview graph is generated by merging the multiple networks of transactional data. Suspicious subgraph regions related to an adversarial activity are identified in the worldview graph through activity detection. The suspicious subgraph regions are used to generate and transmit an alert of the adversarial activity.

    Automated system to identify polarized groups on social media
    2.
    发明授权
    Automated system to identify polarized groups on social media 有权

    公开(公告)号：US11126689B1

    公开(公告)日：2021-09-21

    申请号：US16201924

    申请日：2018-11-27

    Applicant: HRL Laboratories, LLC

    Inventor： Dana Warmsley ,  Jiejun Xu ,  Tsai-Ching Lu

    IPC: G06F16/00 ,  G06F17/16 ,  G06F16/9536 ,  G06F16/28

    Abstract: Described is a system for identifying and communicating with polarized groups in social media platforms. The system generates a tripartite graph from online social network data. The tripartite graph incorporates user data, post data, and tag data obtained from the online social network data. Nonnegative matrix factorization is performed on a decomposed tripartite graph to obtain an optimization function. The optimization function is solved to identify polarized groups in the online social network. Based on the identified polarized groups, the system sends pre-determined communications to members of each group aimed at targeted escalation or de-escalation of polarization in an online social media platform.

    System for inferring network dynamics and sources within the network
    3.
    发明授权
    System for inferring network dynamics and sources within the network 有权

    公开(公告)号：US10652104B1

    公开(公告)日：2020-05-12

    申请号：US15782668

    申请日：2017-10-12

    Applicant: HRL Laboratories, LLC

    Inventor： Steven J. Munn ,  Kang-Yu Ni ,  Jiejun Xu

    IPC: G06F15/173 ,  H04L12/24 ,  H04L12/26 ,  G06F17/16 ,  G06F9/50

    Abstract: Described is a system for inferring network dynamics and their sources within the network. During operation, a vector representation is generated based on states of agents in a network. The vector representation including attribute vectors that correspond to the states of the agents in the network. A matrix representation is then generated based on the changing states of agents by packing the attribute vectors at each time step into an attribute matrix. Time-evolving states of the agents are learned using dictionary learning. Influential source agents in the network are then identified by performing dimensionality reduction on the attribute matrix. Finally, in some aspects, an action is executed based on the identity of the influential source agents. For example, marketing material may be directed to a source agent's online account, or the source agent's online account can be deactivated or terminated or some other desired action can be taken.

    Fast open doorway detection for autonomous robot exploration
    7.
    发明授权
    Fast open doorway detection for autonomous robot exploration 有权
    快速开门检测自动机器人探测

    公开(公告)号：US09251417B1

    公开(公告)日：2016-02-02

    申请号：US14515414

    申请日：2014-10-15

    Applicant: HRL Laboratories, LLC

    Inventor： Jiejun Xu ,  Deepak Khosla ,  Kyungnam Kim

    IPC: G06K9/00 ,  G06T3/40 ,  G06K9/46

    CPC classification number: G06K9/00664 ,  G06K9/00208 ,  G06T2210/21

    Abstract: Described is a system for open doorway detection for autonomous robot exploration, the system includes an onboard range sensor that is operable for constructing a three-dimensional (3D) point cloud of a scene. One or more processors that receive the 3D point cloud from the range sensor. The 3D point cloud is then filtered and downsampled to remove cloud points outside of a predefined range and reduce a size of the point cloud and, in doing so, generate a filtered and downsampled 3D point cloud. Vertical planes are extracted from the filtered and downsampled 3D point cloud. Finally, open doorways are identified from each extracted vertical plane.

    Abstract translation: 描述了一种用于自动机器人探测的开门检测系统，该系统包括可用于构建场景的三维（3D）点云的车载范围传感器。 从范围传感器接收3D点云的一个或多个处理器。 然后，3D点云被过滤和下采样，以除去预定义范围之外的云点，并减小点云的大小，并在此过程中生成滤波和下采样的3D点云。 从过滤和下采样的3D点云中提取垂直平面。 最后，从每个提取的垂直平面识别开门。

    State transition network analysis of multiple one-dimensional time series
    8.
    发明授权
    State transition network analysis of multiple one-dimensional time series 有权

    公开(公告)号：US11106989B1

    公开(公告)日：2021-08-31

    申请号：US15912209

    申请日：2018-03-05

    Applicant: HRL Laboratories, LLC

    Inventor： Alex N. Waagen ,  Tsai-Ching Lu ,  Jiejun Xu

    IPC: G06N7/00 ,  G06N5/02 ,  G06N5/04 ,  G06F9/448 ,  G06F16/2458 ,  G06F16/901 ,  G06Q50/00

    Abstract: Described is a system for predicting an occurrence of large-scale events using social media data. A collection of time series is acquired from social media data related to an event of interest. The collection of time series is partitioned into time intervals and semantic features are extracted from the time intervals as a set of semantic intervals. The semantic features are encoded into a multilayer network. Subgraphs of the multilayer network are transformed into a state transition network. A prediction of a future event of interest is generated by analyzing the encoded network using the state transition network. Using the analyzed encoded network, a device is controlled based on the prediction of the future event of interest.

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