Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

Abstract: Techniques are described for using computing devices to perform automated operations for automatically generating information about attributes of buildings from automated analysis of building information that includes floor plans and acquired building images and to subsequently using the generated building information in one or more further automated manners. In some situations, such automated generation of building information includes automatically determining objects in a building and other attributes of the building, and automatically generating descriptions about the determined building attributes. Information about such determined attributes and generated descriptions may be used in various automated manners, including for updating and/or validating information in existing building descriptions, for determining matching buildings that have similarities to indicated building descriptions or other specified criteria, for controlling device navigation (e.g.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for dynamic container auto-configuration. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. An auto-configuration application determines a representative container point cloud and (a) loads an initial pre-configuration file that defines a digital bounding box having dimensions representative of the predefined search space and an initial front board area; (b) applies the digital bounding box to the container point cloud to remove front board interference data from the container point cloud based on the initial front board area; (c) generates a refined front board area based on the shipping container type; (d) generates an adjusted digital bounding box based on the refined front board area; and (e) generates an auto-configuration result comprising the adjusted digital bounding box containing at least a portion of the container point cloud.

Abstract: Techniques are described for using computing devices to perform automated operations for determining matching buildings and using corresponding information in further automated manners, such as by determining buildings having similarities to other indicated buildings based at least in part on building floor plans and/or other attributes, and by automatically generating descriptions of the similarities. In some situations, such matching building determinations are based on generating and using adjacency graphs for floor plans that represent inter-connections between rooms and other attributes of the buildings, and further generating and comparing vector-based embeddings that concisely represent the information of the adjacency graphs for use in performing inter-building comparisons and generating the descriptions. Information about such determined buildings may be used in various automated manners, including for controlling device navigation (e.g.

Abstract: The present invention discloses a foldable FRP plate, comprising a plurality of first regions and one or a plurality of second regions which are integrated in one piece; the second region is located between two adjacent first regions, so that the adjacent first regions being folded and unfolded relative to each other with the second region as a rotating shaft; the first regions are plate-like products manufactured by impregnating fiber woven fabric with resin for curing, are rigid and cannot be folded; the second region is flexible fiber woven fabric and has a width being two times a design thickness of the FRP plate. The present invention also discloses a manufacturing method, including laying the fiber woven fabric according to a design thickness and a layer layout; dividing the first regions and the second region according to an origami design method.

Abstract: In some examples, touch data can include noise. Machine learning techniques, such as gated recurrent units and convolutional neural networks can be used to mitigate noise present in touch data. In some examples, a gated recurrent unit stage and a convolutional neural network stage can be arranged in series, such as by providing the output of the gated recurrent unit as input to the convolutional neural network. The gated recurrent unit can remove noise caused by a first component of the electronic device and the convolutional neural network can remove noise caused by a second component of the electronic device, for example. Thus, together, the gated recurrent unit and the convolutional neural network can remove or substantially reduce the noise in the touch data.

Abstract: Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for assessing an orientation with respect to a container. A 3D-depth camera captures 3D image data of a shipping container. A container feature assessment application determines a representative container point cloud and (a) converts the 3D image data into 2D depth image data; (b) compares the 2D depth image data to one or more template image data; (c) performs segmentation to extract 3D point cloud features; (d) determines exterior features of the shipping container and assesses the exterior features using an exterior features metric; (e) determines interior features of the shipping container and assesses the interior features using an interior features metric; and (f) generates an orientation adjustment instruction for indicating to an operator to orient the 3D-depth camera in a second direction for use during a shipping container loading session, wherein the second direction is different than the first direction.

Abstract: A basket and a stroller with the basket are disclosed. The stroller includes a frame, a seat and a basket. The seat is disposed on the frame, and the basket is connected to the frame and located under the seat. The basket includes a bottom, a front stopper, a rear stopper and two side stoppers. The front stopper, the rear stopper and the side stoppers are connected to a front side, a rear side, and left and right sides of the bottom respectively. By the movement of the rear stopper with respect to the bottom, the deformation of the rear stopper itself, or the detachable connection of the rear stopper to the bottom, an opening for objects to enter the basket is formed at a rear side of the basket.

Abstract: A method for implementing graph-based reinforced text representation learning (GRTR) is presented. The method includes, in a training phase, generating a dependency tree for training text data, training a GRTR agent by learning to navigate in the dependency tree and selectively collecting semantic information, learning GRTR agents, and storing, in a GRTR-specific memory, parameters of the learned GRTR agents. The method further includes, in a testing phase, generating a dependency tree for testing the text data, retrieving and evaluating the learned GRTR agents of the training phase to evaluate testing samples, making task-specific decisions for the testing samples, and reporting the task-specific decisions to a computing device operated by a user.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for dynamic container auto-configuration. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. An auto-configuration application determines a representative container point cloud and (a) loads an initial pre-configuration file that defines a digital bounding box having dimensions representative of the predefined search space and an initial front board area; (b) applies the digital bounding box to the container point cloud to remove front board interference data from the container point cloud based on the initial front board area; (c) generates a refined front board area based on the shipping container type; (d) generates an adjusted digital bounding box based on the refined front board area; and (e) generates an auto-configuration result comprising the adjusted digital bounding box containing at least a portion of the container point cloud.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for assessing an orientation with respect to a container. A 3D-depth camera captures 3D image data of a shipping container. A container feature assessment application determines a representative container point cloud and (a) converts the 3D image data into 2D depth image data; (b) compares the 2D depth image data to one or more template image data; (c) performs segmentation to extract 3D point cloud features; (d) determines exterior features of the shipping container and assesses the exterior features using an exterior features metric; (e) determines interior features of the shipping container and assesses the interior features using an interior features metric; and (f) generates an orientation adjustment instruction for indicating to an operator to orient the 3D-depth camera in a second direction for use during a shipping container loading session, wherein the second direction is different than the first direction.

Abstract: Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for dynamic container auto-configuration. A 3D-depth camera captures 3D image data of a shipping container located in a predefined search space during a shipping container loading session. An auto-configuration application determines a representative container point cloud and (a) loads an initial pre-configuration file that defines a digital bounding box having dimensions representative of the predefined search space and an initial front board area; (b) applies the digital bounding box to the container point cloud to remove front board interference data from the container point cloud based on the initial front board area; (c) generates a refined front board area based on the shipping container type; (d) generates an adjusted digital bounding box based on the refined front board area; and (e) generates an auto-configuration result comprising the adjusted digital bounding box containing at least a portion of the container point cloud.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for assessing an orientation with respect to a container. A 3D-depth camera captures 3D image data of a shipping container. A container feature assessment application determines a representative container point cloud and (a) converts the 3D image data into 2D depth image data; (b) compares the 2D depth image data to one or more template image data; (c) performs segmentation to extract 3D point cloud features; (d) determines exterior features of the shipping container and assesses the exterior features using an exterior features metric; (e) determines interior features of the shipping container and assesses the interior features using an interior features metric; and (f) generates an orientation adjustment instruction for indicating to an operator to orient the 3D-depth camera in a second direction for use during a shipping container loading session, wherein the second direction is different than the first direction.

Abstract: Three-dimensional (3D) depth imaging systems and methods are disclosed for assessing an orientation with respect to a container. A 3D-depth camera captures 3D image data of a shipping container. A container feature assessment application determines a representative container point cloud and (a) converts the 3D image data into 2D depth image data; (b) compares the 2D depth image data to one or more template image data; (c) performs segmentation to extract 3D point cloud features; (d) determines exterior features of the shipping container and assesses the exterior features using an exterior features metric; (e) determines interior features of the shipping container and assesses the interior features using an interior features metric; and (f) generates an orientation adjustment instruction for indicating to an operator to orient the 3D-depth camera in a second direction for use during a shipping container loading session, wherein the second direction is different than the first direction.

Abstract: Some embodiments disclosed here provide a method performed by a radio node in a narrowband communication system. The method comprises deploying a standalone narrowband carrier for the narrowband communication system outside of an in-band and a guardband of a wideband carrier for a wideband communication system. The method further comprises transmitting deployment information to a wireless device indicating that the standalone narrowband carrier is deployed in the in-band or the guardband of the wideband carrier.

Abstract: A versatile and mobile keyboard and a highly energy efficient keyboard scanning method are provided. The keyboard may be used with a variety of mobile devices such as PDAs, cellular phones, and tablet PCs, through various interfaces such as an IR, USB, or Bluetooth™ interface. In particular, an IR head assembly is provided that includes an IR head, a movable arm on which the IR head is mounted, and a connector for attaching to a docking structure. The docking structure may be attached to a keypad, which is electrically coupled to the IR head for transmitting keystroke data through the IR head. When a mobile device is docked on the docking structure, the arm in the IR assembly may be moved to an optimal distance from the IR port of the mobile device to ensure high communication performance between the keyboard and the mobile device. An energy efficient keyboard scanning method utilizes higher-valued pull-up resistors for energy conservation.

Abstract: A versatile and mobile keyboard and a highly energy efficient keyboard scanning method are provided. The keyboard may be used with a variety of mobile devices such as PDAs, cellular phones, and tablet PCs, through various interfaces such as an IR, USB, or Bluetooth™ interface. In particular, an IR head assembly is provided that includes an IR head, a movable arm on which the IR head is mounted, and a connector for attaching to a docking structure. The docking structure may be attached to a keypad, which is electrically coupled to the IR head for transmitting keystroke data through the IR head. When a mobile device is docked on the docking structure, the arm in the IR assembly may be moved to an optimal distance from the IR port of the mobile device to ensure high communication performance between the keyboard and the mobile device. An energy efficient keyboard scanning method utilizes higher-valued pull-up resistors for energy conservation.

Abstract: A versatile and mobile keyboard and a highly energy efficient keyboard scanning method are provided. The keyboard may be used with a variety of mobile devices such as PDAs, cellular phones, and tablet PCs, through various interfaces such as an IR, USB, or Bluetooth™ interface. In particular, an IR head assembly is provided that includes an IR head, a movable arm on which the IR head is mounted, and a connector for attaching to a docking structure. The docking structure may be attached to a keypad, which is electrically coupled to the IR head for transmitting keystroke data through the IR head. When a mobile device is docked on the docking structure, the arm in the IR assembly may be moved to an optimal distance from the IR port of the mobile device to ensure high communication performance between the keyboard and the mobile device. An energy efficient keyboard scanning method utilizes higher-valued pull-up resistors for energy conservation.