Abstract: A virtual network device may identify a cloud provider associated with the virtual network device, and may provide a request for public network addresses and private network addresses associated with the cloud provider. The virtual network device may receive the public network addresses and the private network addresses from the cloud provider based on the request, and may generate a translation table that maps the public network addresses and the private network addresses. The virtual network device may utilize the translation table to establish a secure communication between an endpoint device and a server device, where the secure communication is associated with at least one packet that requires an inner payload network address change.

Abstract: Described is a multiple-camera system and process for detecting, tracking, and re-verifying agents within a materials handling facility. In one implementation, a plurality of feature vectors may be generated for an agent and maintained as an agent model representative of the agent. When the object being tracked as the agent is to be re-verified, feature vectors representative of the object are generated and stored as a probe agent model. Feature vectors of the probe agent model are compared with corresponding feature vectors of candidate agent models for agents located in the materials handling facility. Based on the similarity scores, the agent may be re-verified, it may be determined that identifiers used for objects tracked as representative of the agents have been flipped, and/or to determine that tracking of the object representing the agent has been dropped.

Abstract: This disclosure describes a system for managing inventory as it transitions into a materials handling facility, as it transitions between locations within a materials handling facility and/or as it transitions out of a materials handling facility. In some instances, a user (e.g., picker or picking agent) may retrieve an item from an inventory location and place the item into a tote. The systems described herein detect the item when it is added to or removed from the tote.

Abstract: Where an event is determined to have occurred at a location within a vicinity of a plurality of actors, imaging data captured using cameras having the location is processed using one or more machine learning systems or techniques operating on the cameras to determine which of the actors is most likely associated with the event. For each relevant pixel of each image captured by a camera, the camera returns a set of vectors extending to pixels of body parts of actors who are most likely to have been involved with an event occurring at the relevant pixel, along with a measure of confidence in the respective vectors. A server receives the vectors from the cameras, determines which of the images depicted the event in a favorable view, based at least in part on the quality of such images, and selects one of the actors as associated with the event accordingly.

Abstract: This disclosure describes architectures and techniques to provide information to a user about items with which the user interacts. In some instances, a user may utilize a wearable device that is configured to interact with one or more components of an information discovery system to obtain information about items in the user's environment.

Abstract: A method and system for autonomous parking are disclosed. The system employs Homography guided motion segmentation (HGMS) to determine the availability of a parking slot. A free parking slot is determined based on an occupancy status check and identified structures of parking lines. For example, motion analysis is performed on successive images captured by a camera unit to determine the occupancy status of a parking slot. Information of the free parking slot, for example, the dimensions and orientation, may be determined from identifying the structure of parking lines. This information is used by the system to guide a vehicle from its current location to the detected free parking slot.

Abstract: An identification system includes one or more infrared light sources and a camera that acquires images of a user's palm. For example, at a first time, one or more first images may be acquired by the camera using infrared light with a first polarization that represent external characteristics of the user's palm. At a second time, one or more second images may be acquired using infrared light with a second polarization that represent internal characteristics of the user's palm. These images are processed to determine a first set of feature vectors and a second set of feature vectors. A current signature may be determined using the first set of feature vectors and the second set of feature vectors. In addition, a user may be identified based on a comparison of the current signature and previously stored reference signatures that are associated with candidate user identifiers.

Abstract: The systems and methods disclosed herein comprise computer-based platforms configured for automated early-stage application security monitoring for allowing users (e.g., application developers) to make decisions at the early stage of the application development.

Abstract: A system may use sensor data from a facility to generate tentative values associated with an event, such as the identification of an item removed from a shelf of the facility. A confidence value associated with each of the tentative values may be less than a confidence threshold. In response, inquiry data seeking confirmation of a tentative value from an associate is generated and sent to one or more associates in the facility. Responses from the associates are collected to determine a selection of one of the tentative values. The selected tentative value is designated as output data for the system. Thereafter, the output data and the original sensor data are designated as training data, which can then be used to train or update machine learning systems. Subsequent use of the updated machine learning systems can yield more accurate results.

Abstract: A neural network is trained to process input data and generate a classification value that characterizes the input with respect to an ordered continuum of classes. For example, the input data may comprise an image and the classification value may be indicative of a quality of the image. The ordered continuum of classes may represent classes of quality of the image ranging from “worst”, “bad”, “normal”, “good”, to “best”. During training, loss values are determined using an ordered classification loss function. The ordered classification loss function maintains monotonicity in the loss values that corresponds to placement in the continuum. For example, the classification value for a “bad” image will be less than the classification value indicative of a “best” image. The classification value may be used for subsequent processing. For example, biometric input data may be required to have a minimum classification value for further processing.

Abstract: Methods, apparatus, systems and articles of manufacture to generate regex and detect data similarity are disclosed. An example apparatus includes a token graph generator to generate a token graph including nodes based on a cluster of strings corresponding to a group of messages that are known to be spam; a pivot engine to identify pivot nodes in the cluster of strings; a pivot applicator to tag corresponding ones of the nodes of the token graph as the pivot nodes; and a regex converter to generate the anti-spam signature based on: (a) the tagged nodes and (b) at least one of the node of the token graph that is not tagged as a pivot node.

Abstract: Devices are dynamically paired at a new location based on historical context with respect to previous device pairing and a pairing environment. A request is received at a computer to pair a user device to a device at a new location. Historical context of the user device paired at a previous location to one or more devices at the previous location is analyzed. Pairing of the user device to the device at the new location is automatically initiated based on the analysis of the historical context, and the automatic initiation includes adjusting settings on the user device based on the historical context. Pairing the user device to the device at the new location automatically.

Abstract: This disclosure is directed to techniques in which a first user in an environment scans visual indicia associated with an item, such as a barcode, before handing the item to a second user. One or more computing devices may receive an indication of the scan, retrieve image data of the interaction from a camera within the environment, identify the user that received the item, and update a virtual cart associated with the second user to indicate addition of the item.

Abstract: This disclosure describes a system for disambiguating between multiple potential users that may have performed an item action (e.g., item removal or item placement) at an inventory location. For example, if there are three picking agents (users) standing near an inventory location and one of the agents removes an item (item action) from the inventory location, the example systems and processes described herein may utilize various inputs to disambiguate between the users and determine which of the potential users performed the item action.

Abstract: This disclosure describes techniques for identifying users that are enrolled for use of a user-recognition system and updating identification data of these users over time. To enroll in the user-recognition system, the user may initially scan his or her palm. The resulting image data may later be used when the user requests to be identified by the system by again scanning his or her palm. However, because the characteristics of user palms may change over the time, the user-recognition system may periodically perform processes for updating the identification data stored in association with the user in order to maintain or increase an accuracy of the user-recognition system.

Abstract: A pair of input images acquired using a first modality and a second modality is processed using a multi-classifier trained to determine classification data indicative of whether the pair is normal or abnormal. A pair may be deemed abnormal if one or both input images are obscured or inconsistent with one another. Training data comprising normal and abnormal images are used to train the multi-classifier. During training, the multi-classifier uses an objective function that includes cross entropy loss, distance loss, and discrepancy loss to process the training data. During use, the trained multi-classifier processes a pair of input images. If the resulting classification data indicates the pair of input images are normal, the pair of input images may be processed to assert an identity.

Abstract: A process includes accessing by a management controller of a computer platform, a platform certificate that is stored in a secure memory. The platform certificate includes data representing a reference inventory for the computer platform. The platform certificate includes data representing information designated to bind the platform certificate to a security processor. The security processor is accessible by the management controller. The security processor is inaccessible by an operating system of the computer platform. The process includes verifying the platform certificate. Verifying the platform certificate includes validating, by the management controller, a signature of the platform certificate; and validating, by the management controller, the information designated to bind the platform certificate to the security processor. Verifying the platform certificate includes comparing, by the management controller, a second inventory of the computer platform to the reference inventory.

Abstract: This disclosure describes a system for managing inventory as it transitions into a materials handling facility, as it transitions between locations within a materials handling facility and/or as it transitions out of a materials handling facility. In some instances, a user (e.g., picker or picking agent) may retrieve an item from an inventory location and place the item into a tote. The systems described herein detect the item when it is added to or removed from the tote.

Abstract: A method for performing data prediction includes: obtaining a dataset; generating prediction parameters using the dataset; identifying significant variables in the dataset; predicting seasonality of the dataset based on the significant variables; determining uncertainty of the prediction parameters; performing the data prediction by minimizing randomness and uncertainty of the dataset; and displaying the data prediction on a graphical user interface.

Abstract: This disclosure describes a system for tracking removal or placement of items at inventory locations with a materials handling facility. In some instances, a user may remove an item from an inventory location and the inventory management system may detect that removal and update a user item list associated with the user to include an item identifier representative of the removed item. Likewise, if the user places an item at an inventory location, the inventory management system may detect that placement and update the user item list to remove an item identifier representative of the placed item.