Abstract: Systems, methods and media for adaptive real time modeling and scoring are provided. In one example, a system for automatically generating predictive scoring models comprises a trigger component to determine, based on a threshold or trigger, such as a detection of new significant relationships, whether a predictive scoring model is ready for a refresh or regeneration. An automated modeling sufficiency checker receives and transforms user-selectable system input data. The user-selectable system input data may comprise at least one of email, display or social media traffic. An adaptive modeling engine operably connected to the trigger component and modeling sufficiency checker is configured to monitor and identify a change in the input data and, based on an identified change in the input data, automatically refresh or regenerate the scoring model for calculating new lead scores. A refreshed or regenerated predictive scoring model is output.

Abstract: A logical graph is generated using at least a portion of log data received from a set of agents executing on one or more nodes in one or more data centers. The logical graph is augmented using data obtained from one or more agents executing in containerized environments, including by representing communications between pods within the logical graph. The augmented logical graph is used to detect an anomaly.

Abstract: Detecting anomalous behavior using a browser extension, including: gathering first information describing activity associated with a user and generated by a browser extension on a user device; gathering second information describing activity associated with the user and generated by an application executed on the user device; and determining, based on the first information and the second information, whether the user has deviated from normal activity.

Abstract: An apparatus for wireless communications includes a processor, and a memory coupled with the processor and storing instructions operable, when executed by the processor, to cause the apparatus to output a pre-configured uplink resource (PUR) configuration information comprising a least significant bit (LSB) associated with a hyper system frame number (H-SFN). Execution of the instructions further cause the apparatus to obtain, from a user equipment (UE) at a PUR start time, a first message on a PUR associated with the PUR configuration information, the PUR start time associated with the H-SFN corresponding to the LSB.

Abstract: Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may start a timer after a repetition of a communication, where calculation of a length of the timer includes an offset that corresponds to a propagation delay between the UE and a base station via a non-terrestrial network (NTN) entity. The UE may start a discontinuous reception (DRX) retransmission timer after expiration of the timer. Numerous other aspects are described.

Abstract: Embodiments described herein provide a processing apparatus comprising compute circuitry to generate neural network data for a convolutional neural network (CNN) and write the neural network data to a memory buffer. The compute circuitry additionally includes a direct memory access (DMA) controller including a hardware codec having encode circuitry and a decode circuitry. The DMA controller reads the neural network data from the memory buffer, encode the neural network data via the encode circuit, writes encoded neural network data to a memory device coupled with the processing apparatus, writes metadata for the encoded neural network data to the memory device coupled with the processing apparatus, and decodes encoded neural network data via the decode circuit in response to a request from the compute circuitry.

Abstract: Transactions between entities may be validated using LoRaWAN communications to more quickly process those transactions. In the example of check depositing, a first entity associated with a check payee may transmit a check clearance request to a second entity associated with a check payor using LoRaWAN communications. The LoRaWAN signaling and communications may be performed between devices within LoRaWAN range. For example, a check depositing device may receive a check deposit where the payor is associated with a different financial institution or entity. Accordingly, the check depositing device may communicate with another device associated with the payor institution or entity using LoRaWAN signals to confirm check clearance.

Abstract: Communication, authentication, and/or validation may be required in various types of device interactions. For example, for some types of device functions, user or device authentication may be required to confirm that the function can and should be performed. Authentication information may be communicated and authentication and/or validation may be performed using LoRaWAN signaling. LoRaWAN signaling provides low power and long-range communication capabilities to efficiently and effectively perform authentication processes. In some arrangements, LoRaWAN signals may be generated by both a first device (e.g., a user device) and a second device (e.g., a function-performing device) and subsequently compared to determine whether they correspond. This correspondence may provide the requisite authentication or validation of the first device and/or an associated user.

Abstract: Transactions may be performed using a mobile device such as a user-wearable device using LoRaWAN protocol to minimize power consumption while maximizing communication range. Transaction information may be stored in the user-wearable device and transmitted to a transaction processing device using LoRaWAN communications. Transaction information may include user-specific information, payment information, merchant information, product information, user preference information and the like. In some instances, the only communication interface (or wireless communication interface) provided in the mobile device is a LoRaWAN communication interface.

Abstract: Device information may be obtained using LoRaWAN communications. A device information request may be generated and transmitted by a user device and include request parameters such as a device type for which information is desired, a type of information requested, a geographic range for the request, and the like and/or combinations thereof. Device information requests may also be propagated or repeated by intermediary devices to achieve the desired request range. Accordingly, a recipient device of the original request may propagate or repeat, using LoRaWAN, a request signal if it determines that the request range has not been met. If the request range has been met, the recipient device might not repeat the request.

Abstract: Generating user-specific polygraphs for network activity, including: gathering information describing network activity associated with a user and generating, based on the information, a user-specific polygraph that includes one or more destinations associated with the network activity.

Abstract: Methods, systems, and devices for wireless communications are described. A user equipment (UE) may determine a position (e.g., a global navigation satellite system (GNSS)) position of the UE based on a relative position of the UE to another UE. For example, a first UE may determine (e.g., calculate, receive) a relative position of the first UE to a second UE that is selected from a set of UEs including the first UE and the second UE to perform a position acquisition procedure to determine a position of the second UE. The first UE may determine (e.g., calculate, receive) its position based on the relative position of the first UE to the second UE and the position of the second UE and may reset a timer that indicates for the first UE to perform a position acquisition procedure upon expiration of the timer.

Abstract: Detecting deviations from typical user behavior, including: identifying a geographic location of a device that is associated with a user; determining device activity associated with the user; and detecting, based on a profile associated with the user, that the device activity associated with the user deviates from normal activity for the user.

Abstract: A method, comprising: measuring a rotational speed of a wheel of a vehicle, determining a longitudinal acceleration of the vehicle relative to a plurality of ambient terrestrial objects using a measurement system onboard the vehicle, determining a longitudinal speed of the vehicle relative to the plurality of ambient terrestrial objects using the measurement system, judging a slippage state of the vehicle using the longitudinal acceleration, and if a result of the judging indicates the vehicle is in a low-slippage state, estimating a coefficient of traction using the rotational speed and the longitudinal speed, wherein the measurement system is structured to radiate electromagnetic energy and to receive reflections of the electromagnetic energy reflected from the plurality of ambient terrestrial objects.

Abstract: Detecting anomalous behavior of a device, including: generating, using information describing historical activity associated with a user device, a trained model for detecting normal activity for the user device; gathering information describing current activity associated with the user device; and determining, by using the information describing current activity associated with the user device as input to the trained model, whether the user device has deviated from normal activity.

Abstract: An illustrative method includes generating, based on log data associated with at least one user session in a network environment associated with a user, a logical graph, wherein the logical graph comprises: (1) a first node corresponding to the user, (2) a plurality of additional nodes, and (3) a set of edges connecting the first node to one or more of the additional nodes, wherein each edge in the set of edges represents a change in behavior of the user; using the logical graph to detect an anomaly, wherein detecting the anomaly includes determining that a change has been made to at least one edge included in the set of edges; and generating, in response to detecting the anomaly, an alert.

Abstract: A method for estimating a grip of a tire supporting a vehicle includes generating a first set of data from a tire-mounted sensor unit, generating a second set of data from the tire-mounted sensor unit and from data obtained from the vehicle, and generating a third set of data from data obtained from the vehicle and from the Internet. A grip estimation module is provided. The first, second and third sets of data are received in the grip estimation module. A friction probability distribution is calculated with the grip estimation module using the first, second and third sets of data, and the friction probability distribution is input into at least one vehicle system.

Abstract: A data platform receives data associated with activities in an environment, generates a logical graph using at least a portion of the received data, at least in part by clustering multiple nodes into a node of the logical graph based at least in part on behaviors in the network environment, and provides data representing a portion of the logical graph to a computer, the data representing the portion of the logical graph configured to be processed by the computer to display a visualization of the portion of the logical graph. The data platform may provide the data to the computer in response to receiving a query from the computer. The data platform may use the logical graph to detect an anomaly in the environment.

Abstract: This disclosure describes techniques for updating planogram data associated with a facility. The planogram may indicate, for different shelves and other inventory locations within the facility, which items are on which shelves. For example, the planogram data may indicate that a particular item is located on a particular shelf. Therefore, when a system identifies that a user has taken an item from that shelf, the system may update a virtual cart of that user to indicate addition of the particular item. In some instances, however, a new item may be stocked on the example shelf instead of a previous item. The techniques described herein may use sensor data generated in the facility to identify this change and update the planogram data to indicate an association between the shelf and the new item.

Abstract: Detecting anomalous behavior using a browser extension, including: gathering first information describing activity associated with a user and generated by a browser extension on a user device; gathering second information describing activity associated with the user and generated by an application executed on the user device; and determining, based on the first information and the second information, whether the user has deviated from normal activity.