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: 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: Establishing a location profile for a user device, including: gathering information associated with the location of a user device; determining, based on the information associated with the location of a user device, whether the user device is being accessed at a known location; responsive to determining that the user device is being accessed at a known location: determining a characterization of the known location; and determining, based on the characterization of the known location, whether device utilization is anomalous; and responsive to determining that the user device is not being accessed at a known location: determining a characterization of the unknown location; and determining, based on the characterization of the unknown location, whether device utilization is anomalous.

Abstract: A tire load estimation system includes at least one tire supporting a vehicle, in which the at least one tire includes a pair of sidewalls extending to a circumferential tread. A sensor is mounted to the at least one tire. A footprint is formed by the tread and includes a centerline with a footprint centerline length. The footprint centerline length is measured by the sensor. A tire load estimator receives a precalibrated sensitivity, the footprint centerline length during straight-line driving conditions, a reference footprint value, and a reference load value as inputs. The tire load estimator determines an estimation of tire load and outputs the estimation to at least one of a vehicle control system and a vehicle electronic control unit. A method for estimating the load of a tire is also provided.

Abstract: A tire pressure monitoring system monitors the pressure in at least one tire supporting a vehicle. The system includes at least one sensor mounted on the tire for measuring a pressure and a temperature of the tire. Transmission means transmit the measured pressure data and temperature data to a processor, and a tire pressure model is executed on the processor. The tire pressure model includes a driving event extractor to extract cold pressure data from the measured pressure data, and a temperature compensator to generate a compensated cold tire pressure from the cold pressure data. A noise filter filters sensor noise and generates a filtered cold tire pressure from the compensated cold tire pressure. A detection module receives the filtered cold tire pressure and determines an air pressure leak rate of the tire, and a leak notification corresponding to the air pressure leak rate is generated.

Abstract: A tire wear state estimation system includes at least one tire that supports a vehicle. A sensor is mounted on the tire and measures tire parameters. At least one sensor is mounted on the vehicle and measures vehicle parameters. Each one of a plurality of sub-models receives selected tire parameters from the tire mounted sensor and selected vehicle parameters from the vehicle mounted sensor. Each one of the sub-models generates a sub-model wear state estimate, and a model reliability is determined for each one of the sub-models. A supervisory model receives the wear state estimate from each sub-model and the model reliability for each sub-model, and generates a combined wear state estimate for the tire.

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 generated at least in part by clustering a first set of nodes using a first clustering criteria. The logical graph is augmented at least in part by performing a reclustering operation using a second clustering criteria.

Abstract: A tire wear estimation system is provided. The system includes at least one tire that supports a vehicle. At least one sensor is affixed to the tire to generate a first predictor. A lookup table or a database stores data for a second predictor. One of the predictors includes at least one vehicle effect. A model receives the predictors and generates an estimated wear rate for the at least one tire.

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: First information associated with a first user login activity is received, as is second information associated with a second user login activity. A determination is made, using the received first and second information, that the first user login activity and second user login activity have a parent-child relationship. The first user login activity and the second user login activity are linked to at least one user and a process.

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: 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 vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold.

Abstract: Log data associated with at least one user session in a network environment associated with an original user is received. A logical graph is generated using at least a portion of the received log data. One example of such a logical graph is a privilege change graph that models privilege changes between processes. Another example of such a logical graph is a user login graph that models machines with which the original user interacts. Another example of such a logical graph is a machine-server graph that clusters machines into nodes based on resources executing on the machine. The generated logical graph is used to detect an anomaly.

Abstract: A tire wear state estimation system includes a first sensor unit that is mounted on a tire and includes a footprint centerline length measurement sensor. A second sensor unit is mounted on the tire and includes a shoulder length measurement sensor. At least one of the first sensor unit and the second sensor unit includes a pressure sensor, a temperature sensor, and electronic memory capacity for storing tire identification information. A processor is in electronic communication with the sensor units and receives the measured centerline length, the measured pressure, the measured temperature, the identification information, and the measured shoulder length. The identification information is correlated to tire construction data. An analysis module stored on the processor receives the measured values, the identification information, and the tire construction data as inputs. The analysis module includes a prediction model that generates an estimated wear state for the tire from the inputs.

Abstract: A method for extracting changes in characteristics of a tire supporting a vehicle is provided. The method includes extracting selected tire characteristics from at least one sensor mounted on the tire. The selected tire characteristics are transmitted to a remote processor and are stored in a historical data log that is in communication with the remote processor. At least one tire characteristic of interest is selected, and a time series decomposition model is applied to data from the historical data log to delineate exogenous inputs from an underlying trend in the selected tire characteristic of interest. A learning model is applied to the underlying trend in the selected tire characteristic of interest to model a relationship between the selected tire characteristic of interest and a condition of the tire. A prediction value for a condition of the tire is output from the learning model.

Abstract: A tire wear state estimation system includes a tire that supports a vehicle. A sensor unit is mounted on the tire and includes a footprint centerline length measurement sensor, a pressure sensor, a temperature sensor, and electronic memory capacity for storing identification information for the tire. A processor is in electronic communication with the sensor unit and receives the measured centerline length, the measured pressure, the measured temperature and the identification information. A tire construction database stores tire construction data and is in electronic communication with the processor. The identification information is correlated to the tire construction data. An analysis module is stored on the processor and receives the measured centerline length, the measure pressure, the measured temperature, the identification information, and the tire construction data as inputs. The analysis module includes a prediction model that generates an estimated wear state for the tire from the inputs.

Abstract: Log data associated with an environment that includes containers is received. An example of such an environment is one managed by Kubernetes. A logical graph is generated using at least a portion of the received log data. The logical graph is used to detect an anomaly. In response to the anomaly being detected, the anomaly is recorded.

Abstract: Embodiments disclosed herein relate to cryptology, and more particularly to secure sharing of data objects stored in the at least one cloud device between two user devices using the PRE. Embodiments herein disclose methods and systems for enabling a first user device to subscribe with a key server for uploading encrypted data object to at least one cloud device using the PRE. Embodiments herein disclose methods and systems for allowing the first user device to share the encrypted data object stored in the at least one cloud device with a second user through the key server using the PRE.

Abstract: A tire data information system includes at least one tire that supports a vehicle and a processor. At least one sensor is mounted on the tire and is in electronic communication with the processor. The sensor measures a temperature and a pressure of the tire. A memory for storing tire identification information is in electronic communication with the processor. Tire wear state means generate a wear state of the tire and are in electronic communication with the processor. Tire wear rate means generate a wear rate of the tire and are in electronic communication with the processor. Transmission means transmit the temperature, pressure, identification information, wear state, and wear rate of the tire to a display device that is accessible to a user of the vehicle. The display device includes indicators for showing the temperature, pressure, identification information, wear state, and wear rate to the user.