Abstract: A system, method and architecture for providing driving recommendations to a vehicle based on network-assisted scanning of a surrounding environment. In one embodiment, the system is operative for receiving environmental data from one or more collection agents, each operating with a plurality of sensors configured to sense data relating to the environment. At least one collection agent is operative in association with the vehicle for providing vehicular condition data and vehicular navigation data. Road condition information and obstacle condition information relating to a road segment, the vehicle is traversing may be obtained. Responsive to the road condition/navigation data, vehicle type and positioning, etc., a routing path over the road segment may be determined. In one configuration, the routing path may be presented via a display (e.g., AR/MR) associated with the vehicle.

Abstract: A method of a node in a radio access network that optimizes radio access network operations. The method of the node includes determining a topology of the radio access network, exchanging optimization information and network metric information with neighbor nodes in the topology, determining an updated local configuration for the node based on a negotiated optimization with the neighbor nodes, and updating an optimization function based on collected updated network metric information of the node executing the updated local configuration.

Abstract: According to some embodiments, a method performed by a network node for validating minimization of drive test (MDT) reports comprises: receiving a MDT report generated by a wireless device: determining to perform validation on the received MDT report: correlating the MDT report with MDT reports from one or more wireless devices in proximity to the wireless device to determine a first correlation value; determining a trust score for the MDT report based on one or more correlation values, the one or more correlation values at least comprising the first correlation value; determining whether the trust score is below a validation threshold; and upon determining the trust score is below the validation threshold, performing a corrective action with respect to the received MDT report.

Abstract: A scheme (300) for facilitating automated AR-based rendering with respect to expert guidance or assistance provided in a connected work environment based on contextualization is disclosed. In one aspect, a method comprises a worker (102) requiring assistance with respect to a given task generating (302) one or more suitable queries. Responsive thereto, real-time context data is gathered (304), which may comprise data relating to the work environment. A remote expert (118) may generate (306) appropriate guidance relevant to the task and assistance query, which may be rendered as AR content (308) for worker consumption by using an AR rendering module (112). The worker (102) consumes or uses the AR content for performing (310) an action with respect to the task. The performance of the worker (102) may be used as a measure to improve the AR rendering in automated fashion (312, 314) using one or more machine learning modules (114).

Abstract: A system, method and architecture for network-assisted scanning of a surrounding environment. In one example arrangement, the system is operative for receiving one or more pieces of environmental data from a plurality of collection agents, each collection agent operating with a plurality of sensors configured to sense data relating to a respective location area of an environment, wherein the one or more environmental data is received responsive to determining that a portion of the environmental data is necessary for updating a consumption point report. Based on the received data, an updated consumption point report may be generated, which may be transmitted to a data consumption point.

Abstract: A system, method and architecture for network-assisted scanning of a surrounding environment. In one example arrangement, the system is operative for receiving real world object identification and spatial mapping data relative to a plurality of real world scenarios sensed by sensing points, each operating as a collection agent configured to collect environmental data in association with a plurality of sensors. The data may be prioritized responsive to at least one of a determination of RAN resource capacity available to the collection agent, e.g., compute resources, storage resources, radio transmission resources, cost of the transmission capacity available to the collection agent, etc. in view of urgency/relevance of the data. Responsive to prioritizing the environmental data, one or more pieces of the environmental data may be transmitted to a nearest edge compute location and/or a cloud-based datacenter network node.

Abstract: A method performed by a first network node operating in a communications network is disclosed. The method is for handling a performance of a radio access network (RAN) including one or more radio network nodes. The first network node determines a configuration of one or more parameters in the RAN based on one or more machine-implemented reinforcement learning (RL) procedures to optimize the performance of the RAN based on the one or more parameters. The RL procedures are further based on at least one of: i) one or more physical characteristics of a deployment of the RAN, ii) one or more radio characteristics of the RAN, and iii) a location of users or traffic load in the RAN. The first network node then initiates providing one or more indicators of the determined configuration to a second network node.

Abstract: A method determines outlier inputs for a machine learning system. The method includes receiving a classification and activation values of a trained classifier or a first input processed by the trained classifier, determining whether an entropy score derived from the first input is below a threshold entropy-based distance metric, and changing the classification in response to the entropy score not being below the threshold.

Abstract: A system, method and architecture for providing driving recommendations to a vehicle based on network-assisted scanning of a surrounding environment. In one embodiment, the system is operative for receiving environmental data from one or more collection agents, each operating with a plurality of sensors configured to sense data relating to the environment. At least one collection agent is operative in association with the vehicle for providing vehicular condition data and vehicular navigation data. Road condition information and obstacle condition information relating to a road segment, the vehicle is traversing may be obtained. Responsive to the road condition/navigation data, vehicle type and positioning, etc., a routing path over the road segment may be determined. In one configuration, the routing path may be presented via a display (e.g., AR/MR) associated with the vehicle.

Abstract: A system, method and architecture for providing highly granular driving recommendations to a vehicle based on network-assisted scanning of a surrounding environment. In one embodiment, the system is operative for receiving environmental data from one or more collection agents, each operating with a plurality of sensors configured to sense data relating to the environment. At least one collection agent is operative in association with the vehicle for providing vehicular condition data and vehicular navigation data. The system is configured to calibrate the vehicle to identify a vehicle type responsive to the vehicular condition/navigation data. Road condition information and obstacle condition information relating to a road segment, the vehicle is traversing may also be obtained. Responsive to the road condition/navigation data, vehicle type and positioning, etc., an optimal racing line path over the road segment may be determined.

Abstract: A method generates synthetic data by a data collection system where the synthetic data meets a first threshold for accuracy and a second threshold for protecting sensitive data from recovery from the synthetic data. The method includes collecting data including sensitive data and non-sensitive data, executing a first machine learning model to generate the synthetic data from the collected data where the synthetic data meets the first threshold, executing a second machine learning model to update the synthetic data to meet the second threshold, checking whether the updated synthetic data meets the first threshold, releasing the updated synthetic data where the first threshold is met, and re-executing the first machine learning model and second machine learning model to update the synthetic data where the first threshold is not met during the checking.

Abstract: A system, method and architecture for network-assisted scanning of a surrounding environment. In one example arrangement, the system is operative for receiving real world object identification and spatial mapping data relative to a plurality of real world scenarios sensed by sensing points, each operating as a collection agent configured to collect environmental data in association with a plurality of sensors. The data may be prioritized responsive to at least one of a determination of RAN resource capacity available to the collection agent, e.g., compute resources, storage resources, radio transmission resources, cost of the transmission capacity available to the collection agent, etc. in view of urgency/relevance of the data. Responsive to prioritizing the environmental data, one or more pieces of the environmental data may be transmitted to a nearest edge compute location and/or a cloud-based datacenter network node.

Abstract: A scheme (300) for facilitating automated AR-based rendering with respect to expert guidance or assistance provided in a connected work environment based on contextualization is disclosed. In one aspect, a method comprises a worker (102) requiring assistance with respect to a given task generating (302) one or more suitable queries. Responsive thereto, real-time context data is gathered (304), which may comprise data relating to the work environment. A remote expert (118) may generate (306) appropriate guidance relevant to the task and assistance query, which may be rendered as AR content (308) for worker consumption by using an AR rendering module (112). The worker (102) consumes or uses the AR content for performing (310) an action with respect to the task. The performance of the worker (102) may be used as a measure to improve the AR rendering in automated fashion (312, 314) using one or more machine learning modules (114).

Abstract: A method performed by a first network node operating in a communications network is disclosed. The method is for handling a performance of a radio access network (RAN) including one or more radio network nodes. The first network node determines a configuration of one or more parameters in the RAN based on one or more machine-implemented reinforcement learning (RL) procedures to optimize the performance of the RAN based on the one or more parameters. The RL procedures are further based on at least one of: i) one or more physical characteristics of a deployment of the RAN, ii) one or more radio characteristics of the RAN, and iii) a location of users or traffic load in the RAN. The first network node then initiates providing one or more indicators of the determined configuration to a second network node.

Abstract: A system and method are described herein for improving content-based (CB) filtering to find other items similar to a particular item. For example, the system and method can operate to (1) collect ground truth from users, experts or non-experts for a subset of items in a library; (2) build similarity model(s) against the collected ground truth and evaluate this/these similarity model(s) for generalizability to all items in the library, and (3) build recommendations for users based on knowledge elicited from the users themselves, combined with the similarity scores in the built similarity model(s).

Abstract: A system, method and architecture for providing highly granular driving recommendations to a vehicle based on network-assisted scanning of a surrounding environment. In one embodiment, the system is operative for receiving environmental data from one or more collection agents, each operating with a plurality of sensors configured to sense data relating to the environment. At least one collection agent is operative in association with the vehicle for providing vehicular condition data and vehicular navigation data. The system is configured to calibrate the vehicle to identify a vehicle type responsive to the vehicular condition/navigation data. Road condition information and obstacle condition information relating to a road segment, the vehicle is traversing may also be obtained. Responsive to the road condition/navigation data, vehicle type and positioning, etc., an optimal racing line path over the road segment may be determined.

Abstract: Exemplary techniques for analytics-driven hybrid concurrency control in clouds are disclosed that include a hybrid resource allocation module that can concurrently utilize an optimistic allocation scheme alongside a pessimistic allocation scheme. Machine learning techniques utilizing previous activity history of applications can be used to train a cluster model that is integrated by a hybrid resource allocation module to classify applications in either a pessimistic cluster or an optimistic cluster that identifies under which scheme requests from the applications will be processed.

Abstract: A sensor data system, method and computer program configured to handle sensor data are disclosed. The system includes a reception unit adapted to receive sensor data, a storing unit adapted to store at least some of the received sensor data in an observations database, a recording unit adapted to record usage of the sensor data, and a relevance determination unit adapted to determine the relevance of the sensor data based on the recorded usage. A decision unit is adapted to remove excess sensor data with determined relevance below a limit from the observations database, and to retain relevant sensor data with determined relevance above the limit in the sensor database. Sensor data in an observations database may thereby be limited to a desired size, by removal of excess sensor data from the database.

Abstract: Exemplary techniques for policy-controlled analytic data collection in large-scale systems are described. A policy engine receives predicate/action pairs and an alerts policy, each predicate identifying an operating condition at a reporting module that can be evaluated as true or false, and a corresponding action identifying what the reporting module is to do upon the corresponding predicate being evaluated as true. The policy engine provides the predicate/action pairs to reporting modules to be installed as rules, which generate analytic data vectors and apply those vectors against the rules. The actions may cause the reporting modules to send the analytic data vectors as analytic report data to an analytics engine, which has been configured with the alerts policy received by the policy engine. The analytics engine applies received analytic report data against the alerts policy to determine whether to send alert event data to the policy engine or to perform a responsive action.

Abstract: According to one embodiment, a method in a server end station of a cloud for determining whether a service level agreement (SLA) violation has occurred or is expected to occur is described. The method includes receiving one or more insight models from an insight model builder, wherein each insight model is a based on one or more metrics previously collected from a virtualized infrastructure, and wherein each insight model models a particular behavior in the virtualized infrastructure and receiving real time metrics from the virtualized infrastructure. The method further includes for each of the one or more insight models, determining based on the received real time metrics that one or more services on the virtualized infrastructure is in an abnormal state or is expected to enter the abnormal state, wherein the abnormal state occurs when the insight model indicates that the associated modeled behavior violates a predetermined indicator.