Abstract: In one embodiment, a service in a network computes an expected information gain associated with rerouting traffic from a first tunnel onto a backup tunnel in the network. The service initiates, based on the expected information gain, rerouting of the traffic from the first tunnel onto the backup tunnel. The service obtains performance measurements for the traffic rerouted onto the backup tunnel. The service uses the performance measurements to train a machine learning model to predict whether rerouting traffic from the first tunnel onto the backup tunnel will satisfy a service level agreement (SLA) of the traffic.

Abstract: In some examples, a first storage system receives a transfer of a synchronous replication role and an asynchronous replication role of a second storage system to the first storage system, where in the synchronous replication role the second storage system is to synchronously replicate data to the first storage system, and in the asynchronous replication role the second storage system is to asynchronously replicate a first data storage object to a third storage system. The first storage system uses, at the first storage system, translation information to identify a second data storage object that the second storage system is to asynchronously replicate to the third storage system following the transfer.

Abstract: Predictive scaling of containers is provided based on obtaining, by one or more processors, prior transaction data on one or more of a user's prior transactions on one or more transaction-centric applications, and analyzing by the processor(s), the prior transaction data to predict whether a current transaction of the user on an application is deterministic or non-deterministic as to one or more functional layers of multiple functional layers of the application. Based on predicting that the current transaction is deterministic as to the functional layer(s) of the application, the processor(s) determines compute resource requirements for the deterministic transaction, and launches one or more containers in advance to run the functional layer(s) based, at least in part, on the computed resource requirements of the deterministic transaction.

Abstract: Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing one-click driver image labeling to facilitate the assignment of drive time to a driver. Various aspects of the driver and/or passengers may be determined, which may include a driver identifier previously associated with the driver, whether the driver is wearing a facial covering, whether an image of the driver is partially occluded, whether the driver is facing in the direction of the camera, and whether an image of the driver is expected to be easily recognizable to a person tasked with assigning representative driver images to driver identifiers. These determinations may be used to improve the efficacy and ease-of-use of a visual login system.

Abstract: The present invention relates to a system and a method for providing 5G services to at least one user device [302], The at least one user device [302] transmits a first connection request to the gNodeB [304], The gNodeB [304] attaches a first protocol message to the first connection request received from the at least one user device [302] and transmits the attached first connection request to an interworking unit [306], The interworking unit [306] generates a third request from the attached first connection request based on an application protocol and transmits the generated third request to an EPC [308], The EPC [308] establishes a connection with the at least one user device [302] and 5G services to the at least one user device [302] via the gNodeB [304].

Abstract: A telematics analysis (TA) computing device including a processor in communication with a memory device for monitoring driving behavior of a driver of a vehicle may be provided.

Abstract: In one embodiment, a service computes a data fidelity metric for network telemetry data used by a machine learning model to monitor a computer network. The service detects unacceptable performance of the machine learning model. The service determines a correlation between the data fidelity metric and the unacceptable performance of the machine learning model. The service adjusts generation of the network telemetry data for input to the machine learning model, based on the determined correlation between the data fidelity metric and the unacceptable performance of the machine learning model.

Abstract: In one embodiment, a device makes a first determination as to whether a time series of a performance metric for a first network path exhibits seasonality. The device makes a second determination as to whether the time series exhibits a trend. The device predicts, based on the first determination and on the second determination, the performance metric for the first network path during a future time period. The device assesses, based on the performance metric predicted for the first network path, whether a measure of application experience will degrade for application traffic sent via the first network path. The device causes the application traffic to be proactively switched to a second network path, when the measure of application experience is expected to degrade.

Abstract: In one embodiment, a networking device reroutes traffic in a network from a first path to a second path, based on a prediction that the first path will not satisfy a service level agreement associated with the traffic. The networking device enters a fast monitoring state during which the networking device performs fast probing of the first path and of the second path onto which the traffic was rerouted. The networking device makes, based on the fast probing, a determination as to whether the first path would have violated the service level agreement and whether the second path violates the service level agreement. The networking device enacts a routing decision for the traffic by applying a routing policy to the determination.

Abstract: In one embodiment, a device obtains traffic telemetry data regarding a first path in a network and an alternate path in the network. The device predicts, based on the traffic telemetry data, an amount of traffic for an application that is expected at a particular time. The device makes, based on the traffic telemetry data and on the amount of traffic for the application that is predicted to be expected at the particular time, a counterfactual prediction as to whether the alternate path would violate a service level agreement associated with the traffic, should the traffic be routed via the alternate path at the particular time. The device causes, based on the counterfactual prediction, the traffic for the application to be rerouted from the first path in the network to the alternate path, prior to the particular time.

Abstract: In one embodiment, a device forms a telemetry cluster by applying clustering to telemetry data regarding a plurality of network connections over an access network between a plurality of entities and a remote application. The device applies anomaly detection to the telemetry cluster, to identify one or more anomalies. The device makes a determination as to whether the one or more anomalies are attributable to the remote application or to the access network. The device initiates a corrective measure, based on the determination as to whether the one or more anomalies are attributable to the remote application or to the access network.

Abstract: A gate-based vehicle image capture system for analyzing vehicle image data is presented. The system may include a portable imaging gate apparatus configured to capture vehicle image data of a vehicle. The portable imaging gate apparatus may include a plurality of imaging assemblies positioned at a plurality of viewing angles. The system may further include an external processing server configured to receive the vehicle image data from the portable imaging gate apparatus. The external processing server may also analyze the vehicle image data to identify a plurality of vehicle features, and determine a first vehicle feature from the plurality of vehicle features. The first vehicle feature may be related to a vehicle incident. The system may also include a provider server configured to receive the first vehicle feature from the external processing server, and update an aspect of a risk evaluation based on the first vehicle feature.

Abstract: Methods and systems for handling power saving signals to improve power saving performance of UE. A method disclosed herein includes enabling, a User Equipment (UE), to monitor a Physical Downlink Control Channel (PDCCH) for downlink control information based on a reception of power saving signals from a Base Station with respect to a discontinuous-reception (DRX) cycle. The power saving signals include a Wake Up Signal (WUS), Go To Sleep (GTS) signal and PDCCH adaptation signal. The method further includes enabling the UE to monitor the PDCCH, if the WUS indicates presence of the PDCCH and enables the UE to skip the monitoring of the PDCCH during an On-duration of the DRX cycle, if the WUS indicates absence of the PDCCH. The method further includes enabling the UE to skip the monitoring of the PDCCH in an active time of the DRX cycle, on receiving the GTS signal.

Abstract: Aspects of the invention are directed towards a system and a method for locking an item. The item is locked in a housing comprising cavity for receiving the item. The housing comprises an electronic circuit for receiving a command from a user terminal to lock or unlock an item in the cavity of the housing. A motor resides in the housing which is actuated by the electronic circuit based on the command received from the user terminal. The housing further comprises a housing lock to lock or unlock the item on actuation of the motor.

Abstract: In one embodiment, a network assurance service that monitors one or more networks identifies changes in a key performance indicator for each of a plurality of network entities in the one or more networks. The service forms a peer group of network entities from the plurality of network entities whose changes in the key performance indicator are correlated. The service monitors the key performance indicator for network entities in the peer group of network entities. The service, based on the monitoring, detects an anomalous change in the key performance indicator for a particular network entity in the peer group of network entities relative to other network entities in the peer group of network entities.

Abstract: The present disclosure describes methods of operating user equipment in 5G NR communications system.

Abstract: In one embodiment, a device obtains, from a plurality of routers in a network, a set of routing patches that collectively specify a first set of paths in the network, a second set of paths in the network, and time periods during which traffic is to be rerouted from one of the first set of paths to one of the second set of paths in the network. The device identifies overlapping path segments of the second set of paths in the network. The device makes, based in part on the overlapping path segments, a prediction that two or more of the set of routing patches will cause congestion along paths with overlapping path segments. The device adjusts, based on the prediction, the set of routing patches, to avoid causing the congestion.

Abstract: According to an example aspect of the present invention, there is provided a rapid, economically feasible method for producing continuous films with excellent properties using nanocellulose suspensions at high consistency.

Abstract: In one embodiment, a device calculates one or more distributions of bitrates associated with an application whose traffic is conveyed via one or more paths in a network. The device detects throughput modes of the application, based on the one or more distributions of bitrates associated with the application. The device associates each throughput mode with a quality of experience label, to form a plurality of pairs of throughput modes and quality of experience labels. The device estimates a quality of experience metric for the application, based on a bitrate of the application and the plurality of pairs of throughput modes and quality of experience labels.

Abstract: Some embodiments provide a method for configuring an edge computing device to implement a logical router belonging to a logical network. The method configures a datapath executing on the edge computing device to use a first routing table associated with the logical router for processing data messages routed to the logical router. The method configures a routing protocol application executing on the edge computing device to (i) use the first routing table for exchanging routes with a network external to the logical network and (ii) use a second routing table for exchanging routes with other edge computing devices that implement the logical router.