Abstract: Systems and methods described herein direct an end device to a multi-access edge computing (MEC) service instance among MEC instances with different service levels for different geographic areas. A network device stores a map of a geographic area with unique identifiable regions (UIRs) that each include cells for different wireless stations of a transport network. The network device receives application parameters, for a designated coverage area, for an application to be serviced using MEC resources. The network device associates the designated coverage area with one or more target UIRs from the map and deploys, when the MEC resources are available to support the application parameters, an instance of the application at a MEC cluster. The deployed instance of the application meets the application parameters for the target UIRs. The network device updates a MEC-domain name service (DNS) for the deployed instance of the application at the MEC cluster.

Abstract: A system described herein may train an explanation model based on a set of images and a set of explanation labels. The system may receive input data, and may provide the input data to the explanation model and a second model. The second model may provide a set of output labels, which may include performing unknown or “black box” processing on the input data. The explanation model may generate one or more images based on the input data, compare the images to the set of images based on which the explanation model was trained, and accordingly identify one or more explanation labels with bounding boxes associated with the generated one or more images. The system may output, in response to the input data, the set of output labels provided by the second model as well as the identified explanation labels.

Abstract: A method, a device, and a non-transitory storage medium are described in which an application layer network management service is provided. The service may allow third parties associated with an application layer network to develop and on-board application-aware logic that manages application services. The application-aware logic may include optimization of the application service and remedial procedures that address events associated with degradation and/or performance of the application service. The application-aware logic may be implemented in conjunction with other logic provisioned by a network provider.

Abstract: A system described herein may provide a technique for identifying and remediating potential threat vectors in a system, such as containers or applications in a virtual or cloud computing environment. Attributes of potential threat vectors may be identified, and the potential threat vectors may be scored based on the attributes. Values or scores of individual attributes may be determined through machine learning or other suitable techniques. Scores exceeding a threshold may indicate that a remedial measure should be performed. A remedial measure may be identified using machine learning or other suitable techniques. After the remedial measure is performed, the threat vector may be scored again, and a machine learning model may be refined based on whether the remedial measure was successful.

Abstract: In some implementations, a device may receive historical content data indicating historical characteristics associated with one or more groups of content. The device may determine, based on the historical content data, one or more characteristics associated with a time period. The device may determine, based on the one or more characteristics, a new group of content associated with the time period. The device may generate a display element for accessing content included in the new group of content during the time period.

Abstract: Systems and methods provide for configuring, by a first wireless station using radio resource control (RRC), a plurality of Fifth Generation (5G) wireless stations associated with 5G secondary cell groups (SCGs), wherein the first wireless station is associated with a first anchor cell for the SCGs; determining, by the wireless station, that a dual connectivity (DC) user equipment (UE) device has a wireless connection to a first 5G wireless station via a first 5G cell, wherein a second 5G SCG is associated with a second one of the 5G wireless stations and the DC-UE device is located in an area associated with the first anchor cell and in an area associated with a second anchor cell; and switching, using a media access control (MAC) control element (CE) of the DC-UE device, the wireless connection from the first 5G cell to the second 5G cell.

Abstract: A device may receive customer data associated with customers of items, and may identify customer characteristics based on the customer data. The device may process the customer characteristics, with a first model, to group the customers into groups of customers, and may process item data and data identifying the groups of customers, with a second model, to determine associations between the item data and the groups of customers. The device may receive, from a user device associated with a particular customer, particular customer data, and may identify particular customer characteristics based on the particular customer data. The device may assign the particular customer to a particular group, of the groups of customers, based on the particular customer characteristics, and may generate an item recommendation, for the particular customer, based on the item data associated with the particular group. The device may provide the item recommendation to the user device.

Abstract: Techniques for stateless address auto-configuration with support for multiple, different subnets are disclosed. In one embodiment, a method is disclosed comprising receiving, by a network device, a router solicitation (RS) message from a network node, generating, by the network device, a router advertisement (RA) message for use in determining the network node's network address, the RA message including a network address prefix and subnetting information, the subnetting information comprising a subnet address, a subnet address length and a subnet option type, and transmitting, by the network device, the RA message to the network node.

Abstract: A user equipment may generate one or more temperature thresholds based on thermal mitigation guidelines. The one or more temperature thresholds may define a first temperature zone and a second temperature zone. The user equipment may detect a temperature of the user equipment. The user equipment may identify whether the temperature occurs in the first temperature zone or the second temperature zone. The user equipment may send information to a network device to cause one or more network actions to be performed. The one or more network actions may be configured to maintain or reduce the temperature within the first temperature zone or to reduce the temperature to cause the temperature to switch from the second temperature zone to the first temperature zone.

Abstract: A system may receive a first definition for a virtualized instance of a network function. The first definition may include a first set of declarations in a first format that is different than respective formats supported by different virtualized environments. The system may select a first virtualized environment to run the virtualized instance based on requirements specified within the first definition, and may generate a second definition with a second set of declarations that map the first set of declarations from the first format to a second format supported by the first virtualized environment. The system may deploy the virtualized instance to the first virtualized environment using the second set of declarations from the second definition. Deploying the virtualized instance may include configuring its operation based on some of the second set of declarations matching a configuration format supported by the first virtualized environment.

Abstract: A system described herein may use automated techniques to verify network connection integrity and provide visual indicators of connection validity or invalidity. The system may generate a unique identifier related to a user and/or web resource during an initial use. The unique identifier may be utilized to transform a secure image and store the transformed image. Upon subsequent use of the web resource, a visit identifier may be generated and utilized to perform an inverse transformation of the transformed image and display the result, where the resulting image will be the same as the secure image if the visit identifier matches the unique identifier.

Abstract: A method for performing service authorization for private networks based on an enhanced PLMN identifier. The method includes receiving an attach request from a user equipment device (UE) via a private network, where the attach request includes an international mobile subscriber identity value (IMSI). The method further includes determining, based on the IMSI, an organization identifier and a token associated with the private network, where the token is included in an enhanced PLMN for granting the UE access to resources in the private network. The method further includes sending the token to the UE and a network proxy within the private network.

Abstract: A system described herein may provide a technique for the use of machine learning techniques to perform authentication, such as biometrics-based user authentication. For example, user biometric information (e.g., facial features, fingerprints, voice, etc.) of a user may be used to train a machine learning model, in addition to a noise vector. A representation of the biometric information (e.g., an image file including a picture of the user's face, an encoded file with vectors or other representation of the user's fingerprint, a sound file including the user's voice, etc.) may be iteratively transformed until the transformed biometric information matches the noise vector, and the machine learning model may be trained based on the set of transformations that ultimately yield the noise vector, when given the biometric information.

Abstract: A device may receive network data identifying a topology of network devices of a network, quality of service (QoS) rules for user equipment (UEs) and application flows associated with the network, QoS profiles for a radio access network (RAN) of the network, and service data flow (SDF) templates. The device may process the network data, with a model, to calculate a network device service level agreement (SLA) score, for each of the network devices on a sliding window, to generate network device SLA scores, and may aggregate the network device SLA scores to generate an end-to-end SLA score on the sliding window. The device may generate QoS configuration data based on the end-to-end SLA score, and may implement the QoS configuration data.

Abstract: Uplink congestion control based on session initiation protocol (SIP) messaging includes receiving SIP messages from user equipment devices (UEs) via established wireless uplink connections; and monitoring a rate of the SIP messages received from the UEs. Uplink congestion control further includes identifying whether the rate of received SIP messages exceeds a first threshold during a first period of time; and sending SIP response messages to the UEs to reduce congestion on the wireless uplink connections upon identifying that the rate of received SIP messages exceeded the first threshold over the first period of time, where the sent SIP response messages instruct the UEs to resend the SIP messages after predetermined time delays.

Abstract: A network device may receive, from a user device, a call associated with a telephone number, where the call is intended for a priority service associated with the telephone number. The network device may identify the call as a priority call based on the telephone number associated with the call, and may determine that a network overload condition exists. The network device may associate a priority tag with the call based on identifying the call as a priority call. The network device may cause the call to be provided preferential treatment, while the call is transmitted for the priority service during the network overload condition, based on the priority tag.

Abstract: In some implementations, a device may obtain load information indicating a load of a first base station, wherein the load is associated with devices connected to the first base station. The device may determine that the load satisfies a load threshold of the first base station. The device may determine that a network repeater is connected to the first base station. The device may determine that a plurality of devices are connected to the network repeater. The device may determine that the network repeater is capable of connecting to a second base station. The device may cause the network repeater to connect to the second base station to reduce the load based on: the load satisfying the load threshold, the plurality of devices being connected to the network repeater, and the network repeater being capable of connecting to the second base station.

Abstract: A device may generate first scores for sentences of text based on a cumulative frequency of words in each sentence, may generate second scores for the sentences based on a cumulative frequency of domain entities in each sentence, and may generate third scores for the sentences based on a sentiment analysis of each sentence. The device may generate a summary of the text, may filter the sentences to extract a first set of sentences, may filter the sentences to extract a second set of sentences, and may filter the sentences to extract a third set of sentences. The device may identify and assign weights to a first group of sentences, a second group of sentences, and a third group of sentences, may generate a ranked list of sentences based on the weighted first group, second group, and third group, and may perform actions based on the final summary.

Abstract: A device may receive device data identifying activation and usage of different types of IoT devices, and may identify a device activation sequence based on the device data. The device may train a model with the device activation sequence, and may generate output data based on training the model with the device activation sequence. The device may calculate weights and a loss function for the output data, and may retrain the model based on the weights and the loss function to generate a retrained model. The device may generate device representations of the different types of IoT devices using the retrained model, and may determine groups of similar IoT devices based on the device representations. The device may generate configuration data for each of the groups of the similar IoT devices, and may cause the different types of IoT devices to be configured based on the configuration data.

Abstract: In some implementations, a device may receive spatial data corresponding to an interior of a building and objects located in the interior of the building. The device may generate a digital map of the interior of the building based on the spatial data. The device may generate a geographic coordinate map of the interior of the building. The device may receive, from a sensor mounted on an uncrewed aerial vehicle (UAV), sensor data indicating three-dimensional geographic points. The device may compare the sensor data to the geographic coordinate map to localize the UAV on the geographic coordinate map. The device may generate coordinate data indicating geographic coordinates associated with the geographic coordinate map and formatted in a global positioning system coordinate format and a National Marine Electronics Association format. The device may transmit, to a controller of the UAV, at least a subset of the coordinate data.