Abstract: In some implementations, a device may identify a network node that supports a first radio access technology (RAT), wherein the network node is associated with sessions with user equipments (UEs). The device may calculate a percentage of traffic from the sessions that is associated with the first RAT and not a second RAT. The device may determine that the network node is associated with a score based on the percentage of traffic associated with the first RAT not satisfying a threshold. The device may collect, based on the network node being associated with the score, device data associated with the UEs. The device may determine a network issue associated with the network node based on the device data. The device may determine a root cause for the network issue. The server may transmit an indication of the root cause.

Abstract: A device may receive a query, and may generate an embedding based on the query. The device may determine context for the query based on the embedding and historical data, and may select, based on the context, a base LLM from a plurality of base LLMs. The device may fine-tune the base LLM with LLM fine-tuning data to generate a fine-tuned LLM, and may process the embedding, with the fine-tuned LLM, to identify tasks associated with the query. The device may determine recommended tasks based on the tasks, and may select a task LLM from a plurality of task LLMs. The device may process the tasks and the recommended tasks, with the task LLM, to determine final tasks, and may cause the final tasks to be executed. The device may perform one or more actions based on the final tasks.

Abstract: Embodiments of the present application provide a data processing method, system, device and storage medium, where a processing request for target data sent by a requesting party having an access permission is received; a first identifier of a first user to which the target data belongs and a second identifier of the requesting party are obtained; a data processing rule pre-agreed upon by the first user and the requesting party is searched for according to the first identifier and the second identifier; and when a processing operation specified by the processing request complies with the data processing rule, the target data is processed according to the data processing rule to feedback corresponding response information to the requesting party, which can meet processing requirements of the requesting party for the target data, and effectively improve security protection effect during a process in which the target data is secure processing.

Abstract: In some implementations, a device may identify a network node that supports a first radio access technology (RAT), wherein the network node is associated with sessions with user equipments (UEs). The device may calculate a percentage of traffic from the sessions that is associated with the first RAT and not a second RAT. The device may determine that the network node is associated with a score based on the percentage of traffic associated with the first RAT not satisfying a threshold. The device may collect, based on the network node being associated with the score, device data associated with the UEs. The device may determine a network issue associated with the network node based on the device data. The device may determine a root cause for the network issue. The server may transmit an indication of the root cause.

Abstract: A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.

Abstract: Systems and methods described herein intelligently multiplex different types of traffic in a fixed wireless access (FWA) environment by communicating with the user/application directly so that the network capacity can be maximized for different types of traffic. A routing device in a customer premises network receives a routing policy and receives an indication of network congestion conditions. The routing device identifies one or more sources of delay-tolerant traffic in the customer premises network, such as a local area network (LAN), and applies the routing policy to delay data transmissions from the source based on identifying the indication of network congestion conditions.

Abstract: A system described herein may provide a technique for using modeling techniques to identify events, trends, etc. in a set of data, such as streaming video or audio content. The system may perform lightweight pre-processing operations on a different set of data, such as object position data, to identify timeframes at which an event may potentially have occurred, and the modeling techniques may be performed at portions of the streaming content that correspond to such timeframes. The system may forgo performing such modeling techniques at other portions of the streaming content, thus conserving processing resources.

Abstract: A traceback solution is provided. For a network of autonomous systems, the traceback solution traces the autonomous system path taken by traffic flows. Every link in the traceback path is created, verified, and audited by autonomous systems. Multiple autonomous systems may take part in the process, making the system robust against fake information. The database used to store the validated traceback paths is a decentralized and distributed storage. Multiple copies of the database may be maintained by the network of autonomous systems. The database may be accessible by any participating autonomous system; and is not accessible from outside the network of autonomous systems. The traceback solution achieves both validation and non-repudiation property among the ASes. The traceback solution mitigates some important attack scenarios that might be targeted specifically at the traceback solution.

Abstract: A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Abstract: A method for processing data, task processing system and electronic equipment are disclosed.

Abstract: A device may include a processor configured to collect historical location data for a user equipment (UE) device associated with a user and determine a movement pattern for the user based on the collected historical location data. The processor may be further configured to obtain current location data for the UE device over a time period; determine a mobility score for the user for the time period based on the determined movement pattern and the obtained current location data, wherein the mobility score is associated with the user's movements during the time period with respect to the determined movement pattern for the user; and provide the determined mobility score for the user to another device configured to adjust one or more parameters of a radio access network (RAN) associated with the UE device based on the determined mobility score.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A traceback solution is provided. For a network of autonomous systems, the traceback solution traces the autonomous system path taken by traffic flows. Every link in the traceback path is created, verified, and audited by autonomous systems. Multiple autonomous systems may take part in the process, making the system robust against fake information. The database used to store the validated traceback paths is a decentralized and distributed storage. Multiple copies of the database may be maintained by the network of autonomous systems. The database may be accessible by any participating autonomous system; and is not accessible from outside the network of autonomous systems. The traceback solution achieves both validation and non-repudiation property among the ASes. The traceback solution mitigates some important attack scenarios that might be targeted specifically at the traceback solution.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A device may collect network performance data associated with a user equipment of a network. The network performance data may include information associated with a plurality of performance indicators of the network. The device may process information associated with a first portion of the plurality of performance indicators to determine a first performance category experience score, and information associated with a second portion of the plurality of performance indicators to determine a second performance category experience score. The device may process the first performance category experience score and the second performance category experience score to determine a network experience score. The device may determine whether the network experience score satisfies a threshold value. The device may perform one or more actions based on determining that the network experience score satisfies the threshold value.

Abstract: A method, a computer-readable medium, and an apparatus for IP traceback are provided. The apparatus may generate a group public key shared by a plurality of routers controlled by a service provider. The apparatus may generate a unique private signing key for a router of the plurality of routers. The private signing key may be used to generate a group signature for a session of network traffic. The group public key may be applied to the group signature to identify the service provider. The apparatus may identify the router by applying the group public key and a master secret key to the group signature. The apparatus may deploy preventive or mitigate action on the identified router.

Abstract: A Self-Organizing Network (SON) collects data pertaining to a first number of cells of a wireless network. The SON splits the collected data into a second number of groups, and, for each of the second number of groups, repeatedly set a third number of clusters to a different number between a low limit and a high limit. The SON, for each of the settings, clusters the cells into the third number of clusters and trains a deep neural network to perform a regression analysis on the third number of clusters. For each of the second number of groups, the SON also determines an optimum number of clusters based on the regression analyses, re-clusters the cells into the optimum number of clusters; and tunes engineering parameters based on the re-clustering to optimize performance of the wireless network and quality of experience pertaining to the wireless network.

Abstract: A method, a device, and a non-transitory storage medium for estimating voice call quality include performing automatic speech recognition, for each of a plurality of voice calls, to generate recognized text for both an originating device acoustic signal and a receiving device acoustic signal. The recognized text for both the originating device acoustic signal and the receiving device acoustic signal are compared to the reference text to identified recognition errors and a voice call quality score for each of the originating device acoustic signal and the receiving device acoustic signal are determined. A correlation between the network conditions and the voice call quality scores is then determined.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.

Abstract: A system may collect, from a wireless network, first data pertaining to nodes in the wireless network. Each datum of the first data belongs to one of two or more categories/For each of the nodes, for each of the categories, and for each datum belonging to the category, the system may determine if the datum is outside of a first range of values, and if the datum is inside the first range, the system may calculate a first base network performance health (NPH) score that is a function of the nodes, the categories, the data, and time. The system may also apply first deep learning to a first neural network among a plurality of neural networks to update first coefficients for correlating the first base NPH score to a mean opinion score, for each of the categories.