Abstract: A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to train a generator neural network to simulate a network entity using a discriminator neural network that discriminates output associated with the network entity from output generated by the generator neural network. The computer device may be further configured to receive a set of input parameters associated with the simulated network entity; use the generator neural network to generate output for the simulated network entity based on the received set of input parameters; and apply the generated output for the simulated network entity to manage a communication network.

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: One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.

Abstract: A system described herein may provide a technique for the augmentation of one or more models based on target distributions of values associated with the models. Such models may be associated with a wireless network, and may associate a set of states to a set of values and/or to a set of actions. Such states may include particular sets of configuration parameters associated with the wireless network, the values may include Key Performance Indicators (“KPIs”) associated with a randomness state model or other metrics associated with the wireless network, and the actions may include one or more actions to perform with respect to the wireless network when given a particular state and/or set of KPIs.

Abstract: Provided is a controller for configuring network devices at different network locations with rules that prevent different sets of clients from accessing specific network resources. The controller may receive a request with an identifier of a first resource from a particular network point of access. The controller may identify one or more network devices (e.g., wireless access point, router, switch, firewall, gateway, etc.) that are in the network path between the particular network point of access and the first resource. The controller may select a particular network device in the network path, may establish a connection to the particular network device, and may configure the particular network device with a rule that prevents access to the first resource from the particular network point of access, while permitting access to other resources from the particular network point of access.

Abstract: A system described herein may provide a technique for analyzing metrics, parameters, attributes, and/or other information associated with networks or other devices or systems associated with high-dimensional data in order to determine potential configuration changes that may be made to such networks or other devices or systems in order to optimize and/or otherwise enhance the operation of such networks or other devices or systems. Multiple autoencoders associated with multiple dimensions may be used to calculate reconstruction errors or other features of data (e.g., metrics, parameters, etc.) that may be used to define operating or performance states of the network. Operating or performance states of network components may be mapped to quantum state objects (“QSOs”) for analysis using artificial intelligence and/or machine learning techniques or other suitable techniques.

Abstract: A device may receive network resource model (NRM) data identifying application layer data, user related data, network layer data, and physical layer data associated with a network, and may generate a graphical NRM that is a causal graph representation of the NRM data. The device may merge key performance indicators (KPIs) and network interventions with the graphical NRM, and may generate a hypergraph NRM. The device may determine first embeddings that preserve a structure of the graphical NRM and second embeddings that preserve a structure of the hypergraph NRM, and may train a causal impact model, based on the first embeddings, the second embeddings, a pre-intervention period, and a post-intervention period, to generate learned relationships. The device may retrain the causal impact model based on the learned relationships to generate a trained causal impact model, and may perform one or more actions with the trained causal impact model.

Abstract: Disclosed are systems and methods for performing distributed, keyless authorized electronic transactions. The disclosed systems and methods provide an electronic transaction framework where hardware and/or software devices can be used to authenticate users and/or authorize transactions involving such users. The disclosed framework operates as a distributed system in that it can be built without an entity that all parties must trust. The framework relies entirely on one-way functions and avoids using both symmetric and asymmetric cryptography; therefore, the framework does not have secret keys that can be compromised. The framework provides a distributed, electronic authorization system that does not require a reference clock to which components are synchronized, thereby enabling dynamic, agile authorization with improved security for user and transactional data.

Abstract: A system described herein may provide a technique for the augmentation of one or more models based on target distributions of values associated with the models. Such models may be associated with a wireless network, and may associate a set of states to a set of values and/or to a set of actions. Such states may include particular sets of configuration parameters associated with the wireless network, the values may include Key Performance Indicators (“KPIs”) associated with a randomness state model or other metrics associated with the wireless network, and the actions may include one or more actions to perform with respect to the wireless network when given a particular state and/or set of KPIs.

Abstract: One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.

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 system described herein may provide a technique for the augmentation of one or more models based on target distributions of values associated with the models. Such models may be associated with a wireless network, and may associate a set of states to a set of values and/or to a set of actions. Such states may include particular sets of configuration parameters associated with the wireless network, the values may include Key Performance Indicators (“KPIs”) associated with a randomness state model or other metrics associated with the wireless network, and the actions may include one or more actions to perform with respect to the wireless network when given a particular state and/or set of KPIs.

Abstract: One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.

Abstract: A system described herein may provide a technique for analyzing metrics, parameters, attributes, and/or other information associated with networks or other devices or systems associated with high-dimensional data in order to determine potential configuration changes that may be made to such networks or other devices or systems in order to optimize and/or otherwise enhance the operation of such networks or other devices or systems. Multiple autoencoders associated with multiple dimensions may be used to calculate reconstruction errors or other features of data (e.g., metrics, parameters, etc.) that may be used to define operating or performance states of the network. Operating or performance states of network components may be mapped to quantum state objects (“QSOs”) for analysis using artificial intelligence and/or machine learning techniques or other suitable techniques.

Abstract: A system described herein may provide a technique for the augmentation of one or more models based on target distributions of values associated with the models. Such models may be associated with a wireless network, and may associate a set of states to a set of values and/or to a set of actions. Such states may include particular sets of configuration parameters associated with the wireless network, the values may include Key Performance Indicators (“KPIs”) associated with a randomness state model or other metrics associated with the wireless network, and the actions may include one or more actions to perform with respect to the wireless network when given a particular state and/or set of KPIs.

Abstract: A system described herein may provide a technique for analyzing metrics, parameters, attributes, and/or other information associated with networks or other devices or systems associated with high-dimensional data in order to determine potential configuration changes that may be made to such networks or other devices or systems in order to optimize and/or otherwise enhance the operation of such networks or other devices or systems. Multiple autoencoders associated with multiple dimensions may be used to calculate reconstruction errors or other features of data (e.g., metrics, parameters, etc.) that may be used to define operating or performance states of the network. Operating or performance states of network components may be mapped to quantum state objects (“QSOs”) for analysis using artificial intelligence and/or machine learning techniques or other suitable techniques.

Abstract: One or more computing devices, systems, and/or methods for system parameter identification and network component configuration are provided. A state comprising a system parameter combination, a traffic model, and a channel assignment may be generated. A network traffic scenario is executed through a virtualized testbed using the state. A reward for the system parameter combination may be generated based upon key performance indicators output by the network traffic scenario. A reward policy and rewards generated for system parameter combinations are used to select a system parameter combination that is used to configure a network component of a communication network.

Abstract: Disclosed are systems and methods for performing distributed, keyless authorized electronic transactions. The disclosed systems and methods provide an electronic transaction framework where hardware and/or software devices can be used to authenticate users and/or authorize transactions involving such users. The disclosed framework operates as a distributed system in that it can be built without an entity that all parties must trust. The framework relies entirely on one-way functions and avoids using both symmetric and asymmetric cryptography; therefore, the framework does not have secret keys that can be compromised. The framework provides a distributed, electronic authorization system that does not require a reference clock to which components are synchronized, thereby enabling dynamic, agile authorization with improved security for user and transactional data.

Abstract: A system described herein may provide a technique for analyzing metrics, parameters, attributes, and/or other information associated with networks or other devices or systems associated with high-dimensional data in order to determine potential configuration changes that may be made to such networks or other devices or systems in order to optimize and/or otherwise enhance the operation of such networks or other devices or systems. Multiple autoencoders associated with multiple dimensions may be used to calculate reconstruction errors or other features of data (e.g., metrics, parameters, etc.) that may be used to define operating or performance states of the network. Operating or performance states of network components may be mapped to quantum state objects (“QSOs”) for analysis using artificial intelligence and/or machine learning techniques or other suitable techniques.

Abstract: A computer device may include a memory configured to store instructions and a processor configured to execute the instructions to train a generator neural network to simulate a network entity using a discriminator neural network that discriminates output associated with the network entity from output generated by the generator neural network. The computer device may be further configured to receive a set of input parameters associated with the simulated network entity; use the generator neural network to generate output for the simulated network entity based on the received set of input parameters; and apply the generated output for the simulated network entity to manage a communication network.