Abstract: A document object may be transmitted to a plurality of user devices. The document object may include at least one field for display of a content object of a group of content objects. The field may be associated with an identifier corresponding to the group of content objects. When the document object is accessed, then the access may trigger a request to a server, which may select a content object of the group of content objects using a content object identification function. The content object identification function may be dynamically updated based at least in part on the document object being accessed by one or more of the plurality of user devices. Responsive to the request, a unique content object identifier corresponding to the selected content object may be transmitted to the user device and displayed at the accessed document object.

Abstract: Aspects of the present disclosure include systems, methods, computing devices, computer-implemented methods, and the like for modeling and/or simulating performance of a telecommunications network during one or more failure scenarios that reduces computational time and/or power over previous simulation techniques. Modeling and simulating the network may include generating an initial network model from network data information and applying one or more transformations to the initial network model to reduce the size of the model. Following transformation, simulation methods may be applied to the generated network model based on routing characteristics of the components of the network. To reduce the computations utilized to simulate such components and/or routing decisions in the network, one or more simulation algorithms may be applied to the transformed network model to reduce the number of routing decisions simulated.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving home data for a first property; (2) determining, using a trained machine learning evaluation model, one or more home score factors based upon at least the home data; (3) generating, based upon the one or more home score factors, a home score for the first property; (4) retrieving past hazard data associated with a second property; and (5) generating based upon at least the past hazard data, a home modification recommendation.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving at least one of home data for a property or user data for a user; (2) determining, using a trained machine learning evaluation model, one or more home score factors based upon at least one of the home data or the user data; (3) receiving, from the user, a home modification indication; (4) modifying, based upon the home modification indication, at least one of the one or more home score factors to create one or more modified home score factors; and (5) generating, based upon the one or more modified home score factors, a home score for the property.

Abstract: Recommending changes to a storage system, including: identifying, using predicted characteristics of one or more workloads executing on the storage system, one or more configuration changes to the storage system that would improve the operation of the storage system; selecting, from the one or more configuration changes to the storage system that would improve the operation of the storage system, a preferred configuration change; and recommending the preferred configuration change.

Abstract: A multiphase motor control system and method of operation. Discloses embodiments comprise receiving multiphase reference commands, transforming the multiphase reference commands into corresponding reference symmetrical components, receiving feedback information representative of multiphase drive signals in a motor, transforming the feedback information into corresponding feedback symmetrical components, generating multiphase motor control symmetrical components based on the reference symmetrical components and the feedback symmetrical components, and transforming the multiphase motor control symmetrical components into corresponding multiphase motor drive control signals.

Abstract: Systems and methods for assessing cybersecurity risk of a computer network include the use of a risk model application that is configured to determine an initial cyber risk score value based upon an underwriting process. A cyber risk data stream is sent from the client's computer network to the system processor to periodically calculate an updated cyber risk score based upon actual data. The system processor is adapted to use the data stream to generate client information that is accessible by the client via a web-based client portal. In embodiments, the cyber risk data stream can be actively monitored to identify a threat of a cybersecurity breach.

Abstract: Aspects of the present disclosure include systems, methods, computing devices, computer-implemented methods, and the like for modeling and/or simulating performance of a telecommunications network during one or more failure scenarios that reduces computational time and/or power over previous simulation techniques. Modeling and simulating the network may include generating an initial network model from network data information and applying one or more transformations to the initial network model to reduce the size of the model. Following transformation, simulation methods may be applied to the generated network model based on routing characteristics of the components of the network. To reduce the computations utilized to simulate such components and/or routing decisions in the network, one or more simulation algorithms may be applied to the transformed network model to reduce the number of routing decisions simulated.

Abstract: Systems and methods for assessing cybersecurity risk of a computer network include the use of a risk model application that is configured to determine an initial cyber risk score value based upon an underwriting process. A cyber risk data stream is sent from the client's computer network to the system processor to periodically calculate an updated cyber risk score based upon actual data. The system processor is adapted to use the data stream to generate client information that is accessible by the client via a web-based client portal. In embodiments, the cyber risk data stream can be actively monitored to identify a threat of a cybersecurity breach.

Abstract: Systems and methods are described for generating and/or displaying an overall home score for a property. The method may include: retrieving one or more attributes of a property; determining one or more home score factors from the attributes; generating an overall home score; generating a neighborhood score; and displaying the overall home score, the neighborhood score and/or a map.

Abstract: Systems and methods are described for generating and/or displaying an overall home score for a property. The method may include: retrieving one or more attributes of a property; determining one or more home score factors from the attributes; generating an overall home score; generating a neighborhood score; and displaying the overall home score, the neighborhood score and/or a map.

Abstract: A method and system for predicting a state of a cell in a radio access network are described herein. The method includes obtaining information of the cell, determining one or more sets of conditions based on the information and predicting that the cell will enter a sleeping state when at least one set of the one or more sets of conditions is fulfilled. The method further includes outputting an action to prevent the cell from entering the sleeping state based on the probability and a number of wireless devices currently connected to the cell.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) receiving home telematics data associated with a property for a user; (2) analyzing, according to a machine learning model, the home telematics data to determine age data for one or more components of the property, wherein the one or more components of the property are associated with a structural integrity of the property; (3) determining, based upon at least the determined age data, a home health indicator for the property, wherein the home health indicator for the property is associated with a determined age for the property; and (4) generating, based upon at least the determined home health indicator, a home score for the property.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving at least one of home data for a property or user data for a user; (2) determining, using a trained machine learning evaluation model, one or more home score factors based upon at least one of the home data or the user data; (3) receiving, from the user, a home modification indication; (4) modifying, based upon the home modification indication, at least one of the one or more home score factors to create one or more modified home score factors; and (5) generating, based upon the one or more modified home score factors, a home score for the property.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving at least one of home data for a property or user data for a user; (2) determining, using a trained machine learning evaluation model, one or more home score factors based upon at least one of the home data or the user data; (3) receiving, from the user, a home modification indication; (4) modifying, based upon the home modification indication, at least one of the one or more home score factors to create one or more modified home score factors; and (5) generating, based upon the one or more modified home score factors, a home score for the property.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving home data for a property; (2) determining, based upon the home data for the property, one or more home score factors, wherein the determining may include: (i) analyzing, using a trained machine learning data evaluation model, the home data for the property to determine home characteristic data for the property, (ii) analyzing, using the trained machine learning data evaluation model, the home data for the property to determine a likelihood of loss associated with the property, and (iii) determining, based upon the home characteristic data for the property and the likelihood of loss associated with the property; and (3) generating, based upon the one or more home score factors, a home score for the property.

Abstract: Systems and methods are described for analyzing home data to generate a home score. The method may include: retrieving home data for a first property; determining that the first property shares home characteristics with a second property; retrieving past hazard data associated with the second property; determining, based upon at least the home data and the past hazard data, one or more first home score factors, wherein the determining includes: analyzing, using a trained machine learning data evaluation model, the home data to determine home characteristic data for the first property, determining second home score factors for the second property based at least upon the past hazard data, and determining, based upon the home characteristic data and the second home score factors, the one or more first home score factors; and generating, based upon the one or more first home score factors, a home score for the first property.

Abstract: Systems and methods are described for evaluating and analyzing home data to generate a home score. The method may include: (1) retrieving home data for a property; (2) determining, based upon the home data for the property, one or more home score factors, wherein the determining may include: (i) analyzing, using a trained machine learning data evaluation model, the home data for the property to determine home characteristic data for the property, (ii) analyzing, using the trained machine learning data evaluation model, the home data for the property to determine a likelihood of loss associated with the property, and (iii) determining, based upon the home characteristic data for the property and the likelihood of loss associated with the property; and (3) generating, based upon the one or more home score factors, a home score for the property.