Abstract: Computer systems that use artificial intelligence to predict strategies, scenarios and or game plans for sports games that are already in progress. In some embodiments a feedback loop is used so that the artificial intelligence takes into account events occurring in and/or around the game as they occur to provide updated predictions.

Abstract: A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.

Abstract: A computer-implemented method, according to one embodiment, includes mapping hosts in communication with a storage system to compartment constructs that are logical partitions of the storage system, analyzing interoperability of the hosts and the compartment constructs and defining, based on the analysis, risk profiles for applications run on the hosts. Ownership of storage objects to the compartment constructs is assigned based on the risk profiles, where each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

Abstract: A method of mitigating risks in a service level agreement (SLA), including: identifying relationships between an infrastructure and an application associated with the SLA; identifying changes to a technological environment of the SLA based on collected data associated with the SLA and the identified relationships, the collected data including the infrastructure and the application; establishing a baseline for a service level objective (SLO) of the SLA by analyzing the collected data of the infrastructure and the application; determining risk impact to the SLA based on an assessment of requirements to the SLO and the changes to the technological environment of the SLA; generating a solution option for the SLA by applying an explainable artificial intelligence (XAI) model based on processing the risk impact with the baseline in the XAI model; and updating the SLA based on the solution option.

Abstract: A computer-implemented method, in accordance with one embodiment, includes collecting touch data from one or more touch sensors coupled to a first product, the one or more touch sensors being configured to indicate when a human touches the one or more touch sensors and/or first product. Product vector information about the first product is received. Classification on the touch data and product vector information is performed using a hierarchical multilabel classification system for assigning the touch data to predefined patterns for each level of a classifier used by the hierarchical multilabel classification system. Features of a second product, e.g., a touch vector and a touch pattern, are transformed into a second feature vector. A featurewise difference detection is performed on the feature vectors to calculate a difference in distribution for features of the products to generate and output a caption indicative of the differences between the products.

Abstract: An approach for providing solutions to ad in the process recovery in manufacturing systems to resume manufacturing activity is disclosed. The outcome of the approach can include the following advantages, minimizing waste, reducing cost of recovery, and increasing efficiency in a manufacturing process. The approach includes collecting initial factory data; identifying and categorizing one or more shop floor activities; determining initial recovery process; creating a digital twin copy of the factory; performing one or more initial simulation scenarios; generating factory improvement plan; and executing factory improvement plan.

Abstract: A tool for providing contextual policy-based computer vision control. The tool identifies one or more camera devices in an environment. The tool determines a plurality of vision areas in a field of view within the environment. The tool determines one or more contextual policies for the plurality of vision areas in the field of view. The tool determines one or more vision augmentations for the plurality of vision areas based, at least in part, on an aggregate computer vision capability of the one or more camera devices in the environment and the one or more contextual policies for the plurality of vision areas in the field of view. The tool applies the one or more vision augmentations to at least one of the one or more camera devices in the environment.

Abstract: A computer-implemented method, according to one embodiment, includes logically partitioning a storage system into a plurality of compartment constructs, and mapping hosts in communication with the storage system to the compartment constructs, thereby enabling interoperability among the hosts and the compartment constructs. The interoperability of the hosts and the compartment constructs is analyzed, and the interoperability is based on storage software and/or firmware versions being run by the hosts. The method further includes defining, based on the analysis, risk profiles for applications run on the hosts, and determining, based on the risk profiles, recommendations for assignment and mapping of the hosts with the compartment constructs. Ownership of storage objects is assigned to the compartment constructs based on the recommendations. Each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

Abstract: A computer-implemented method includes: advertising, by a computing device, at least one service including information from a learned knowledge base by a service provider vehicle within an autonomous vehicle ecosystem; connecting, by the computing device, the service provider vehicle to a service consumer vehicle within the autonomous vehicle ecosystem through a communication network; and sharing, by the computing device, the at least one service including the information from the learned knowledge base through a connection between the service provider vehicle and the service consumer vehicle using the communication network.

Abstract: A method of mitigating risks in a service level agreement (SLA), including: identifying relationships between an infrastructure and an application associated with the SLA; identifying changes to a technological environment of the SLA based on collected data associated with the SLA and the identified relationships, the collected data including the infrastructure and the application; establishing a baseline for a service level objective (SLO) of the SLA by analyzing the collected data of the infrastructure and the application; determining risk impact to the SLA based on an assessment of requirements to the SLO and the changes to the technological environment of the SLA; generating a solution option for the SLA by applying an explainable artificial intelligence (XAI) model based on processing the risk impact with the baseline in the XAI model; and updating the SLA based on the solution option.

Abstract: A computer-implemented method, according to one embodiment, includes logically partitioning a storage system into a plurality of compartment constructs, and mapping hosts in communication with the storage system to the compartment constructs, thereby enabling interoperability among the hosts and the compartment constructs. The interoperability of the hosts and the compartment constructs is analyzed, and the interoperability is based on storage software and/or firmware versions being run by the hosts. The method further includes defining, based on the analysis, risk profiles for applications run on the hosts, and determining, based on the risk profiles, recommendations for assignment and mapping of the hosts with the compartment constructs. Ownership of storage objects is assigned to the compartment constructs based on the recommendations. Each of the storage objects define a logical partition of one of the hosts and a logical partition of a storage volume of the storage system.

Abstract: Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Abstract: A computer implemented method is provided that includes using historic migration data to label key performance indicators (KPIs) in a migration model including a scale that indicates a level of successful migration to a remote provider. Employing the migration model to predict successful migration of a local application having one or more of said one or more of local key performance indicators for the local application. Migrating the local application to a remote provider when the model to predict successful migration indicates a greater than threshold value for successful migration.

Abstract: A method of mitigating risks in a service level agreement (SLA), including: identifying relationships between an infrastructure and an application associated with the SLA; identifying changes to a technological environment of the SLA based on collected data associated with the SLA and the identified relationships, the collected data including the infrastructure and the application; establishing a baseline for a service level objective (SLO) of the SLA by analyzing the collected data of the infrastructure and the application; determining risk impact to the SLA based on an assessment of requirements to the SLO and the changes to the technological environment of the SLA; generating a solution option for the SLA by applying an explainable artificial intelligence (XAI) model based on processing the risk impact with the baseline in the XAI model; and updating the SLA based on the solution option.

Abstract: A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.

Abstract: A method, computer program product, and system include a processor(s) obtaining an instruction to perform an inspection of a given type at a geographic site. The processor(s) deploys a robotic drone to the geographic site, wherein based on the deployment, the robotic drone performs a contextual analysis on the geographic site to identify a use case and to collect locational data. The processor(s) obtains the locational data. Based on the locational data, the given type of the inspection, and the use case, the processor(s) generates an inspection plan comprising tasks. The processor(s) identifies robotic drone(s) to complete the tasks and distributes the tasks. The robotic drone( )automatically self-optimize/s to complete the tasks. The processor(s) obtain the collected data from the self-optimized identified one or more robotic drones. The processor(s) analyze the collected data to identify issue(s) at geographic site.

Abstract: Disclosed are techniques for dynamically prioritizing subsets of data within datasets based on context. Historical analysis logs, the underlying datasets for the historical analysis logs, and context data are used to train a machine learning model to determine subsets of data within an input dataset when provided the input dataset and a context information input set. When an input dataset and context information input set are received, the machine learning model determines subsets of data (if any) that should be prioritized for processing ahead of other sets of data in the input dataset, based on the context information input set. Subsets of data within an input dataset with higher priority values are processed before other sets of data within the input dataset.

Abstract: An approach for providing solutions to ad in the process recovery in manufacturing systems to resume manufacturing activity is disclosed. The outcome of the approach can include the following advantages, minimizing waste, reducing cost of recovery, and increasing efficiency in a manufacturing process. The approach includes collecting initial factory data; identifying and categorizing one or more shop floor activities; determining initial recovery process; creating a digital twin copy of the factory; performing one or more initial simulation scenarios; generating factory improvement plan; and executing factory improvement plan.

Abstract: A processor may receive object data associated with a position and an orientation of a first object in an environment from IoT sensors. The processor may generate a digital twin simulation of the first object. In some embodiments, the digital twin simulation may include data associated with the relative positions and orientations of one or more other objects to the first object. The processor may calculate forces acting on the first object. The processor may identify whether the first object is in a state of instability.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations. The operations may include collecting organism data and analyzing the organism data. The operations may also include predicting a organism movement pattern based on the organism data and relaying the organism movement pattern to a participant.