Abstract: Predicting infection risk by generating a first temporal graph of a first set of disease progression data, generating a second temporal graph of a second set of disease progression data, combining a first temporal graph node embedding and a second temporal graph node embedding, and generating a predicted infection risk according to the first temporal graph node embedding and the second temporal graph node embedding.

Abstract: Streamlining compliance reporting and remediation through clustering compliance deviations by receiving inspection scan compliance deviation report data, analyzing the inspection scan compliance deviation report data, in response to the analysis, creating normalized deviation report documents, comparing the normalized deviation report documents, in response to the comparisons, clustering the normalized deviation report documents, creating a common cluster deviation profile comprising clustered deviation reports, and generating a summary system asset compliance report comprising the common cluster deviation profile.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate generation of microservices from a monolithic application based on runtime traces are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that learns cluster assignments of classes in a monolithic application based on runtime traces of executed test cases. The computer executable components can further comprise a cluster component that employs the model component to generate clusters of the classes based on the cluster assignments to identify one or more microservices of the monolithic application.

Abstract: A system can include a memory that stores computer-executable components and a processor, operably coupled to the memory, that can execute the computer-executable components stored in the memory. The computer-executable components can include a model generator component that can execute a search function on an application based on a set of application component topics to identify application components of the application that facilitate execution of the application. A configuration property extractor component can analyze the application based on the application components and can determine configuration properties associated with operating the application components. An application builder component can process the application components and the configuration properties and can generate instructions that facilitate execution of the application in a target computing environment.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate discovery of an inexplicit link between a change and an incident in a computing environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an analysis component that determines a defined link strength corresponding to links between change data and incident data in a computing environment. The computer executable components further comprise an extraction component that employs a model to identify an inexplicit link between the change data and the incident data based on the defined link strength.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate transformation of an enterprise application into a cloud native container are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an analysis component that employs a model to discover attributes of an enterprise application. The computer executable components can further comprise a transformation component that generates artifacts based on the attributes to transform the enterprise application into a cloud native container.

Abstract: Aspects of the invention include receiving, using a processor, a plurality of values of a performance indicator. A statistical analysis of the plurality of values of the performance indicator is performed, using the processor, to detect an anomaly pattern in the plurality of values of the performance indicator. A warning message about the detected anomaly pattern is sent to an alert recipient that is selected by a machine learning model trained to identify alert recipients based at least in part on detected anomaly patterns. Feedback about the warning message is received from the alert recipient. The feedback includes an interest of the alert recipient in receiving warning messages about the detected anomaly pattern. The machine learning model is updated based at least in part on the feedback.

Abstract: An approach to optimized migration of user assets to the cloud using artificial intelligence is presented. This approach may user input and artificial intelligence trained with historical knowledge to generate rules. Migration models may be generated from the rules. A user may verify the migration models were successful. A task portfolio may be generated from the verified wave migration models. Runbook applications may be generated from the task portfolio and the migration may be executed using the runbooks.

Abstract: Techniques facilitating mitigation of dangerous activities are provided. In one example, a computer-implemented method can include detecting, by a system operatively coupled to a processor, a trigger event based on a determination that a security risk level satisfies a defined condition associated with the security risk level. The security risk level can be associated with an item of interest and a monitored behavior related to the item of interest. The computer-implemented method can also include implementing, by the system, an action that mitigates an impact of the security risk level. The action can be implemented as the trigger event is detected and the action can be determined based on the item of interest and the monitored behavior.

Abstract: An example operation may include one or more of receiving user profile identification data associated with a user profile, creating a smart contract on a blockchain with the user profile identification data, validating an identity of the user profile based on the user profile identification data, performing a predictive analysis by the smart contract to determine one or more future tasks to be performed by a user device associated with the user profile, generating one or more tokens associated with the user profile, the one or more tokens include access rights for the user device to perform the one or more future tasks, and storing the one or more tokens in the blockchain.

Abstract: Systems, computer-implemented methods, and computer program products that can facilitate creating and querying a knowledge base of identified topics, computer code actions, and parameters, are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a parameter component that can identify a result parameter in ones of one or more results, wherein the one or more results comprise topics and computer code actions. The computer executable components can further comprise a result component that can select a result of the one or more results based on a mapping of a query to the one or more results, the mapping being based on the result parameter identified in the result and a criterion.

Abstract: Systems and methods to facilitate sleep are described. In on example, a cognitive alarm clock system for children learns sleep patterns and activities towards recommending sleep schedules and teaching independence. The system may detect the cognitive state of a child based on voice or cry pattern recognition, a time of day or night, scheduled activities, and social context, among other factors. The system may initiate actions to facilitate sleep in response to the cognitive factors. For example, the system may adjust lighting or push back a wakeup time. In another example, the system may use the cognitive analysis to teach children good sleeping habits by making recommendations to facilitate a good night's rest and encourage independence.

Abstract: Systems and methods to facilitate sleep are described. In on example, a cognitive alarm clock system for children learns sleep patterns and activities towards recommending sleep schedules and teaching independence. The system may detect the cognitive state of a child based on voice or cry pattern recognition, a time of day or night, scheduled activities, and social context, among other factors. The system may initiate actions to facilitate sleep in response to the cognitive factors. For example, the system may adjust lighting or push back a wakeup time. In another example, the system may use the cognitive analysis to teach children good sleeping habits by making recommendations to facilitate a good night's rest and encourage independence.

Abstract: A method provides for controlling compliance remediation that includes performing compliance inspection runs by account nodes for multiple accounts. Inspection results of the inspection runs from each account node are aggregated by an account cognitive policy advisory (CPA) service. The inspection results from each account are aggregated. It is determined whether remediations are required by analyzing the inspection results combined with a current compliance mode of a server. Upon a determination that the current compliance mode of the server is a first mode, the account CPA service determines whether a policy fingerprint has changed. Upon a change to the policy fingerprint, compliance enforcement runs on the account nodes are temporarily suspended.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate generation of microservices from a monolithic application based on runtime traces are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that learns cluster assignments of classes in a monolithic application based on runtime traces of executed test cases. The computer executable components can further comprise a cluster component that employs the model component to generate clusters of the classes based on the cluster assignments to identify one or more microservices of the monolithic application.

Abstract: An information processing system, a computer readable storage medium, and a method for providing a recommendation for a plaything as a recommended item can include analyzing information received from a person monitoring system to provide an analysis for providing the recommendation for the plaything, and based on the analysis, sending a representation of the recommended item in a signal to a shopping cart such as an online shopping cart. The system can include an analysis module that receives information from a person monitoring system, and at least one processor configured to analyze information received from the person monitoring system (for one or more persons) to provide an analysis. The analysis provides a recommendation for a plaything. The processor can further send a representation of the recommended item in a signal to a shopping cart to upload into the shopping cart based on the analysis. Other embodiments are disclosed.

Abstract: Various embodiments provision resources of a large-scale enterprise network for a first user. The large-scale enterprise network has a multiplicity of resources and users. The actions of users of the network are monitored to determine resource usage history and current resource usage of all resources for each user. A resource usage frequency is determined for all resources used by the first user. A set of related users is created by identifying users grouped with the first user in a common user group. At least one resource usage configuration for the first user is predicted based on the resource usage history, current resource usage, and resource usage frequency of the first user and resource usage history and current usage of each of the related users. A computing device of the first user is provisioned according to the predicted resource usage configuration.

Abstract: Embodiments of the present invention are directed to a computer-implemented method for generating a configurable conversational agent. A non-limiting example of the computer-implemented method includes providing available conversational agents to a user's computing device. The user uses a graphical user interface to choose desired characteristics of a conversational agent. The conversational agent generating service receives the chosen characteristics to generate a customized conversational agent and integrate the conversational agent into the user's system.

Abstract: The present invention is a system and method that generates a Table of Contents (TOC) customized to the user knowledge about the concept(s) in the user query and the specific context and preferences of the user. The invention identifies search concepts within the search queries, receives search results, and splits the search results into one or more result segments. In a preferred embodiment, a correlation strength between concepts in one or more of the result segments and the user search query, along with reference to the user knowledge, context, and/or preferences determine which result segments are selected in sequenced to form the TOC.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate generation of microservices from a monolithic application based on runtime traces are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a model component that learns cluster assignments of classes in a monolithic application based on runtime traces of executed test cases. The computer executable components can further comprise a cluster component that employs the model component to generate clusters of the classes based on the cluster assignments to identify one or more microservices of the monolithic application.