Abstract: According to one embodiment, a method, computer system, and computer program product for identifying cloud storage destination. The embodiment may include identifying content of a data file being created or updated. The embodiment may include identifying cloud storage criteria of the data file. The embodiment may include determining whether an existing storage solution within a cloud storage service provider (CSSP) knowledge corpus is applicable to the data file. In response to determining that an existing storage solution is not applicable to the data file, the embodiment may include creating a new storage solution for the data file based on the identified content and cloud storage criteria of the data file.

Abstract: A method, computer program product, and a system where a processor(s) ingests content from a source(s) with an attribute(s) comprising a verified temporal context(s) of the source(s). The processor(s) cognitively analyzes the content, by applying an entity recognition algorithm(s) to identify and extract entities in the source(s). The processor(s) classifies each extracted entity into a given grouping from a plurality of groupings based on at least one attribute comprising the verified temporal context of the source from which the extracted entity was extracted. The processor(s) generates a corpus comprising the groupings; each grouping comprises extracted entities with verified temporal contexts consistent with a defined time period.

Abstract: In an embodiment, cognitive adjustment of responses from a virtual assistant includes detecting verbal content in an audio signal, determining that the verbal content includes a voice command associated with a task by correlating the verbal content with voice command data stored in memory, determining a state of the user from the audio signal by cognitively comparing aspects of the verbal content with aspects of profile data associated with the user, and retrieving task data associated with the task for preparing a response to the voice command.

Abstract: Techniques for composite imagery rendering in a diminished reality environment for medical diagnosis are provided. In one aspect, a method for composite image rendering in a diminished reality environment for medical diagnosis includes: analyzing medical imagery scans for a patient visiting a medical office for consultation with a physician for a diagnosis; obtaining real-time images of the patient who is physically located in the medical office; creating a composite image of the patient comprising relevant portions of the medical imagery scans combined with the real-time images of the patient; selecting one or more portions of the composite image to diminish for the diagnosis; and rendering the composite image in a diminished reality environment.

Abstract: Disclosed embodiments provide a virtual learning environment by generating one or more virtual students (VS) to interact with a real student (RS). A real student is a live student, while virtual students are computer-generated and exist in the virtual learning environment. The virtual students interact with the real student and/or other virtual students, in order to facilitate a collaborative learning environment.

Abstract: A machine learning model repeatedly scans data exhaust for workstations (for which there are retention requirements regarding data exhaust) over a period of time. The machine learning model analyzes the respective data exhaust and the retention requirements to determine respective data exhaust logging parameters for each workstation. The machine learning model monitors respective data exhaust activity for each workstation over a subsequent period of time. An instance is identified in which logging activity of data exhaust for an identified workstation fails the retention requirements. An ameliorative action designed to satisfy the respective retention requirements for the identified workstation is executed.

Abstract: A harvesting supply chain logistics system for coordinating the harvest and transport of crops from multiple food producers. The system receives first crop data of a first crop grown at a first location by a first food producer. The system predicts a first harvesting time and a first harvesting amount of the first crop. The system receives second crop data of a second crop grown at a second location by a second food producer. The system predicts a second harvesting time and a second harvesting amount of the second crop. The system determines whether a transport for the first crop and the second crop can be combined based on the first harvesting time, first harvesting amount, the second harvesting time, and the second harvesting amount. The system schedules a combine transport of the first crop and the second crop when the first crop and the second crop can be combined.

Abstract: An embodiment includes receiving, by a processor, an indication that a first device transmitted a file having tracking metadata to a second device. The embodiment also includes receiving, by the processor, an indication of a modification to the file by the second device. The embodiment also includes evaluating, by the processor, the modification to the file using a cognitive process that analyzes the modification as it relates to profile information for a user and generates a significance value associated with the change. The embodiment also includes automatically initiating, by the processor and responsive to the generating of the significance value, a selected responsive action from among a plurality of responsive actions based at least in part on the significance value, where the automatic initiation of the selected responsive action includes automatic transmission of a notification to the first device regarding the modification to the file.

Abstract: One or more computer processors detect a computing device within a proximity to a rollable device. The one or more computer processors determine a location and an orientation of the rollable device relative to the computing device. The one or more computer processors attach the rollable device to the computing device utilizing the set of biometric locks engaged programmatically such that at least one side of the rollable device is locked and hinged to the computing device. The one or more computer processors responsive to the rollable device attached to a side of the computing device, extend a display area of the computing device to the rollable device.

Abstract: Head movements can be detected by various sensors. The head movements are tracked over time, and the data is stored in a knowledgebase. User actions are also detected and tracked in the knowledgebase. Therefore, associations between head movements and user actions can be learned and predicted. Upon detecting a head movement associated with a user action, the user action can be executed.

Abstract: According to one embodiment, a method, computer system, and computer program product for customizing a mixed reality experience based on the experiential state of users in a queue to join the mixed reality experience or immersed in the mixed reality experience is provided. The present invention may include modeling the experiential state of the at least one user participating in the mixed-reality experience; modeling one or more relationships between the experiential state of the at least one user and one or more physical or virtual experience parameters comprising the mixed-reality experience; based on the one or more modeled relationships, predicting one or more alterations to the one or more physical or virtual experience parameters to enhance the experiential state of the at least one user; and operating a mixed reality system to perform one or more remedial actions to execute the one or more predicted alterations.

Abstract: One or more computer processors detect a contextual need for sound generation on a rollable display device, wherein the rollable display device comprises an array of micro-speakers mounted on one or more microfluidics panels and a plurality of embedded piezoelectric strips. The one or more computer processors identify one or more environmental parameters associated with an environment surrounding the rollable display device. The one or more computer processors determine a rolling profile associated with the rollable based on the detected contextual need and the one or more identified environmental parameters. The one or more computer processors roll the rollable display device based on the determined rolling profile utilizing the plurality of piezoelectric strips. The one or more computer processors adjust an output direction of each micro-speaker in the array of micro-speakers utilizing the respective microfluidics panel.

Abstract: A method, system, and computer program product for preventing resource exploitation in mobile devices are provided. The method receives a resource request from a mobile application stored on a mobile computing device. A device context is determined for the mobile computing device. A resource consumption context is determined for the mobile computing device based on the device context and resource consumption of one or more mobile applications operating on the mobile computing device. In response to the resource request, the method generates a set of resource consumption profiles based on the device context and the resource consumption context. A resource consumption profile is selected for the mobile application from the set of resource consumption profiles. The method manages consumption of resources, of the mobile computing device, by the mobile application based on the selected resource consumption profile.

Abstract: Taking security actions according to calculated sensitivity levels of objects. Sensitivity levels are determined according to learned security measures taken on objects having certain content. Security actions are taken upon determining that an object in view of recently added content has an actionable sensitivity level. Additional considerations apply to baseline aspects of the object as well as ongoing changes to the baseline considerations.

Abstract: According to one embodiment, a method, computer system, and computer program product for computing device control. The embodiment may include segmenting an augmented reality (AR) view of an environment of a user into distinct segmented areas of the AR view. The AR view is created by an AR device of the user. The segmenting is performed using the AR device. The embodiment may include mapping a computing device within the environment to a distinct segmented area of the AR view. The mapping results in a user interface (UI) of the computing device being displayed within the distinct segmented area of the AR view. The embodiment may include controlling the computing device via the UI of the computing device displayed within the distinct segmented area of the AR view.

Abstract: A machine learning model repeatedly scans data exhaust for workstations (for which there are retention requirements regarding data exhaust) over a period of time. The machine learning model analyzes the respective data exhaust and the retention requirements to determine respective data exhaust logging parameters for each workstation. The machine learning model monitors respective data exhaust activity for each workstation over a subsequent period of time. An instance is identified in which logging activity of data exhaust for an identified workstation fails the retention requirements. An ameliorative action designed to satisfy the respective retention requirements for the identified workstation is executed.

Abstract: One or more computer processors generate a network fractal based on one or more predicted network conditions for a network that includes a change in user density, user device latency, and network throughput, wherein the network fractal is a deployment template comprised of a plurality of nodes. The one or more computer processors select a configuration of network infrastructure devices placed at each node in the plurality of nodes comprised in the generated network fractal. The one or more computer processors modify the network utilizing the generated network fractal and the selected configuration of network infrastructure devices. The one or more computer processors deploy the modified network.

Abstract: In an embodiment, a correlated forecast is computer generated by a processor that receives input data for historic values of a first input variable, creates forecast data for future values of the first input variable using the historic values of the first input variable, generates diagnostic data based on a diagnostic analysis of the forecast data, creates a first diagnostic variable that includes a first diagnostic value from a first cognitive process, generates a feature vector based on a second cognitive process that determines the feature vector by identifying a correlation between the first diagnostic variable and a second diagnostic variable, and generates a final forecast using the feature vector as an input for a cognitive forecasting process, where the first cognitive process determines the first diagnostic value based on the diagnostic data.

Abstract: A computer-implemented method includes: receiving, by a computer device, preference information from a plurality of people, the preference information including an individual level of preference for various activities for each of the people; receiving, by the computer device, biometric data of the people, the biometric data relating to the various activities; building, by the computer device, a corpus of relationships between the preference information and the biometric data; receiving, by the computer device, biometric data of a user; and predicting, by the computer device, a user level of preference with a particular activity of the various activities, the predicting being based on a comparison of the biometric data of the user and the biometric data of the people who participated in the particular activity.

Abstract: In an approach for derivation of progressively variant dark data utility for legacy system candidate microservices, a processor analyzes a variability of data stored in a legacy system for a plurality of data metrics. A processor measures, for each data metric, a utility of the data using an intra-analysis and a meta-analysis. A processor correlates the plurality of data metrics to candidate microservices. A processor generates insights for the candidate microservices based on the utility of the data.