Abstract: A method for introducing a new IoT device into an existing ecosystem with multiple IoT devices includes identifying existing IoT devices at a location and generating a digital twin model for each existing IoT device. The method also includes identifying tasks performed by each existing IoT devices at the location and receiving a selection for a new IoT device for introduction with the existing IoT devices based on the tasks performed by each of the existing IoT devices and the generated digital twin models for the plurality of existing IoT devices. In response to determining the new IoT device is suitable for the introduction to the location with the existing IoT devices, the method also includes performing a configuration for the new IoT device based on the tasks performable by the new IoT device.

Abstract: A processor may analyze an external device for one or more activity data collection devices. A processor may identify an activity the external device will perform in a protected boundary using the one or more activity data collection devices. A processor may deactivate the one or more activity data collection devices associated with the external device. A processor may generate activity data based, at least in part, on the activity and the protected boundary. A processor may output the activity data to the external device. In some embodiments, the external device may perform the activity using the activity data.

Abstract: A driverless vehicle system comprises a processor that is configured to communicate information related to attributes of a focus autonomous vehicle (FAV) to an other peer vehicle (PV) and/or a central repository system (CRS). The processor is further configured to communicate information about a corrective action by at least one of the FAV and a previously contacted vehicle to the CRS or to a further peer vehicle that is within a predefined region.

Abstract: A computer system provides performs defect tracking and remediation. Video data is received, from a client computing device, corresponding to a recording of a graphical user interface of an application of the client computing device, wherein the video data is obtained in response to the application encountering an error. Feedback is obtained from a user of the client computing device by engaging the user via a natural language processing model. The video data and the feedback are analyzed to determine one or more operations to reproduce the error. One or more corrective actions, based on the determined one or more operations, are provided to remediate the error in the application. Embodiments of the present invention further include a method and program product for performing defect tracking and remediation in substantially the same manner described above.

Abstract: Provided are techniques for dynamically creating and playing a scenario replication for an activity on one or more visual and audio devices. One or more patterns and one or more contexts are stored. One or more goals are stored for a user. A current context is identified from data from a plurality of data sources. The current context is matched to a stored context of the one or more contexts. A pattern associated with the matched context is identified. A recommendation of an activity is provided based on the pattern and based on a goal of the one or more goals. A scenario replication is created with at least one of visual elements and audio elements for the recommendation. One or more visual and audio devices are identified. The scenario replication is played on the one or more visual and audio devices.

Abstract: A method includes determining a plurality of voice assistance systems located in a plurality of environments and receiving, from a headset of a user, a voice command from the user. The voice command lacks an identifier for a first voice assistance system of the plurality of voice assistance systems in a first environment of the plurality of environments. The method also includes predicting, based on the voice command, a subset of the plurality of voice assistance systems for executing the voice command and communicating, to the headset, images of environments of the plurality of environments in which the subset of the plurality of voice assistance systems are located. The method further includes detecting that the user selected, from the images, an image of the first environment that contains the first voice assistance system and in response, communicating the voice command to the first voice assistance system.

Abstract: The present specification describes a computer-implemented method. According to the method, an object within a range of vision of a user is identified via a capture device on a wearable augmented reality (AR) system worn by the user. A user interaction with an identified object within the range of vision of the user is monitored. A notification related to the identified object is generated and presented on a display device of the wearable AR system.

Abstract: In an approach to safely facilitate driver responses to road traffic event alerts, computer-implemented methods, computer program products, and computer systems for reducing vehicle occupant distractions are described. The computer-implemented method includes processors configured for receiving vehicle alert data corresponding to a road traffic event, generating a user alert prompt corresponding to the vehicle alert data, transmitting the user alert prompt to a user vehicle satisfying a first condition, and receiving a user response from an occupant of the user vehicle. Responsive to receiving an affirmative user response, activating one or more vehicle activity systems to reduce vehicle cabin activity by occupants within the user vehicle.

Abstract: Provided are a computer program product, system, and method for using a predictive model to determine virtual usages of storage spaces in a storage infrastructure. Information on the storage infrastructure indicates storage spaces in the storage infrastructure, including physical boundaries and usages of the storage spaces and a predictive model used to determine storage spaces for items to store in the storage spaces. The predictive model receives as inputs physical boundaries and usages of a selected storage space. The predictive model processes the inputs to output a virtual usage of the selected storage space within the physical boundaries of the selected storage space. The virtual usage is defined by coordinates in the physical boundaries of the selected storage space. Augmented reality representation overlaying the virtual usage at the coordinates within a view of the selected storage space are generated in a computer display.

Abstract: Provided are a computer program product, system, and method for using a predictive machine learning to determine storage spaces to store items in a storage infrastructure. Information on the storage infrastructure indicates storage spaces in the storage infrastructure, including physical configurations of the storage spaces, usages of the storage spaces, items placed in the storage spaces, and a predictive model. The predictive model receives as inputs, attributes of a target item to add to the storage spaces, the physical configuration of the storage spaces, attributes of the storage spaces, and the usages of the storage spaces. The predictive model processes the inputs to output an available storage space to optimize storage of the target item. An augmented reality representation of information on storage of the target item with respect to the available storage space is generated on a computer display.

Abstract: Provided are techniques for enhancing applications based on effectiveness scores. Application functionalities of applications in an application landscape are identified. Application logs for the applications are analyzed to identify how the application functionalities are executing. Groups of application functionalities are formed, where each of the groups includes different application functionalities that perform a similar function. For a group of the groups, an effectiveness score is assigned to each of the different application functionalities in that group. For each application functionality, a corresponding recommendation is identified on how that application functionality is to be modified based on the effectiveness score for that application functionality compared to the effectiveness score of each of the other application functionalities in the group. Each application functionality to be modified is automatically updated based on the corresponding recommendation.

Abstract: A computer collects data relevant to a primary activity occurring in a data gathering zone. The computer receives from an Activity Indication Source (AIS), an indication of a primary activity associated with a Data Gathering Zone (DGZ). The computer determines, uses a first Machine Learning (ML) model to identify data streams relevant for data gathering activities, a Target Set of Data Streams (TSDS) associated with the primary activity. The computer identifies within a set of sensors having a monitoring arrangement with the DGZ, a set of preferred sensors to provide the TSDS. The computer determines using a second Machine Learning (ML) model trained to identify Preferred Data Stream Attributes (PDSAs) for data gathering activities, a target set of PDSAs. The computer retrieves, a data stream relevant to the TSDS. The computer compares attributes of the data stream to the PDSAs and initiates corrective action as necessary.

Abstract: A processor may receive instructional information. The instructional information may be associated with an instructional media. A processor may analyze the instructional information for one or more instructions. A processor may identify a target device from the one or more smart devices in the smart environment that are associated with the one or more instructions. A processor may perform the one or more instructions with the target device.

Abstract: Disclosed are techniques for automatically aligning a wrist-mounted display device to a primary display device to facilitate simultaneous use of both display devices. Historical usage information corresponding to a user using both devices is leveraged to determine usage patterns for the devices together. Subsequently, when the primary display device is used by the user, the usage patterns are used to determine when the wrist-mounted display device can be used together with the primary display device. Position information from each device is used to determine a position for the wrist-mounted display device to automatically relocate to for aligning the wrist-mounted display device and the primary display device to have similar viewing directions. The wrist-mounted display device is automatically aligned to the position, and a usage of the aligned wrist-mounted display device is initiated based on the historical usage patterns.

Abstract: A processor may receive an information dataset. The information dataset may include geographical area information and electronic waste information. A processor may generate a digital twin of the physical geographical area. The digital twin may be based, at least in part, on the information dataset. A processor may simulate one or more factors on the physical geographical area. A processor may predict, responsive to simulating the one or more factors, one or more electronic waste impacts on the physical geographical area.

Abstract: Aspects of this disclosure include a method for proactive cooling, a vehicle, and a computer program product for a proactive cooling system. One embodiment of the method may comprise creating a machine learning model of a physical object, and instrumenting the physical object with sensors to generate real-time data about the physical object and its operational environment. The method may further comprise generating, with the machine learning model, a predicted future temperature for the physical object using the real-time data about the physical object and its operational environment, and in response to the predicted future temperature exceeding a threshold temperature within a threshold time period, proactively cooling the physical object.

Abstract: Provided are techniques for performing edge processing by selecting edge devices based on security levels. A security policy is identified for a job to determine a security level for the data. A number of the edge devices that are to be included in a participating group to execute the job is identified. A subset of edge devices that meet the security level are identified by comparing security data of each of the edge devices with the security level. In response to determining that the subset of edge devices includes that number of edge devices, the edge devices from the subset are added to the participating group. The job is executed using the edge devices in the participating group and the one or more cloud nodes, while maintaining the security level in processing the data and in communications across the edge devices and the one or more cloud nodes.

Abstract: A mechanism is provided in a data processing system for proactive deployment of connected vehicles to a connected vehicle ecosystem based on computerized simulation and prediction of contextual situations in accordance with an illustrative embodiment. The mechanism collects real-world data from sensors in a connected vehicle ecosystem and simulates the connected vehicle ecosystem based on the real-world data and a simulation model data of the connected vehicle ecosystem. The mechanism predicts changes in contextual situations in the connected vehicle ecosystem that affect connected vehicle services provided in the connected vehicle ecosystem based on the simulation of the connected vehicle ecosystem, the real-world data, and the simulation model data.

Abstract: The present specification describes a computer-implemented method. According to the method, a request is received to execute a computing task. The request includes parameters for the computing task. A processor identifies, based on the parameters for the computing task and from a pool of registered nodes, a set of assigned nodes amongst which the computing task is to be distributed. A computing assignment of the computing task is transmitted to a secure and isolated container on each of the assigned nodes. A completed computing assignment is received from each of the assigned nodes and the completed computing task is assembled and distributed to a requesting device.

Abstract: Provided are a computer program product, system, and method for dynamic determination of retention periods for digital content. Metadata is generated for instances of digital content including an access pattern of the digital content by a user of the computing device, attributes of the digital content, and a retention period during which the digital content stored is retained in the storage. A machine learning module is trained with input comprising the metadata for instances of the digital content to produce the retention period of the digital content. Input, comprising metadata determined from digital content, received after training the machine learning module, is provided to the machine learning module to produce an output retention period for the digital content received after the training. The output retention period is used to determine when to delete the digital received after the training content from the storage.