Abstract: An automated digital badge management system and method for automating and providing digital badge recommendations is provided. The method may include receiving role data in response to a selection at an automated digital badge management graphical user interface and proficiency data collected in part from an automated digital badge management client device. The method may further include creating an instantiation of an automated digital badge management system data structure. The method may also include populating the automated digital badge management system data structure with the role data and the proficiency data. The method may additionally include determining a recommended digital badge to achieve and one or more recommended actions to perform to achieve the recommended digital badge based on the role data and the proficiency data in the automated digital badge management system data structure. Moreover, the method may include providing the recommended digital badge to achieve.

Abstract: Embodiments for managing voice commands by one or more processors are described. The receiving of a voice command from an individual is detected. An action associated with the voice command is caused to be at least temporarily prevented from being executed based on at least one data source associated with the individual.

Abstract: Using a location sensor, a location including a latitude and a longitude of a device is determined. Using an air pressure sensor, a first elevation of a device is determined. Using the location, a corresponding street address is determined. Using the street address and the first elevation, a floor number is determined. The street address and the floor number are reported to an emergency response system.

Abstract: Embodiments disclosed herein generate visual summaries of data and improve the same using cognitive feedback. In one embodiment, a data visualization application determines properties associated with data or portion(s) thereof, and then generates and combines visualizations of the determined properties into visual data summaries. Examples of visualizations that may be generated include box plots, histograms, graphs, charts, and word clouds. Examples of visual summaries that combine such visualizations include videos, animated GIF (Graphics Interchanged Format) files, powerpoints, PDF (Portable Document Format) files, and web pages. Subsequent to generating visual summaries, the data visualization application may monitor user interactions with those visual summaries and update the same by rebuilding the visual summaries based on the user interactions.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for identifying pathways in a graph of interconnected nodes. A time series of graph data is captured, which corresponds to events occurring at different nodes in the graph. The graph is traversed to identify consistent and variable pathways, respectively, within the graph. The identified pathways are scored according to a particular feature to establish an expected variance model for the events contained within the graph. Newly received data are compared against the expected variance model to determine a degree to which the newly received data deviates from the expected variance. The variance model is adjusted based on the newly encountered data.

Abstract: Cognitive pre-loading of referenced content in electronic mail (e-mail) messages includes determining, using computer hardware, metadata for an electronic message directed to a user, detecting, using the computer hardware, a universal resource locator (URL) within a body portion of the electronic message, determining, using the computer hardware, a content type for the URL, and performing, using the computer hardware, natural language processing on the electronic message to determine an action importance corresponding to the URL. At least a portion of content specified by the URL can be pre-fetched from a data processing system based on the metadata, the content type of the URL, and the action importance. The electronic message and at least a portion of the content can be provided to a client device of the user.

Abstract: At an emergency response system, a first location including a latitude and a longitude at which a first device is located is received. At an emergency response system, an air pressure reading measured by the first device is received. Using the air pressure reading and a mean sea-level barometric measurement, a first elevation of a device is computed. Using the first location, a corresponding street address is computed. Using the street address and the first elevation, a floor number is computed. The street address and the floor number are transmitted to a second device.

Abstract: Disclosed aspects relate to counter-fraud operation management. A counter-fraud operation may be executed using an initial set of parameter values for a set of parameters of the counter-fraud operation. A set of user counter-fraud activities of a user may be monitored corresponding to a user interface. A set of user feedback data may be captured to determine a feedback-driven set of parameter values for a set of parameters of the counter-fraud operation. The feedback-driven set of parameter values may be determined for the set of parameters of the counter-fraud operation. The counter-fraud operation using the feedback-driven set of parameter values may be executed.

Abstract: Embodiments for intelligent media data filtering in an Internet of Things (IoT) computing environment by a processor. User preferences for the media data may be determined according to a plurality of identified contextual factors. At least a portion of the media data may be filtered according to one or more user classifications.

Abstract: A computer-implemented method includes generating, by a computer device, a plurality of clusters of unmanned aerial vehicles (UAVs) from a plurality of UAVs having different UAV characteristics, the clusters being generated based on the UAV characteristics of each of the UAVs; determining, by the computer device, task characteristics for a first task; selecting, by the computer device, one of the clusters based on the task characteristics of the first task; assigning to the first task, by the computer device, a first UAV of the plurality of UAVs from the selected cluster; receiving, by the computer device, task feedback regarding an attempt by the first UAV to complete the first task; and reassigning, by the computer device, and based on the task feedback, the first UAV to a determined one of the plurality of clusters.

Abstract: Embodiments relate to a system, program product, and method for use with an intelligent computer platform and cognitive processing and associated distribution. The embodiments support a mechanism for dynamically sharing critical and non-critical information responsive to a classification model, such that only relevant information or part of the information is shared. As the classification model is subject to modification, the dynamic sharing mechanism is dynamically updated to reflect such modification. Similarly, as an associated document or file is subject to modification, dynamic processing of the document or file takes place responsive to the classification model. The dynamic classification and document processing employ NLP and ML models to support the associated functionality.

Abstract: Embodiments of the present invention disclose a method, a computer program product, and a computer system for generating a wind map based on multimedia analysis. A computer receives a multimedia source configuration and builds a wind scale reference database. In addition, the computer extracts and processes both wind speed data and contextual data from the multimedia. Moreover, the computer analyses temporal and spatial features, as well as generates a wind map based on the extracted context, extracted wind speed, and analysed temporal and spatial features. Lastly, the wind map generator validates and modifies the wind scale reference database.

Abstract: Known geospatial device coordinates are clustered using a machine learning-based clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including latitude, longitude, and elevation, are received, and a geographic location of the device is determined using the latitude and longitude. An approximate current floor upon which the first device is located is determined by mapping the current device data of the first device to a closest cluster centroid, and calculating the approximate current floor using the elevation of the first device and the annotated regional floor-height value of the closest cluster centroid. An individual is directed to the device's geographic location and approximate current floor.

Abstract: Automatically managing ameliorative process that controls undesired code use in an application based on predicted valuation and risk vectors. A multidimensional risk vectors may be received in relation to current and future use of undesired code. Amelioration actions may be classifies by using a custom trained machine learning model. An optimal amelioration action may be selected based on at least one optimization objective function. The selected amelioration action may be taken to control undesired code use in application. Examples of an amelioration action may include changing of the characteristics of a user interface and stopping an application or a component of the application from running.

Abstract: An exemplary method includes obtaining a training set of code data that includes deprecated code; training a multi-layer neural network on the training set of code data to predict usage events for the deprecated code; predicting usage events for the deprecated code, in a first set of code data, using the trained multi-layer neural network; estimating a value of risk associated with the deprecated code in response to the predicted usage events; comparing the estimated value of risk to a threshold; and, in response to the estimated value of risk exceeding the threshold, implementing an ameliorative action regarding the deprecated code.

Abstract: Provided are a computer program product, system, and method for determining tags to recommend for a document. A natural language processing module determines a document keyword for a document. A tag database search module determines, a tag in a tag database associated with the document keyword. A domain specific search module determines a domain specific tag in a domain specific knowledge base associated with the document keyword. A recommendation is made of at least one of the tag and the domain specific tag as a recommended tag for the document.

Abstract: Known geospatial device coordinates are clustered using a clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including geographic location and elevation, are received. An approximate current floor upon which the first device is located is determined using the elevation of the first device and the annotated regional floor-height value of a closest cluster centroid, the closest cluster centroid determined based, at least in part, on the geographic location of the first device. An individual is directed to the device's geographic location and approximate current floor. The device clusters are re-computed based upon feedback from the individual regarding the device's actual floor.

Abstract: At an emergency response system, a first location including a latitude and a longitude at which a first device is located is received. At an emergency response system, an air pressure reading measured by the first device is received. Using the air pressure reading and a mean sea-level barometric measurement, a first elevation of a device is computed. Using the first location, a corresponding street address is computed. Using the street address and the first elevation, a floor number is computed. The street address and the floor number are transmitted to a second device.

Abstract: Historical device positioning data captured from one or more devices over a period of time is received. The historical device positioning data includes historical latitude, longitude, and elevation data of the one or more devices. Building boundaries for a give building are identified based upon the historical latitude and longitude data. The historical device positioning data corresponding to locations within the building boundaries of the building is clustered using a machine learning-based clustering algorithm, resulting in clusters with corresponding cluster centroids. The cluster centroids are associated with respective floors within the building. A current floor of the building on which a specific device is located is determined by mapping current device positioning data of the specific device to the closest cluster centroid.

Abstract: Known geospatial device coordinates are clustered using a machine learning-based clustering algorithm into device clusters with cluster centroids. Each device cluster corresponds to a geographical location. Each cluster centroid is annotated with a regional floor-height value of the respective geographical location. Current device data of a device, including latitude, longitude, and elevation, are received, and a geographic location of the device is determined using the latitude and longitude. An approximate current floor upon which the first device is located is determined by mapping the current device data of the first device to a closest cluster centroid, and calculating the approximate current floor using the elevation of the first device and the annotated regional floor-height value of the closest cluster centroid. An individual is directed to the device's geographic location and approximate current floor.