Abstract: Embodiments are related to providing external device communication and localization for virtual reality based equipment using radio-frequency identification (RFID). At least two receivers and a transmitter are used to recognize an external device and determine a location of the external device relative to the headset, based on tags coupled to the external device. A three-dimensional (3D) model is downloaded of the external device based on information received by the at least two receivers from the tags. A location of the external device is matched to the 3D model based on the tags. A virtual image is displayed of the external device corresponding to the location of the external device.

Abstract: A method, computer system, and a computer program product for smart SDN is provided. The present invention may include recording and clustering a pod's behavior to generate a behavior transition model for the pod. The present invention may include watching a behavior of the pod and comparing the behavior to the generated behavior transition model. The present invention may include triggering a network policy change based on determining that the behavior of the pod is a misbehavior.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to generating a security threat prediction. 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 prediction component that analyzes a public data graph model to generate a primary security threat determination, wherein the prediction component can further generate a secondary security threat prediction based on the primary security threat determination and on a proprietary data graph model, wherein the proprietary data graph model comprises proprietary security threat data from a source, and wherein the proprietary security threat data has been scrubbed of source-identifiers.

Abstract: A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.

Abstract: Embodiments are related to providing external device communication and localization for virtual reality based equipment using radio-frequency identification (RFID). At least two receivers and a transmitter are used to recognize an external device and determine a location of the external device relative to the headset, based on tags coupled to the external device. A three-dimensional (3D) model is downloaded of the external device based on information received by the at least two receivers from the tags. A location of the external device is matched to the 3D model based on the tags. A virtual image is displayed of the external device corresponding to the location of the external device.

Abstract: Embodiments of the invention are directed to a computer system that includes a processor electronically coupled to a memory. The processor is operable to perform processor operations that includes accessing hypertext virtual markup language encoded (HTVML-encoded) text. The HTVML-encoded text is displayed in an immersive video frame of a display of a virtual reality (VR) environment. HTVML content associated with the HTVML-encoded text is also displayed in the 360-degree video frame. The HTVML content include a three-dimensional (3D), immersive presentation format.

Abstract: Embodiments of present invention provide a method of taking photo in a virtual reality environment. The method includes defining a group that includes multiple entities; detecting multiple directions that the multiple entities are facing based upon orientations of the multiple entities; identifying multiple extension lines that start from the multiple entities in the group and extend in the multiple directions; identifying one or more intersections associated with the multiple directions; identifying one or more connection lines that connect a center of the group to the one or more intersections; defining a camera line based on the one or more connection lines; determining a camera location along the camera line; taking an image of virtual view of the multiple entities by a virtual camera; and providing the image as a photo to the multiple entities in the group. A non-transitory storage medium and a computing environment are also provided.

Abstract: A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to generating a security threat prediction. 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 prediction component that analyzes a public data graph model to generate a primary security threat determination, wherein the prediction component can further generate a secondary security threat prediction based on the primary security threat determination and on a proprietary data graph model, wherein the proprietary data graph model comprises proprietary security threat data from a source, and wherein the proprietary security threat data has been scrubbed of source-identifiers.

Abstract: A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.

Abstract: An approach is provided in which a method, system, and program product build a time series prediction model based on one or more relationships between a first set of keywords in a set of first news articles and a second set of keywords in a set of second news articles. The time series prediction model includes a time-based interest level adjustment corresponding to a publication time between the set of first news articles and the set second of news articles. The method, system, and program product use the time series prediction model to compute an inherited initial interest level of a third news article that includes a set of new keywords based on the set of new keywords and the time-based interest level adjustment. The method, system, and program product assign the inherited initial interest level to the third news article.

Abstract: Computing systems, methods and management tools for scheduling, optimizing and completing a dynamically adjustable workflow process. The computing systems, methods and management tools being capable of evaluating the availability of resources available for completing the workflow process and ascertaining the reliability of the resources in order to pre-generate a workflow process schedule. The computing systems, methods and management tools are further able to communicate with the assigned resources to incrementally negotiate and receive approval for proposed improvements to the pre-generated workflow schedule prior to implementation of the workflow schedule in order to optimize the cycle time of the process and increase probability of successfully completing the workflow process. The computing systems, methods and management tools may dynamically track the due dates for completing particular tasks and generate amended workflow process schedules in the event a failure occurs.

Abstract: An approach to workflow management in response to a detected security incident in a computer system. The approach may include an inference driven response based on prior artifacts. The inference driven response may predict the condition of the system and the outcomes of actions in response to the security incident. The predictions made by the inference drive response may be based on a machine learning model. The inference driven response may pause or prevent scheduled actions of the system based on the predictions. The inference driven response may continue to monitor the system and dynamically update its predictions for the condition of the system. In response to the updated predictions, the inference driven response may cancel or execute the previously scheduled actions of the system.

Abstract: A method, computer system, and a computer program product for smart SDN is provided. The present invention may include recording and clustering a pod's behavior to generate a behavior transition model for the pod. The present invention may include watching a behavior of the pod and comparing the behavior to the generated behavior transition model. The present invention may include triggering a network policy change based on determining that the behavior of the pod is a misbehavior.

Abstract: A computer-implemented method for automatically adjusting a Uniform Resource Locator (URL) seed list. The method includes crawling for documents based on a seed URL list. The method generates relations data from the documents using a Natural Language Processing (NLP) model. The method analyzes the relations data using an auto-seed model. The method modifies the seed URL list.

Abstract: Content for products can be identified. For each identified content, at least one class to which the first content pertains can be predicted using an artificial intelligence multiclass model. For each identified first content that corresponds to the at least one class, a support level of the product with regard to at least one class can be predicted using artificial intelligence binary class models. For each identified product, data indicating the product and the support level of the product with regard to the at least one class can be added to a data table. A product features map based on the data indicating the products and a support level of each product with regard to the at least one class can be generated and output.

Abstract: Generating a solution keyword tag cloud is provided. The solution keyword tag cloud is generated for a product based on matching keywords identified in a question asking how to resolve an issue experienced by a user with the product with keyword tags included in a set of condition-solution trees corresponding to the product. In response to receiving an indication that a tried solution in the solution keyword tag cloud did not resolve the issue experienced by the user, the solution keyword tag cloud is graphically updated by moving the tried solution that failed to resolve the issue from a solution section of the solution keyword tag cloud to a condition section of the solution keyword tag cloud. The solution keyword tag cloud is presented in a graphical user interface display on a client device corresponding to the user.

Abstract: Embodiments of the invention are directed to a computer-implemented method of operating a multi-screen virtual reality environment. The computer-implemented method includes performing a wall arrangement and transmission (WA&T) protocol that includes receiving at a second module a function transmitted by a first module over a network to the second module. The second module and the function received over the network are used to generate priority data that identifies a priority of each of a plurality of individual video streams generated by a plurality of video sources. Based at least in part on the priority, the second module is used to generate reduced-size video streams that include selected ones of the plurality of individual video streams. The second module transmits the priority data and a multi-screen video stream that includes the reduced-size video streams and non-reduced-size video streams of the plurality of individual video streams.

Abstract: Embodiments for graph computing are provided. A graph including a plurality explicit nodes and at least one implicit node is generated. A first of the plurality of explicit nodes and a second of the plurality of explicit nodes are traversed between utilizing deductive reasoning. A third of the plurality of explicit nodes and a fourth of the plurality of explicit nodes are traversed between through the at least one implicit node utilizing inductive reasoning.

Abstract: A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.