Abstract: Embodiments of the present invention provide a method, system and computer program product for teaming in swarm intelligent robot sets. The method includes programming a multiplicity of robots in a multi-robot set with a particular locomotive model and assigning each of the robots to different individual tasks corresponding to different individual objectives of a problem based upon the particular locomotive model of the corresponding robots. The method additionally includes deploying the multi-robot set into a confined geographic area and surveilling each robot to ensure that each robot assigned to a corresponding task is achieving the assigned task. Finally, the method includes responding to one of the robots appearing to be unable to complete a corresponding assigned one of the tasks by selecting a different robot with a locomotive model considered compatible with the corresponding assigned one of the tasks to complete the corresponding assigned one of the tasks.

Abstract: A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The events corresponding to the collections are classified, using an LTSM network, into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.

Abstract: Physical object addition and removal based on affordance and view. A method tracks eye gaze of a group of users who view an environment, the group of users each having a respective field of view to the environment. The method assesses, based on the tracking, whether an object present in the respective fields of view of the group of users is clutter. The method also initiates a physical change to the environment based on the assessing. In some examples, assessing whether the object is clutter includes determining an extent to which the group of users avoid the object by looking around or away from the object, measuring visual dwell time on and around the object by the group of users, and/or identifying affordances of the object based on input from the group of users and/or on correlating the object to another object having known affordances associated therewith.

Abstract: A method for performing the following operations (not necessarily in the following order): (i) receiving by an augmented reality system, a series of images corresponding to views of the real world; (ii) processing, by the augmented reality system, the series of images to determine presence of a first object; (iii) determining, by the augmented reality system, that the first object meets a first set of conditions such that the first object belongs to a first object category; and (iv) responsive to the determination that the first object belongs to the first object category, providing an audio response in the form of one at least one of the following types of audio responses: communicating a predefined sound to an augmented reality user and/or changing audio characteristic(s) of a sound not generated by the augmented reality system which is being experienced by the augmented reality user.

Abstract: Social media inputs specific to a content of a broadcast program and received from a plurality of users are analyzed to determine a sentiment value expressed relative to a portion of the content. A group is formed of those users whose sentiment values expressed relative to the portion of the content are within a specified tolerance value of sentiment value and are expressed within a defined period during the broadcast of the program. A historical data from the inputs unrelated to the content is analyzed to extract an entity. A time zone of a geographical location of the entity is associated with the group. a sentiment of the group expressed relative to the portion of the content is shifted by an amount of time on a program continuum timeline, such that the sentiment of the group aligns with a time when the portion was broadcast in the time zone.

Abstract: A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The set of collections is input at a first input and the second set of collections is input at a second input in the LSTM. The events corresponding to the collections are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.

Abstract: An experience feedback of a user is measured, the experience feedback being in response to a presentation of a stream of a content. In response to the determining that an experience value corresponding to the experience feedback has to be adjusted to a target experience value, an optional content selected, the optional content including data indicative of an option experience value, the option experience value being at least a component of a difference between the experience value and the target experience value. The optional content is steganographically combined with the content in the stream to form a combined stream. During the presentation of the combined stream, a switch is made to the optional content such that a portion of the content is replaced by the optional content in the presentation, the switching causing in the user a change from the experience value towards the target experience value.

Abstract: An application is deployed on a first cloud from a set of clouds in a hybrid cloud environment. A simulator is deployed on each cloud in the set. A vertical relationship function is computed between a time-series of the application and a time-series of the simulator on the first cloud. A first actual value in the time-series of the application is forecasted for a future time. A horizontal relationship function is computed between a first simulator value in the time-series of the simulator on the first cloud and a second simulator value in the time-series of the simulator on a second cloud. A second actual value in the time-series of a hypothetical deployment of the application on the second cloud is forecasted for the future time without deploying the application on the second cloud. The application is placed on the second cloud when the second actual value satisfies a condition.

Abstract: A method for performing the following operations (not necessarily in the following order): (i) receiving by an augmented reality system, a series of images corresponding to views of the real world; (ii) processing, by the augmented reality system, the series of images to determine presence of a first object; (iii) determining, by the augmented reality system, that the first object meets a first set of conditions such that the first object belongs to a first object category; and (iv) responsive to the determination that the first object belongs to the first object category, providing an audio response in the form of one at least one of the following types of audio responses: communicating a predefined sound to an augmented reality user and/or changing audio characteristic(s) of a sound not generated by the augmented reality system which is being experienced by the augmented reality user.

Abstract: A set of collections of forecasted feature vectors is selected from a repository for a future time window after a present time, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set having feature vectors that are indicative of an event related to the cyber-attack in a region of the environment at a discrete time. The events corresponding to the collections in the set are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase is determined that corresponds to the class. The determined phase is predicted as likely to occur during the future time window in the region.

Abstract: A set of collections of forecasted feature vectors is selected from a repository for a future time window after a present time, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set having feature vectors that are indicative of an event related to the cyber-attack in a region of the environment at a discrete time. The events corresponding to the collections in the set are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase is determined that corresponds to the class. The determined phase is predicted as likely to occur during the future time window in the region.

Abstract: A set and a second set of collections of forecasted feature vectors are selected from a repository for a future time window, a cyber-attack being in progress in a data processing environment at the present time, a collection in the set and a collection in the second set indicating an event related to the cyber-attack in a first region and a second event in a second region, respectively, of the environment at a discrete time. The set of collections is input at a first input and the second set of collections is input at a second input in the LSTM. The events corresponding to the collections are classified into a class of cyber-attack. From a mapping between a set of phases of the cyber-attack and a set of classes, a phase that corresponds to the class is predicted as likely to occur during the future time window in the region.