Abstract: Embodiments of the present invention provide a method, a computer program product, and a system for generating a haptic signal representing a fabric composition. Embodiments of the present invention can be used to generate a haptic signal that is based on a user selection. For example, embodiments of the present invention can combine characteristic signals corresponding to a plurality of textiles to generate the haptic signal for output to a haptic device. Embodiments of the present invention can be used to recommend similar fabric compositions based upon similarity between a characteristic signal of a fabric composition and the haptic signal.

Abstract: A computer scans user data for upcoming events and identifies a potential gift giving event from these events using contextual information about the events to determine the events' nature. The computer identifies a plurality of event attendees and analyzes information about two or more of the event attendees. The computer groups a plurality of people into a group of potential gift collaborators, wherein the plurality of people is chosen from the plurality of attendees. The computer generates a first potential gift list. The computer analyzes gift giving abilities of the group members and selects, based partially upon the analysis, a gift giver. The computer generates a second potential gift list with detailed information about one or more potential gifts wherein the gifts are based partially on the first potential gift list. The computer provides at least a portion of the second potential gift list to the selected gift giver.

Abstract: Systems and methods are provided to implement intelligent recommendations to users by modeling user profiles through deep learning of multimodal user data. For example, a recommendation computing platform collects multimodal user data from a computing device of a registered user, wherein the multimodal user data include time-series data, unstructured textual data, and multimedia data. A first deep learning classification engine is utilized to extract features from the multimodal user data. A second deep learning classification engine is utilized to generate a profile of the registered user based on the extracted features. A deep recommendation classification engine is utilized to determine a recommendation for the registered user based on the profile of the registered user, wherein the recommendation identifies at least one additional registered user. The recommendation is presented to the registered user on the computing device of the registered user.

Abstract: A mechanism is provided in a data processing system for aggregating sentiment about an entity from a corpus of documents. The mechanism identifies a plurality of sentiment passages in the corpus of documents. Each of the plurality of sentiment passages includes a statement of sentiment about the entity. The mechanism determines a plurality of passage sentiment scores for the plurality of sentiment passages and an actual aggregate sentiment score from the plurality of passage sentiment scores based on a k-valued model. The mechanism determines a sentiment confidence score for the actual aggregate sentiment score based on the raw aggregate sentiment score and the actual aggregate sentiment score and presents the actual aggregate sentiment score and the sentiment confidence score.

Abstract: Embodiments describing an approach to evaluate text and image consistency. Receiving one or more images. Receiving one or more text documents. Identifying relevant text in the one or more text documents. Determining the consistency between the one or more images and the one or more text documents. Creating one or more image and text consistency scores based on the determined consistency between the one or more images and the one or more text documents, and outputting the one or more image and text consistency scores for evaluating text and image consistency.

Abstract: Metadata about a movie is retrieved. The metadata includes a plurality of associated viewer responses from at least one previous audience viewing of the video. The plurality of associated viewer responses from the at least one previous audience viewing are associated with one or more segments of the video. A segment of the video associated with a type of viewer reaction based on emotion and sentiment recognition is identified. Additional media content based on the identified video segment is retrieved. A segment of the additional media content that exceeds a threshold of similarity with the segment of the video is determined. A video clip that includes the segment of the additional media content is created.

Abstract: In an approach to managing images and captions, one or more computer processors receive one or more captured images of including one or more subjects. The one or more computer processors identify the one or more subjects from the first image. The one or more computer processors identify the context of the first image of the one or more captured images containing the one or more subjects. The one or more computer processors analyze one or more social networking histories and relationships associated with the one or more subjects using recognition techniques. The one or more computer processors create one or more captions associated with the first image of the one or more captured images based on the social networking histories and relationships of the one or more subjects and the identified context of the first image of the one or more captured images containing the one or more subjects.

Abstract: Methods, systems, and computer program products for driving anomaly detection based on spatio-temporal landscape-specific driving models are provided herein.

Abstract: A mechanism is provided in a data processing system for generating poll information from a chat session. The mechanism monitors chat content in the multiple user chat session. The mechanism identifies a poll question in a message within the chat content. The mechanism identifies a plurality of answers to the poll question in subsequent messages within the chat content. The mechanism generates a poll user interface presenting the poll question and the plurality of answers as selectable options and presents the poll user interface to the multiple users of the chat session.

Abstract: A mechanism is provided in a data processing system for generating poll information from a chat session. The mechanism monitors chat content in the multiple user chat session. The mechanism identifies a poll question in a message within the chat content. The mechanism identifies a plurality of answers to the poll question in subsequent messages within the chat content. The mechanism generates a poll user interface presenting the poll question and the plurality of answers as selectable options and presents the poll user interface to the multiple users of the chat session.

Abstract: Methods, systems, and computer program products for transferring context with delegation are provided herein. A computer-implemented method includes detecting an anticipated unavailability of a first individual for a given future event, based on inputs generated by the first individual and databases of stored calendar information pertaining to the first individual and the given future event; identifying tasks, related to the given future event, requiring a delegation as a result of the anticipated unavailability of the first individual, wherein identifying is based on data related to the given future event; determining additional individuals to whom the tasks can be delegated, based on qualifications of the additional individuals and one or more constraints; generating a summary of the delegation comprising a description of the tasks and information pertaining to the given future event; and outputting the summary to at least one of the additional individuals.

Abstract: Methods, systems, and computer program products for driving anomaly detection based on spatio-temporal landscape-specific driving models are provided herein.

Abstract: A method for object popularity detection includes receiving expression data for a user viewing a first object. The expression data for the user viewing the first object includes a viewing duration, a variation in heart rate of the user, one or more facial expressions of the user, and a variation in eye movement of the user. The expression data for the user viewing the first object is aggregated into one or more models and a response is determined for sending to an electronic device associated with the user, based on the one or more models from which the expression data was aggregated. The response based on the one or more models from which the expression data was aggregated is transmitted to the electronic device associated with the user for viewing.

Abstract: A method for object popularity detection includes receiving expression data for a user viewing a first object. The expression data for the user viewing the first object includes a viewing duration, a variation in heart rate of the user, one or more facial expressions of the user, and a variation in eye movement of the user. The expression data for the user viewing the first object is aggregated into one or more models and a response is determined for sending to an electronic device associated with the user, based on the one or more models from which the expression data was aggregated. The response based on the one or more models from which the expression data was aggregated is transmitted to the electronic device associated with the user for viewing.

Abstract: In an approach to managing images and captions, one or more computer processors receive one or more captured images of including one or more subjects. The one or more computer processors identify the one or more subjects from the first image. The one or more computer processors identify the context of the first image of the one or more captured images containing the one or more subjects. The one or more computer processors analyze one or more social networking histories and relationships associated with the one or more subjects using recognition techniques. The one or more computer processors create one or more captions associated with the first image of the one or more captured images based on the social networking histories and relationships of the one or more subjects and the identified context of the first image of the one or more captured images containing the one or more subjects.

Abstract: Methods, systems, and computer program products for cognitive based optimal grouping of users and trip planning are provided herein. A computer-implemented method includes generating, for each of multiple users, a temporally-related driving skill model pertaining to each of multiple topographies, wherein the model is based on items of driving-related data associated with the users and topography-related information of trips driven by the users; selecting users to participate in a ride-sharing trip in a given vehicle based on the models and details of the ride-sharing trip; determining a route for the trip based on the models of the selected users and environmental factors; creating a schedule for the trip by assigning the selected users to drive during distinct portions of the route based on the models of the selected users and characteristics of the determined route; and outputting the schedule to at least the selected users.

Abstract: In an approach to managing images and captions, one or more computer processors receive one or more captured images of including one or more subjects. The one or more computer processors identify the one or more subjects from the first image. The one or more computer processors identify the context of the first image of the one or more captured images containing the one or more subjects. The one or more computer processors analyze one or more social networking histories and relationships associated with the one or more subjects using recognition techniques. The one or more computer processors create one or more captions associated with the first image of the one or more captured images based on the social networking histories and relationships of the one or more subjects and the identified context of the first image of the one or more captured images containing the one or more subjects.

Abstract: Aspects include engaging a user in a chat flow by a dialog system. A user sentiment associated with a system response provided by the dialog system as part of the chat flow is determined based on observation of the user. A next system response is rerouted from a planned sequence of the chat flow to a sentiment-based repair sequence to alter content delivered to the user based on a detected aspect of the user sentiment.

Abstract: Methods, systems, and computer program products for deep learning based unsupervised event learning for economic indicator predictions are provided herein. A computer-implemented method includes extracting multiple events from a collection of documents; determining characteristics of the extracted events, wherein the characteristics comprise (i) one or more actions occurring within each event, (ii) one or more actors participating in each event, and (iii) one or more objects affected by each event; deriving structured data, related to an economic indicator value to be predicted, from multiple data sources; combining items of the derived structured data into one or more groups based on (i) semantic similarity of the items and (ii) a temporal aspect attributed to each of the items; and generating a prediction for the economic indicator value based on a comparison of the one or more groups to the characteristics of each of the extracted events.

Abstract: Aspects include accessing a chat flow tree structure defining a plurality of response sequences for a chat flow of a dialog system. A sentiment and flow history associated with the chat flow tree structure is analyzed. The sentiment and flow history defines an observed user sentiment captured for one or more system responses of the response sequences. Each of the one or more system responses is flagged having a negative sentiment in the sentiment and flow history and a sample size greater than a minimum adjustment threshold. A portion of the chat flow tree structure is adjusted that includes at least one of the flagged system responses to reduce the negative sentiment.