Abstract: An example operation may include one or more of identifying a first conferred asset exchange request from a first user account and a second conferred asset exchange request from a second user account which are capable of being used to settle each other, requesting an intermediary blockchain to perform a conferred asset settlement transaction for the first and second conferred asset exchange requests, determining that first conferred assets of the first user account and second conferred assets of the second user account have been transferred to temporary intermediary trading addresses, respectively, and releasing the first conferred assets to the second user account and the second loyalty assets to the first user account, in response to the determining.

Abstract: One embodiment provides a method for predicting a next action in a conversation system that includes obtaining, by a processor, information from conversation logs and a conversation design. The processor further creates a dialog graph based on the conversation design. Weights and attributes for edges in the dialog graph are determined based on the information from the conversation logs and adding user input and external context information to an edge attributes set. An unrecognized user input is analyzed and a next action is predicted based on dialog nodes in the dialog graph and historical paths. A guiding conversation response is generated based on the predicted next action.

Abstract: A method and system of evaluating a user experience (UX) design are provided. A UX design is received. All objects that are identified to be part of a background of the input UI screen are removed to create a filtered input UI screen. The input UI screen is assigned to a cluster. A target UI screen of the input screen is determined and its background removed, to create a filtered target UI cluster. The target UI screen is assigned to a cluster. The filtered input UI screen is used as an input to a deep learning model to predict a target UI cluster. The predicted target UI cluster is compared to the filtered target UI cluster based on the clustering. Upon determining that the filtered target UI cluster is similar to the target UI screen, the UX design is classified as being successful.

Abstract: One embodiment provides a computer program product for cognitive visual and ontological mapping of tabular data. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to receive a data query and ontology information relating to the data query, receive context information relating to a display device, determine a measure of interest for the data query and one or more constraints for the measure of interest, determine saliency features based on the context information, and generate a user interface comprising query results corresponding to the data query arranged in a dynamic tabular format for display on the display device. The dynamic tabular format is based on the ontology information, the measure of interest, the one or more constraints for the measure of interest, and the saliency features.

Abstract: In one general aspect, a computer-implemented method includes identifying current choices with different verbosity levels for a current turn in a conversation; normalizing multi-dimensional verbosity vectors for each of the current choices to obtain a normalized value for each of the current choices; determining a state definition for the current turn in the conversation, utilizing the normalized values for each of the current choices; providing the state definition for the current turn in the conversation and the normalized values for each of the current choices to a trained reinforcement learning module; receiving, from the trained reinforcement learning module, a score associated with each of the current choices for the current turn in the conversation; and selecting one of the current choices to be entered for the current turn in the conversation, based on the score associated with each of the current choices for the current turn in the conversation.

Abstract: A method, a computer program product, and a computer system counterbalance a developed bias of user reviews. The method includes determining a developed bias of an existing plurality of first user reviews for a first item. The method includes determining a tendency value of a designated user indicative of a tendency of a user sentiment exhibited in user reviews for respective second items provided by the designated user deviating from an average sentiment of the respective second items. The method includes determining an influential prompt in which the designated user provides an input for the first item, the influential prompt being offset by an offset value based on the developed bias and the tendency value. The method includes prompting the designated user with the influential prompt and receiving the input from the designated user. The method includes updating the tendency value based on the input.

Abstract: One embodiment provides a method comprising generating a conversational interface for display on an electronic device. The conversational interface facilitates a communication session between a user and an automated conversational agent. The method further comprises performing a real-time analysis of a portion of a user input in response to the user constructing the user input during the communication session, and updating the conversational interface to include real-time feedback indicative of whether the automated conversational agent understands the portion of the user input based on the analysis. The real-time feedback allows the user to adjust the user input before completing the user input.

Abstract: A method and system of evaluating a user experience (UX) design are provided. A UX design is received. All objects that are identified to be part of a background of the input UI screen are removed to create a filtered input UI screen. The input UI screen is assigned to a cluster. A target UI screen of the input screen is determined and its background removed, to create a filtered target UI cluster. The target UI screen is assigned to a cluster. The filtered input UI screen is used as an input to a deep learning model to predict a target UI cluster. The predicted target UI cluster is compared to the filtered target UI cluster based on the clustering. Upon determining that the filtered target UI cluster is similar to the target UI screen, the UX design is classified as being successful.

Abstract: A method and system of automatically identifying topics of a conversation are provided. An electronic data package comprising a sequence of utterances between conversation entities is received by a computing device. Each utterance is classified to a corresponding social action. One or more utterances in the sequence are grouped into a segment based on a deep learning model. A similarity of topics between adjacent segments is determined. Upon determining that the similarity is above a predetermined threshold, the adjacent segments are grouped together. A transcript of the conversation including the grouping of the adjacent segments is stored in a memory.

Abstract: A computer-implemented method, system and computer program product for generating a user interface. A sketch (e.g., wireframe) of a portion of a user interface is received. The sketch is analyzed to predict a set of intended sketches using artificial intelligence based on historical data and/or the user's asset library. A set of intended final sketch renderings of the user interface is then generated and displayed using the set of predicted intended sketches based on historical data or a model trained to extract visual characteristics from existing user interface screens. If the user selects one of the intended final sketch renderings of the user interface as being directed to the intended design of the user interface and indicates that the selected intended final sketch rendering of the user interface corresponds to the final intended design, then code is generated to render the selected final sketch rendering of the user interface.

Abstract: A computer-implemented method, according to one embodiment, is used to creating customized templates and template recommendations. The computer-implemented method includes: extracting text content from user information which corresponds to a user, and using the text content extracted from the user information to generate feature vectors. The feature vectors are further used to form one or more clusters of existing user editions. For each of the one or more formed clusters, a tree structure is generated for each user edition in the cluster. Moreover, the tree structures in the cluster are combined into a merged tree structure. A customized template is generated for each of the one or more formed clusters using the respective merged tree structure, and at least one of the customized templates is recommended to the user based on the user information.

Abstract: One embodiment provides a method for predicting a next action in a conversation system that includes obtaining, by a processor, information from conversation logs and a conversation design. The processor further creates a dialog graph based on the conversation design. Weights and attributes for edges in the dialog graph are determined based on the information from the conversation logs. An unrecognized user input the conversation system is detected. The unrecognized user input is analyzed and a next action is predicted based on dialog nodes in the dialog graph and historical paths. A guiding conversation response is generated based on the predicted next action.

Abstract: A method and system of evaluating a user experience (UX) design are provided. A UX design is received. All objects that are identified to be part of a background of the input UI screen are removed to create a filtered input UI screen. The input UI screen is assigned to a cluster. A target UI screen of the input screen is determined and its background removed, to create a filtered target UI cluster. The target UI screen is assigned to a cluster. The filtered input UI screen is used as an input to a deep learning model to predict a target UI cluster. The predicted target UI cluster is compared to the filtered target UI cluster based on the clustering. Upon determining that the filtered target UI cluster is similar to the target UI screen, the UX design is classified as being successful.

Abstract: Embodiments relate to an intelligent computer platform to utilize machine learning techniques to minimize compliance risk. Features are extracted from one or more data sources across two or more domains and activity data is identified. Features are identified within the activity data which are used to create one or more models. Domain activity impact is measured and a risk probability is calculated. A policy is generated based on the calculated risk probability and one or more compliance activities are orchestrated. One or more encoded actions are executed in compliance with the orchestration.

Abstract: Facilitation of optimized transportation selection relative to user travel objectives and user context is provided. A system can include a memory and a processor that executes computer executable components. The computer executable components can include: an objective component that determines travel objectives of a user; a context component that determines context of the user; an analyzer component that analyzes transportation related data relevant to the travel objectives of the user, wherein the analyzer component performs a utility-based analysis that weighs costs versus benefits associated with respective transportation options relative to the travel objectives of the user; and an optimization component that outputs a travel itinerary to the user that includes an optimized transportation selection relative to the user travel objectives and user context.

Abstract: A method and system of matching a participant to a task are provided. A project is received and its parameters determined. Tasks of the project are determined from the parameters. For each task, parameters of the task are determined, comprising a time to perform the task and a threshold level of expertise to perform the task. Profile information of potential participants is received that is filtered based on the time to perform the task, to conserve a memory and a processing power of the computer device. The task is assigned to a participant of the filtered potential participants based on multi-variable optimization on cost, time, and quality of a deliverable of the task.

Abstract: A computer implemented method includes receiving a first set of data comprising financial data of a company and at least one of holiday data, industrial event data, or weather data. A probabilistic hotel demand forecast is computed for a hotel in a location based on a statistical analysis of prior hotel stays at the hotel in the location with an adjustment based on at least the financial data of the first set of data. The adjustment can be computed by the computer based on a prior effect of at least the financial data on a number of prior hotel stays. The resulting probabilistic hotel demand forecast can be displayed on a user terminal device. This forecast may be used to generate a future pricing strategy.

Abstract: One embodiment provides a computer program product for improving accuracy of a transcript of a spoken interaction. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to identify a plurality of patterns in the transcript. The plurality of patterns are indicative of a group of acoustically similar words in the transcript and a corresponding local, sequential context of the group of acoustically similar words. The program instructions are further executable by the processor to cause the processor to predict conditional probabilities for the group of acoustically similar words based on a predictive model and the plurality of patterns, detect one or more transcription errors in the transcript based on the conditional probabilities, and correct the one or more transcription errors by applying a multi-pass correction on the one or more transcription errors.

Abstract: One embodiment provides a method for assessing probability of winning an in-flight deal. The method comprises receiving information for the in-flight deal. The information for the in-flight deal comprises a set of price points for the in-flight deal and metadata relating to the in-flight deal. The method further comprises, for each price point of the set of price points, predicting a probability of winning the in-flight deal at the price point based on a predictive analytics model.

Abstract: One embodiment provides a computer program product for cognitive visual and ontological mapping of tabular data. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to receive a data query and ontology information relating to the data query, receive context information relating to a display device, determine a measure of interest for the data query and one or more constraints for the measure of interest, determine saliency features based on the context information, and generate a user interface comprising query results corresponding to the data query arranged in a dynamic tabular format for display on the display device. The dynamic tabular format is based on the ontology information, the measure of interest, the one or more constraints for the measure of interest, and the saliency features.