Abstract: The present invention refers to an online monitoring system for the gravitational separation of oil emulsions, more specifically, a monitoring system for the properties related to the emulsions, such as the drop size distribution (DSD) and the water content (WC), simultaneously, in pressurized and heated systems at high pressures and temperatures, respectively, using near-infrared region (NIR) spectroscopy coupled with optical microscopy. The online monitoring system is innovative as it overcomes the current need of the oil industry to remove aliquots from emulsified systems under high pressure and temperature, preferably up to 70 bar (7 MPa) and 150° C., respectively, for determination of the properties of emulsions, which causes disturbances and affects the physicochemical characteristics of emulsions.

Abstract: When human users traverse physical space or traverse computer software, they take “navigational paths.” Some embodiments of the present invention are directed to machine logic for identifying a favorable navigation paths by monitoring physical or computer software navigation paths used by human users as they use a set of computer program(s). This favorable navigation path, through physical space or the logic of the set of computer program(s), can then be suggested to future users who want to navigate from a similar starting point to a similar end point.

Abstract: When human users traverse physical space or traverse computer software, they take “navigational paths.” Some embodiments of the present invention are directed to machine logic for identifying a favorable navigation paths by monitoring physical or computer software navigation paths used by human users as they use a set of computer program(s). This favorable navigation path, through physical space or the logic of the set of computer program(s), can then be suggested to future users who want to navigate from a similar starting point to a similar end point.

Abstract: A method of automatically creating new intents and assigning examples to those new intents in a dialog system includes generating at least one predicted intent from an intent concept graph by combining two or more nodes of the intent concept graph. A graph embedding is generated of the at least one predicted intent. A sentence embedding is generated from examples from a call log. The graph embedding of the at least one predicted intent is then matched with the examples from the sentence embedding and the at least one predicted intent, with the examples, is connected into a dialog system.

Abstract: A system may include a memory and a processor in communication with the memory. The processor may be configured to perform operations that include analyzing interactions by a user within a network and generating a user profile for the user. The operations by the processor may further include identifying an attempt by the user to share a post via the network and prompting the user with a personalized alert to evaluate the post, wherein the personalized alert is generated based on the interactions, the user profile, and the properties of the post.

Abstract: To reduce misinformation consumption in the media, a computer-implemented method is described for presenting thought-provoking information about a media product that includes receiving media consumption data indicating a media product was consumed via a computing device user interface; determining claims for the media product; identifying a plurality of related media products based at least on a topic of the media product; determining positions for the plurality of related media products with respect to the one or more claims; determining a most contested claim as a claim that satisfies a condition corresponding to having a predetermined number of disagreeing related media products; generating a question based on the most contested claim and a paragraph including the most contested claim; generating an answer to the question based on the question and the related media product that disagrees with the most contested claim; and presenting the question and answer via the user interface.

Abstract: A method of automatically creating new intents and assigning examples to those new intents in a dialog system includes generating at least one predicted intent from an intent concept graph by combining two or more nodes of the intent concept graph. A graph embedding is generated of the at least one predicted intent. A sentence embedding is generated from examples from a call log. The graph embedding of the at least one predicted intent is then matched with the examples from the sentence embedding and the at least one predicted intent, with the examples, is connected into a dialog system.

Abstract: A method of providing examples to a computerized conversation agent includes associating one or more triplets of two concepts and a relationship therebetween with an intent related to a query. The triplet is embedded in a knowledge graph and the concepts in the knowledge graph are mapped to the intent. A call log is scanned for examples of the intent based on the concepts in the knowledge graph and the examples are mapped to the intent.

Abstract: A method of providing examples to a computerized conversation agent includes associating one or more triplets of two concepts and a relationship therebetween with an intent related to a query. The triplet is embedded in a knowledge graph and the concepts in the knowledge graph are mapped to the intent. A call log is scanned for examples of the intent based on the concepts in the knowledge graph and the examples are mapped to the intent.

Abstract: A user expertise classifying method, system, and computer program product, include analyzing an input by a user based on at least one of vocabulary, orthography, and grammar of the user input, processing user background data obtained from a database, and calculating an expertise score of the user based on the analyzed user input and the processed background data.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes assessing a performance, a personality and a cognitive trait of a conversational agent based on natural written language by a second conversational agent by combining an analysis of a plurality of metrics that each compare a metric from the conversational agent with a metric from the second conversational agent and producing a report detailing the performance, the personality, and the cognitive trait of the conversational agent.

Abstract: A method for indexing a user interface test recording includes capturing, during a session, a recording of a plurality of interactions with the user interface, capturing, during the session, an event stream including a plurality of user interface events, synchronizing, in time, the plurality of interactions and the plurality of user interface events, identifying a point of interest in the event stream, wherein the point of interest is correlated to a time in the recording by the synchronization, and annotating the recording at a time correlated to when the point of interest occurred.

Abstract: A user expertise classifying method, system, and computer program product, include analyzing an input by a user based on at least one of vocabulary, orthography, and grammar of the user input, processing user background data obtained from a database, and calculating an expertise score of the user based on the analyzed user input and the processed background data.

Abstract: A user expertise classifying method, system, and computer program product, include analyzing an input by a user based on at least one of vocabulary, orthography, and grammar of the user input, processing user background data obtained from a database, and calculating an expertise score of the user based on the analyzed user input and the processed background data.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes obtaining data to create at least one scenario for testing a conversational agent, performing a set of tests using a scenario of the at least one scenario created to assess a capability of the conversational agent, and comparing a result of the capability from the set of tests with an expected result of the scenario.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes assessing a performance, a personality and a cognitive trait of a conversational agent based on natural written language by a second conversational agent by combining an analysis of a plurality of metrics that each compare a metric from the conversational agent with a metric from the second conversational agent and producing a report detailing the performance, the personality, and the cognitive trait of the conversational agent

Abstract: A method for surveilling a monitored environment includes classifying an individual detected in the monitored environment according to a role fulfilled by the individual within the monitored environment, generating a trajectory that illustrates movements and locations of the individual within the monitored environment, and detecting when the trajectory indicates an event that is inconsistent with an expected pattern for the role.

Abstract: A conversational agent capability assessment method, system, and computer program product, includes obtaining data to create at least one scenario for testing a conversational agent, performing a set of tests using a scenario of the at least one scenario created to assess a capability of the conversational agent, and comparing a result of the capability from the set of tests with an expected result of the scenario.

Abstract: A user expertise classifying method, system, and computer program product, include analyzing an input by a user based on at least one of vocabulary, orthography, and grammar of the user input, processing user background data obtained from a database, and calculating an expertise score of the user based on the analyzed user input and the processed background data.

Abstract: Embodiments focus on the real-time processing and analysis of event-related images posts on social media. By computing the most posted images and grouping them in accordance to their similarity, these images can be organized in a visual interface to provide the users an image-driven social media topic detection system. The groups of images with the highest frequency represent the most prominent topics. By keeping track of the temporal occurrence of the images, it is possible to visualize the temporal evolution of the topics.