Abstract: An approach for automatically generate the Natural Language Interface (NLI) directly from the Graphical User Interface (GUI) code is disclosed. The approach leverages the use of mapping between GUI components to pre-defined NLI components in order to generate the necessary NLI components (e.g., intent example, entities, etc.) from the GUI code representation. The approach can leverage pre-defined patterns in order to generate these intent examples for each kind of NLI components. The created NLI dialog can be used simultaneously with the GUI or as a standalone feature.

Abstract: An example system includes processor to receive a frame of video and a number of detected feature points for the frame. The processor can calculate a position of a relative anchor for the frame based on a density of the feature points. The processor can add the relative anchor to the frame. The processor can then transmit the frame with the added relative anchor to a second computing device.

Abstract: The method provides for receiving a plurality of trained models from a corresponding plurality of clients, wherein a respective trained model predicts a condition of an asset and is based on a data set associated with the asset of a respective client. The trained model is based on a seed model that includes a canonical set of features. The trained model includes a component that converts the data at a site to the canonical set of features used by the seed model. The plurality of trained models from the corresponding plurality of clients is assigned to two or more groupings, wherein a grouping includes trained models providing similar analysis. The one or more processors generate an improved model for a client with a limited amount of training data, obtaining the improvement by using multiple models that belong to the same grouping of the first client's model.

Abstract: An approach for automatically generate the Natural Language Interface (NLI) directly from the Graphical User Interface (GUI) code is disclosed. The approach leverages the use of mapping between GUI components to pre-defined NLI components in order to generate the necessary NLI components (e.g., intent example, entities, etc.) from the GUI code representation. The approach can leverage pre-defined patterns in order to generate these intent examples for each kind of NLI components. The created NLI dialog can be used simultaneously with the GUI or as a standalone feature.

Abstract: The method provides for receiving a plurality of trained models from a corresponding plurality of clients, wherein a respective trained model predicts a condition of an asset and is based on a data set associated with the asset of a respective client. The trained model is based on a seed model that includes a canonical set of features. The trained model includes a component that converts the data at a site to the canonical set of features used by the seed model. The plurality of trained models from the corresponding plurality of clients is assigned to two or more groupings, wherein a grouping includes trained models providing similar analysis. The one or more processors generate an improved model for a client with a limited amount of training data, obtaining the improvement by using multiple models that belong to the same grouping of the first client's model.

Abstract: The method provides for receiving a plurality of trained models from a corresponding plurality of clients, wherein a respective trained model predicts a condition of an asset and is based on a data set associated with the asset of a respective client. The trained model is based on a seed model that includes a canonical set of features. The trained model includes a component that converts the data at a site to the canonical set of features used by the seed model. The plurality of trained models from the corresponding plurality of clients is assigned to two or more groupings, wherein a grouping includes trained models providing similar analysis. The one or more processors generate an improved model for a client with a limited amount of training data, obtaining the improvement by using multiple models that belong to the same grouping of the first client's model.

Abstract: A solution is proposed for controlling usage of computing services. A corresponding method comprises allocating a new usage token to a user in response to a usage entitlement thereof being insufficient to meet a usage weight of a new computing service. The usage entitlement is updated according to the new usage token and the usage weight of the new computing service, and the usage of the new computing service is enabled in response thereto. A computer program and a computer program product for performing the method are also proposed. Moreover, a system for implementing the method is proposed.

Abstract: Facilities of a shared environment are automatically optimized by inferring sentiment from unstructured conversational data towards various environmental entities such as heat, light, service levels, etc. Conversational audio streams from different areas are analyzed to identify an entity and associated sentiment, and a heatmap is created representing the sentiment across the different areas. The conversational audio streams are captured by directional microphones and are assigned metadata such as a location tag indicating a position of a microphone within the shared environment. Heatmap creation can be supplemented by other sensory data. A cognitive system is used to generate actions for control of the facilities based on the heatmap. A suggested action may still be subject to operational policies for the facility. In some scenarios a first suggested facility action compensates for an effect of a second suggested facility action.

Abstract: A method of expert peer identification includes receiving, from a user, a request for an expert peer for an asset type, and obtaining asset interaction data relevant to the request including a list of assets, and, for each asset on the list, values for each of a set of pre-defined interaction categories for each worker that has interacted with the asset. The method further includes, for each asset on the list, respectively multiplying the interaction category values by a corresponding set of category weights to obtain a set of weighted interaction values, and, for each worker, processing the sum of their weighted interaction values to obtain an overall centrality measure. The method also includes selecting one of the workers as the expert peer based, at least in part, on their overall centrality measure, and identifying the expert peer to the user.

Abstract: Facilities of a shared environment are automatically optimized by inferring sentiment from unstructured conversational data towards various environmental entities such as heat, light, service levels, etc. Conversational audio streams from different areas are analyzed to identify an entity and associated sentiment, and a heatmap is created representing the sentiment across the different areas. The conversational audio streams are captured by directional microphones and are assigned metadata such as a location tag indicating a position of a microphone within the shared environment. Heatmap creation can be supplemented by other sensory data. A cognitive system is used to generate actions for control of the facilities based on the heatmap. A suggested action may still be subject to operational policies for the facility. In some scenarios a first suggested facility action compensates for an effect of a second suggested facility action.

Abstract: An access management process orchestration method, an access management governance orchestrator, and a computer program product. One embodiment may comprise receiving a request for accessing a managed resource of an information system, querying an authorization for accessing the resource from an access manager, and in response to the querying of the authorization, requesting an access control policy update to grant the access to the managed resource. Receiving the request, querying the authorization, and requesting the access control policy update may comprise generating a transaction record, and adding the transaction record to a distributed ledger, wherein the distributed ledger simultaneously maintains the transaction record at multiple nodes throughout a network.

Abstract: A method for adjusting a resource within an area utilizing voting by users. The method includes a computer processor receiving a request from one or more of the users to adjust a resource. The method further includes generating a voting poll for the resource. The method further includes communicating the generated voting poll to a plurality of users, wherein the plurality of users includes the one or more users. The method further includes identifying valid responses to the communicated voting poll based on a first policy related to the resource. The method further includes determining a response to the voting poll, wherein the response is based, at least in part, on the first policy related to the resource and aggregated valid responses of the voting poll. The method further includes applying the determined response to the resource.

Abstract: An image captured at a first moment in time is received. A user is identified by comparing at least one facial feature extracted from the image with user information stored in a database. It is determined whether the user observed a visual display by determining whether the visual display was within the visual field of the user. Upon determining that the user observed the visual display, the image is processed with a sentiment analysis module to determine a sentiment of the user. Further, it is determined, based on a record stored in a transaction database, that the user purchased the first product at a second moment in time, and an engagement index is generated for the visual display based on processing the determined sentiment of the user and a period of elapsed time between the second moment in time and the first moment in time.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for determining a veridicality metric of a user profile stored in an electronic information system. The method, computer program product and computer system may include a computing device that inputs an identifier of a user profile and retrieves from an information system at least one element of the user profile based on the identifier. The computing device may submit a search request to at least one further information system. The search request may include a search criterion with at least a part of the element. The computing device may receive at least one search result related to the search request, and determine the veridicality metric based on the at least one element and the at least one search result.

Abstract: A method of expert peer identification includes receiving, from a user, a request for an expert peer for an asset type, and obtaining asset interaction data relevant to the request including a list of assets, and, for each asset on the list, values for each of a set of pre-defined interaction categories for each worker that has interacted with the asset. The method further includes, for each asset on the list, respectively multiplying the interaction category values by a corresponding set of category weights to obtain a set of weighted interaction values, and, for each worker, processing the sum of their weighted interaction values to obtain an overall centrality measure. The method also includes selecting one of the workers as the expert peer based, at least in part, on their overall centrality measure, and identifying the expert peer to the user.

Abstract: An image captured at a first moment in time is received. A user is identified by comparing at least one facial feature extracted from the image with user information stored in a database. It is determined whether the user observed a visual display by determining whether the visual display was within the visual field of the user. Upon determining that the user observed the visual display, the image is processed with a sentiment analysis module to determine a sentiment of the user. Further, it is determined, based on a record stored in a transaction database, that the user purchased the first product at a second moment in time, and an engagement index is generated for the visual display based on processing the determined sentiment of the user and a period of elapsed time between the second moment in time and the first moment in time.

Abstract: An approach is provided to present controlled heterogeneous digital content to users. The approach receives topics corresponding to content items that were previously accessed by a user, such as having been watched by the user on a display device. Opposing topics are associated to the identified topics with each of the opposing topics being opposite to the corresponding topic. A user position score is calculated based upon at least some of the opposing topics. This position score is used to identify content recommendations which are then presented to the user.

Abstract: A diagnosis server for collaborating with electronic noses, a related mobile diagnosis unit and a related method may be provided. A diagnosis server may comprise a receiver unit for receiving a set of data from one out of a plurality of e-noses. The set of data may comprise a sensor identifier, a sensor output value, and a relevance flag for a predefined diagnosis. A determination unit may determine a probability factor for the predefined diagnosis based on the set of data, a relevance function and a distribution function.

Abstract: Techniques are disclosed for distributing online content (e.g., news articles, blog posts, and the like) to a social media provider at a point in time determined based on topic maturity. One or more metrics are obtained from each of a plurality of social media providers. The metrics relate to a topic associated with a document for publishing on the social media providers. A measure is determined for each of the social media providers. The measure indicates when to publish the document on the social media provider based on the metrics associated with that provider. Upon determining that the measure exceeds a threshold for at least one of the social media providers, the document is published to the social media provider.

Abstract: A method and system or monitoring a resource consumption of an application running on a computer. A first metric describes the resource consumption of the application as a first function of time. A wavelet analysis of the first function of time is performed. A discontinuity in the first function is detected at a first time at which a coefficient determined from the wavelet analysis exceeds a specified threshold. A cross-correlation of the first metric and a second metric describing the resource consumption of the application as a second function of time are calculated. A diagnostic level associated with the discontinuity in the first function is adjusted based on the calculated cross-correlation. In response to adjusting the diagnostic level, a debugging function on the application is started in response to the cross correlation having a value indicative of an anomaly within the application.