Abstract: An asset stored on a storage device of a computer system is detected to be omitted from an asset-specific access policy. Using a utilization history of the asset, a criticality of the asset is scored, generating a first criticality score for the asset. Using the first criticality score, the asset is classified into a policy category. A selected access policy selected from a set of access policies assigned to a set of assets in the policy category is applied to the asset, the selected access policy specifying an access restriction of the asset.

Abstract: An approach for intelligent optimization of machine learning models for a target environment may be provided herein. The approach may include extracting metadata from a training pipeline for a first machine learning model that has been configured to operate within a first computing environment. The approach may also include mapping the extracted metadata to one or more constraints associated with a second machine learning model that has been configured to operate within a second computing environment. The approach may also include training the second machine learning model configured to the second computing environment, with the dataset that was used to train the first machine learning model, based on the mapped constraints. The approach may also include comparing performance metrics of the first machine learning model to corresponding metrics of the now trained second machine learning model.

Abstract: A processor may receive data regarding a context for a first dialog turn. The processor may monitor a voice input from a user for the first dialog turn. The processor may detect a first pause in the voice input, the first pause having a duration that satisfies a time threshold. The processor may receive, based on the first pause, first voice input data. The processor may analyze the first voice input data. The processor may determine that additional time is recommended for the voice input to be provided by the user.

Abstract: In an approach to monitoring and querying autobiographical events, one or more computer processors receive data from one or more sensors associated with a user. Based on the received data from the one or more sensors, one or more computer processors detect an autobiographical event associated with the user. One or more computer processors convert the received data from the one or more sensors into one or more observations associated with the autobiographical event. One or more computer processors convert the one or more observations into one or more questions associated with the one or more observations. One or more computer processors query the user with the one or more questions. One or more computer processors receive at least one response to the one or more questions from the user.

Abstract: The present invention provides a method, computer program product, and system of performing personality profile automated measurements. In an embodiment, the method, computer program product, and system includes receiving, by a computer system, gaming history data associated with a gamer profile, assigning, by the computer system, the gamer profile to a first cluster of gamers, recording, by the computer system, game play data associated with the gamer profile, and based on the game play data associated with the gamer profile and the gaming history data associated with the gamer profile, reassigning the gamer profile into a second cluster of gamers.

Abstract: Embodiments relate to an intelligent computer platform to identify a lexical answer type (LAT), a first concept relevant to the received request and a second concept related to the identified first concept. The LAT, together with the first and second concepts are utilized to create a first and second cluster. Documents are selectively populated into the clusters. The clusters are subject to sorting based on a relevancy protocol.

Abstract: Microservices are predictively deployed on edge devices in a network. An application is run on a client device, the application comprising a set of microservices runnable on any edge device in a set of two or more edge devices. A state of the client device at a first time is determined, the state including one or more microservices currently being run for the client device, and for each microservice currently being run, an edge device running the microservice. One or more microservices that are likely to be run at a second time subsequent to the first time and a location of the client device at the second time are predicted. Based on the predicted location, a next edge device in the set of edge devices is determined for running the one or more microservices predicted to be run at the second time.

Abstract: A method identifies and removes bias from a machine learning model. A user/computer inputs a plurality of input training data into a machine learning system to generate an output of labeled output data. The user/computer evaluates the labeled output data according to a consistency metric to associate the labeled output data with a corresponding consistency assessment. The user/computer selects each labeled output data having a consistency assessment indicating a consistency assessment that is greater than a predetermined threshold to form a labeled output data subset, and then creates additional labeling for the labeled output data subset. The user/computer utilizes the additional labeling to distinguish each labeled training data from labeled output data subset as being mislabeled and biased, and then adjusts the learning machine based on the labeled output data subset being mislabeled and biased.

Abstract: A method and system for delivering a package is provided. The method includes receiving online order data comprising details associated with a product being ordered by a user. The online order data and environmental data associated with delivery of the product is analyzed and a resulting a travel route for delivering the product to a specified location of the user is generated. A delivery vehicle for delivering the product to the specified location of the user is selected and directed to a storage location of the product for loading. The delivery vehicle is directed from the storage location to the specified location of the user via the travel route. Delivery actions associated with the product with respect to the specified location are determined based on data indicating current environmental conditions. The delivery actions are executed.

Abstract: An asset stored on a storage device of a computer system is detected to be omitted from an asset-specific access policy. Using a utilization history of the asset, a criticality of the asset is scored, generating a first criticality score for the asset. Using the first criticality score, the asset is classified into a policy category. A selected access policy selected from a set of access policies assigned to a set of assets in the policy category is applied to the asset, the selected access policy specifying an access restriction of the asset.

Abstract: A processor may receive data regarding a context for a first dialog turn. The processor may monitor a voice input from a user for the first dialog turn. The processor may detect a first pause in the voice input, the first pause having a duration that satisfies a time threshold. The processor may receive, based on the first pause, first voice input data. The processor may analyze the first voice input data. The processor may determine that additional time is recommended for the voice input to be provided by the user.

Abstract: Software that performs the following operations: (i) receiving descriptive information associated with a domain expert; (ii) receiving a question and a corresponding candidate answer for the question; (iii) determining a set of scoring features to be used to evaluate the candidate answer, wherein the set of scoring features includes: at least one scoring feature pertaining to the question, at least one scoring feature pertaining to the candidate answer, and at least one scoring feature pertaining to the descriptive information; (iv) receiving a score from the domain expert, wherein the score is based, at least in part, on the domain expert's evaluation of the candidate answer; (v) generating a feature vector based on the set of scoring features; (vi) cross-correlating the feature vector with the score; and (vii) clustering the domain expert with one or more other domain experts according to the cross-correlation, thereby creating a first cluster.

Abstract: A method administers a therapeutic treatment for a neurological impairment of a patient. One or more processor(s) display query content on a display, and then receive responses to the query content from a patient. The processor(s) identify a physiological locus of a neurological impairment of the patient based on the responses to the query content from the patient, and administer a therapeutic treatment to the patient based on the identified physiological locus of the neurological impairment of the patient.

Abstract: Provided are techniques for OCR induction for multi-page changes. A plurality of documents of a document type are processed to generate text area data for a text area in one or more documents of the plurality of documents, where the text area data includes coordinate locations of a zone for the text area based on expansion and direction of shift of the text area. A page flow model is trained using the plurality of documents and the text area data. In response to receiving a new document comprising the text area, a scanning script is received from the page flow model, where the page flow model identifies a new zone for the text area in the new document and determines how to adjust another zone for an element in the new document. The scanning script is used to scan the new document to generate digital text.

Abstract: Microservices are predictively deployed on edge devices in a network. An application is run on a client device, the application comprising a set of microservices runnable on any edge device in a set of two or more edge devices. A state of the client device at a first time is determined, the state including one or more microservices currently being run for the client device, and for each microservice currently being run, an edge device running the microservice. One or more microservices that are likely to be run at a second time subsequent to the first time and a location of the client device at the second time are predicted. Based on the predicted location, a next edge device in the set of edge devices is determined for running the one or more microservices predicted to be run at the second time.

Abstract: Utilizing a computing device executing a master chatbot and one or more modular chatbots to respond to one or more chat messages. A computing device executing the master chatbot receives one or more chat messages. The computing device parses the received one or more chat messages to discover an intent and entities contained within the chat messages. A ranking algorithm is employed to rank the master chatbot and a plurality of modular chatbots, the ranking algorithm scoring the master chatbot and the plurality of modular chatbots based upon the intent and entities contained within the one or more chat messages. The master chatbot responds to the chat message if the ranking algorithm rates the master chatbot highest or forwards automatically the one or more chat messages to a ranked modular chatbot for response if the ranking algorithm rates the ranked modular chatbot highest.

Abstract: A method and system are provided for implementing enhanced cognitive searches optimized to integrate deployment with development testing. An initial body of works is ingested into a system capable of answering questions. A series of queries is analyzed against the initial body of works utilizing a set of scorers utilizing criteria to form assessments, wherein each scorer uses the criteria against the query and the initial body of works to form the assessment. The assessments are analyzed to determine a usefulness of a set of entries in the initial body of works. Content are deployed using the identified selected first set of entries as deployment ready, enabling enhanced cognitive search results.

Abstract: Natural language contractual geographic resource restrictions (CGRR) are converted by computer into a set of machine logic based rule(s), and then the set of machine logic based rule(s) is used to control access to a set of computing resources in accordance with the CGRR in the operative contract. In some embodiments, the CGRR restrict access based on the geography of the prospective user, or customer, of the computing resources.

Abstract: According to aspects of the present disclosure, systems, methods and computer program products can be provided for dynamic workload orchestration based on data complexity. Methods, computer program products and/or systems are provided for dynamic workload orchestration that perform the following operations: (i) receiving a workload for orchestration; (ii) computing complexity scores for respective portions of the workload, where the complexity scores are computed based at least on parameters describing data associated with the portions of the workload; and (iii) using an orchestration engine to assign the portions of the workload to corresponding compute resources, based on their respective complexity scores.

Abstract: A method, computer system, and computer program product for form-based conversation system design are provided. The embodiment may include ingesting, by a processor, a plurality of forms from a given domain. The embodiment may also include extracting indicators of required input fields from the ingested plurality of forms. The embodiment may further include generating a required input list based on the extracted indicators of the required input fields to update a size of the required input list. The embodiment may also include determining transactional intents based on the required input list. The embodiments may further include generating a dialog flow that satisfies the determined transactional intents.