Abstract: Computer-implemented methods, computer program products, and computer systems for detecting and resolving fraudulent calls. The computer-implemented methods include one or more processors configured for receiving voice call data corresponding to an incoming telephone call placed to a user device, wherein the voice call data comprises caller voice data. Further, the computer-implemented methods may include one or more processors configured for converting the caller voice data to caller text data comprising one or more text phrases. Further embodiments of the computer-implemented method include processors configured for determining that the one or more text phrases satisfies a first condition, and responsive to determining that the one or more text phrases satisfies the first condition, transmitting a user alert to the user device.

Abstract: An example operation may include one or more of computing historical patterns related to fraudulent attempts from a transaction log, predicting future fraud attempts from public data, correlating the historical patterns and the predicted future fraud attempts, modifying one or more endorsement policies based on the correlations, and adding the modified one or more endorsement policies to a smart contract.

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: identifying two or more distinct elements in an image; generating a sub-image for each of the two or more distinct elements; generating adjectives descriptive of content associated with a distinct element for each sub-image; displaying a response list including the adjectives associated with the distinct element of a selected sub-image in response to an interaction with the image; obtaining annotation data based in part on the response list displayed for the distinct element of the selected sub-image; and assigning the annotation data to the distinct element of the selected sub-image, wherein the annotation data is displayed in response to an interaction with the distinct element in the image.

Abstract: A method comprising retrieving a plurality of invalid user first commands and a plurality of user responses stored in a memory, wherein each of plurality of invalid user verbal commands are commands to a smart hub requesting a first smart device perform an action, wherein each of the plurality of invalid commands includes at least a first name for the first smart device, wherein the first name is not a valid name for the first smart device. Determining a trend within the retrieved plurality of invalid user verbal commands and a plurality of user responses, wherein the trend identifies the first name as being used a plurality of times and identifies the first smart device the user was referring to when the user used the first name. Automatically updating a namespace database to include the first name as an alias for a setup name for the first smart device.

Abstract: Systems and techniques that facilitate quantum circuit valuation are provided. In various embodiments, a system can comprise an input component that can access a first quantum circuit. In various embodiments, the system can further comprise a valuation component that can appraise the first quantum circuit based on one or more factors (e.g., frequency factor, complexity factor, resource factor, similarity factor), thereby yielding a value score that characterizes the first quantum circuit. In various instances, the system can further comprise an execution component that can recommend deployment of the first quantum circuit based on determining that the value score exceeds a threshold.

Abstract: A method, computer system, and a computer program product for personalized voice responses is provided. The present invention may include gathering a plurality of user data from an Internet of Things (IoT) connected sensor. The present invention may include identifying a personalized vocabulary based on the gathered plurality of user data. The present invention may include training a voice response system based on the gathered plurality of user data and the identified personalized vocabulary. The present invention may include receiving a verbal request. The present invention may include responding to the received verbal request using the trained voice response system.

Abstract: A processor may analyze, using an AI system, an application, where the application includes one or more application modules. The processor may determine, using the AI system, that an application module is critical based on a contextual scenario. The AI system may be trained utilizing data regarding heat generation of hardware on which the application module is operating. The processor may identify, using the AI system, required resources of the hardware for the application module to function during the contextual scenario. The processor may allocate an availability of the required resources for the application module.

Abstract: A method, a computer program product, and a computer system determine a customized augmented reality workout based on a cognitive state of a user. The method includes determining a current cognitive score of the user based on physical descriptions. The method includes determining the cognitive state of the user based on the current cognitive score. The cognitive state is indicative of a stress level being experienced by the user. The method includes determining the customized augmented reality workout from a plurality of augmented reality workouts based on the cognitive state of the user. The method includes generating the customized augmented reality workout to include at least one virtual component and incorporating at least one feature of real world surroundings. The method includes presenting the customized augmented reality workout to the user via an augmented reality device worn by the user.

Abstract: In an approach for generating personalized clues for multiple-choice test questions, a processor analyzes one or more multiple-choice test questions to identify one or more concepts required to be understood to correctly answer each multiple-choice test question. A processor generates a dependency graph corresponding to each multiple-choice test question. A processor monitors a user answer the one or more multiple-choice test questions. Responsive to the user answering at least one of the one or more multiple-choice test questions, a processor assesses whether the user answered the at least one of the one or more multiple-choice test questions correctly. A processor generates a known concept database for the user. Responsive to determining the user is answering a second multiple-choice test question, a processor generates at least one personalized clue based on the dependency graph and the known concept database. A processor presents the user with the at least one personalized clue.

Abstract: A delivery location of a first user is linked to a tokenized identifier of a package including an ordered item and delivery is initiated. A second order for the same item is received from a second user also requesting expedited delivery while the item ordered by the first user is en-route for delivery. An incentive is offered to a computing device of the first user in exchange for agreeing to a delayed delivery of the item ordered by the first user. An expedited delivery timeframe in exchange for additional compensation is offered to a computing device of the second user, and in response to receiving an acceptance of the incentive for delayed delivery and the expedited delivery in exchange for additional compensation the link to the tokenized identifier is changed to a delivery location of the second user while the package including the item is en-route.

Abstract: Using a digital twin model of a user, a digital twin model of a geographical location, a digital twin model of an event venue located at the geographical location, and a plurality of digital twin models of clothing items, a product recommendation customized to the user and a planned event is generated, the planned event planned to occur at the event venue. A product recommendation depiction is generated, the product recommendation depiction comprising a depiction of the product recommendation being worn by the user at the planned event. An answer to a natural language query regarding the product recommendation depiction is generated.

Abstract: A method includes: predicting a jeopardy associated with an edge server included in the server cluster that communicates with a central server; responsive to predicting the jeopardy, detaching the edge server from the server cluster; determining a patch based on the jeopardy; pushing the patch to the edge server; validating the patch by performing regression testing; and responsive to validating the patch, inducting the edge server into the server cluster.

Abstract: Aspects of the present disclosure relate generally to reminder systems and, more particularly, to a contextual item discovery and pattern inculcated reminder mechanism and methods of use. A computer-implemented method includes: determining, by a computing device, an event in which a user will participate; associating, by the computing device, one or more items with the event; determining a location of the one or more items associated with the event including that the user does not possess any combination of the one or more items; and providing an alert to the user that the any of the one or more items associated with the event is not in the possession of the user.

Abstract: A first plurality of machine learning operations are performed on Internet of Things (IoT) input data in an IoT ecosystem. The first plurality of machine learning operations are performed using a first machine learning level. One or more first machine learning outputs are received from the first machine learning level. A second plurality of machine learning operations are executed on the one or more first machine learning outputs. The second plurality of machine learning operations are executed using a second machine learning level. One or more second machine learning outputs are obtained from the second machine learning level. A third plurality of machine learning operations run on the one or more second machine learning outputs. The third plurality of machine learning operations run using a third machine learning level. An IoT output is identified from the third machine learning level.

Abstract: Using a sentiment analysis model, a reaction to a presentation of a first content is measured. Using the first content and the reaction, a content generation parameter is determined. Using a first content generation model and the content generation parameter, second content corresponding to the first content is generated. Using a second content generation model and the content generation parameter, third content corresponding to the first content is generated. The first content is adjusted by combining the first content with the second content and the third content, the adjusting resulting in an adjusted content.

Abstract: A computer implemented method includes receiving a dataset for use with respect to a current machine learning model, wherein the dataset comprises one or more features, analyzing one or more external datasets to identify a set of similar features, appending the similar features to the received dataset to generate an updated dataset, applying the updated dataset to the current machine learning model to generate an updated machine learning model, and assessing performance of the updated machine learning model. The method may further include categorizing the features of the dataset into categorical text features and unstructured text features. The method may additionally include recommending one or more actions based on the performance assessment of the updated machine learning model. The method may further include converting the one or more features into numerical feature vectors and identifying a vectoral distance between the one or more features and the set of similar features.

Abstract: A computer implemented method and system are disclosed for dynamically enabling a first user device operated by a first user to provide wireless hotspot capability for a wireless hotspot session to a second user device operated by a second user. The wireless hotspot session is accepted based at least on urgency and bandwidth requirement of the second user device. When the first user device accepts a connection to the second user device to provision a wireless hotspot for the wireless hotspot session, the first user restricts use of the second user device in the wireless hotspot session, based on bandwidth and context of use of the second user device. The first user device may refuse a connection by rejecting the wireless hotspot session request wireless hotspot session transactions information is provided as blockchain ledgers.

Abstract: A computer-implemented method, including: receiving, by a computing device, a post referring to a multimedia content; identifying, by the computing device, a time in the post; generating, by the computing device, a validity score based on analyzing contextual data of the time in the post; determining, by the computing device, a correlation between the time in the post to the multimedia content based on the validity score; and publishing, by the computing device, the post with an interactive link to a corresponding time of the multimedia content based on the determined correlation.

Abstract: The present invention includes an embodiment that may determine an access level within an organization. The embodiment may generate a simulated scenario based on the access level. The embodiment may identify responses of the user to the generated simulated scenario. The embodiment may capture one or more input frames. The embodiment may analyze the responses and the one or more input frames and generate education for the user based on the responses and the one or more input frames.

Abstract: A method, computer system, and a drone for in-flight drone structure modification are provided. A first sensor of a drone may detect damage to a first arm of the drone during a flight of the drone. In response to the detecting the damage, the damaged first arm of the drone may be detached via a computer of the drone and during the flight of the drone.