Abstract: A method of this disclosure may include performing a named entity recognition on text information related to requirements for a wireframe by a first artificial intelligence (AI) model, so as to extract entities and relations of the entities from the text information. The method may further comprise inputting the extracted entities and relations to a second AI model to generate the wireframe, wherein the second AI model is trained so that a difference between resultant relations of the entities of the generated wireframe and the extracted relations of the entities from the first AI model is decreased.

Abstract: According to an embodiment of the disclosure, a method performed by an electronic apparatus may include extracting at least one key frame and at least one non-key frame from a video. According to an embodiment of the disclosure, a method performed by an electronic apparatus may include inpainting the at least one key frame based on at least one mask corresponding to the at least one key frame. According to an embodiment of the disclosure, a method performed by an electronic apparatus may include inpainting the at least one non-key frame based on the at least one inpainted key frame.

Abstract: A method may include acquiring a first image that is obtained by adding noise to a second image, the second image comprising a target restoration region. The method may include performing at least one first denoising process on the first image using a first artificial intelligence (AI) network to obtain a first denoising result. The method may include restoring the target restoration region based on the first denoising result using a second AI network to obtain a restored image.

Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Abstract: Techniques are provided for determining an identity of a scanned person in computed tomography (CT) scanning is provided. The techniques include the use of a system for assisting in determining an identity of a scanned person in CT scanning and a CT scanner.

Abstract: Executing serverless application functions is provided. A response to a user request for a service is received with an include callback parameter and a transaction identifier of the user request included in a header of the response from an external service via a network. A checkpoint of a container corresponding to the service is retrieved from a data store using the transaction identifier of the user request. The container corresponding to the service is restored using the checkpoint to process the response received from the external service.

Abstract: Embodiments of the invention are directed to a computer system that includes a processor electronically coupled to a memory. The processor is operable to perform processor operations that includes accessing hypertext virtual markup language encoded (HTVML-encoded) text. The HTVML-encoded text is displayed in an immersive video frame of a display of a virtual reality (VR) environment. HTVML content associated with the HTVML-encoded text is also displayed in the 360-degree video frame. The HTVML content include a three-dimensional (3D), immersive presentation format.

Abstract: Embodiments of present invention provide a method of taking photo in a virtual reality environment. The method includes defining a group that includes multiple entities; detecting multiple directions that the multiple entities are facing based upon orientations of the multiple entities; identifying multiple extension lines that start from the multiple entities in the group and extend in the multiple directions; identifying one or more intersections associated with the multiple directions; identifying one or more connection lines that connect a center of the group to the one or more intersections; defining a camera line based on the one or more connection lines; determining a camera location along the camera line; taking an image of virtual view of the multiple entities by a virtual camera; and providing the image as a photo to the multiple entities in the group. A non-transitory storage medium and a computing environment are also provided.

Abstract: A method of this disclosure may include performing a named entity recognition on text information related to requirements for a wireframe by a first artificial intelligence (AI) model, so as to extract entities and relations of the entities from the text information. The method may further comprise inputting the extracted entities and relations to a second AI model to generate the wireframe, wherein the second AI model is trained so that a difference between resultant relations of the entities of the generated wireframe and the extracted relations of the entities from the first AI model is decreased.

Abstract: A method of this disclosure may include performing a named entity recognition on text information related to requirements for a wireframe by a first artificial intelligence (AI) model, so as to extract entities and relations of the entities from the text information. The method may further comprise inputting the extracted entities and relations to a second AI model to generate the wireframe, wherein the second AI model is trained so that a difference between resultant relations of the entities of the generated wireframe and the extracted relations of the entities from the first AI model is decreased.

Abstract: Determining a class-specific predictor indicator importance can include generating, using machine learning, a prediction model having a tree structure including nodes, class label subgroups, and class-specific predictor indicators. A class-specific predictor indicator importance can be generated for a selected class-specific predictor indicator by merging a term predictor indicator frequency and a purity predictor indicator frequency at each instance of the selected class-specific predictor indicator within one or more nodes of the tree structure. A representation of the tree structure displayed in a graphical user interface can depict each path between a root node of the tree structure and a leaf node mapped to the selected class label subgroup having a node containing an instance of the selected class-specific predictor indicator in a manner that contrasts with other paths between the root node and other leaf nodes of the tree structure.

Abstract: System and methods for adaptive multi-granularity event groupings are provided. In embodiments, a method includes: determining to group IT operations data at a first level of granularity for similar events or at a second level of granularity for related events based on user input of a data grouping event; parsing, by an event parser, the IT operations data into one or more groups of similar events based on text information and parser rules in response to determining to group the IT operations data at the first level of granularity; obtaining user feedback indicating the one or more groups of similar events require modification; determining one or more keywords of the IT operations data using an artificial intelligence model in response to the user feedback; and updating the parser rules for the event parser based on the one or more keywords, thereby generating updated parser rules.

Abstract: An approach is provided that trains an artificial intelligence (AI) system, such as a neural network, to process IT ticket data. The approach receives IT tickets from various ticket sources. Ticket vectors corresponding to each of the IT tickets are computed. An analysis is performed using the ticket vectors and a node vector that corresponds to a network topology. The analysis is performed using a corpus of IT ticket data. An IT ticket model used by the AI system is trained based on the analysis. Responses are provided to requestors of the AI system using the trained IT ticket model.

Abstract: Content for products can be identified. For each identified content, at least one class to which the first content pertains can be predicted using an artificial intelligence multiclass model. For each identified first content that corresponds to the at least one class, a support level of the product with regard to at least one class can be predicted using artificial intelligence binary class models. For each identified product, data indicating the product and the support level of the product with regard to the at least one class can be added to a data table. A product features map based on the data indicating the products and a support level of each product with regard to the at least one class can be generated and output.

Abstract: Embodiments of the invention are directed to a computer-implemented method of operating a multi-screen virtual reality environment. The computer-implemented method includes performing a wall arrangement and transmission (WA&T) protocol that includes receiving at a second module a function transmitted by a first module over a network to the second module. The second module and the function received over the network are used to generate priority data that identifies a priority of each of a plurality of individual video streams generated by a plurality of video sources. Based at least in part on the priority, the second module is used to generate reduced-size video streams that include selected ones of the plurality of individual video streams. The second module transmits the priority data and a multi-screen video stream that includes the reduced-size video streams and non-reduced-size video streams of the plurality of individual video streams.

Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Abstract: The present disclosure relates to the field of biology, in particular, to an anti-CLD18A2 VHH. The present disclosure provides an anti-CLD18A2 VHH. A complementarity determining region (CDR) of the anti-CLD18A2 VHH includes CDR1-CDR3 having amino acid sequences as shown below: CDR1 having an amino acid sequence represented by one of SEQ ID NO. 4-11, CDR2 having an amino acid sequence represented by one of SEQ ID NO. 19-26, and CDR3 having an amino acid sequence represented by one of SEQ ID NO.33-39. The anti-CLD18A2 VHH of the present disclosure can specifically bind to the epitope on CLD18A2, and the corresponding fusion protein thereof has good specificity and affinity for CLD18A2, and has a noticeable tumor suppression effect.

Abstract: Executing serverless application functions is provided. A response to a user request for a service is received with an include callback parameter and a transaction identifier of the user request included in a header of the response from an external service via a network. A checkpoint of a container corresponding to the service is retrieved from a data store using the transaction identifier of the user request. The container corresponding to the service is restored using the checkpoint to process the response received from the external service.

Abstract: Content for products can be identified. For each identified content, at least one class to which the first content pertains can be predicted using an artificial intelligence multiclass model. For each identified first content that corresponds to the at least one class, a support level of the product with regard to at least one class can be predicted using artificial intelligence binary class models. For each identified product, data indicating the product and the support level of the product with regard to the at least one class can be added to a data table. A product features map based on the data indicating the products and a support level of each product with regard to the at least one class can be generated and output.