Abstract: Methods, system, and non-transitory processor-readable storage medium for a component development migration system are provided herein. An example method includes a reverse proxy server that receives a Hypertext Transfer Protocol (HTTP) request from a client system. The reverse proxy server intercepts the HTTP request between the client system and a server. A listener module receives a digital footprint of the HTTP request, where the digital footprint identifies a feature associated with an enterprise application. The method determines a feature score associated with the feature, and an overall feature score using a weighted feature score and a weighted feature priority score. The method then migrates development of a component of the enterprise application to a second enterprise application according to the overall feature score.

Abstract: A fuel cell system, e.g., of a motor vehicle, includes a fuel cell stack having fuel cells and non-repeating hardware components, the latter including wet and dry end units. The cells are positioned between the end units. The dry end unit includes an end plate and a compression plate assembly that uniformly compresses the cells against the end plate, and a terminal plate surrounded by a seal plate. An insulator frame disposed adjacent to the compression plate assembly is engaged with the insulator frame via a frictional interface provided by o-ring compression seals having a respective post connected to the terminal plate and the seal plate, and a pocket connected to the insulator frame. The post and pocket of at least one of the compression seals together define a fluid passage. One or more shim plates may be disposed between the end plate and insulator frame.

Abstract: Methods, apparatus, and processor-readable storage media for implementing synchronization systems for ALM are provided herein. An example computer-implemented method includes identifying ALM tools in conjunction with at least one software application development task; establishing a connection between at least a first and at least a second of the ALM tools using one or more APIs associated with the at least a first ALM tool and the at least a second ALM tool; determining data mapping rules and data transformation rules associated with the at least a first ALM tool and the at least a second ALM tool; and synchronizing data, related to the at least one software application development task, from the at least a first to the at least a second ALM tool, via the connection and in accordance with at least a portion of the data mapping rule(s) and the data transformation rule(s).

Abstract: Methods, apparatus, and processor-readable storage media for generating context-based and user-related predictions using artificial intelligence techniques are provided herein. An example computer-implemented method includes determining one or more user parameters by processing information related to a user in association with at least one web application; determining context information associated with the user accessing one or more portions of the at least one web application; generating one or more predictions associated with future use of the at least one web application by the user by processing at least a portion of the one or more user parameters and at least a portion of the context information using one or more artificial intelligence techniques; and performing one or more automated actions based at least in part on the one or more predictions associated with future use of the at least one web application by the user.

Abstract: An apparatus comprises a processing device configured to select first and second versions of a web application, to obtain first and second sets of source code files used for generating front ends of the first and second versions of the web application, and to generate first and second sets of tree structures based at least in part on the first and second sets of source code files, respectively. The processing device is also configured to identify node differences between the first and second sets of tree structures and to generate, based at least in part on the identified node differences, information characterizing differences between web elements of the first and second versions of the web application. The processing device is further configured to initiate automated action based at least in part on the generated information characterizing differences between web elements of the first and second versions of the web application.

Abstract: The technology described herein is directed towards product classification across multiple contexts within an enterprise environment, e.g., based on stock keeping unit (SKU-) related data. Relevant SKU attributes and patterns for each context are used to train machine learning-based classification models, which are then used to classify descriptive data of a product, such as one or more modules of a SKU. In one implementation, natural language processing and vectorization of input data are described to provide appropriate input to the trained classifier. Context-specific rules derived from domain knowledge and regulatory guidelines can be integrated to refine the classifications made by the model. The system incorporates integration with enterprise resource planning (ERP) systems, enabling efficient data management and classification within an existing infrastructure.

Abstract: An apparatus comprises at least one processing device configured to obtain information characterizing a user interface (UI) of an application, the obtained information comprising an image of the UI and textual data associated with the UI. The processing device is also configured to generate, by processing the image utilizing a first machine learning model, a first data structure characterizing data fields corresponding to UI components in the UI. The processing device is further configured to generate, by processing the textual data utilizing a second machine learning model, a second data structure characterizing relationships between the data fields. The processing device is further configured to determine, based on the first and second data structures, a database 10 configuration comprising an organization of the data fields into tables for a database associated with the application.

Abstract: A biopsy forceps includes an end effector with opposing first and second jaws coupled together and configured to move from an open configuration to a closed configuration. Each of the first jaw and the second jaw includes a plurality of teeth having a base. The bases of two adjacent teeth of the first jaw are spaced apart to form a first gap, and the bases of two adjacent teeth on the second jaw are spaced apart to form a second gap. The end effector in the closed configuration may define one or more windows between edges of the first and the second jaws. And, at least one of these windows may include at least a portion of one or both of the first and second gaps.

Abstract: A system and a method for a single sign-on (SSO) authentication process that enables digital communication between multiple entities. The system includes a server with one or more processors. These processors receive a first authentication request from a first entity to access a second entity, which employs a first authentication process. Based on the request, the processors generate a second authentication request corresponding to the second authentication process used by the second entity. The second authentication request is then communicated to the second entity. After receiving a first response from the second entity, the processors generate a second response corresponding to that first response. This second response is communicated back to the first entity. Upon validating the second response, the processors allow communication between the first and second entities.

Abstract: Methods, apparatus, and processor-readable storage media for generating test cases for software testing using machine learning techniques are provided herein. An example computer-implemented method includes obtaining user input data associated with at least one software application; identifying one or more predetermined types of information, including one or more of topic-related information and entity-related information, from at least a portion of the user input data using at least a first set of one or more machine learning techniques; generating one or more test cases for testing at least a portion of the at least one software application by processing at least a portion of the identified information using at least a second set of one or more machine learning techniques; and performing one or more automated actions based at least in part on at least one of the one or more generated test cases.

Abstract: Techniques and solutions are provided for database testing. In particular, aspects of the present disclosure facilitate “automatic exploration” of testing of database features, such as database commands. In particular, test definitions can be created for particular database features, where a test definition can have variants. Different test definitions can be combined into a composite test, which can include different variants for different tests. An executable test can be created that allows a database to execute a composite test. The present disclose also provides techniques and solutions for analyzing test results, particularly when voluminous test results are returned. In one example, test results for multiple iterations of a test can be compared. A change in test results, for example, can cause a test result to be flagged for further review. The composite tests, or result analysis techniques, can help provide better testing of database software to help identify software errors/bugs.

Abstract: An apparatus comprises a processing device configured to detect a user login to access a console of a computing device implementing a multi-level console interface comprising two or more levels each associated with a set of criteria, goals to be achieved, and actions relevant for achieving the goals. The processing device is also configured to determine a current state of the computing device, and to select a given level of the multi-level console interface based at least in part on mapping the current state to a given set of criteria associated with the given level. The processing device is further configured to provide, via the console of the computing device, an interface screen corresponding to the given level, the interface screen displaying the actions relevant for achieving the goals of the given level to facilitate transitioning of the computing device from the current state to a desired state.

Abstract: An apparatus comprises a processing device configured to determine specifications for an information technology asset to be developed, and to identify, utilizing at least one machine learning model, whether at least one of the specifications for the information technology asset is defect-prone, wherein a given specification is identified as defect-prone responsive to at least one output of the at least one machine learning model indicating that the given specification has at least a threshold likelihood of resulting in one or more defects during development of the information technology asset. The processing device is also configured to establish a mapping between the one or more identified defect-prone specifications for the information technology asset and one or more components of the information technology asset, and to modify one or more development processes for the information technology asset based at least in part on the established mapping.

Abstract: Methods, system, and non-transitory processor-readable storage medium for a digifootprint use case capturing system are provided herein. An example method includes a listener module that receives a Hypertext Transfer Protocol (HTTP) request from a client system, where the reverse proxy server intercepts the HTTP request between the client system and a server. A listener module associated with the reverse proxy server captures a digital footprint of the HTTP request, and a transmitter module updates an automation code repository with normalized test data obtained from the digital footprint.

Abstract: An apparatus comprises a processing device configured to receive a search query associated with a given user, to determine a user context specifying topics of interest and a level of detail sought by the given user, and to execute the search query to obtain search results. The processing device is also configured to apply natural language processing to the search results to assign classifications from a set defined based at least in part on the topics of interest of the given user. The processing device is further configured to utilize machine learning to filter the search results for relevancy based at least in part on the classifications assigned to the search results. The processing device is further configured to generate and provide to the given user a search response to the search query in a format selected based on the level of detail sought by the given user.

Abstract: A User Plane Function (UPF) of 5G Core network performs a search for the next hop in the data path, using utilities (e.g., Internet Control Message Protocol (ICMP) trace route), and determines the capability of the next router and/or other hops in the path. The UPF updates (e.g., using the PATCH command) the Network Repository Function (NRF) with the gathered information. The UPF also updates the NRF the UPF's position in the current route, and the role(s) the UPF is playing at a given time, e.g., Branching Point (BP), Intermediate User Plane Function (I-UPF), and the like. The SMF is enabled to identify the UPF's capability for a given PDU Session. The selection of a given UPF for non-suitable roles can be prevented, and the UPF can be selected for those roles in which the UPF is more suitable at a given time.

Abstract: The present application provides a hazardous gas detection system to determine hazardous gas concentrations and/or temperatures within a flow of exhaust air in an exhaust duct of a gas turbine compartment. The hazardous gas detection system may include one or more sensors positioned within or in communication with the exhaust duct and a static mixer positioned upstream of the one or more sensors to promote mixing of the flow of exhaust air.

Abstract: Techniques for validating a cloud service for multiple programming stacks are disclosed. A computer system may configure a corresponding set of requests for each programming stack client in a plurality of programming stack clients based on usage metrics of a cloud service. Each programming stack client in the plurality of programming stack clients may be configured to be used to build a software application for the cloud service. The usage metrics may indicate historical requests executed on the cloud service via the plurality of programming stack clients. The computer system may then, for each programming stack client in the plurality of programming stack clients, trigger execution of the corresponding set of requests for the programming stack client on the cloud service via the programming stack client.

Abstract: Methods, apparatus, and processor-readable storage media for task-related data processing using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data related to at least one decision-making context in connection with at least one user; classifying at least a portion of the obtained data into one or more of multiple categories of action-related requirements associated with the at least one decision-making context; recommending one or more tasks in furtherance of the at least one decision-making context by processing at least a portion of the classified data using one or more machine learning techniques; and performing one or more automated actions based at least in part on the one or more recommended tasks.

Abstract: Methods, apparatus, and processor-readable storage media for converting text data to video data using artificial intelligence techniques are provided herein. An example computer-implemented method includes converting text data associated with a task into multiple action statements relating to user performance of at least a portion of the task; generating video data by processing at least a portion of the multiple action statements using one or more artificial intelligence techniques; compiling at least a portion of the generated video data into at least one video sequence corresponding to at least one action workflow for the user performance of the at least a portion of the task; and performing one or more automated actions based at least in part on the at least one video sequence.