Abstract: A design application receives an exemplary design from an end-user having one or more functional attributes relevant to solving a design problem. The design application then generates a set of labels that describes the functional attributes of the exemplary design. Based on the set of labels, the design application explores a functional space to retrieve one or more system classes having functionally descriptive labels that are similar to the set of labels generated for the exemplary design. The one or more system classes include different approaches to solving the design problem, and represent systems having at least some functional attributes in common with the exemplary design.

Abstract: Provided herein is a moving object simulation method including: a simulation preprocessing step in which a first server generates environment information for the simulation target, including at least one of road data, nearby vehicle data, nearby pedestrian data, nearby obstacle data, nearby traffic light data, nearby sign data, and event data, for a predetermined period of time; and a simulation step in which a second server calculates sensor unit output data of a simulation target vehicle according to movement of the simulation target vehicle for the predetermined period of time using the environment information, preventing computational overload due to limitations of server resources and promoting quality improvement for algorithm verification.

Abstract: In an approach for building a machine learning model that predicts the appropriate action to resolve a malfunction or system error, a processor receives an alert that a malfunction or a system error has occurred. A processor creates a workspace on a ChatOps platform integrated with a chatbot and one or more tools. A processor inputs data relating to the alert in a natural language format. A processor processes the data using a natural language processing algorithm. Responsive to determining a pre-set threshold for outputting the appropriate action is not met, a processor establishes a conversation between two or more support service agents in the workspace. A processor monitors the conversation using the natural language processing algorithm. A processor analyzes a transcript of the conversation using text analytics or pattern matching. A processor creates and trains a machine learning model to predict the appropriate action in future iterations.

Abstract: An application programming interface (API) as a service is disclosed. In embodiments, a client provides code to be executed along with a configuration file for that code. Based on that, virtual machine(s) and load balancer(s) may be selected, a domain name service configured, and throttling and scaling configured. Through this, an API as a service may be provided on behalf of a client with minimal configuration required by the client or an administrator of a web service platform that provides the API as a service.

Abstract: Systems and methods for zero-footprint and safe execution of quantum computing programs are disclosed. According to one embodiment, in an electronic device comprising at least one computer processor, a method for cloud-based execution of quantum-computing programs may include: (1) receiving, from a user interface on a client device, a serialized file comprising a domain, an application, and an algorithm; (2) receiving, from the user interface, problem data and an identification of a quantum computing backend for executing the problem data; (3) instantiating a quantum program for execution and communicating the quantum program and the problem data to the quantum computing backend for execution; (4) receiving, from the quantum computing backend, an output of the execution; and (5) communicating the output to the user interface on the client device.

Abstract: A method of performing a remotely-initiated procedure on a computing device is provided. The method includes (a) receiving, by memory of the computing device, a request from a remote device via remote direct memory access (RDMA); (b) in response to receiving the request, assigning processing of the request to one core of a plurality of processing cores of the computing device, wherein assigning includes the one core receiving a completion signal from a shared completion queue (Shared CQ) of the computing device, the Shared CQ being shared between the plurality of cores; and (c) in response to assigning, performing, by the one core, a procedure described by the request. An apparatus, system, and computer program product for performing a similar method are also provided.

Abstract: Techniques for generating plug-in application recipe (PIAR) extensions are disclosed. A PIAR management application discovers a particular data type within one or more data values for a particular field of a plug-in application, where the particular data type is (a) different from a data type of the particular field as reported by the plug-in application and (b) narrower than the data type of the particular field while complying with the data type of the particular field. The PIAR management application identifies one or more mappings between (a) the particular data type and (b) one or more data types for fields accepted by actions of plug-in applications. The PIAR management application presents a user interface including one or more candidate PIAR extensions based on the mapping(s). Based on a user selection of a candidate PAIR extension, the PIAR management application executes a PIAR that includes the selected PIAR extension.

Abstract: A method, computer program product, and computer system for processing instances of message sequences. Two or more instances are received. Each instance has an instance number and an associated instance Id. Each instance includes a stream of one or more messages. Each message in each stream is associated with a respective task. All messages in the two or more instances whose respective tasks have been completed are distributed into partitions in the broker. Different messages in each instance of at least one instance are distributed into different partitions. The messages distributed in the partitions include parallel task messages. The parallel task messages are sequenced by being grouped by the instance number or instance Id and are sequentially ordered in each group in an ascending order of a timestamp of arrival of each parallel task message at the broker. The parallel task messages in each instance are dispatched to a service.

Abstract: An equipment performance modeling platform is disclosed. In certain embodiments, an adaptive sensing coordinator acquires sensor measurements, configures and processes the sensor measurements for a specific statistical model, and sends the measurements to a server. A server performs data processing, provides storage (e.g., local or in a database), and provides an interface for data extraction. Statistical models are used to interpreting sensor values for a type of equipment, and a labeling mechanism labels performance occurrences.

Abstract: A service may be provided that reads sensors, and that communicates information based on the sensor readings to applications. In one example, an operating system provides a sensor interface that allows programs that run on a machine to read the values of sensors (such as an accelerometer, light meter, etc.). A service may use the interface to read the value of sensors, and may receive subscriptions to sensor values from other programs. The service may then generate messages that contain the sensor value, and may provide these messages to programs that have subscribed to the messages. The messages may contain raw sensor data. Or, the messages may contain information that is derived from the sensor data and/or from other data.

Abstract: Intra-application messaging that is non-coupled may be facilitated through byte code injection. For example, a consumer class may be annotated with an annotation that describes data with which the function is to be called to consume data. A producer class may annotate a field/variable of an annotation to indicate that the data is generated by the producer class. At run time, a customized class loader scans each class as it is loaded for these annotations. When a consumer class is found, a byte code injector may write byte code that registers a consumer object instantiated from the consumer class to receive the data from a message router. When a producer class is found, a byte code injector may write byte code that notifies the message router that the data is available.

Abstract: A system is provided. The system includes a processor for implementing a computer-aided technology (CAx) system, the CAx system including a graphical-user-interface (GUI) configured to present a first computer-aided design (CAD) model, the first CAD model including a first part. The system includes memory storing instructions configured to cause the processor to present the GUI and to display, via the GUI, a first product manufacturing information (PMI), wherein the first PMI is linked to a first feature of the first part included in the first CAD model. The instructions further cause the processor to iterate through a second CAD model to identify a second feature related to the first feature and to copy the PMI to the second feature of the second CAD model.

Abstract: A first processing unit such as a graphics processing unit (GPU) pipelines that execute commands and a scheduler to schedule one or more first commands for execution by one or more of the pipelines. The one or more first commands are received from a user mode driver in a second processing unit such as a central processing unit (CPU). The scheduler schedules one or more second commands for execution in response to completing execution of the one or more first commands and without notifying the second processing unit. In some cases, the first processing unit includes a direct memory access (DMA) engine that writes blocks of information from the first processing unit to a memory. The one or more second commands program the DMA engine to write a block of information including results generated by executing the one or more first commands.

Abstract: Embodiments for recording application program interface (API) calls through an API recorder component or process that captures API calls as they are used by a number of users. API flows can be recorded to define recorded macros. An API collector collects API data for executed APIs and macros for storage in an API collection database and API performance analysis using at least one of a sequence analyzer, a runtime analyzer, a usage frequency analyzer, and an error analyzer. Output from the analyzers is provided to a user for review and determination of API improvement with respect to error reduction, API sequencing, runtime reduction, and usage frequency.

Abstract: Systems, computer program products, and methods are described herein for dynamically orchestrating API calls using quantum computing. The present invention may be configured to receive API calls from clients, determine whether each API call is acceptable using a first machine learning model, and determine whether a subset or multiple subsets of the acceptable API calls are combinable using a second machine learning model to obtain API call categories. The present invention may be configured to determine whether to route each API call category to a cache or to an endpoint using a third machine learning model and obtain data from the endpoint that includes responses of the endpoint. In some embodiments, the first machine learning model, the second machine learning model, and the third machine learning model may be trained using one or more quantum computing engines.

Abstract: The application provides a task scheduling simulation system, comprising a data preprocessing subsystem and a task scheduling subsystem. The data preprocessing subsystem filters the input cloud computing log information for abnormal data and extracts the running time of each task. The task scheduling subsystem enqueues or dequeues tasks from the batch task and real-time task running queues of each node, and keeps the tasks currently running in the cluster consistent with the actual production environment, and updates the number of CPU cores and the used and available memory capacity of each node according to resource requirement of each task. The mixed scheduling simulation of batch tasks and online tasks can be realized, and the resource simulation of the heterogeneous CPU core number and memory capacity of the cluster nodes can be simulated.

Abstract: Systems and methods are configured to perform prioritized processing of a plurality of processing objects under a time constraint. In various embodiments, a priority policy that includes deterministic prioritization rules, probabilistic prioritization rules, and a priority determination machine learning model is applied to the objects to determine high and low priority subsets. Here, the subsets are determined using the deterministic prioritization rules and a probabilistic ordering of the low priority subset is determined using the probabilistic prioritization rules and the priority determination machine learning model. In particular embodiments, the ordering is accomplished by determining a hybrid priority score for each object in the low priority subset based on a rule-based priority score and a machine-learning-based priority score.

Abstract: A computer-implemented method of mapping a process model of a software application executed by a hosting platform. A series of actions taken by a user with respect to user interfaces generated by the software application are simulated through one or more application programming interface (API) calls. The user interfaces include a form and the series of actions includes opening the form. A plurality of user interface fields of the form are then identified through one or more other API calls executed while impersonating a session of a user under test. At least some of the user interface fields are set to known values and the form is submitted to the software application after the fields have been set. Changes to field-related and record-related information resulting from the submission are then gathered and a process model of the software application is determined based upon the gathered information.

Abstract: The invention relates to a computer-implemented system and method for end-to-end deal execution for primary issuance of securities in equity capital markets and debt capital markets. The system may comprise: a user interface layer that is programmed to execute a unidirectional data flow; a connectivity layer that is programmed to provide scalable, low latency data streaming across a plurality of channels, regions and users and that utilizes a protocol buffer for data streaming; a data access layer that utilizes an in-memory data grid to distribute processing of the requests to a plurality of processors; a microservices layer that is programmed to provide modular microservices that subscribe to events published by the data access layer and that use a command query responsibility segregation (CQRS) pattern; a database layer; and a plurality of external application programming interfaces (APIs) to enable data exchange with third parties.

Abstract: A processing method includes: performing an I/O preparation process and an I/O request for a data block in an application program thread; activating, by the application program thread, a journaling thread; waiting for completion of an I/O for the data block and a commit of the journaling thread; preparing an I/O for the journal block and requesting the I/O for the journal block during the waiting for the completion of the I/O for the data block and the commit of the journaling thread, in the journaling thread; preparing an I/O for a journal commit block, before waiting for completion of the I/O for the journal block after the requesting of the I/O for the journal block; after waiting for the completion of the I/O for the journal block, requesting the I/O for the journal commit block; and waiting for completion of the I/O for the journal commit block.