Abstract: Techniques for updating a client device are provided that include receiving, by a client device, a software update and a certificate associated with the software update, verifying, by the client device, the certificate associated with the software update based on a stored public key of the client device, extracting an update scope value from the certificate, comparing the update scope value against a corresponding attribute of the update, and either applying the software update based on the comparing, or rejecting the software update based on the comparing.

Abstract: A method, computer system, and a computer program product for project development in a collaborative platform is provided. The present invention may include identifying one or more features of an open source project. The present invention may include analyzing the one or more features of the open source project and assigning a sentiment to each of the one or more features. The present invention may include generating a requirement mapping for the open source project, wherein the requirement mapping is a series of the one or more features.

Abstract: A system may execute a pipelined multiple-tier test stack to support migration of computing resources via a migratory data stream. Via the pipelined multiple-tier test stack, the system may perform extract, transform, and load operations on the migratory data stream. The extract, transform, and load operations may be used to identify applications that may undergo testing. At a generation tier of the pipelined multiple-tier test stack, the system may generate test scripts, which may be used to test the application. The tests may be validated by the system via a validation tier of the pipelined multiple-tier test stack. To govern the operations, the pipelined multiple-tier test stack may rely on a multi-point reference data model.

Abstract: Functionality is provided for the automated creation, testing, training, adaptation and deployment of AI models and changes thereto. Base classes are provided that enable practicable creation of new models from existing one. New models are tested on live data sets offline from user sites. New training methods are provided for the production of particular outcomes. Efficient adaptation of new AI models is facilitated, encompassing data scientist and development team control over how fast to train and deploy new models.

Abstract: An apparatus includes processing circuitry configured to: obtain first trained parameters for a model, wherein the first trained parameters have been generated by training the model using data from a first data cohort; obtain second trained parameters for the model, wherein the second trained parameters have been generated by training the model using data from a second, different data cohort; determine a first evaluation value by inputting data from the first data cohort into a model having the first trained parameters; and determine a second evaluation value by inputting data from the first data cohort into a model having the second trained parameters.

Abstract: Systems and methods are provided for identifying a safe window for creating a firmware update package. In some embodiments, an Information Handling System (IHS), may include a processor and a memory coupled to the processor, the memory having program instructions stored thereon that, upon execution, cause the IHS to: generate tokens based upon a log of historical firmware update package creation events; identify a plurality of workflows based upon the tokens; predict, based upon the plurality of workflows, a future workload of a firmware update package creation service; and indicate, based upon the future workload, a time window for creating a firmware update package using the firmware update package creation service that increases a probability of successful creation.

Abstract: An algorithm download method, a device, and a related product. The method includes: obtaining an algorithm identifier of an algorithm and a capability description of a client; sending the algorithm identifier and the capability description to a cloud; and receiving a version code that is of the algorithm and that is returned by the cloud, where the version code is obtained by the cloud by searching based on the algorithm identifier and the capability description. According to the foregoing solution, an algorithm can be easily downloaded.

Abstract: A method of analyzing code is provided. The method includes generating an abstract representation of the code, identifying conditional statements in the abstract representation, populating a truth table for each conditional statement that has been identified with all possible outcomes of the conditional statement and assessing the truth table for each conditional statement to identify issues.

Abstract: Disclosed is a method of updating a control unit for a vehicle, the method including: generating raw difference data by comparing an old version of data with a new version of data; generating divided difference data by dividing the raw difference data for each memory sector; and updating the old version to the new version by using the divided difference data.

Abstract: A system and method for bi-directional design to code are disclosed. In one embodiment, the system includes a processor; a memory storing instructions that, when executed, cause the system to obtain, by a plugin, a web application design, the web application design generated using a first design tool; generate, by the plugin, an object model based on the web application design; access, by a configurator, the object model; and generate, by the configurator, for presentation to a developer, the web application design.

Abstract: The invention provides a system, a method and a computer program product that facilitate access to one or more applications by a computing device. The invention includes determining one or more contexts associated with at least one of the computing device and a user of the computing device, such that the one or more contexts describe at least one of an environment and an activity of the at least one of the user and the computing device. Thereafter at least one contextual tag corresponding to the one or more contexts is generated. Subsequently, the one or more applications associated with the at least one contextual tag are identified and the computing device is enabled to access the one or more applications.

Abstract: An industrial integrated development environment (IDE) supports collaborative tools that allow multiple designers and programmers to remotely submit design input to the same automation system project in parallel while maintaining project consistency. The industrial IDE also permits localized development of system projects, and provides an infrastructure for sharing and selectively synchronizing project edits among multiple locally stored versions of the system project.

Abstract: A method and system of designing control logic for an avionics system, the method and system including receiving a function requirement defining a desired control logic for the desired control system, designing, by a user in a user interface (UI) of a toolset, the desired control logic comprising an arrangement of predefined library blocks to enable the functional requirement in the desired control system, and generating, by the toolset, a data file representative of the desired control logic to enable the functional requirement during run-time operation in the avionics system.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for predicting hardware failure events are presented. A time series comprising event log data for a plurality of events and a plurality of event types that occurred on a server computing device may be received. The time series may be filtered for a subset of the plurality of event types. The filtered time series may be processed with a recurrent neural network that has been trained to predict hardware failure events from time series data comprising the subset of the plurality of event types. A prediction may be made that a hardware failure event will occur on the server computing device within a threshold duration of time. A prophylactic follow-up action corresponding to the predicted hardware failure event may be performed.

Abstract: A method including the following steps: receiving user input; resolving a feature of the input using a trainable algorithm, the trainable algorithm being trainable to resolve a feature by application of the algorithm to a dataset including a plurality of labelled dataset entries, the label of each labelled dataset entry describing a feature; wherein the trainable algorithm resolves the features in user input by identifying in the user input a dataset entry labelled with said feature; forming a UI that incorporates the resolved feature; presenting the formed UI; obtaining feedback in relation to the presented UI or a feature thereof; applying the feedback to train the trainable algorithm to resolve features of a UI, wherein feedback for training the trainable algorithm derives from any one or more of, or a combination of: user selection/validation/customisation of features presented to the user and/or user observation.

Abstract: A GUI testing device may be configured to execute a testing state machine for interacting with a software application to generate an initial screen of a GUI. The GUI testing device may be configured to determine a current state in the testing state machine based upon a matching trigger target in the initial screen to a given state. The current state may include an operation, and the operation may associate with a trigger target to operate on. The trigger may include a source state, a destination state, and a trigger target. The operation may include a user input operation, and an operation trigger target. The GUI testing device may be configured to perform the operation on the matching trigger target in the initial screen to generate a next screen of the GUI, and advance from the current state to a next state based upon the trigger.

Abstract: A system and method for generating and using fixed-point operations for neural networks includes converting floating-point weighting factors into fixed-point weighting factors using a scaling factor. The scaling factor is defined to minimize a cost function and the scaling factor is derived from a set of multiples of a predetermined base. The set of possible scaling function is defined to reduce the computational effort for evaluating the cost function for each of a number of possible scaling factors. The system and method may be implemented in one or more controllers that are programmed to execute the logic.

Abstract: A semiconductor device includes three integrated circuits. One of the integrated circuits includes: a first connector configured to connect to a device; and a transmitter. The transmitter is configured to transmit to another integrated circuit, first data on each of a plurality of pieces of packet data. The transmitter is also configured to, when the first connector is connected to the device, while a second controller is performing a second process, transmit, to a first controller, a request to process data transmitted from the device.

Abstract: Methods, systems and computer program products are provided for automated runtime configuration for dataflows to automatically select or adapt a runtime environment or resources to a dataflow plan prior to execution. Metadata generated for dataflows indicates dataflow information, such as numbers and types of sources, sinks and operations, and the amount of data being consumed, processed and written. Weighted dataflow plans are created from unweighted dataflow plans based on metadata. Weights that indicate operation complexity or resource consumption are generated for data operations. A runtime environment or resources to execute a dataflow plan is/are selected based on the weighted dataflow and/or a maximum flow. Preferences may be provided to influence weighting and runtime selections.

Abstract: A method of processing test execution logs to determine error location and source includes creating a set of training examples based on previously processed test execution logs, clustering the training examples into a set of clusters using an unsupervised learning process, and using training examples of each cluster to train a respective supervised learning process to label data where each generated cluster is used as a class/label to identify the type of errors in the test execution log. The labeled data is then processed by supervised learning processes, specifically a classification algorithm. Once the classification model is built it is used to predict the type of the errors in future/unseen test execution logs. In some embodiments, the unsupervised learning process is a density-based spatial clustering of applications with noise clustering application, and the supervised learning processes are random forest deep neural networks.