Abstract: The present disclosure is directed to automated generation and management of update estimates relative to application of an update to a computing device. One or more updates to be applied to a computing device are identified. A trained artificial intelligence (AI) model is applied that is adapted to generate an update estimate predicting an amount of time that is required to apply an update to the computing device. An update estimate is generated based on a contextual analysis that evaluates one or more of: parameters associated with the update; device characteristics of the computing device to be updated; a state of current user activity on the computing device; historical predictions relating to prior update estimates for one or more computing devices (e.g., that comprise the computing device); or a combination thereof. A notification of the update estimate is then automatically generated and caused to be rendered.

Abstract: A computer-implemented method is carried out on an IT framework and a relative apparatus including: an orchestrator module; an optimizer module; a configurator module; a load generator module; and a telemetry module. The method includes: identifying tunable parameters representing a candidate configuration for a System Under Test (SUT), and applying the candidate configuration to the SUT using the configurator module; performance testing the SUT to determine a performance indicator; supplying performance metrics to the optimizer module's machine learning model to generate an optimized candidate configuration. The model provides as output, in correspondence of a candidate set of parameters, an expected value of the performance indicator and a prediction uncertainty thereof, used by the optimizer module to build an Acquisition Function used to derive a candidate configuration and by the load generator module to build the test workload. The test workload is computed through the machine learning model.

Abstract: A computer-implemented method includes: registering, by a computer device, a device to a network; collecting, by the computer device, device data from the device through the network; compiling, by the computer device, training data from the collected device data; training, by the computer device, a machine learning model using the training data; predicting, by the computer device and using the machine learning model, a time when the device will be in an inactive system state; and automatically scheduling, by the computer device and based on the predicting, an application of an update for the time when the device is in the inactive system state.

Abstract: A method for operating at least a container application and a component application. The container is an application hosting the component application. The method includes the following: providing a library of code files, a bootloader and further executable code files, wherein the component creates a probing message to the container application, and the container creates a response message containing references to code files to be loaded by the component.

Abstract: A system for executing software, wherein a computing device stores first software instructions for a code execution module such that, when the first software instructions are executed, the computing device will: receive, for execution by the code execution module, second software instructions; create one or more immutable software nodes described in the second software instructions; determine that the second software instructions comprise an instruction to begin a simulated change at runtime of the one or more immutable software nodes; store the simulated change in a simulated change apparatus; using the simulated change apparatus, perform one or more operations of the second software instructions as if the one or more immutable software nodes had been changed in memory, during a period of time where each of the one or more immutable software nodes is guaranteed to retain logical immutability; and output results of the one or more operations.

Abstract: A testing system includes a client library that functions as a proxy for remote procedure calls to allow commands to be issued directly from a test framework including an interface that enables remote procedure call commands to be sent to a device under test from a mobile device and responses from the device under test to be received and forwarded to the mobile device, even where the mobile device and device under test have different operating systems. A client program adapted to run on the device under test sends and receives remote procedure call commands from the device under test to and from a test server that implements the test framework and manages a device state of the device under test. In a pairing test, the remote procedure call commands include commands adapted to emulate a button press for initiating pairing between the mobile device and the device under test.

Abstract: Techniques for an ultra-fast installation of a new operating system are provided. Conventional dependencies are ignored in a way that allows multiple components to be installed at once, even when certain components traditionally could not be installed until one or more other components had successfully completed their installation. An operating system executing on a computing device receives a container with files that collectively include a new operating system and a definition with the locations for the files in memory. An uninstalled state may be assigned to each file. Each file may be moved from the container to the location specified in the definition in parallel and independently of other files. After each file is moved, each file may be switched from the uninstalled state to the installed state. The new operating system may be transitioned from an uninstalled state to an installed state once files are switched to installed states.

Abstract: Embodiments relate to interactive graphical display. A request is processed by a generative system to generate multiple hypotheses of an output in response to the request, a primary hypothesis of the multiple hypotheses having a highest confidence for the output, multiple secondary hypotheses of the multiple hypotheses having a lower confidence than the highest confidence. At least one region of divergence is determined from the primary hypothesis by the multiple secondary hypotheses, the at least one region of divergence having alternatives in the multiple secondary hypotheses, the alternatives in the multiple secondary hypotheses differing from the primary hypothesis. A graphical user interface displays the at least one region of divergence in the primary hypothesis and alternatives in the multiple secondary hypotheses for the at least one region of divergence, the alternatives and primary hypothesis for the at least one region of divergence being displayed as selectable options for a user.

Abstract: Methods, non-transitory computer readable media, and computing devices are disclosed that provision an analytics application in an instance of a software development platform. The analytics application comprises a schema, one or more functions, and one or more dashboard templates. Raw analytics data is extracted according to the schema. The raw analytics data is logged by the software development platform in one or more databases and is based on monitored activity associated with a software development process performed on the software development platform. The one or more functions are then applied to the extracted raw analytics data to generate performance metrics for the software development process. The one or more dashboard templates are populated based on the performance metrics and the populated one or more dashboard templates are embedded into a user interface of the software development platform to facilitate visualization of the raw analytics data.

Abstract: A data integration device, including input interfaces to which a device which delivers runtime data to the input interface is connectable in each case. The runtime data of the particular device are characterized with the aid of at least one aspect model that is associated with the particular input interface and that characterizes in each case an aspect of the runtime data. The particular input interface is associated with the particular aspect model.

Abstract: Described herein are embodiments for managing comments in a program code file. A system may select program code and compile it to an intermediary code. The system may compare the intermediary code to a library of intermediary code snippets associated with comments. Based on the comparison, a system may recognize the code to be obsolete. In some embodiments, a system may generate one or more recommendations to update a code. Based on received feedback regarding a recommendation, a system may accordingly update a code.

Abstract: An automated system for resolving program merges uses a multi-task neural transformer with attention. Each component of a merge conflict tuple (A, B, O) is represented as an AST and transformed into aligned AST-node sequences and aligned editing sequences. The multi-task neural transformer model predicts the tree editing steps needed to resolve the merge conflict and applies them to the AST representation of the code base. The tree editing steps include the edit actions that needed to be applied to the AST of the code base and the edit labels that are inserted or updated with the edit actions.

Abstract: A taint report represents a taint flow from a source value at a source program point to a sink value at a sink program point. Candidate watchpoints that correspond to taint-like values similar to the source value may be inferred from an execution trace. Different subsets of candidate watchpoints represent solutions to the problem of determining an optimal subset of watchpoints contributing to a taint flow. Using a hill-climbing heuristic, incremental improvements are efficiently applied to a solution until no more improvements are found. An objective function may determine whether one solution improves another solution. The objective function may be based on validity, understandability, and performance. Validity favors candidate watchpoints that reduce the edit distance between the source and sink values. Understandability favors candidate watchpoints included in a call chain from the source program point to the sink program point. Performance favors small subsets of candidate watchpoints.

Abstract: Embodiments are disclosed for learning structural similarity of user experience (UX) designs using machine learning. In particular, in one or more embodiments, the disclosed systems and methods comprise generating a representation of a layout of a graphical user interface (GUI), the layout including a plurality of control components, each control component including a control type, geometric features, and relationship features to at least one other control component, generating a search embedding for the representation of the layout using a neural network, and querying a repository of layouts in embedding space using the search embedding to obtain a plurality of layouts based on similarity to the layout of the GUI in the embedding space.

Abstract: Embodiments described herein provide techniques for in-line compiling of UDFs in other programming languages. These techniques enable faster programming iterations because it allows users to compile directly in the cloud processing system. Moreover, it allows the UDFs to tie into existing libraries. The compiled results are treated as sensitive and handled with appropriate security policies, as with any other user data in the system.

Abstract: A system including a machine learning accelerator (MLA) hardware configured to perform machine-learning operations according to native instructions; an interpreter computing module configured to: generate, based on virtual instructions, machine language instructions configured to be processed by a processing hardware implementing the interpreter computing module; and cause the processing hardware to perform machine-learning operations according to the machine language instructions; and a compiler computing module associated with the MLA hardware, the compiler computing module configured to: receive instructions for performing an inference using a machine-learning model; based on the received instructions: generate the native instructions configured to be processed by the MLA hardware, the native instructions specifying first machine-learning operations associated with performing the inference; and generate the virtual instructions configured to be processed by the interpreter computing module, the virtual ins

Abstract: According to an aspect, there is provided a computing device for performing the following. The computing device maintains, in a database, a first deviation detection algorithm for evaluating values of a first deviation metric. The computing device obtains, in the code editor, a plurality of logical lines of code and performs, for at least one logical line of code, the following. The computing device evaluates, using the first deviation detection algorithm, for a program instruction defined in a logical line of code, a value of the first deviation metric based at least on one or more other program instructions. The computing device selects a first color based on the first deviation metric from a first colormap. Finally, the computing device colors, in part or fully, a background or a text of the logical line of code or a graphical element associated with it with the first color.

Abstract: Systems and methods are disclosed herein for switching an application executing on an ambulatory medical device to a new application without interrupting therapy provided by the ambulatory medical device to a subject. The ambulatory medical device may receive an indication that an update to an application executing on the ambulatory insulin pump is available, establish a communication connection to a host computing system, download and install the application update, while a prior version of the application continues to run. The disclosed systems and methods can confirm successful installation of the application update on the ambulatory medical device and switch control of the ambulatory medical device from the prior version to the new version of the application without interrupting therapy provided to the subject.

Abstract: The technology disclosed herein enhances a fault-based communication channel between a virtual machine and a hypervisor. An example method may include: configuring, by a hypervisor, a first memory location to generate one or more faults when accessed by a virtual machine process, wherein the first memory location is mapped to a device and a second memory location is mapped to memory; detecting, by the hypervisor, a fault caused by a first execution of an instruction of the virtual machine process, wherein the instruction comprises a reference to a register comprising the first memory location; responsive to the detecting the fault, the hypervisor performing a computing task for the virtual machine process and updating the register to comprise the second memory location; and initiating, by the hypervisor, a second execution of the instruction of the virtual machine process, wherein the second execution of the instruction accesses the second memory location.

Abstract: Disclosed examples to detect and annotate backedges in data-flow graphs include: a characteristic detector to store a node characteristic identifier in memory in association with a first node of a dataflow graph; a characteristic comparator to compare the node characteristic identifier with a reference criterion; and a backedge identifier generator to generate a backedge identifier indicative of a backedge between the first node and a second node of the dataflow graph based on the comparison, the memory to store the backedge identifier in association with a connection arc between the first and second nodes.