Abstract: An information processing apparatus of an embodiment includes one or more hardware processors. The one or more hardware processors receive input of parameter of a model to be estimated by machine learning and input of first input data. The one or more hardware processors train, by using the first input data as training data, the model using a cost function for which a cost is smaller as a change in the parameter is smaller.

Abstract: Embodiments described herein include methods and/or systems for updating a medical device. Embodiments include medical devices which are configured for updates in response to various events including connection of a peripheral device to the medical device, a user initiated event, or based on received recommendations.

Abstract: A control apparatus includes a controller. The controller is configured to obtain a measurement value of a state of mechanical equipment corresponding to a period in which the mechanical equipment reaches a second state from a first state, extract at least one predetermined feature value by using the measurement value, and extract data for machine learning from data of the at least one predetermined feature value on a basis of a separation degree for distinguishing the first state and the second state from each other.

Abstract: A peripheral unit for use in a water regulation system that controls the intake of water in a water circulation system of a building; it has a power source; a user input interface; a motion detector; a communication interface that is configured to establish a connection with the control panel; and a controller that is configured to send a first transmission to the control panel via the connection for causing the controller of the control panel to actuate opening of the water valve for a given period of time; and send a second transmission to the control panel via the connection for causing the controller of the control panel to actuate closing of the water valve.

Abstract: Tools and techniques are described to attach a device to a controller, whereby the controller analyzes the device inputs, looks up information about the device in a database, and then determines which inputs on the device match the defined device inputs. It then may translate information received from the device into an intermediate language. It may also use the information received from the device, the location of the device, and information about the device to create a digital twin of the device.

Abstract: A site monitoring system may analyze information from sites to determine when a device, a sensor, a controller, or other structure associated with optically switchable devices has a problem. The system may, if appropriate, act on the problem. In certain embodiments, the system learns customer/user preferences and adapts its control logic to meet the customer's goals.

Abstract: An industrial device supports device-level data modeling that pre-models data stored in the device with known relationships, correlations, key variable identifiers, and other such metadata to assist higher-level analytic systems to more quickly and accurately converge to actionable insights relative to a defined business or analytic objective. Data at the device level can be modeled according to modeling templates stored on the device that define relationships between items of device data for respective analytic goals (e.g., improvement of product quality, maximizing product throughput, optimizing energy consumption, etc.). This device-level modeling data can be provided to higher level systems together with their corresponding data tag values to high level analytic systems, which discovers insights into an industrial process or machine based on analysis of the data and its modeling data.

Abstract: Disclosed embodiments relate to perform operations for receiving and integrating a delta file in a vehicle. Operations may include receiving, at an Electronic Control Unit (ECU) in the vehicle, a delta file, the delta file comprising a plurality of deltas corresponding to a software update for software on the ECU and startup code for executing the delta file in the ECU; executing the delta file, based on the startup code, in the ECU; and updating memory addresses in the ECU to correspond to the plurality of deltas from the delta file.

Abstract: A method and a system for programming one or more behavior of a field device connected to a network comprising an input programming language to define the one or more behaviors to create an input program, transmitting over the network the input program to a translator coupled to the field device, translating the input program to generate a field program comprising a plurality of tasks and executing said field program by an executor coupled to said field device.

Abstract: The present invention provides a deployment platform that enables solution modules to be created and deployed without writing new code. The solution modules may include existing solutions, solution components, connectors, and the like selected from a solution library. The deployment platform includes a development engine providing functionality for generating deployment information for the solution module. The deployment information may include a blueprint or other information for deploying the solution module to target infrastructure. The deployment platform also includes a deployment engine providing functionality for deploying the solution module to the target infrastructure automatically. During deployment, the deployment engine pushes components of the solution module to the target infrastructure in accordance with the deployment information.

Abstract: A system for generating a cognitive disorder nourishment program comprises a computing device configured to obtain a cognitive indicator element, produce a cognitive appraisal as a function of the cognitive indicator element, wherein producing further comprises identifying a cognitive function as a function of an experience label, and producing the cognitive appraisal as a function of the cognitive function and cognitive indicator element using a cognitive machine-learning model, determine an edible as a function of the cognitive appraisal, and generate a nourishment program as a function of the edible.

Abstract: An information processing apparatus includes a controller. The controller is configured to obtain a measurement value of a sensor provided in mechanical equipment. The controller is configured to generate a first model by machine learning using the measurement value of the sensor measured in a first period of the mechanical equipment and store the first model in a storage portion. The controller is configured to generate a second model by machine learning using the measurement value of the sensor measured in a second period after a trigger event has occurred in the mechanical equipment and store the second model in the storage portion. The controller is configured to determine a state of the mechanical equipment by using the measurement value of the sensor measured in an evaluation period and the first model and the second model stored in the storage portion.

Abstract: A task definition is received. The task definition indicates at least a location from which one or more software image can be obtained and information usable to determine an amount of resources to allocate to one or more software containers for the one or more software image. A set of virtual machine instances in which to launch the one or more software containers is determined, the one or more software image is obtained from the location included in the task definition and is launched as the one or more of software containers within the set of virtual machine instances.

Abstract: In overview, methods, computer programs products and devices for securing software are provided. In accordance with the disclosure, a method may comprise attaching a debugger process to a software process. During execution of the software process, operations relevant to the functionality of the code process are carried out within the debugger process. As a result, the debugger process cannot be replaced or subverted without impinging on the functionality of the software process. The software process can therefore be protected from inspection by modified or malicious debugging techniques.

Abstract: Transient computing clusters can be temporarily provisioned in cloud-based infrastructure to run data processing tasks. Such tasks may be run by services operating in the clusters that consume and produce data including operational metadata. Techniques are introduced for tracking data lineage across multiple clusters, including transient computing clusters, based on the operational metadata. In some embodiments, operational metadata is extracted from the transient computing clusters and aggregated at a metadata system for analysis. Based on the analysis of the metadata, operations can be summarized at a cluster level even if the transient computing cluster no longer exists. Further relationships between workflows, such as dependencies or redundancies, can be identified and utilized to optimize the provisioning of computing clusters and tasks performed by the computing clusters.

Abstract: Techniques are disclosed for inferring design-time information based on run-time artifacts generated by services operating in a distributed computing cluster. In an embodiment, a metadata system extracts metadata including run-time artifacts generated by services in a distributed computing cluster while processing a workflow including multiple jobs. The extracted metadata is processed to identify entities and entity relationships which can then be used to generate lineage information. Using the lineage information, the metadata system can infer design-time information associated with the workflow. The inferred design-time information can then be utilized to, for example, recreate the workflow, recreate previous versions of the workflow, optimize the workflow, etc.

Abstract: Automatic application of software patches to software associated with container images based upon image relationships in a dependency tree. The computing device determines whether software associated with a base container image requires software patches. The computing device accesses dependency trees maintaining image relationships between the base container image and dependent container images. The computing device determines based upon the accessed one or more dependency trees whether the base container image has dependent container images derived from the base container image. The computing device applies software patches to the software associated with the base container image. The computing device rebuilds the base container image with the applied software patches. The computing device then rebuilds the dependent container images dependent upon the rebuilt base container image.

Abstract: An approach is provided for autonomous and optimal cloning, reinstating, and archiving of a containerized application. Application metadata is obtained from a catalog. Cloning rules specifying cloning an application are obtained. Application components are selected for cloning and the cloning is determined to be compliant with the cloning rules. The application components are cloned and data for the clone is saved to a clone repository. The catalog is updated with specifications of the cloned application components. Reinstating rules specifying reinstating a clone of the application are obtained. Reinstating the clone is determined to be compliant with the reinstating rules. The catalog is updated with specifications of the reinstated clone. In one embodiment, the catalog is updated with inferred associations among applications and identified rules associated with the application, where the inferred associations and identified rules are generated by a trained machine learning-based classifier.

Abstract: An analysis system receives a time series. The data values of the time series correspond to a metric describing a characteristic of the computing system that changes over time. The analysis system stores a statistic value that represents the stationarity of the time series. In response to receiving a most recent value, the analysis system assigns the most recent value as the leading value in a window before retrieving the trailing value of the window. The analysis system updates the statistic value to add an influence of the most recent value and remove an influence of the trailing value. If the statistic value is less than a threshold, the analysis system determines that the time series is stationary. In response to determining the time series is stationary, the analysis system assigns an alert to the metric. The analysis system detects an anomaly in the metric based on the assigned alert.

Abstract: Capturing dependencies between variables using a variable agnostic object is disclosed. A system is configured to obtain an indication of a first dependency of a first variable to a second variable via a programming interface and depict the first dependency, the first variable, and the second variable in a first instance of a variable agnostic object in a source code. The system is also configured to obtain an indication of a second dependency of a third variable to a fourth variable via the programming interface and depict the second dependency, the third variable, and the fourth variable in a second instance of the variable agnostic object in the source code. The system is also configured to compile the source code to generate a computer-executable program capturing the first dependency and the second dependency based on the first instance and the second instance of the variable agnostic object.