Abstract: In an example, a method is described. The method comprises receiving an indication of a change to a computing system useable in a computing network. The method further comprises establishing whether a rule system is affected by the change. In response to establishing that the rule system is affected by the change, the method causes the rule system to be modified to account for the change.

Abstract: In some examples, a computing device comprises a first service function instance to apply a service function and a service function forwarder to: receive a first layer 3 routing protocol route advertisement that includes service function instance data for a second service function instance, the service function instance data indicating a service function type and a service identifier for the service function instance; receive a second layer 3 routing protocol route advertisement that includes service function chain data for a service function chain, the service function chain data indicating a service path identifier and one or more service function items; and send, to the second service function instance and based at least on determining a service function item of the one or more service function items indicates the second service function instance, a packet classified to the service function chain.

Abstract: An apparatus and method is described comprising: classifying service ticket data relating to a service request into a service topic, wherein the service ticket data is obtained from the service request relating to a device; determining, for the service request, an extent to which the service topic matches a telemetry data class, wherein the telemetry data class relates to activities at the device; and providing an output according to said determination.

Abstract: Disclosed are various embodiments for identifying fault injection impact zones. A test for an application can be received from a test service. An application schema can then be created that represents the systems or services that are involved in the execution of the application. Applications with a similar application schema are then identified using a first machine-learning model. A subset of the plurality of applications that have experienced a similar test are then identified with a second machine-learning model. Log files associated with each of the subset of applications are then analyzed to identify errors that occurred during the similar test. Each of the identified errors are then provided to the test service in response to receipt of the test.

Abstract: In some embodiments, a compensated power detector can include a power detector that includes a first detection cell having a bias input and an output, and a second detection cell having a signal input, a bias input and an output. The power detector can further include an error amplifier having a first input coupled to the output of the first detection cell, and a second input for receiving a reference voltage. The error amplifier can be configured to provide an output voltage to each of the bias inputs of the first and second detection cells, such that an output of the second detection cell is representative of power of a radio-frequency signal received at the signal input with an adjustment for one or more non-signal effects as measured by the first detection cell and the error amplifier.

Abstract: In implementations of systems for generating review likelihoods for sets of code, a computing device implements a review system to compile input data based on code data describing information associated with a set of new code to be incorporated into a set of existing code and reviewer data describing information associated with a potential reviewer of sets of code. The review system processes the input data using a machine learning model trained on training data to generate review likelihoods for potential reviewers of sets of code to be selected to review sets of new code. A review likelihood for the potential reviewer of sets of code to be selected to review the set of new code is generated using the machine learning model based on processing the input data. The review system generates an indication of the review likelihood for display in a user interface.

Abstract: A head mountable device for detecting a functional disorder of a brain in a patient, includes: an opaque visor unit for positioning over eyes of the patient, a plurality of reference sensors adapted to detect a reference electrical potential for the patient during exposure to the visual stimulus, and a sensor housing including at least one electroencephalogram (EEG) electrode for measuring electrical potential generated by the brain of the patient. The visor unit includes an arrangement of a plurality of LEDs positioned on an inside surface of said visor unit to which, during use, the eyes of the patient are exposed, and an LED driver, said LED driver being configured to control each of said LEDs independently to display a visual stimulus.

Abstract: In an example, a hypervisor measuring the state of a protected virtual machine using a Trusted Platform Module (TPM) filter. Using the TPM filter, the system ensures an untrusted operating system attempting to access the TPM is secure, without having to trust the security of the operating system or the operating system's built in hypervisor.

Abstract: A clamping circuit comprises a first field-effect transistor (FET) having a gate, a source, and a drain, a diode, a first voltage source, and coupling circuitry configured to couple the first voltage source to the drain of the first FET and the diode to the source of the first FET.

Abstract: In implementations of systems for generating review likelihoods for sets of code, a computing device implements a review system to compile input data based on code data describing information associated with a set of new code to be incorporated into a set of existing code and reviewer data describing information associated with a potential reviewer of sets of code. The review system processes the input data using a machine learning model trained on training data to generate review likelihoods for potential reviewers of sets of code to be selected to review sets of new code. A review likelihood for the potential reviewer of sets of code to be selected to review the set of new code is generated using the machine learning model based on processing the input data. The review system generates an indication of the review likelihood for display in a user interface.

Abstract: A cluster enrollment system remote to a device cluster performs a cluster enrollment process according to a cluster enrollment policy to determine whether a target device is authenticated to join the device cluster.

Abstract: A computing device may receive data associated with an event from an originating apparatus in the form of a data item. The computing device may then apply an evaluation function to the data item, wherein applying the evaluation function generates a processing result characterizing an aspect of the data item. The computing device may generate a tag based on the processing result. The computing device may associate the generated tag with the data, wherein the tag is transmitted with the data outside the network environment where the data item is not accessible for processing outside the network environment, but the data tag is accessible. The computing device may provide the data and the tag for transmission.

Abstract: In some embodiments, a compensated power detector can include a power detector that includes a first detection cell having a bias input and an output, and a second detection cell having a signal input, a bias input and an output. The power detector can further include an error amplifier having a first input coupled to the output of the first detection cell, and a second input for receiving a reference voltage. The error amplifier can be configured to provide an output voltage to each of the bias inputs of the first and second detection cells, such that an output of the second detection cell is representative of power of a radio-frequency signal received at the signal input with an adjustment for one or more non-signal effects as measured by the first detection cell and the error amplifier.

Abstract: At least in some examples, a non-transitory machine-readable storage medium can be encoded with instructions for analysing an operating system configuration, the instructions executable by a processor of a system, whereby to cause the system to generate a set of malware samples, each malware sample defined by a set of actions forming an attack chain representing a sequence of procedures, execute each of the malware samples on an instance of the operating system configuration to generate a set of logs, and using the set of logs, determine a set of actions detected by an anti-malware process of the operating system configuration.

Abstract: In an example, a method is described. The method comprises receiving identifying information associated with an occurrence of an activity within a computing network. The method further comprises receiving an indication of a sequence value generated by a sequence function that iterates the sequence value in response to a metric associated with activity of the computing network triggering iteration of the sequence value. The method further comprises producing a data element representative of the identifying information by using the indicated sequence value as an input to a transformation function for at least partially concealing the identifying information when producing the data element.

Abstract: In implementations of systems for classifying incorporation metrics, a computing device implements a classification system to receive code data describing information associated with a set of new code defining new functionality for an application. The set of new code is to be incorporated into a set of existing code defining existing functionality of the application. The classification system processes the code data using a machine learning model trained on training data to generate classifications of incorporation metrics for sets of new code defining new functionalities to be incorporated into sets of existing code defining existing functionalities. A classification of an incorporation metric for the set of new code is output using the machine learning model based on processing the code data. The classification system generates an indication of the classification of the incorporation metric for the set of new code for display in a user interface.

Abstract: A humidification system can include a heater base, a humidification chamber, and a breathing circuit. A cartridge can be removably coupled to the heater base. The cartridge can include various sensors, probes, sensor wire connectors, heater wire connectors, and/or other features. The cartridge can include features configured to mate with corresponding features on the humidification chamber and the heater base. The cartridge includes a memory, such as an EEPROM, or other suitable storage device. When the cartridge is installed on the heater base, the memory is electrically connected to a processor and/or memory of the heater base. Various models of cartridges can be produced for use with different humidification chambers, breathing circuits, and/or therapies. A connector can be configured to couple an inspiratory conduit to an outlet port of the humidification chamber. The connector can provide a pneumatic connection to the outlet port and an electrical connection to the cartridge.

Abstract: In an example, a computing device includes a processor which in a reimaging operation of the computing device, may determine if a backup image to be used in the reimaging operation is available from a memory device connected to a local area network of the computing device. When the backup image is available from the memory device, the processor may acquire the backup image over the local area network. When the backup image is not available from the memory device, the processor may determine if a backup image is available from a wide area network of the computing device.

Abstract: In an example, a computing device is described. The computing device comprises a memory to store a set of states and a corresponding set of non-overlapping time intervals. The computing device further comprises a timing unit to indicate a time at which a signature is to be produced. The computing device further comprises a processor to: identify which time interval of the set of non-overlapping time intervals includes the indicated time; generate a signing key based on a state associated with the identified time interval; and produce a signature, under a stateful signature scheme, with the signing key.

Abstract: In an example, a computing device is described. The computing device comprises an interface to receive a request from a signer for a state. The state is to be used as an input to generate a key under a stateful signature scheme. The computing device further comprises a processor. The processor is to identify an available state that the signer is authorized to use in response to the request received via the interface. The available state is identified from a set of states that can be used by the signer to maintain statefulness of the stateful signature scheme. The processor is further to instruct a reply to be sent to the signer via the interface. The reply comprises an indication of the state that the signer is authorized to use.