Abstract: Disclosed in some examples are methods, systems, and machine readable mediums for secure, low end-user effort computing device configuration. In some examples the IoT device is configured via a user's computing device over a short range wireless link of a first type. This short range wireless communication may use a connection establishment that does not require end-user input. For example, the end user will not have to enter, or confirm a PIN number or other authentication information such as usernames and/or passwords. This allows configuration to involve less user input. In some examples, to prevent man-in-the-middle attacks, the power of a transmitter in the IoT device that transmits the short range wireless link is reduced during a configuration procedure so that the range of the transmissions to and from the user's computing device are reduced to a short distance.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums for secure, low end-user effort computing device configuration. In some examples the IoT device is configured via a user's computing device over a short range wireless link of a first type. This short range wireless communication may use a connection establishment that does not require end-user input. For example, the end user will not have to enter, or confirm a PIN number or other authentication information such as usernames and/or passwords. This allows configuration to involve less user input. In some examples, to prevent man-in-the-middle attacks, the power of a transmitter in the IoT device that transmits the short range wireless link is reduced during a configuration procedure so that the range of the transmissions to and from the user's computing device are reduced to a short distance.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to monitor telemetry data in computing system. An example apparatus includes interface circuitry to obtain telemetry data; computer readable instructions; and programmable circuitry to instantiate: aggregation circuitry to analyze the telemetry data using an artificial intelligence model to detect an event; and action controller circuitry to: determine a telemetry collection resolution associated with the event; and instruct a telemetry collection operation associated with the telemetry data to adjust collection of the telemetry data according to the determined telemetry collection resolution.

Abstract: Methods, apparatus, and systems are disclosed for mapping active assurance intents to resource orchestration and life cycle management. An example apparatus disclosed herein is to reserve a probe on a compute device in a cluster of compute devices based on a request to satisfy a resource availability criterion associated with a resource of the cluster, apply a risk mitigation operation based on the resource availability criterion before deployment of a workload to the cluster, and monitor whether the criterion is satisfied based on data from the probe after deployment of the workload to the cluster.

Abstract: Methods, apparatus, systems, and articles of manufacture to migrate cloud-based workloads are disclosed. An example instructions cause programmable circuitry to at least cause transmission of anonymized information corresponding to a user device to a network device; and cause migration of a virtual execution environment from a first compute device to a second compute device based on a response from the network device, the virtual execution environment to execute at least a portion of a workload for an end user device.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to identify and manage IoT protocols and associated devices. An example apparatus includes a gateway device to communicate according to a first protocol. The example gateway device includes a plugin agent to discover a first device and probe the first device to gather data regarding a protocol of the first device. The example plugin agent is to transmit the gathered data to a plugin manager to determine whether the first device is to communicate via the first protocol, and, when the first device is unable to communicate via the first protocol, determine a plugin for the gateway device to enable the gateway device to communicate with the first device, the plugin agent to provision the plugin for the gateway device with respect to the first device.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: Technologies for context-aware dynamic bandwidth allocation include a network compute device configured to collect context inputs from a plurality of compute devices communicatively coupled to the network compute device. The network compute device is further configured to identify a context of each compute device based on the collected context inputs and determine a bandwidth priority for each compute device based on the identified context. Additionally, the network compute device is configure to determine an amount of bandwidth from a total available bandwidth to allocate to the compute device based on the determined bandwidth priority and update a moderated bandwidth allocation policy to reflect the determined amount of bandwidth allocated to the compute device. Other embodiments are described herein.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes processor circuitry to execute computer readable instructions to: execute a machine learning model to generate a first code recommendation for programming code, the first code recommendation being associated with security of the programming code; cause output of the first code recommendation via a user interface; update the machine learning model based on feedback obtained via the user interface; determine a performance of the programming code; generate a second code recommendation, the second code recommendation being associated with the performance of the programming code; and cause output of the second code recommendation via the user interface.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: Methods, apparatus, systems and articles of manufacture to provide machine assisted programming are disclosed. An example apparatus includes a feature extractor to convert compiled code into a first feature vector; a first machine leaning model to identify a cluster of stored feature vectors corresponding to the first feature vector; and a second machine learning model to recommend a second algorithm corresponding to a second feature vector of the cluster based on a comparison of a parameter of a first algorithm corresponding to the first feature vector and the parameter of the second algorithm.

Abstract: Disclosed in some examples are methods, systems, and machine readable mediums for secure, low end-user effort computing device configuration. In some examples the IoT device is configured via a user's computing device over a short range wireless link of a first type. This short range wireless communication may use a connection establishment that does not require end-user input. For example, the end user will not have to enter, or confirm a PIN number or other authentication information such as usernames and/or passwords. This allows configuration to involve less user input. In some examples, to prevent man-in-the-middle attacks, the power of a transmitter in the IoT device that transmits the short range wireless link is reduced during a configuration procedure so that the range of the transmissions to and from the user's computing device are reduced to a short distance.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to identify and manage IoT protocols and associated devices. An example apparatus includes a gateway device to communicate according to a first protocol. The example gateway device includes a plugin agent to discover a first device and probe the first device to gather data regarding a protocol of the first device. The example plugin agent is to transmit the gathered data to a plugin manager to determine whether the first device is to communicate via the first protocol, and, when the first device is unable to communicate via the first protocol, determine a plugin for the gateway device to enable the gateway device to communicate with the first device, the plugin agent to provision the plugin for the gateway device with respect to the first device.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to coordinate and manage secure shipment of a package. An example shipment coordination apparatus includes an address generator and a verification engine. The example apparatus includes a shipping group coordinator to generate a group including a sender and a receiver based on a) a first digital address associated with the sender, b) a second digital address associated with the receiver, and c) at least one encryption key associated with the first digital address and/or the second digital address, the shipping group coordinator to initiate delivery instruction and manage receipt confirmation of a package at a second physical address corresponding to the second digital address based on verification of a token identifying the receiver and to provide messaging between the sender and the receiver in the group using a group encryption key to keep messages private in the group.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture for intent-based machine programming are disclosed. An example system categorize source code blocks includes a code repository accessor to access a code repository and select a source code block. The example system also includes a signature generator to generate a signature for the source code block, a collateral miner to extract collateral associated with the source code block, and a tokenizer to transform the source code block into tokens. In addition, the example system includes a function assessor to determine a function of the source code block based on the collateral and the tokens and an input/output determiner to determine an input and an output of the source code block based on the collateral and the signature. The example system further includes a tagger to categorize the source code block with the function, input, and output.

Abstract: A wireless sensor network is provided comprising: a node that listens for radio messages with periodic control messages, and listens for radio messages with periodic sensor data messages, and determines whether to skip listening for radio messages with a periodic sensor data message during a sensor data time period based upon a skip indicator included in a periodic control message.

Abstract: Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.