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 at least one of the first digital address 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: 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: An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

Abstract: A method for discovery of devices is described herein. The method includes connecting, via a processor, to a discovery node service. The method also includes sending, via the processor, a node name to the discovery node service. The method further includes sending, via the processor, data and content to be sent to a discovery node associated with the node name. The method also further includes receiving data and content from the discovery node, the data to include a list of devices subscribed to the discovery node.

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 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 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 are disclosed for code review assistance for dynamically typed languages. An example apparatus to analyze a segment of code includes a function identifier to identify a first input of a first function call included in the segment of the code, a parameter type vector (PTV) estimatior model to estimate a first data structure based on the first input, the PTV estimatior model generated via a set of reviewed code, a PTV determiner to generate a second data structure based on a data parameter type of the first input, an error comparator to determine a first reconstruction error based on the first data structure, and the second data structure and a recommendation generator to, if the first reconstruction error does not satisfy a recommendation threshold, generate a first recommendation to review the first function call.

Abstract: Apparatus, systems, methods, and articles of manufacture for automated quality assurance and software improvement are disclosed. An example apparatus includes a data processor to process data corresponding to events occurring with respect to a software application in i) a development and/or a testing environment and ii) a production environment. The example apparatus includes a model tool to: generate a first model of expected software usage based on the data corresponding to events occurring in the development and/or the testing environment; and generate a second model of actual software usage based on the data corresponding to events occurring in the production environment. The example apparatus includes a model comparator to compare the first model to the second model. The example apparatus includes a correction generator to generate an actionable recommendation to adjust the development and/or the testing environment to reduce a difference between the first model and the second model.

Abstract: An apparatus includes a feature extractor to extract features from input data, the features including descriptive information corresponding to a function of the input data, an inference generator to classify the features into a group indicative of a semantic property, a programming pattern, or a compliance type of the function of the input data, assign a cluster identifier to the features based on a prediction that the features are classified into the group, and retrieve solutions from a database that correspond to the cluster identifier, and a suggestion determiner to generate a suggestion list by building a pool of suggestions to present to a user.

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 method for discovery of devices is described herein. The method includes connecting, via a processor, to a discovery node service. The method also includes sending, via the processor, a node name to the discovery node service. The method further includes sending, via the processor, data and content to be sent to a discovery node associated with the node name. The method also further includes receiving data and content from the discovery node, the data to include a list of devices subscribed to the discovery node.

Abstract: Technologies for trusted device on-boarding include a first computing device to generate a first public Diffie-Hellman key based on a private Diffie-Hellman key and a first unique identifier of the first computing device. The first unique identifier is retrieved from secure memory of the first computing device. The first computing device transmits the first public Diffie-Hellman key to a second computing device and receives, from the second computing device, a second public Diffie-Hellman key of the second computing device. The second public Diffie-Hellman key incorporates a second unique identifier of the second computing device. Further, the first computing device removes a contribution of the second unique identifier from the second public Diffie-Hellman key to generate a modified public Diffie-Hellman key and generates a shared Diffie-Hellman key based on the modified public Diffie-Hellman key and the private Diffie-Hellman key of the first computing device.

Abstract: An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

Abstract: In embodiments, an apparatus for selectively delivering software updates to nodes in a network includes a receiver to receive a software update and a list of nodes of the network scheduled to receive the software update. In embodiments, the apparatus further includes a device management agent (DMA) to: identify a set of traversals to leaf nodes of the list of nodes necessary to traverse all nodes on the list, and distribute the software updates to the nodes on the list using the set of traversals.

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 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 at least one of the first digital address 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: 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: A method for discovery of devices is described herein. The method includes connecting, via a processor, to a discovery node service. The method also includes sending, via the processor, a node name to the discovery node service. The method further includes sending, via the processor, data and content to be sent to a discovery node associated with the node name. The method also further includes receiving data and content from the discovery node, the data to include a list of devices subscribed to the discovery node.

Abstract: Embodiments include apparatuses, methods, and systems including a wireless transceiver, a processor coupled to the wireless transceiver, and a group management module operated by the processor to control the wireless transceiver to receive information pertaining to services offered by a plurality of devices co-located with the apparatus at a location, transmit the received information pertaining to the services offered by the plurality of devices, and information pertaining to services offered by the apparatus; and detect a response to the transmission by a new device; and manage, as a master device, the received information pertaining to the services offered by the plurality of devices and the services offered by the apparatus to enable the new device to join and cooperate with the plurality of devices and the apparatus at the location. Other embodiments may also be described and claimed.