Systems, apparatus, and methods that can elect a broker on a Message Queuing Telemetry Transport (MQTT) bus are disclosed. One system includes an MQTT bus and a set of client devices on the MQTT bus. Each client device maintains a set of attributes for each other client device and casts one or more votes for a particular client device on the MQTT bus to elect the particular client device as a new broker on the MQTT bus in response to a current broker on the MQTT bus becoming unavailable. The votes cast for the particular client device are based on a first value corresponding to one or more attributes for the particular client device relative to respective second values to the corresponding attribute(s) for each of the other client devices on the MQTT bus as calculated by each respective client device on the MQTT bus.

Systems, apparatus, and methods that can elect a broker on a Message Queuing Telemetry Transport (MQTT) bus are disclosed. One system includes an MQTT bus and a set of client devices on the MQTT bus. Each client device maintains a set of attributes for each other client device and casts one or more votes for a particular client device on the MQTT bus to elect the particular client device as a new broker on the MQTT bus in response to a current broker on the MQTT bus becoming unavailable. The votes cast for the particular client device are based on a first value corresponding to one or more attributes for the particular client device relative to respective second values to the corresponding attribute(s) for each of the other client devices on the MQTT bus as calculated by each respective client device on the MQTT bus.

A data search apparatus includes a logical search circuit and a memory, and the logical search circuit is connected to the memory through a databus. The databus can access all memory resources, and each part of databus resource can access all the memory resources. A logical search resource provided by the logical search circuit can be divided into a plurality of parts as required, and each part of logical resource can access node data in the memory through the bus resource.

Systems, apparatus, and methods that can elect a broker on a Message Queuing Telemetry Transport (MQTT) bus are disclosed. One system includes an MQTT bus and a set of client devices on the MQTT bus. Each client device maintains a set of attributes for each other client device and casts one or more votes for a particular client device on the MQTT bus to elect the particular client device as a new broker on the MQTT bus in response to a current broker on the MQTT bus becoming unavailable. The votes cast for the particular client device are based on a first value corresponding to one or more attributes for the particular client device relative to respective second values to the corresponding attribute(s) for each of the other client devices on the MQTT bus as calculated by each respective client device on the MQTT bus.

Systems, apparatus, and methods that can elect a broker on a Message Queuing Telemetry Transport (MQTT) bus are disclosed. One system includes an MQTT bus and a set of client devices on the MQTT bus. Each client device maintains a set of attributes for each other client device and casts one or more votes for a particular client device on the MQTT bus to elect the particular client device as a new broker on the MQTT bus in response to a current broker on the MQTT bus becoming unavailable. The votes cast for the particular client device are based on a first value corresponding to one or more attributes for the particular client device relative to respective second values to the corresponding attribute(s) for each of the other client devices on the MQTT bus as calculated by each respective client device on the MQTT bus.

A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a reshaping layer to two-dimensionally concatenate features in each group comprised of corresponding K channels of a training image or its processed feature map, to thereby generate a reshaped feature map, and instructing a subsequent convolutional layer to apply the 1×K or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map; and (b) instructing an output layer to refer to features on the adjusted feature map or its processed feature map, and instructing a loss layer to calculate losses by referring to an output from the output layer and its corresponding GT.

A method for learning parameters of a CNN using a 1×K convolution operation or a K×1 convolution operation is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a reshaping layer to two-dimensionally concatenate features in each group comprised of corresponding K channels of a training image or its processed feature map, to thereby generate a reshaped feature map, and instructing a subsequent convolutional layer to apply the 1×K or the K×1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map; and (b) instructing an output layer to refer to features on the adjusted feature map or its processed feature map, and instructing a loss layer to calculate losses by referring to an output from the output layer and its corresponding GT.

Systems, apparatuses, methods, and computer-readable media are provided for managing operations associated with multi-device serial read for communication buses. Embodiments include a protocol controller coupled to a transmitter and receiver assembly of a device to control the transmitter and receiver assembly to perform a multi-device read protocol to read from a plurality of devices coupled to the serial bus using a single device group read command. Other embodiments may be described and/or claimed.

A method for learning parameters of a CNN using a 1K convolution operation or a K1 convolution operation is provided to be used for hardware optimization which satisfies KPI. The method includes steps of: a learning device (a) instructing a reshaping layer to two-dimensionally concatenate features in each group comprised of corresponding K channels of a training image or its processed feature map, to thereby generate a reshaped feature map, and instructing a subsequent convolutional layer to apply the 1K or the K1 convolution operation to the reshaped feature map, to thereby generate an adjusted feature map; and (b) instructing an output layer to refer to features on the adjusted feature map or its processed feature map, and instructing a loss layer to calculate losses by referring to an output from the output layer and its corresponding GT.

A system for predicting the occurrence of an event includes an event detector and a reporting processor. The event detector is configured to: receive data that defines a plurality of social media items; receive a real-time data feed; and predict the occurrence of an event based on a correlation between information in the plurality of social media items and activity associated with the real-time data feed. The reporting processor is configured to determine an event type associated with the event; identify a sentiment of the predicted event based on historical data in the real-time data feed, and generate a recommendation for preventing the occurrence of the event based on at least one of the event type and the sentiment of the predicted event. The recommendation includes a plurality of actions. The reporting processor is coupled to a knowledge graph database that corresponds to an ontology that defines one or more relationships between event types, and response types. The reporting processor determines an order of the actions of the recommendation based on the knowledge graph ontology.