A detecting device obtains a network log and a host log of a device. The detecting device converts the network log into a network feature value in a form which is inputtable to a multimodal generative model which generates output data on the basis of multiple latent variables represented by random variables. The detecting device converts the host log into a host feature value in a form which is inputtable to the generative model. The detecting device also inputs at least one of the network feature value and the host feature value to the generative model and calculates output data. The detecting device detects an abnormality about the device using an anomaly score calculated on the basis of the output data.

A detecting device obtains a network log and a host log of a device. The detecting device converts the network log into a network feature value in a form which is inputtable to a multimodal generative model which generates output data on the basis of multiple latent variables represented by random variables. The detecting device converts the host log into a host feature value in a form which is inputtable to the generative model. The detecting device also inputs at least one of the network feature value and the host feature value to the generative model and calculates output data. The detecting device detects an abnormality about the device using an anomaly score calculated on the basis of the output data.

One or more embodiments of the present disclosure describe an RFID-enabled roofing accessory. The roofing accessory may include a roofing shingle, such as an asphalt roofing shingle. The roofing accessory may include an RFID tag incorporated thereon. The RFID tag may be adhered to the roofing accessories.

A method for processing a query for data stored in a distributed database includes receiving, at an edge device, the query for data stored in the distributed database from a query device. The method includes causing, by the edge device, the query to be stored on a dynamic ledger maintained by the distributed database. The method includes detecting, by the edge device, that summary data has been stored on the dynamic ledger. The method includes generating, by the edge device, an approximate response to the query based on the summary data stored on the dynamic ledger. The method includes transmitting, to the query device, the approximate response.

A wireless sensing system includes a first tape node and a second tape node. The first tape node has a low-power wireless-communications interface and an environmental sensor operable to capture and transmit a first set of environmental data of at least one environmental characteristic to the second tape node. The second node includes an environmental sensor, a low-power wireless-communication interface, a first processor, and a first memory communicatively coupled with the first processor, the first memory storing machine-readable instructions that, when executed by the first processor, cause the first processor to: capture a second set of environmental data; compute an environmental differential between the first set of environmental data and the second set of environmental data; compare the environmental differential to a predetermined environmental threshold; and transmit a notification to a client application of the wireless sensing system running on a client device of the wireless sensing system when the environmental differential exceeds the predetermined environmental threshold.

A method and a system for managing a notification for missed events in an Internet of things (IoT) environment are provided. The method includes determining at least one missed event in response to a user command to an IoT appliance after an expiry of a threshold wait time, determining a plurality of attributes associated with at least one of appliance settings and environment properties after the detection of at least one missed event based on a processing of the user command to obtain an attribute report corresponding to the plurality of attributes respective to each of the IoT appliance, predicting a requirement for notifying the user with respect to the determined at least one missed event based on the attribute report, and managing the notification corresponding to the at least one missed event based on the predicted requirement for notifying the user.

A method and a system for managing a notification for missed events in an Internet of things (IoT) environment are provided. The method includes determining at least one missed event in response to a user command to an IoT appliance after an expiry of a threshold wait time, determining a plurality of attributes associated with at least one of appliance settings and environment properties after the detection of at least one missed event based on a processing of the user command to obtain an attribute report corresponding to the plurality of attributes respective to each of the IoT appliance, predicting a requirement for notifying the user with respect to the determined at least one missed event based on the attribute report, and managing the notification corresponding to the at least one missed event based on the predicted requirement for notifying the user.

A computer system is provided which includes a sensor system and a processor. The sampling rate of the sensor system is not predetermined, and instead depends on the acquisition rate of the algorithm acquiring the data stream provided by the sensor system. A corresponding method is also provided.

A computer system is provided which includes a sensor system and a processor. The sampling rate of the sensor system is not predetermined, and instead depends on the acquisition rate of the algorithm acquiring the data stream provided by the sensor system. A corresponding method is also provided.

A computer-implemented method of training an autoencoder to recover missing data is provided. The autoencoder includes an encoder for encoding its inputs into a latent space and a decoder for decoding the encodings from the latent space. The method comprises creating a first training set including a valid data set of multiple dimensions, and training the encoder and the decoder in a first training stage using the first training set to reduce a difference between the valid data set provided to the encoder and a data set decoded by the decoder. The method further comprises creating a second training set comprising an invalid data set, and training the encoder in a second training stage using the second training set to reduce a difference between encodings of valid data instances and encodings of their corresponding invalid data instances.