Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Abstract: Example task assignment methods disclosed herein for video analytics processing in a cloud computing environment include determining a graph, such as a directed acyclic graph, including nodes and edges to represent a plurality of video sources, a cloud computing platform, and a plurality of intermediate network devices in the cloud computing environment. Disclosed example task assignment methods also include specifying task orderings for respective sequences of video analytics processing tasks to be executed in the cloud computing environment on respective video source data generated by respective ones of the video sources.

Abstract: A delivery confirmation of a package at a delivery location may be determined based on a communication protocol between a user device and a package device associated with the package. A user can order an item and anticipate a delivery of the item to a delivery location associated with the user. A delivery device can determine that it has entered a geographic region associated with the delivery location. The delivery device and/or a user device associated with the delivery location can determine, using the communication protocol, that the delivery device is within a range of the user device. Then, the user device can receive a package device identifier associated with the package device, determine that the package device is within the range of the user device, and determine a delivery confirmation of the package.

Abstract: A weighting value is determined for each of a plurality of decision trees in a random forest model hosted on a particular device, where the weighting is based on entropy of the respective decision tree. A new decision tree is received at the particular device and a weighting value is determined for the new decision tree based on entropy of the new decision tree. Based on the determined weighting value, it is determined whether to add the new the decision tree to the random forest model. A classification for data generated at the particular device is predicted using the random forest model.

Abstract: Disclosed are various embodiments for estimating missing wireless attributes of a wireless fingerprint generated for a given location and confirming that the given location is a correct location. A collection of wireless fingerprint samples for the given location can be used to train an attribute prediction model that can estimate missing attributes in wireless fingerprints. For example, a wireless fingerprint may be incomplete as the fingerprint may fail to include attribute data for some AP devices associated with the given location. The attribute prediction model can be used to reconstruct wireless fingerprints to include the missing attributes. The reconstructed wireless fingerprints can then be used to train a location confirmation model which can be used for accurate location confirmation.

Abstract: In one embodiment, an apparatus comprises a display interface to communicate with a display device and a processor. The processor is to: identify a mobile transaction associated with a user, wherein the mobile transaction comprises a potential transaction to be completed using a mobile device of the user; access transaction data associated with the mobile transaction; encrypt the transaction data based on an encryption key associated with the user; generate a visual code associated with the encrypted transaction data, wherein the visual code comprises visually encoded data, and wherein the visual code is generated within a protected execution environment of the processor; establish a protected datapath between the processor and the display device via the display interface; and transmit the visual code to the display device via the protected datapath, wherein the visual code is to be displayed on the display device.

Abstract: In one embodiment, an apparatus comprises a communication interface to communicate over a network, and a processor. The processor is to: receive a workload provisioning request from a user, wherein the workload provisioning request comprises information associated with a workload, a network topology, and a plurality of potential hardware choices for deploying the workload over the network topology; receive hardware performance information for the plurality of potential hardware choices from one or more hardware providers; generate a task dependency graph associated with the workload; generate a device connectivity graph associated with the network topology; select, based on the task dependency graph and the device connectivity graph, one or more hardware choices from the plurality of potential hardware choices; and provision a plurality of resources for deploying the workload over the network topology, wherein the plurality of resources are provisioned based on the one or more hardware choices.

Abstract: A gateway is provided with configuration management logic to identify a set of configurations corresponding to a deployment of a particular application, and automatically send corresponding configuration data to a set of devices in range of the gateway. Service management logic of the gateway determines that assets on the set of devices correspond to one or more asset abstractions defined for the particular application, where the configuration data is sent to the set of devices based on the assets corresponding to the asset abstractions. Sensor data is received during the deployment as generated by a sensor asset of one of the devices, the sensor data is processed according to service logic of the particular application to generate a result, and actuating data is generated and sent during the deployment to an actuator asset on the set of devices based on the result.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: A method for optimizing camera layout for areas requiring surveillance comprises constructing a three-dimensional model of a scene subject to surveillance and related scene variables, configuring a computation range, constructing a plurality of simulation scenes using the three-dimensional model and the scene variables and recording the framing of pixels in the plurality of simulation scenes by a plurality of cameras according to the computation range, and further calculating the number of pixels required for visibility of an object to be recognized from the recorded framing of pixels. A camera set is selected from the plurality of cameras according to a convergence requirement, and a computation as to camera optimization layout is performed with the camera set to obtain one or more layout schemes.

Abstract: In one embodiment, an apparatus comprises processing circuitry to: receive, via a communication interface, a compressed video stream captured by a camera, wherein the compressed video stream comprises: a first compressed frame; and a second compressed frame, wherein the second compressed frame is compressed based at least in part on the first compressed frame, and wherein the second compressed frame comprises a plurality of motion vectors; decompress the first compressed frame into a first decompressed frame; perform pixel-domain object detection to detect an object at a first position in the first decompressed frame; and perform compressed-domain object detection to detect the object at a second position in the second compressed frame, wherein the object is detected at the second position in the second compressed frame based on: the first position of the object in the first decompressed frame; and the plurality of motion vectors from the second compressed frame.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a visual computing device over a network. The processor is to: access visual data captured by a camera; detect a particular feature in the visual data, wherein the particular feature comprises a visual indication of privacy-sensitive information; sanitize the visual data to mask the privacy-sensitive information associated with the particular feature, wherein sanitizing the visual data causes sanitized visual data to be produced; and transmit, via the communication interface, the sanitized visual data to the visual computing device over the network, wherein the visual computing device is to use the sanitized visual data to process a visual query associated with the visual data.

Abstract: A set of data is identified that includes a plurality of observed values generated by a plurality of sensor devices located in a plurality of different locations. For each of the plurality of observed values, a modality of the value, a spatial location of the value, and a timestamp of the value is determined. Values for one or more missing values in the set of data are determined from the modalities, spatial locations, and timestamps of the plurality of observed values.

Abstract: A method for optimizing camera layout for areas requiring surveillance comprises constructing a three-dimensional model of a scene subject to surveillance and related scene variables, configuring a computation range, constructing a plurality of simulation scenes using the three-dimensional model and the scene variables and recording the framing of pixels in the plurality of simulation scenes by a plurality of cameras according to the computation range, and further calculating the number of pixels required for visibility of an object to be recognized from the recorded framing of pixels. A camera set is selected from the plurality of cameras according to a convergence requirement, and a computation as to camera optimization layout is performed with the camera set to obtain one or more layout schemes.

Abstract: Example task assignment methods disclosed herein for video analytics processing in a cloud computing environment include determining a graph, such as a directed acyclic graph, including nodes and edges to represent a plurality of video sources, a cloud computing platform, and a plurality of intermediate network devices in the cloud computing environment. Disclosed example task assignment methods also include specifying task orderings for respective sequences of video analytics processing tasks to be executed in the cloud computing environment on respective video source data generated by respective ones of the video sources.

Abstract: A method for ensuring safety of humans within operating area or in close proximity to an automatic apparatus is applied in and by a control apparatus. The control apparatus is coupled to one or more cameras arranged around the operating area of the automatic apparatus. The control apparatus uses deep learning techniques to analyze images captured by the cameras to determine whether there is a person in the operating area and powers off the automatic apparatus if any person is deemed present.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data, wherein the sensor data is captured by a plurality of sensors within an educational environment. The processor is to: access the sensor data captured by the plurality of sensors; identify a student within the educational environment based on the sensor data; detect a plurality of events associated with the student based on the sensor data, wherein each event is indicative of an attention level of the student within the educational environment; generate a report based on the plurality of events associated with the student; and send the report to a third party associated with the student.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.