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 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: In one embodiment, an apparatus comprises a processor to: identify a workload comprising a plurality of tasks; generate a workload graph based on the workload, wherein the workload graph comprises information associated with the plurality of tasks; identify a device connectivity graph, wherein the device connectivity graph comprises device connectivity information associated with a plurality of processing devices; identify a privacy policy associated with the workload; identify privacy level information associated with the plurality of processing devices; identify a privacy constraint based on the privacy policy and the privacy level information; and determine a workload schedule, wherein the workload schedule comprises a mapping of the workload onto the plurality of processing devices, and wherein the workload schedule is determined based on the privacy constraint, the workload graph, and the device connectivity graph.

Abstract: Various embodiments are disclosed for providing machine learning routines with peripheral device data to infer driver activity and location. Peripheral device data may be collected on a peripheral device having a machine learning routine executing thereon to infer driver activity and perform improved estimation of driver location. Using driver activity and location estimation, contextually relevant delivery workflow assistance may be automatically provided to a delivery driver or other individual without requiring manual input, thereby improving driver safety and operational efficiency.

Abstract: A mechanism is described for facilitating portable, reusable, and shareable Internet of Things-based services and resources according to one embodiment. A method of embodiments, described herein, includes selecting a recipe, wherein the recipe includes instructions associated with a first trigger to activate a first set of resources, the first set of resources and instructions correspond to a first category, accessing a second set of resources associated with a second trigger to activate the second set of resources, the second set of resources and instructions correspond to a second category of user preferences, the second category different from the first category, modify the recipe to replace a first resource block of the first set of resources with a second resource block of the second set of resources, and deploy the recipe at a computing device, the computing device receiving at least one of the first trigger or the second trigger.

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: A system can be configured to receive and analyze an image captured by a user device at a delivery location. The system can utilize local algorithms tailored to the delivery location to confirm that the delivery location is the intended delivery location for a package. The system can utilize global algorithms to generate three-dimensional models of the delivery environment and an augmented reality interface providing delivery instructions to the deliverer. The delivery instructions include indications of approved delivery locations and rejected delivery locations associated with the general delivery location of the customer. The delivery instructions and the user device can be utilized to ensure package placement for secure delivery to a customer.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: A mechanism is described for facilitating portable, reusable, and shareable Internet of Things-based services and resources according to one embodiment. A method of embodiments, as described herein, includes wherein receiving a recipe request for selecting a recipe relating to Internet of Things (IoT) services, where selecting includes at least one of searching the recipe and modifying the recipe. The method may further include selecting the recipe, where the recipe includes a set of resource requirements and business logic. The method may further include modifying the set of resource requirements, where the modified set of resource requirements is associated with the business logic to modify the recipe, and deploying the modified recipe at one or more computing devices, where the modified recipe to facilitate management for one or more IoT devices at one or more locations.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

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: Disclosed are various embodiments for generating location confirmation models using crowdsourced wireless fingerprints. Wireless fingerprints can be generated and associated with a given location during events that use proximity to specific locations. The wireless fingerprints can be processed to generate location confirmation models that can be used for location confirmation. Periodically, the collected wireless fingerprints can be analyzed and compared to previously collected wireless fingerprints to detect a change in a wireless infrastructure at the given location. Upon determining that a previously generated location confirmation model is invalid according to a level of significance of the change, the outdated wireless fingerprints can be identified and removed from storage or otherwise ignored for future models. An updated location confirmation model can be generated using the up-to-date wireless fingerprints.

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: User inputs received through a graphical user interface of a programming tool are used to define a set of capability abstractions for a particular application and further define, based on the one or more user inputs, relationships between the set of capability abstractions. The particular application is to utilize a machine-to-machine network, and the set of capability abstractions include: a sensor capability abstraction, an actuator capability abstraction, a computation logic capability abstraction, an input user interface (UI) capability abstraction, and an output UI capability abstraction for the particular application. The relationships include a relationship between the input UI capability abstraction and the computation logic capability abstraction, where the input UI capability is to provide an input to the computation logic capability abstraction.

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: With respect to a package delivery system configured to optimize efficiencies associated with package delivery, a server computing device may monitor locations and conditions associated with a fleet of vehicles. A vehicle including a first delivery person may be dispatched with packages for delivery. The server computing device may provide updated delivery routes based on traffic and/or other conditions in the environment. At the completion of a work shift associated with the first delivery person, the vehicle may be parked at a location proximate the delivery route. The server computing device may dispatch a second delivery person to the location. Based on an indication that the second delivery person is proximate the vehicle, the server computing device may cause a user interface to surface on a computing device of the second delivery person, enabling the second delivery person to unlock the vehicle and continue delivering the packages.

Abstract: Systems and methods for automated generation and presentation of thematic content objects to consumers in a retail environment are provided. A portable device may include a transmitter or transceiver that emits, produces or otherwise transmits a signal that includes information and/or data representative of an identifier that uniquely identifies the portable device. Upon entering an establishment thematic content delivery circuitry detects the presence of the portable device and obtains the identifier included in the signal emitted by the portable device. Using the identifier, the thematic content delivery circuitry determines a theme logically associated with the identifier. The thematic content delivery circuitry generates thematic content output that is logically associated with the theme and communicates the thematic content output to an output device. The thematic content output may include display output, audio output, tactile output, hardcopy output, or combinations thereof.

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: 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 visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.