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: 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: 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: In embodiments, one or more non-transitory computer-readable storage media comprise a set of instructions, which, when executed on a processor of a server, causes the server to receive sensor data from at least one sensor proximate to an entity, the entity is a human under care of at least one temporary guardian (TG) pursuant to a set of guardianship rules, the guardianship rules including a pre-defined geographic boundary in which the entity is to remain while under the care of the at least one TG. When executed, the instructions further cause the server to extract location metadata of the entity from the sensor data, and based at least in part on the metadata, send notifications to the TG and to a primary guardian (PG) of the entity when the entity is outside of the pre-defined boundary.

Abstract: An embodiment includes at least one computer readable storage medium comprising instructions that when executed enable a system to: receive (a)(i) first radio signal location data for a first object from a radio sensor; and (a)(ii) first visual signal location data for the first object from a camera sensor; perform feature extraction on (b)(i) the first radio signal location data to determine first extracted radio signal features; and (b)(ii) the first visual signal location data to determine first extracted visual signal features; solve a first association problem between the first extracted radio signal features and the first extracted visual signal features to determine first fused location data; and store the first fused location data in the at least one computer readable storage medium. Other embodiments are described herein.

Abstract: Techniques are provided for adaptive distribution of video analysis workload over a network of video processor nodes. The nodes may include, for example, internet protocol (IP) cameras, video recorders and/or data centers. The network may also include a management system configured to assign video analysis tasks to the nodes based on the node resources and predictive modelling of the node workload. The management system may re-distribute the tasks based on performance monitoring. Some assigned tasks may be bound to the node while other tasks may be transferrable, by the node, to other nodes. The nodes may be configured to determine which of the transferrable tasks will be locally executed or transferred based on a check of resource usage against a usage policy that specifies thresholds for the determinations. The nodes may be configured to transmit video analysis packets, including image data, analysis completion status and analysis results, to other nodes.

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 lifting handle (1, 2) for enhancing the convenience of carrying containers. The lifting handle (1, 2) comprises: a base board (11, 21), the base board (11, 21) comprising two end edges (111, 211) and two side edges (112, 212); the two end edges (111, 211) form the two ends of the base board (11, 21), and the two ends of the two side edges (112, 212) respectively connect to the two end edges (111, 211); the base board (11, 21) is provided with at least one cutting part (12, 22), and the at least one cutting part (12, 22) extending from the position of one end edge (111, 211) near the base board (11, 21) to the position of the other end edge (111, 211) near the base board (11, 21), such that the base board (11, 21) forms two carrying parts (13, 23) and a lifting handle part (14, 24).

Abstract: In one embodiment, an apparatus comprises a storage device and a processor. The storage device may store a plurality of compressed images comprising one or more compressed master images and one or more compressed slave images.

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: In one embodiment, an apparatus comprises a memory and a processor. The memory stores visual data captured by one or more sensors. The processor detects one or more first objects in the visual data based on a machine learning model and one or more first reference templates. The processor further determines, based on an object ontology, that the visual data is expected to contain a second object, wherein the object ontology indicates that the second object is related to the one or more first objects. The processor further detects the second object in the visual data based on the machine learning model and a second reference template. The processor further determines, based on an inference rule, that the visual data is expected to contain a third object. The processor further detects the third object in the visual data based on the machine learning model and a third reference template.

Abstract: Systems, apparatuses and methods may receive, at a local Internet of Things (IOT) device, a request to deploy an IOT application. Additionally, the IOT application may be partitioned into a plurality of atomic nodes, wherein configuration information for the plurality of atomic nodes may be sent, at runtime, to a plurality of remote IOT devices having abstracted resources that support operation of the first plurality of atomic nodes. In one example, the configuration information is sent via a device independent message protocol having a universal namespace.

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 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: Systems and methods for autonomously generating temporally limited targeted offers and/or directed advertisements based on detected interaction between customers and products are provided. A regional monitoring system may detect the movement of a customer in the establishment. A customer that moves within a limited area of the establishment may be identified by targeted offer distribution circuitry as interested in one or more products in the area. A customer interest monitoring device detects interaction between the customer and the product. Using information obtained from the customer interest monitoring device, the targeted offer distribution circuitry autonomously generates a temporally limited targeted offer or directed advertisement. The targeted offer distribution circuitry delivers the targeted offer or directed advertisement to an output device proximate the customer or to a processor-based device or media carried or possessed by the customer.

Abstract: Classification techniques are disclosed that take into account the “cost” of each type of classification error for minimizing total cost of errors. In one example embodiment, a pre-trained cost-sensitive auto-encoder can be used in combination with a training (fine-tuning) stage for cost-sensitive deep learning. Thus, cost information is effectively combined with deep learning by modifying the objective function in the pre-training phase. By minimizing the modified objective function, the auto-encoder not only tries to capture underlying pattern, it further “learns” the cost information and “stores” it in the structure. By later fine-tuning at the training stage, the classification system yields improved performance (lower cost) than a typical classification system that does not take cost information into account during pre-training.

Abstract: This disclosure presents embodiments for performing privacy-preserving distributed computing. For example, a workload that includes a plurality of tasks may be identified, and a workload graph may be generated to represent the workload. In addition, a device connectivity graph with connectivity information for a plurality of processing devices may be identified. A privacy policy associated with the workload, along with privacy level information associated with the plurality of processing devices, may also be identified. A privacy constraint may then be identified based on the privacy policy and the privacy level information. A workload schedule that includes a mapping of the workload onto the plurality of processing devices may be determined based on the privacy constraint, the workload graph, and the device connectivity graph. The workload schedule may be sent to the plurality of processing devices, which may execute the workload based on the workload schedule.

Abstract: A particular device is provided with a communications module to receive signals of a plurality of devices within range of the particular device and further provisioned with grouping logic. The grouping logic is executable by one or more processors to determine from each of the signals a respective identifier for each of the plurality of devices, determine, based at least in part on the identifiers, that a particular subset of the plurality of devices are also included with the particular device in a particular one of a plurality of defined groups, and converge data received from the particular subset of devices based on the particular group.

Abstract: An embodiment of a semiconductor package apparatus may include technology to associate an asset to a fixture with a device positioned proximate to the fixture, and determine a location of the fixture based on a location of the device. Other embodiments are disclosed and claimed. Non-limiting example applications may include shipping, logistics, warehouse asset tracking, retail, etc.

Abstract: A simulation is defined to include a particular test environment and one or more device models, where each device model simulates a respective device, the test environment simulates a physical environment, and the one or more device models are populated in the IoT testing environment to model a deployment of devices corresponding to the device models in the physical environment. Modeled device data is generated, based on the one or more device models, to simulate data generated by corresponding devices. The modeled device data is transmitted to an application, and application data is received in response from the application and used in the simulation.