A data processing device and a computer-implemented method are configured to execute in parallel a data hub process (6) comprising at least a segmentation sub-process (61) which segments input data into data segments and at least one keying sub-process (62) which provides keys to the data segments creating keyed data segments, wherein the data hub process (6) stores the keyed data segments in a shared memory device (4) as shared keyed data segments and a plurality of processes in the form of computation modules (7) wherein each computation module (7) is configured to access the at least one shared memory device (4) to look for modulo-specific data segments which are shared keyed data segments that are keyed with at least one key which is specific for at least one of the computation modules (7) and to execute a machine learning method on the module-specific data segments, said machine learning method comprising data interpretation and classification methods using at least one pre-trained neuronal network (71) and to output the result of the executed machine learning method to the shared memory device (4) or another computation module.

Urgent screening in high-throughput, secure environments such as emergency rooms, or security, typically involves multiple devices. Devices that are used for screening are diverse, and typically sourced from multiple vendors. Currently, devices are typically stand-alone. Further, large devices, are costly, having high capital expenses with long commercial lifetimes, sometimes approaching a decade or more. The result of these features leads to duplication of computational resources, obsolescent computational infrastructure, and lack of interconnection between elements. Aspects of this invention include a device, system, and methods to provide vendor-agnostic interconnection between the multiple elements of a defined environment. The disclosed approach untethers AI algorithms from data generation system and increases flexibility in deployment of newer technologies and algorithms. Example systems can be updated or replaced with new hardware, as computational capabilities develop on short or emergent quality improvement cycles, and can adapt nimbly to changes in threats, regulatory requirements or market developments.

An image processing apparatus includes: a receiving unit configured to receive an addition instruction for adding a page to album data including a plurality of pages; an image obtaining unit configured to obtain a user selection image as an image to be arranged in the page to be added in response to the addition instruction, the user selection image being selected by a user; a first analysis information obtaining unit configured to obtain analysis information of the user selection image; and a selecting unit configured to select an image to be arranged in the added page other than the user selection image, based on the analysis information of the user selection image.

An image processing apparatus includes: a receiving unit configured to receive an addition instruction for adding a page to album data including a plurality of pages; an image obtaining unit configured to obtain a user selection image as an image to be arranged in the page to be added in response to the addition instruction, the user selection image being selected by a user; a first analysis information obtaining unit configured to obtain analysis information of the user selection image; and a selecting unit configured to select an image to be arranged in the added page other than the user selection image, based on the analysis information of the user selection image.

A vehicle behavior of a monitored vehicle is learned. A vehicle illumination of the monitored vehicle is detected and monitored. If a light-pattern occurs in the detected vehicle illumination, wherein the light-pattern corresponds to a frequency, intensity and/or color dependent glowing of the vehicle illumination, and further wherein the light-pattern starts with a flashing up of the detected vehicle illumination and ends after a certain time without glowing of the respective part of the detected vehicle illumination, then the method further monitors the light-pattern; monitors a vehicle movement of the monitored vehicle during the occurrence of the light-pattern; and compares the monitored light-pattern with a known light-pattern from a light-pattern data entry stored in an light-pattern database. If the comparison results in the monitored light-pattern being unknown, the method stores the light-pattern and the vehicle movement together as a new light-pattern data entry in the light-pattern database.

Methods, systems, and apparatus, including medium-encoded computer program products, for a secure edge platform that uses image classification machine learning models. An edge platform can include at least one camera and can identify image classification models that generate classification output data from image data generated by the cameras. The edge platform can receive image data generated by the camera, and provide the image data to the models. In response to providing the image data classification models, the edge platform can receive classification output data. In response to receiving the classification output data from the image classification models, the edge platform can generate augmentation data that is associated with the image data, then transmit detection data to a central server platform. The detection data can include (i) the classification output data and (ii) the augmentation data associated with the image data. Data can be made recordable, reportable, searchable, and alarmable.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Automation is the key to build efficient workflows with minimum effort consumption. However, there is a large gap in workflow synthesis for automated AI application development. Computer vision workflow synthesis largely rely on domain expert due to lack of generalization over solution search space for given goal. This search space for creating suitable solution(s) using available algorithms is quite vast, which makes exploratory work of solution building a time-, effort- and intellect intensive endeavor. Embodiments of the present disclosure provide system and method for goal-driven algorithm selection approach for building computer vision workflows on the fly. The system generates one or more task workflows with associated success probability depending on initial conditions and input natural language goal query by combining various image processing algorithms. Symbolic AI planning is aided by Reinforcement Learning to recommend optimal workflows that are robust and adaptive to changes in the environment.

Methods and systems are described herein for hosting and arbitrating algorithms for the generation of structured frames of data from one or more sources of unstructured input frames. A plurality of frames may be received from a recording device and a plurality of object types to be recognized in the plurality of frames may be determined. A determination may be made of multiple machine learning models for recognizing the object types. The frames may be sequentially input into the machine learning models to obtain a plurality of sets of objects from the plurality of machine learning models and object indicators may be received from those machine learning models. A set of composite frames with the plurality of indicators corresponding to the plurality of objects may be generated, and an output stream may be generated including the set of composite frames to be played back in chronological order.