Abstract: Systems and methods described herein which can involve for a first input of a plurality of labeled images of a new domain task, processing the first plurality of labeled images through a plurality of backbone snapshots, each of the backbone snapshots representative of a model trained across a plurality of other domain tasks, each of the plurality of backbone snapshots configured to output a first plurality of features responsive to the input; processing a second input of second plurality of unlabeled images through the plurality of backbone snapshots to output a second plurality of features responsive to the second input; and generating a representative model for the new domain task from the clustering and transformation of the first plurality of features and as associated from the clustered and transformed second plurality of features.

Abstract: Example implementations described herein involve systems and methods for efficient learning for mixture of domains which can include applying a clustering technique to a set of data comprised of multiple domains to obtain an initial domain separation of the set of data into one or more clusters; training one or more experts associated with each of the one or more clusters based on the initial domain separation where each expert corresponds with one domain of the multiple domains; inputting all data points to the one or more experts for refining each of the one or more clusters using expert output probabilities; retraining the one or more experts based on the refined one or more clusters; and training a gating mechanism to route an input to an appropriate expert of the one or more experts based on the refined one or more clusters.

Abstract: An over-the-air (OTA) mobility service platform (MSP) is disclosed that provides a variety of OTA services, including but not limited to: updating software OTA (SOTA), updating firmware OTA (FOTA), client connectivity, remote control and operation monitoring. In some exemplary embodiments, the MSP is a distributed computing platform that delivers and/or updates one or more of configuration data, rules, scripts and other services to vehicles and IoT devices. In some exemplary embodiments, the MSP optionally provides data ingestion, storage and management, data analytics, real-time data processing, remote control of data retrieving, insurance fraud verification, predictive maintenance and social media support.

Abstract: Example implementations described herein can involve systems and methods involving, for receipt of input data from one or more assets, identifying and separating different event contexts from the input data; training a plurality of machine learning models for each of the different event contexts; selecting a best performing model from the plurality of machine learning models to form a compound model; selecting a best performing subset of the input data for the compound model based on maximizing a metric; and deploying the compound model for the selected subset.

Abstract: Example implementations involve systems and methods to create robust visual inspection datasets and models. The novel method learns and transfers damage representation from few samples to new images. The proposed method introduces a generative region-of-interest based adversarial network with the aim of learning a common damage representation and transferring it to an unseen image. The proposed approach shows the benefit of adding damage-region-based component, since existing methods fail to transfer the damages. The proposed method successfully generated images with variations in context and conditions to improve model generalization for small datasets.

Abstract: Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.

Abstract: An over-the-air (OTA) mobility service platform (MSP) is disclosed that provides a variety of OTA services, including but not limited to: updating software OTA (SOTA), updating firmware OTA (FOTA), client connectivity, remote control and operation monitoring. In some exemplary embodiments, the MSP is a distributed computing platform that delivers and/or updates one or more of configuration data, rules, scripts and other services to vehicles and IoT devices. In some exemplary embodiments, the MSP optionally provides data ingestion, storage and management, data analytics, real-time data processing, remote control of data retrieving, insurance fraud verification, predictive maintenance and social media support.

Abstract: An over-the-air (OTA) mobility service platform (MSP) is disclosed that provides a variety of OTA services, including but not limited to: updating software OTA (SOTA), updating firmware OTA (FOTA), client connectivity, remote control and operation monitoring. In some exemplary embodiments, the MSP is a distributed computing platform that delivers and/or updates one or more of configuration data, rules, scripts and other services to vehicles and IoT devices. In some exemplary embodiments, the MSP optionally provides data ingestion, storage and management, data analytics, real-time data processing, remote control of data retrieving, insurance fraud verification, predictive maintenance and social media support.

Abstract: Example implementations involve systems and methods for predicting failures and remaining useful life (RUL) for equipment, which can involve, for data received from the equipment comprising fault events, conducting feature extraction on the data to generate sequences of event features based on the fault events; applying deep learning modeling to the sequences of event features to generate a model configured to predict the failures and the RUL for the equipment based on event features extracted from data of the equipment; and executing optimization on the model.

Abstract: Example implementations involve systems and methods to advance data acquisition systems for automated visual inspection using a mobile camera infrastructure. The example implementations address the uncertainty of localization and navigation under semi-controlled environments. The approach combines object detection models and navigation planning to control the quality of visual inputs in the inspection process. The solution guides the operator (human or robot) to collect only valid viewpoints to achieve higher accuracy. Finally, the learning models and navigation planning are generalized to multiple type and size of inspection objects.

Abstract: A wireless multi-edge computing (MEC)-based road-side unit (RSU) (RSU-MEC) system and method for traffic management.

Abstract: Example implementations described herein involve systems and methods for generating an ensemble of deep learning or neural network models, which can involve, for a training set of data, generating a plurality of model samples for the training set of data, the plurality of model samples generated from deep learning or neural network methods; and aggregating output of the model samples to generate an output of the ensemble models.

Abstract: A machine learning system and method configured to receive information from a plurality of sensors being located on a computational front-end; a deep cellular recurrent neural network configured to receive time-series data input from each of the plurality of sensor; and one or more feed-forward layers being located on a computational back-end configured to receive data output, the data output being processed by the deep cellular recurrent neural network. The deep cellular recurrent neural network further includes a plurality cellular long short-term memory networks arranged in corresponding nodes, wherein each of the plurality of cellular long short-term memory networks are interconnected to at least one adjacent cellular long short-term memory module.

Abstract: The disclosed embodiments are directed to a mobility services platform for self-healing mobility clients. In an embodiment, a method comprises: obtaining, by one or more server computers, diagnostic data from a plurality of mobility clients; applying, by the one or more server computers, machine learning to the diagnostic data; identifying, by the one or more server computers, outlier data resulting from the machine learning; identifying, by the one or more server computers and from the outlier data, a new error class; determining, by the one or more server computers, an impact of the error class (e.g., short-term, medium-term, long-term impact) on the plurality of mobility clients; and generating, by the one or more server computers and based on the determined impact, an update for the plurality of mobility clients.

Abstract: Flexible, multidimensional infrastructure scaling for cloud-based over-the-air (OTA) systems is disclosed.

Abstract: Flexible, multidimensional infrastructure scaling for cloud-based over-the-air (OTA) systems is disclosed.

Abstract: The disclosed embodiments are directed to a mobility services platform for self-healing mobility clients. In an embodiment, a method comprises: obtaining, by one or more server computers, diagnostic data from a plurality of mobility clients; applying, by the one or more server computers, machine learning to the diagnostic data; identifying, by the one or more server computers, outlier data resulting from the machine learning; identifying, by the one or more server computers and from the outlier data, a new error class; determining, by the one or more server computers, an impact of the error class (e.g., short-term, medium-term, long-term impact) on the plurality of mobility clients; and generating, by the one or more server computers and based on the determined impact, an update for the plurality of mobility clients.

Abstract: An over-the-air (OTA) mobility service platform (MSP) is disclosed that provides a variety of OTA services, including but not limited to: updating software OTA (SOTA), updating firmware OTA (FOTA), client connectivity, remote control and operation monitoring. In some exemplary embodiments, the MSP is a distributed computing platform that delivers and/or updates one or more of configuration data, rules, scripts and other services to vehicles and IoT devices. In some exemplary embodiments, the MSP optionally provides data ingestion, storage and management, data analytics, real-time data processing, remote control of data retrieving, insurance fraud verification, predictive maintenance and social media support.

Abstract: A vehicle software update system is contemplated. The system may be utilized to facilitate Over-the-Air (OTA) software updates according to various functional requirements, the interaction of modules with other modules and/or the relationship of the modules with engines. The vehicle software update system may include a message engine, a transformation engine, an operation engine, an intelligent engine, and/or an analytical engine.

Abstract: Apparatus, methods and logic for vehicles to determine vehicle to vehicle (V2V) safety message transmission rates for transmitting V2V safety messages based on how frequently the vehicles actually need to exchange safety messages, including factors such as vehicle velocities, distances among vehicles, and on how quickly the inter-vehicle distances are closing up. The determined V2V safety message transmission rates are selectively dynamically adjusted in accordance with detected significant changes in one or more of the inter-vehicle distances or inter-vehicle speeds. To avoid needless frequent changes to the transmission rate, statistical modeling techniques including hypothesis testing and sequential change detection are selectively used to more accurately detect significant changes in inter-vehicle distances or inter-vehicle speeds that warrant a change to the message transmission rate.