Abstract: Disclosed is a method for serving by a distributed communication system a vehicle traveling from an origin to a destination. The distributed communication system comprises an initial set of computer systems. The method comprises: predicting a route of the vehicle from a current location of the vehicle to the destination. Resource information may be used for selecting from the initial set of computer systems a set of computer systems that can provide machine learning based content to the vehicle along the route. A subset of one or more computer systems of the set of computer systems may be selected for generating a predicted content. A generation of the predicted content may be offloaded to the subset of computer systems. Content delivery computer systems of the initial set of computer systems may be controlled to deliver the generated content to the vehicle in accordance with the specific set of space-time points.

Abstract: A system for enhancing a classifier prediction in respect to underrepresented classes may be provided. A classifier system trained with training data to build a model is used for classifying unknown input data, and an evaluator engine adapted for a determination of an underrepresented class. Additionally, the system comprises an extractor engine adapted for an extraction of relating data from an additional source, and a similarity engine adapted for a selection of data sets out of the relating data wherein the similarity engine is also adapted for comparing features of the relating data and a representative data set for the underrepresented class. Finally, the system comprises a recursion unit adapted for triggering the evaluator engine, the extractor engine and the similarity engine treating selected data set as input data until the evaluator engine classifies the selected data set with a confidence level which is above a confidence threshold level.

Abstract: A system for enhancing a classifier prediction in respect to underrepresented classes may be provided. A classifier system trained with training data to build a model is used for classifying unknown input data, and an evaluator engine adapted for a determination of an underrepresented class. Additionally, the system comprises an extractor engine adapted for an extraction of relating data from an additional source, and a similarity engine adapted for a selection of data sets out of the relating data wherein the similarity engine is also adapted for comparing features of the relating data and a representative data set for the underrepresented class. Finally, the system comprises a recursion unit adapted for triggering the evaluator engine, the extractor engine and the similarity engine treating selected data set as input data until the evaluator engine classifies the selected data set with a confidence level which is above a confidence threshold level.