Nec Corporation

Nec Laboratories Europe Gmbh

Jonathan Fuerst

Mauricio Fadel Argerich

Bin Cheng

Apostolos Papageorgiou

Ernoe Kovacs

Flavio Cirillo

Atsushi Kitazawa

Masahiro Hayakawa

Gurkan Solmaz

Guerkan Solmaz

Kaifei Chen

Everton Luis Berz

A method for using knowledge infusion for robust and transferable machine learning models includes receiving a plurality of adaptive and programmable knowledge functions comprising a plurality of strong functions and a plurality of weak functions. A knowledge model is generated based on the plurality of strong functions and the plurality of weak functions. A machine learning model is trained based on the generated knowledge model.

Automated knowledge infusion for robust and transferable machine learning

A method for reinforcement machine learning uses a reinforcement learning system that has an environment and an agent. The agent has a policy providing a mapping between states of the environment and actions. The method includes: determining a current state of the environment; determining, using the policy, a current policy output based on the current state; determining, using a knowledge function, a current knowledge function output based on the current state; determining an action based on the current policy output and the current knowledge function output; applying the action to the environment resulting in updating the current state and determining a reward; and updating the policy based on at least one of the current state and the reward.

Weakly supervised reinforcement learning

A method is for matching a set of first classes assigned to a first data set with a set of second classes assigned to a second data set. The method includes constructing, via a set of pre-processing functions, a plurality of alignment profiles such that at least one alignment profile is assigned to each of the first classes and each of the second classes. The method includes generating a comparison matrix for each group of the alignment profiles, such that each group includes at least one of the first classes and at least one of the second classes. The method includes training a first machine learning model, through supervised training, based on the generated comparison matrices and based on probabilistic labels generated by a second machine learning model.

Ontology matching based on weak supervision

A method for traffic control includes ingesting public transport data with one or more extract-transform-load procedures. A route of a public transport vehicle is reconstructed based on the ingested public transport data. The reconstructed vehicle route is partitioned into discrete route segments, each of the discrete route segments being defined by parameters spanning multiple dimensions. The discrete route segments are clustered into multiple groups based on the respective multidimensional parameters of each of the discrete route segments. The route segments are scored based on the clustering to create a traffic model which is useable to implement traffic control measures or to change mobility infrastructure.

Automated control through a traffic model

A platform host for deploying a runtime adaptable application has in-line application scope parameters and is capable of interacting with code selection logic when executed. The platform host includes one or more processors coupled to a non-transitory processor-readable storage medium having processor-executable instructions that, when executed by the processor, cause the platform host to: instantiate the code selection logic in an execution platform, the code selection logic being based, at least in part, on the in-line application scope parameters; determine values of platform configuration parameters at runtime, and execute the runtime adaptable application based on the code selection logic and the values of the platform configuration parameters.

System and method for runtime adaptable applications

A method for localization of a vulnerable road user (VRU) includes receiving a received signal strength indication (RSSI) level of a wireless signal of a mobile device carried by the VRU detected by a wireless sensor in an area of interest. The detected RSSI level is compared to RSSI fingerprints stored in a fingerprinting database (DB) so as to identify an RSSI fingerprint having a closest match to the detected RSSI level. The VRU is localized at a position stored in the fingerprinting DB for the identified RSSI fingerprint.

Calibration for wireless localization and detection of vulnerable road users

A method performs wireless localization data acquisition and calibration using a visual 3D model. The method includes: receiving image data and transmitter measurement data from a device; localizing the image data in the visual 3D model to determine a device location in a physical space; and using the device location and the transmitter measurement data to perform at least one of: determining transmitter fingerprint localization data and storing the transmitter fingerprint localization data in a fingerprint database; or calibrating a radio frequency (RF) propagation model for proximity estimation.

Growing community of inventors

Heidelberg, Germany

Jonathan Fuerst