Abstract: A method preserving privacy in federated machine learning system is provided. In the method, a first computing entity in the federated learning system determines a first labeling matrix based on applying a first set of labeling functions to first data points. The first labeling matrix includes a plurality of first labels. The first computing entity obtains a similarity matrix indicating similarity scores between the first data points and second data points associated with a second computing entity. The first computing entity augments the first labeling matrix by transferring labels from a second labeling matrix into the first labeling matrix using the similarity scores between the first data points and the second data points. The first computing entity trains a discriminative machine learning model associated with the first computing entity based on the first augmented labeling matrix.

Abstract: A method augments data labels of a machine learning task. The method includes applying at least one labeling function for each of a plurality of pairs of semantic concepts and producing a labeling matrix, computing pairwise similarity scores using similarity metrics for each of the pairs of semantic concepts, augmenting the labeling matrix using the pairwise similarity scores to increase a density of the labeling matrix, and inputting the labeling matrix to a label aggregator to apply a single label for each of the pairs of semantic concepts labeled by the at least one labeling function.

Abstract: A method for supporting autonomous driving of an autonomous vehicle includes detecting, by an in-vehicle internet-of-things (IoT) platform of the autonomous vehicle, a vulnerable road user (VRU) having a mobile device in a vicinity of the autonomous vehicle. A mobility application runs on the mobile device of the VRU and sends VRU-specific data to the in-vehicle IoT platform of the autonomous vehicle. The VRU is detected based on the VRU-specific data and/or in-vehicle sensor data of the autonomous vehicle. The method further includes determining, by the in-vehicle IoT platform, a movement intention prediction based on the VRU-specific data. The movement intention prediction is computed by use of a machine learning model. The VRU-specific data of the mobile device are provided as input data for the machine learning model. In addition, the method includes performing an autonomous driving decision for the autonomous vehicle based on the movement intention prediction.

Abstract: 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.

Abstract: 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.

Abstract: 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.