Abstract: A method for training a speech-emotion recognition classifier under a continuously updatable and re-trainable ASER machine learning model. The quantified training data is generated by first processing the utterances of a human speech source and the associated texts in an emotion evaluation and rating process with normalization; then, extracting the features of the utterance; quantifying the feature attributes of the extracted features by labelling, tagging, and weighting the feature attributes, with their values assigned under measurable scales. The quantified training data comprises the normalized results of the emotion evaluation and rating process, the extracted features, the quantified emotional feature attributes, and the hash values of the quantified emotional feature attributes.

Abstract: A method for training a speech-emotion recognition classifier under a continuous self-updating and re-trainable ASER machine learning model, wherein the training data is generated by: obtaining an utterance of a human speech source; processing the utterance in an emotion evaluation and rating process with normalization; extracting the features of the utterance; quantifying the feature attributes of the extracted features by labelling, tagging, and weighting the feature attributes, with their values assigned under measurable scales; and hashing the quantified feature attributes in a feature attribute hashing process to obtain hash values for creating a feature vector space.

Abstract: Systems and methods providing adaptive path planning techniques utilizing localized learning with global planning are described. The adaptive path planning of embodiments provides global guidance and performs local planning based on localized learning, wherein the global guidance provides a planned path through the dynamic environment from a start location to a selected destination while the local planning provides for dynamic interaction within the environment in reaching the destination, such as in response to obstacles entering the planned path. Global guidance may combine an initial global path with history information for providing a global path configured to avoid points of frequent traffic conflicts. Local planning may utilize localized deep reinforcement learning to direct interactions of an automated vehicle traversing the global path in a dynamic environment, such as in response to obstacles entering the global path.

Abstract: The present invention includes apparatuses, systems, and methods for peer group formation for mobile devices by proximity sensing. The described embodiments further include methods, systems, and apparatuses configured to easily establish an ad-hoc peer network in which each of the devices in the network automatically discovers and is connected to other devices in the network upon establishing a connection with at least one member of the ad-hoc network. The connection is established using a tap of NFC devices in the two wireless devices. Further, the wireless devices in the ad-hoc network may be divided into logical peer groups.

Abstract: The present invention includes apparatuses, systems, and methods for peer group formation for mobile devices by proximity sensing. The described embodiments further include methods, systems, and apparatuses configured to easily establish an ad-hoc peer network in which each of the devices in the network automatically discovers and is connected to other devices in the network upon establishing a connection with at least one member of the ad-hoc network. The connection is established using a tap of NFC devices in the two wireless devices. Further, the wireless devices in the ad-hoc network may be divided into logical peer groups.