Abstract: An example operation may include one or more of connecting, by an identity server, to a blockchain configured to store an identity trait of a user, retrieving, by an identity server, the identity trait from the a blockchain, establishing, by the identity server, a trust group homomorphism digital signature algorithm (DSA) for the user associated with the identity trait based on a public key PK1, creating, by the identity server, a zero-knowledge proof function with a public key PK2 based on a DSA member of the trust group homomorphism for witness data, providing, by the identity server, the witness data to a challenger for the zero-knowledge proof function, and receiving, by the identity server, a validation of the user as a proved user based on execution of the zero-knowledge proof function based on the witness data.

Abstract: An utterance is analyzed to identify an absence of a known invocation phrase. A skill set is constructed in response to the absence, the skill set including a first skill corresponding to the utterance and a first skill score corresponding to a likelihood that the first skill corresponds to the utterance. The first skill score is adjusted, based on the presence of the first skill in a skill history, where the skill history stores a set of history skills in an order of recency of use of each history skill in the set of history skills. The first skill score is adjusted, based on an association of the first skill with a default installed skill. An installed skill is selected, based on the adjusted first skill score, the installed skill performing an action in response to the utterance.

Abstract: An example operation may include one or more of connecting to a blockchain configured to store an identity trait of a user, retrieving the identity trait from the blockchain, establishing a trust group homomorphism digital signature algorithm (DSA) for the user based on a private key, creating a zero knowledge succinct non-interactive argument of knowledge (zkSNARK) proof constructed based on a DSA of a trait in the trust group homomorphism DSA as witness data, and obtaining further trust information related to the user from the witness data.

Abstract: An example operation may include one or more of connecting to a blockchain configured to store an identity trait of a user, retrieving the identity trait from the blockchain, establishing a trust group homomorphism digital signature algorithm (DSA) for the user based on a private key, creating a zero knowledge succinct non-interactive argument of knowledge (zkSNARK) proof constructed based on a DSA of a trait in the trust group homomorphism DSA as witness data, and obtaining further trust information related to the user from the witness data.

Abstract: An example operation may include one or more of connecting, by an identity server, to a blockchain configured to store an identity trait of a user, retrieving, by an identity server, the identity trait from the a blockchain, establishing, by the identity server, a trust group homomorphism digital signature algorithm (DSA) for the user associated with the identity trait based on a public key PK1, creating, by the identity server, a zero-knowledge proof function with a public key PK2 based on a DSA member of the trust group homomorphism for witness data, providing, by the identity server, the witness data to a challenger for the zero-knowledge proof function, and receiving, by the identity server, a validation of the user as a proved user based on execution of the zero-knowledge proof function based on the witness data.

Abstract: A computer-implemented method includes: receiving a web service definition file that identifies multiple web services; parsing the web service definition file; identifying respective voice commands to associate with each of the multiple web service based on the parsing; generating a skillset utterances file that maps the respective voice commands to the multiple web services; generating a skillset implementation file that maps the multiple web services to a respective set of instructions, wherein each of the respective set of instructions is used to invoke a corresponding web service; receiving an input voice command from a user via a user device; identifying a particular web service, of the multiple web services, associated with the input voice command based on information included in the skillset utterances file; and invoking the particular web service in accordance with the set of instructions associated with the particular web service included in the skillset implementation file.

Abstract: An utterance is analyzed to identify an absence of a known invocation phrase. A skill set is constructed in response to the absence, the skill set including a first skill corresponding to the utterance and a first skill score corresponding to a likelihood that the first skill corresponds to the utterance. The first skill score is adjusted, based on the presence of the first skill in a skill history, where the skill history stores a set of history skills in an order of recency of use of each history skill in the set of history skills. The first skill score is adjusted, based on an association of the first skill with a default installed skill. An installed skill is selected, based on the adjusted first skill score, the installed skill performing an action in response to the utterance.

Abstract: A system and method for recording a collaborative session includes two phases. One is performed in real-time and includes determining portions of a composite image of collaborative session content that are constant over time or preprocessed. The portions are removed from the composite image of the collaborative session content. Remaining content of the composite image and any of the portions not already compressed is compressed and stored. A second phase is performed when off-line and includes decoding the remaining content of the composite image and the portions to reconstruct the composite image of the collaborative session content and formatting the composite image.

Abstract: A system and method for recording a collaborative session includes two phases. One is performed in real-time and includes determining portions of a composite image of collaborative session content that are constant over time or preprocessed. The portions are removed from the composite image of the collaborative session content. Remaining content of the composite image and any of the portions not already compressed is compressed and stored. A second phase is performed when off-line and includes decoding the remaining content of the composite image and the portions to reconstruct the composite image of the collaborative session content and formatting the composite image.

Abstract: A system and method for recording a collaborative session includes two phases. One is performed in real-time and includes determining portions of a composite image of collaborative session content that are constant over time or preprocessed. The portions are removed from the composite image of the collaborative session content. Remaining content of the composite image and any of the portions not already compressed is compressed and stored. A second phase is performed when off-line and includes decoding the remaining content of the composite image and the portions to reconstruct the composite image of the collaborative session content and formatting the composite image.

Abstract: A system and method for recording a collaborative session includes two phases. One is performed in real-time and includes determining portions of a composite image of collaborative session content that are constant over time or preprocessed. The portions are removed from the composite image of the collaborative session content. Remaining content of the composite image and any of the portions not already compressed is compressed and stored. A second phase is performed when off-line and includes decoding the remaining content of the composite image and the portions to reconstruct the composite image of the collaborative session content and formatting the composite image.

Abstract: The illustrative embodiments provide a method, a system, a computer program code, and a computer implemented method for sending events between vehicles. A vehicle detects an event, wherein the event is for a user action that indicates an intent to change movement of the vehicle. The vehicle determines whether the event should be sent to another vehicle. If the event should be sent to another vehicle, then the vehicle sends the event to the another vehicle.