Abstract: A system for providing secure access to digital resources is provided that utilizes a blockchain platform. Using this blockchain platform, digital resource vendors create new digital tracking ledgers for their digital resource products such that updates to the digital resource products are accessible directly from a blockchain. Accordingly, these updates are deliverable in a protected and secure manner to consumers of the digital resources.

Abstract: One embodiment of the present invention sets forth a technique for detecting malicious domains via registration profiling. The technique includes receiving domain registration information associated with a plurality of malicious domains and generating a plurality of domain clusters based on the domain registration information. The technique further includes comparing a domain registration profile associated with a candidate domain to the plurality of domain clusters to generate a similarity score and classifying the candidate domain as a malicious domain based on the similarity score.

Abstract: A system for providing secure access to digital resources is provided that utilizes a blockchain platform. Using this blockchain platform, digital resource vendors create new digital tracking ledgers for their digital resource products such that updates to the digital resource products are accessible directly from a blockchain. Accordingly, these updates are deliverable in a protected and secure manner to consumers of the digital resources.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: Automated user-machine interaction is gaining attraction in many applications and services. However, implementing and offering smart automated user-machine interaction services still present technical challenges. According to at least one example embodiment, a dialogue manager is configured to handle multiple dialogue applications independent of the language, the input modalities, or output modalities used. The dialogue manager employs generic semantic representation of user-input data. At a step of a dialogue, the dialogue manager determines whether the user-input data is indicative of a new request or a refinement request based on the generic semantic representation and at least one of a maintained state of the dialogue, general knowledge data representing one or more concepts, and data representing history of the dialogue. The dialogue manager then responds to determined user-request with multi-facet output data to a client dialogue application indicating action(s) to be performed.

Abstract: In an embodiment, speech may be acquired from a user. A concept, that may be associated with the user, may be identified from the acquired speech. The concept may be identified by fuzzy matching one or more words in the acquired speech with data contained in a data store. The data store may be associated with the user. An action may be performed based on the identified concept.