Abstract: Examples of ensemble knowledge transfer in collaborative learning include: receiving, at a primary node, from a plurality of remote nodes, a plurality of trained proxy machine learning (ML) models, wherein each proxy ML model is received from a different one of the plurality of remote nodes, and wherein each of the plurality of remote nodes is remote across a network from the primary node; training a primary ML model using the plurality of proxy ML models, wherein training the primary ML model comprises: for each of a plurality of training cases of a primary training dataset, weighting results from each of the proxy ML models based on at least a confidence of the respective proxy ML model regarding the training case.

Abstract: A personalized natural language processing system tokenizes a plurality of sets of raw text data to generate a plurality of sets of tokenized text data for the plurality of users, respectively. The tokenized text data includes a sequence of tokens corresponding to the raw text data, the tokens at least identifying distinct words or portions of words in the raw text. The system appends predetermined user-specific tokens to the sets of tokenized text data from the users, respectively. Each predetermined user-specific token corresponds to one of the users. The system processes the sets of tokenized text data using the NLP model in accordance with the appended predetermined user-specific tokens to predict a personalized classification for the sets of tokenized text data from each of the users, and outputs the personalized classifications of the tokenized text data for each of the users.

Abstract: The present disclosure relates to systems and methods for an interactive, intelligent hub built around the completion of a task. This hub brings together resources, information, suggested steps, and other automated assistance to facilitate the completion of the task. AI-based assistance may indicate which steps can be completed by automated processes, and dispatch those processes, or suggest resources to assist in the completion of other steps. The hub displays the current status of the task, and lives until the completion of the task, or abandonment by the user.

Abstract: The present disclosure relates to systems and methods for an interactive, intelligent hub built around the completion of a task. This hub brings together resources, information, suggested steps, and other automated assistance to facilitate the completion of the task. AI-based assistance may indicate which steps can be completed by automated processes, and dispatch those processes, or suggest resources to assist in the completion of other steps. The hub displays the current status of the task, and lives until the completion of the task, or abandonment by the user.

Abstract: The present disclosure relates to systems and methods for an interactive, intelligent hub built around the completion of a task. This hub brings together resources, information, suggested steps, and other automated assistance to facilitate the completion of the task. AI-based assistance may indicate which steps can be completed by automated processes, and dispatch those processes, or suggest resources to assist in the completion of other steps. The hub displays the current status of the task, and lives until the completion of the task, or abandonment by the user.

Abstract: A system and method to provide computer support for a meeting of invitees comprises accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more of the invitees during the meeting, the one or more sensory data streams including at least one audio stream. The method also comprises subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the audio stream and to assign each word to an invitee, subjecting the sequence of words to semantic computer modeling to recognize a sequence of directives in the sequence of words, and releasing one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications.

Abstract: A system and method to provide computer support for a meeting of invitees comprises accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more of the invitees during the meeting, the one or more sensory data streams including at least one audio stream. The method also comprises subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the audio stream and to assign each word to an invitee, subjecting the sequence of words to semantic computer modeling to recognize a sequence of directives in the sequence of words, and releasing one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications.

Abstract: The present disclosure relates to systems and methods for discovering relatedness between entities from a corpora of information by automatically extracting attributes from the plurality of heterogeneous entities in a graph. A standardized representation of the extracted attributes from the plurality of heterogeneous entities are propagated across the graph and these propagated attributes are used to find a degree to which the plurality of heterogeneous entities are associated with the extracted attributes. The degree to which the plurality of heterogeneous entities are associated with the extracted attributes is used to create a representation space illustrating a level of relatedness of an entity to another entity of the plurality of heterogeneous entities.

Abstract: Embodiments described herein are implemented in authentication brokering systems where an authentication broker issues security tokens that represent its authentications of users. Client devices operated by the users store the security tokens and send them to resource providers. The resource providers authenticate and grant access to the users based on validation of the security tokens. Authentication related messages exchanged between the resource providers and the authentication broker are used to exchange authentication risk data that is obtained or derived by the resource providers and the authentication broker. The resource providers obtain authentication risk data directly from the authentication broker and indirectly, via the authentication broker, from each other. As security tokens are used or managed, authentication risk data is shared among the participants in the authentication brokering system.

Abstract: Embodiments described herein are implemented in authentication brokering systems where an authentication broker issues security tokens that represent its authentications of users. Client devices operated by the users store the security tokens and send them to resource providers. The resource providers authenticate and grant access to the users based on validation of the security tokens. Authentication related messages exchanged between the resource providers and the authentication broker are used to exchange authentication risk data that is obtained or derived by the resource providers and the authentication broker. The resource providers obtain authentication risk data directly from the authentication broker and indirectly, via the authentication broker, from each other. As security tokens are used or managed, authentication risk data is shared among the participants in the authentication brokering system.

Abstract: A system and method to provide computer support for a meeting of invitees comprises accessing one or more sensory data streams providing digitized sensory data responsive to an activity of one or more of the invitees during the meeting, the one or more sensory data streams including at least one audio stream. The method also comprises subjecting the at least one audio stream to phonetic and situational computer modeling to recognize a sequence of words in the audio stream and to assign each word to an invitee, subjecting the sequence of words to semantic computer modeling to recognize a sequence of directives in the sequence of words, and releasing one or more output data streams based on the sequence of directives, the one or more output data streams including one or more notifications.

Abstract: Embodiments described herein are implemented in authentication brokering systems where an authentication broker issues security tokens that represent its authentications of users. Client devices operated by the users store the security tokens and send them to resource providers. The resource providers authenticate and grant access to the users based on validation of the security tokens. Authentication related messages exchanged between the resource providers and the authentication broker are used to exchange authentication risk data that is obtained or derived by the resource providers and the authentication broker. The resource providers obtain authentication risk data directly from the authentication broker and indirectly, via the authentication broker, from each other. As security tokens are used or managed, authentication risk data is shared among the participants in the authentication brokering system.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for file acquisition for reputation evaluation. A reputation service may be configured to evaluate files and provide reputations of such files to clients (e.g., an indication as to whether a file is safe or malicious). If the reputation service receives a reputation request for a file that is unknown to the reputation service (e.g., a file not yet fully acquired by the reputation service), then the reputation service may identify a set of chunks into which the file can be partitioned. The reputation service may obtain chunks from various clients, such as a first chunk from a first client and a second chunk from a second client. Such chunks may be evaluated to assign a reputation to the file. In this way, the reputation service may retrieve portions of a file in a distributed manner for reputation evaluation.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.

Abstract: One or more techniques and/or systems are provided for file acquisition for reputation evaluation. A reputation service may be configured to evaluate files and provide reputations of such files to clients (e.g., an indication as to whether a file is safe or malicious). If the reputation service receives a reputation request for a file that is unknown to the reputation service (e.g., a file not yet fully acquired by the reputation service), then the reputation service may identify a set of chunks into which the file can be partitioned. The reputation service may obtain chunks from various clients, such as a first chunk from a first client and a second chunk from a second client. Such chunks may be evaluated to assign a reputation to the file. In this way, the reputation service may retrieve portions of a file in a distributed manner for reputation evaluation.

Abstract: One or more techniques and/or systems are provided for risk assessment. Historical authentication data and/or compromised user account data may be evaluated to identify a set of authentication context properties associated with user authentication sessions and/or a set of malicious account context properties associated with compromised user accounts (e.g., properties indicative of whether a user recently visited a malicious site, created a fake social network profile, logged in from unknown locations, etc.). The set of authentication context properties and/or the set of malicious account context properties may be annotated to create an annotated context property training set that may be used to train a risk assessment machine learning model to generate a risk assessment model. The risk assessment model may be used to evaluate user context properties of a user account event to generate a risk analysis metric indicative of a likelihood the user account event is malicious or safe.