Abstract: Some embodiments provide a method that receives a piece of network data. Based on a set of values in a set of headers of the piece of network data, the method determines whether the piece of network data is precision time protocol (PTP) network data and a particular port in the set of ports of the network device through which the piece of network data is to be forwarded. Based on the particular port, the method determines a port profile from a set of port profiles. Based on a value of the field in the port profile and the determination of whether the piece of network data is PTP network data, the method determines whether to bypass a set of data security operations on the piece of network data. The method transmits the piece of network data through the particular port of the network device.

Abstract: Systems and methods for identifying data labels for submitting to additional data labeling routines based on embedding clusters are described. For example, the system may retrieve an unlabeled dataset, wherein the unlabeled dataset comprises a plurality of unlabeled samples. The system may generate a plurality of embeddings based on the unlabeled dataset, wherein the plurality of embeddings comprises a respective embedding for each unlabeled sample in the plurality of unlabeled samples. The system may cluster the plurality of embeddings into a plurality of clusters. The system may, based on the first cluster, determine a first labeled sample of the first labeled dataset corresponding to the first cluster and determine to submit the first labeled sample to a second data labeling routine. The system may generate, using the second data labeling routine, a second labeled sample based on the first labeled sample.

Abstract: Systems and methods are described for using generative artificial intelligence to automatically label training prompts for training a classification model. Interaction data comprising communications between clients and a chatbot may be used to identify a representative set of topics. Each communication can be assigned a label associated with one or more topics from the set of topics. First training data comprising the set of labels and one or more of the communications assigned to that label may be generated and used to train a generative artificial intelligence model to generate metadata comprising a description of each label from the set of labels. Second training data comprising a plurality of sample prompts, the set of labels, and the metadata; may be generated and used to train a classification model to autonomously label each of the plurality of sample prompts with one or more labels from the set of labels.

Abstract: In some embodiments, intent labeling may be facilitated for machine learning model training. In some embodiments, a set of natural language inputs may be obtained. Based on an action set, content related to intent labeling may be presented on a user interface. Based on an entity hierarchy and user selection of an agent-side action of the action set, a first entity subset of entities associated with the agent-side action may be presented on the user interface. Based on the entity hierarchy and user selection of a first entity from the first entity subset, a second entity subset of entities associated with the first entity may be presented on the user interface. Based on user selection of a second entity from the second entity subset, the natural language input may be associated with an ordered intent label that includes the agent-side action, the first entity, and the second entity.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: In some implementations, a device may obtain a natural language input including an emoji. The device may identify one or more appearance modifiers associated with the emoji. The device may generate a token associated with the emoji that removes the one or more appearance modifiers, wherein the token is associated with multiple emojis including the emoji, and wherein the token is a modified code associated with the emoji or is associated with a cluster that is associated with the multiple emojis. The device may provide, to a natural language processing (NLP) model, the token associated with the emoji. The device may obtain, from the NLP model, an output that indicates an interpretation of the natural language input based on providing the token to the NLP model.

Abstract: Systems and methods for improvements to data labeling using a hybrid artificial intelligence labeling approach that is ambiguous to training data requirements are described. For example, the system may generate a first labeled dataset. The system may train, using the first labeled dataset, a model for a data labeling routine. The system may determine a confidence metric for a first labeled sample from a second plurality of labeled samples. The system may compare the first confidence metric to a threshold confidence metric.

Abstract: Systems and methods for identifying data labels for submitting to additional data labeling routines based on embedding clusters are described. For example, the system may retrieve an unlabeled dataset, wherein the unlabeled dataset comprises a plurality of unlabeled samples. The system may generate a plurality of embeddings based on the unlabeled dataset, wherein the plurality of embeddings comprises a respective embedding for each unlabeled sample in the plurality of unlabeled samples. The system may cluster the plurality of embeddings into a plurality of clusters. The system may, based on the first cluster, determine a first labeled sample of the first labeled dataset corresponding to the first cluster and determine to submit the first labeled sample to a second data labeling routine. The system may generate, using the second data labeling routine, a second labeled sample based on the first labeled sample.

Abstract: The present disclosure describes a machine learning model trained to identify social engineering attacks. A prompt may be provided to a generative artificial intelligence to create communications resembling social engineering attacks. The communications may be inputted into a machine learning model to train the machine learning model to identify social engineering attacks. The machine learning model may also be trained on actual social engineering attacks. Once trained, the machine learning model may be deployed to monitor a plurality of communication channels to detect social engineering attacks. Upon detecting a social engineering attack, a system may implement one or more remedial actions to mitigate the detected social engineering attack.

Abstract: Methods and systems are disclosed herein to recommend load rebalancing based on learned patterns of recurrence. In some aspects, the method receives a requested load usage from a user system and a load usage dataset. Each entry in the load usage dataset corresponds to an instance of load use. The method determines similar instances based on a respective amount and respective category of each entry. The method then clusters the similar instances into a first cluster and a second cluster based on frequencies of the similar instances. The method combines load uses corresponding to the first cluster into a first recurring load use and load uses corresponding to the second cluster into a second recurring load use. The method compares the requested load usage to the second recurring load use to generate a recommendation to the user system for rebalancing load usage.

Abstract: Systems and methods for detecting unauthorized access are disclosed herein. In some aspects, the system receives a combined activity dataset. The system updates a base breach detection model based on the combined activity dataset to generate a combined breach detection model. The system duplicates the combined breach detection model to generate a first breach detection model for a first user. The system receives a first activity dataset for the first user and trains a labeling model to associate activities from the first activity dataset with the first user. The system processes the combined activity dataset using the labeling model to associate activities from a portion of the combined activity dataset with the first user. The system updates the base breach detection model based on activities from the first activity dataset and the portion of the combined activity dataset to generate a second breach detection model to detect breach activity.

Abstract: A method includes obtaining a first dataset associated with a first set of classes and a second dataset associated with a second set of classes, where the first and second datasets share features. The method also includes creating an aggregated dataset having a quality score for the second set of classes that satisfy a first threshold. Creating the aggregated dataset includes determining a first and second set of clusters based on the first and second datasets. The method includes determining a distance based on a record of the first set of clusters and a second record of the second set of clusters in a feature space. The method further includes determining a set of analysis scores based on the distance and generating an indication for a set of records associated with the set of analysis scores based on the set of analysis scores satisfying a second threshold.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: In some embodiments, intent labeling may be facilitated for machine learning model training. In some embodiments, a set of natural language inputs may be obtained. Based on an action set, content related to intent labeling may be presented on a user interface. Based on an entity hierarchy and user selection of an agent-side action of the action set, a first entity subset of entities associated with the agent-side action may be presented on the user interface. Based on the entity hierarchy and user selection of a first entity from the first entity subset, a second entity subset of entities associated with the first entity may be presented on the user interface. Based on user selection of a second entity from the second entity subset, the natural language input may be associated with an ordered intent label that includes the agent-side action, the first entity, and the second entity.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: Systems and methods are described for maintaining bifurcated data management while labeling data for artificial intelligence model development. For example, the system may receive a first label for a first sample from a first dataset, wherein the first dataset is accessible to a first subset of a plurality of users, and wherein the first subset comprises a first attribute. The system may receive first version metadata of the first label, wherein the first version metadata comprises a proposed label for the first sample assigned by a first user. The system may determine, based on a first user input from the first user, a first grouping of source code files for storing the first version metadata, wherein the first grouping of source code files is accessible to a second subset of the plurality of users, and wherein the second subset comprises a second attribute.

Abstract: In some implementations, a device may obtain a natural language input including an emoji. The device may identify one or more appearance modifiers associated with the emoji. The device may generate a token associated with the emoji that removes the one or more appearance modifiers, wherein the token is associated with multiple emojis including the emoji, and wherein the token is a modified code associated with the emoji or is associated with a cluster that is associated with the multiple emojis. The device may provide, to a natural language processing (NLP) model, the token associated with the emoji. The device may obtain, from the NLP model, an output that indicates an interpretation of the natural language input based on providing the token to the NLP model.