Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: A method performed by a device may include identifying a plurality of samples of textual content; performing tokenization of the plurality of samples to generate a respective plurality of tokenized samples; performing embedding of the plurality of tokenized samples to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network; determining context relationships between the groupings of attributes using a bidirectional long short term memory (LSTM) technique; selecting predicted labels for the plurality of samples using a model, wherein the model selects, for a particular sample of the plurality of samples, a predicted label of the predicted labels from a plurality of labels based on respective scores of the particular sample with regard to the plurality of labels and based on a nonparametric paired comparison of the respective scores; and providing information identifying the predicted labels.

Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: Disclosed herein are systems, products, and/or methods for determining a dependency between a task and a hardware component executing the task. The method may include: accessing an execution log storing information related to a plurality of tasks, each task of the plurality of tasks being executed by a respective computing device of a plurality of computing devices distributed across a network architecture; identifying a task of the plurality of tasks to obtain application layer information of the identified task; determining which respective computing device executed the identified task to obtain network layer information of the respective computing device; generating a dependency map illustrating a relationship between the identified task and the respective computing device that executed the identified task, the relationship including the application layer information and the network layer information; and displaying, using an interactive graphical user interface (GUI) on a user device, the dependency map.

Abstract: A method performed by a device may include identifying a plurality of samples of textual content; performing tokenization of the plurality of samples to generate a respective plurality of tokenized samples; performing embedding of the plurality of tokenized samples to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network; determining context relationships between the groupings of attributes using a bidirectional long short term memory (LSTM) technique; selecting predicted labels for the plurality of samples using a model, wherein the model selects, for a particular sample of the plurality of samples, a predicted label of the predicted labels from a plurality of labels based on respective scores of the particular sample with regard to the plurality of labels and based on a nonparametric paired comparison of the respective scores; and providing information identifying the predicted labels.

Abstract: A method performed by a device may include identifying a plurality of samples of textual content; performing tokenization of the plurality of samples to generate a respective plurality of tokenized samples; performing embedding of the plurality of tokenized samples to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network; determining context relationships between the groupings of attributes using a bidirectional long short term memory (LSTM) technique; selecting predicted labels for the plurality of samples using a model, wherein the model selects, for a particular sample of the plurality of samples, a predicted label of the predicted labels from a plurality of labels based on respective scores of the particular sample with regard to the plurality of labels and based on a nonparametric paired comparison of the respective scores; and providing information identifying the predicted labels.

Abstract: Systems and methods are provided for access management using machine learning. An exemplary system may include at least one processor and a storage medium storing instructions that, when executed by the processor, cause the processor to perform operations. The operations may include obtaining user access information from an access directory and generating a network comprising nodes and edges. The nodes may include a first type of nodes representing users and a second type of nodes representing access rights. The edges may include a first type of edges indicating that users have access rights, and a second type of edges indicating degrees of similarity between two users. The operations may also include determining a group of nodes of the first type representing a community of users sharing a degree of commonality higher than a degree of commonality shared by other users outside the community.

Abstract: A method performed by a device may include identifying a plurality of samples of textual content; performing tokenization of the plurality of samples to generate a respective plurality of tokenized samples; performing embedding of the plurality of tokenized samples to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network; determining context relationships between the groupings of attributes using a bidirectional long short term memory (LSTM) technique; selecting predicted labels for the plurality of samples using a model, wherein the model selects, for a particular sample of the plurality of samples, a predicted label of the predicted labels from a plurality of labels based on respective scores of the particular sample with regard to the plurality of labels and based on a nonparametric paired comparison of the respective scores; and providing information identifying the predicted labels.