Abstract: Methods and systems for customizing a hearing device. The disclosed methods involve receiving an audio sample associated with a target entity, calculating at least one acoustic parameter from the audio sample, generating an audio stimulus using the at least one calculated acoustic parameter, presenting the audio stimulus to a user, receiving a response to the audio stimulus, and adjusting the hearing device based on an optimal parameter.

Abstract: Methods and systems for classifying social media users. The system computes a plurality of subgraphs from a user's social graph network and considers which types of subgraphs are overly represented in the user's social network to determine whether a user belongs to a certain class. The system may also consider features based on metadata of the user's network and social interactions occurring in the user's network.

Abstract: Systems, methods, and apparatus for automatically parallelizing code segments are provided. For example, an environment includes a profiling agent, a parallelization agent, and a verification agent. The profiling agent executes a code segment and generates a profile of the executed code segment. The parallelization agent analyzes the code segment to determine whether a parallelizable portion is present in the code segment. When a parallelizable portion is present, the parallelization agent determines, based on the profile of the executed code segment, whether to parallelize the parallelizable portion of the code segment. If it is determined to parallelize the parallelizable portion of the code segment, the parallelization agent automatically parallelizes the parallelizable portion of the code segment. The verification agent verifies the functionality and/or correctness of the parallelized code segment.

Abstract: Methods and systems for mutating a network topology on which various containers run. The system includes a host controller to assign each of a plurality of hosts an unchanging public virtual IP address that maps to changing real IP address, a threat detection module to detect a mutation stimuli, and a management module configured to receive a mutation policy and execute the mutation policy to enact a container mutation upon the threat detection module detecting the mutation stimuli.

Abstract: Systems, methods, and apparatus for automatically parallelizing code segments are provided. For example, an environment includes a profiling agent, a parallelization agent, and a verification agent. The profiling agent executes a code segment and generates a profile of the executed code segment. The parallelization agent analyzes the code segment to determine whether a parallelizable portion is present in the code segment. When a parallelizable portion is present, the parallelization agent determines, based on the profile of the executed code segment, whether to parallelize the parallelizable portion of the code segment. If it is determined to parallelize the parallelizable portion of the code segment, the parallelization agent automatically parallelizes the parallelizable portion of the code segment. The verification agent verifies the functionality and/or correctness of the parallelized code segment.

Abstract: Systems, methods, and apparatus for automatically parallelizing code segments are provided. For example, an environment includes a profiling agent, a parallelization agent, and a verification agent. The profiling agent executes a code segment and generates a profile of the executed code segment. The parallelization agent analyzes the code segment to determine whether a parallelizable portion is present in the code segment. When a parallelizable portion is present, the parallelization agent determines, based on the profile of the executed code segment, whether to parallelize the parallelizable portion of the code segment. If it is determined to parallelize the parallelizable portion of the code segment, the parallelization agent automatically parallelizes the parallelizable portion of the code segment. The verification agent verifies the functionality and/or correctness of the parallelized code segment.

Abstract: Methods and systems for providing secure computing environments. Features of the present invention use a plurality of integrated security controls to ensure security of a computing environment. More specifically, features of the present invention detect discrepancies between a node's behavior and a defined policy to identify and remedy malicious behavior.

Abstract: Systems, methods, and apparatus for identifying anomalous behavior are provided. For example, a method may include receiving raw data, generating a behavior profile for the entity based on the raw data, receiving comparison data, determining whether the comparison data deviates from a pattern of behavior defined in the behavior profile, and identifying the comparison data as anomalous behavior when the comparison data deviates from the pattern of behavior. In one embodiment, the raw data includes recorded activity for the entity. In one embodiment, the behavior profile defines a pattern of behavior for the entity. In one embodiment, a countermeasure is performed upon identifying anomalous behavior. The countermeasure may include at least one of revoking the entity's credentials, denying the entity access to a resource, shutting down access to a port, and denying access to the entity. The method may further include providing a report of the anomalous behavior.

Abstract: Hearing device configuration and hearing treatment using categorical perception; systems and methods for categorical perception based configuration of hearing devices and hearing treatment.

Abstract: Methods and systems for motif recognition in graph sets. Distribution scores comparing the canonical representations of a compressed graph in the set of graphs versus the canonical representations of a compressed graph in a reference graph are used to determine the significance of the compressed graph.

Abstract: Systems, methods, and apparatus for identifying anomalous behavior are provided. For example, a method may include receiving raw data, generating a behavior profile for the entity based on the raw data, receiving comparison data, determining whether the comparison data deviates from a pattern of behavior defined in the behavior profile, and identifying the comparison data as anomalous behavior when the comparison data deviates from the pattern of behavior. In one embodiment, the raw data includes recorded activity for the entity. In one embodiment, the behavior profile defines a pattern of behavior for the entity. In one embodiment, a countermeasure is performed upon identifying anomalous behavior. The countermeasure may include at least one of revoking the entity's credentials, denying the entity access to a resource, shutting down access to a port, and denying access to the entity. The method may further include providing a report of the anomalous behavior.

Abstract: Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.

Abstract: Systems, methods, and apparatus for automatically parallelizing code segments are provided. For example, an environment includes a profiling agent, a parallelization agent, and a verification agent. The profiling agent executes a code segment and generates a profile of the executed code segment. The parallelization agent analyzes the code segment to determine whether a parallelizable portion is present in the code segment. When a parallelizable portion is present, the parallelization agent determines, based on the profile of the executed code segment, whether to parallelize the parallelizable portion of the code segment. If it is determined to parallelize the parallelizable portion of the code segment, the parallelization agent automatically parallelizes the parallelizable portion of the code segment. The verification agent verifies the functionality and/or correctness of the parallelized code segment.

Abstract: Systems, methods, and apparatus for identifying anomalous behavior are provided. For example, a method may include receiving raw data, generating a behavior profile for the entity based on the raw data, receiving comparison data, determining whether the comparison data deviates from a pattern of behavior defined in the behavior profile, and identifying the comparison data as anomalous behavior when the comparison data deviates from the pattern of behavior. In one embodiment, the raw data includes recorded activity for the entity. In one embodiment, the behavior profile defines a pattern of behavior for the entity. In one embodiment, a countermeasure is performed upon identifying anomalous behavior. The countermeasure may include at least one of revoking the entity's credentials, denying the entity access to a resource, shutting down access to a port, and denying access to the entity. The method may further include providing a report of the anomalous behavior.

Abstract: Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.

Abstract: Methods and systems for node and link detection in social network analysis. Interactive noise reduction allows reduction of the data set under analysis to enable substantially real time detection of links and nodes.

Abstract: Systems, methods, and apparatus for clustering resources using rare features are provided. For example, an environment includes an extraction module, an index module, and a cluster module. The extractions module identifies a set of resources and extracts a plurality of features from the resources. The plurality of features may be rare features. The index module identifies and generates a rare features index. The cluster module identifies at least two resources that share rare features, creates one or more clusters based on the identified at least two resources, and associates resources that share similar features with the one or more clusters. Resources that do not share similar features are not associated with the one or more clusters. Identifying at least two resources that share rare features is based at least upon a threshold.

Abstract: Systems, methods, and apparatus for identifying anomalous behavior are provided. For example, a method may include receiving raw data, generating a behavior profile for the entity based on the raw data, receiving comparison data, determining whether the comparison data deviates from a pattern of behavior defined in the behavior profile, and identifying the comparison data as anomalous behavior when the comparison data deviates from the pattern of behavior. In one embodiment, the raw data includes recorded activity for the entity. In one embodiment, the behavior profile defines a pattern of behavior for the entity. In one embodiment, a countermeasure is performed upon identifying anomalous behavior. The countermeasure may include at least one of revoking the entity's credentials, denying the entity access to a resource, shutting down access to a port, and denying access to the entity. The method may further include providing a report of the anomalous behavior.