Patent classifications
G06N5/048
Machine learning model score obfuscation using coordinated interleaving
An artefact is received. Features are extracted from this artefact which are, in turn, used to populate a vector. The vector is then input into a classification model to generate a score. The score is then modified to result in a modified score by interleaving the generated score or a mapping thereof into digits of a pseudo-score. Thereafter, the modified score can be provided to a consuming application or process. Related apparatus, systems, techniques and articles are also described.
Machine learning and validation of account names, addresses, and/or identifiers
Systems and methods are disclosed for determining if an account identifier is computer-generated. One method includes receiving the account identifier, dividing the account identifier into a plurality of fragments, and determining one or more features of at least one of the fragments. The method further includes determining the commonness of at least one of the fragments, and determining if the account identifier is computer-generated based on the features of at least one of the fragments, and the commonness of at least one of the fragments.
Machine learning and validation of account names, addresses, and/or identifiers
Systems and methods are disclosed for determining if an account identifier is computer-generated. One method includes receiving the account identifier, dividing the account identifier into a plurality of fragments, and determining one or more features of at least one of the fragments. The method further includes determining the commonness of at least one of the fragments, and determining if the account identifier is computer-generated based on the features of at least one of the fragments, and the commonness of at least one of the fragments.
Static and dynamic non-deterministic finite automata tree structure application apparatus and method
A method includes processing a user input for generating a non-deterministic finite automata tree (NFAT) correlation policy. The user input indicates one or more of a static condition or a dynamic condition for inclusion in the NFAT correlation policy. The static condition includes a comparison between a defined entity and a first fixed parameter. The dynamic condition includes a comparison between the defined entity and a variable parameter. An applicable NFAT element is generated that includes at least one of the NFAT correlation policy generated based on a determination that the user input indicates the static condition or a NFAT template generated based on a determination that the user input indicates the dynamic condition. Event data received from a network device is processed to detect a status of a network entity associated with a communication network based on the applicable NFAT element.
Method and system for fuzzy matching and alias matching for streaming data sets
A method, system, and computer-usable medium for streaming or processing data streams. Raw text data is cleansed to a standard format. A fuzzy matching algorithm is performed on the text data. For data where domain expertise is required, alias matching is performed. End state categorizing or grouping is provided for the cleansed raw text data.
Method and system for fuzzy matching and alias matching for streaming data sets
A method, system, and computer-usable medium for streaming or processing data streams. Raw text data is cleansed to a standard format. A fuzzy matching algorithm is performed on the text data. For data where domain expertise is required, alias matching is performed. End state categorizing or grouping is provided for the cleansed raw text data.
FACILITATING DEVICE FINGERPRINTING THROUGH ASSIGNMENT OF FUZZY DEVICE IDENTIFIERS
Various device attributes associated with a current event may be obtained. Similarity metrics may be determined that indicate a degree of similarity between the device attributes that are associated with the current event and stored device attributes that are associated with previous events and previously created fuzzy device identifiers. A fuzzy device identifier may be assigned to the current event based at least in part on a comparison of the similarity metrics with a threshold. If none of the similarity metrics compare favorably with the threshold, then a new fuzzy device identifier may be created for the current event. However, if at least one of the similarity metrics compares favorably with the threshold, then the previously created fuzzy device identifier whose stored device attributes are most similar to the device attributes that are associated with the current event may be assigned to the current event.
Apparatuses and methods for selectively inserting text into a video resume
Aspects relate to apparatuses and methods for selectively inserting text into a video resume. An exemplary apparatus includes a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the processor to receive a video resume from a user, divide the video resume is into temporal sections, acquire a plurality of textual inputs from a user, wherein the plurality of textual inputs pertains to the same user of received video resume, classify the plurality of textual inputs to corresponding temporal sections of the received video resume and display, as a function of the classification, the received video resume with a corresponding plurality of textual inputs.
Swarm system including an operator control section enabling operator input of mission objectives and responses to advice requests from a heterogeneous multi-agent population including information fusion, control diffusion, and operator infusion agents that controls platforms, effectors, and sensors
Systems and methods are provided relating to a complex adaptive command guided swarm system including an operator section comprising a first command and control section and a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs). The first command and control section includes a user interface, computer system, network interface, and plurality of command and control systems executed or running on the computer system. The networked SAASoSPs each include a second command and control section, wherein the second command and control section utilizes artificial intelligence (AI) configured with a combination of both symbolic and probabilistic machine learning for various functions including pattern recognition and new pattern identification. The AI is also configured to combine advice-based learning with active learning, wherein the AI solicits advice from a domain expert user via the first command and control section as necessary during both training and operational stages of the system.
Systems and methods involving hybrid quantum machines, aspects of quantum information technology and/or other features
Systems and methods involving quantum machines, hybrid quantum machines, aspects of quantum information technology and/or other features are disclosed. In one exemplary implementation, a system is provided comprising a quantum register that stores quantum information using qubits, wherein the qubits are configured to store the quantum information using particles or objects arranged in a lattice of quantum gates, a clock that provides a clock cycle to the quantum register, and a qubit-tie computing component coupled to the quantum register, wherein the qubit-tie computing component is configured to shift the quantum information between the qubits, wherein the system stores the qubits in different states using physical qualities, which may define qubits that are configured to be entangled and superposed at a same time. Further, the quantum register may comprise an entanglement component, and/or the qubit-tie computing component may comprise a superposition component.