Abstract: There is provided a computer system and method for orchestrating a user interface, the method include: obtaining a first data set representative of intercepted data communication messages between a user interface of a user and a merchant hosting server; obtaining a second data set representing an instruction set for loading visual elements on the user interface provided from the merchant hosting server; analyzing the first data set to obtain one or more user-specific characteristics; determining if the user-specific characteristics associated with the user satisfy a trigger condition associated with a current resource offering; and responsive to a positive determination: injecting, into the instruction set for loading the visual elements on the user interface provided from the merchant hosting server, code corresponding to an interactive visual element corresponding to the current resource offering.

Abstract: Systems, methods and data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions and other secure data processes. Aspects of such systems include trusted platforms by which networked communication devices and merchant systems may registered as trusted entities. Information associated with particular payment means, such as accounts or payment tokens, can be stored on device(s) secure data sets known as virtual or electronic wallets, or in the form of secure payment tokens. Among other improvements, the invention enables the use of multiple payment accounts to fund purchases and other electronic transactions.

Abstract: Systems and methods for machine learning architecture for time series data prediction. The system may include a processor and a memory storing processor-executable instructions. The processor-executable instructions, when executed, may configure the processor to: obtain time series data associated with a data query; generate a predicted value based on a sampled realization of the time series data and a continuous time generative model, the continuous time generative model trained to define an invertible mapping to maximize a log-likelihood of a set of predicted values for a time range associated with the time series data; and generate a signal providing an indication of the predicted value associated with the data query.

Abstract: A method for detecting network anomalies comprises monitoring a network that provides public-facing application services and monitoring at least one external public Internet platform outside of the network to obtain volumetric problem report data about the application services. The external public Internet platform is nonspecific to the application services. Responsive to the volumetric problem report data from the external public Internet platform(s) exceeding a threshold, at least one internal network event logging tool is queried for alerts, and from the alerts, at least one anomaly associated with the volumetric problem report data is identified and an anomaly report about the at least one anomaly is generated. Responsive to generating the anomaly report, it may be determined whether the at least one anomaly has a known remediation, and if so, the known remediation may be initiated automatically. Network administrator(s) may also be automatically notified.

Abstract: Systems and methods for managing electronic resource access are disclosed. An example computer system may include: a processor; and a non-transitory memory storing one or more sets of instructions that when executed by the processor, causes the system to: receive a user request for accessing or modifying an electronic resource; process the user request to obtain text data; apply feature engineering to the text data to output a feature matrix, the feature engineering comprising application of natural language processing to the text data; use a trained machine learning model to determine a probability score indicating a likelihood of incident occurrence as a result of the user request; and generate signals for displaying a decision granting or denying the user request based on the probability score.

Abstract: Systems, methods, and machine-executable data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions. Aspects of such methods, systems, and data structures include providing at an electronic device, an output indicating that a dynamically-configured electronic token is in a transaction-ready state, where the dynamically-configured electronic token is associated with a plurality of loyalty accounts; in response to one or more signals providing information regarding a location of the electronic device, obtaining token data associated with a loyalty account of the plurality of loyalty accounts corresponding to the location of the electronic device; and via a data communication interface, route a token, generated from the token data, for processing at a transaction processing system.

Abstract: Described in various embodiments herein is a technical solution directed to decomposition of time as an input for machine learning, and various related mechanisms and data structures. In particular, specific machines, computer-readable media, computer processes, and methods are described that are utilized to improve machine learning outcomes, including, improving accuracy, convergence speed (e.g., reduced epochs for training), and reduced overall computational resource requirements. A vector representation of continuous time containing a periodic function with frequency and phase-shift learnable parameters is used to decompose time into output dimensions for improved tracking of periodic behavior of a feature. The vector representation is used to modify time inputs in machine learning architectures.

Abstract: A system for computational estimation sampling from non-trivial probability distributions. The system comprises a processor, operating in conjunction with computer memory. The processor is configured to conduct importance sampling using normalizing flows where a base distribution has a set of parameters that can be adjusted to account for heavy-tailed distributions.

Abstract: A de-coupled computing infrastructure is described that is adapted to provide domain specific contextual engines based on conversational flow. The computing infrastructure further includes, in some embodiments, a mechanism for directing conversational flow in respect of a backend natural language processing engine. The computing infrastructure is adapted to control or manage conversational flows using a plurality of natural language processing agents.

Abstract: An approach for increasing security of biometric templates is described. An improved system is adapted to split a full set of features or representations of a trained model into a first partial template and a second partial template, the second partial template being stored on a secure enclave accessible only through zero-knowledge proof based interfaces. During verification using the template, a new full set of features is received for comparison, and a model is loaded based on the available portions of the model. Comparison utilizing the second partial template requires the computation of zero-knowledge proofs as direct access to the underlying second partial template is prohibited by the secure enclave.

Abstract: Systems and methods for maintaining IT assets are disclosed. An example computer system may include: a processor; and a non-transitory memory storing one or more sets of instructions that when executed by the processor, causes the system to: access or receive asset data for a plurality of IT assets located in a plurality of physical locations; determine, based on the asset data, a plurality of assets due for maintenance; access or receive, in real time, map data for physical locations associated with the plurality of assets; determine a cost matrix for scheduling a route plan for visiting the physical locations associated with the plurality of assets; compute the route plan based on the cost matrix and the map data; generate signals for displaying, at a display of a user device, the route plan; and transmit said signals to the user device for displaying the route plan.

Abstract: Systems and methods for adaptively identifying anomalous network communication traffic. The system includes a processor and a memory coupled to the processor. The memory includes processor-executable instructions that configure the processor to: obtain data associated with a sequence of network communication events; determine that the sequence of communication events is generated by a computing agent based on a symmetricity measure associated with the sequence of network communication events; generate a threat prediction value for the sequence of network communication events prior-generated by the computing agent based on a combination of the symmetricity measure and a randomness measure associated with the network communication events; and transmit a signal for communicating that the sequence of network communication events is a potential malicious sequence of network communication events based on the threat prediction value.

Abstract: Systems, methods, and corresponding non-transitory computer readable media describe a proposed system adapted as a platform governing the loading of data in a multiparty secure computing environment. In the multiparty secure computing environment described herein, multiple parties are able to load their secure information into a data warehouse having specific secure processing adaptations that limit both access and interactions with data stored thereon.

Abstract: Systems are methods are provided for training an automated agent. The automated agent maintains a reinforcement learning neural network and generates, according to outputs of the reinforcement learning neural network, signals for communicating resource task requests. First and second task data are received. The task data are processed to compute a first performance metric reflective of performance of the automated agent relative to other entities in a first time interval, and a second performance metric reflective of performance of the automated agent relative to other entities in a second time interval. A reward for the reinforcement learning neural network that reflects a difference between the second performance metric and the first performance metric is computed and provided to the reinforcement learning neural network to train the automated agent.

Abstract: Systems, methods, and machine-executable data structures for the processing of data for the secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of electronic payment transactions and other secure data processes. Aspects of such systems include trusted platforms by which networked communication devices and merchant systems may be registered as trusted entities. Information associated with particular payment means, such as accounts or payment tokens, can be stored on device(s) secure data sets known as virtual or electronic wallets, or in the form of secure payment tokens. Common application programming interfaces executed by devices may facilitate push and pull processes between electronic wallets and other secure data stores. Users may thereby initiate and complete electronic transactions directly from within applications on trusted devices.

Abstract: A system for reinforcement learning in a dynamic resource environment includes at least one memory device and at least one processor configured to provide an electronic resource environment comprising: a matching engine and the resource generating agent configured for: obtaining from a historical data processing task database a plurality of historical data processing tasks, each historical data processing task including respective task resource requirement data; for a historical data processing task of the plurality of historical data processing tasks, generating layers of data processing tasks wherein a first layer data processing task has an incremental variant in its resource requirement data relative to resource requirement data for a second layer data processing task; and providing the layers of data processing tasks for matching by the machine engine.

Abstract: Methods, systems, and techniques for facilitating identification of electronic data exfiltration. A message transmission log and screenshot metadata are obtained. A screenshot corresponding to the screenshot metadata is matched to a sent electronic message, such as an email, having a file attachment represented in the message transmission log to generate an event. The screenshot metadata indicates that the screenshot was captured prior to when the message transmission log indicates the electronic message was sent. An anomaly score is determined for the sent electronic message is determined by applying unsupervised machine learning, such as by applying an isolation forest, to score the sent electronic message relative to a baseline. The anomaly score meeting or exceeding an anomaly threshold is treated as potentially being indicative of electronic data exfiltration.

Abstract: A computer implemented system for controlling access to data associated with an entity includes a data storage device having a computer memory, and one or more processors. The one or more processors are configured for: storing a secret key associated with the entity in a computer memory associated with the entity; upon receiving entity data, storing the entity data in the computer memory; and upon receiving an access grant signal, enabling communication of information relating to the entity data.

Abstract: A computer implemented system for anonymous electronic verification of location credentials including at least one processor and data storage is described in various embodiments. The system includes cryptographic mechanisms and electronic communication between one or more computing systems that in concert, provide verification of a prover's location credentials in accordance to logical conditions of a verifier's policy without providing additional information to a verifier entity.

Abstract: A system and method for adversarial vulnerability testing of machine learning models is proposed that receives as an input, a representation of a non-differentiable machine learning model, transforms the input model into a smoothed model and conducts an adversarial search against the smoothed model to generate an output data value representative of a potential vulnerability to adversarial examples. Variant embodiments are also proposed, directed to noise injection, hyperparameter control, and exhaustive/sampling-based searches in an effort to balance computational efficiency and accuracy in practical implementation. Flagged vulnerabilities can be used to have models re-validated, re-trained, or removed from use due to an increased cybersecurity risk profile.