Abstract: System and methods providing for categorizing individual virtual machines, as well as the associated application that they form by working in concert, into groups based on the feasibility of hosting the processes that occur on a virtual machine within a container, as well as the relative difficulty of doing so on a virtual machine and application level. The data used to create these scores is collected from the individual machines, at regular intervals through the use of an automated scoring engine that collects and aggregates the data. Said data is then analyzed by the system, that with the aid of passed in configuration data, is configured to generate the scores to allows for an educated and focused effort to migrate from hosting applications on virtual machines to hosting applications on containers.

Abstract: Systems (100, 900), 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 (100) include trusted platforms (120) by which networked communication devices (110) and merchant systems (130) may registered as trusted entities 110?, 130. 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 (112), 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: A computer implemented system for controlling access to data associated with an entity includes a data storage device having a protected memory region, and one or more processors, at least one of which is operable in the protected memory region. The one or more processors are configured for: storing a secret key associated with the entity in a portion of the protected memory region associated with the entity; upon receiving entity data, storing the entity data in the portion of the protected memory region associated with the entity; and upon receiving an access grant signal, generating a smart contract, the smart contract defining the entity data to be accessed and a recipient of the entity data to be accessed.

Abstract: Systems and methods for expanding a multi-relational data structure tunable for generating a non-linear dataset from a time-dependent query. The systems include a processor and a memory. The memory may store processor-executable instructions that, when executed, configure the processor to: receive the query of the multi-relational data structure, wherein the query includes at least one entity node at a queried time relative to the time data; obtain, based on the query, a temporal representation vector based on a diachronic embedding of the multi-relational data structure, the diachronic embedding based on a combination of a first sub-function associated with a temporal feature and a second sub-function associated with a persistent feature; determine, from the temporal representation vector, at least one time-varied score corresponding to the queried time; and generate a response dataset based on the at least one time-varied score determined from the temporal representation vector.

Abstract: A system and method for handling crypto-asset transactions includes: receiving from a payment processing system an electronic transaction request including: a payment token corresponding to a payment identifier associated with the customer account, and a transaction amount in a fiat currency; determining current price data corresponding to a first crypto-asset and a second crypto-asset associated with the customer account; associating the electronic transaction request with at least one data processing task for executing at least one crypto-asset transaction; and when at least one crypto-asset confidence condition is satisfied based on the current price data of at least one of the first crypto-asset or the second crypto-asset, generating signals for providing, via the payment processing system, an indication that the electronic transaction request is authorized without waiting for confirmation of execution of the at least one crypto-asset transaction in the respective distributed ledger.

Abstract: Systems (100), 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 (100) include trusted platforms (120) by which networked communication devices (110) and merchant systems (130) may registered as trusted entities 110?, 130. 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 (112), or in the form of secure payment tokens. Common application programming interfaces executed by devices (110) may facilitate push and pull processes between electronic wallets (112) and other secure data stores (136, 120, 160). Users (190) may thereby initiate and complete electronic transactions directly from within applications on trusted devices (110?).

Abstract: System and method for facilitating management of a time-varying resource pool. The system includes a processor and a memory coupled to the processor. The memory stores processor-executable instructions that, when executed, configure the processor to: obtain a time-series data set including data entries associated with one or more consumed resources; identify one or more recurring resource allocations based on recurring data entries of the time-series data set; identify additional resource allocations based on irregularly-timed data entries of the time-series data set; determine a forecasted resource pool value based on a combination of the identified recurring resource allocations and the additional resource allocations; and upon detection of a trigger condition, generate data for display, via a user interface, a scaled resource allocation value based on the forecasted resource pool value, the scaled resource allocation value corresponding to at least one time-based reference.

Abstract: Systems, methods, and devices for communication are described. A communication system includes a first communication device configured to communicate with a backup device and a destination; and a first tapping device for monitoring messages sent over a first communication link between the first communication device and the backup device. The first communication device includes at least one processor configured to: before sending a first message destined for the destination, send a backup message corresponding to the first message over the communication link for backup at the backup device; and upon confirmation of a tap copy of the backup message from the first tapping device, send the first message to the destination.

Abstract: A computer system and method for machine inductive learning on a graph is provided. In the inductive learning computational approach, an iterative approach is used for sampling a set of seed nodes and then considering their k-degree (hop) neighbors for aggregation and propagation. The approach is adapted to enhance privacy of edge weights by adding noise during a forward pass and a backward pass step of an inductive learning computational approach. Accordingly, it becomes more technically difficult for a malicious user to attempt to reverse engineer the edge weight information. Applicants were able to experimentally validate that acceptable privacy costs could be achieved in various embodiments described herein.

Abstract: Systems and methods for knowledge distillation provide supervised training of a student network with a teacher network, including inputting a batch to the teacher network, inputting the batch to the student network, generating a teacher activation map at a layer of the teacher network, generating a student activation map at a layer of the student network corresponding to the layer of the teacher network, generating a pairwise teacher similarity matrix based on the teacher activation map, generating a pairwise student similarity matrix based on the student activation map, and minimizing a knowledge distillation loss defined as a difference between the pairwise teacher similarity matrix and the pairwise student similarity matrix.

Abstract: Systems, devices, methods, and computer readable media for training a machine learning architecture include: receiving one or more observation data sets representing one or more observations associated with at least a portion of a state; and training the machine learning architecture with the one or more observation data sets, where the training includes updating the plurality of weights based on an error value, and at least one time-varying step-size value; wherein the at least one step-size value is based on a set of meta-weights which vary based on a stochastic meta-descent.

Abstract: Systems and methods for monitoring suspicious communication network traffic. The methods include obtaining data associated with a sequence of communication events transmitted via the communication network and determining an entropy approximation measure associated at least one event attribute for the sequence of communication events. The method includes generating a threat prediction value based on an anomaly classification model and the entropy approximation measure. The anomaly classification model is trained based on prior sequences of communication events to identify a non-outlier anomaly range associated with the at least one event attribute. The threat prediction value is generated based on classification of the entropy approximation measure relative to the non-outlier anomaly range associated with the at least one attribute for identifying a potential threat. The method includes transmitting a signal for communicating that the sequence is a potential threat within the communication network.

Abstract: A variational hyper recurrent neural network (VHRNN) can be trained by, for each step in sequential training data: determining a prior probability distribution for a latent variable from a prior network of the VHRNN using an initial hidden state; determining a hidden state from a recurrent neural network (RNN) of the VHRNN using an observation state, the latent variable and the initial hidden state; determining an approximate posterior probability distribution for the latent variable from an encoder network of the VHRNN using the observation state and the initial hidden state; determining a generating probability distribution for the observation state from a decoder network of the VHRNN using the latent variable and the initial hidden state; and maximizing a variational lower bound of a marginal log-likelihood of the training data. The trained VHRNN can be used to generate sequential data.

Abstract: Systems, methods, and non-transient machine-interpretable data representing executable instruction sets and/or other products for the processing of data for the 5 secure creation, administration, manipulation, processing, and storage of electronic data useful in the processing of payment transactions and other secure data processes. In various aspects and embodiments the disclosure provides secure means for the authorization of sensitive and other data processes subject to controlled access. Such processes include, for example the creation, administration, 10 authorization, virtualization, storage, and other manipulation or processing of electronic data representing characteristics of, instructions for, and information associated with consumer, business, and other payment accounts, and other forms of secure payment elements, such as payment tokens; and data useful in processing transactions using such accounts and elements.

Abstract: Systems 100; devices 110, 120, 130, 150, 160; methods 2400, 2500; and machine-executable programming structures stored in persistent (i.e., non-transitory), computer-readable media 604, 606, 618, 126, 139 for the rapid and secure negotiation, authorization, execution, and confirmation of multi-party data processes, including payment transactions conducted between purchasers 190 having electronic access to bank accounts and other sources of payment, merchants operating e- and/or m-commerce transaction systems 132, 134, 136, and banks and other financial institutions 120 capable of electronically communicating with both.

Abstract: Systems and methods of updating a multi-level data structure for controlling an agent. The method may include: accessing a data structure defining one or more nodes. A non-leaf node of the one or more nodes may be associated with one or more edges for traversing to a subsequent node. An edge of the one or more edges may be associated with a visit count and a softmax state-action value estimation. The method may include identifying a node trajectory including a series of nodes based on an asymptotically converging sampling policy, where the node trajectory includes a root node and a leaf node of the data structure, determining a reward indication associated with the node trajectory; and for at least one non-leaf node, updating the visit count and the softmax state-action value estimate associated with one or more edges of the non-leaf node based on the determined reward indication.

Abstract: A system, non-transitory computer-readable medium, and method are provided. The system comprises at least one processor and memory storing instructions which when executed by the at least one processor configure the at least one processor to perform the method. The non-transitory computer-readable medium has instructions thereon, which when executed by a processor, perform the method. The method comprises determining a similarity score between a first webpage and a second webpage, determining a popularity score of a link between the first webpage and the second webpage, determining a difference between the similarity score and the popularity score, and determining that the link between the first webpage and the second webpage may be improved if the difference is greater than a threshold value.

Abstract: A computer system and method for extending parallelized asynchronous reinforcement learning for training a neural network is described in various embodiments, through coordinated operation of plurality of hardware processors or threads such that each functions as a worker agent that is configured to simultaneously interact with a target computing environment for local gradient computation based on a loss determination and to update global network parameters based at least on local gradient computation to train the neural network through modifications of weighted interconnections between interconnected computing units as gradient computation is conducted across a plurality of iterations of a target computing environment, the loss determination including at least a policy loss term (actor), a value loss term (critic), and an auxiliary control loss. Variations are described further where the neural network is adapted to include terminal state prediction and action guidance.

Abstract: Systems and methods for processing natural language statements. Based on historical records of data associated with an entity, systems and methods provide models for inferring publication of data content associated with the particular entity. The systems and methods may compare newly observed data content to predicted content associated with an entity for evaluating novelty or impact of the newly observed data content.