Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed in inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

Abstract: The invention relates to optimizing platform conversion through dynamic management of capacity in an ecommerce environment. The invention commences when a request is received from a user to place an order for an item in the ecommerce environment. The request is then transmitted to a server. A first buying cohort with an associated first prioritization value is then identified. A utilization value of a first capacity reservation for the identified first buying cohort is then determined. Thereafter, if the determined utilization value of the first capacity reservation is more than a predefined threshold value, a extraction capacity value from at least one second capacity reservation is dynamically assigned to the first capacity reservation based on a dynamically forecasted demand and a risk factor associated with at least one second capacity reservation.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

Abstract: The present invention relates to systems and associated methods for generating geo-coordinates for any given geographic element such as an address, while using unstructured or structured address data. According to the embodiments of the present invention, a region is divided into grids with each grid encompassing certain addresses with their locations. A grid is then treated as a label for said addresses and with the <address, grid> paired data, an appropriate grid for a new address is then predicted based on the correspondence of tokens between the address and the grid. The centroid of the predicted grid is then outputted as the latitude, longitude coordinates for the address.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can predict one or more inventory management parameters that result in a particular probability of achieving a target service level while minimizing a cost.

Abstract: Network flow classification can include clustering a network flow database into a number of at least one of applications and network flows. Network flow classification can include classifying the number of the at least one of applications and network flows.

Abstract: The present disclosure provides systems and methods that may advantageously apply machine learning to accurately manage and predict inventory variables with future uncertainty. In an aspect, the present disclosure provides a system that can receive an inventory dataset comprising a plurality of inventory variables that indicate at least historical (i) inventory levels, (ii) inventory holding costs, (iii) supplier orders, and/or (iv) lead times over time. The plurality of inventory variables can be characterized by having one or more future uncertainty levels. The system can process the inventory dataset using a trained machine learning model to generate a prediction of the plurality inventory variables. The system can provide the processed inventory dataset to an optimization algorithm. The optimization algorithm can be used to predict a target inventory level for optimizing an inventory holding cost. The optimization algorithm can comprise one or more constraint conditions.

Abstract: Performance testing based on variable length segmentation and clustering of time series data is disclosed. One example is a system including a training module, a performance testing module, and an interface module. The training module generates a trained model to learn characteristics of a system of interest from training time series data by segmenting the training time series data into homogeneous windows of variable length, clustering the segments to identify patterns, and associating each cluster with a cluster score. The performance testing module analyzes system characteristics from testing time series data by receiving the testing time series data, and determining a performance metric for the testing time series data by analyzing the testing time series data based on the trained model. The interface module is communicatively linked to the performance testing module, and provides the performance metric via an interactive graphical user interface.

Abstract: Examples disclosed herein relate to: computing, by a computing device at a party among a plurality of parties, a sum of local data owned by the party. The local data is horizontally partitioned into a plurality of data segments, with each data segment representing a non-overlapping subset of data entries owned by a particular party; computing a local gradient based on the horizontally partitioned local data; initializing each data segment; anonymizing aggregated local gradients received from the mediator, wherein the aggregated local gradients comprise gradients computed based on a plurality of data entries owned by the plurality of parties; receiving, from a mediator, a global gradient based on the aggregated local gradients; learning a global analytic model based on the global gradient; and performing privacy-preserving multi-party analytics on the horizontally partitioned local data based on the learned global analytic model.

Abstract: The present disclosure discloses a method comprising: dividing, by a computing device at a first party among a plurality of parties, local data into a plurality of data segments; recursively encrypting, by the computing device, each data segment using a plurality of public keys corresponding to the plurality of parties and a mediator; sharing, by the computing device, the local data comprising the encrypted plurality of data segments with the mediator; anonymizing, by the computing device, aggregated local data received from the mediator; and communicating, by the computing device from the mediator, a global sum that preserves privacy of the plurality of parties in a multi-party environment, wherein the global sum is computed by the mediator based on the collection of data segments that are decrypted recursively using the private key corresponding to each party and the private key corresponding to the mediator.

Abstract: Example computing devices described herein enable computation of a machine learning model on distributed multi-party data that is vertically partitioned, in a privacy preserving fashion. The computing device computes at a party a sum of local data owned by the party, wherein the local data is vertically partitioned into a plurality of data segments, each data segment representing a non-overlapping subset of data features; transforms a cost function of a data analytics task to a gradient descent function, wherein the cost function comprises a summation of a plurality of cost function values; anonymizes aggregated data shards received from a mediator; updating local model parameters based on the aggregated data shards; and performs privacy-preserving multi-party analytics on the vertically partitioned local data based on a learned global analytic model. It leverages a secure-sum protocol that provides strong security guarantees against collusion and prior-knowledge attacks.

Abstract: Techniques for secure linking of attributes between a first node and a plurality of nodes are provided. In one aspect, the first node and the plurality of nodes maintain a distributed data set. The techniques may include encoding the attributes as integer values at the first node. The nodes of the plurality of nodes that include matching attributes may be determined using a secure list matching protocol.

Abstract: Overlay networks of application components are managed. Applicant components may create overlay networks based on policies of the application components and an environment of the overlay network. The overlay network may be adjusted based on changes to the policies or the environment.

Abstract: Examples disclosed herein relate to: computing, by a computing device at a party among a plurality of parties, a sum of local data owned by the party. The local data is horizontally partitioned into a plurality of data segments, with each data segment representing a non-overlapping subset of data entries owned by a particular party; computing a local gradient based on the horizontally partitioned local data; initializing each data segment; anonymizing aggregated local gradients received from the mediator, wherein the aggregated local gradients comprise gradients computed based on a plurality of data entries owned by the plurality of parties; receiving, from a mediator, a global gradient based on the aggregated local gradients; learning a global analytic model based on the global gradient; and performing privacy-preserving multi-party analytics on the horizontally partitioned local data based on the learned global analytic model.

Abstract: Examples disclosed herein relate to: computing, by a computing device at a party among a plurality of parties, a sum of local data owned by the party, wherein the local data is vertically partitioned into a plurality of data segments, each data segment representing a non-overlapping subset of data features; transforming a cost function of a data analytics task to a gradient descent function, wherein the cost function comprises a summation of a plurality of cost function values; initializing each data segment; anonymizing aggregated data shards received from a mediator; updating local model parameters based on the aggregated data shards; learning a global analytic model based on the updated local parameters and cost function values; and performing privacy-preserving multi-party analytics on the vertically partitioned local data based on the learned global analytic model.

Abstract: The present disclosure discloses a method comprising: dividing, by a computing device at a first party among a plurality of parties, local data into a plurality of data segments; recursively encrypting, by the computing device, each data segment using a plurality of public keys corresponding to the plurality of parties and a mediator; sharing, by the computing device, the local data comprising the encrypted plurality of data segments with the mediator; anonymizing, by the computing device, aggregated local data received from the mediator; and communicating, by the computing device from the mediator, a global sum that preserves privacy of the plurality of parties in a multi-party environment, wherein the global sum is computed by the mediator based on the collection of data segments that are decrypted recursively using the private key corresponding to each party and the private key corresponding to the mediator.

Abstract: Techniques for secure linking of attributes between a first node and a plurality of nodes are provided. In one aspect, the first node and the plurality of nodes maintain a distributed data set. The techniques may include encoding the attributes as integer values at the first node. The nodes of the plurality of nodes that include matching attributes may be determined using a secure list matching protocol.

Abstract: Examples herein involve managing overlay networks of application components. In examples herein, application components may create overlay networks based on policies of the application components and an environment of the overlay network. The overlay network may be adjusted based on changes to the policies or the environment.

Abstract: Examples relate to distributed anomaly management. In one example, a computing device may: receive real-time anomaly data for a first set of client devices, wherein the received anomaly data includes: anomalous network behavior data received from a network intrusion detection system (NICKS) monitoring network traffic behavior, anomalous host event data received from a host intrusion detection system (HIDS) monitoring host events originating from client devices in the first set, and anomalous process activity data received from a trace intrusion detection system (TIDS) monitoring process activity performed by client devices in the first set; for each client device in the first set of client devices for which anomaly data is received, associate the received anomaly data with the client device; and determine, for a particular client device, a measure of risk, wherein the measure of risk is dynamically adjusted based on the received real-time anomaly data.

Abstract: Performance testing based on variable length segmentation and clustering of time series data is disclosed. One example is a system including a training module, a performance testing module, and an interface module. The training module generates a trained model to learn characteristics of a system of interest from training time series data by segmenting the training time series data into homogeneous windows of variable length, clustering the segments to identify patterns, and associating each cluster with a cluster score. The performance testing module analyzes system characteristics from testing time series data by receiving the testing time series data, and determining a performance metric for the testing time series data by analyzing the testing time series data based on the trained model. The interface module is communicatively linked to the performance testing module, and provides the performance metric via an interactive graphical user interface.