Abstract: Obtain, from an existing machine learning classifier, original probabilistic scores classifying samples taken from two or more groups into two or more classes via supervised machine learning. Associate the original probabilistic scores with a plurality of original Lagrange multipliers. Adjust values of the plurality of original Lagrange multipliers via low-dimensional convex optimization to obtain updated Lagrange multipliers that satisfy fairness constraints as compared to the original Lagrange multipliers. Based on the updated Lagrange multipliers, closed-form transform the original probabilistic scores into transformed probabilistic scores that satisfy the fairness constraints while minimizing loss in utility. The fairness constraints are with respect to the two or more groups.

Abstract: A machine-implemented method for use at a destination node for transferring data from a source node, the method comprising: establishing one or more connections with the source node; sending to the source node a request for a data stream, the request including control information to be applied at the source node, the control information including a number of degrees of freedom (DoFs) or frames to be transmitted across each of the one or more connections; and receiving one or more coded frames through the data stream over the one or more connections.

Abstract: In some embodiments, a method comprises: transmitting first data units from a source node to a destination node over a first path; receiving information about the availability of one or more second paths between the source and destination nodes; determining a cost associated with each of the one or more second paths; allocating a first number of second data units to the first path and a second number of second data units to the one or more second paths based on the determined costs; and transmitting the second data units from the source node to the destination node over the first path and/or the one or more second paths according to the allocation, wherein the first data units, the second data units, or both the first and second data units comprise network coded data.

Abstract: Obtain, from an existing machine learning classifier, original probabilistic scores classifying samples taken from two or more groups into two or more classes via supervised machine learning. Associate the original probabilistic scores with a plurality of original Lagrange multipliers. Adjust values of the plurality of original Lagrange multipliers via low-dimensional convex optimization to obtain updated Lagrange multipliers that satisfy fairness constraints as compared to the original Lagrange multipliers. Based on the updated Lagrange multipliers, closed-form transform the original probabilistic scores into transformed probabilistic scores that satisfy the fairness constraints while minimizing loss in utility. The fairness constraints are with respect to the two or more groups.

Abstract: In some embodiments, a method comprises: transmitting first data units from a source node to a destination node over a first path; receiving information about the availability of one or more second paths between the source and destination nodes; determining a cost associated with each of the one or more second paths; allocating a first number of second data units to the first path and a second number of second data units to the one or more second paths based on the determined costs; and transmitting the second data units from the source node to the destination node over the first path and/or the one or more second paths according to the allocation, wherein the first data units, the second data units, or both the first and second data units comprise network coded data.

Abstract: A machine-implemented method for use at a destination node for transferring data from a source node, the method comprising: establishing one or more connections with the source node; sending to the source node a request for a data stream, the request including control information to be applied at the source node, the control information including a number of degrees of freedom (DoFs) or frames to be transmitted across each of the one or more connections; and receiving one or more coded frames through the data stream over the one or more connections.

Abstract: Techniques facilitating using a distribution system for incentivizing and accelerating data driven scientific research are described herein. The distribution system can track the input of various parties involved in scientific research, and when a reward, monetary or otherwise, is realized for one or more outcomes of the scientific research, the distribution system can distribute the reward among the parties that provided the input. The relative levels and contributions of the parties can be tracked to ensure that an equitable portioning of the reward is realized. A directed graph can be formed based on the transactions, wherein the nodes correspond to entities, researchers, publications, and the edges correspond to relationships between the entities. The directed graph can be analyzed to determine the relative or absolute levels of contributions from each of the entities, and the rewards can be distributed based on the contribution levels.

Abstract: An example operation may include one or more of partitioning a data set from a data provider into a training data set and a test data set, exposing the training data set to a learning service provider while preventing the learning service provider from being able to access the test data set, wherein the preventing comprises encrypting the test data set and storing the encrypted test data set in an immutable ledger, receiving a learning model that is generated by the learning service provider based on the exposed training data set, executing the received learning model using the test data set as input to verify whether the learning model satisfies a predefined performance threshold, and in response to verifying the learning model satisfies the predefined performance threshold, outputting information about the verification to a computing node.

Abstract: Techniques for improving products based on data-driven models are provided. In one example, a system comprises a receiving component that receives product data representing information about a set of products, wherein a first product of the set of products comprises a first combination of a first set of ingredients, and wherein the product data comprises product composition data representing a composition of the first product. The system further comprises a learning component that generates product space data representing a product space that characterizes the set of products and respective degrees of similarity between members of the set of products, wherein a degree of similarity between the first product and a second product of the set of products is determined based on product distance data representing a determined distance metric resulting from a comparison of the first set of ingredients to a second set of ingredients combined to produce the second product.

Abstract: Techniques facilitating using a distribution system for incentivizing and accelerating data driven scientific research are described herein. The distribution system can track the input of various parties involved in scientific research, and when a reward, monetary or otherwise, is realized for one or more outcomes of the scientific research, the distribution system can distribute the reward among the parties that provided the input. The relative levels and contributions of the parties can be tracked to ensure that an equitable portioning of the reward is realized. A directed graph can be formed based on the transactions, wherein the nodes correspond to entities, researchers, publications, and the edges correspond to relationships between the entities. The directed graph can be analyzed to determine the relative or absolute levels of contributions from each of the entities, and the rewards can be distributed based on the contribution levels.

Abstract: Techniques facilitating using a distribution system for incentivizing and accelerating data driven scientific research are described herein. The distribution system can track the input of various parties involved in scientific research, and when a reward, monetary or otherwise, is realized for one or more outcomes of the scientific research, the distribution system can distribute the reward among the parties that provided the input. The relative levels and contributions of the parties can be tracked to ensure that an equitable portioning of the reward is realized. A directed graph can be formed based on the transactions, wherein the nodes correspond to entities, researchers, publications, and the edges correspond to relationships between the entities. The directed graph can be analyzed to determine the relative or absolute levels of contributions from each of the entities, and the rewards can be distributed based on the contribution levels.

Abstract: An example operation may include one or more of partitioning a data set from a data provider into a training data set and a test data set, exposing the training data set to a learning service provider while preventing the learning service provider from being able to access the test data set, wherein the preventing comprises encrypting the test data set and storing the encrypted test data set in an immutable ledger, receiving a learning model that is generated by the learning service provider based on the exposed training data set, executing the received learning model using the test data set as input to verify whether the learning model satisfies a predefined performance threshold, and in response to verifying the learning model satisfies the predefined performance threshold, outputting information about the verification to a computing node.

Abstract: Techniques facilitating using a distribution system for incentivizing and accelerating data driven scientific research are described herein. The distribution system can track the input of various parties involved in scientific research, and when a reward, monetary or otherwise, is realized for one or more outcomes of the scientific research, the distribution system can distribute the reward among the parties that provided the input. The relative levels and contributions of the parties can be tracked to ensure that an equitable portioning of the reward is realized. A directed graph can be formed based on the transactions, wherein the nodes correspond to entities, researchers, publications, and the edges correspond to relationships between the entities. The directed graph can be analyzed to determine the relative or absolute levels of contributions from each of the entities, and the rewards can be distributed based on the contribution levels.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for generating a special map of a plurality of products based on relationships between products. A query is received, from a user, via a user device, wherein the query includes an associated target product. A set of data associated with a plurality of products is received. An earth mover's distance value is calculating for at least one pair of products of the plurality of products. The earth mover's distance is communicated value to a user. A weight is received value based on a user input selection. The earth mover's distance value is modified based on the received weight. A flow vector is determining based on the modified earth mover's distance value of the at least one pair of products and each product of the at least one pair of products is mapped to a vector graph.

Abstract: Secrecy scheme systems and associated methods using list source codes for enabling secure communications in communications networks are provided herein. Additionally, improved information-theoretic metrics for characterizing and optimizing said secrecy scheme systems and associated methods are provided herein. One method of secure communication comprises receiving a data file at a first location, encoding the data file using a list source code to generate an encoded file, encrypting a select portion of the data file using a key to generate an encrypted file, and transmitting the encoded file and the encrypted file to an end user at a destination location, wherein the encoded file cannot be decoded at the destination location until the encrypted file has been received and decrypted by the end user, wherein the end user possesses the key.

Abstract: A compositional artifact may be identified, and a set of logical coordinates within a composition model may be determined for the compositional artifact. The set of logical coordinates may be determined based on the components of the compositional artifact. Tolerance parameters may be used in conjunction with the set of logical coordinates to calculate a logical distance, and other artifacts in the composition model whose logical coordinates fall within the logical distance may be displayed to a user.

Abstract: Techniques facilitating using a distribution system for incentivizing and accelerating data driven scientific research are described herein. The distribution system can track the input of various parties involved in scientific research, and when a reward, monetary or otherwise, is realized for one or more outcomes of the scientific research, the distribution system can distribute the reward among the parties that provided the input. The relative levels and contributions of the parties can be tracked to ensure that an equitable portioning of the reward is realized. A directed graph can be formed based on the transactions, wherein the nodes correspond to entities, researchers, publications, and the edges correspond to relationships between the entities. The directed graph can be analyzed to determine the relative or absolute levels of contributions from each of the entities, and the rewards can be distributed based on the contribution levels.

Abstract: Techniques facilitating using a distribution system for incentivizing and accelerating data driven scientific research are described herein. The distribution system can track the input of various parties involved in scientific research, and when a reward, monetary or otherwise, is realized for one or more outcomes of the scientific research, the distribution system can distribute the reward among the parties that provided the input. The relative levels and contributions of the parties can be tracked to ensure that an equitable portioning of the reward is realized. A directed graph can be formed based on the transactions, wherein the nodes correspond to entities, researchers, publications, and the edges correspond to relationships between the entities. The directed graph can be analyzed to determine the relative or absolute levels of contributions from each of the entities, and the rewards can be distributed based on the contribution levels.

Abstract: Techniques, devices, systems, and protocols are disclosed herein that relate to data transfer between communication nodes via multiple heterogeneous paths. In various embodiments, network coding may be used to improve data flow and reliability in a multiple path scenario. Transmission control protocol (TCP) may also be used within different paths to further enhance data transfer reliability. In some embodiments, multiple levels of network coding may be provided within a transmitter in a multiple path scenario, with one level being applied across all paths and another being applied within individual paths.

Abstract: Techniques for improving products based on data-driven models are provided. In one example, a system comprises a receiving component that receives product data representing information about a set of products, wherein a first product of the set of products comprises a first combination of a first set of ingredients, and wherein the product data comprises product composition data representing a composition of the first product. The system further comprises a learning component that generates product space data representing a product space that characterizes the set of products and respective degrees of similarity between members of the set of products, wherein a degree of similarity between the first product and a second product of the set of products is determined based on product distance data representing a determined distance metric resulting from a comparison of the first set of ingredients to a second set of ingredients combined to produce the second product.