Abstract: An example operation may include one or more of receiving, from an executing client, a blockchain transaction comprising an anonymous rating related to an authorizing client, a merkle tree root node value, a proof, and a nullifier, and in response, executing, by a smart contract, a valid historical value assert call on a lookback key storing the merkle tree root node value, verifying, through a valid historical value assert call, that the merkle tree root node value is a current or previous value of the merkle tree root node value, verifying the proof with the merkle tree root node value and the nullifier, adding the anonymous rating to a shared ledger, marking the nullifier as used, and storing the marked nullifier to the shared ledger.

Abstract: Network identifiers are extracted from route advertisements. A table associates virtual network identifiers with provider edge devices. When a virtual network identifier extracted from a route advertisement matches a virtual network identifier in the table, the route advertisement is propagated to the provider edge devices associated with that virtual network identifier in the table. The route advertisement is not propagated to provider edge devices not associated with that virtual network identifier in the table.

Abstract: Methods, systems, and computer program products for verifying confidential machine learning models are provided herein. A computer-implemented method includes obtaining (i) a set of training data and (ii) a request, from a requestor, for a machine learning model, wherein the request is accompanied by at least a set of test data; obtaining a commitment from a provider in response to the request, the commitment comprising a special hash corresponding to parameters of a candidate machine learning model trained on the set of training data; revealing the set of test data to the requestor; obtaining, from the requestor, (i) a claim of performance of the candidate machine learning model for the test data and (ii) a proof of the performance of the candidate machine learning model; and verifying the claimed performance for the requestor based on (i) the special hash and (ii) the proof of the claimed performance.

Abstract: There is provided a system and a method comprising obtaining a sequence of a plurality of frames of an area of a specimen, wherein at least one frame of the sequence is transformed with respect to another frame, obtaining a reference frame based at least on a first frame of the sequence, determining, based on the reference frame, a reference pattern, wherein the reference pattern is informative of a structural feature of the specimen in the area, for a given frame of the sequence, determining, based on the given frame, a pattern informative of said structural feature in the area, determining data Dshrinkage informative of an amplitude of a spatial transformation between the reference pattern and the pattern, generating a corrected frame based on said pattern and Dshrinkage and generating an image of the area.

Abstract: Methods, systems, and computer program products for anomaly and mode inference from time series data are provided herein. A computer-implemented method includes receiving time-series sensor data for each one of a group of devices; extracting a set of states for each device in the group from the time-series sensor data; constructing a state-transition graph for each of the devices, wherein each of the state-transition graphs comprises nodes corresponding to each state in the set and edges corresponding to a probability of transition between the extracted states over time; identifying, for each set, a given state as one of: a mode, a normal state and an anomalous state based on the state-transition graph; and detecting one or more anomalous devices in the group by computing similarities between different devices in the group, based at least in part on the determined state-transition graphs.

Abstract: An example operation may include one or more of receiving, by a document validation node, documents from a plurality of document owner nodes over a blockchain network, generating, by the document validation node, commitments for the documents on the blockchain network, deriving, by the document validation node, proofs to verify predicates of the documents, and generating, by the document validation node, a document relationship graph (DRG) based on the commitments and the predicates.

Abstract: An example operation may include one or more of storing a tree structure via a blockchain storage, the tree structure comprising anonymous behavior data of a plurality of blockchain participants stored in a plurality of nodes in a hierarchical structure, receiving a request to add new anonymous behavior data to the tree structure, the request comprising a zero-knowledge proof generated by a blockchain participant, identifying an active leaf on the tree structure which stores previously recorded anonymous behavior data of the blockchain participant associated with the request based on the zero-knowledge proof, generating a new active leaf for the blockchain participant based on the new anonymous behavior data and the previously recorded anonymous behavior, and storing the new active leaf as a leaf node on the tree structure in the blockchain storage.

Abstract: One embodiment provides a method, including: obtaining historical information for equipment having at least one control, wherein the historical information indicates a setting for the at least one control during operation of the equipment and identifies operating performance of the equipment corresponding to the indicated setting; receiving a goal for the equipment, wherein the goal is related to a desired operating performance of the equipment; identifying, a plurality of sets of contiguous good reference segments, wherein a contiguous set of good reference segments comprises a plurality of operating time segments where the desired operating performance goal was achieved for a predetermined of time; identifying, a subset of sets comprising reference segments that are achievable from a current operating state of the equipment; selecting, a reference segment that is attainable based upon exogenous factors related to an operating environment of the equipment; and providing a recommendation to an operator of the

Abstract: Methods, systems, and computer program products for verifying confidential machine learning models are provided herein. A computer-implemented method includes obtaining (i) a set of training data and (ii) a request, from a requestor, for a machine learning model, wherein the request is accompanied by at least a set of test data; obtaining a commitment from a provider in response to the request, the commitment comprising a special hash corresponding to parameters of a candidate machine learning model trained on the set of training data; revealing the set of test data to the requestor; obtaining, from the requestor, (i) a claim of performance of the candidate machine learning model for the test data and (ii) a proof of the performance of the candidate machine learning model; and verifying the claimed performance for the requestor based on (i) the special hash and (ii) the proof of the claimed performance.

Abstract: Techniques for implementing neighbor equivalence groups on a network device are provided, where a neighbor equivalence group is a group of peers of the network device that communicate identical control plane state information for a given network protocol to the network device. In the context of Border Gateway Protocol (BGP), these techniques can include (1) creating, by the network device, a neighbor equivalence group for a set of BGP peers that advertise the same BGP paths to the device, (2) maintaining, by the network device, a single path database for the neighbor equivalence group (rather than one path database for each BGP peer in the group), and (3) immediately processing by the network device, BGP path update/withdrawal messages received from any of the BGP peers in the group against the single path database, without waiting for the same message to be received from every peer.

Abstract: An example operation may include one or more of receiving a content request to add a content to a blockchain storage structure implemented on a blockchain database, temporarily storing the content of the content request in a queue implemented via the blockchain database based on when the request is received, receiving a request to flush the queue which is invoked by chaincode, and in response to the flush request, removing the content from the queue and adding the content to the blockchain storage structure.

Abstract: Disclosed herein are systems and methods for device authentication or pairing. In an aspect, a wearable display system comprises a display, an image capture device configured to capture images of a companion device, a computer-readable storage medium configured to store the images of the companion device, and a processor in communication with the image capture device and the storage medium. The processor can be programmed with executable instructions to receive a first image of a first optical pattern displayed by the companion device captured by the image capture device, wherein the first optical pattern is generated by the companion device based on first shared data, extract first data from the first optical pattern in the received first image, authenticate the companion device based on the first data extracted from the first optical pattern, and notify a user of the wearable display system that the companion device is authenticated.

Abstract: Network identifiers are extracted from route advertisements. A table associates virtual network identifiers with provider edge devices. When a virtual network identifier extracted from a route advertisement matches a virtual network identifier in the table, the route advertisement is propagated to the provider edge devices associated with that virtual network identifier in the table. The route advertisement is not propagated to provider edge devices not associated with that virtual network identifier in the table.

Abstract: Techniques for implementing neighbor equivalence groups on a network device are provided, where a neighbor equivalence group is a group of peers of the network device that communicate identical control plane state information for a given network protocol to the network device. In the context of Border Gateway Protocol (BGP), these techniques can include (1) creating, by the network device, a neighbor equivalence group for a set of BGP peers that advertise the same BGP paths to the device, (2) maintaining, by the network device, a single path database for the neighbor equivalence group (rather than one path database for each BGP peer in the group), and (3) immediately processing. by the network device, BGP path update/withdrawal messages received from any of the BGP peers in the group against the single path database, without waiting for the same message to be received from every peer.

Abstract: Disclosed herein are systems and methods for device authentication or pairing. In an aspect, a wearable display system comprises a display, an image capture device configured to capture images of a companion device, a computer-readable storage medium configured to store the images of the companion device, and a processor in communication with the image capture device and the storage medium. The processor can be programmed with executable instructions to receive a first image of a first optical pattern displayed by the companion device captured by the image capture device, wherein the first optical pattern is generated by the companion device based on first shared data, extract first data from the first optical pattern in the received first image, authenticate the companion device based on the first data extracted from the first optical pattern, and notify a user of the wearable display system that the companion device is authenticated.

Abstract: A provider edge device, capable of accessing a first type of memory and a second type of memory, may determine a network address associated with a customer edge device. The provider edge device may determine whether the customer edge device is categorized as a leaf device in an Ethernet Tree service provided by the provider edge device. The provider edge device may selectively store the network address in the first type of memory or the second type of memory based on determining whether the customer edge device is categorized as a leaf device in the Ethernet Tree service.

Abstract: An example operation may include one or more of receiving, by a document validation node, documents from a plurality of document owner nodes over a blockchain network, generating, by the document validation node, commitments for the documents on the blockchain network, deriving, by the document validation node, proofs to verify predicates of the documents, and generating, by the document validation node, a document relationship graph (DRG) based on the commitments and the predicates.

Abstract: An example operation may include one or more of receiving, from an executing client, a blockchain transaction comprising an anonymous rating related to an authorizing client, a merkle tree root node value, a proof, and a nullifier, and in response, executing, by a smart contract, a valid historical value assert call on a lookback key storing the merkle tree root node value, verifying, through a valid historical value assert call, that the merkle tree root node value is a current or previous value of the merkle tree root node value, verifying the proof with the merkle tree root node value and the nullifier, adding the anonymous rating to a shared ledger, marking the nullifier as used, and storing the marked nullifier to the shared ledger.