International Business Machines Corporation

Nec Corporation

Globalfoundries Inc.

Eric Kevin Butler

Divyesh Jadav

Thomas D Griffin

Pawan Raghunath Chowdhary

Hovey R Strong, Jr

Anca Andreea Chandra

Sunhwan Lee

Robert John Moore

Jeanette L Blomberg

Aameek Singh

Chung-hao Tan

Susanne M Glissmann-Hochstein

Sandeep Gopisetty

Shun Jiang

Kaladhar Voruganti

Prasenjit Sarkar

Kavita Chavda

Gabriel Alatorre

Scott Joseph Colbeck

Pi-Wei Chin

Yang Song

Marc Henri Coq

Mu Qiao

Luis Angel Bathen

Ramani Ranjan Routray

Nagapramod S Mandagere

Aly Megahed

Sandip Agarwala

Rakesh Jain

Mihail Corneliu Constantinescu

Akil Khamisi Sutton

Reshu Jain

Robert Engel

Yuya Jeremy Ong

Patrick B Heywood

Sandra C Thompson

Nitin Ramchandani

Kiyoung Yang

Heiko Hary Ludwig

Seshashayee Sankarshana Murthy

Nikolaos Anerousis

James E Olson

Guangjie Ren

Margaret Helen Szymanski

Raphael I Arar

Shubhi Asthana

Mark V Chitti

Cheryl Ann Kieliszewski

German H Flores

Cedric D Cook

Kevin P Roche

Nathalie Baracaldo-Angel

Richard J Ayala, Jr

Manuel Hernandez

Steven P Gronefeld

William Anthony Sherrill

Preethi Anantharaman

Wenqi Wei

Stephen Buckley

A computer-implemented method for validating ownership of a digital asset is disclosed. The computer-implemented method includes transmitting a public key for encrypting a digital asset to an owner of the digital asset responsive to receiving a request for the public key. The computer-implemented method further includes receiving from the owner of the digital asset, an encrypted digital asset and a first liveness hash, wherein the digital asset is encrypted using the public key, and the first liveness hash is generated based, at least in part, on the digital asset in an unencrypted form and a first nonce. The computer-implemented method further includes determining whether the first liveness hash is valid. The computer-implemented method further includes generating in response to determining that the first liveness hash is valid, a digital asset record, wherein the digital asset record includes the encrypted digital asset and the first liveness hash.

Method to validate ownership and authentication of a digital asset

Migration of smart contracts across blockchain ledgers is described herein. Different decentralized ledgers are first cross-compiled. A verifiable smart contract is employed that will adhere to the rules of the ledger being targeted from the decentralized ledgers, and each of the smart contracts can be modeled as a state machine. Each of the smart contracts can be verified for correctness. At least one smart contracts that completes the verification process is installed across the different decentralized ledgers. At least one smart contract is approved with a verification process for confirming equivalency to deploy across the decentralized ledgers. The smart contracts are deployed across the decentralized ledgers.

Nomadic smart contracts migrated across blockchain ledgers

A processor can be configured to receive data associated with, and/or access to, a computing system's file system structure. The processor can also be configured to determine file patterns, file path patterns and/or graph patterns associated with the computing system. The processor can also be configured to build a graph structure having nodes and edges, the graph structure representing the file patterns, file path patterns and graph patterns, wherein the nodes of the graph structure represent files and attributes of the files and the edges of the graph structure represent connectivity between the files. The processor can also be configured to train, based on the graph structure, a first machine learning model to learn a feature vector associated with a file. The processor can also be configured to train, based on the feature vector, a second machine learning model to identify a vulnerable ransomware target.

Machine learning based vulnerable target identification in ransomware attack

A method of using a computing device to compare performance of multiple algorithms. The method includes receiving, by a computing device, multiple algorithms to assess. The computing device further receives a total amount of resources to allocate to the multiple algorithms. The computing device additionally assigns a fair share of the total amount of resources to each of the multiple algorithms. The computing device still further executes each of the multiple algorithms using the assigned fair share of the total amount of resources. The computing device additionally compares the performance of each of the multiple based on at least one of multiple hardware relative utility metrics describing a hardware relative utility of any given resource allocation for each of the multiple algorithms.

Fair simultaneous comparison of parallel machine learning models

Techniques are disclosed, using one or more computers. for bidding on digital advertising space associated with media content on a digital platform. Popularity is determined of a plurality of electronic communication and associated clips on a digital platform. A clip is detected which meets a threshold for approaching an activity level of user activity indicating the popularity of the clip. The clip meeting the threshold is labeled as a non-fungible token (NFT) indicating available digital advertising space associated with the clip. In response to the labeling of the clip, monetary bidding is received, based on the NFT by entities buying digital advertising space for digital advertising content associated with the NFT.

Electronic marketplace for digital advertising space

Embodiments of the invention are directed to a computer system that includes a memory electronically coupled to a processor system. The processor system is operable to perform processor system operations that include accessing a graph model representation of a computer network. The graph model is used to implement a resiliency-problem identification analysis that identifies a set of resiliency problems in the graph model. The graph model is used to apply a resiliency-problem solution analysis to a resiliency problem in the set of resiliency problems to generate a set of resiliency-problem solutions. Each resiliency-problem solution in the set of resiliency-problem solutions is ranked.

Data center resiliency recommender

A processor distributes, from a server, a trained supervised machine learning (ML) model and supervised and unsupervised feature information to a plurality of client devices; at each client device, trains the supervised ML model using local data to generate a local supervised ML model, constructs a local unsupervised ML model using the unsupervised feature information, and deploys the local supervised and unsupervised ML models; determining when a detection performance difference between the local supervised and unsupervised ML models reaches a threshold; identifies a proposed change to the supervised or unsupervised feature information; deploys the proposed change on one client device; responsive to determining the proposed change improves the detection performance of that client device, communicates the proposed change to a sampled set of client devices; and responsive to determining the proposed change improves the detection performance of a majority of the sampled set, communicates the proposed change to the server.

Dynamically federated data breach detection

A method, computer system, and a computer program for quick disaster recovery of cloud-native environments is provided. The present invention may include replicating at a secondary server site software executing in a cloud-native environment on a primary server site. The present invention may also include detecting a failure associated with the software executing in the cloud-native environment. The present invention may then include whether the detected failure is causing down time for the software executing in the cloud environment. The present invention may further include deploying the replicated software on the secondary server site in response to determining that the detected failure is causing down time.

Quick disaster recovery in distributed computing environment

Systems and methods are described for a data breach detection based on snapshot analytics. The described systems and methods identify a plurality of snapshots of a data structure, identify a plurality of leaf nodes of the data structure for each of the snapshots, generate a vector of data attributes for each of the leaf nodes, assign a weight to each of the vectors to produce a set of weighted vectors for each of the snapshots, compute a distance metric between each pair of the snapshots based on the corresponding sets of weighted vectors, and detect an abnormal snapshot among the plurality of snapshots based on the distance metrics.

Data breach detection

A computer-implemented method according to one embodiment includes identifying a topic associated with a received notification, determining a plurality of policies associated with the topic, determining a current environmental context, determining a generalization level, utilizing the plurality of policies and the current environmental context, modifying the notification, based on the generalization level, and presenting the modified notification.

Contextually adjusting device notifications

Techniques for augmenting data visualizations based on user interactions to enhance user experience are provided. In one aspect, a method for providing real-time recommendations to a user includes: capturing user interactions with a data visualization, wherein the user interactions include images captured as the user interacts with the data visualization; building stacks of the user interactions, wherein the stacks of the user interactions are built from sequences of the user interactions captured over time; generating embeddings for the stacks of the user interactions; finding clusters of embeddings having similar properties; and making the real-time recommendations to the user based on the clusters of embeddings having the similar properties.

Interaction-based visualization to augment user experience

One embodiment provides a method comprising receiving a flight plan request for a drone. The flight plan request comprises a drone identity, departure information, and arrival information. The method further comprises constructing a modified flight plan for the drone based on the flight plan request, wherein the modified flight plan represents an approved, congestion reducing, and executable flight plan for the drone, and the modified flight plan comprises a sequence of four-dimensional (4D) cells representing a planned flight path for the drone. For each 4D cell of the modified flight plan, the method further comprises attempting to place an exclusive lock on behalf of the drone on the 4D cell, and in response to a failure to place the exclusive lock on behalf of the drone on the 4D cell, rerouting the modified flight plan around the 4D cell to a random neighboring 4D cell.

Drone air traffic control and flight plan management

One embodiment provides a method that includes generating, by a processor, a topic graph by converting information to topic nodes and edges in the topic graph. Bridges from a given topic node are provided to a neighbor node based on a weight assignment. The bridges are converted to an electronic form of communication. The processor orders suggested paths based on a determined distance from at least one other topic node to a topic goal node via an earliest topic node on each suggested path. The determined distance is found by a first search of the topic graph.

Topic bridging determination using topical graphs

Embodiments of the present invention provide a method comprising receiving a task set comprising multiple tasks, receiving operational information identifying one or more operating characteristics of multiple drones, and obtaining an initial heuristic ordering of the multiple tasks based on the operational information and the climate information. Each task has a corresponding task location. The method further comprises scheduling the multiple tasks to obtain a final ordering of the multiple tasks. The final ordering represents an order in which the multiple tasks are scheduled, and the final ordering may be different from the initial heuristic ordering.

Drone carrier

One embodiment provides a method comprising maintaining a multi-dimensional data structure partitioned into cells utilizing a tree data structure ('tree') comprising intervals for each dimension of a multi-dimensional space. To partition an interval for a node of the tree into multiple subintervals, multiple leaf nodes ('leaves') are generated, each leaf descending from the node. To merge multiple intervals for multiple nodes of the tree, a parent node (&#x201c;parent&#x201d;) and multiple leaves descending from the parent are generated, the parent and the leaves are time constrained, and the leaves are scheduled for a merger. When transient data in cells included in a list that corresponds to a leaf scheduled for merger expires, each cell in the list is converted into a cell for inclusion in a different list corresponding to a parent of the leaf, each leaf of the parent removed, and the parent turned into a leaf.

Drone management data structure

Growing community of inventors

San Jose, CA, United States of America

Eric Kevin Butler