International Business Machines Corporation

Other

Komminist S Weldemariam

Clifford A Pickover

Michael S Gordon

Shikhar Kwatra

James R Kozloski

Maja Vukovic

Abdigani Mohamed Diriye

Thomas David Erickson

Sarbajit Kumar Rakshit

Ashish Kundu

Celia Cintas

Jonathan Lenchner

Kala Fleming

David W Kaguma

Girmaw Abebe Tadesse

Sushain Pandit

Paul N Krystek

Skyler Speakman

Kamal Bhattacharya

Toby Kurien

Naweed Aghmad Khan

Nelson Kibichii Bore

Ashley D Gritzman

Andrew Kinai

Juliet K Mutahi

Elizabeth Ondula

Thomas E Chefalas

Edgar Adolfo Zamora Duran

Aisha Walcott

Francois Pierre Luus

Etienne Eben Vos

Charity Wayua

Samuel Karumba

Zachary A Silverstein

Andrzej P Kochut

Amit Anil Nanavati

Heike E Riel

Anup Kalia

Smitkumar Narotambhai Marvaniya

Lin Zhou

Osamuyimen Stewart

Robert J Schloss

Ismail Yunus Akhalwaya

Meenal Pore

Isaac Waweru Wambugu

Charles Muchiri Wachira

Reha Yurdakul

Srihari Sridharan

Shelby Solomon Darnell

Fred Ochieng Otieno

Isaac Markus Serfaty

Kala K Fleming

Muuo Wambua

Timothy Kotin

Oliver Bent

Alan Kalton

Lucile Ter-Minassian

Rick A Hamilton, Ii

Valentina Salapura

Jeremy A Greenberger

Itzhack Goldberg

Maharaj Mukherjee

Sharathchandra UmapathiRao Pankanti

Vittorio R Castelli

John David Wilson

Nalini Kanta Ratha

Jinho Hwang

Solomon Assefa

Anca Sailer

Praveen Jayachandran

Zachary M Greenberger

Adam Lee Griffin

Evelyn Duesterwald

Roxana Monge Nunez

Daby Mousse Sow

Shantanu Ravindra Godbole

Bikram Sengupta

Campbell D Watson

Chen Lin

Kedar Kulkarni

Reginald Bryant

Richard Allen Young

Victor Abayomi Akinwande

Darlington Shingirirai Mapiye

Lianna C Samuel

Percival Silva De Lucena

Samuel Osebe

Madeleine Eve Barker

Abhishek Kumar

Blaise Schaeffer

Peninah Mugure Waweru

Joan Byamugisha

Sally Simone R F L Fobi Nsutezo

Mattia Zeni

Temitope Ogunyoku

Simone Fobi

Arphaxade Wanjala Sifuna

Tonya Nyakeya

Clifton Kipchirchir

Timothy Nyota

An example operation may include one or more of connecting, by a migration server, to a legacy system configured to store legacy data, identifying, by the migration server, artifacts of the legacy system subject to a migration to a blockchain, generating, by the migration server, transactions that correspond to the artifacts of the legacy system, mapping the artifacts of the legacy system to artifacts of the blockchain based on the transactions, and executing, by the migration server, a smart contract to migrate the legacy data corresponding to the mapped artifacts into the blockchain.

Migration of a legacy system

An example operation may include one or more of receiving sensor data from one or more sensors associated with a building, storing a block including the sensor data to a shared ledger of a blockchain network, the one or more sensors associated with one or more buildings, requesting a risk assessment for the sensor data, by a blockchain node, calculating the risk assessment with one or more machine learning algorithms based on the sensor data, historical sensor blockchain data, importance of the one or more sensors, and a degree of concern related to the sensor data, providing the risk assessment to the blockchain node, and generating an alert in response to the risk assessment above a threshold.

Sensor risk assessment database

Train a machine learning model, using an image-based knowledge graph of tropical cyclone data, for implementing a surface field modeling architecture that produces images of at least surface wind fields and surface rainfall fields from images of at least tropical cyclone tracks and pressure intensities. Generate model images of a modeled surface wind field and a modeled surface rainfall field by providing images of at least a user-generated tropical cyclone track and pressure intensity to the trained machine learning model.

Modeling tropical cyclone surface fields for impact assessment

An exemplary method includes vehicle-mounted sensors continuously detecting vehicle speed and vehicle tire and steering vibrations; a processor implementing a machine-learning program that continuously monitors signals from the vehicle-mounted sensors and compares detected vehicle tire and steering vibrations to upper bounds corresponding to detected vehicle speed; and the processor alerting a vehicle driver that wheel or tire service is required based on the detected vehicle tire and steering vibrations exceeding the upper bounds. An exemplary apparatus includes a vehicle; tires mounted to the vehicle; a speed sensor mounted to the vehicle; a vibration sensor mounted to the vehicle; and a processor connected in communication with the speed sensor and the vibration sensor. The processor is adapted to implement any of the method steps above.

Predicting tire imbalance and/or wheel misalignment

Techniques are described for providing live first aid response guidance using a machine learning based cognitive aid planner. In one embodiment, a computer-implemented method is provided that comprises classifying, by a system operatively coupled to a processor, a type of an injury endured by a patient. The method further comprises, employing, by the system, one or more machine learning models to estimate a risk level associated with the injury based on the type of the injury and a current context of the patient.

Providing live first aid response guidance using a machine learning based cognitive aid planner

Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of code datasets prior to a data migration; analyzing the identified code datasets for a plurality of parameters; dynamically predicting a carbon footprint associated with the analyzed code datasets based on the plurality of parameters for each analyzed code dataset; and automatically optimizing the analyzed code datasets based on the predicted carbon footprint for data migration.

Carbon-aware code optimization

A method comprising retrieving a plurality of invalid user first commands and a plurality of user responses stored in a memory, wherein each of plurality of invalid user verbal commands are commands to a smart hub requesting a first smart device perform an action, wherein each of the plurality of invalid commands includes at least a first name for the first smart device, wherein the first name is not a valid name for the first smart device. Determining a trend within the retrieved plurality of invalid user verbal commands and a plurality of user responses, wherein the trend identifies the first name as being used a plurality of times and identifies the first smart device the user was referring to when the user used the first name. Automatically updating a namespace database to include the first name as an alias for a setup name for the first smart device.

Dynamic namespace modification for virtual agents

Aspects of the present invention disclose a method for synchronizing a voice reply from AI voice assistant based on user activities. The method includes one or more processors identifying a task of a user that corresponds to a voice query of the user. The method further includes generating a sequence of sub-activities of the task corresponding to the voice query of the user. The method further includes determining a completion status of each sub-activity of the sequence of sub-activities of the task corresponding to the voice query of the user based at least in part on activity data received from one or more computing devices in an operating environment of the user. The method further includes synchronizing a voice reply of a computing device with the sequence of sub-activities of the task based at least in part on the completion status of each sub-activity of the sequence.

Synchronizing a voice reply of a voice assistant with activities of a user

An illustrative embodiment includes a method for detecting whether a given item within input data is anomalous. The method includes: extracting activations at respective nodes of an autoencoder trained on the data, the activations comprising activations at the respective nodes for the given item within the data and for one or more other items within the data; calculating p-values corresponding to the respective nodes, wherein the p-value for a given node represents a proportion of the activations at the given node for the other items which are greater than the activations at the given node for the given item; determining at least one score at least in part by scanning for a subset of the respective nodes which maximizes a statistical scoring function applied to the corresponding p-values; and providing a visualization of at least the given item within the input data to a human user.

Identifying adversarial attacks with advanced subset scanning

Embodiments of the present invention describe predictively reconstructing a physical event using augmented reality. Embodiments describe, identifying relative states of objects located in a physical event area by using video analysis to analyze collected video feeds from the physical event area before and after a physical event involving at least one of the objects, creating a knowledge corpus including the video analysis and the collected video feeds associated with the physical event and historical information, and capturing data, by a computing device, of the physical event area. Additionally, embodiments describe identifying possible precursor events based on the captured data and the knowledge corpus, and generating a virtual reconstruction of the physical event using the possible precursor events, displaying, by the computing device, the generated virtual reconstruction of the predicted physical event, wherein the displayed virtual reconstruction of the predicted physical event overlays an image of the physical event area.

Predictive virtual reconstruction of physical environments

A first set of user data is received and a user profile is constructed based on the user data and in accordance with a sensitive service involving the user. A situational context is analyzed based on the first set of data. Personalized questions are generated, responsive to the user profile and to the situational context. The personalized questions are presented to a user corresponding to the user data and responses to same are received, including detection of user micro-expressions. The responses are analyzed, according to one or more machine learning models. A neural network model selects an action to be performed in response to analyzing the responses from the user; the action is a sensitive service involving the user. An apparatus is triggered to send a simple message service (SMS) message to a point of care service professional; the message recommends performance of the sensitive service on the user.

Enhanced user screening for sensitive services

Predicting infection risk by generating a first temporal graph of a first set of disease progression data, generating a second temporal graph of a second set of disease progression data, combining a first temporal graph node embedding and a second temporal graph node embedding, and generating a predicted infection risk according to the first temporal graph node embedding and the second temporal graph node embedding.

Predicting infection risk using heterogeneous temporal graphs

Embodiments for managing user concentration in a work environment are described. Information associated with a user is received. At least one characteristic of a task of the user is identified based on the information associated with the user. A concentration risk level associated with the user performing the task is determined based on the at least one characteristic of the task. A computing environment associated with the user performing the task is provided based on based on the concentration risk level.

Methods and systems for managing concentration in work environments

A system, method, and computer program product for verifying signatures. The system includes at least one processing component, at least one memory component, and a reference storage comprising a set of reference signatures. The system also includes a model generator configured to generate a signature model based on the set of reference signatures. Further, the system includes a verification component configured to receive a signature, and determine whether the signature is valid.

Multifactor handwritten signature verification

Smart speaker system mechanisms, associated with a smart speaker device comprising an audio capture device, are provided for processing audio sample data captured by the audio capture device. The mechanisms receive, from the audio capture device of the smart speaker device, an audio sample captured from a monitored environment. The mechanisms classify a sound in the audio sample data as a type of sound based on performing a joint analysis of a plurality of different characteristics of the sound and matching results of the joint analysis to criteria specified in a plurality of sound models. The mechanisms determine, based on the classification of the sound, whether a responsive action is to be performed based on the classification of the sound. In response to determining that a responsive action is to be performed, the mechanisms initiate performance of the responsive action by the smart speaker system.

Smart speaker system with cognitive sound analysis and response

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Nairobi, Kenya

Komminist S Weldemariam