International Business Machines Corporation

Manish Anand Bhide

Phani Kumar V U Ayyagari

Mukesh Kumar Mohania

Srinivas Kiran Mittapalli

Kuntal Dey

Sameep Mehta

PurnaChandra Rao Jasti

Madan K Chukka

Seema Nagar

Sriram Kolinjivadi Padmanabhan

Ravi Chandra Chamarthy

Ajay Gupta

Sumit Negi

Madhavi Katari

Diptikalyan Saha

Krishna Kishore Bonagiri

Prateek Goyal

Pranay Kumar Lohia

Manoj Kumar Agarwal

Bhavani Kumar Eshwar

Girish Venkatachaliah

Nishtha Madaan

Vijay Kumar Ananthapur Bache

Himanshu Gupta

Kiran Kumar Anumalasetty

Vishal Singh Batra

Nithinkrishna Shenoy

Purna Chandra Jasti

Arunkumar Kalpathi Suryanarayanan

Sai Prasanth Vuppala

Harivansh Kumar

Sarbajit Kumar Rakshit

Jean-Claude Mamou

Shyam R Mudambi

Venkat Chakravarthy

Nagaraju Narla

Rajesh K Pandey

Venkata R Madugundu

Ruchir Puri

Sam Sampson Lightstone

Sougata Mukherjea

Krishnasuri Narayanam

Scott Schumacher

Namit Kabra

Pramod Vadayadiyil Raveendran

Rema Ananthanarayanan

Daniele Costante Zilio

Jhilam Bera

Laurent Sebastien Mignet

Samiulla Zakir Hussain Shaikh

Rajmohan Chandrahasan

Alan P Plante

Veera Maddipati

Anil Kumar Thiramdas

Prashant Pandurang Mundhe

Vivek S Tirumalaraju

Govind Kothari

Prem Piyush Goyal

Phani Kumar Ayyagari V U

Jasti Purnachandra Rao

Samiulla Shaikh

Shikhar Kwatra

Vijay Ekambaram

Zachary A Silverstein

Yannick Saillet

Sachindra Joshi

Biplav Srivastava

Sudheesh S Kairali

Amit Anil Nanavati

Ashish Verma

Saravanan Sadacharam

Kush Raj Varshney

Paul Sherwood Taylor

Srinivasan S Muthuswamy

Rajeev Gupta

Subhendu Das

Sri Harsha Varada

Om Dadaji Deshmukh

Amir Bar-Or

Jonathan Limburn

Karthikeyan Natesan Ramamurthy

Gagandeep Singh

Harikrishna K Manchineni

Calvin Stacy Powers

Aniya Aggarwal

Binoy Thomas

Sudhir Alluri

Sandeep Hans

Srikanth Kondapaneni

Rajesh V Patil

Negandhi Vrunda Ravindra

Atul Laxmikant Kalantri

William B Lobig

Sunita Rani Nayak

Swapna Somineni

Purna Chandra R Jasti

Bhanu Mudhireddy

Sebastian Ertel

Srilakshmi Kotwal

Mukul Madhukar Joshi

A method includes receiving, by a computing device, concepts of a domain; determining, by the computing device, objects relevant to the concepts; generating, by the computing device, a new image by stitching the relevant objects together; determining, by the computing device, whether the new image is accurate or inaccurate; and in response to determining the new image is inaccurate, propagating, by the computing device, the inaccurate new image back to a convolutional neural network (CNN).

Object stitching image generation

A machine learning model data quality improvement detection tool is provided for identifying an accurate reference group and an accurate monitored group of a machine learning model. The tool monitors a behavior of the machine learning model for a predetermined time frame. The tool compares a determined fairness metric a pre-defined fairness threshold. Responsive to the fairness metric failing to meet the pre-defined fairness threshold, the tool modifies the monitored group to include a first portion of the reference group. The tool compares a newly determined fairness metric to the pre-defined fairness threshold. Responsive to the newly determined fairness metric meeting the pre-defined fairness threshold, the tool identifies the modified monitored group including the first portion of the user-defined reference group as a new monitored group and the modified reference group without the first portion of the user-defined reference group as a new reference group.

Decreasing error in a machine learning model based on identifying reference and monitored groups of the machine learning model

Computer technology for determining a possibility of an emerging hearing issue in a user based on the user's auditory interactions with a mobile device (for example, a smartphone), a wearable computer device (for example, a smart watch) and/or a virtual assistant (for example, an internet of things (IoT) enabled doorbell).

Computer technology for prediction of hearing issues

Provided are a computer program product, system, and method for rendering an extended-reality representation of a virtual meeting. An extended reality representation is generated of a virtual meeting including avatars associated with participants in the virtual meeting to render in extended-reality displays of the participants. An extended-reality representation of a virtual controller corresponding to a real-world controller in a real-world location is generated to render in at least one extended-reality display of at least one participant in the virtual meeting. Participant input is received from one of the at least one participant to control the virtual controller. The participant input is translated to control the virtual controller to commands to control the real-world controller. The commands are transmitted to the real-world controller at the real-world location to control the real-world controller to perform actions in the real-world location to carry out the transmitted commands.

Rendering an extended-reality representation of a virtual meeting including virtual controllers corresponding to real-world controllers in a real-world location

Disclosed aspects include computer-implemented method for preventing Carpal Tunnel Syndrome (CTS), a computer program product, and a laptop computer. One embodiment of the method may comprise collecting, by a device having an associated keyboard, data of a user's interaction with the keyboard. The method may further comprise analyzing, by a first machine learning model, the audiovisual data to identify ergonomic issues in the user's interaction with the keyboard. The method may further comprise notifying the user of the identified ergonomic issues in real time.

Preventing carpal tunnel syndrome

A computer-implemented method according to one embodiment includes identifying an occurrence of accuracy drift by a trained model; identifying data associated with the accuracy drift, utilizing a drift detection model (DDM) constructed for the trained model; applying the data associated with the accuracy drift to a decision tree to determine a feature space and specific subset of the data causing the accuracy drift; analyzing a distribution of features within the feature space for the specific subset of the data causing the accuracy drift to determine specific features of the data causing the accuracy drift; and returning the specific features of the data causing the accuracy drift.

Explaining accuracy drift in production data

A method, system, and computer program product for generating lineage events of machine learning models. The method may include identifying a machine learning model with missing lineage. The method may also include generating a creation event and deployment event for the machine learning model. The method may also include generating a version change event for the machine learning model. Generating the version change event may include identifying one or more predicted data points with a low model confidence; rescoring the one or more predicted data points based on the machine learning model at a second time period; determining that the updated one or more predicted data points are significantly different than the one or more predicted data points; and determining that there is a new version of the machine learning model. The method may also include creating a lineage path for the machine learning model.

Generation of machine learning model lineage events

A method, computer system, and computer program product for explaining time series machine learning model are provided. The embodiment may include determining a first order difference in time series input data and historical training data. The embodiment may also include performing perturbation of time series input data based on the determined first order difference and the determined historical training data. The embodiment may further include computing closeness of the determined first order difference in the historical training data to the determined first order difference in the time series input data. The embodiment may also include generating a uniform random sample of first value input to a time series machine learning model. The embodiment may further include determining values of other inputs to the time series machine learning model based on the generated random sample and a random sample from the historical training data first order differences.

Explaining machine learning based time series models

In an approach to facilitate contactless remote shopping assistance, a computer-implemented method includes one or more processors configured for receiving first order data from a user device corresponding to an order of items to be retrieved from a parking lot of a retail entity proximate to a retail entity location, transmitting the first order data to the retail entity, and assigning the order to a mobility device stationed at the retail entity. The method further includes transmitting order retrieval instructions to the mobility device causing the mobility device to navigate the retail to secure the items, receiving an indication that the mobility device secured the items, and transmitting order delivery instructions to the mobility device. The method further includes receiving a second indication that the mobility device transported the items to the parking lot and placed the items in a vehicle associated with a user of the user device.

Contactless remote shopping assistance

In an approach, a processor receives a request to access an electronic resource from a device. A processor causes the device to generate a Completely Automated Public Turing test (CAPTCHA), where the CAPTCHA comprises: a virtual keyboard; an ordered string of characters required to be input; and presentation of a highlighted key of the virtual keyboard on the device, wherein (i) the highlighted key is a first key visually distinct from other keys of the virtual keyboard and (ii) the first key corresponds to a character of the ordered string of characters. A processor receives a result of the CAPTCHA from the device. A processor performs an action based on the result.

Virtual keyboard CAPTCHA

A computer-implemented method includes: receiving, by a computing device and from a user device, a request to validate an application program interface (API); validating, by the computing device, the API by performing a fetch analysis using different user profiles; returning, by the computing device and to the user device, a result of the fetch analysis; validating, by the computing device, the API by performing an insert/update analysis using the different user profiles; and returning, by the computing device and to the user device, a result of the insert/update analysis.

Data governance policy recommendations for application program interfaces

An example operation may include one or more of storing a batch scoring engine and an application programming interface (API) for the batch scoring engine, receiving a trigger to perform a batch prediction process, reading input data from a source data store and executing, via the batch scoring engine, one or more predictive models on the input data to generate a predictive output and metadata associated with the predictive output, storing the predictive output and the metadata in a target data store, and updating the API with a location of the predictive output within the target data store and a location of the metadata within the target data store.

API management for batch processing

In an approach, a processor receives a photograph requirement from an image of an application, the photograph requirement associated with a photograph to be uploaded to the application. A processor adjusts a camera setting based on the photograph requirement. A processor captures the photograph in accordance with the adjusted camera setting.

Automatically upload photographs according to requirements

A system and related method score a fairness of an outcome model. The method comprises receiving a set of original transaction records (OTRs), and selecting an OTR subset of the OTRs according to a subset selection criteria in order to reduce a number of OTRs to send to outcome model. For each OTR in the subset a perturbed transaction record (PTR) is created based on the OTR that includes changing at least one attribute in the PTR from the OTR, sending the OTR and the PTR to the outcome model, receiving an OTR outcome and a PTR outcome from the outcome model, and determining a record bias score for the OTR outcome and the PTR outcome respectively that indicates bias in the respective outcome. The OTR and the PTR bias score are stored in a bias determination system (BDS) database.

Batch scoring model fairness

Early indications of application programming interface (API) usage are identified by correlation to particular issues with the API including singular and mutual consistency, completeness, accuracy, and staleness. Analysis of API input and output along with data type and formatting information facilitates identification of the API issues. Establishing a correlation between API usage and issues supports early detection of potential usage reduction on a case-by-case level. Corrective action to resolve identified issues may be performed in a timely manner to maintain usage levels.

Correlating usage to issues in application programming interfaces

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Hyderabad Telangana, India

Manish Anand Bhide