International Business Machines Corporation

Sekou Lionel Remy

Roman Vaculin

Kush Raj Varshney

Aisha Walcott

Michael James Hind

Nelson Kibichii Bore

Ravi Kiran Raman

Eleftheria K Pissadaki

Celia Cintas

Charles Muchiri Wachira

William Ogallo

Jonathan Lenchner

Victor Abayomi Akinwande

Samuel Osebe

Maroun Touma

Kugamoorthy Gajananan

Akil Khamisi Sutton

Toby Kurien

Girmaw Abebe Tadesse

Otis Smart

Reha Yurdakul

Myneeka Holloway

Stephen Oduor Odhiambo

Fiona Mugure Matu

Lauren Quigley

A computer-implemented method, in accordance with one aspect of the present invention, includes analyzing timing data for stages in a pipeline running in an edge system for detecting starvation of one or more of the stages in the pipeline. In response to detecting one or more of the stages being starved, starvation avoidance is performed for mitigating the starvation of the starving stage(s). A computer-implemented method, in accordance with another aspect of the present invention, includes analyzing timing data for pipelines running in parallel in an edge system for detecting starvation of one or more of the pipelines. In response to detecting one or more of the pipelines being starved, starvation avoidance is performed for mitigating the starvation of the starving pipeline(s).

Prevention of resource starvation across stages and/or pipelines in computer environments

Computer-implemented methods selecting a cloud computing system for deployment of a workload are provided. Aspects include obtaining an input function that characterizes the workload and obtaining a plurality of performance models, wherein each of the plurality of performance models corresponds to one of a plurality of cloud computing systems including the cloud computing system. Aspects also include applying the input function into each of the plurality of performance models, calculating one or more of a gain, a phase shift, and a stability margin for each of the plurality of performance models, and selecting the cloud computing system from the plurality of cloud computing systems based at least in part on the one or more of the gain, the phase shift, and the stability margin.

Benchmarking and prediction of cloud system performance

In an approach to improve platform modification by dynamically modifying content based on user feedback. Embodiments suggest one or more solutions and recommendations, via a user interface, to one or more users that comply with one or more standards, regulations, and resources of an organization. Further, embodiments verify the solutions and recommendations that comply with the one or more standards, regulations, and resources of the organization, and automatically generate a prototype solution. Additionally, embodiments provide, by the user interface, one or more augmented solutions for the one or more users, wherein augmented solutions are provided to the one or more users requiring a temporary solution; and label and cluster user feedback and associated solutions of the one or more users to provide effective solutions for future users based on a similarity of the feedback of the one or more users.

Dynamically modifying a platform based on user feedback

An example operation may include one or more of generating a plurality of successive data points of an iterative simulation based on predefined checkpoints, each data point identifying an evolving state of the iterative simulation with respect to a previous data point among the successive data points, transmitting a blockchain request for validating state data within a first data point among the plurality of successive data points to a first subset of endorsing nodes of a blockchain network, and transmitting a blockchain request for validating state data within a second data point among the plurality of successive data points to a second subset of endorsing nodes which are mutually exclusive from the first subset of endorsing nodes of the blockchain network for parallel endorsement of the first and second data points.

Distributed platform for computation and trusted validation

An example operation may include one or more of obtaining data of a simulation, identifying checkpoints within the simulation data, generating a plurality of sequential data structures based on the identified checkpoints, where each data structure identifies an evolving state of the simulation with respect to a previous data structure among the sequential data structures, and transmitting the generated sequential data structures to nodes of a blockchain network for inclusion in one or more data blocks within a hash-linked chain of data blocks.

A computer system may create a language model corpus including multilingual alignment for training a combined language model and train (or pre-train) the combined language model. The computer system may create an adverse medication reaction corpus to include adverse medication reaction utterances and label an N-gram of an utterance in the adverse medication reaction utterances as a response to query, for multiple N-grams. The computer system may generate a code-mixed utterance model to perform code-mixed utterances in a turn by turn dialogue, by at least adding additional output layer including at least a start vector, language vector, and a query vector including at least the labeled N-gram, which are additional to the combined language model's predicted next words.

Goal-oriented conversation with code-mixed language

A translation-engine suggestion method, system, and computer program product include identifying probes for third-party translation-engines for an input text, segmenting sections of the input text into a plurality of segments according to the identified probes, fragmenting the input text into fragments according to the segments, applying each fragment to the identified probe using the corresponding third-party translation-engine, and outputting a translation by combining each fragment.

Translation engine suggestion via targeted probes

An example operation may include one or more of generating a data frame storing content of a simulation, compressing the simulation content within the data frame based on previous simulation content stored in another data frame to generate a compressed data frame, and transmitting the compressed data frame via a blockchain request to one or more endorsing peer nodes of a blockchain network for inclusion of the compressed data frame within a hash-linked chain of blocks of the blockchain network.

A bias detection method, system, and computer program product include creating a context of an applicant based on a profile of the applicant and a context of a reviewer based on a profile of the reviewer, predicting a probability of overlapping data points between the applicant and the reviewer, building enriched embeddings for a deep learning model based on the context of the applicant, the context of the reviewer, the overlapping data points, and text from a review and a final decision by the reviewer, and calculating a bias score via a deep learning model run over the enriched embeddings.

Bias detection and estimation under technical portfolio reviews

A physical device may include an embedded microprocessor and memory. The memory is loadable with information associated with a holder of the card device. The microprocessor communicates with another device responsive to detecting said another device is within a threshold proximity. The microprocessor further may negotiate for exchanging of the information with said another device based on current context.

Computer-implemented agent assisted electronic business cards

Systems and methods for detecting gaps in netting include constructing a depth map from electromagnetic signals associated with netting surrounding an area, the depth map including measurements of distance of each strand of the netting along an axis perpendicular to a plane formed by the netting relative to the detector. Each segment of netting is identified including each strand of the netting in a side of the netting facing the detector. Locations of gaps in the netting are identified according to the depth map. A user is alerted to the presence and locations of gaps by sending a communication to a computing device.

Growing community of inventors

Nairobi, Kenya

Sekou Lionel Remy