International Business Machines Corporation

Merative US L.p.

Globalfoundries Inc.

Kyndryl, Inc.

Ramani Ranjan Routray

Yang Song

Mu Qiao

Rakesh Jain

Nagapramod S Mandagere

Kavita Chavda

Sandeep Gopisetty

James E Olson

Sumant Padbidri

Sandeep Madhav Uttamchandani

Bernhard Julius Klingenberg

Luis Angel D Bathen

Gabor Madl

Kaladhar Voruganti

Chung-hao Tan

John Jason Auvenshine

Stanley C Wood

Steven Pantridge

Sunhwan Lee

Pankaj S Bavishi

Kang-Won Lee

Sheng Hua Bao

Gauri Sudhir Shah

Billy S Brewer

Dinesh C Verma

Dakshi Agrawal

Prasenjit Sarkar

Pin Zhou

Seshashayee Sankarshana Murthy

Sandip Agarwala

Gabriel Alatorre

Eitan D Farchi

Roger C Raphael

Akshat Verma

Ann Corrao

Orna Raz

Abhinandan Kelgere Ramesh

Riyazahamad Moulasab Shiraguppi

Dean Hildebrand

Murthy V Devarakonda

Norbert George Vogl

Rajesh M Desai

David Alson George

Konstantinos Magoutis

Luis Angel Bathen

David P Goodman

Bruce Light Hillsberg

Tom Hagan

Robin H Lewis

Jeff N Marinstein

Esha Seth

Seraphin Bernard Calo

Divyesh Jadav

Jarir Kamel Chaar

Aameek Singh

Eric Kevin Butler

Robert Michael Rees

David Lynn Merbach

Venkat K Balagurusamy

Cartic Ramakrishnan

Vasily Tarasov

Rohit Jain

Shripad Jayant Nadgowda

Donna N Eng Dillenberger

Samir Tata

Radha P Ratnaparkhi

Patrick B Heywood

Srinivasan Balasubramanian

Roberto C Pineiro

Jorge D Acuña

Ashwin Dhinesh Kumar

Kiyoung Yang

Subhojit Roy

Min Li

Feng Wang

Dulce Beatriz Ponceleon

Colin Scott Dawson

Sangeetha Seshadri

Chatschik Bisdikian

Heiko Hary Ludwig

David Randall Blea

Inderpal Singh Narang

Steve Richard White

Aly Megahed

Shripad Nadgowda

Mickey Iqbal

Dhaval Kiritbhai Shah

Upendra Sharma

Mohamed Mohamed

Thomas K Clark

Diego A Masini

Jorge Daniel Acuna

Michael L Lamb

Marcel Zalmanovici

Smita J Raut

Yuya Jeremy Ong

Pathirage D Perera

Norman Jerome Pass

Nan Liu

Harsha D Gunatilaka

An apparatus includes a cable having two ends and at least two object markers coupled to the cable configured to enable augmented reality (AR) detection of each end of the cable among a plurality of cables. A computer-implemented method using augmented reality (AR) technology includes selecting a cable of interest and identifying an object marker positioned toward a first end of the cable of interest. The method also includes storing the object marker, scanning a plurality of cables, and identifying a second end of the cable of interest based an instance of the object marker positioned toward the second end of the cable of interest. A computer program product for detecting ends of cables using augmented reality (AR) technology includes a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a computer to cause the computer to perform the foregoing method.

Identifying cable ends using augmented reality

An example operation may include one or more of receiving a request to certify a digital record, retrieving a first hashed data value of the digital record from a data block included among a first hash-linked chain of blocks on a first blockchain, retrieving a second hashed data value of the digital record from a second data block included among a second hash-linked chain of blocks on a second blockchain which is different from the first blockchain, determining whether the digital record is valid based on a cross-validation of the first hashed data value and the second hashed data value, and storing the determination of the validity of the digital record in a data block among a third hash-linked chain of blocks.

Cross-chain validation

Mechanisms are provided for training a computer implemented model. The mechanisms perform multiple instances of training of the computer implemented model, where each instance of training of the computer implemented model comprises training the computer implemented model using a different training data set to generate a different instance of a trained computer implemented model. The mechanisms generate computer implemented model results after each instance of training by executing the corresponding instance of the trained computer implemented model. The mechanisms record differences in the instances of training of the computer implemented model in association with corresponding identifiers of the instances of trained computer implemented model and corresponding computer implemented model results. The mechanisms analyze the recorded differences and the corresponding computer implemented model results, and generate an output indicating a correlation between recorded differences and corresponding computer implemented model results.

Computer model machine learning based on correlations of training data with performance trends

A mechanism is provided in a data processing system having a processor and a memory. The memory comprises instructions which are executed by the processor to cause the processor to implement a training system for finding an optimal surface for hierarchical classification task on an ontology. The training system receives a training data set and a hierarchical classification ontology data structure. The training system generates a neural network architecture based on the training data set and the hierarchical classification ontology data structure. The neural network architecture comprises an indicative layer, a parent tier (PT) output and a lower leaf tier (LLT) output. The training system trains the neural network architecture to classify the training data set to leaf nodes at the LLT output and parent nodes at the PT output. The indicative layer in the neural network architecture determines a surface that passes through each path from a root to a leaf node in the hierarchical ontology data structure. The training system trains a classifier model for a cognitive system using the surface and the training data set.

Optimizing hierarchical classification with adaptive node collapses

Techniques are provided detecting diluted drugs using machine learning. Measurements and images corresponding to a product are obtained, wherein the product is formulated as a liquid, and wherein the measurements and images capture physical, spectral, optical, and/or chemical properties of the product. The measurements and images are provided to a machine learning model, wherein the machine learning model is trained using data generated from interactive learning modules (e.g., a generative adversarial network). The machine learning model detects whether the product or chemical is a real or counterfeit product. In addition, these techniques may be used by practitioners (e.g., medical personnel dispensing a prescribed dosage of a drug with a specific dilution level) to detect prescription errors at the point of administration.

Artificial intelligence for robust drug dilution detection

Embodiments relate to a system, program product, and method for smart contract implementation and management. A request for resources is modeled and a reservation of resources is captured in a first computation model interface. A provision of services is modeled as a second computation model interface. Compatibility of the first and second computation model interfaces is verified. Input and output actions are synchronized between the first and second computation model interfaces responsive to the compatibility verification. A smart contract is composed as a third computation model interface to model negotiation of contractual terms, including the captured resources with the provision of services. The composed smart contract is recorded in an operatively coupled immutable venue.

Formal verification of smart contracts

Techniques are provided for detecting counterfeit products. Measurements and images corresponding to a product are obtained, wherein at least a portion of the measurements or images are obtained from a mobile/IoT/IoB device. The measurements and images are provided to a trained machine learning model to progressively analyze the measurements and images. Based on the progressive analysis, a determination/prediction is made with an associated confidence score as to whether the product is real or counterfeit.

Counterfeit pharmaceutical and biologic product detection using progressive data analysis and machine learning

Methods, computer program products, and systems are presented. The methods include, for instance: analyzing a dataset associated with a service provided by the data protection service provider in order to determine a policy for when and how to replicate the respective components of the dataset corresponding to the service from a source site to a target site, such that the target site may perform the service with a minimum cost.

Optimized disaster-recovery-as-a-service system

A method, computer system, and a computer program product for assessing a likelihood of success associated with developing at least one machine learning (ML) solution is provided. The present invention may include generating a set of questions based on a set of raw training data. The present invention may also include computing a feasibility score based on an answer corresponding with each question from the generated set of questions. The present invention may then include, in response to determining that the computed feasibility score satisfies a threshold, computing a level of effort associated with developing the at least one ML solution to address a problem. The present invention may further include presenting, to a user, a plurality of results associated with assessing the likelihood of success of the at least one ML solution.

Estimating feasibility and effort for a machine learning solution

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to configure the processor to implement a cognitive service for cognitive model tuning with rich deep learning knowledge. The mechanism performs a first model training operation and records training data set and hyperparameter information for the model in a database. The mechanism performs a model testing operation using a testing data set and records metric values that result from the model testing in the database. For a next model training operation for a given model, the mechanism performs an anomalies check for the given model. The mechanism performs a difference comparison on the training data set, hyperparameter information, and the metric values. The mechanism generates a recommendation of a training data set and hyperparameters for the next model training operation. The mechanism performs the next model training operation.

Cognitive model tuning with rich deep learning knowledge

A computer system trains a machine learning model. A vector representation is generated for each document in a collection of documents. The documents are clustered based on the vector representations of the documents to produce a plurality of clusters. A training set is produced by selecting one or more documents from each cluster, wherein the selected documents represent a sample of the collection of documents to train the machine learning model. The machine learning model is trained by applying the training set to the machine learning model. Embodiments of the present invention further include a method and program product for training a machine learning model in substantially the same manner described above.

Generating training sets to train machine learning models

A mechanism is provided in a data processing system for training a computer implemented model. The mechanism determines an operation for which the computer implemented model is to be trained. The mechanism performs a statistical analysis of an enterprise dataset for an enterprise to generate one or more statistical distributions of cases and features correlated with the operation for which the computer implemented model is to be trained. The mechanism selects a subset of cases in the enterprise dataset for annotation based on the one or more statistical distributions of cases and features. The mechanism annotates the selected subset of cases to generate an annotated training dataset. The mechanism trains the computer implemented model, using the annotated training dataset, to perform the operation.

Cognitive data preparation for deep learning model training

Mechanisms are provided to implement an annotation mechanism allows users to annotate documents with annotations for processing by a cognitive medical system. The annotation mechanism receives, via a user interface, a user selection of an electronic document for annotation, and determines one or more domains associated with the selected electronic document from an analysis of metadata associated with the selected electronic document. The annotation mechanism retrieves a predefined set of annotations associated with each determined domain, and presents the predefined set of annotations as user selectable elements. The annotation mechanism receives, via the user interface, a selection of one or more annotations in the predefined set of annotations to be associated with the selected portion of the selected electronic document, and generates annotation metadata associating the selected portion using the selected one or more annotations. The annotation mechanism then generates an annotated electronic document using the annotated metadata.

Annotating documents for processing by cognitive systems

Intelligent cognitive assistants for decision-making are provided. A first plurality of decisions made by a first healthcare provider during treatment of a first patient is monitored. For each respective decision of the first plurality of decisions, one or more corresponding medical attributes of the first patient that were present at a time when the respective decision was made are determined. A cognitive assistant is trained, using an imitation learning model, based on each of the first plurality of decisions and the corresponding one or more medical attributes of the first patient. Subsequently, one or more medical attributes of a second patient are received, and a first medical decision is generated by processing the one or more medical attributes of the second patient using the cognitive assistant.

Cognitive assistant for aiding expert decision

From metadata corresponding to a narrative text, a first encoding is constructed, the first encoding comprising a standardized text string, the first encoding formed according to an encoding scheme. A specified portion of the standardized text string of the first encoding is marked as an anchor term. A correspondence between the first encoding and a second encoding is tested using the encoding scheme and a Natural Language Processing engine, responsive to finding the anchor term within the narrative text. The second encoding corresponds to a text window. The text window comprises a portion of the narrative text comprising an instance of the anchor term and a word within a predetermined distance from the instance. Responsive to the second encoding being identical to the first encoding, the narrative text is annotated, the annotating creating new data linking the narrative text with the second encoding.

Encoding-assisted annotation of narrative text

Growing community of inventors

San Jose, CA, United States of America

Ramani Ranjan Routray