International Business Machines Corporation

Daniel Gruhl

Alfredo Alba

Linda Ha Kato

Steven R Welch

Varun Bhagwan

Chad Eric DeLuca

Neal Ryan Lewis

Clemens Drews

Anna Lisa Gentile

Petar Ristoski

Cartic Ramakrishnan

Pablo N Mendes

Chris Kau

Meenakshi Nagarajan

Ismini Lourentzou

Andrew Stephen Tomkins

Dharmendra S Modha

Alexander Andreopoulos

Michael S Karasick

Jan Hendrik Pieper

April L Webster

Christian Benjamin Kau

Anni Rosa Coden

Tyrone W A Grandison

Ramanathan V Guha

Joseph F Terdiman

Chandrasekhar Narayan

Nathaniel H Park

Sridhar Rajagopalan

Mark Andrew Smith

Matthew Denesuk

Kevin Snow McCurley

Dmitry Zubarev

Daniel Norin Meredith

James L Hedrick

Daniel Sanders

Rajesh M Desai

Kenneth Lee Clarkson

Kevin Haas

Jeffrey Alan Kusnitz

Shanmugasundaram Ravikumar

Nimrod Megiddo

Sandeep Gopisetty

Roberto Javier Bayardo

Laura Chiticariu

Savitha Srinivasan

Jason Yeong Zien

John T E Timm

Karen Wolfe Brannon

Ronald Fagin

Jasmine Novak

Christine M Robson

Joerg Meyer

Tyrone Wilberforce Andre Grandison

Jonathan Rogelio Razo

Candidate material for polymerization can be received. One or more desired features in the candidate material can be identified. A machine learning model can be trained to generate a new material having one or more of the desired features. Permissively, the candidate material can be determined from running a machine learning classification model that ranks a plurality of material as candidates. Permissively, the generated new material can be input to the machine learning classification model, for the machine learning classification model to include in ranking the plurality of material as candidates.

Expert-in-the-loop AI for materials generation

A set of material candidates expected to yield materials with target properties can be generated. A subject matter expert's decision indicating accepted and rejected material candidates from the set of material candidates can be received. Based on the subject matter expert's input, a machine learning model can be trained to replicate the subject matter expert's decision.

Expert-in-the-loop AI for materials discovery

An embodiment includes generating a query prompting a user to select from among a plurality of response options related to a first query set of objects. The embodiment also receives, responsive to the query, user input representative of a selected response option selected by the user from among the plurality of response options. The embodiment also calculates a plurality of weight values for respective ones of a plurality of similarity matrices based on the selected response option, where the plurality of similarity matrices include respective different sets of similarity values, each set of similarity values comprising similarity values representative of similarities of respective pairs of the plurality of objects. The embodiment stores a designated similarity matrix that is selected from among the plurality of similarity matrices based at least in part on a weight value from among the plurality of weight values assigned to the designated similarity matrix.

Identifying similarity matrix for derived perceptions

A computer-implemented method according to one embodiment includes receiving snapshot data for a node within a data center; determining one or more candidate labels for one or more software applications running on the node, utilizing the snapshot data; implementing a validation of the one or more candidate labels to determine one or more validated labels; and training a machine learning model, utilizing the one or more validated labels and the snapshot data.

Labeling software applications running on nodes of a data center

A method for ranking relevance of documents includes using a set of queries, searching a corpus of documents for a set of candidate documents with information relevant to the set of queries. The method further includes ranking the set of candidate documents by a deep learning processing system according to relevance to respective ones of the set of queries. The method additionally includes responsive to user input, revising the ranked set of candidate documents to produce a revised ranked set of candidate documents. The method further includes using the revised ranked set of candidate documents to retrain the deep learning processing system. The method still further includes performing a categorization of the set of candidate documents by the retrained deep learning processing system.

Cognitive horizon surveillance

Systems, computer-implemented methods, and computer program products to transform a lexicon that describes an information asset are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a term validation component that can determine from a subject matter expert, a validated term that can indicate validation of a candidate term that describes an information asset. The computer executable components can further comprise a lexicon transforming component that, based on the validated term, can transform a lexicon that describes the information asset, by incorporating the validated term into the lexicon.

Transforming a lexicon that describes an information asset

One embodiment provides a method that includes determining candidate ontologies for alignment from multiple available knowledge bases. An initial target ontology is selected from the candidate ontologies and correcting the initial selected ontology with received refinement input. Concepts in the selected initial ontology are aligned with concepts of the target ontology using a deep learning hierarchical classification with received review input. A user is assisted to build, change and grow the selected initial ontology exploiting both the target ontology and new facts extracted from unstructured data.

User-centric ontology population with user refinement

Embodiments relate to a system, program product, and method for use with a physical computing device to process a data access request. The requested data is encrypted with two keys, including a physical device authentication key and a transient key. Access to the data requires authentication on both the device level and situational level. Device situational data is monitored, which includes selectively enabling access to the requested data and de-activation of the transient key in response to a change in the monitored situational data. The transient key de-activation removes access to the requested data.

Transient management of data encryption and authentication

Embodiments relate to a computer system, computer program product, and method to prevent unauthorized file dissemination and replication. A file parameter is defined, with the defined file parameter including a file dissemination characteristic. The file is encoded with the defined file parameter as file metadata. Dissemination and replication of the file is managed responsive to the encoded file parameter. The defined parameter is assessed along with a physical replication destination. The file is selectively replicated or transmitted responsive to the file parameter and the destination assessment.

Geography aware file dissemination

Embodiments relate to a system, program product, and method for information extraction and annotation of a data set. Neural models are utilized to automatically attach machine annotations to data elements within an unlabeled data set. The attached machine annotations are evaluated and a score is attached to the annotations. The score reflects a confidence of correctness of the annotations. A labeled data set is iteratively expanded with selectively evaluated annotations based on the attached score. The labeled data set is applied to an unexplored corpus to identify matching corpus data to populated instances of the labeled data set.

Collaborative information extraction

At least some embodiments are directed to a computer-implemented method that comprises receiving original input text that includes a term, comparing a definition of the term to definitions of multiple candidate replacement terms to generate a set of candidate replacement terms, and substituting each of the candidate replacement terms in the set for the term in the original input text to produce a plurality of modified input texts. The method also comprises determining the grammatical accuracy of each of the plurality of modified input texts, comparing meanings of the modified input texts to a meaning of the original input text, and modifying the set of candidate replacement terms based on the determinations of grammatical accuracy and the comparisons of the meanings. The method still further comprises ranking the modified set of candidate replacement terms using one or more criteria, and displaying the ranking on a display.

Identifying linguistic replacements to improve textual message effectiveness

A verification and authorization method, system, and computer program product include verifying, via a receiving device that receives a verification sound packet, an identity of a trusted caller via the verification sound packet, the verification sound packet including an asymmetrically encrypted payload sent by the trusted caller.

Authentication and authorization via vocal track frequency channel

In an approach to corpus expansion using lexical signatures, one or more computer processors retrieve a donor corpus of text, wherein the donor corpus includes a plurality of documents. One or more computer processors generate a document signature for each of the plurality of documents in the donor corpus. One or more computer processors retrieve a target corpus of text for expansion. One or more computer processors generate a corpus signature for the target corpus. One or more computer processors compare each document signature to the corpus signature. Based on the comparison, one or more computer processors determine a similarity score for each document signature. One or more computer processors rank the plurality of documents by the similarity score. One or more computer processors add one or more top-ranked documents of the plurality of documents to the target corpus.

Corpus expansion using lexical signatures

Embodiments relate to a system, program product, and method for dictionary membership management directed at identifying ambiguity in semantic resources. A dictionary of seed terms is applied to a text corpus and matching items in the corpus are identified. The linguistic properties for each matching item are characterized and a context pattern of each matching item is constructed. Each context pattern is applied to the dictionary and matching content between the seed terms and the context pattern is identified and quantified. Lexicon items from the dictionary that have anomalous behavior reflected in the quantification are identified. One or more seed words identified as having anomalous behavior are selectively removed from the dictionary.

Identifying ambiguity in semantic resources

One embodiment provides a method including identifying all computing nodes and connections associated with the computing nodes in a data center based on running processes in the data center that communicate with one another. For each computing node, running processes are identified using natural language processing (NLP) by: iteratively refining a rule set that enables processing of surveillance information from the data center into an initial map of systems and applications in the data center, and extracting known process entities according to predetermined rules from the rule set. A visual dependency representation of the computing nodes and the processes running on the computing nodes is generated.

Data center cartography bootstrapping from process table data

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San Jose, CA, United States of America

Daniel Gruhl