International Business Machines Corporation

Slingshot Iot LLC

Kyndryl, Inc.

Doordash, Inc.

Arkose Labs Holdings, Inc.

Ashish Kundu

Clifford A Pickover

Michael S Gordon

James R Kozloski

Peter Kenneth Malkin

Suresh N Chari

Ajay Mohindra

Dimitrios Pendarakis

Amit Anil Nanavati

Komminist S Weldemariam

Arup Acharya

Ian M Molloy

Kapil Kumar Singh

Arjun Natarajan

Joshua F Payne

Valentina Salapura

Ruchi Mahindru

Vijay Krishnarao Naik

Kirk Alan Beaty

Sambit Sahu

Kevin W Brew

Qi Zhang

Dong Su

Ted A Habeck

Maja Vukovic

Arun Kwangil Iyengar

Mahesh Viswanathan

Roger C Raphael

David R Safford

Danny Soroker

Anuradha Bhamidipaty

Josyula R Rao

Mihai Christodorescu

Sreekrishnan Venkateswaran

Debasisha Padhi

Hasini Gunasinghe

Nalini Kanta Ratha

Nataraj Nagaratnam

Petr Novotny

Sreekanth Ramakrishna Iyer

Justin David Weisz

Rajesh Ramkrishna Bordawekar

Scott Schumacher

Paul Anthony Ashley

He Huang

Zehra Noman Sura

Sridhar R Muppidi

Ronald B Williams

Maryam Ashoori

Gong Su

Tian Cheng Liu

Stefan Berger

Abhishek Malvankar

Byung Chul Tak

Shubhi Asthana

Yasuharu Katsuno

John G Buckley

Julia L O'Halloran

Li Feng Zhang

Vittorio R Castelli

Ravi Nair

John Alan Bivens

Min Li

Eugen Schenfeld

Mukesh Kumar Mohania

Richard Harold Boivie

Biplav Srivastava

Arun Kumar

Harigovind Venkatraj Ramasamy

Yaoping Ruan

Oded Shmueli

Marc Philippe Stoecklin

Mu Qiao

Kenneth Alan Goldman

Rajesh M Desai

Thomas David Erickson

Yuan Yuan

Vikas Agarwal

Salman Abdul Baset

Neeran Mohan Karnik

Hugo M Krawczyk

Naga A Ayachitula

Pradeep Varma

Zhe Zhang

Girish Bhimrao Chafle

Apurva Kumar

Stacy Fay Hobson

Aniya Aggarwal

Hoang Tam Vo

Manas R Kumar Singh

Manish P Kurhekar

Johara Shireen Shahabuddin

Sanjana Sahayaraj

Alexandre Rademaker

Abhishek Kumar

Mahesh Gajwani

Megha Gupta

An apparatus and related method defend against adversarial queries. A policy enforcement hypergraph is constructed to express a set of security policies. Then, the hypergraph is repeatedly traversed to determine whether a user behavior is changing over time. The user behavior is measured by reference to a vertex or an edge in the hypergraph. If it is determined that the user behavior has changed over time an enforcement action is taken based on a security policy.

Defending against adversarial queries in a data governance system

A deep-learning based method evaluates similarities of entities in decentralized identity graphs. One or more processors represent a first identity profile as a first identity graph and a second identity profile as a second identity graph. The processor(s) compare the first identity graph to the second identity graph, which are decentralized identity graphs from different identity networks, in order to determine a similarity score between the first identity profile and the second identity profile. The processor(s) then implement a security action based on the similarity score.

Deep learning-based similarity evaluation in decentralized identity graphs

A computer-implemented method in accordance with one embodiment includes, in response to a submission of an input dataset to an artificially intelligent application, receiving an explanation from each module of the application. The modules are configured within the application in a serial sequence in which each module, upon receiving the input dataset and any input generated by an immediately preceding module of the serial sequence, generates output that is forwarded as input to a next module, if any, in the sequence. A determination is made that at least two of the received explanations are semantically inconsistent.

Semantic consistency of explanations in explainable artificial intelligence applications

Anomalous control and data flow paths in a program are determined by machine learning the program's normal control flow paths and data flow paths. A subset of those paths also may be determined to involve sensitive data and/or computation. Learning involves collecting events as the program executes, and associating those event with metadata related to the flows. This information is used to train the system about normal paths versus anomalous paths, and sensitive paths versus non-sensitive paths. Training leads to development of a baseline 'provenance' graph, which is evaluated to determine 'sensitive' control or data flows in the &#x201c;normal&#x201d; operation. This process is enhanced by analyzing log data collected during runtime execution of the program against a policy to assign confidence values to the control and data flows. Using these confidence values, anomalous edges and/or paths with respect to the policy are identified to generate a &#x201c;program execution&#x201d; provenance graph associated with the policy.

Policy-based detection of anomalous control and data flow paths in an application program

A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.

Controlling driving modes of self-driving vehicles

A method, computer system, and a computer program product for text bias identification and correction are provided. A first text corpus may be received. A designation of a second text corpus may be received. Words of the first text corpus may be embedded as a first word embedding in an embedding model. The first word embedding may be compared to a second word embedding in the embedding model to identify a first biased text in the first text corpus. The second word embedding may be from the second text corpus. A first replacement text portion may be generated as a substitute for the first biased text. The first replacement text portion may include a first unbiased text. The first biased text and the first replacement text portion may be presented.

Bias identification and correction in text documents

Preserving distributions of data values of a data asset in a data anonymization operation is provided. Anonymizing data values is performed by transforming sensitive data in a set of columns over rows of the data asset while preserving distribution of the data values in the set of transformed columns to a defined degree using a set of autoencoders and loss function. The autoencoders are base trained from preexisting data in a data assets catalog and actively trained during data dissemination. Parametric coefficients of the loss function are configured and the threshold is generated using policies from an enforcement decision for the data asset and data consumer. The loss function value of a selected row is compared to the threshold. Transformed data values of the selected row are transcribed to an output row when the loss function value is greater than the threshold and disseminated to the data consumer.

Learning to transform sensitive data with variable distribution preservation

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

A method provides a security action based on identity profile scores. One or more processors represent an identity profile as a knowledge graph. The processor(s) associate a set of changes of the identity profile across a plurality of identity networks with a fraud score. The processor(s) then implement a security action based on the fraud score.

Deep learning-based identity fraud detection

An example operation may include one or more of creating, by a blockchain user of a blockchain network, a world state checkpoint transaction requesting world state validation, endorsing, by one or more endorser nodes or peers, the world state checkpoint transaction, transferring endorsements to the blockchain user, recording, by an orderer node or peer, the endorsed world state checkpoint transaction into a block, validating and committing all transactions in the block, calculating and signing a hash of a current world state, by all blockchain nodes or peers of the blockchain network, and verifying, by the blockchain user, world state integrity from the calculated and signed hashes of the current world state.

Database world state integrity validation

A secure chain of data blocks is maintained at a given computing node. The given computing node is part of a set of computing nodes in a distributed network of computing nodes wherein each of the set of computing nodes maintains the secure chain of data blocks. The secure chain of data blocks maintained at each computing node comprises one or more data blocks that represent one or more accident related transactions associated with a vehicle. In response to a risk assessment operation, one or more data blocks are added to the secure chain of data blocks maintained at the given computing node.

Blockchain and cryptocurrency for real-time vehicle accident management

A computer-implemented method, system, and/or computer program product controls self-driving vehicles (SDVs). An emergency message is transmitted to a receiver within a self-driving vehicle (SDV). The emergency message describes an emergency state of an emergency vehicle and an identified future route of the emergency vehicle, where the identified future route is a planned route to an emergency destination for the emergency vehicle that includes a first pathway. In response to the SDV receiving the emergency message, the SDV is redirected, via an auto-control hardware system on the SDV, to drive to a second pathway that does not conflict with the identified future route of the emergency vehicle.

Self-driving vehicle's response to a proximate emergency vehicle

An explainable artificially intelligent (XAI) application contains an ordered sequence of artificially intelligent software modules. When an input dataset is submitted to the application, each module generates an output dataset and an explanation that represents, as a set of Boolean expressions, reasoning by which each output element was chosen. If any pair of explanations are determined to be semantically inconsistent, and if this determination is confirmed by further determining that an apparent inconsistency was not a correct response to an unexpected characteristic of the input dataset, nonzero inconsistency scores are assigned to inconsistent elements of the pair of explanations. If the application's overall inconsistency score exceeds a threshold value, the system forwards information about the explanation, the offending modules, and the input dataset to a downstream machine-learning component that uses this information to train the application to better respond to future input that shares certain characteristics with the current input.

An example operation may include one or more of generating a proposal to perform a ledger operation at a first node, informing one or more second nodes of the proposal, receiving a decision on consensus among the first node and the one or more second nodes for the proposal, and performing the ledger operation at the first node when there is consensus, wherein the ledger operation changes a state database of a ledger of the first node and wherein the state database corresponds to a blockchain stored in the ledger.

Index management for a database

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Elmsford, NY, United States of America

Ashish Kundu