International Business Machines Corporation

Daby Mousse Sow

John Sidney Davis, Ii

Deepak Srinivas Turaga

Nagui Halim

Maria Rene Ebling

Archan Misra

John Alan Bivens

Min Wang

Youssef Drissi

Srinivasan Parthasarathy

Joel W Branch

Marion Lee Blount

Alain E Biem

Hui Lei

Chatschik Bisdikian

Lisa Amini

Gang Luo

Venkata Nagaraju Pavuluri

Timothy Rea Dinger

Abdulhamid Adebowale Adebayo

Mohamed Ghalwash

Veronique Perret

Gelonia L Dent

James R Kozloski

Moon Jung Kim

Komminist S Weldemariam

Jonathan Lenchner

Qinghuang Lin

Pritish Ranjan Parida

Robert G Farrell

Anca Sailer

David Frederick Bantz

Olivier Verscheure

Nicholas C M Fuller

Rafah A Hosn

Muhammed Fatih Bulut

Constantin Mircea Adam

Wim De Pauw

Stefan Berger

Guruduth Somasekhara Banavar

Mahmoud Naghshineh

Chitra Venkatramani

Nicholas Matthew Mitchell

Stacy Fay Hobson

Long H Vu

Nathan J Lee

Norman Howard Cohen

Jeremy Brand Sussman

Yu Xuan Zhang

Steven Ocepek

Guerney Douglass Holloway Hunt

Eric T C Yee

Viatcheslav Gurev

James V Codella

Ijeoma M Nnebe

Charity Wayua

Shelby Solomon Darnell

Paolo Di Achille

Jaimit Parikh

Felix Bonard Kwizera

Guruduth Somesekhara Banavar

Zijun Yao

Lilian Mathias Ngweta

Ting Dai

Prithwish Chakraborty

Zachary Shahn

Muhammed Faith Bulut

Guerney Douglas S Holloway Hunt

Rajarsh Idas

Purity Mugambi

Ya-Ti Peng

Juan Fernando Argüello

John Allan Bivens

Stefan Ibm United Kindgom Limited Berger

Mahmoud Nagshineh

Nathan Junsup Lee

Chatschick Bisdikian

One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to compliance mapping, and more particularly to aggregated mapping of one or more sets of context-based compliance data with standard compliance data, such as from a target domain and one or more associate domains. A system can comprise a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components can comprise a mapping component that can map a compliance control for a target domain based on a model trained by an active learning process that incorporates a plurality of contexts representing relationships between entities and associate domain specific dependencies.

AI-assisted compliance mapping

An apparatus, a method, and a computer program product are provided that combine policy compliance with vulnerability management to provide a more accurate risk assessment of an environment. The method includes training a policy machine learning model using a first training dataset to generate a policy machine learning model to produce mitigation technique classifications and training a vulnerability machine learning model using a second training dataset to generate a vulnerability machine learning model to produce weakness type classifications. The method also includes mapping the mitigation technique classifications to attack techniques to produce a policy mapping and mapping the weakness type classifications to the attack techniques to produce a vulnerability mapping. The method further includes producing a risk assessment of a vulnerability based on the policy mapping and the vulnerability mapping.

Combining policy compliance and vulnerability management for risk assessment

Techniques regarding neuromodulation are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include a mapping component that can generate a stimulus map by mapping a stimulus parameter to a response from an entity to application of a neuromodulating stimulus, with the first neuromodulating stimulus being applied to the entity based on the first stimulus parameter, to therapeutically cause or prevent a sensation.

Neurostimulation therapy

Dual machine translators are trained by generating a translated medical image by operation of an illustrative model on an original medical record, generating information based on whether the translated medical image is natural in a modality of medical imaging, producing a back-translated medical record by operation of an interpretive model on the translated medical image, calculating a reward by comparing the back-translated medical record to the original medical record, updating parameters of the illustrative model in response to the information and the reward, and updating parameters of the interpretive model in response to the reward.

Predictive dual machine translation

Systems and methods that use multi-tasking and transfer learning with sparse gating mechanisms and domain knowledge to generate pheno-embeddings in a scalable manner that can improve the relevance of the patient embeddings from Electronic Health Records. A system, comprises at least one processor that executes the following computer executable components stored in memory: a structural pheno-embedding model that employs a hierarchical knowledge graph; a data augmentation component that expands on a sparse data set associated with the knowledge graph; and an embedding component that generates a specialized embedding for phenotypes using the structural pheno-embedding model and the augmented data set for a selected cohort.

System for generating specialized phenotypical embedding

An example operation may include one or more of connecting to a blockchain configured to store replicated Uniform Resource Locator (URL) shortening tables, receiving an indication from a blockchain node that a destination of a shortened URL has changed, informing other blockchain nodes of the changed destination of the shortened URL, receiving a shortened URL request from the blockchain node and checking if the requested shortened URL on other blockchain nodes points to a same destination, and connecting the blockchain node to the destination of the requested shortened URL if the destination associated with the shortened URL points to the same destination across all the blockchain nodes.

Secure URL shortening

An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to transform inter-arrival times between a plurality of input events into discrete time symbols and map the input events and the discrete time symbols using a dictionary to output a time gram representing a temporal dimension between a sequences of events.

Encoding for time grams

A method of managing lifelong learner events on a blockchain includes detecting an event related to a learner using a blockchain-enabled digital learning system, determining a concern/risk level of the learner by performing a risk assessment, determining parameters to generate a transaction related the learner's event based on the parameters and the concern/risk level, determining the values of the parameters by measuring the value or importance of the event and its associated metadata and documents, generating a list of transactions corresponding to the parameters, and validating the transactions using validating distributed peer-to-peer devices that run one or more chaincodes related to the management of the lifelong learner events.

Managing lifelong learner events on a blockchain

A method of analyzing physiological data streams. According to the method, physiological data is received into a computerized machine. The physiological data comprises numerical data and medical symptoms of a patient. Features are extracted from the physiological data based on development of the physiological data over a period of time. The features are converted into a textual representation using natural language generation. Input terms for an information retrieval system operating on the computerized machine are automatically generated based on the features. The input terms are input to the information retrieval system. A corpus of data is automatically searched to retrieve results to the input terms using the information retrieval system.

Hypothesis-driven, real-time analysis of physiological data streams using textual representations

A method for performing assisted knowledge discovery includes receiving a dataset. Each of a plurality of analytical techniques is applied to the received data set to generate a plurality of corresponding analytical results. A composite validation metric is applied to each of the plurality of analytical results. The composite validation metric is a single scoring/ranking function that is created from a plurality of different scoring/ranking functions. The plurality of analytical results is presented to a user arranged in accordance with the results of the applying the composite validation metric to each of the plurality of analytical results. A selection from the user from among the plurality of analytical results is recorded. The user's selection is used to modify the composite validation metric such that the analytical techniques responsible for generating the selected analytical result is scored/ranked more highly.

Ranking data analytics results using composite validation

An encoding system for encoding an event time series, the system including an inter-arrival time computing device configured to compute an inter-arrival time between a plurality of input events and computes a sequence of events, a transformation device configured to transform the inter-arrival time between the plurality of input events into discrete time symbols, and a mapping device configured to map the input events and the discrete time symbols using a dictionary to output a time gram.

Encoding system, method, and recording medium for time grams

A method of meta-learning includes receiving a prediction objective, extracting a plurality of subsets of data from a distributed dataset, generating a plurality of local predictions, wherein each local prediction is based on a different subset of the plurality of subsets of data and the prediction objective, combining the plurality of local predictions, and generating a final prediction based on the combined local predictions.

Scalable online hierarchical meta-learning

A method of pre-fetching and preparing content in an information processing system is provided. The method includes the steps of generating at least one content pre-fetching policy and at least one content preparation policy, wherein each of the policies are at least in part a function of context information associated with a user. The content is pre-fetched based on information contained within the at least one content pre-fetching policy. Once the content has been pre-fetched, it is prepared based on information contained within the at least one content preparation policy. The context information associated with the user includes at least one of the user's usage patterns, current location, future plans and preferences.

Content pre-fetching and preparation

Techniques are disclosed for determining and validating provenance data in such data stream processing systems. For example, a method for processing data associated with a data stream received by a data stream processing system, wherein the system comprises a plurality of processing elements, comprises the following steps. Input data elements and output data elements associated with at least one processing element of the plurality of processing elements are obtained. One or more intervals are computed for the processing element using data representing observations of associations between inputs elements and output elements of the processing element, wherein, for a given one of the intervals, one or more particular input elements contained within the given interval are determined to have contributed to a particular output element. In another method, intervals are specified, and then validated by comparing the specified intervals against intervals computed based on observations.

Determining and validating provenance data in data stream processing system

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Croton on Hudson, NY, United States of America

Daby Mousse Sow