International Business Machines Corporation

Philips Electronics North America Corporation

Maplebear Inc.

Koninklijke Philips Corporation N.v.

Globalfoundreis Inc.

Dinesh C Verma

Seraphin Bernard Calo

Dakshi Agrawal

Vasileios Pappas

Kang-Won Lee

Bong Jun Ko

Mudhakar Srivatsa

Franck Vinh Le

Erich Miles Nahum

Douglas Morgan Freimuth

Robert Bruce Nicholson

Ramya Raghavendra

Theodoros Salonidis

Maroun Touma

Supriyo Chakraborty

Christian Makaya

Chatschik Bisdikian

Thai Franck Le

Swati Rallapalli

Nirmit V Desai

Xiping Wang

David P Olshefski

Bruce Oliver Anthony, Jr

Charles P Wright

John Michael Tracey

Petros Zerfos

Hubertus Franke

Sambit Sahu

Ravindranath Kokku

Mathews Thomas

Renu Tewari

Kaladhar Voruganti

Raghu Kiran Ganti

Sandeep Gopisetty

Michael E Baskey

Ramani Ranjan Routray

Yang Song

Ronald Patrick Doyle

Shivkumar Kalyanaraman

Dinakaran B Joseph

Lap Thiet Huynh

Anees Abdul Shaikh

James R Giles

Debanjan Saha

Yongcheng Li

Arup Acharya

Raymond Byars Jennings, Iii

Murtaza Zafer

Victor S Moore

Gregory E McBride

Martin Oberhofer

David Louis Kaminsky

Bodhisatwa Sadhu

David Michael Shackelford

Robert Beverley Basham

Namik Hrle

Donna N Dillenberger

Zon-Yin Shae

Brent William Yardley

Khalid Filali-Adib

Kiyoshi Maruyama

Michael D Williams

Ronald Leroy Billau

Jarir Kamel Chaar

Canio Cillis

Reiner Sailer

Bernhard Julius Klingenberg

Einar Lueck

Robert Michael Rees

Roch A Guerin

Martin G Kienzle

Dilip Dinkar Kandlur

Edith H Stern

Philip Shi-lung Yu

Alberto Valdes Garcia

Tamer E Abuelsaad

Gennaro A Cuomo

John Maxwell Cohn

Mukesh Kumar Mohania

Samir N Hulyalkar

Kelly Abuelsaad

Alessandro Donatelli

Pawan Raghunath Chowdhary

Sumedh Wasudeo Sathaye

Malolan Chetlur

James Corvin Fletcher

Ravi Kothari

Jing Li

Mandayam Thondanur Raghunath

Peter Hans Westerink

Graham Anthony Bent

Heather D Achilles

Steven Dale Ims

Cesar Augusto Gonzales

Tova Roth

David Alson George

Daniel Joseph Martin

Marcia L Stockton

Umamaheswari C Devi

Kai R Schleupen

Mechanisms are provided for customizing an avatar in a virtual environment. A user that is interacting with the virtual environment, and to whom the avatar is to be rendered in the virtual environment, is identified and classified with regard to a plurality of affinity groups specifying levels of affinity between the first user and an operator of the avatar. A user security and privacy policy (USPP) data structure, associated with the operator, is retrieved that specifying security and privacy policies for each affinity group in the plurality of affinity groups. Based on the user classification and the security and privacy policy, an anonymization operation is applied to static and/or dynamic characteristics data for the operator which are used to render the avatar, to thereby generate anonymized characteristic data. The anonymized characteristic data is output for rendering the avatar in the virtual environment.

Personalized avatars with custom levels of privacy

A computer-implemented method of determining an agent data attribution and selection to perform a collaborative data-related task includes computing an agent data attribution score for each agent of the plurality of agents associated with the collaborative data-related task. A subset of the plurality of agents that participate in the collaborative data-related task is selected based on the agent data attribution score. An instruction is transmitted to the selected subset of the plurality of agents for each agent to conduct a respective portion of the collaborative data-related task.

Private computation of an agent data attribution score in collaborated task

A method for targeted advertisement includes transmitting a pre-filter to the user device, responsive to contextual information from a user device, to determine, using a processor, one or more inferences based on physical browsing information, collected at the user device, in compliance with one or more privacy policies of the user. The method also includes receiving one or more inferences determined by the pre-filter from the user device and transmitting one or more targeted advertisements to the user device based on one or more inferences.

Recommendations based on private data using a dynamically deployed pre-filter

The present inventive concept provides for a method of unmanned machine synchronization using robotic sensing. The method includes generating at least one physical signal in the vicinity of at least one unmanned machine. The at least one generated physical signal is received by the at least one unmanned machine. At least one task is performed by the at least one unmanned machine based on the at least one received generated physical signal.

Unmanned machine synchronization using robotic sensing

A method, computer system, and a computer program product is provided for establishing a dynamically adaptive network firewall. A firewall model is established that identifies resources that can be used and a plurality of nodes that connect to one another to form a network. A set of external rules and policies are provided to each resource. The firewall implemented using the firewall model upon receiving of incoming data and upon traffic flow. The firewall expands or contracts the network by adding or removing resources according to work that needs to be performed. The work includes both tasks to be completed and efforts that has to be taken by the firewall to ensure security by preventing unauthorized access into the network.

Polymorphic dynamic firewall

A computer-implemented method, according to one approach, includes: receiving, from a remote location, a request to generate a unique equipment code identifying a device. Identifying characteristics of the device are determined, and a unique equipment code is generated. The unique equipment code is generated by applying policy based rules summarized in one or more predetermined information dependency graphs while combining at least some of the determined identifying characteristics in a specific order. Moreover, the combined identifying characteristics are converted into a corresponding alphanumeric string. The unique equipment code is assigned to the device and the unique equipment code is also transmitted to the remote location.

Managing distributed devices using unique codes

In an approach to identifying occluded objects, a computer retrieves a first image that includes an object at least partially occluded by one or more occlusions. A computer removes the one or more occlusions from the first image to create a partial object in a second image. A computer runs a detection model with the second image to predict one or more identifications of a symbol represented by the partial object. A computer determines top predictions of the one or more identifications of the symbol by the detection model. A computer identifies at least one geometric property associated with the one or more identifications of the symbol included in the one or more top predictions. A computer applies the at least one geometric property to the partial object. A computer determines a probability of the one or more top predictions correctly identifying the symbol represented by the partial object.

Identifying occluded objects in engineering drawings

Techniques for determining system performance without ground truth include receiving a trained model and one or more generator models, the trained model having been trained on training data. The trained model is used on testing data to produce labeled testing data, and the labeled testing data are used to train a proxy model. The one or more generator models are used to produce synthetic training data that are representative of the training data. The proxy model is used on the synthetic training data to produce predictions, and performance of the trained model is determined based on the predictions by the proxy model.

Determining system performance without ground truth

Methods and systems for emulating an application include generating a log template to match one or more patterns in a set of application logs collected from an original application. Semantic state representations are learned for the original application from the log template. A classifier is trained to predict a next action template based on a sequence of prior action templates. A regressor is trained to generate a parameter value for a template based on a sequence of prior action templates and a particular semantic state of the original application.

Machine learning to emulate software applications

Embodiments related to using a foundational model for network packet traces. A technique includes receiving network traffic of a network and extracting features from the network traffic, the features having a function related to communications in the network. The technique includes generating tokens from the features, each of the features corresponding to a respective one of the tokens, training a machine learning model by inputting the tokens, the machine learning model being trained to output contextual embeddings for the tokens, and using the contextual embeddings to determine an anomaly in the network traffic.

Foundational model for network packet traces

A computer-implemented method for grouping devices in a massive multiple-input and multiple-output (MIMO)-based cellular network, in accordance with one embodiment, includes determining movement states of end devices in a cell of the massive MIMO-based cellular network, estimating payload requirements of the end devices, and grouping the end devices in a group based on the determined movement states and the estimated payload requirements.

Dynamic massive MIMO end device pairing based on predicted and real time connection state

Methods and systems for execution of distributed analytics include building a global linked structure that describes correspondences between dataset metadata structures, analytics metadata structures, and location metadata structures and that encodes compatibility between respective datasets, analytics, and locations. A set of analytics and compatible datasets for execution is determined based on the dataset metadata structures, analytics metadata structures, and global linked structure. An optimal execution location is determined based on the determined set of analytics and compatible datasets, the location metadata structures, and the global linked structure. The set of analytics and compatible datasets are deployed to the optimal location for execution.

Linking, deploying, and executing distributed analytics with distributed datasets

A computer implemented method determines a placement of an application being added to a network. The method includes determining a logical-physical mapping for the application. Sub-graphs in the logical-physical mapping are identified. A stored application in a catalog of applications previously used in the network is located. The stored application includes one of the sub-graphs in the logical-physical mapping. An experiment is generated, performed by a computing device running a neural network model, using the sub-graphs. The experiment includes inducing a physical node mapping for the sub-graph. A cost function associated with a placement of the application being added to the network to one or more physical nodes in the induced physical node mapping is determined.

Generative experiments for application deployment in 5G networks

A data mining method, system, and non-transitory computer readable medium include obtaining a subset of public records of data in a public domain and performing data mining, via private domain data, within the subset of the public records of data to find data in the public domain corresponding to a particular individual.

System, method, and recording medium for data mining between private and public domains

Techniques for non-iterative federated learning include receiving local models from agents, generating synthetic datasets for the local models, and producing outputs using the local models and the synthetic datasets. A global model is trained based on the synthetic datasets and the outputs.

Non-iterative federated learning

Growing community of inventors

New Castle, NY, United States of America

Dinesh C Verma