International Business Machines Corporation

Bloom Value Corporation

Beijing Zitiao Network Technology Co., Ltd.

Sharathchandra Umapathirao Pankanti

Rudolf Maarten Bolle

Nalini Kanta Ratha

Lin Hong

Arun Hampapur

Quanfu Fan

Yun Zhai

Karthik Nandakumar

Erik Rueger

Jonathan Hudson Connell, Ii

Roman Vaculin

Ying Li

Anil K Jain

Lei Ding

Sampoorna Hegde

Scott Eric Colville

Roona Shree

Russell Patrick Bobbitt

Shai Halevi

Ankur Datta

Noel C Codella

Karthikeyan Shanmugam

Jonathan H Connell

Sachiko Miyazawa

Charles A Otto

Gaurav Goswami

Yu Cheng

Tomas Krojzl

Balamanohar Paluri

Venu M Kondle

Kanthi Sarpatwar

Rolf Schaefer

Blair Bryson

Shravan Conjeevaram

Praveen Guledgudda

Megha Kuchhal

Yashpal Gulab Dhawle

Priyanka Rajesh

Moumita Dhar

A drug delivery device includes a capsule, a logic circuit disposed within the capsule, and an actuator connected to the logic circuit and configured to expose an interior of the capsule to an exterior of the capsule upon activation by the logic circuit.

Drug delivery device having controlled delivery and confirmation

Techniques regarding adaptive data recovery schemes 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 comprise a data management component that can modify a data recovery scheme based on performance data exhibited by a network of data centers and a data recovery requirement. The data recovery scheme can direct a relocation of data within the network.

Data recovery modification based on performance data exhibited by a network of data centers and data recovery requirement

ML model(s) are created and trained using training data from user(s) to create corresponding trained ML model(s). The training data is in FHE domains, each FHE domain corresponding to an individual one of the user(s). The trained machine learning model(s) are run to perform inferencing using other data from at least one of the user(s). The running of the ML model(s) determines results. The other data is in a corresponding FHE domain of the at least one user. Using at least the results, it is determined which of the following issues is true: the results comprise objectionable material, or at least one of the trained ML model(s) performs prohibited release of information. One or more actions are taken to take to address the issue determined to be true. Methods, apparatus, and computer program product are disclosed.

Searching over encrypted model and encrypted data using secure single-and multi-party learning based on encrypted data

Configurations of a system and a method for a dynamic trust-based authorization system (DTBAS), are described. In one aspect, the DTBAS may implement an execution of operations of multiple engines, models, framework, circuits, code, etc., for dynamically authorizing a request for a service or an operation. The DTBAS, when deployed in a managing entity, may receive a request from a managed entity to execute an operation or task. Based on parameters or attributes associated with the managed entity, the DTBAS may execute operations to evaluate a trust score. Based on the evaluated trust score, the DTBAS may compute a weight decision matrix that may include multiple weights associated with the managed entity. Based on the computed weight decision matrix and the evaluated trust score, the DTBAS may authorize or deny the execution of the requested operation.

Dynamic trust-based authorization system

An entity learning recognition method, system, and computer program product include learning (i.e., in a training phase) from at least one entity to produce augments entities such that an augmented entity is still recognizable as the original entity but differs sufficiently to produce a different feature representation of the entity to create a database for use (i.e., in an implementation phase).

Entity learning recognition

A system and method to authorize requests for procedures or medical claim requests is described. In one aspect, datasets from multiple data sources, including historical authorizations associated with various medical claims, are processed. The machine learning engines and the machine learning models are trained based on the received data. Simultaneously, real-time authorization requests are received and analyzed from secondary data sources. Each request is split into service requests based on second attributes, which include identifiers linked to services. The services are encoded using fixed-length encoding, and unique numeric identifiers are assigned to categorical variables. For each encoded service request, contexts, actions, and decisions are determined by comparing the first attributes of historical authorization requests with the second attributes of real-time requests. Service requests are then categorized into cohorts based on similarity metrics between their second attributes and those of historical authorizations. This comparison enables efficient processing and informed decision-making.

Intelligent authorization system

An entity learning recognition method and computer program product include learning training a model based on a combination of an original entity and an augmented entity in an augmented database, where the entity includes an image that is used for a training of the model and where the training is based on a visual element portion of the image with added noise.

A compression method, system, and computer program product include creating compressed data via a first system from input data, sending information to a second system detailing a compression strategy for the compressed data, and learning, via the second system, from the information how to recreate the input to the first system using the compressed data.

Data compression by using cognitive created dictionaries

Securely re-encrypting homomorphically encrypted data by receiving fully homomorphically encrypted (FHE) information from a client device, training a machine learning model using the FHE information, yielding FHE ciphertexts, applying a first transform to the FHE ciphertexts, yielding obfuscated FHE ciphertexts, sending the obfuscated FHE ciphertexts to a secure device, receiving a re-encrypted version of the obfuscated FHE ciphertexts from the secure device, applying a second transform to the re-encrypted version of the obfuscated FHE ciphertexts yielding de-obfuscated re-encrypted FHE ciphertexts, determining FHE ML model parameters according to the de-obfuscated re-encrypted ciphertexts, and sending the FHE ML model parameters to the client device.

Secure re-encryption of homomorphically encrypted data

A face clustering system for video face clustering in a video sequence, the system including an inherent supervision summarization device configured to collect group-level supervision and instance level supervision within a same chunklet based on a user input of face images for a person, a discriminative projection learning device configured to embed group constraints of the group-level supervision into a transformed space, and configured to generate an embedding space from the original image feature space, and a clustering device, in the embedding space, configured to execute pair-wise based clustering to cluster the video images into different clusters with the instance level supervision collected by the inherent supervision summarization device.

System, method, and recording medium for detecting video face clustering with inherent and weak supervision

A camera field indicating method, system, and non-transitory computer readable medium for a camera including a light sensor, imaging optics, and an extended light source situated near the light sensor and sharing the imaging optics, including sensing incoming light via the imaging optics and the light sensor and intermittently activating the extended light source when the light sensor does not sense incoming light such that a projection of the extended light source through the imaging optics matches a field of view of the light sensor.

Camera apparatus and system, method and recording medium for indicating camera field of view

Local models learned from anomaly detection are used to rank detected anomalies. The local models include image feature values extracted from an image field of video image data with respect to different predefined spatial and temporal local units, wherein anomaly results are determined by failures to fit to applied anomaly detection module local models. Image features values extracted from the image field local units associated with anomaly results are normalized, and image feature values extracted from the image field local units are clustered. Weights for anomaly results are learned as a function of the relations of the normalized extracted image feature values to the clustered image feature values. The normalized values are multiplied by the learned weights to generate ranking values to rank the anomalies.

Efficient retrieval of anomalous events with priority learning

Human behavior alerts are determined from a video stream through application of video analytics that parse a video stream into a plurality of segments, wherein each of the segments are either temporally related to at least one of a plurality of temporally distinct transactions in an event data log; or they are each associated with a pseudo transaction marker if not temporally related to at least one of the temporally distinct transactions and an image analysis indicates a temporal correlation with at least one of the distinct transactions is expected. Visual image features are extracted from the segments and one-SVM classification is performed on the extracted features to categorize segments into inliers or outliers relative to a threshold boundary. Event of concern alerts are issued with respect to the inlier segments associated with the associated pseudo transaction marker.

Activity determination as function of transaction log

Human behavior is determined by sequential event detection by constructing a temporal-event graph with vertices representing adjacent first and second primitive images of a plurality of individual primitive images parsed from a video stream, and also of first and second idle states associated with the respective first and second primitive images. Constructing the graph is a function of an edge set between the adjacent first and second primitive images, and an edge weight set as a function of a discrepancy between computed visual features within regions of interest common to the adjacent first and second primitive images. A human activity event is determined as a function of a shortest distance path of the temporal-event graph vertices.

Sequential event detection from video

In an embodiment, automated analysis of video data for determination of human behavior includes providing a programmable device that segments a video stream into a plurality of discrete individual frame image primitives which are combined into a visual event that may encompass an activity of concern as a function of a hypothesis. The visual event is optimized by setting a binary variable to true or false as a function of one or more constraints. The optimized visual event is processed in view of associated non-video transaction data and the binary variable by associating the optimized visual event with a logged transaction if associable, issuing an alert if the binary variable is true and the optimized visual event is not associable with the logged transaction, and dropping the optimized visual event if the binary variable is false and the optimized visual event is not associable.

Growing community of inventors

Mt. Kisco, NY, United States of America

Sharathchandra Umapathirao Pankanti