Emc IP Holding Company LLC

Dell Products Inc.

Dell & Products L.p.

Emc Corporation

Landmark Graphics Corporation

Vinicius Michel Gottin

Paulo Abelha Ferreira

Pablo Nascimento Da Silva

Rômulo Teixeira De Abreu Pinho

Tiago Salviano Calmon

Jonas Furtado Dias

Joel Evans Christner

Hugo De Oliveira Barbalho

Alex Laier Bordignon

Roberto Nery Stelling Neto

Romulo D Pinho

Eduardo Vera Sousa

Daniel Sadoc Menasché

Angelo E M Ciarlini

Paulo De Figueiredo Pires

Werner Spolidoro Freund

Herberth Birck Fröhlich

Romulo Teixeira De Abreu Pinho

Keshava Prasad Rangarajan

Scott David Senften

Nalinkumar Mistry

Joseph Blake Winston

Ashwani Dev

Jaumir Valença Da Silveira Junior

Julia Drummond Noce

Xi Wang

David Michael Holmes

Eric L Caron

Raul Gracia

Chandra Yeleshwarapu

Edward Pacheco

Angelo Ciarlini

Andre Maximo

Jaumir Valenca Da Silveira Junior

Clifton Brent Walker

Adriana Bechara Prado

André De Almeida Maximo

Edward José Pacheco Condori

Fábio André Machado Porto

Wagner Dos Santos Vieira

Yania Molina Souto

Bruno Carlos Da Cunha Costa

Nagaraj Srinivasan

David Burth Kurka

Nagaraj Sirinivasan

Renan De Campos

Percy Rivera Salas

Franklin Jordan Ventura Quico

Norton Zamboni Junior

Fagner Scheibel Reinaldo

Aline Rodrigues Da Cruz

Paulo Acassio Martins Oliveira

Franklin Ventura

Thais Luca Marques De Almeida

Techniques are disclosed for dynamic edge-weighted quantization. For example, a system can include at least one processing device including a processor coupled to a memory, the at least one processing device being configured to implement the following steps: selecting edge nodes for sampling based on an edge node sampling algorithm configured to use a specified number of edge nodes to be sampled; causing the selected edge nodes to execute a quantization selection procedure; receiving, from the selected edge nodes, identifications of a quantization procedure based on the quantization selection procedure; and selecting a quantization procedure for each edge node, based on the identifications of the quantization procedures for the selected edge nodes.

Edge-weighted quantization for federated learning

One example method includes identifying active device data streams, based on the active device data streams identified, selecting applicable ML models, obtaining a respective classification inference from each of the ML models, wherein each of the classification inferences applies to a respective active device data stream, identifying a current agreement between the respective classification inferences generated by the models, comparing the classification inferences associated with the current agreement with historical classification inferences associated with historical agreements, and based on the comparing, determining whether or not one or more of the data streams comprises a piece of data of an unknown class.

Open set classification based on heterogenous model ensemble in multisensor environments

The disclosure relates to information retrieval and includes a preprocessing stage and a retrieval stage. The preprocessing stage includes ingesting data in a semantic manner that incorporates domain expert knowledge and that retains contextual information. The ingested data or objects are chunked and chunk embeddings are generated. A knowledge graph is created to hierarchically represent each of the objects. During the retrieval stage, the top k embeddings closest to an embedding of a query are used to identify the semantic cells and metadata cells of a document corresponding to the top k embeddings. This generates a return object that is used an input for content generation.

Semantic segmentation of data for information retrieval

One example method includes receiving, by a central node, respective data and statistics from each edge node in a group of edge nodes, performing, by the central node, a similarity-based clustering of the edge nodes so that different clusters of edge nodes are defined, sampling, by the central node, edge nodes from each of the clusters to perform a quantization selection method, receiving, by the central node, from the sampled edge nodes, a respective indication of a best-performing quantization method, and electing, by the central node, a quantization method to be used by all the edge nodes.

Similarity-based quantization selection for federated learning with heterogeneous edge devices

Occupancy aware decision-making is disclosed. Positions of entities in an environment are determined. Stationary entities are used as markers to segment the environment. Positions of non-stationary entities are used to determine an occupancy score for each of the segments. Decision making operations, such as pathfinding for mobile entities, can be performed using the occupancy scores of the segments. The occupancy scores and segmentation adapt to changes in the environment.

Dynamic segmentation and occupancy scoring in edge environments with mobile devices for real-time decision making

An event driven detection model is disclosed. A model operates at a node to identify relevant video data from video streams generated by cameras. Video data that is not relevant is discarded. An objectness score is generated for the relevant video data. The objectness score and position data from position sensors is used to infer an event. When an event is inferred by the model, a decision may be made and performed.

Efficient event-driven object detection at the forklifts at the edge in warehouse environments

One example method includes selecting items to be repatriated from a cloud site to an on-premises site, and the items include a workload and a data set accessed by the workload, transmitting a repatriation request from the on-premises site to the cloud site, and the repatriation request identifies the selected items, receiving, by the on-premises site from the cloud site, a compressed data set which includes the data set in compressed form, receiving, by the on-premises site from the cloud site, a compressed workload which includes the workload in compressed form, and the compressed workload and the compressed data set have been compressed with a compression algorithm automatically selected based on content, and/or context, of data in the data set, decompressing, at the on-premises site, the compressed data set and the compressed workload, and deploying the decompressed data set and the decompressed workload locally at the on-premises site.

Opportunistic on-prem data repatriation in hybrid clouds

One example method includes mapping of a set of environment constraints to various elements of a dataset distillation process, and then performing the dataset distillation process based upon the mapping, and the dataset distillation process is performed in a distributed manner by a group of edge nodes and a central node with which the edge nodes communicate. The environment constraints may include computing resource availability, and privacy constraints concerning the data of the dataset.

Adaptable dataset distillation for heterogeneous environments

The quality of decompressed data that was compressed in a lossy manner to generate lossy compressed data, is improved. Lossy compressed data may be generated at a device and transmitted to a gateway. The gateway includes a reconstruction engine that is trained to reduce the compression loss by generating a learned residue. The residue is added to the lossy decompressed data to generated corrected decompressed data that has a smaller loss than the lossy decompressed data.

Quality of lossy compressed sensor data with machine learning

One example method includes performing operations, in a computing environment that includes multiple layers, one of which is a functional edge layer, that include obtaining input parameters, and one of the input parameters identifies one of the layers of the computing environment where a machine learning model is located, based on the input parameters, placing a control module in one of the layers of the computing environment, determining, by the control module, candidate layers of the computing environment for respective instances of a drift module, and placing a respective one of the instances of the drift module in each of the candidate layers, running the control module to identify an instance of the drift module to be activated, and activating the identified instance of the drift module, and detecting, by the activated instance of the drift module, drift in the machine learning model.

Self-distributed multimodal drift detection at the edge

One example method includes identifying features for operational actions that are to be performed at an edge node of an edge computing system. The features include information used by the edge node to execute the operational actions. A first set of the features is received during an input processing stage. The first set of the features are used in the execution of the operational actions. In response to the execution of the operational actions, a second set of the one or more features are obtained, In response to retrieving the first set of the features or in response to obtaining the second set of the features, a local map is updated to include the features. The local map includes a subset of a global map, where the local map and the global map represent a shared representation of a map structure.

Orchestration of action-input representations for decision making in edge environments

Method for model updating in a federated learning environment, including distributing a current model to client nodes; receiving a first set of gradient sign vectors, wherein each gradient sign vector of the first set of gradient sign vectors is received from one client node; generating a first updated model based on the first set of gradient sign vectors; distributing the first updated model to the plurality of client nodes; storing a first shape parameter and a second shape parameter; receiving, in response to distributing the first updated model, a second set of gradient sign vectors, wherein each gradient sign vector of the second set of gradient sign vectors is received from one client node; generating a second updated model based on the second set of gradient sign vectors, the first shape parameter, and the second shape parameter; and distributing the second updated model to the plurality of client nodes.

Fast converging gradient compressor for federated learning

Peer-based positioning of nodes in an environment is disclosed. A node is able to determine its own position using sources. The sources may include fixed and/or mobile beacons. Each of the sources is associated with a distance error and a position error. These errors, along with distance measurements between the node and each of the sources, are used to determine a position and a total positioning error, which reflects the accuracy of the determined position.

Peer-based positioning of mobile devices

Systems and methods for decoupling information into multiple levels of detail based on semantics while providing a compact global representation structure. A tree-based graph represents objects in an environment. The top node provides complete environment information at the lowest level of detail. Leaves of the tree-based graph complement the parent nodes with more detailed local information. This achieved efficient processing, communication, and data compaction.

So-map: a semantic-aware algorithm for optimizing the representation structure of Octomaps

Techniques are provided for customer service ticket similarity determination using an updated encoding model based on similarity feedback from a user. One method comprises obtaining encodings of customer service tickets in a vector space using an encoding model; determining pairwise similarities for the encodings of the customer service tickets; obtaining feedback from a user regarding the pairwise similarities for a subset of the encodings; updating pairwise similarities for the subset of the encodings using the feedback from the user; generating an updated encoding model by processing the updated pairwise similarities for the subset of the encodings of the customer service tickets using a supervised learning algorithm; and processing at least one customer service ticket based at least in part on the updated encoding model. The feedback from the user may indicate a similarity of two or more of the plurality of customer service tickets.

Customer service ticket similarity determination using updated encoding model based on similarity feedback from user

Growing community of inventors

Rio de Janeiro, Brazil

Vinicius Michel Gottin