Intel Corporation

Mohammad Mejbah Ul Alam

Justin E Gottschlich

Shengtian Zhou

Alexander F Heinecke

Sara S Baghsorkhi

Jeffrey Ota

Jason Martin

Thijs Metsch

David Israel Gonzalez Aguirre

Mats Gustav Agerstam

Marcos Emanuel Carranza

Joseph Butler

Maria E Ramirez Loaiza

Kathiravetpillai Sivanesan

Bahareh Sadeghi

Ignacio Javier Alvarez

Liuyang L Yang

Sridhar G Sharma

Li Chen

Robert Colby

Cesar Martinez-Spessot

Patrick Mead

Methods, apparatus, systems and articles of manufacture are disclosed that optimize workflows. An example apparatus includes an intent determiner to determine an objective of a user input, the objective indicating a task to be executed in an infrastructure, a configuration composer to compose a plurality of workflows based on the determined objective, a model executor to execute a machine learning model to create a confidence score relating to the plurality of workflows, and a workflow selector to select at least one of the plurality of workflows for execution in the infrastructure, the selection of the at least one of the plurality of workflows based on the confidence score.

Methods and apparatus to optimize workflows

Methods, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus at least one memory; instructions; and processor circuitry to execute the instructions to: determine a danger level associated with an object, the danger level indicative of a first measure of damage corresponding to a trajectory of the object compared to a trajectory of a vehicle; determine, based on the first danger level, a danger measure based on at least one of a position of the object, a velocity of the object, an acceleration of the object, a direction of travel of the object, a weight or mass of the object; and generate instructions to transmit to a steering system or a braking system of the vehicle based on the determination.

Methods and apparatus to mitigate hard-braking events

Methods, apparatus, systems and articles of manufacture to identify a side-channel attack are disclosed. Example instructions cause one or more processors to generate an event vector based on one or more counts corresponding to tasks performed by a central processing unit; determine distances between the event vector and weight vectors of neurons in a self-organizing map; select a neuron of the neurons that results based on a determined distance; identify neurons that neighbor the selected neuron; and update at least one of a weight vector of the selected neuron or weight vectors of the neighboring neurons based on the determined distance of the selected neuron.

Methods and apparatus to detect side-channel attacks

Methods, apparatus, systems, and articles of manufacture are disclosed herein that mitigate hard-braking events. An example apparatus includes a world generator to generate a deep learning model to identify and categorize an object in a proximity of a vehicle, a data analyzer to determine a danger level associated with the object, the danger level indicative of a likelihood of a collision between the vehicle and the object, a vehicle response determiner to determine, based on the danger level, a response of the vehicle to avoid a collision with the object, and an instruction generator to transmit instructions to a steering system or a braking system of the vehicle based on the determined vehicle response.

Methods, apparatus, systems and articles of manufacture are disclosed that provide an apparatus to analyze vehicle perspectives, the apparatus comprising a profile generator to generate a first profile of an environment based on a profile template and first data generated by a first vehicle; a data analyzer to: determine a difference between the first profile and a second profile obtained from a first one of one or more nodes in the environment; and in response to a trigger event, update the first profile based on the difference; and a vehicle control system to: in response to the trigger event, update a first perspective of the environment based on one or more of second data from the first one of the one or more nodes or the updated first profile; update a path plan for the first vehicle based on the updated first perspective; and execute the updated path plan.

Methods and apparatus to update autonomous vehicle perspectives

Methods, apparatus, systems and articles of manufacture to recommend instruction adaptations to improve compute performance are disclosed. An example apparatus includes a pattern detector to detect an execution pattern from an execution profile provided by a server, the execution profile associated with an instruction stored in an instruction repository. An adaptation identifier is to identify a possible instruction adaptation that may be applied to the instruction associated with the execution pattern. A model processor is to predict, using a machine learning model, an expected performance improvement of the adaptation. A result comparator is to determine whether the expected performance improvement meets an threshold. An instruction editor is to, in response to the result comparator determining that the expected performance improvement meets the threshold, apply the possible instruction adaptation to the instruction in the instruction repository.

Methods and apparatus to recommend instruction adaptations to improve compute performance

Apparatus, systems, articles of manufacture, and methods for a context and complexity-aware recommendation system for efficient software development. An example apparatus includes a current state generator to generate a representation of a current state of a new function, an instruction predictor to generate a first recommended software component based on the current state of the new function, a complexity cost determiner to rank the first recommended software component based on a weighted sum of associated partial cost values, the software component to be ranked against second recommended software components based on a comparison of partial cost values corresponding to respective ones of the second recommended software components, a risk identifier to detect vulnerabilities based on an attack surface of a portion of the first recommended software component, and a ranking determiner to generate a third recommended software component, the third recommended software component corresponding to respective ranking metrics.

Methods, systems, articles of manufacture, and apparatus for a context and complexity-aware recommendation system for improved software development efficiency

Methods, apparatus, systems and articles of manufacture are disclosed for code review assistance for dynamically typed languages. An example apparatus to analyze a segment of code includes a function identifier to identify a first input of a first function call included in the segment of the code, a parameter type vector (PTV) estimator model to estimate a first data structure based on the first input, the PTV estimator model generated via a set of reviewed code, a PTV determiner to generate a second data structure based on a data parameter type of the first input, an error comparator to determine a first reconstruction error based on the first data structure, and the second data structure and a recommendation generator to, if the first reconstruction error does not satisfy a recommendation threshold, generate a first recommendation to review the first function call.

Methods, systems, articles of manufacture and apparatus for code review assistance for dynamically typed languages

Methods, apparatus, systems and articles of manufacture to identify a side-channel attack are disclosed. An example apparatus includes a vector-to-neuron processor to map an event vector to a neuron of a trained self-organizing map; a buffer processor to identify a task pair based on the neuron and an adjacent neuron of the neuron; a buffer to store data corresponding to the identified task pair; an attack identifier to, when information stored in the buffer corresponds to more than a threshold number of task pairs corresponding to the identified task pair, identify a malware attack; and a mitigation technique selector to select a technique for mitigating the malware attack.

An apparatus includes a software parser to generate a plurality of abstract syntax trees based on a plurality of software files, the ASTs including subtrees corresponding to a plurality of functions of the software files, a subtree encoder to generate a plurality of code vectors representative of one or more semantic properties of the subtrees, a function identifier to determine a plurality of clusters for the subtrees and assign a cluster identifier and a function label to the subtrees, a tree database to store the subtrees and map the plurality of subtrees to respective ones of cluster identifiers and function names, and a processor to: train a model based on a feature vector and the plurality of clusters stored in the tree database and predict the cluster identifier for the subtrees, based on the trained model, to identify a name of the function.

Methods and apparatus for recommending computer program updates utilizing a trained model

Methods, apparatus, systems and articles of manufacture are disclosed. An example system includes a memory accessed by a program of interest, a performance monitoring unit to collect first memory access information and second memory access information about an object accessed in the memory by the program of interest; and a leak detector to: determine a non-access period based on the first memory access information and an unsupervised machine learning model trained based on the program of interest; and detect a potential memory leak of the program of interest based on the second memory access information and the non-access period.

Methods and apparatus to detect memory leaks in computing systems

An apparatus includes a data interface to obtain first sensor data from a first sensor and second sensor data from a second sensor of a monitored system; a data analyzer to extract a feature based on analyzing the first and second sensor data using a model, the model trained based on historical sensor data, the model to determine the feature as a deviation between the first and second sensor data to predict a future malfunction of the monitored system; an anomaly detector to detect an anomaly in at least one of the first sensor data or the second sensor data based on the feature, the anomaly corresponding to the future malfunction of the monitored system; and a system applicator to modify operation of the monitored system based on the anomaly.

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Milpitas, CA, United States of America

Mohammad Mejbah Ul Alam