586 results about "Machine-readable medium" patented technology

The Internet can be configured to provide communications to a large number of Internet-of-Things (IoT) devices. An example non-transitory, machine readable medium comprises instructions that, when executed, direct a processor to: cause a device to connect to a network of a decentralized database, discover a namespace of a node of the decentralized database, create a shared database partition in response to being accepted by the node, advertise a service to the decentralized database; and route data of a service between a private database partition and the shared database partition

Various embodiments concern a method of training a large language model (LLM) for an unseen task T′ comprising: obtaining a plurality of pre-trained Low-Rank Adaptation (LoRA) modules, using a set of examples Q to obtain a set of weights for the plurality of pre-trained LoRA modules, using the set of weights on the plurality of pre-trained LoRA modules to obtain a fused LoRA module, and applying the fused LoRA module to the LLM to get an adapted LLM for the unseen task T′. Systems and machine-readable media are also provided.

A system, apparatus, method, and machine-readable medium are described for personalizing and pre-registering an authenticator. For example, one embodiment of a method comprising: confirming an identity of a user by a relying party using a first identity verification technique responsive to the user performing a first transaction with the relying party; generating or collecting initial user verification reference data upon verification of the identity of the user through the first identity verification technique; requesting personalization of an authenticator to be provided to the user; receiving credential registration data of the authenticator after personalization; receiving a transaction request from the user that requires user authentication; and authenticating the user based on the authenticator and the credential registration data of the authenticator using a second identity verification technique.

Systems, methods, and non-transitory, machine-readable media may facilitate testing within sub-environments with respect to resource capacity data corresponding to resources. Specifications of resource allocations for resources may be collected. Observation data, including resource metrics data, may be consolidated and used to develop resource data composites with mapped resource metrics data. A sub-environment interface may be configured to allow creation of sub-environments based on the resource data composites. Source schemas and data structures corresponding to the resource data composites may be selected. A migration plan to migrate the selected data structures to a destination schema for testing in a sub-environment may be created. A migration according to the migration plan may be executed. The destination schema may be created to match the selected source data structures and the selected source schemas. The selected data structures may be migrated to the destination schema to allow for testing in the sub-environment.

Methods, systems, and machine-readable mediums to encode at least one vector associated with a log using a neural network. In at least one embodiment, a processor is to encode at least one log message using at least one neural network trained, at least in part, by: obtaining a similarity score associated with a first vector and a second vector, the first vector to be associated with one or more first log messages, and the second vector to be associated with one or more second log messages; generating at least one similarity value indicating similarity between the first vector and the second vector; and determining a metric indicating similarity between the similarity score and the at least one similarity value.

A computer program product interacts with machine-readable mediums with instructions for automated target recognition. It captures a video of a specified region using detectors on a vehicle. Raw image frames from the video undergo pre-processing, followed by feeding through long and short-range target detection pipelines. Image frames are downscaled in the short-range pipeline, generating image windows and applying a trained convolutional neural network (CNN). Full resolution and non-redundant image chips are created. Detection region of interest (ROI) proposal lists are generated and analyzed with the CNN, producing frame detection lists with detected targets. These lists are stacked and processed using a multi-target Kalman filter, ultimately creating a track list of targets for monitoring.

Methods, apparatuses, systems, and machine-readable media are disclosed for improving packet filtering efficiency by reducing processing time and / or by reducing memory usage. Any of various types of data structures, such as flat hash maps and / or ruletrees, may be used by a packet filtering appliance to search for cybersecurity policy packet filtering rules that should be applied to in-transit packets. The packet filtering appliance may search the index data structures for matches of search objects, in the form of values that the packet filtering appliance extracts from in-transit packets, to threat indicator matching criteria of the policy rules. Each of the index data structures may map rule identifiers (rule IDs) of policy rules to keys that are based on (or that comprise) the matching criteria of those rules.

A system, apparatus, method, and machine-readable medium are described for speculative attestation conveyance, such as over an out-of-band channel. For example, one embodiment of a method comprises: associating a credential provider with a credential provider instance, the credential provider instance to generate credentials for authenticating a user to relying parties from a client device; associating a new credential with the credential provider instance, the new credential having a public portion and a non-public portion; transmitting over an out-of-band communication channel, a request for an identifying attestation to a plurality of trusted credential provider servers, the request including an identifier based on the new credential or the public portion of the new credential; receiving from a trusted credential provider server of the plurality of trusted credential provider servers the identifying attestation; attempting to validate the identifying attestation; and using or storing the identifying attestation when validated.

Methods, systems, and machine-readable mediums to perform a neural network to encode log data. In at least one embodiment, a processor comprising one or more circuits to encode at least one log message, at least in part, by encoding a first type of information in the at least one log message to obtain a first encoding, encoding a second type of information in the at least one log message to obtain a second encoding, and obtaining a resultant encoding at least in part by combing at least the first and second encodings.

Systems, methods, and non-transitory, machine-readable media may facilitate adaptive resource capacity prediction and control using cloud infrastructures with a capacity prediction interface. Specifications of resource allocations for resources provided by a cloud infrastructure system may be collected. Observation data may be collected and may include resource metrics data. Prediction rules may be selected as a function of particular resource metrics. A subset of the resources may be identified. The selected prediction rules may be used to predict resource capacities for the subset of the resources as a function of the particular resource metrics and generate resource capacity predictions. Incidents may be identified based on the resource capacity predictions. A capacity prediction interface may be generated and may be configured to represent the resource capacity predictions and facilitate preemptive actions with respect to the incidents identified based on the resource capacity predictions.

Methods, systems, and machine-readable mediums to encode at least one vector associated with a log using a neural network. In at least one embodiment, a neural network is trained, at least in part, by obtaining first, second, and third encoded vectors by encoding a first vector associated with a first log sequence, a second vector associated with a second log sequence similar to the first log sequence, and a third vector associated with a third log sequence dissimilar from the first log sequence; and selecting at least one model weight that increases a likelihood that the first encoded vector is closer to the second encoded vector than the third encoded vector.

One or more systems, methods and / or machine-readable mediums are described herein for adaptively quantizing the parameters that are aimed to be deployed on in-memory computing systems based on magnetic memory devices. The method includes quantizing parameters based on a conductance shift sensing process, a sensed conductance shift value, a conductance shift lookup table, and a parameter to be quantized. The parameter can be quantized by using a value recorded in the lookup table, such as rounding to the nearest value. The lookup table can be generated by the conductance shift sensing process which can enabling recording of a sensed conductance of a magnetic memory device and an associated device state. The conductance shift sensing process can set the magnetic memory device (MMD) to different states and can measure the conductance shift of the MMD, caused by the state setting, using suitable equipment.

Methods, systems, and machine-readable mediums that enhance transaction authentication of a transaction of a first application by checking that a state of one or more other applications matches prespecified states or that one or more of those applications transition states within a prespecified period of time. For example, the prespecified states may correspond to the application being installed on a specified device (e.g., of the user) and having an authenticated session with a specified user.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for automating the recording of an evidentiary history (e.g., versions audit trail) of a graphical user interface with an embedded clickwrap agreement. The snapshot server captures and outputs snapshot records of a graphical user interface displaying the click-wrap agreement on an end user computing platform. The snapshot records are created using a virtual machine that emulates an end user computing platform for navigation of an application rendered on the platform. Aspects may also include generating a snapshot record of metadata associated with the captured snapshot image.

Methods, systems, and machine-readable mediums to perform a neural network to classify one or more logs. In at least one embodiment, a processor comprising one or more circuits to classify one or more log entries to obtain one or more classified log entries, obtain combined information at least in part by combing at least the one or more classified log entries and telemetry information, and use at least one machine learning process to classify the combined information.

To effectively resolve the issue of inappropriate handovers during communication sessions, methods, systems, and machine-readable mediums which utilize speech features captured by microphones in the original wireless peripheral device and / or the wireless peripheral device to which the communication session is to be handed over to determine if a handover should proceed or should be reversed. Speech features refer to the composite attributes of spoken language that encompass both acoustic and linguistic features. Acoustic features characterize the sound properties of speech and include, but are not limited to, timbre, pitch, intonation, speaking rate, articulation, prosody, melody, spectral features, formant frequencies, and the like. Linguistic features pertain to the actual content conveyed, comprising words, phrases, syntax, and semantics. This includes the analysis of words, phrases, syntax, and semantics to understand the context and continuity of the conversation.

The present disclosure generally relates to improved centroid predictions. In some aspects, a method of the disclosed technology includes: collecting, from a sensor, sensor data comprising data points; segmenting, via a first network, the sensor data into a first portion of the sensor data and a second portion of the sensor data; determining a semantic label for the first portion of the sensor data; determining local semantic information for each data point of the first portion of the sensor data; removing, using a point mask, the second portion of the sensor data; and determining, via a second network, a centroid of the first portion of the sensor data based on the first portion of the sensor data, the semantic label, and the local semantic information. Systems and machine-readable media are also provided.

Systems and methods for training a machine learning model to classify defects with utility-function-based active learning are described. In one embodiment, one or more non-transitory, machine-readable mediums are configured to cause a processor to at least determine a utility function value for unclassified measurement images, based on a machine learning model, wherein the machine learning model is trained using a pool of labeled measurement images. Based on a determination that the utility function value for a given unclassified measurement image is less than a threshold value, the unclassified measurement image is output for classification without the use of the machine learning model. The unclassified measurement images classified via the classification without the use of the machine learning model are added to the pool of labeled measurement images. The machine learning model is trained based on the measurement images classified via the classification without the use of the machine learning model.

In some examples, a computerized system for analyzing images comprises at least one programmable processor and a machine-readable medium having instructions stored thereon which, when executed by the at least one programmable processor, cause the at least one programmable processor to execute operations comprising training an autoencoder using a plurality of image model training samples, the autoencoder comprising a plurality of interconnected layers and combined instances of neural networks, passing input data into a trained autoencoder model, the input data including at least one pixel image, encoding the input data into a compressed version of the input data, and decoding the compressed version of the input data to generate to create an output, the output including a sparse reconstruction of the input data, the output including a predicted pixel image label or score.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for generating time-based interactive digital representations of a subject. Various aspects may include receiving a request for generating the interactive digital representations, wherein the request specifies the subject. Aspects may also include determining a time period covering a time span associated with the request. Aspects may also include retrieving contextual information associated with the subject and restricted by the time period. Aspects may also include generating, based on the contextual information, a model representing a persona of the subject in the time period. Aspects may include outputting an interactive digital representation of the model in a user interface (e.g., a conversational agent in a messaging application or avatar in a virtual environment), enabling user interactions with the model.

Methods, systems, and machine-readable mediums that enhance transaction authentication of a transaction of a first application by checking that a state of one or more other applications matches prespecified states or that one or more of those applications transition states within a prespecified period of time. For example, the prespecified states may correspond to the application being installed on a specified device (e.g., of the user) and having an authenticated session with a specified user.

Embodiments described herein provide for a non-transitory machine-readable medium storing instructions to cause one or more processor to perform operations to share a set of keys used to communicate with a wireless accessory device. By sharing the set of keys, functionality of the wireless accessory device can be delegated by an owner to other individuals.

Methods, systems, and machine-readable mediums that enhance transaction authentication of a transaction of a first application by checking that a state of one or more other applications matches prespecified states or that one or more of those applications transition states within a prespecified period of time. For example, the prespecified states may correspond to the application being installed on a specified device (e.g., of the user) and having an authenticated session with a specified user.

Systems, methods, and non-transitory, machine-readable media may facilitate testing within sub-environments with respect to resource capacity data corresponding to resources. Specifications of resource allocations for resources may be collected. Observation data, including resource metrics data, may be consolidated and used to develop resource data composites with mapped resource metrics data. A sub-environment interface may be configured to allow creation of sub-environments based on the resource data composites. Source schemas and data structures corresponding to the resource data composites may be selected. A migration plan to migrate the selected data structures to a destination schema for testing in a sub-environment may be created. A migration according to the migration plan may be executed. The destination schema may be created to match the selected source data structures and the selected source schemas. The selected data structures may be migrated to the destination schema to allow for testing in the sub-environment.

This disclosure describes methods, systems and machine readable media that can provide position solutions using, for example, pattern matching with GNSS signals in urban canyons. In one method, based upon an approximate location in an urban canyon and a set of 3D data about building structures in the urban canyon, an expected signal reception data can be generated for both line of sight and non-line of sight GNSS signals from GNSS satellites, or other sources of GNSS signals, at each point in a set of points in a grid (or other model) in the vicinity of the approximate location). This expected signal reception data can be matched to a received set of GNSS signals that have been received by a GNSS receiver, and the result of the matching can produce an adjustment to the approximate location that is used in the position solution of the GNSS receiver.

Various aspects of the subject technology relate to systems, methods, and machine-readable media for improving the privacy of transfers in a distributed ledger. Various aspects may include creating account state commitments for a transaction including at least an encrypted amount and the account state commitment. Aspects may also include receiving the transaction and storing the account state commitment in a first data structure. Aspects may also include determining an encrypted balance for a receiver based on the encrypted amount included in the transaction. Aspects may also include storing, in a second data structure, the encrypted balance mapped to a public key of the user. Aspects may also include atomically updating an encrypted outgoing balance of the user and a current encrypted balance of the receiver according to the transaction.

The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to perform rounds of federated machine learning training including: processing client reports from a plurality of clients of the edge computing network; selecting a candidate set of clients from the plurality of clients for an epoch of the federated machine learning training; causing a global model to be sent to the candidate set of clients; and performing the federated machine learning training on the candidate set of clients. The processor may perform rounds of federated machine learning training including: obtaining coded training data from each of the selected clients; and performing machine learning training on the coded training data.

A computer program product interacts with machine-readable mediums with instructions for automated target recognition. It captures a video of a specified region using detectors on a vehicle. Raw image frames from the video undergo pre-processing, followed by feeding through long and short-range target detection pipelines. Image frames are downscaled in the short-range pipeline, generating image windows and applying a trained convolutional neural network (CNN). Full resolution and non-redundant image chips are created. Detection region of interest (ROI) proposal lists are generated and analyzed with the CNN, producing frame detection lists with detected targets. These lists are stacked and processed using a multi-target Kalman filter, ultimately creating a track list of targets for monitoring.

The present disclosure generally relates to improved autonomous vehicle (AV) navigation in foggy conditions and, more specifically, to determining a fog intensity level and adjusting the speed of the AV based on the fog intensity level. In some aspects, a method of the disclosed technology includes steps for collecting sensor data for an environment around an AV; determining, based on the collected sensor data, that fog exists in the environment around the AV; determining, based on the collected sensor data, a fog proxy level; determining, based on the fog proxy level, a track spawning range of the AV; and adjusting, based on the track spawning range of the AV, a speed of the AV. Systems and machine-readable media are also provided.

Methods, systems, and machine-readable mediums that enhance transaction authentication of a transaction of a first application by checking that a state of one or more other applications matches prespecified states or that one or more of those applications transition states within a prespecified period of time. For example, the prespecified states may correspond to the application being installed on a specified device (e.g., of the user) and having an authenticated session with a specified user.