Abstract: Techniques are provided for email protection using email signatures based on signing tokens. One method comprises obtaining a signed email from an email sender. The signed email comprises a second hash value generated using a source version of the signed email embedded with a signing token of the email sender. A data record stores a first hash value, based on the source version, and the second hash value. The second hash value is obtained from the signed email and compared to: (i) a copy of the second hash value obtained from the data record and/or (ii) a comparison value generated using a regenerated version of the source version embedded with the signing token of the email sender. A delivery of the signed email to an email recipient may be based on a result of the comparison.

Abstract: A method for verifying a user agent includes transmitting, by a core node, to a user agent, a predefined set of user requests. For each user request in the predefined set of user requests, the core node receives an embedding generated based on the user request, thereby receiving a plurality of embeddings based on the predefined set of user requests. The core node determines whether the plurality of generated embeddings satisfy a similarity criterion. The core node transmits, to the user agent, a task to generate a query plan and receives a query plan generated by the user agent for processing a user request identified in the task. The core node determines whether the query plan satisfies a query plan criterion, thereby producing query plan adequacy output. The core node determines whether to approve the user agent based on the similarity output and the query plan adequacy output.

Abstract: A method for identifying and clustering worker agents for processing requests includes receiving, by a core node, from a user agent, a user request. The core node updates, for each of the plurality of worker agents, an availability status, thereby producing a plurality of availability statuses. The core node computes, for each of the plurality of worker agents, a value of a drift metric. The core node clusters the plurality of worker agents to produce a plurality of clusters of worker agents, wherein each of the plurality of clusters contains worker agents that have similar semantic capabilities. Based at least on the user request, the plurality of availability statuses, and the plurality of clusters, the core node identifies a subset of the plurality of worker agents that are both available to process the user request and that are suitable for processing the user request.

Abstract: Disclosed is a double chamber honey packaging developed for use in the food industry, to enable honeycomb and extracted honey to be stored and/or stacked in the same package without contacting each other and to extend the shelf life of the product.

Abstract: A computer-implemented method for generating machine learning training data may include obtaining mechanism of action (MOA) data that is indicative of a hierarchical tree structure of relationships between the MOA data; generating linear representations of branches of the hierarchical tree structure; determining association rules for the MOA data by applying one or more frequent pattern mining algorithm to the linear representations; and determining, as at least a portion of the generated machine learning training data, MOA clusters by applying a clustering model to the linear representations and the association rules.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program may provide a client device a collection of data objects in a subset of the plurality of collections of data objects. The program may receive a selection of a data object in the collection of data objects. The program may determine a set of policies associated with the data object. The program may apply the set of policies on the data object. Based on the application of the set of policies on the data object, the program may determine whether to process the data object.

Abstract: A method for identifying and clustering worker agents for processing requests includes receiving, by a core node, from a user agent, a user request. The core node updates, for each of the plurality of worker agents, an availability status, thereby producing a plurality of availability statuses. The core node computes, for each of the plurality of worker agents, a value of a drift metric. The core node clusters the plurality of worker agents to produce a plurality of clusters of worker agents, wherein each of the plurality of clusters contains worker agents that have similar semantic capabilities. Based at least on the user request, the plurality of availability statuses, and the plurality of clusters, the core node identifies a subset of the plurality of worker agents that are both available to process the user request and that are suitable for processing the user request.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing a target function for execution on a target processor. In particular, the target function is optimized by searching through candidate computer programs that each represent an approximation of the target function.

Abstract: Methods of treating a nerve injury are disclosed. The methods include administering to a target site of the nerve injury a thermally stable preparation having a purified amphiphilic peptide in an aqueous biocompatible solution, being configured to self-assemble into a hydrogel, and administering to the target site a buffer having an effective amount of an ionic salt and a biological buffering agent to form the hydrogel. The methods include administering to the target site a biological material suspension in an amount effective to treat the nerve injury. The methods include administering to the target site an anti-scarring agent in an amount effective to treat the nerve injury. The target site is associated with central nervous system tissue or peripheral nervous system tissue. The nerve injury includes spinal cord injury and peripheral nerve injury.

Abstract: A method for identifying and clustering worker agents for processing requests includes receiving, by a core node, from a user agent, a user request. The core node updates, for each of the plurality of worker agents, an availability status, thereby producing a plurality of availability statuses. The core node computes, for each of the plurality of worker agents, a value of a drift metric. The core node clusters the plurality of worker agents to produce a plurality of clusters of worker agents, wherein each of the plurality of clusters contains worker agents that have similar semantic capabilities. Based at least on the user request, the plurality of availability statuses, and the plurality of clusters, the core node identifies a subset of the plurality of worker agents that are both available to process the user request and that are suitable for processing the user request.

Abstract: A method for verifying a user agent includes transmitting, by a core node, to a user agent, a predefined set of user requests. For each user request in the predefined set of user requests, the core node receives an embedding generated based on the user request, thereby receiving a plurality of embeddings based on the predefined set of user requests. The core node determines whether the plurality of generated embeddings satisfy a similarity criterion. The core node transmits, to the user agent, a task to generate a query plan and receives a query plan generated by the user agent for processing a user request identified in the task. The core node determines whether the query plan satisfies a query plan criterion, thereby producing query plan adequacy output. The core node determines whether to approve the user agent based on the similarity output and the query plan adequacy output.

Abstract: A method for assigning a plurality of worker agents to perform a plurality of sub-tasks includes generating, by a user agent, an embedding of a user request using a first pre-trained large language model. A core node in communication with the user agent identifies and ranks a plurality of candidate worker agents based on the embedding of the user request. A second LLM decomposes the user request into a plurality of sub-tasks. For each of the plurality of sub-tasks, the user agent identifies a subset of the plurality of candidate worker agents to perform the sub-task and identifies a best worker agent from the subset. For each of the plurality of sub-tasks, the user agent assigns the sub-task to, and receives a corresponding result from, the best worker agent; the user agent provides the corresponding result to the user.

Abstract: A method for ranking a plurality of worker agents based on a user request includes receiving a user request, the user request including a description of a task. The method incudes identifying a plurality of tasks to be performed to process the user request. The method includes, for each of a plurality of worker agents, computing a corresponding value for each of a plurality of metrics, thereby computing a plurality of metric values for each of the plurality of worker agents. The method includes generating, for each of the plurality of worker agents, a corresponding ranking, based on the metrics computed for the plurality of worker agents above, thereby generating a plurality of worker agent rankings, each of which corresponds to a distinct worker agent in the plurality of worker agents.

Abstract: A method includes recording user-agent interaction data associated with a plurality of interactions between a user and a user agent, the user-agent interaction data including a plurality of requests provided by the user to the user agent, a plurality of responses provided by the user agent, and feedback provided by the user in response to at least some of the plurality of responses. The user-agent interaction data is summarized to produce summarized user-agent interaction data, which is stored in a scalable database optimized for quick retrieval. A user request is received and parsed to identify an intent of the user request. A similarity search of the user request is performed against the summarized user-agent interaction data, thereby identifying a matching subset of the summarized user-agent interaction data. The matching subset of the summarized user-agent interaction data is concatenated with the user request to produce an enhanced context.

Abstract: A method for verifying a user agent includes transmitting, by a core node, to a user agent, a predefined set of user requests. For each user request in the predefined set of user requests, the core node receives an embedding generated based on the user request, thereby receiving a plurality of embeddings based on the predefined set of user requests. The core node determines whether the plurality of generated embeddings satisfy a similarity criterion. The core node transmits, to the user agent, a task to generate a query plan and receives a query plan generated by the user agent for processing a user request identified in the task. The core node determines whether the query plan satisfies a query plan criterion, thereby producing query plan adequacy output. The core node determines whether to approve the user agent based on the similarity output and the query plan adequacy output.

Abstract: A method includes receiving a user request from a user. A user agent identifies a current worker agent capable of handling a current part of the user request and of satisfying the set of requirements of the current part of the user request. The current worker agent processes the current part of the user request to generate and send a response to the user agent, which parses the response to extract relevant context information. The user agent enables the extracted relevant context information to be appended to subsequent user requests. The user agent selects, based on an additional part of the user request, an additional worker agent to process the additional part. An augmented user request is constructed based on the additional part of the user request and the extracted relevant context information.

Abstract: A method for identifying and clustering worker agents for processing requests includes receiving, by a core node, from a user agent, a user request. The core node updates, for each of the plurality of worker agents, an availability status, thereby producing a plurality of availability statuses. The core node computes, for each of the plurality of worker agents, a value of a drift metric. The core node clusters the plurality of worker agents to produce a plurality of clusters of worker agents, wherein each of the plurality of clusters contains worker agents that have similar semantic capabilities. Based at least on the user request, the plurality of availability statuses, and the plurality of clusters, the core node identifies a subset of the plurality of worker agents that are both available to process the user request and that are suitable for processing the user request.

Abstract: A method for verifying a worker agent includes receiving, by a core node, from a worker agent, a capability description describing a plurality of tasks and, for each of the plurality of tasks, (i) at least one parameter of the task and (ii) an outcome expected to be produced by performing the task. The method includes generating, based on the capability description, a plurality of request-output pairs, each representing a particular request and a corresponding baseline output expected to be produced upon processing the request. The core node receives, from the worker agent, a plurality of outputs, each of the plurality of outputs generated by the worker agent and corresponding to one of the plurality of request-output pairs. The core node compares the plurality of baseline outputs to the plurality of actual outputs to produce comparison output and determines whether to approve the worker agent based on the comparison output.

Abstract: In some implementations, a data merger may receive a configuration associated with a first data source. The data merger may receive a configuration associated with a second data source. The data merger may receive a configuration associated with a first output endpoint. The data merger may receive an indication of a first transformation to apply to first data received from the first data source and second data received from the second source, such that the first output endpoint transmits a combination of the first data and the second data after application of the first transformation. The data merger may provide the first data, received from the first data source, to a machine learning model and receive an indication of a second transformation recommended by the machine learning model. The data merger may transmit the indication of the second transformation.

Abstract: This disclosure relates to method and system for evaluating through the Artificial intelligence (AI) model. The method includes receiving input data comprising video data and audio data corresponding to an interview of a candidate. The method further includes extracting in near real-time a set of video and audio features from each of the plurality of frames of the video data using a first self-learning AI model and audio data using a second self-learning AI model. The method further includes comparing the set of video features and the set of audio features with predefined threshold values corresponding to the set of predefined parameters. The method further includes generating a score corresponding to each of the set of predefined parameters of the candidate using the first self-learning AI model and the second self-learning AI model.