Abstract: A system for time-series message management using directed property graphs is provided. A directed property graph is generated using various messages. The directed property graph comprises various message nodes and attribute nodes, with each message node representing a message, having some attributes of the message associated as node properties thereof, and being associated with attribute nodes that represent shared attributes of the message. To execute a query indicative of time-series computation functions, one or more attribute nodes are identified in the directed property graph. Based on the identified attribute nodes, a set of message nodes is determined. Further, from node properties of each determined message node, one or more message timestamps are identified. Based on the identified message timestamps, the time-series computation functions are executed.

Abstract: This disclosure relates to method and system for facilitating legacy code transformation. The method includes receiving legacy code data and natural language document from one or more data sources. Each of the one or more data sources is one of an external data source or an internal data source. Further, the method includes generating a first natural language output based on the legacy code data through a first LLM, and a second natural language output based on the natural language document through a second LLM. Further, the method includes fine-tuning one of the first LLM or the second LLM based on the first natural language output and the second natural language output, through a third LLM. Further, the method includes generating a natural language specification document corresponding to the legacy code data based on the first natural language output and the second natural language output through the third LLM.

Abstract: The present disclosure facilitates optimized storage of historical data. A graph is generated using various messages. For each message, a message node is instantiated in the graph. In a message-driven architecture, processing of a message leads to the generation of a data node in the graph. The data node represents an output associated with the message processing. In response to the data node generation, another message node is instantiated in the graph. The other message node represents a message that is indicative of the generation of the data node and comprises data and transactional information associated with the data node. Further, a history node is created linking the data node and the two message nodes to indicate that the data node is an output associated with the message processing and the newly generated message node stores a change associated with the data node.

Abstract: A system for message management using directed property graphs is provided. A directed property graph is generated using various messages. To generate the graph, for each message, a message node representing the message is instantiated. Each message has various attributes. Some message attributes are associated as node properties of the message node. Further, one or more shared attributes are derived for each message and corresponding attribute nodes are instantiated. One or more attributes are associated as node properties of these attribute nodes. Further, each message node is coupled to a corresponding attribute node by way of an edge. Each edge has edge attributes associated as edge properties. The edge attributes may be derived from node properties of the message node and the corresponding attribute node and may indicate an association therebetween. The generated graph facilitates query response generation.

Abstract: The disclosure relates to system and method for continuously tracking humans in an area. The method includes receiving video data of the area from overhead cameras. Each of overhead cameras includes Field of View (FoV), FoV includes overlapping region and non-overlapping region, and overlapping region corresponds to region of intersection between at least two FoVs. The method further includes detecting presence humans in first FoV through object detection and classification models; for each human of humans, assigning unique global identity (ID) corresponding to human in first FoV, and reassigning unique global ID to human when human moves from first FoV to second FoV through overlapping region between first FoV and second FoV using weighted combination of resource assignment algorithm, intersection-over-union (IOU) based track detection, and velocity and direction estimation of subsequent frame of video data; and continuously tracking, in real-time, each of humans in the area through unique global ID.

Abstract: A system and/or a method for a cognitive platform for autonomous data orchestration, comprising edge computing devices, data platforms and cognitive computing engine. The cognitive computing engine is configured to receive digital events either generated by external computing devices or within the cognitive computing engine. Context and state information is accessed from a central intelligence store to determine the intent of the received digital events. A target entity is identified based on at least one of the determined intent. A data package is composed for the identified at least one target entity, transformed to a natural language text and communicated to the identified at least one target entity, inciting the target entity to take action on the package delivered. The central intelligence store is updated new context and intents for subsequent digital events making it an autonomous learning platform.

Abstract: This disclosure relates to a method and system for sanitization of sensitive data. The method includes analyzing sensitive data present within a page of an application based on a deterministic algorithm. Further, the method includes classifying the sensitive data into a high risk sensitive data and a low risk sensitive data based on a ML classification algorithm. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a destructive sanitization on each of the high risk sensitive data. For the one or more sensitive data classified as the high risk sensitive data, the method further includes performing a non-destructive sanitization on each of the high risk sensitive data.

Abstract: An overlay system is provided that includes processing circuitry and a storage element that stores an executable graph-based model including various active nodes, various history message nodes, and various history overlay nodes. Each active node is associated with one or more history overlay nodes that facilitate the creation and maintenance of one or more history message nodes associated therewith. The processing circuitry receives a contextualized stimulus and identifies an active node and one or more history message nodes in the executable graph-based model based on the context. The processing circuitry creates one or more history nodes (e.g., one or more historical versions of the corresponding active node) based on the one or more history message nodes. Further, the processing circuitry executes an operation associated with the stimulus based on the identified active node, the one or more history overlay nodes, and the created one or more history nodes.

Abstract: The disclosure relates to system and method for smart test execution in a network test automation platform. The method includes retrieving set of parameter values for each of plurality of test cases in test suite corresponding to set of risk parameters based on latest release of product code. The method further includes calculating risk index for each of plurality of test cases based on set of parameter values and predefined set of weightage scores. The method further includes determining execution order of plurality of test cases based on calculated risk index. The method further includes executing set of priority test cases from plurality of test cases based on execution order and predefined threshold risk escape value. The method further includes dynamically rendering in real-time, one or more live charts based on current risk escape value upon execution of the set of priority test cases via GUI.

Abstract: A system for improving performance of variational quantum classifiers (VQCs) using large language models (LLMs) is provided. Features and classes are extracted from a classification dataset. Weights are assigned to the features based on contribution of the features in class identification. Further, segregation parameters are determined for the features such that a segregation parameter indicates a degree of separation between two classes based on a correlation between two features. A prompt is generated based on the weights, the segregation parameters, current ansatz and feature map of a VQC, a performance metric of the VQC for the current ansatz and feature map, details of hardware executing the VQC, and a number of qubits of the VQC. Using an LLM, a response to the prompt is generated. Further, using the response, the ansatz and the feature map are updated. The update continues until the performance metric is within a desired range.

Abstract: This disclosure relates to method and system for framework agnostic smart test orchestration in network test automation in a network. The method includes receiving input data for executing one or more test scripts to test one or more functions on one or more devices in the network. The input is received from a user device via a web-based application. The method comprises parsing the input data and selecting an execution module from a plurality of execution modules for executing the one or more test scripts on the one or more devices based on the parsed input data. The method comprises executing the one or more test scripts to test the one or more functions on the one or more devices using the selected execution module.

Abstract: An overlay system is provided that includes a storage element and processing circuitry coupled thereto. The processing circuitry receives a stimulus associated with the overlay system and creates an executable graph-based model that implements an external datastore in the storage element, based on the stimulus. The processing circuitry creates, in the executable graph-based model, a plurality of node groups that implements a plurality of schemas associated with the external datastore, with a first node group created for a first schema. The processing circuitry creates, in the first node group, a first set of active nodes that implements a first database object associated with the first schema. The processing circuitry creates, in the first node group, a second set of active nodes that implements a first set of database records associated with the first database object. The processing circuitry executes one or more operations in the executable graph-based model.

Abstract: A system for context-based graphical element replacement is provided. Various graphical elements (e.g., images, icons, or the like) are detected in a source file. For each graphical element, a visual context of the graphical element and one or more additional contexts are determined. The one or more additional contexts include a context associated with information present within a predefined range of the graphical element, a visual context of at least one other graphical element, and a context associated with the source file. A replacement graphical element is then generated for each detected graphical element based on the determined visual context and the one or more additional contexts associated with the corresponding graphical element. The graphical elements in the source file are replaced with the replacement graphical elements to generate a replacement file. The replacement file is then rendered on a user device.

Abstract: A system for framework selection for task execution is provided. Framework records of various frameworks are analyzed and based on the analysis, a translation instruction set is generated for each framework to translate an operation from a predefined format to a format associated with the corresponding framework. The most suitable framework is determined for a particular operation. A mapping between various operations and the most suitable framework for each operation is generated. During task execution, multiple natural-language tasks are derived from a natural-language task description. Each natural-language task is translated into a neo-syntax operation. Based on the mapping, a suitable framework is selected for the execution of each neo-syntax operation. The operations are translated to the syntax of the suitable frameworks by using the corresponding translation instruction set. A task output for the natural-language task description is generated based on the execution of the translated operations.

Abstract: The disclosure relates to a method and system for dynamically mitigating threats of generative Artificial Intelligence (AI) models. Conventional systems often suffer from inefficiencies due to sequentially applying threat detection checks leading to unnecessary preprocessing and increased computational demands. Additionally, such systems typically focus only on input data, neglecting potential threats in outputs. The disclosed system and method addresses these drawbacks by employing a hierarchical structure of macro and nano classifiers. The system utilizes macro classifiers for broad initial threat categorization followed by specialized nano classifiers for detailed analysis of specific threat subtypes, thereby optimizing processing time and computational resources. The system operates in real time, applying predefined moderation rules to both input and output data to ensure comprehensive threat mitigation.

Abstract: A system for framework selection for task execution is provided. Framework records of various frameworks are analyzed and based on the analysis, a translation instruction set is generated for each framework to translate an operation from a predefined format to a format associated with the corresponding framework. The most suitable framework is determined for a particular operation. A mapping between various operations and the most suitable framework for each operation is generated. During task execution, multiple natural-language tasks are derived from a natural-language task description. Each natural-language task is translated into a neo-syntax operation. Based on the mapping, a suitable framework is selected for the execution of each neo-syntax operation. The operations are translated to the syntax of the suitable frameworks by using the corresponding translation instruction set. A task output for the natural-language task description is generated based on the execution of the translated operations.

Abstract: A method for loading run-time nodes of a template-based executable graph-based model is provided. A node instance manifest state is obtained based on a first node identifier associated with a node instance. The node instance manifest state includes a second node identifier associated with a node template. A node instance manifest is generated from the node instance manifest state. The node instance manifest comprises a first storage location and the first node identifier. A node instance state is obtained from the first storage location. The node template is obtained based on the second node identifier. The node instance is generated from the node instance manifest, the node instance state, and the node template. A run-time node, comprising a composition of the node instance and the node template, is generated.

Abstract: A method for contract-based loading of executable-graph based models is provided. A stimulus and an associated context are received. The stimulus is associated with a contract node having a contract identifier. Based on the contract identifier, a contract node manifest associated with the contract node, a storage location associated with a contract node state, and node identifiers of nodes associated with the contract node are obtained. The contract node is generated based on the contract node manifest and the contract node state. Node manifests associated with the nodes are obtained. Each node manifest comprises a node identifier and a storage location associated with a respective node state. Node states for the nodes are obtained. Each node state is obtained from the storage location associated with the respective node state. The nodes are generated based on the node manifests and the node states.

Abstract: A method and system for prediction based on asynchronous and heterogeneous time-series data streams is provided. The asynchronous and heterogeneous time-series data streams are aligned onto a unified temporal grid. The aligned time-series data streams are synchronous, and sampling frequencies of the aligned time-series data streams are identical. Cross-attention is executed on the aligned time-series data streams across multiple attention windows. Each attention window is associated with a different time duration. A cross-attention output is generated based on the execution of the cross-attention for each attention window. Fused embeddings are generated based on the cross-attention outputs generated for the multiple attention windows. A prediction output is generated based on the plurality of fused embeddings for the time-series data streams.

Abstract: An overlay system is provided that includes a storage element and processing circuitry. The storage element stores an executable graph-based model that includes various executable nodes that communicate with each other by way of messages. The executable graph-based model further includes a message node for each message associated with the overlay system. Each message node is associated with one or more analytics overlay nodes that execute a corresponding set of analytics operations on composition of the corresponding message node to generate one or more analytic insights. The composition includes data and transactional information associated with the corresponding message node. A publisher overlay node associated with the one or more analytics overlay nodes may generate and publish an analytics outcome based on an output of the execution of the set of analytics operations. The analytics outcome is indicative of performance of the overlay system.