Abstract: The present disclosure is of a method for automated authoring of purposive models from Natural Language (NL) documents. Conventional model extractors to automatically extract models from NL documents are specific to a metamodel, do not consider document structure and are not configurable. Initially, the system receives a plurality of Natural Language (NL) documents, a metamodel, a plurality of pattern trees corresponding to the metamodel, and a configurable domain dictionary. Each of the plurality of pattern trees includes a plurality of pattern elements. Further, a document information is generated from each of the plurality of NL documents using a document information reading technique. Finally, a plurality of purposive models are generated for each of the plurality of NL documents by interpreting a corresponding document information based on the plurality of pattern trees and the metamodel using a pattern interpretation technique.

Abstract: Enterprises need assurance that their internal documents like policies, procedures, controls, standard operating procedures (SOPS) are adherent to the regulatory obligations. In conventional practice this requires a manual effort where legal, business and IT experts collaborate for assuring completeness and consistency in enterprise documents with respect to regulatory obligations, thereby establishing regulatory hygiene. Governance risk and compliance (GRC) frameworks help experts with collaboration but do not provide automation aids necessary to reduce the analysis and synthesis burden. Present disclosure provides system and method for determining hygiene in the plurality of enterprise documents with respect to the plurality of regulatory obligations by extracting concept ontology models from multiple enterprise documents and multiple regulations and enabling navigation across multiple documents via the ontology.

Abstract: Organizations are struggling to ensure business continuity without compromising on delivery excellence in the face of pandemic related uncertainties which exists along multiple dimensions effected by authorities. This uncertainty plays out in a non-uniform manner thus leading to highly heterogeneous evolution of pandemic. Present disclosure provides digital twin based systems and methods for business continuity plan and safe return to workplace wherein a simulation-based data-driven evidence-backed approach is implemented that captures details pertaining to virus, individualistic characteristics of employees and their dependents, offices, locations of the employees and offices, and various pandemic control measures that are in effect and need to be explored using a hybrid modelling and simulation approach that combines fine-grained actor/agent model and coarse-grained stock-and-flow model.

Abstract: The present disclosure provides a dynamic functional adaptation of software systems based on real time need, which is unaddressed by the conventional methods. Initially, the system receives an output generated by a software system under execution corresponding to a given input. Further, a deviation in the generated output is identified based on a comparison between the generated output and a corresponding expected output. Further, an optimal policy is identified based on the generated output and the identified deviation using a reward based Reinforcement Learning (RL) model. Further, an adaptation specification is generated based on the identified optimal policy using an Adaptation Specification Generation (ASG) technique, Further, a software code is generated dynamically for the identified optimal policy using an ASG based code generation technique.

Abstract: This disclosure relates to the field of project related document analysis. Conventionally, process of retrieving right information related to a stakeholder with relevant project initiation concerns involves manual intervention resulting in more time consumption. The method of the present disclosure describes a system and method for automated extraction and classification of project initiation related information from request for proposal response documents The RFP response document is parsed using document structure based parsing technique to identify questions and answers. The questions from the RFP response document are classified into different classes of interest and important information from the answers are extracted and mapped to an identified class. The method of the present disclosure demonstrates significant improvement in terms of time consumption by reducing volume of information and providing a quick access to class-specific relevant information from the RFP response document.

Abstract: The present disclosure extracts contextual product feature model from requirement specification documents where the conventional methods fail to perform. Initially, the system receives a plurality of requirement specification documents pertaining to a product, a domain dictionary, a plurality of configuration parameters, and a plurality of extraction patterns. A product feature model is generated using a NLP based feature extraction technique. The product feature model includes a plurality of product feature elements comprising a feature area, a major feature and a plurality of features arranged hierarchically and classified into feature types. Further, a plurality of ContextType associations like core, client, geography and market are extracted for each of the plurality of features using a ContextType extraction technique. Finally, the plurality of ContextType associations is updated in the product feature model to obtain a contextual product feature model.

Abstract: The present disclosure provides a holistic model for feature specification and dependency representation for requirement specification documents where the conventional models fail to provide. The present disclosure receives a plurality of requirement specification documents and a related data. A product feature model is generated based on the plurality of requirement specification documents and the related data using a feature model generation technique. The product feature model includes a plurality of product feature elements. The plurality of product feature elements includes a feature area, a major feature and a plurality of features. A specification model is generated further for each of the plurality of features using a specification extraction technique. Post generating the specification model, a plurality of dependency associations are generated for each of a plurality of specification elements of the specification model using a dependency extraction technique.

Abstract: This disclosure relates generally to the method and system for dynamically optimizing the operations of logistics management system. The existing methods for optimizing the operational processes in the sorting terminals are not dynamic as the sorting terminal operations are largely manual and experience driven. The proposed method and system describe an actor-based representation of simulatable digital twin of the sorting terminal that enables in-silico quantitative exploration of design space to help human experts arrive at the right decisions related to the logistics management system. The proposed method and system initially construct a high-fidelity simulatable digital twin of the sorting terminal, validate it, set it up with real data, and simulate various adaptation and design alternatives to understand their impacts on the key performance indicator values.

Abstract: A method is provided for decoding an encoded video stream on a processor having a plurality of processing cores includes receiving and examining a video stream to identify any macroscopic constructs present therein that support parallel processing. Decoding of the video stream is divided into a plurality of decoding functions. The plurality of decoding functions is scheduled for decoding the video stream in a dynamic manner based on availability of any macroscopic constructs that have been identified and then based on a number of bytes used to encode each block into which each picture of the video stream is partitioned. Each of the decoding functions is dispatched to the plurality of processing cores in accordance with the scheduling.

Abstract: The disclosure generally related to a learning-based modelling of an emergent behavior of a complex system. Existing decision-making at complex systems primarily relies on qualitative approaches, which often results in inaccurate outputs. The disclosed system includes a digital twin of the complex system and a digital twin of an environment of said complex system, and captures an interaction and dynamic behavior of agents of the digital twins. The agents of the digital twins are simulated and modelled using learning-based models such as RL and genetic algorithms that learns the behavior (i.e. actions and their outcomes) over a period of time. Hence, the agents (or actors) of the digital twins are dynamic in nature. The actor-based bottom up simulation approach is capable of producing sufficient insight for effective decision making prior to implementation.

Abstract: This disclosure relates to method of extracting an information associated with design of formulated products and representing as a graph. A graph domain model of a plurality of vertices, and at least one formulation text as text file are received as an input. The information extraction is applied to identify at least one sentence and extract at least one subject-verb-object triple from every sentence of the at least one formulation text. A sentence including an ingredient listing and associated weights indicated by presence of weight numerals, and a sentence including at least one verb from the at least one subject-verb-object based on the graph domain model are classified. A representation of the recipe text is generated in terms of at least one action, ingredients on which the at least one action is performed, and condition. An insert query string is generated and executed to store the formulations as the graph.

Abstract: A method is provided for decoding an encoded video stream on a processor having a plurality of processing cores includes receiving and examining a video stream to identify any macroscopic constructs present therein that support parallel processing. Decoding of the video stream is divided into a plurality of decoding functions. The plurality of decoding functions is scheduled for decoding the video stream in a dynamic manner based on availability of any macroscopic constructs that have been identified and then based on a number of bytes used to encode each block into which each picture of the video stream is partitioned. Each of the decoding functions is dispatched to the plurality of processing cores in accordance with the scheduling.

Abstract: This disclosure relates to automatic creation of an image processing pipeline that has a dependency on image processing domain experts. Given the image processing problem has multiple viable solutions, search space of creating a solution for a given goal in a given constrained infrastructure is large and, choosing an optimal image processing solution is an effort, time and intellect intensive endeavor. The present disclosure models image processing goals in abstract task categories and externalizes domain knowledge as an object model and a set of rules defined over it. Rules codify task template selection criteria and algorithm selection criteria for implementing a task. Recommendations are provided to choose suitable algorithm (s) for carrying out image processing tasks. Effects of algorithm execution are recorded for subsequent tasks recommendations. The stored state of execution and trace information enables rerouting the execution and knowledge upgrade.

Abstract: Generally, in complex systems such as business organizations and supply chain networks, decisions making and control are challenging due to dynamic environment thereof. The embodiments herein provide method and system that uses reinforcement learning (RL) for exploring policies and deciding control actions, and actor-based modelling and simulation for performing accurate long-term rollouts of the policies, in order to optimize operation of the complex systems. In an embodiment, a method for actor based simulation of complex system includes modeling, actors of the complex system using an actor abstraction, where actors includes RL agents embodied in each subsystem of the complex system, and executes micro-level interactions amongst the subsystems. An emergent macro-behavior of the complex system is simulated by the using the RL agents based on the micro-level interactions. Optimal decisions pertaining to actions of the RL agents are learnt based on the emergent macro-behavior of the complex subsystem.

Abstract: A method is provided for determining a context-index when performing Context-based Adaptive Binary Arithmetic Coding (CABAC) for video compression or decompression includes initializing to an initialized value each of a plurality of context-indexes of chosen syntax elements associated with a given block (e.g., a macroblock). The context-index of dependent neighboring blocks of the given block is evaluated. The dependent neighboring blocks are blocks that have a context-index that depends on coding of a current bin position. The context-index of the dependent neighboring blocks is updated if and only if their context-index changes from the initialized values.

Abstract: This disclosure relates to method of extracting an information associated with design of formulated products and representing as a graph. A graph domain model of a plurality of vertices, and at least one formulation text as text file are received as an input. The information extraction is applied to identify at least one sentence and extract at least one subject-verb-object triple from every sentence of the at least one formulation text. A sentence including an ingredient listing and associated weights indicated by presence of weight numerals, and a sentence including at least one verb from the at least one subject-verb-object based on the graph domain model are classified. A representation of the recipe text is generated in terms of at least one action, ingredients on which the at least one action is performed, and condition. An insert query string is generated and executed to store the formulations as the graph.

Abstract: This disclosure relates to automatic creation of an image processing pipeline that has a dependency on image processing domain experts. Given the image processing problem has multiple viable solutions, search space of creating a solution for a given goal in a given constrained infrastructure is large and, choosing an optimal image processing solution is an effort, time and intellect intensive endeavor. The present disclosure models image processing goals in abstract task categories and externalizes domain knowledge as an object model and a set of rules defined over it. Rules codify task template selection criteria and algorithm selection criteria for implementing a task. Recommendations are provided to choose suitable algorithm (s) for carrying out image processing tasks. Effects of algorithm execution are recorded for subsequent tasks recommendations. The stored state of execution and trace information enables rerouting the execution and knowledge upgrade.

Abstract: A method is provided for decoding an encoded video stream on a processor having a plurality of processing cores includes receiving and examining a video stream to identify any macroscopic constructs present therein that support parallel processing. Decoding of the video stream is divided into a plurality of decoding functions. The plurality of decoding functions is scheduled for decoding the video stream in a dynamic manner based on availability of any macroscopic constructs that have been identified and then based on a number of bytes used to encode each block into which each picture of the video stream is partitioned. Each of the decoding functions is dispatched to the plurality of processing cores in accordance with the scheduling.

Abstract: A method is provided for determining a context-index when performing Context-based Adaptive Binary Arithmetic Coding (CABAC) for video compression or decompression includes initializing to an initialized value each of a plurality of context-indexes of chosen syntax elements associated with a given block (e.g., a macroblock). The context-index of dependent neighboring blocks of the given block is evaluated. The dependent neighboring blocks are blocks that have a context-index that depends on coding of a current bin position. The context-index of the dependent neighboring blocks is updated if and only if their context-index changes from the initialized values.

Abstract: A method is provided for determining a context-index when performing Context-based Adaptive Binary Arithmetic Coding (CABAC) for video compression or decompression includes initializing to an initialized value each of a plurality of context-indexes of chosen syntax elements associated with a given block (e.g., a macroblock). The context-index of dependent neighboring blocks of the given block is evaluated. The dependent neighboring blocks are blocks that have a context-index that depends on coding of a current bin position. The context-index of the dependent neighboring blocks is updated if and only if their context-index changes from the initialized values.