Abstract: Considering the number of OSS components and the number of OSS license types available today, the number of license attributes to be considered for analyzing a product at a granular level is a challenge to perform manually, prudently considering legal implications of non-compliance and contamination and also within the limited time available today before going to market in the software industry. Systems and methods of the present disclosure intelligently facilitates a matrix which is able to identify OSS components in a software product and also facilitates the product owner to identify proprietary IP that can be suitably protected and licensed without contamination by the accompanying OSS components and generated components in the software product under consideration. License attributes of the OSS components are mapped suitably, and a final attribute is derived for each OSS component embedded in the product under consideration.

Abstract: Considering the number of OSS components and the number of OSS license types available today, the number of license attributes to be considered for analyzing a product at a granular level is a challenge to perform manually, prudently considering legal implications of non-compliance and contamination and also within the limited time available today before going to market in the software industry. Systems and methods of the present disclosure intelligently facilitates a matrix which is able to identify OSS components in a software product and also facilitates the product owner to identify proprietary IP that can be suitably protected and licensed without contamination by the accompanying OSS components and generated components in the software product under consideration. License attributes of the OSS components are mapped suitably, and a final attribute is derived for each OSS component embedded in the product under consideration.

Abstract: This disclosure relates to a system and method for quantitative measure of subject specific sentiment analysis of a text input. The text input comprises subjects and objects. The text input is tokenized, and each word of the tokenized text input is tagged based on a part-of-speech (POS) and a universal dependency tag. A universal dependency tag tree is prepared based on dependency tags. Further, the subjects and objects are identified using a subject-verb-object (SVO) detection. The universal dependency tree is analyzed for each identified subject to determine a token dependency of the subject. The identified subject is quantified using a deep learning-based sentiment analyzer and finally a sentiment score is recommended for the subject using a probability score and a class score is assigned to the subject.

Abstract: System and method for multi-core processing based time series management, capable of handling high volume time series data with high speed processing for time series forecasting. The method includes storing each time series as a Structured Query Language (SQL) array in a single row using a plurality of time series parameters. Further, stored plurality of time series are analyzed on receiving a forecast request using a correlation based pattern detection mechanism. The pattern detection mechanism enables deriving a subset of forecasting models, from which an optimal or best-fit forecasting model is identified to generate one or more forecasted time series.

Abstract: This disclosure relates generally to a method and a system for extraction of key-terms and synonyms for the key-terms. The method discloses to extract the key-terms and further determine a set of relevant synonyms for the extracted key-terms, wherein the key-terms is a collection of small sub-set of words and phrases which are individually meaningful and collectively provide a generic context of a given input text. The method discloses techniques for extraction of key-terms that is independent of any specific supervised training, does not require a huge amount of training data and computationally less intensive. The key-terms are determined in several stages using frequency based techniques based on a relevancy scoring of the key-terms. Further a set of synonyms are determined for the identified key-terms based on a language-based approach or domain specific approach.

Abstract: This disclosure relates generally to a method and a system for extraction of key-terms and synonyms for the key-terms. The method discloses to extract the key-terms and further determine a set of relevant synonyms for the extracted key-terms, wherein the key-terms is a collection of small sub-set of words and phrases which are individually meaningful and collectively provide a generic context of a given input text. The method discloses techniques for extraction of key-terms that is independent of any specific supervised training, does not require a huge amount of training data and computationally less intensive. The key-terms are determined in several stages using frequency based techniques based on a relevancy scoring of the key-terms. Further a set of synonyms are determined for the identified key-terms based on a language-based approach or domain specific approach.

Abstract: This disclosure relates to a system and method for quantitative measure of subject specific sentiment analysis of a text input. The text input comprises subjects and objects. The text input is tokenized, and each word of the tokenized text input is tagged based on a part-of-speech (POS) and a universal dependency tag. A universal dependency tag tree is prepared based on dependency tags. Further, the subjects and objects are identified using a subject-verb-object (SVO) detection. The universal dependency tree is analyzed for each identified subject to determine a token dependency of the subject. The identified subject is quantified using a deep learning-based sentiment analyzer and finally a sentiment score is recommended for the subject using a probability score and a class score is assigned to the subject.

Abstract: System and method for multi-core processing based time series management, capable of handling high volume time series data with high speed processing for time series forecasting. The method includes storing each time series as a Structured Query Language (SQL) array in a single row using a plurality of time series parameters. Further, stored plurality of time series are analyzed on receiving a forecast request using a correlation based pattern detection mechanism. The pattern detection mechanism enables deriving a subset of forecasting models, from which an optimal or best-fit forecasting model is identified to generate one or more forecasted time series.

Abstract: Processing backup data based on file system authentication is described. A system request authentication information from an application in response to a receipt of a request from the application to process backup data. The system receives encrypted authentication information associated with an authentication system corresponding to a file system. The system requests a permission level from the authentication system based on the encrypted authentication information. The system receives the permission level from the authentication system. The system determines whether the permission level permits the request from the application to process the backup data. The system processes the backup data for the application in response to a determination that the permission level permits the request from the application to process the backup data.

Abstract: Methods and systems for selecting an appropriate forecasting model for accurate workload prediction of a processor is disclosed. The processor is configured to monitor workload and extract workload history of the processor for a given time interval. Further, the processor is configured to create plurality of forecasting models based on the extracted workload history and apply the group of forecasting model on the extracted workload history to obtain a plurality of predicted future workload for the given time interval. Further, the processor is configured to compute an error measure of the plurality of predicted future workload in reference to an actual workload of the processor and select the appropriate forecasting model from plurality of the forecasting models having least error measure among the computed error measures for dynamically scaling frequency and voltage required by the processor and thereby optimizing energy consumption in the processor.

Abstract: Methods and systems for selecting an appropriate forecasting model for accurate workload prediction of a processor is disclosed. The processor is configured to monitor workload and extract workload history of the processor for a given time interval. Further, the processor is configured to create plurality of forecasting models based on the extracted workload history and apply the group of forecasting model on the extracted workload history to obtain a plurality of predicted future workload for the given time interval. Further, the processor is configured to compute an error measure of the plurality of predicted future workload in reference to an actual workload of the processor and select the appropriate forecasting model from plurality of the forecasting models having least error measure among the computed error measures for dynamically scaling frequency and voltage required by the processor and thereby optimizing energy consumption in the processor.

Abstract: There is provided a method of nucleic acid analysis which allows analysis of genetic diversity in multiple populations to be performed rapidly and simultaneously. The method comprises (a) isolating nucleic acid from said sample; (b) providing at least two pairs of labelled primers, wherein each said primer pair is complementary to a marker sequence in a nucleic acid of at least one member; (c) amplifying the nucleic acid; (d) digesting the labelled amplified nucleic acid with at least one restriction enzyme to produce restriction fragments, and size sorting said fragments to produce a restriction fragment length profile, and (e) analysing said restriction fragment length profile so obtained; wherein the primer pairs provided for each marker have a different sequence to the sequence of the primer pairs for each other marker, and wherein each said primer pair is uniquely labelled relative to the other primer pair(s). In one embodiment each primer pair is uniquely labelled at the 5? end with a fluorophore.

Abstract: There is provided a method of nucleic acid analysis which allows analysis of genetic diversity in multiple populations to be performed rapidly and simultaneously. The method comprises (a) isolating nucleic acid from said sample; (b) providing at least two pairs of labelled primers, wherein each said primer pair is complementary to a marker sequence in a nucleic acid of at least one member; (c) amplifying the nucleic acid; (d) digesting the labelled amplified nucleic acid with at least one restriction enzyme to produce restriction fragments, and size sorting said fragments to produce a restriction fragment length profile, and (e) analysing said restriction fragment length profile so obtained; wherein the primer pairs provided for each marker have a different sequence to the sequence of the primer pairs for each other marker, and wherein each said primer pair is uniquely labelled relative to the other primer pair(s). In one embodiment each primer pair is uniquely labelled at the 5? end with a fluorophore.