Abstract: The present disclosure relates to system(s) and method(s) for generating an Omni-channel support platform. The method comprises integrating a multi-channel support system with a blockchain framework. Further, the method comprises generating an Omni-channel support platform based on the integration. The Omni-channel support platform comprises an Omni-channel support block for a user from a set of users. The Omni-channel support block comprises a support ledger and a support smart contract for the user. The Omni-channel support block further comprises capturing transaction data associated with the user from the multiple support channels. Further, the Omni-channel support block comprises recommending one or more resolutions to each user upon based on a support request.

Abstract: Disclosed is a method and system for generating test scripts. The method comprises receiving at least one of a video and/or an audio captured during manual testing of a Device Under Test (DUT) comprising an output unit or a Graphical User Interface (GUI) based application. At least one of the video and/or the audio is processed for generating a test script for the DUT or the GUI based application. Generation of the test script may include allowing a user to pause at least one of the video and/or the audio at a particular time frame. Using a script generator user interface, input events corresponding to the particular time frame are received. A type of validation is selected for the output unit of the DUT or the GUI based application, and inputs are provided for the validation. The validation is device specific and performed using during runtime test execution.

Abstract: A method for transmitting media content from a cloud content delivery network (CCDN) to a plurality of in-flight wireless media servers (WMSs) is disclosed. In some embodiments, the method includes receiving, by each of the plurality of in-flight WMSs, position data with respect to at least one of a movement, an altitude, or a geo-spatial location of an aircraft; identifying, by the plurality of in-flight WMSs, one or more elevated in-flight WMSs for receiving the media content. The method for identifying further includes determining a respective content-reception state of each of the plurality of in-flight WMSs; determining a respective content-reception suitability score for each of the plurality of in-flight WMSs; and identifying the one or more elevated in-flight WMSs. The method further includes receiving, by each of the one or more elevated in-flights WMSs, the media content from a suitable CCDN server in the CCDN.

Abstract: Disclosed is a method for providing visual guidance in a medical surgery. The method comprises registering, within a virtual assistance device having a combination of a virtual medical implant, an Intra-Medullary (IM) nail model and a virtual insertion handle model, each of a physical IM nail implant and a physical insertion handle. Further, a physical drill gun is registered within the virtual assistance device having a drill gun model. Further, coordinates of the one or more holes on the physical IM nail implant are registered within the virtual assistance device. The physical IM nail is inserted into a target. The virtual impression of the physical drill gun is aligned over the one or more holes based on the coordinates. Further, one or more surgical steps are performed by the physical drill gun based on the aligning.

Abstract: Disclosed is a reconfigurable parallel 3-Dimensional (3-D) convolution engine for performing 3-D Convolution and parallel feature map extraction on an image. The reconfigurable parallel 3-D convolution engine further comprises a plurality of CNN reconfigurable engines configured to perform 3-D convolution, in parallel, to process a plurality of feature maps, a kernel memory space, present in each instance of CNN reconfigurable engine, capable for holding a set of parameters associated to a network layer having each operational instance of CNN reconfigurable engine, and at least one memory controller, an Input Feature Map Memory (FMM) cluster and an Output FMM cluster.

Abstract: The present disclosure relates to system(s) and method(s) for building an ARIMA based Time Series prediction/forecast model for Key Performance Indicators (KPIs) and Performance Management (PM) counters in a telecommunication network. The system receives historical data, for a predefined period, associated with a prediction/forecast model. The system further pre-processes the historical data in order to evaluate statistical characteristics of stationarity of the historical data. Based on the evaluation, the system stationarizes the data first by backfilling anomalies and missing data and then applying techniques associated with differencing, moving averages and auto-correlation. The system further builds the Time Series based prediction/forecast model using the data using ACF and PACF correlation functions.

Abstract: Disclosed is a method and system for verifying a regex sanitizer and a validator. The method comprises verifying of at least one of a regex sanitizer and a validator by applying the regex sanitizer and a validator over multiple predefined tainted inputs. An output obtained after applying at least one of the regex sanitizer is checked for one of a tainted output or a non-tainted output. The at least one of the regex sanitizer and validator may be qualified as a valid regex sanitizer and validator based upon the checking. The valid regex sanitizer may be tagged with a validation signature. The valid regex sanitizer is used for checking the tainted input in the user's input.

Abstract: The present disclosure relates to a system and a method for processing distributed data files. The processor executes instructions to receive a set of instructions from a primary device, wherein the set of instructions comprises verification rules, validators, primary transformers and structure query transformers; generate processed data files by processing the distributed data files. The distributed data files are processed by performing at least one of: executing one of the verification rules, the validators and the primary transformers on the distributed data files; and transforming the distributed data files by executing the structure query transformers. The execution of the structured query transformers comprises steps of generating a dependency graph based upon dependencies between the structure query transformers; and determining a sequence of execution of the structured query transformers based upon the dependency graph; and transfer the processed data files to a data warehouse.

Abstract: The present disclosure relates to system(s) and method(s) for verifying data loading requirements of an avionics unit. The system receives a request for data loading. The request comprises file data, and data loading requirements associated with the avionics unit. Further, the system obtains target file from a repository based on an analysis of the request. The system further generates valid data set and invalid data set in the target file based on an analysis of the data loading requirements. Upon generation, the system verifies predefined data loading requirements of the avionics unit using the invalid data set from the target file.

Abstract: Disclosed is a method and system for joining datasets in a distributed computing environment. The system comprises a memory 206 and a processor 202. The processor 202 identifies a skewed dataset from two or more datasets to be joined. The processor 202 identifies a replication parameter from a configuration file. The processor 202 then assigns a randomly assigned machine number to each chunk of the skewed dataset owned by the nodes/machines involved in the join operation. The processor 202 forms copies of the non-skewed dataset equal to the replication parameter and adds the copy number to each sample of the copy of the non-skewed dataset formed. Further, the processor 202 merges each non-skewed dataset into the final copy of the non-skewed dataset, forming a single non skewed dataset. The processor 202 then repeats these steps for all the non-skewed datasets involved in the join operation resulting in generation of merged copies of all the non-skewed datasets and then performs the joining operation.

Abstract: Disclosed is a convolution operator system for performing a convolution operation concurrently on an image. An input router receives image data. A controller allocates image data to a set of computing blocks based on the size of the image data and number of available computing blocks. Each computing block produces a convolution output corresponding to each row of the image. The controller allocates a plurality of group having one or more computing blocks to generate a set of convolution output. Further, a pipeline adder aggregates the set of convolution output to produce an aggregated convolution output. An output router transmits either the convolution output or the aggregated convolution output for performing subsequent convolution operation to generate a convolution result for the image data.

Abstract: The present disclosure relates to system(s) and method(s) for decomposing a monolithic application into one or more micro services. The method identifies a subset of functionalities, from a set of functionalities associated with the monolithic application. The method further determines a number of micro services based on a functionality priority, a functionality complexity score, and a functionality predefined complexity score associated with each functionality. Further, the method generates one or more groups of methods from a plurality of methods associated with the subset of functionalities. Further, the method decomposes the monolithic application into the one or more micro services based on the one or more groups of methods, and the number of micro services.

Abstract: Disclosed is a system for allowing secure access to a microservice. An Application Programming Interface (API) gateway receives a request comprising a Uniform Resource Locator (URL) associated to the microservice. A set of input parameters indicating information about the user device and the microservice is identified from the URL. The system performs validation of input parameters, extraction of request patterns, tracking of IP address, and detection of user credentials to provide output parameters. A decision tree comprising rules is generated by using a supervised machine learning technique on the output parameters. Further, the API gateway creates a stateless identity token to encrypt the request. The stateless identity token is created based on the user credentials and at least one rule applicable to the request. Once the stateless identity token is created, the stateless identity token is verified to allow the secure access to the microservice.

Abstract: Disclosed is a method and system for verifying a regex group. The method comprises verifying of a regex group by creating a flow id through a processor for the regex group when source reaches the sink. The flow id is used for tracking the flow of the regex group. The processor checks in case the flow id is a previously tested flow id. When the flow id is not the previously tested flow id, the processor passes one or more run tasks through a processor forming a queue. The processor tests for one or more vulnerabilities to be associated with the regex group based on the passing, wherein the testing is used to qualify the regex group as a valid regex group.

Abstract: A Convolution Multiply and Accumulate (CMAC) system for performing a convolution operation is disclosed. The CMAC system receives image data pertaining to an image. The image data comprises a set of feature matrix, a kernel size and depth information. Further, the CMAC system generates a convoluted data based on convolution operation for each feature matrix. The CMAC system performs an accumulation of the convoluted data to generate accumulated data, when the convolution operation for each feature matrix is performed. The CMAC system further performs an addition of a predefined value to the accumulated data to generate added data. Further, the CMAC system filters the added data to provide a convolution result for the image, thereby performing the convolution operation of the image.

Abstract: Disclosed is a method and system for classifying elements of a product. The method comprises identifying elements of the product. Thereupon, features of the one or more elements are determined, using a feature recognition technique. The features correspond to manufacturing operations required for manufacturing the elements, and include sheet metal operations, turn operations, injection moulding operations, and machining operations. The manufacturing operations are determined in a priority order with the sheet metal operation having a highest priority and the machining operation having a least priority.

Abstract: Disclosed is a reconfigurable convolution engine for performing a convolution operation on an image. A data receiving module receives image data. A determination module determines a kernel size based on the image data, clock speed associated to the convolution engine and number of available on-chip resources. An allocation module allocates a plurality of instances based on the kernel size. Each instance of the plurality of instances further comprises a set of computing blocks operating concurrently. Each computing block is configured to perform convolution operation on the feature map of the image. An aggregation module aggregates the convolution output of each computing block for each instance of the plurality of instances to produce a convolution result for the image.

Abstract: Disclosed is a method and system for joining datasets in a distributed computing environment. The system comprises a memory 206 and a processor 202. The processor 202 identifies a skewed dataset from two or more datasets to be joined. The processor 202 identifies a replication parameter from a configuration file. The processor 202 then assigns a randomly assigned machine number to each chunk of the skewed dataset owned by the nodes/machines involved in the join operation. The processor 202 forms copies of the non-skewed dataset equal to the replication parameter and adds the copy number to each sample of the copy of the non-skewed dataset formed. Further, the processor 202 merges each non-skewed dataset into the final copy of the non-skewed dataset, forming a single non skewed dataset. The processor 202 then repeats these steps for all the non-skewed datasets involved in the join operation resulting in generation of merged copies of all the non-skewed datasets and then performs the joining operation.

Abstract: The present disclosure relates to system(s) and method(s) for monitoring each activity in a process. The method comprises capturing target data associated with a target type, corresponding to an activity in a process, selected by a user. The target type is at least one of a cut-off, an uptime, a count, a duration, a throughput, and a comparison. Further, the method comprises analysing the target data using threshold data and data stream in real time. Furthermore, the method comprises monitoring the activity in the process based on the analysis of the target type.

Abstract: Disclosed is a method for providing visual guidance in a medical surgery. The method comprises registering, within a virtual assistance device having a combination of a virtual medical implant, an Intra-Medullary (IM) nail model and a virtual insertion handle model, each of a physical IM nail implant and a physical insertion handle. Further, a physical drill gun is registered within the virtual assistance device having a drill gun model. Further, coordinates of the one or more holes on the physical IM nail implant are registered within the virtual assistance device. The physical IM nail is inserted into a target. The virtual impression of the physical drill gun is aligned over the one or more holes based on the coordinates. Further, one or more surgical steps are performed by the physical drill gun based on the aligning.