Abstract: Disclosed is a sterile cover (100) for a Mixed Reality (MR) device (101). The sterile cover (100) may comprise a first case (102) arranged for covering a front side of the MR device (101). The first case (102) may comprise a hard case with an embedded clear layer (114) for covering a glass area of the front side of the MR device (101). The sterile cover (100) may further comprise a second case (104). The second case (104) may be arranged for covering a headband side of the MR device (101). The second case (104) may comprise a stretchable case for allowing a flexible adjustment of the headband side of the MR device (101) within the second case (104).

Abstract: The present disclosure relates to system(s) and method(s) to perform control testing to mitigate risks in an organization. The system may extract sentences from control documents, and may classify the sentences into one of questions and non-questions, based on at least one of active learning and pro-active learning. Interpretations of the questions may thereafter be generated. Relevant documents related to each of the interpretations of the questions may be identified and extracted, from repositories. Artificial Intelligence (AI) may be used to identify the relevant documents. A cognitive master may be implemented to organize meetings between control testers and process owners for discussing over effectiveness of design and implementation test of test plans to mitigate the risks.

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) to generate alerts while monitoring a machine learning model in real time. The system is configured to receive, in response to a first input parameter, a first output parameter generated by a first function of a learning model corresponding to a machine learning model. The system is further configured to receive, in response to a second input parameter, a second output parameter generated by a second functionality of a real-time model corresponding to the machine learning model. Further, the system is configured to compare the first output parameter with the second output parameter and the first input parameter with the second input parameter to generate tuning and rebuilding alerts.

Abstract: Disclosed is a system for translating data, extracted from disparate data sources, into a homogeneous dataset to provide meaningful information. The database schema definition module defines a database schema in order to extract meaningful information pertaining to a specific use-case. The data source determination module determines one or more disparate data sources pertinent to extract the meaningful information. The data extraction module extracts heterogeneous dataset from the one or more disparate data sources. The data extraction module further passes the heterogeneous dataset to a Data-Translate Markup Language (DTML) executer to translate the heterogeneous dataset into a homogeneous dataset. The data translation module translates the heterogeneous dataset into the homogeneous dataset by using at least one data adapter.

Abstract: Disclosed is a system (102) for determining a location of an explosive device. The system (102) detects an explosive device using one or more devices (204) based on one or more nano-explosive detection sensors (206) associated with the one or more devices (204). The system (102) further identifies a type, a quantity and a signal strength associated with the explosive device. The system (102) computes a distance between the explosive device and the one or more devices (204). The system (102) determines explosive device co-ordinates based on the computed distance and device co-ordinates associated with each device (204). The system (102) receives a data packet comprising data associated with the explosive device from the one or more devices (204). The system (102) determines a location of the explosive device based on an analysis of the data packet received from the one or more devices (204).

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: 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: A Convolution Multiply and Accumulate-Xtended (CMAC-X) system (102) for performing a convolution operation with functional safety mechanism is disclosed. The CMAC-X system (102) 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-X system (102) generates a convoluted data based on convolution operation for each feature matrix, The CMAC-X system (102) performs an accumulation of the convoluted data to generate accumulated data, when the convolution operation for each feature matrix is performed. The CMAC-X system (102) further performs an addition of a predefined value to the accumulated data to generate added data. Further, the CMAC-X system (102) filters the added data. Further, the CMAC-X system (102) comprises a functional safety unit to verify a functionality of the CMAC-X system (102), thereby performing the convolution operation of the image with functional safety mechanism.

Abstract: A half rate bang—bang phase detector for high-speed Analog Clock and Data Recovery (CDR) is disclosed. In some embodiments, the half rate bang—bang phase detector includes a first set of flip flops. Each of the first set of flip flops is configured to receive an input data sampled at each of a four phases of a Voltage Controlled Oscillator (VCO) clock. The half rate bang—bang phase detector includes a first set of logic gates configured to generate a set of four exclusive—OR (XOR) outputs. The half rate bang—bang phase detector includes a second set of flip flops configured to generate a set of clean XOR outputs. The half rate bang—bang phase detector includes a second set of logic gates configured to generate a set of final outputs based on the set of clean XOR outputs.

Abstract: An embodiment of the invention may include a method, computer program product, and system for detecting email messages sent from an automated mailing system. The embodiment may include analyzing email metadata of a user to detect an indication of automation. The embodiment may include identifying any email messages sent corresponding to the pattern of automation. The embodiment may include performing an action in response to the identified email messages.

Abstract: The invention relates to system and method for automating software development life cycle. In some embodiments, the method includes receiving a plurality of code snippets being utilized for developing a software application from a plurality of sources in a software development life cycle, generating a plurality of embedding vectors corresponding to the plurality of code snippets, and generating a high-level feature vector corresponding to each of the plurality of code snippets based on the corresponding embedding vector using a deep learning model. The method further includes generating a final merged code comprising a final sequence of code lines by combining the sequence of code lines corresponding to the plurality of code snippets using the deep learning model. The deep learning model is trained to generate the high-level feature vector and arrange the sequence of code lines based on historical data from the software development life cycle.

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: The present disclosure relates to system(s) and method(s) for interactively controlling the course of a functional simulation of DUV/SUV. The system comprises a testbench and the DUV/SUV connected to the testbench. The testbench generates a set of input data/packets as a stimulus to be processed by the DUV/SUV. The set of input data/packets is generated to simulate and verify the DUV/SUV. Further, the testbench identifies a pre-defined event at runtime during the simulation. Upon identification of the event, the testbench is configured to pause the simulation and transmit a notification message to a user indicating the occurrence of the event. Further, the testbench waits for a pre-defined time interval to receive one or more user inputs. The testbench further generates new stimulus based on the one or more user inputs and resumes the paused simulation with the new stimulus, thereby controlling the course of the functional simulation.

Abstract: The present disclosure relates to system(s) and method(s) for generating an alert based on change in traffic pattern. The system receives historic traffic data and current traffic data, associated with each road segment, from a set of road segments. Further, the system identifies a change traffic pattern based on analysing the historic traffic pattern and the current traffic pattern, using data analytics and a machine learning algorithm. Furthermore, the system identifies a sub-set of road segments, from the set of road segments, based on comparison of the change in traffic pattern and a pre-defined threshold. The system further determines root cause of change in traffic pattern by analysing the sub-set of road segments. Further, the system generates an alert for updating one or more road segments, from the sub-set of road segments, based on the root cause of change in traffic pattern.

Abstract: The present disclosure relates to a system (102) for assigning dynamic operation of devices in a communication network (106). The system (102) receives one or more behavioral attributes and one or more contextual attributes associated with one or more devices (232) in a communication network (106). The system (102) further determines one or more clusters (234) associated with each device from the one or more devices (232). The system (102) further determines, dynamically, one or more network slices, from a set of network slices associated with the one or more clusters (234). The system (102) further determines, dynamically, one or more analytics models associated with the one or more clusters (234). The system (102) further assigns dynamic operation of the one or more clusters (234) based on the one or more contextual attributes, the one or more network slices and the one or more analytics models.

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.