Abstract: Increasing use of web-based applications or Software-as-a-Service and IoT devices within enterprise networks increases the variety of network traffic and variables for consideration in managing security posture, which includes policy management. A security posture management system as disclosed herein leverages application identification and device discovery from ongoing collection and analysis of network traffic data to manage policies at device granularity allowing tailored security posture management. The system can tailor policies to handle network traffic depending on identified application and device type inputs obtained from the ongoing collection and analysis. The security posture management system can configure SD-WAN construct based parameters of a policy to tailor policies for different application traffic from different types of devices.

Abstract: A machine learning model can be trained using a first set of degraded images for each of a plurality of combinations and corresponding reference images, where a number of degraded images in the first set corresponding to a particular combination of the plurality of combinations is selected in accordance with a probability value associated with the particular combination. A validation process can be used to determine a loss value for each of the plurality of combinations of degradations. Updates to the probability values associated with the plurality of combinations can be calculated based on the loss values. The machine learning model can be updated using a second set of degraded images for each of the plurality of combinations, and the corresponding reference images, where a number of degraded images in the second set corresponding to the particular combination is selected based on the updated probability value.

Abstract: A machine learning model can be trained to perform super-resolution by using high-frequency loss. One or more degradations of a first type can be applied to reference images to generate corresponding degraded images that include a reduced amount of high-frequency texture information when compared to the corresponding reference images. A mapping function associated with a machine learning process can used to generate predicted images. One or more degradations of a second type can be applied to the predicted images and the reference images to generate corresponding low-frequency images. The low frequency images corresponding to the predicted images can be compared to the low-frequency images corresponding to the reference images. Based at least partially on the comparison, a loss value can be calculated. If the loss value exceeds a loss value threshold, the mapping function can be updated in accordance with the loss value.

Abstract: A machine learning model can be trained using a first set of degraded images for each of a plurality of combinations and corresponding reference images, where a number of degraded images in the first set corresponding to a particular combination of the plurality of combinations is selected in accordance with a probability value associated with the particular combination. A validation process can be used to determine a loss value for each of the plurality of combinations of degradations. Updates to the probability values associated with the plurality of combinations can be calculated based on the loss values. The machine learning model can be updated using a second set of degraded images for each of the plurality of combinations, and the corresponding reference images, where a number of degraded images in the second set corresponding to the particular combination is selected based on the updated probability value.

Abstract: A machine learning model can be trained to perform super-resolution by using high-frequency loss. One or more degradations of a first type can be applied to reference images to generate corresponding degraded images that include a reduced amount of high-frequency texture information when compared to the corresponding reference images. A mapping function associated with a machine learning process can used to generate predicted images. One or more degradations of a second type can be applied to the predicted images and the reference images to generate corresponding low-frequency images. The low frequency images corresponding to the predicted images can be compared to the low-frequency images corresponding to the reference images. Based at least partially on the comparison, a loss value can be calculated. If the loss value exceeds a loss value threshold, the mapping function can be updated in accordance with the loss value.

Abstract: A method includes selecting a plurality of test cases. The method also includes designing the plurality of test cases to perform the automated quality assurance. The method further includes calibrating the plurality of test cases and manage the plurality of test cases in a visual hierarchy. The method also includes reflecting the functional modules in a cohesive group based on the calibration. The method further includes executing the plurality of test cases through at least one of a manual testing mode and an automatic testing mode. The method includes registering information associated with the plurality of test cases. Moreover the method includes generating one or more reports for the plurality of test cases. Furthermore the method includes displaying the one or more reports generated for the plurality of test cases on a visual interface.

Abstract: The present invention relates to a recombinant calf-Chymosin protein as set forth in SEQ ID No. 1; a recombinant calf-Chymosin gene as set forth in SEQ ID No. 2 encoding the protein comprising amino acid sequence of SEQ ID NO.1; an E. coli comprising the recombinant chymosin gene of SEQ ID No. 2; an expression vector pET21b comprising recombinant calf-chymosin gene as set forth in SEQ ID No. 2; and lastly a method for producing recombinant calf-chymosin protein as set forth in SEQ ID No. 1 which comprises steps of isolating calf-chymosin gene, cloning the same in bacterial expression vector pET21b, transforming said cloned vector into cells of E. coli, fermenting said E. coli to produce pro-chymosin, converting said pro-chymosin to chymosin and subsequently recovering the recombinant calf-chymosin.

Abstract: The present invention relates to a recombinant calf-Chymosin protein as set forth in SEQ ID No. 1; a recombinant calf-Chymosin gene as set forth in SEQ ID No. 2 encoding the protein comprising amino acid sequence of SEQ ID NO. 1; an E. coli comprising the recombinant chymosin gene of SEQ ID No. 2; an expression vector pET21b comprising recombinant calf-chymosin gene as set forth in SEQ ID No. 2; and lastly a method for producing recombinant calf-chymosin protein as set forth in SEQ ID No. 1 which comprises steps of isolating calf-chymosin gene, cloning the same in bacterial expression vector pET21b, transforming said cloned vector into cells of E. coli, fermenting said E. coli to produce pro-chymosin, converting said pro-chymosin to chymosin and subsequently recovering the recombinant calf-chymosin.