Abstract: Synthetic data is an annotated information that computer simulations or algorithms generate as an alternative to real-world data. synthetic data is created in digital worlds rather than collected from or measured in the real world. Embodiments herein provide a method and system for generating synthetic data with domain adaptable features using a neural network. The system is configured to receive seed data from a source domain as an input data. The seed data is considered as a normal state of a machine. The normal state, which is an initial stage of the source domain, consists of a set of features with a certain range of values. Further, a neural network based model is used to generate high quality data with adaptation of the domain specific features. To obtain large amount data for training robust deep learning models to adapt domains emphasizing set of features/providing higher importance selectively.

Abstract: Conventional Question and Answer (QA) datasets are created for generating factoid questions only and the present disclosure generates longform technical QA dataset from textbooks. Initially, the system receives a technical textbook document and extracts a plurality of contexts. Further, a first plurality of questions are generated based on the plurality of contexts. A plurality of answerable questions are generated further based on the plurality of contexts using an unsupervised template-based matching technique. Further, a combined plurality of questions are generated by combining the first plurality of questions and the plurality of answerable questions. Further, an answer for the combined plurality of questions are generated using an autoregressive language model and a mapping score is computed. Further, a plurality of optimal answers are selected based on the corresponding mapping score.

Abstract: In a digital computer with a cache comprised of N sets labeled 0 to N-1, cache tag memory for each set is divided into primary and mirror parts, each part with sufficient capacity to store a number of cache tags equal to the number of cache blocks storable in a cache memory associated with each set. Every modification or installation of cache tags in the primary part of a set x is accompanied or followed by identical modification or installation of cache tags in the mirror part of a set F(x), where F is a one-to-one function that maps the set of integers from 0 to N-1 onto itself. Cache tag lookup retrieves a first set of N cache tags from the primary part of each cache tag memory, and parity checking is performed on each tag. If a parity error is found, a set of cache tags is retrieved from the mirror part of the cache tag memories, and parity checking is again performed. If no error is found, cache processing proceeds normally.