Abstract: Artificially structured materials are created artificially and offer customizable properties. They are being used in various fields. A system and method for designing artificially structured materials have been provided. The system and method is based on neural networks for approximating the electromagnetic (EM) responses of the artificially structured materials. By treating the EM spectral data as time-varying sequences and the inverse problem as a single-input, multi-output model, the architecture is forced to learn the geometry of the designs from the training data as opposed to abstract features thereby addressing both the forward and the inverse design problems.

Abstract: Simulation of dynamic physical systems is done using iterative solvers. However, this iterative process is a time consuming and compute intensive process and, for a given set of simulation parameters, the solution does not always converge to a physically meaningful solution, resulting in huge waste of man hours and computation resource. Embodiments herein provide a method and system for stabilizing a diverged numerical simulation and accelerating a converged numerical simulation by changing one or more control parameters. An automatic monitoring mechanism of residue history (to interpret convergence or divergence) and a subsequent control logic to auto-tune the under-relaxation factor would help in stabilizing a diverging simulation and reaching faster convergence by accelerating converging simulation.

Abstract: Machine Learning approaches in literature for determining optimal solver-preconditioner-smoother for solving matrix equations in computer modelling of any systems are directly dependent on matrix property calculation as an intermediate step. However, in CFD domain, this matrix system is generated from simulation input parameters. Also, part of simulation parameter's relation with the matrix equations can be derived from the theory. Embodiments of the present disclosure provide a method and system for prediction of fastest solver combination for solution of matrix equations during CFD simulations. The system trains a Machine Learning (ML) model using a set of relevant input parameters, based on domain knowledge of a CFD problem of interest, as a plurality of input features. The ML model is a multi-class classification model for the prediction of solver combination taking the CFD simulation parameters as an input.

Abstract: This disclosure relates generally to method and system for generating key performance indicator prediction model for multi-cloud applications. The disclosed method determines an optimized resource model and a predictive cost structure for one or more multi-cloud applications. The method receives a composite usage request to obtain a current resource consumption metrics and a cost structure for each cloud application identifier (ID). Further, a set of cloud provider API endpoints are invoked to obtain a plurality of usage tracking metrics. Further, a plurality of views are generated for each cloud application ID by processing every record associated with each API response file with allocated resource data. Then, a KPI prediction model is generated by leveraging autoregressive integrated moving average on the KPI time series data to determine an optimized resource model and a cost structure.

Abstract: This disclosure relates generally to long-form answer extraction and, more particularly, to long-form answer extraction based on combination of sentence index generation techniques. Existing answer extractions techniques have achieved significant progress for extractive short answers; however, less progress has been made for long form questions that require explanations. Further the state-of-art long-answer extractions techniques result in poorer long-form answers or not address sparsity which becomes an issue longer contexts. Additionally, pre-trained generative sequence-to-sequence models are gaining popularity for factoid answer extraction tasks. Hence the disclosure proposes a long-form answer extraction based on several steps including training a set of generative sequence-to-sequence models comprising a sentence indices generation model and a sentence index spans generation.

Abstract: The present disclosure addresses unresolved problems of task scheduling by adding redundancy in a collection task schedule generated by a ground control station. Embodiments of the present disclosure provide a randomization based redundant collection task scheduling for mitigating occlusions in sensing by small satellite constellations. The randomization based redundant collection task scheduling algorithm receives as input a set of region of observations tessellated into sub-regions, and then collection opportunities for each sub-region is computed based on satellite tracks data. Further, a sub-set of collection opportunities is determined from all the possible collection tasks for each sub-region to further generate the collection task schedule for each of the region of observation.

Abstract: This is a method and a system for designing a personalized therapeutic intervention for an individual. Each individual has a unique composition of microbiome in the gut, one probiotic or dietary regimen may not have same efficacy in different individuals. The disclosure recommends a personalized therapeutic intervention for improving gut health of an individual by designing personalized therapeutics and diet based on functions of microbiome. The personalized therapeutic intervention is recommended based on several steps including generating a set of knowledge bases, identifying a change in the gut health of an individual by monitoring the gut samples and recommending a personalized therapeutic intervention. The personalized therapeutic intervention comprises at least one of a prebiotic, a probiotic and an optimized diet, wherein the optimized diet is estimated based on optimizing a gut food score, where the gut food score is computed based on the change in the gut health of an individual.

Abstract: E-commerce industry is currently expanding rapidly, worldwide. A process of generating product copy for grocery items, which is very challenging as food items, do not have features in common, unlike fashion products. A data associated with one or more grocery products is received as an input. The data is processed to obtain one or more sorted similar grocery products. One or more relevant attributes and allergen information associated with the one or more sorted similar grocery products are extracted. A vocabulary model is created based the one or more relevant attributes and the allergen information associated with the one or more sorted similar grocery products. The vocabulary model is validated based on one or more assigned weights on training data. The one or more descriptive copies associated with grocery products are generated by mapping the validated vocabulary model with the training data.

Abstract: A method and system for automated continuous validation for regulatory compliance of CS with dynamic component. On identification of learning in the CS, a User Acceptance Testing (UAT) is performed using automated test cases of varying types in accordance with what-if scenarios and synthetic data generated using a unique approach. Thereafter, a base validation testing of the CS is performed with clean data (positive scenarios of outcome of the CS) and dirty data (negative scenarios) by conducting repeatability, stability (consistency) and reliability checks. The base validation testing is then followed by learning saturation testing on only if the dynamic component is validated, is rolled out in production environment else is rolled back to the earlier version.

Abstract: Conventional gesture detection approaches demand large memory and computation power to run efficiently, thus limiting their use in power and memory constrained edge devices. Present application/disclosure provides a Spiking Neural Network based system which is a robust low power edge compatible ultrasound-based gesture detection system. The system uses a plurality of speakers and microphones that mimics a Multi Input Multi Output (MIMO) setup thus providing requisite diversity to effectively address fading. The system also makes use of distinctive Channel Impulse Response (CIR) estimated by imposing sparsity prior for robust gesture detection. A multi-layer Convolutional Neural Network (CNN) has been trained on these distinctive CIR images and the trained CNN model is converted into an equivalent Spiking Neural Network (SNN) via an ANN (Artificial Neural Network)-to-SNN conversion mechanism. The SNN is further configured to detect/classify gestures performed by user(s).

Abstract: Motion parameters estimation for localization of differential drive vehicles is an important part of robotics and autonomous navigation. Conventional methods require introceptive as well extroceptive sensors for localization. The present disclosure provides a control command based adaptive system and method for estimating motion parameters of differential drive vehicles. The method utilizes information from one or more time synchronized command signals and generate an experimental model for estimating one or more motion parameters of the differential drive vehicle by computing a mapping function. The experimental model is validated to determine change in the one or more motion parameters with change in one or more factors and adaptively updated to estimate updated value of the one or more motion parameters based on the validation. The system and method of present disclosure provide accurate results for localization with minimum use of extroceptive sensors.

Abstract: This disclosure relates generally to program synthesis for weakly-supervised multimodal question answering using filtered iterative back-translation (FIBT). Existing approaches for chart question answering mainly address structural, visual, relational, or simple data retrieval queries with fixed-vocabulary answers. The present disclosure implements a two-stage approach where, in first stage, a computer vision pipeline is employed to extract data from chart images and store in a generic schema. In second stage, SQL programs for Natural Language (NL) queries are generated in dataset by using FIBT. To adapt forward and backward models to required NL queries, a Probabilistic Context-Free Grammar is defined, whose probabilities are set to be inversely proportional to SQL programs in training data and sample programs from it.

Abstract: Existing systems for behavioural tracking and identification have the disadvantage that they do not analyse data in behavioural aspects. As a result, they lack ability to pre-empt scenarios involving actions that adversely affect user health. The disclosure herein generally relates to behavior prediction, and, more particularly, to a method and system for identifying unhealthy behavior trigger and providing nudges. The system generates a casual inference model, which is a reverse causality model facilitating mapping of context with one or more behaviour of the user. The system further collects and processes real-time data using the casual inference model, to perform behavioral analysis of the user.

Abstract: Works in the literature fail to leverage embedding access patterns and memory units' access/storage capabilities, which when combined can yield high-speed heterogeneous systems by dynamically re-organizing embedding tables partitions across hardware during inference. A method and system for optimal deployment of embeddings tables across heterogeneous memory architecture for high-speed recommendations inference is disclosed, which dynamically partitions and organizes embedding tables across fast memory architectures to reduce access time. Partitions are chosen to take advantage of the past access patterns of those tables to ensure that frequently accessed data is available in the fast memory most of the time. Partition and replication is used to co-optimize memory access time and resources.

Abstract: The present disclosure provides a method for surface wear inspection using millimeter wave radar. The system initially receives a plurality of uncompressed raw Synthetic Aperture Radar (SAR) images. Further, a plurality of reconstructed SAR images are generated based on the plurality of uncompressed raw SAR images using a variable focusing based Range Doppler Algorithm (RDA). Further, a master image and a slave image are selected from the reconstructed SAR images and corresponding anchor points are assigned. Further a plurality of fine level and coarse level shift coordinates are computed based on the corresponding anchor points. Further, a net shift value is computed based on the plurality of fine level and coarse level shift coordinates. The master and the slave images are aligned based on the net shift value and the interferogram is generated. The interferogram is further analyzed to profile the corresponding deformation pertaining to the surface under test.

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: Elderly people suffer from health issues, and timely detection can save lives. State of the art techniques either make certain assumptions or require clinical data in order to perform the frailty detection, which affects the quality as well as cause inconvenience to the users. The disclosure herein generally relates to patient monitoring and, more particularly, to frailty detection using pedometer sensor data, PIR sensor data, and door sensor data. The system determines activity levels of the user being monitored, based on data from the pedometer sensors, PIR sensors, and door sensors, and based on the determined activity levels, further determines whether the user has frailty or not.

Abstract: Data analysis plays a crucial role to get significant information out of the data. A real time system and method for analyzing data streams have been provided. The system can utilize many different types of data formats such as numeric, text, video, audio, image, or combination thereof. The analysis takes place as per the requirement using an analytical engine and an intermediate output is generated. The intermediate output is further processed using a distributed real time business rule processing engine to determine required conditions in the data. The business rules comprise one or more set of meta data. On match of the business rule, the system triggers an alert or propagates the required information to integrating solution for required actions. The system and method are technology and communication protocol agnostic, and designed with highly efficient load balanced technique, thereby facilitating highly concurrent data processing with minimal latency.

Abstract: The present disclosure provides a system and method for delay prediction for scheduled public transport. A multi-architectural deep learning approach has been used to predict the delays of a queried vehicle in the scheduled public transport. For this, historical operational data is transformed into temporal, and spatiotemporal data. While, the spatial data is obtained from geographical information. The system uses different combinations of neural networks architectures. A regressor model uses three separate kinds of architecture. One component is the Fully Connected Neural Network (FCNN), which is good at learning from static features, the second is the Long Short Term Memory (LSTM) network which is good at learning from temporal features, and the third is the 3D Convolutional Neural Network (3DCNN) which is good at learning from spatiotemporal features. Learned encoding from each are fed to another FCNN to produce the predicted delay value.

Abstract: Performance of a machine learning (ML) model in production, is heavily dependent on underlying distribution of data or underlying process generating labels from attributes. Any change in either one or both impacts the ML model performance heavily and inhibits knowledge of true labels. This in turn affects ML model uncertainty. Thus, performance monitoring of ML models in production becomes necessary. Embodiments of the present disclosure provide estimates operating model accuracy at production stage by constructing the correlations between the model accuracy, model uncertainty and deviation of the distributions in absence of ground truth. In the method of present disclosure, the model performance of the machine learning (ML) model deployed in production is estimated in absence of ground truths. Moreover, this can be done without retraining the model, thus saving computational costs and resources. The method of the present disclosure can be used and performed in real time.