Abstract: The present disclosure a method for document structure based unsupervised long-form technical question generation. Initially, the system receives a textbook document. Further, a PDF metadata is extracted from the textbook document using a Natural Language Processing (NLP) technique. Further, a plurality of structures from the textbook document based on the PDF metadata using an NLP based filtering technique. Further, a plurality of index based question templates and Table of Contents (TOC) based question templates are obtained from a plurality of predefined question templates using the plurality of structures. Further, the generated plurality of long-form technical questions are generated using the obtained index and TOC based question templates. The plurality of long-form technical questions are further evaluated by the system using plurality of metrics.

Abstract: Conversational systems are intelligent machines that can understand language and conversing with a customer in writing or verbally. Embodiments herein provide a method for generating a universal conversational system using an ensemble of chatbots and a universal conversational system that adopts wisdom of crowd manifesting as an ensemble of chatbots. The ensemble of chatbots takes responses from NER and rule based conversational models. The knowledge based conversation models where complex queries that require question and answer, and the ensemble of generative knowledge chatbots are relying on a pre-trained models. The pre-trained models are complemented by domain specific training to answer queries that fall outside rule-based chatbot or knowledge graph-based conversation bot capability. The universal conversational system capable of building online virtuous automated learning loop where the models learn from each other and also from human response as wisdom of crowd.

Abstract: Embodiments herein provide a method and system for a vicarious calibration of optical data from satellite sensors for urban scene flat fields. Identifying test sites automatically in the urban scene helps in vicarious calibration or on-board calibration of the hyperspectral/multispectral image. An internal average relative reflectance is calculated to get a relative reflectance of the image. Band ratios for various pixels is determined to assess flatness of the spectrum. Flat field candidates are identified from the various pixels having average band ratio nearing zero and a morphological technique is applied to determine a flat field. Finally, the image is calibrated vicariously based on the determined flat field as a test site. The on-board calibration of the remote sensing image may lead to a faster way to get the reflectance image of the scene, with the help of the calibration constants.

Abstract: This disclosure relates generally to method and system for predicting shelf life of perishable food items. In supply chain management, current technology provides limited capability in providing relation between visual image of food item and a quality parameter value at different storage conditions. The system includes a quality parameter prediction module and a shelf life prediction module. The method obtains input data from user comprising a visual data and a storage data of each food item. The quality parameter prediction module determines a current quality parameter value of the food item from a look-up table. The shelf life prediction module predicts the shelf life of food item based on the current quality parameter value, a critical quality parameter value and the storage data. The look-up table comprising a plurality of weather zones are generated based on relationship dynamics between the visual image of food item and the quality parameter value.

Abstract: The quality of user experience (UX) of an information visualization depends on multiple diverse aspects. These include qualitative, quantitative, and contextual parameters that are unmeasurable and incomparable. Hence, measuring the UX of a visualization is challenging. The disclosure herein relates to a system and method that collects, processes, and analyzes a multiple diverse parameters to measure and profile the UX of a visualization. To accomplish this, the system collects data regarding the usage, effectiveness, and user perception of the visualization. The system creates a quantitative and comparable version of all these parameters to measure holistically the UX of the visualization.

Abstract: The disclosure relates to anomaly detection in an industrial environment including multiple industrial units and systems, generating huge volume of data. The conventional methods rely only on sensor data alone. The techniques of handling missing data plays a crucial role in determining the performance of industrial anomaly detection system. Further, imputation of missing data could cause error in computation, thus affecting the accuracy of the industrial anomaly detection system. The present disclosure addresses the problems associated with missing data by utilizing a masking technique. Further, the present disclosure utilizes quantitative and qualitative metadata associated with industrial system along with the sensor data to improve anomaly detection performance. Furthermore, the present disclosure includes a model recommendation system which provides transfer learning based utilization of existing models for similar industrial systems.

Abstract: Code translation is an evolving field and due to advancements in the infrastructure and compute power. The existing methods for code translation are time and effort intensive. A method and system for translation of codes based on the semantic similarity have been provided. A machine learning model is developed, that understands and encapsulates the semantics of the code in the source side and translates the semantic equivalent code which is more maintainable and efficient compared to one to one translation. The system is configured to group a plurality of statements present in the source code together into blocks of code and comprehend the semantics of the block. The system is also trained to understand syntactically different but semantically similar statements. While understanding the semantics of the block and translating, the unused/duplicate code etc. gets eliminated. The translated code is better architected and native to the target environment.

Abstract: This disclosure relates to a system and method to autonomously manage hybrid IT infrastructure. An end-to-end, integrated, and autonomous IT infrastructure is suggested to offload the repetitive business as usual (BAU) operational tasks, thereby reducing operational cost, noise, and chaos, improve resiliency, thus improving availability of the business. The autonomous IT infrastructure leads to bring in efficiency to customer business, to reduce incident reduction, optimize cost and to provide insight into any future IT infrastructure need. Herein, one or more key characteristics that make the IT infrastructure autonomous includes auto sensing an environment of the infrastructure, learning the infrastructure behavior, predicting one or more events, determining a course of action, and performing one or more actions with minimal or no human intervention.

Abstract: Most techniques to estimate the service life of coatings are experimental in nature, cost expensive and are computationally heavy. Present disclosure provides systems and methods that predict the combined effects of crack path propagation and zones of delamination, that form on coating material and its surface due to weathering. The system of the present disclosure implemented a combined Finite Element Method (FEM) and Monte Carlo based simulation approach to capture the effects of delamination and crack propagation, respectively. The crack paths are predicted using a probabilistic model, considering crack propagation, branching, and keeping a record of crack age. Stress distribution computations are performed using FEM to understand stress concentration zones and delamination behavior with time, which is methodically also combined with the time sequence of cracking as well.

Abstract: Monitoring the progression of a wound is critical, as it involves repeated clinical trips and lab tests over days. An artificial intelligence (AI) based system and method for analyzing wounds on a person is provided. The system is configured to take an image of the wound taken from a camera of a person. This image is then provided to the physician after the analysis and physician is able to provide a feedback to the person in terms of a healing index. In the analysis part, the system provides a fully automatized wound segmentation and quantify the parameters that assist wound care professionals. An Al based estimation module is provided, implemented with morphological operations, connected component analysis, and shape analysis, improving accuracy and providing the wound parameter and metrics such as area, perimeter, circle diameter, major and minor axis length of an ellipse.

Abstract: This disclosure relates generally to system and method for classification of sensitive date using federated semi-supervised learning. Federated learning has emerged as a privacy-preserving technique to learn one or more machine learning (ML) models without requiring users to share their data. In federated learning, data distribution among clients is imbalanced resulting with limited data in some clients. The method includes extracting a training dataset from one or more data sources and pre-processing the training dataset into a machine readable form based on associated data type. Further, a federated semi-supervised learning model is iteratively trained based on a model contrastive and distillation learning to classify sensitive data from the unlabeled dataset. Then, sensitive data from a user query is received as input which are classified using the federated semi-supervised learning model.

Abstract: Portable ECG monitors available in market have the disadvantage that the ECG data they provide as input aren't directly interpretable and requires medical knowledge for the users. The disclosure herein generally relates to Electrocardiogram (ECG), and, more particularly, to a method and system for generating 2d representation of electrocardiogram (ECG) signals. The system provides a mechanism for determining variability between a plurality of segments of an ECG data measured, and uses the information on the determined variability to generate the 2D representation corresponding to the ECG signal. The system further provides means to generate a data model that can be further used for processing real-time ECG data for generating corresponding interpretations. This allows a user to obtain the interpretations as output.

Abstract: The present invention relates to a method and system for Phaseless Passive Synthetic Aperture Radar (PPSAR) imaging. Existing method for image reconstruction requires large number of measurements for satisfactory PPSAR image reconstruction. However, this leads to provisioning of more on-board storage and/or a high-speed data link between a mobile platform and a ground station. These requirements are undesirable in practice as PPSAR image reconstruction systems are deployed on resource constrained platforms. The present disclosure uses a regularized Wirtinger Flow (rWF) based approach that uses appropriate regularizers to facilitate the PPSAR image reconstruction with fewer measurements. Further the PPSAR image reconstruction is achieved using Alternating Direction Method of Multipliers (ADMM) by employing standard denoisers such as Total Variation (TV), Block-matching and 3D filtering (BM3D) and, Deep Image Prior (DIP).

Abstract: This disclosure relates generally to Synthetic Aperture Radar (SAR) reconstruction and finds wide application in remote sensing. Conventional approaches either involve huge computational requirement for processing or require specialized hardware along with many additional Radio Frequency (RF) components. The present disclosure provides two approaches for temporally sampling a received pulse compressed signal at two sub-sampling factors, wherein both methods involve frugal hardware implementation. Reconstruction approach of the art is based on the principle of difference ruler and is not suitable for SAR image reconstruction due to the large measurements and image dimensions. In accordance with the present disclosure, the reconstruction problem is framed as an inverse imaging problem by suitably using a forward model and employing an approach like Alternating Direction Method of Multipliers (ADMM) for solving this model which allows use of readily available Plug and Play (PnP) priors.

Abstract: Garments in their natural form are represented by meshes, where vertices (entities) are connected (related) to each other through mesh edges. Earlier methods largely ignored this relational nature of garment data while modeling garments and networks. Present disclosure provides a particle-based garment system and method that learn to simulate template garments on the target arbitrary body poses by representing physical state of garment vertices as particles, expressed as nodes in a graph, and dynamics (velocities of garment vertices) is computed through a learned message-passing. The system and method exploit this relational nature of garment data and network implemented to enforce strong relational inductive bias in garment dynamics thereby accurately simulating garments on the target body pose conditioned on body motion and fabric type at any resolution without modification even for loose garments, unlike existing state-of-the-art (SOTA) methods.

Abstract: A fully automated and reliable picking of a diverse range of unseen objects in clutter is a challenging problem. The present disclosure provides an optimum grasp pose selection to pick an object from a bin. Initially, the system receives an input image pertaining to a surface. Further, a plurality of sampled grasp poses are generated in a random configuration. Further, a depth difference value is computed for each of a plurality of pixels corresponding to each of the plurality of sampled grasp poses. Further, a binary map is generated for each of the plurality of sampled grasp poses and a plurality of subregions are obtained. Further, a plurality of feasible grasp poses are selected based on the plurality of subregions and a plurality of conditions. Further, the plurality of feasible grasp poses are refined and an optimum grasp pose is obtained based on a Grasp Quality Score (GQS).

Abstract: State of the art Acoustic Models (AM), which are trained using data from one environment, may fail to adapt to another environment, and as a result, application is restricted. The disclosure herein generally relates to speech signal processing, and, more particularly, to a method and system for Automatic Speech Recognition (ASR) using Multi-task Learned Embeddings (MTL). In this approach, MTL embeddings are extracted from an MTL neural network that has been trained using feature vectors from a plurality of speech files. The MTL embeddings are then used for generating an acoustic model, which maybe then used for the purpose of Automatic Speech Recognition, along with the feature vectors and the MTL embeddings.

Abstract: The disclosure relates generally to methods and systems for monitoring lubricant oil condition using a photoacoustic modelling. Conventional techniques in the art for checking the condition of the lubricant oil is laboratory based and thus time consuming, error prone and not efficient. The present disclosure discloses a photoacoustic simulation model which is developed utilizing a photonic model such as a Monte Carlo method-based optical simulation integrated with a finite element model such as a k-wave toolbox-based acoustic measurement. The photoacoustic simulation model of the present disclosure is used to obtain a photoacoustic signal of the lubricant oil sample and a set of statistical features are determined from the obtained photoacoustic signal. The determined set of statistical features are then used as a training data to develop a machine learning (ML) model which is used to classify a type of contamination of the test lubricating oil.

Abstract: Existing approaches for switching between different hardware accelerators in a heterogeneous accelerator approach have the disadvantage that complete potential of the heterogeneous hardware accelerators do not get used as the switching relies on load on the accelerators or a random switching in which entire task gets reassigned to a different hardware accelerator. The disclosure herein generally relates to data model training, and, more particularly, to a method and system for data model training using heterogeneous hardware accelerators. In this approach, the system switches between hardware accelerators when a measured accuracy of the data model after any epoch is below a threshold of accuracy.

Abstract: In the world of digital advertising, optimally allocating an advertisement campaign within a fixed pre-defined budget for an advertising duration aimed at maximizing number of conversions is very important for an advertiser. Embodiments of present disclosure provides a robust and easily generalizable method of optimal allocation of advertisement campaign by formulating it as a constrained Markov Decision Process (MDP) defined by agent state comprising user state and advertiser state, action space comprising a plurality of ad campaigns, state transition routine and a cumulative reward model which rewards maximum total conversions in an advertising duration. The cumulative reward model is trained in conjunction with a deep Q-network for solving the MDP to optimally allocate advertisement campaign for an advertising duration within a constrained budget.