Abstract: Existing data residency compliance techniques suffer from inherent drawbacks to discover the spread of data, understanding the data residency regulations and semantics behind them and most importantly placement of data in cloud datacenters such that it is data residency compliant. Embodiments herein provide a method and system for optimizing placement of data to a cloud datacenter complying data residency regulations. The system selects one serving cloud datacenter for a user center. The selection considers three conflicting objectives such as minimum data placement cost, provide good quality of service (i.e. latency) and to comply with data residency regulations. The system essentially covers data residency compliance problem in three phases namely, violation detection, decision support and recommendation.

Abstract: Conventionally, activity detection has been through one mode i.e., smart watch. Though it works in reasonable cases, there are chances of false positives considerably. Other approaches include surveillance which limits itself to object detection. Embodiments of present disclosure provide systems and methods for detecting activities performed by user from data captured from multiple sensors. A first input (FI) comprising accelerometer data, heart rate and gyroscope data and second input (SI) comprising video data are obtained. Features are extracted from FI and pre-processed for a first activity (FA) detection using activity prediction model. Frames from SI are processed for creating bounding box of user and resized thereof to extract pose coordinates vector. Distance between vector of pose coordinates and training vectors of pose coordinates stored in the system is computed and a second activity (SA) is detected accordingly. Both the FA and SA are validated for determining true and/or false positive.

Abstract: Conventionally material selection of composite structure is performed by using discrete materials available in manufacturer's databases. Thus, tailorable nature of composite materials is not exploited to achieve superior performance. Further, conventional methods perform material selection and sizing separately and do not take into account the influence of sizing on material selection and vice versa. Embodiments of the present disclosure provide systems and methods for multiscale material selection for designing of mechanical systems that incorporates tailoring of material microstructures and sizing to achieve improved solutions. The microstructure properties are obtained by using analytical and computational models for various composite materials. These models compute structure-property relations between bulk material properties and their micro-structural constituents.

Abstract: The present disclosure provides a model for semantic navigation for service robots to find out-of-view objects in an indoor environment. Initially, the system receives a target object to be reached by the mobile robot in the indoor environment. Further, a current location of the mobile robot is identified by a localization technique. An embedding corresponding to each of a plurality of visible regions is computed using a pretrained Graph Neural Network GNN. The GNN is pretrained using a trajectory data and a spatial relationship graph associated with the indoor environment. Further, a similarity score is computed for each of the plurality of visible regions based on the corresponding embedding using a scoring technique. An optimal visible region is identified by comparing the similarity score. Finally, a next action to be taken by the mobile robot selected from a plurality of actions based on the optimal visible region.

Abstract: Rapid development in digitization results in generation of huge amounts of unstructured text data by most organizations from several sources. A major drawback of using existing metrics in the data management is assessing quality of topics associated with context in a document. A processed dataset obtained from a raw dataset is mapped to obtain a relationship between one or more words from one or more topics. A word score is determined based on probability of number of constructs in sentences. At least one repetitive word from the one or more topics are determined. A topic score with degree of contextual association between the one or more words in each topic is determined by mean value of the word score. A contiguity score with degree of contextual association between the one or more topics is determined based on median value of the topic score to obtain one or more connected topics.

Abstract: For performing incremental analysis, analysis tools may reuse analysis information computed earlier for intermediate representation (IR) objects that remain unchanged between versions of program. But identification numbers (IDs) of unchanged IR objects change over versions due to changes with each run of incremental analysis, thereby making it difficult to reuse analysis information as tools require IDs of unchanged IR objects from previous versions. Present disclosure provides method and system for mapping IR objects for facilitating incremental analysis. The system first extracts attributes associated with each IR object present in previous version to create data structure for storing IR objects. The system then determines unchanged lines between versions to create line number map. Thereafter, system uses line number map to create ID-based IR mapping between ID of each unchanged IR object present in each unchanged line in current version and ID of each IR object stored in data structure.

Abstract: This disclosure relates to the field of project related document analysis. Conventionally, process of retrieving right information related to a stakeholder with relevant project initiation concerns involves manual intervention resulting in more time consumption. The method of the present disclosure describes a system and method for automated extraction and classification of project initiation related information from request for proposal response documents The RFP response document is parsed using document structure based parsing technique to identify questions and answers. The questions from the RFP response document are classified into different classes of interest and important information from the answers are extracted and mapped to an identified class. The method of the present disclosure demonstrates significant improvement in terms of time consumption by reducing volume of information and providing a quick access to class-specific relevant information from the RFP response document.

Abstract: The present disclosure provides a dynamic functional adaptation of software systems based on real time need, which is unaddressed by the conventional methods. Initially, the system receives an output generated by a software system under execution corresponding to a given input. Further, a deviation in the generated output is identified based on a comparison between the generated output and a corresponding expected output. Further, an optimal policy is identified based on the generated output and the identified deviation using a reward based Reinforcement Learning (RL) model. Further, an adaptation specification is generated based on the identified optimal policy using an Adaptation Specification Generation (ASG) technique, Further, a software code is generated dynamically for the identified optimal policy using an ASG based code generation technique.

Abstract: The disclosure is a method and a system for root cause identification (RCI) of faults in manufacturing and process industries. With complex interrelated multivariate data in manufacturing and process industries, the process of root RCI of faults is challenging. Further, the existing techniques for RCI have significant dependency on manual inputs and subject matter knowledge/experts. The disclosure is method and a system for root cause identification of a fault based on causal maps. The root cause of fault is identified in several steps including: generation of casual maps using data received from a manufacturing and process industry and root cause identification from the causal maps based on a Fault Traversal and Root Cause Identification (FTRCI) technique. The FTRCI identifies root cause from the causal map by identifying a fault traversal pathway from a leaf node in the causal map, wherein the fault traversal pathway is identified for even cyclic paths.

Abstract: A method and system of a low-latency FPGA framework based on reliable UDP and TCP re-assembly middleware is disclosed. The need for low-latency communication in digital systems has increased drastically. The disclosed FPGA framework enables low-latency communication as a hybrid framework that supports both UDP & TCP communication. As known in art, TCP provides error checking support hence making TCP more reliable as compared to UDP, while UDP is faster but not reliable. Hence the disclosed low-latency FPGA framework latency utilizes the advantage of both UDP and TCP by utilizing UDP for its speed, while switching to TCP in case of a missing sequence in UDP. Further, the disclosed system proposes a TCP re-assembly middleware architecture for processing TCP with a lower-latency, wherein the TCP re-assembly middleware is an independent middleware that is a modular and a plug-play independent middleware.

Abstract: This disclosure relates generally to methods and systems for building an intelligent analytical platform to enable a device fabrication in material science. Material engineers and design engineers may face various challenges with existing knowledge, as more time and efforts are required in finding a relevant knowledge from the existing knowledge, mainly due to the unstructured form, for fabricating new devices. The present disclosure solves the technical problem of finding the relevant knowledge out of the existing knowledge, in a structured form by building an analytical platform. The unstructured format of the existing knowledge of the fabrication process is transformed into a structured format in terms of operation sequence knowledge graphs, using a set of artificial intelligence (AI) and machine learning models, and a knowledge representation model of the fabrication process. The structured format of the existing knowledge is hierarchically arranged to build the analytical platform.

Abstract: This disclosure relates generally to retrieval of prior court cases using witness testimonies. Conventional state-of-the-art methods use supervised techniques for answering basic questions in legal domain using numerous features and do not address interpretability of results and the performance and precision of retrieving prior court cases for these methods are less. Embodiments of the present disclosure obtains an embedded representation for an event structure of a user query and testimony sentences identified from prior court cases using a trained Bi-LSTM classifier and a set of linguistic rules. A similarity is estimated between the embedded representation for the event structure of the user query and the event structure of each testimony sentence from the prior court cases. Further a relevance score is assigned in accordance with the estimated similarity to retrieve the relevant prior court cases. The disclosed method is used to retrieve the relevant prior court cases using witness testimonies.

Abstract: Analysis of the flow of fluxes through the metabolic network of a cell type is useful in gaining knowledge about cellular physiology. Such knowledge can be used in understanding host's interactions with pathogens, drug response etc. However there is dearth of techniques that can incorporate metabolomics data into genome scale metabolic model (GEM) for FBA. A method and system for analyzing metabolic state of a cell at a genome scale by measuring concentrations of a one or more metabolites has been disclosed. The method is utilizing intracellular and/or extracellular metabolite concentrations for constraining reaction fluxes in FBA by incorporating it as part of stoichiometric constraint to metabolic model. The method is used to predict the change in flux flow through all reactions in an organism/cell type under different experimental conditions. The method enables constraining the flow of fluxes through reactions while performing FBA of GEMs using measured metabolite concentrations.

Abstract: Automatic speech recognition techniques are implemented in resource constrained devices such as edge devices in internet of things where on-device speech recognition is required for low latency and privacy preservation. Existing neural network models for speech recognition have a large size and are not suitable for deployment in such devices. The present disclosure provides an architecture of a size constrained neural network and a method of training the size constrained neural network. The architecture of the size constrained neural network provides a way of increasing or decreasing number of feature blocks to achieve an accuracy-model size trade off. The method of training the size constrained neural network comprises creating a training dataset with short utterances and training the size constrained neural network with the training dataset to learn short term dependencies in the utterances. The trained size constrained neural network model is suitable for deployment in resource constrained devices.

Abstract: This disclosure relates generally to method and system for detecting on-street parking violations. The method include capturing, by using an media capturing device embodied in an electronic device mounted in a vehicle, a video stream of a scene during a trip of the vehicle. The video stream is processed at the electronic device to identify target objects such as no-parking signage and vehicles parked in the vicinity thereof. A meta-information associated with the target objects is stored in form of a short-term historian in a repository associated with the electronic device. The absolute locations of the target objects are determined and the historian is updated with the values of the absolute locations. A set of unique target objects is determined from amongst the target objects and a meta-information associated with the unique objects is sent to a cloud server for determining parking violations.

Abstract: Conventionally, cleaning of objects has been proposed by use of gloves wherein objects need to be pre-washed manually. However, the level of cleaning is not spotless. Electric scrubbers have also been proposed in solving scrubbing problem but are not efficient. Embodiment of the present disclosure provide a glove that integrates a detachable power-driven scrubber. The power-driven scrubber is configured to controllably rotate in one or more directions based on an activation of a sensor to clean an object. The glove further includes sensing mechanisms for controlled flow of water discharge and controlled flow of cleansing fluid dispense that enables the detachable power-driven scrubber to clean the object. Rotation of the detachable power-driven scrubber is controlled and driven by a drive shaft that transmits rotary motion/torque generated by a motor coupled thereto. A scrubber locking strap is provided on the glove for lock and release of the detachable power-driven scrubber accordingly.

Abstract: This disclosure provides a simulation platform to study and perform predictive analysis on valvular heart disease, Mitral stenosis (MS) and provides a control approach to correct hemodynamic imbalances during MS conditions. Conventional approaches of valve repair or replacement are often associated with risk of thromboembolism, need for anticoagulation, prosthetic endocarditis, and impaired left ventricle function. The cardiovascular hemodynamics model of the present disclosure helps to create ‘what if’ conditions to study variations in different hemodynamic parameters like blood flow, aortic and ventricular pressure, etc. during normal and pathological conditions. An adaptive control system in conjunction with the hemodynamic cardiovascular system (CVS) is provided to handle hemodynamic disbalance during moderate to severe MS conditions.

Abstract: This disclosure relates generally to methods and systems for text-based target products matching to keep competitive price for increasing customer traffic. The conventional image similarity or image matching based approaches require appropriate configurations and infrastructure, quality images and more computational power. Also, the configurations and infrastructure may not be scalable for new products and may not be suitable for all retail industry types. The present disclosure uses only the text-based data inputs of the retailer and the competitor. The present disclosure first generates a data corpus from the data inputs of the retailer and the competitor. Then, context tokens are identified using a natural language-based technique and a cogent matrix is formed based on the data inputs of the retailer and the competitor, the context tokens, and using a cogent score calculation function, to get the matching results.

Abstract: For various applications (for example, a Virtual Assistant), mechanisms that are capable of collecting user queries and generating responses are being used. While such systems handle structured queries well, they struggle to or fail to interpret an unstructured Natural Language (NL) query. The disclosure herein generally relates to data processing, and, more particularly, to a method and a system for generating responses to unstructured Natural Language (NL) queries. The system collects at least one NL query as input at a time, and generates a sketch, where the sketch is a structured representation of the unstructured NL query. Further by processing the sketch, the system generates one or more database queries. The one or more database queries are then used to search in one or more associated databases and to retrieve matching results, which are then used to generate response to the at least one NL query.

Abstract: Mosquito species cause severe and debilitating illnesses. Despite significant advances in clinical medicine, no specific drugs or vaccines are available for diagnosis, treatment, and management of majority of mosquito-borne illnesses. A method and system for evaluating and reducing the degree of mosquito attractiveness of an individual is provided. The methodology involves computation of a metric based on analysis of the host’s skin microbial profile and is thus expected to estimate individualized attractiveness profiles. A specific set of bacterial species contributing to skin attractiveness for mosquitoes have been identified and relative abundances of the bacterial species in the identified set have been custom engineered to generate novel features. These features have been utilized for computing an index referred to as a ‘Mosquito Attractiveness Quotient (MAQ)’ that quantifies or measures the degree of mosquito-attractiveness of individual.