Abstract: This disclosure relates generally to information processing, and more particularly to method and system for generating a learning path for a topic. The method may include extracting a plurality of key phrases from each of a plurality of learning resources related to the topic, determining a learning context for each of the plurality of learning resources based on the plurality of key phrases, forming a set of key phrase groups from among the plurality of key phrases for each of the plurality of learning resources, determining a relationship among the key phrases in each of the set of key phrase groups based on the learning context, generating a structured graph for the plurality of learning resources based on the plurality of key phrases and the relationship among the key phrases, and generating the learning path for the topic based on the structured graph for the plurality of learning resources.

Abstract: This disclosure relates to method and system for improving relevancy and ranking of a search result for a search query. The method may include accessing documents of the search result. Each document may be associated with derived document features that may be derived based on an evaluation of document features with respect to query features. For each document, the method may further include determining a feature score for each of the derived document features based on one or more of the query features and initializing each element of an order array with the feature score for each ordered document feature. The ordered document features may be the derived document features that are ordered based on pre-defined rules. The method may further include ranking the documents by comparing and ranking the order array for each of the documents and presenting an updated search result based on the ranking of the documents.

Abstract: Disclosed herein is method and system for detecting intent as an ordered sequence from user query. The system identifies word embedding feature for each word using word embedding model and identifies Part of Speech tag feature. Thereafter, system determines dependency label feature based on dependency role and POS tag feature. Further, system provides feature vector comprising POS tag feature of target word, POS tag feature of previous two words of target word, word embedding feature of target word, word embedding feature of head word for each target word and dependency label feature of target word to deep neural network for detecting intent as ordered sequence. The ordered sequence includes desired action in user query, focal point pertaining to which desired action must be performed and one or more descriptors associated with focal point. In this manner, in present disclosure, overall intent from user query is captured for accurately providing response.

Abstract: This disclosure relates generally to image processing, and more particularly to method and system for reconstructing an image. In one embodiment, the method includes pre-processing an input image to generate character images corresponding to characters in the input image, determining a local character thickness threshold value for each character image, determining a global character thickness threshold value for the input image based on the local character thickness threshold values for the character images, and reconstructing each character image based on the local character thickness threshold value for each character image and the global character thickness threshold value to generate reconstructed character images. The local character thickness threshold value in a character image may be based on a set of character pixel values in a pre-determined number of segments in the character image. The method further includes re-constructing the input image based on the reconstructed character images.

Abstract: The present invention relates to a method of identifying a brand and a model of a vehicle. The method comprises obtaining one or more video frames. Further, the method comprises detecting one of presence or absence of text in the at least one vehicle. Upon detecting the presence of the text, the method comprises determining at least one of the brand and the model of the at least one vehicle by accumulating the text recognized in the one or more video frames. Further, identifying at least one of the brand and the model of the at least one vehicle based on an accumulated text. Upon detecting the absence of the text, the method comprises determining the brand of the at least one vehicle based on logo associated with the vehicle.

Abstract: The present invention discloses method and system for multimodal analysis based emotion recognition. The method comprising segmenting video data of a user into a plurality of video segments. A plurality of visual features, voice features and text features from the plurality of video segments is extracted. Autocorrelation values among each of the plurality of visual features, the voice features, and the text features is determined. Each of the plurality of visual features, the voice features and the text features is aligned based on video segment identifier and the autocorrelation values to obtain a plurality of aligned multimodal features. One of two classes of emotions is determined for each of the plurality of aligned multimodal features. The determined emotion for each of the plurality of aligned multimodal features is compared with historic multimodal features from a database, and emotion of the user is determined at real time based on comparison.

Abstract: This disclosure relates to method and system for of dynamically annotating data or validating annotated data. The method may include receiving input data comprising a plurality of input data points. The method may further include one of: a) generating a plurality of annotations for each of the plurality of input data points using at least one of a state-label mapping model and a comparative ANN model, or b) receiving the plurality of annotations for each of the plurality of input data points from an external device or from a user, and validating the plurality of annotations using at least one of the state-label mapping model and the comparative artificial neural network (ANN) model.

Abstract: The present disclosure is related to field of charging of a line follower robot, disclosing method, system, unit for controlling charging of the line follower robot. The charging control unit includes a line follower robot configured with a charging control system. The line follower robot follows a pre-programmed path for performance of a pre-programmed task. The line follower robot includes a ground contact wheel in connection with the pre-programmed path for an entire duration of performance of the pre-programmed task and a segmented wheel in connection with a power-communication track. The segmented wheel is divided into an equal number of different contact surfaces, and the different contact surfaces comprises a charging contact surface, a transmit contact surface, and a receive contact surface. The charging control unit further includes the power-communication-track constructed parallel to the pre-programmed path.

Abstract: This disclosure relates to method and system for providing explanation for output generated by artificial intelligence (AI) model. The method may include receiving encrypted input data and a public encryption key from a client device, wherein the encrypted input data is encrypted using the public encryption key. The method may further include generating an encrypted AI model by encrypting an AI model using the public encryption key. The method may further include generating an encrypted output and an encrypted feature data based on the encrypted input data using the encrypted AI model, and generating an encrypted explanation for the encrypted output based on the encrypted feature data. The method may further include providing the encrypted output and the encrypted explanation to the client device for rendering, wherein the encrypted output and the encrypted explanation are decrypted by the client device using a private encryption key.

Abstract: The present invention relates to a method of generating a facial expression of a user for a virtual environment. The method comprises obtaining a video and an associated speech of the user. Further, extracting in real-time at least one of one or more voice features and one or more text features based on the speech. Furthermore, identifying one or more phonemes in the speech. Thereafter, determining one or more facial features relating to the speech of the user using a pre-trained second learning model based on the one or more voice features, the one or more phonemes, the video and one or more previously generated facial features of the user. Finally, generating the facial expression of the user corresponding to the speech for an avatar representing the user in the virtual environment.

Abstract: A method and system for securing data transmission in communication networks is disclosed. The method includes the steps of allocating a sequence ID (SQID) to each of a plurality of packets. The SQID is embedded in an Internet Protocol (IP) header of an associated packet from the plurality of packets. The method further includes grouping the plurality of packets into at least one cluster based on at least one of a distance amongst at least one IP attribute associated with destination address of each of the plurality of packets and variance in IP attributes associated with destination address of each of the plurality of packets. The method includes transmitting each of the at least one cluster to an associated destination address. Each cluster in the at least one cluster includes a set of packets from the plurality of packets and at least a domain-name is same in destination address.

Abstract: The present invention discloses a method and a system for automatic selection of process automation Robotic Process Automation (RPA) tool and BOT. The method comprising receiving input data associated with a process to be executed, selecting an RPA tool from a plurality of RPA tools for process execution based on the input data and historical process data, wherein the selection is performed by calculating information gain for each parameter of the input data and computing probability for each type of RPA tool based on the information gain and the historical process data, identifying one or more BOTs from a plurality of BOTs based on the selected RPA tool, historical BOT data and the input data for the process execution, and executing the identified one or more BOTs on one or more devices based on selection of the one or more devices from available plurality of devices.

Abstract: The present disclosure relate to effectively determining spatial and temporal features for extracting low-level and high-level features from image frames of a multimedia content. A plurality of image frames are received from an imaging unit. Spatial filters are applied on each image frame to generate a first set of activation maps which provide spatial features in the image frames. Further, a temporal filter is applied on the plurality of image frames at a plurality of levels to generate one or more second set of activation maps corresponding to each level for determining temporal features in the image frames. Thereafter, the spatial feature from each image and temporal feature of the plurality of image frames from each level is extracted, which represent low-level and high-level spatial and temporal features.

Abstract: A method and a system of selecting test cases from existing test cases for a new software testing requirement are disclosed. In an embodiment, the method may include determining a confidence score associated with each of existing test cases, based on comparing a new software testing requirement with the existing test cases using a Recurrent Neural Network (RNN) model, and selecting a set of test cases from the existing test cases based on the confidence score and a predetermined threshold confidence score. The method may further include predicting a defect slippage rate associated with each test case using a linear regression model, and shortlisting a sub-set of test cases from the set of test cases based on the predicted defect slippage rate associated with each test case and a predetermined threshold defect slippage rate.

Abstract: A Functional Safety Timer (FST) and method of for monitoring functional safety in Integrated Circuits (ICs) is disclosed. The FST includes hardware based counter for counting programmed number of cycles associated with an event of peripheral device. The FST includes first logic circuit and second logic circuit. The first logic circuit receives first trigger of peripheral device. In response to first trigger, the counter is initiated by first logic circuit. The second logic circuit receives second trigger of peripheral device. In response to second trigger, the counter being initiated is terminated by the second logic circuit. Further, the FST includes a hardware based comparator for comparing the number of cycles counted by the hardware based counter with a threshold time associated with the event and a plurality of programmable registers for determining one or more faults associated with the event based on result of the comparison and sequence of events.

Abstract: This disclosure relates to method and system for generating cognitive security intelligence for detecting and preventing malwares. In one embodiment, the method includes monitoring instructions being executed by a processor of a computing system, determining events triggered and activities performed by the execution of the instructions, correlating the events and the activities to determine a sequence of events and activities, and mapping the sequence of events and activities with a topographical threat map to detect a pattern match corresponding to a malware. The topographical threat map is event and activity behavior map of a number of categories of malwares, and is built based on a cognitive analysis using deep learning which may also be enriched with external knowledge or historic knowledge. The method further includes effecting a remedial measure, upon detecting the pattern match, to prevent the malware by constructing remedial instructions to be executed by the processor.

Abstract: This disclosure relates to method and system for analyzing IoT data in real-time and predicting future events. In one embodiment, the method may include acquiring the real-time IoT data corresponding to one or more IoT devices, and building a predictive model based on the real-time IoT data. The predictive model may include a machine learning algorithm that generates an output parameter representing a future event based on a set of input parameters derived from the real-time IoT data. The predictive model may be built by training the predictive model for one or more explanatory input parameters and an expected output parameter. The method may further include predicting the future event based on the real-time IoT data using the predictive model, determining a deviation between the future event and an actual event, and tuning the predictive model based on the deviation.

Abstract: A method and system for identifying and mitigating interference caused by a rogue User Equipment (UE) in a Light Fidelity (Li-Fi) communication network is disclosed. The method includes detecting interference caused by the rogue UE to at least one UE from a set of UEs associated with a Li-Fi access point in the Li-Fi communication network. The method further includes assigning a unique first transmission channel to each of the at least one UE in response to detecting the interference. The unique first transmission channel assigned to each of the at least one UE is distinguishable from a second transmission channel associated with the rogue UE. The method further includes scheduling data transmission from each of the at least one UE to the Li-Fi access point based on the associated unique first transmission channel.

Abstract: The disclosure relates to system and method for detecting an instance of lie using a Machine Learning (ML) model. In one example, the method may include extracting a set of features from an input data received from a plurality of data sources at predefined time intervals and combining the set of features from each of the plurality of data sources to obtain a multimodal data. The method may further include processing the multimodal data through an ML model to generate a label for the multimodal data. The label is generated based on a confidence score of the ML model. The label is one of a true value that corresponds to an instance of truth or a false value that corresponds to an instance of lie.

Abstract: The disclosure relates to method and system for transmitting a data packet in downlink in frequency division duplex (FDD) communication mode employing adaptive asynchronous Hybrid Automatic Repeat Request (HARQ) mode. The method includes receiving a current Channel Quality Indicator (CQI) from a user transceiver. The CQI is reported by the user transceiver at a periodic interval based on a radio frequency channel condition. The method further includes evaluating a degradation in the radio frequency channel condition based on a decrease in the current CQI with respect to a previously reported CQI. The method further includes retransmitting at least one of a set of transmitted data packets stored in a HARQ retransmission buffer to the user transceiver, based on the degradation in the radio frequency channel condition and without waiting for a corresponding HARQ negative acknowledgement (NACK) signal from the user transceiver.