Abstract: Systems and methods for displaying media files on a device are provided. The device provides a first mode that displays a subset of media files, determined by a user query, from a plurality of media files. Once activated, the first mode persists on the device prior to becoming inactive. A second mode of the device displays the plurality of media files. The second mode is active when the first mode is inactive. An instruction is received from a user. When the instruction specifies the first mode by inclusion of a user query, a corresponding subset of files is obtained from a remote system. The subset of files is sequentially displayed until the period of time has elapsed according to the first mode. When the instruction does not specify a query, the plurality of files is polled for from a remote device and sequentially displayed according to the second mode.

Abstract: Systems and methods for simulation forecasting in target networks including dynamic realignment are disclosed. A set of target nodes including at least one demand node realignment representative of a demand volume shift for at least one corresponding distribution node in a predetermined time period is received. When the demand volume shift is equal to or above a predetermined threshold, an estimated volume feature for the at least one corresponding distribution node is generated. When the demand volume shift is below the predetermined threshold, an actual volume feature for the at least one corresponding distribution node is generated. The generated one of the estimated volume feature or the actual volume feature is provided to a trained forecasting model to generate a demand forecast data structure based on the generated one of the estimated volume feature or the actual volume feature.

Abstract: Realistic data is an important aspect for training vision based systems. Conventional approaches need complex prerequisites to generate such data and are quite expensive as well. The present disclosure provides one stop solution for different artificial intelligence rendering, scenario generation and future video prediction. The present disclosure provides a provision for fusion of Generative Artificial Intelligence (GenAI), deep learning and image processing techniques and explores the possibilities of generating data from simulated, real, or fused environments. Further, the present disclosure utilizes a panoptic Segmentation approach to create semantic labels and a flow map-based methodology taking cues from a sequence of frames helps to address long time temporal coherence which is a key issue in generated data. This goes as an input to the Generative AI network which generates synthetic dataset based on the aspects of dynamic objects, scene changing and environment variations.

Abstract: In various embodiments, one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising receiving, from an API server, a request to access a service, the request including an API authorization identifier; validating the API authorization identifier to generate a validation result; determining a service tier of the service based on the request; and transmitting, to the API server, a response including the validation result and an indicator of the service tier based on the request.

Abstract: Systems and methods for adjusting light emitted from a display of a device are provided. The adjusting includes obtaining, from light of an environment detected by at least one sensor, a measured color of light of the environment, and obtaining, from light of the environment detected by at least one sensor, a measured brightness of light of the environment. In response to the obtaining the measured color and the measured brightness of light, a color of light emitted from the display is adjusted from an initial color to a target color. A brightness of light emitted from the display is adjusted from an initial brightness to a target brightness.

Abstract: A method for generating a three-dimensional (3D) model of an object includes receiving a two-dimensional (2D) view of at least one object as an input, measuring geometrical shape coordinates of the at least one object from the input, identifying texture parameters of the at least one object from the input, predicting geometrical shape coordinates and texture parameters of occluded portions of the at least one object in the 2D view by processing the measured geometrical shape coordinates of the at least one object, the identified texture parameters of the at least one object, and the occluded portions of the at least one object, and generating a 3D model of the at least one object by mapping the measured geometrical shape coordinates and the identified texture parameters to the predicted geometrical shape coordinates and the predicted texture parameters of the occluded portions of the at least one object.

Abstract: Temperature measurement is an important part of many potential applications in the fields of metallurgy. Conventional temperature measurement methods do not provide accurate and precise average temperature of fluid inside an enclosed chamber. The present disclosure provides multi-sensory techniques for measuring average temperature of mixed fluid inside a chamber. The average temperature is measured based on acoustic interferometry technique on standing wave and inputs received from one or more sensors and radar. The present disclosure utilizes radar to compensate the effect of fumes, noise based on Doppler effect. Further, the inputs received from the one or more sensors are used to determine the concentration of one or more fluids present in the chamber. The method of proposed disclosure depends on the principle of dependence of temperature on sound speed in fluid. So, measurement of sound speed can be mapped to report average temperature of mixed fluid inside the chamber.

Abstract: Provided is a method for generating Augmented Reality (AR) content that includes: receiving a plurality of image frames of at least one scene captured by a plurality of participant devices in an AR or a Virtual Reality (VR) environment; storing the plurality of image frames and metadata associated with the plurality of image frames, in a database; receiving an AR content generation request to generate an AR content view of a user in the AR/VR environment, the AR content generation request including an identifier (ID) of the user and information of the at least one scene; retrieving a set of image frames from a plurality of stored image frames in the database based on the ID of the user, the information of the at least one scene, and metadata associated with the set of image frames, the set of images including the user in the at least one scene in the AR/VR environment; generating the AR content view of the user by combining the set of image frames retrieved from the database, based on the metadata associated

Abstract: Systems and methods for adjusting light emitted from a display of a device are provided. The adjusting includes obtaining, from light of an environment detected by at least one sensor, a measured color of light of the environment, and obtaining, from light of the environment detected by at least one sensor, a measured brightness of light of the environment. In response to the obtaining the measured color and the measured brightness of light, a color of light emitted from the display is adjusted from an initial color to a target color. A brightness of light emitted from the display is adjusted from an initial brightness to a target brightness.

Abstract: Existing semi-supervised and unsupervised approaches for intent discovery require an estimate of the number of new intents present in the user logs. The present disclosure receives labeled utterances from known intents and update parameters of a pre-trained language model (PLM). Representation learning and clustering is performed iteratively using labeled and unlabeled utterances from known intents and unlabeled utterances from unknown intents to fine-tune PLM and a plurality of clusters is generated. Cluster merger algorithm is executed iteratively on generated plurality of clusters. A query cluster is obtained by randomly selecting one cluster from the plurality of clusters and by obtaining a corresponding plurality of nearest neighbors based on a cosine-similarity. A response for merging the query cluster and corresponding plurality of nearest neighbors is obtained, and a new cluster is created.

Abstract: In various embodiments, one or more non-transitory computer-readable media storing program instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising receiving, from an API server, a request to access a service, the request including an API authorization identifier; validating the API authorization identifier to generate a validation result; determining a service tier of the service based on the request; and transmitting, to the API server, a response including the validation result and an indicator of the service tier based on the request.

Abstract: Embodiments disclosed herein model lifelong intent detection as a class-incremental learning where a new set of intents/classes are added at each incremental step. To address the issue of catastrophic forgetting during lifelong intent detection (LID), an incremental learner is provided with Prompt Augmented Generative Replay, wherein unlike existing approaches that store real samples in replay memory, only concept words obtained from old intents are stored, which reduces memory consumption and speeds up incremental training still enabling not forgetting the old intents. Joint training of an incremental learner is carried out for LID and a pseudo-labeled utterance generation with objective is to classify a user utterance into one of multiple pre-defined intents by minimizing a total Loss function comprising a LID loss function, a Labeled Utterance Generation loss function, a Supervised Contrastive Training loss function, and a Knowledge Distillation loss function.

Abstract: Temperature measurement is an important part of many potential applications in process industry. Conventional temperature measurement methods require manual intervention for process monitoring and fail to provide accurate and precise measurement of temperature of an enclosed mixed fluid chamber. The present disclosure provides artificial intelligence based temperature measurement in mixed fluid chamber. A plurality of inputs pertaining to the mixed fluid chamber are received to build a fluid based model. The fluid based model is used to generate one or more fluid parameters. The one or more fluid parameters are used along with a ground truth temperature data and the received plurality of inputs for training an artificial intelligence (AI) based model. However, the AI based model is trained with and without knowledge of fluid flow. The trained AI based model is further used to accurately estimate temperature of the mixed fluid chamber for a plurality of test input data.

Abstract: Systems and methods for adjusting light emitted from a display of a device are provided. The adjusting includes obtaining, from light of an environment detected by at least one sensor, a measured color of light of the environment, and obtaining, from light of the environment detected by at least one sensor, a measured brightness of light of the environment. In response to the obtaining the measured color and the measured brightness of light, a color of light emitted from the display is adjusted from an initial color prior to the adjusting to a target color that matches the measured color. Further, a brightness of light emitted from the display is adjusted from an initial brightness emitted by the display prior to the adjusting to a target brightness that matches the measured brightness of light.

Abstract: There is provided a method for segmenting a point cloud by an electronic device. The method includes receiving the point cloud including colorless data and/or featureless data. Further, the method includes determining a normal vector for the received point cloud and/or a spatial feature for the received point cloud. Further, the method includes segmenting the point cloud based on the at least one of one or more normal vectors and one or more spatial features.

Abstract: Health monitoring of complex industrial assets remains the most critical task for avoiding downtimes, improving system reliability, safety and maximizing utilization. Recent advances in time-series synthetic data generation have several inherent limitations for realistic applications. A method and system have been provided for generating mixed variable type multivariate temporal synthetic data. The system provides a framework for condition and constraint knowledge-driven synthetic data generation of real-world industrial mixed-data type multivariate time-series data. The framework consists of a generative time-series model, which is trained adversarially and jointly through a learned latent embedding space with both supervised and unsupervised losses. The system addresses the key desideratum in diverse time dependent data fields where data availability, data accuracy, precision, timeliness, and completeness are of prior importance in improving the performance of the deep learning models.

Abstract: Systems and methods for displaying media files on a device are provided. The device provides a first mode that displays a subset of media files, determined by a user query, from a plurality of media files. Once activated, the first mode persists on the device prior to becoming inactive. A second mode of the device displays the plurality of media files. The second mode is active when the first mode is inactive. An instruction is received from a user. When the instruction specifies the first mode by inclusion of a user query, a corresponding subset of files is obtained from a remote system. The subset of files is sequentially displayed until the period of time has elapsed according to the first mode. When the instruction does not specify a query, the plurality of files is polled for from a remote device and sequentially displayed according to the second mode.

Abstract: This disclosure relates to a system and method for measuring average temperature of mixed fluid in an enclosed chamber. Measurement is based on two independent principle as measuring variation in acoustic wave velocity and variation in resistivity that works on a single setup. First principle is based on measuring acoustic wave velocity in a known medium, which is isolated from the surrounding. The system comprises a primary pipe and a secondary pipe, wherein the ends of the pipes reside inside the enclosed chamber. The primary pipe is made out of good conductor of heat and filled with air. Ends of the primary pipe is fitted with a transducers have one transmitter at one end and one receiver at another end. Average temperature of the mixed fluid is measured based on the variations in sound velocity of acoustic wave passed through the primary pipe and resistivity variations of the primary pipe.

Abstract: Existing privacy-preserving techniques suffer from inherent drawbacks to retain characteristics of observed, and original industrial time series data for utility in the downstream tasks such as process modelling, control, optimization and etc. The embodiments herein provide a method and system for privacy preserving generative mechanism for data-disclosure of the industrial multivariate mixed-variable time series data. The system fuses an industrial time series data with a random gaussian noise to preserve the privacy of the industrial time series data and trades-off the privacy with the utility of synthetic-private data. Further, the system presents the privacy-preserving synthetic industrial data generative mechanism for data disclosure with minimal risk of AI technique and strong privacy guarantees.

Abstract: Recent advances and techniques in missing data imputation suffer from inherent limitations of preserving the relationship among the input feature attributes and the target variable and temporal relations between observations spanning across timeframes because of which it is also challenging to reconcile missing data for any downstream tasks. Present disclosure provides system and method that implement for congeniality-preserving Generative Adversarial Networks (cpGAN) for imputing low-dimensional incomplete multivariate industrial time-series data. The method minimizes the rubric based on the information theory for Machine Learning (ML) between the empirical probability distributions of the reconcile data and the non-linear original data to preserve the temporal dependencies and retain the input feature-attributes and target-variable relationship and probability distributions of the original data.