Abstract: Robotic Process Automation (RPA) systems face challenges in handling complex processes and diverse screen layouts that require advanced human-like decision-making capabilities. These systems typically rely on pixel-level encoding through drag-and-drop or automation frameworks such as Selenium to create navigation workflows, rather than visual understanding of screen elements. Present disclosure provides systems and methods that implement large language models (LLMs) coupled with deep learning based image understanding which adapt to new scenarios, including changes in user interface and variations in input data, without the need for human intervention. System of the present disclosure uses computer vision and natural language processing to perceive visible elements on graphical user interface (GUI) and convert them into a textual representation.

Abstract: A recovery orchestration pipeline has stages configured to control efficient failover and recovery of applications in a multi-site disaster recovery (DR) environment. The applications run on user virtual machines (UVMs) at a primary site of the DR environment and connect to block storage devices (BSDs) that export virtual disks over a storage protocol to consume data including a recovery plan for disaster recovery. The recovery plan includes a recovery configuration whose generation is triggered by a user via a graphical user interface (GUI) and specifies resource requirements needed to recover the applications at a secondary site in the event of a disaster. The orchestration pipeline is initiated via single click of the GUI and completion of the stages of the pipeline is displayed as progress via the GUI to allow recovery of the applications without user intervention.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for improved network computing for network function testing. In some implementations, a method includes providing an application programming interface (API) to a user device of a network computing system; receiving input data from the user device indicating one or more application requirements for testing one or more network functions; generating one or more virtual processing machines using the one or more application requirements; obtaining testing data from one or more communication networks; providing the testing data to the one or more virtual processing machines configured to generate results based on processing the testing data using the one or more network functions; and providing the results to the user device.

Abstract: The present disclosure relates to a process for treatment of biomass, specifically for pulping and biorefinery applications. The process relates to an organosoly process using high boiling organic solvents for the extraction of high molecular weight lignin and other products from the solution of pulping liquor, by using recycled liquor as the white liquor for pulping. The disclosure also relates to byproducts of pulp & paper industry such as lignin. C5 & C6 sugars.

Abstract: Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.

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: A recovery orchestration pipeline has stages configured to control efficient failover and recovery of applications in a multi-site disaster recovery (DR) environment. The applications run on user virtual machines (UVMs) at a primary site of the DR environment and connect to block storage devices (BSDs) that export virtual disks over a storage protocol to consume data including a recovery plan for disaster recovery. The recovery plan includes a recovery configuration whose generation is triggered by a user via a graphical user interface (GUI) and specifies resource requirements needed to recover the applications at a secondary site in the event of a disaster. The orchestration pipeline is initiated via single click of the GUI and completion of the stages of the pipeline is displayed as progress via the GUI to allow recovery of the applications without user intervention.

Abstract: The need for extracting information trapped in unstructured document images is becoming more acute. A major hurdle to this objective is that these images often contain information in the form of tables and extracting data from tabular sub-images presents a unique set of challenges. Embodiments of the present disclosure provide systems and methods that implement a deep learning network for both table detection and structure recognition, wherein interdependence between table detection and table structure recognition are exploited to segment out the table and column regions. This is followed by semantic rule-based row extraction from the identified tabular sub-regions.

Abstract: Traditional systems that enable extracting information from Piping and Instrumentation Diagrams (P&IDs) lack accuracy due to existing noise in the images or require a significant volume of annotated symbols for training if deep learning models that provide good accuracy are utilized. Conventional few-shot/one-shot learning approaches require a significant number of training tasks for meta-training prior. The present disclosure provides a method and system that utilizes the one-shot learning approach that enables symbol recognition using a single instance per symbol class which is represented as a graph with points (pixels) sampled along the boundaries of different symbols present in the P&ID and subsequently, utilizes a Graph Convolutional Neural Network (GCNN) or a GCNN appended to a Convolutional Neural Network (CNN) for symbol classification. Accordingly, given a clean symbol image for each symbol class, all instances of the symbol class may be recognized from noisy and crowded P&IDs.

Abstract: A solenoid based air ventilation valve (24) comprises of a housing (1), a solenoid coil (2), a flexible diaphragm (6), a plunger (8), a filter inlet (28) and a filter outlet assembly (100). The flexible diaphragm (6) is over molded with the plunger (8) to seal the flexible diaphragm (6) against the high pressure during closed position and has an incorporated O-ring (102) with flexible diaphragm (6) to stop the leakage from the valve (24). The filter outer assembly (100) is fixed to the inlet (25) and outlet port (26) to prevent suspended contamination particles in the valve (24). The valve (24) has a press fitted metal insert/flow controller (55) to achieve a low flow rate. Further, the air ventilation valve (24) allows a low leakage limit, low flow rate to serve high opening and working pressure to work in both over pressure and under pressure (vacuum) conditions.

Abstract: The need for extracting information trapped in unstructured document images is becoming more acute. A major hurdle to this objective is that these images often contain information in the form of tables and extracting data from tabular sub-images presents a unique set of challenges. Embodiments of the present disclosure provide systems and methods that implement a deep learning network for both table detection and structure recognition, wherein interdependence between table detection and table structure recognition are exploited to segment out the table and column regions. This is followed by semantic rule-based row extraction from the identified tabular sub-regions.