Abstract: Systems and methods for generating an output representation are disclosed. A system may include a processor including a representation generator. The representation generator may receive an input data comprising an input content and an instruction. The representation generator may include a parsing engine to parse the input data to obtain parsed information. The representation generator include a mapping engine to map the parsed information with a pre-stored base template pertaining to a pre-defined module, to obtain a mapped template. The representation generator may generate, through a machine learning (ML) model, based on the mapped template, an output representation in a pre-defined format. The output representation may correspond to the expected representation of the input content.

Abstract: Systems and methods for facilitating validation of datasets are disclosed. A system may include a processor. The system may include a data validator implemented via the processor to receive an input dataset including a component metadata. The data validator may perform, using an validation model and through a rules engine, validation of information in the component metadata to obtain a validation dataset. The validation may enable to predict at least one invalid feature in the component dataset. The system may include an insight generator implemented via the processor to generate, based on the validation datasets, automated insights pertaining to mitigation of the at least one invalid feature. In an embodiment, the automated insights may be stored in a distributed ledger to facilitate an authenticated storage of the automated insights. The authenticated storage may be facilitated by a network comprising a plurality of nodes.

Abstract: In some examples, collaborative learning-based cloud migration implementation may include identifying a migration agent that is to perform an application migration from a first cloud environment to a second cloud environment, and identifying a plurality of additional migration agents. A technical context and a migration flow context may be determined for the migration agent and for the plurality of additional migration agents. Executed allowed and error-response migration actions may be identified for states that are similar to a current state of the application migration, and a similarity between the migration agent and each of the migration agents that executed the allowed and error-response migration actions may be determined. A migration action that is to be performed may be identified based on a maximum relevance associated with the allowed and error-response migration actions. The identified migration action may be executed by the migration agent to perform the application migration.

Abstract: A system and method for facilitating digital rationalization of a correspondence is disclosed. The system may include a processor including DTRE. The DTRE may receive a plurality of templates from at least one database coupled to the processor. The plurality of templates may pertain to a given correspondence. The DTRE may process the plurality of templates to identify static objects and dynamic objects. The static objects may be indicative of components that may be common across the plurality of templates. The dynamic objects may be indicative of components that may vary across the plurality of templates. The DTRE may generate, at least one rationalized template based on analysis of the identified static and dynamic objects. The at least one rationalized template may optimally represent the given correspondence, and may enable transmission of the given correspondence having content pertaining to any of the plurality of templates.

Abstract: An authentication and applications access system provides access to a plurality of backend applications via a social media application installed on a user device. Metadata associated with a request to access one of the plurality of backend applications is initially extracted. The metadata is used to authenticate the request via a plurality of validation steps. Upon authentication, the information from the request is provided to the backend application to receive the results responsive to the request. Any sensitive data included in the results is suppressed via data substitution steps from the transmission to the user device. The output from the applications access system with the sensitive data occluded is provided for display on a social media user interface (UI) on a user device.

Abstract: This application discloses a system and method for federated collaborative machine learning model development using local training datasets that are not shared. An adaptive and evolutionary approach is used to select local training nodes that are most fit from one training round to the next training round to optimize an overall cost and performance function for the federated learning, to cross-over model architecture between local training nodes, and to perform model architecture mutation within local training nodes. The local training nodes are further clustered to account for the inhomogeneity in the local datasets. Such adaptive, evolutionary, and collaborative federated learning thus provides cost-effective and high-performance model development.

Abstract: In some examples, collective application portfolio migration control may include determining, for a plurality of applications that are to be clustered for migration to a cloud environment, a coupling coefficient that represents a type of coupling between pairs of applications from the plurality of applications, a proximity coefficient that represents application proximities, and a connectedness coefficient that represents application connections. A combined application relatedness coefficient may be determined based on the coupling coefficient, the proximity coefficient, and the connectedness coefficient. A portfolio graph may be generated based on the combined application relatedness coefficient to generate migration application clusters to duster the plurality of applications. Migration of the plurality of applications to the cloud environment may be controlled based on the migration application clusters.

Abstract: Systems and methods for evaluating reliability of a model are disclosed, including a processor that may include a data augmentor and a model evaluator. The data augmentor may receive a task data pertaining to information related to a pre-defined task to be performed by the model. The data augmentor may augment the task data to obtain an augmented aspect data. The model evaluator may evaluate a trained model based on the augmented aspect data to obtain aspect evaluation metrics. The model may be an artificial intelligence (AI) model that may be trained using the task data. The evaluation may enable to assess performance of the trained model by computing a performance score based on the aspect evaluation metrics. The performance score may help evaluate the reliability of the model in a pre-defined domain.

Abstract: In some examples, stylization-based floor plan generation may include receiving, for a floor plan that is to be generated, a layout graph for which user constraints are encoded as a plurality of room types. The user constraints may include spatial connections therebetween. Based on the layout graph, embedding vectors may be generated for each room type of the plurality of room types. Bounding boxes and segmentation masks may be determined for each room embedding from the layout graph, and based on an analysis of the embedding vectors for each room type of the plurality of room types. A space layout may be generated by combining the bounding boxes and the segmentation masks. A floor plan may be generated based on an analysis of the space layout and an input boundary feature map.

Abstract: Systems and methods for attribute-based modelling are disclosed. A system includes an attribute-based decomposition engine, which when executed using a processor, causes the engine to retrieve one or more product attributes associated with each of a set of products, an importance of the product attributes being determined based on the product sales data, product data, product parameters, and financial data associated with product. The attribute-based decomposition engine using the processor establishes, for a set of products, a relationship between a retrieved one or more product attributes and product sales associated with product, based on implementation of a non-parametric machine learning (ML) modeling on a data model. The attribute-based decomposition engine quantifies contribution of each product attribute on product sales based on an established relationship and a game theoretic framework.

Abstract: Systems and methods for facilitating an automated observability of a ML model are disclosed. A system may include a processor including a model creator and a monitoring engine. The model creator may generate a configuration artifact based on a pre-defined template and a pre-defined input. The configuration artifact may pertain to expected attributes of the ML model to be created. The model creator may generate the ML model based on the configuration artifact. The monitoring engine may monitor a model attribute associated with each ML model based on monitoring rules stored in a rules engine. This may facilitate to identify an event associated with alteration in the model attribute from a pre-defined value. Based on the identified event, the system may execute an automated response including at least one of an alert and a remedial action to mitigate the event.

Abstract: Systems and methods for disability simulations and accessibility evaluations of content is disclosed. A disclosed system runs using an information loss determination engine via a processor, for a given disability, at least one simulation to simulate how a content is experienced by a user having such disability. The system computes information loss based on comparison of the simulated content with desired original content. Further, the system transmits data packets indicative of a content optimization strategy that is determined based on the determined information loss.

Abstract: Systems and methods for facilitating an automated detection of an object in a test document are disclosed. A system may include a processor including a dataset generator. The dataset generator may obtain a first input image and a first original document from a data lake. The dataset generator may prune a portion of the first original document to obtain a pruned image. The dataset generator may blend the first input image with the pruned image to generate a modified image. The modified image may include the pruned image bearing the first pre-defined representation. The modified image may be combined with the first original document to generate a training dataset. The training dataset may be utilized to train a neural network based model to obtain a trained model for the automated detection of the object in the test document.

Abstract: An Artificial Intelligence (AI) based data processing system transforms a plurality of time series data sets for processing by one or more deep learning (DL) models for generating forecasts. The DL models are initially trained on training data generated from the historical data. During operation, a plurality of transformed time series data sets are generated from the plurality of time series data sets associated with different entities in an entity hierarchy via data flattening and data stacking. A primary model of the one or more DL models is trained on first-party data for generating the forecasts. An extended model of the one or more DL models is trained on third-party data from external data sources. Whenever new data is available in the first-party data or the third-party data, the primary model and the extended model are correspondingly updated.

Abstract: A system for auto classification of products includes an entity recognizer and a model selector. The entity recognizer receives training data including an attribute of a product. The model selector selects a feature from the training data using a first statistical model to provide a first feature and a second statistical model to provide a second feature, and trains a probabilistic classifier using the first and the second features for providing a first and a second classification models respectively. Further, the model selector calculates an accuracy score of the obtained classification models for each distinct category in a preset hierarchy of categories and selects a classification model from the obtained classification models based on the accuracy score. The selected classification model has a highest accuracy score for a corresponding category in the preset hierarchy.

Abstract: An Artificial Intelligence (AI)-based regulatory data processing system accesses a regulatory text corpus for training machine learning (ML) models including a topic extraction model, a feature selection model, an entity identification model and a section classification model. The regulatory text corpus includes documents pertaining to a specific domain corresponding to a received domain-specific regulatory text document. Various trained machine learning (ML) models are used to extract topics, identify entities from the new regulatory document and to classify portions of the domain-specific regulatory text document into one of a plurality of predetermined sections. The information in the new regulatory document is therefore converted into machine consumable form which can facilitate automatic execution of downstream processes such as identification of actions needed to implement the regulations and robotic process automation (RPA).

Abstract: An Artificial Intelligence (AI)-based innovation data processing system receives at least one query word related to a category. Information material including textual and non-textual data is retrieved from a plurality of data sources using the at least one query word. The information material is tokenized and parsed using a dependency parser for entity recognition, building entity relationships and for generating knowledge graphs. The output of the dependency parser is accessed by a trained classifier for obtaining respective confidence levels for each of the sentences in the textual data. The confidence levels are compared to a predetermined threshold confidence level for determining if the sentences include references to innovations. In addition, trends in the innovations are determined and responses to user queries are generated based on one or more of knowledge graphs and the trends.

Abstract: A device receives a request associated with standardizing organization-specific roles within an organization, where the request includes data that identifies titles for the organization-specific roles. The device converts the data to vectors that represent semantic meanings of the titles. The device sets a configuration of a data model by assigning weighted values to title-class identifiers that are used to associate titles, of a standardized set of titles, to a hierarchy of role classifications. The device uses the data model to determine scores that indicate likelihoods of the titles mapping to the title-class identifiers. The device identifies, based on scores, a subset of title-class identifiers that associate particular titles, of the standardized set of titles, and particular role classifications. The subset of title-class identifiers is stored in association with information relating to the particular titles. The device performs an action based on the information relating to the particular titles.

Abstract: In some examples, structure-based multi-intent email classification may include receiving an email thread that includes a plurality of emails including an email that is to be classified, and identifying, for the email thread, a process associated with the email thread, Based on the process associated with the email thread and for each sentence of the email that includes a plurality of sentences, a corresponding classifier may be determined from a plurality of classifiers, and applied to each sentence of the email to generate classified sentences. At least one entity may be extracted from each of the classified sentences of the email. Based on the at least one extracted entity, the email may be classified.

Abstract: An Artificial Intelligence (AI) based transaction data processing and reconciliation system analyzes transaction data of different accounts to determine anomalous transactions, tagged transactions with Required Adjustments tag (R-tag), or aging transactions. Different Artificial intelligence (AI) based models are trained to produce corresponding risk scores that enable the determinations. Those transactions having low-risk scores are automatically reconciled whereas transactions having higher risk scores can be flagged for further review. Furthermore, the accounts corresponding to the transactions are also analyzed via different AI-based account-level models to identify accounts that can be R-tagged and/or accounts that are at the risk of being de-certified. Those accounts with higher risk scores can be flagged for further review while accounts with lower risk scores can be automatically certified.