Abstract: A system and method for lead conversion using conversational virtual avatar is disclosed. System comprising processor causes Conversation Virtual Avatar Platform (CVAP) to receive, for first entity, from lead prioritization engine, leads applicable to first entity via lead repository based on scores associated with respective leads. Processor causes CVAP to receive, through conversation management engine (CME) configured in CVAP, from leads, responses to questions pertaining to product attributes and information pertaining to lead.

Abstract: Methods, systems, and apparatus for solving cost optimization problems. In one aspect, a method includes receiving data representing a cost optimization problem in a network, wherein i) the network is represented as a graph of nodes and edges, and ii) each edge comprises an associated cost; mapping the data representing the cost optimization problem in a network to a quadratic unconstrained binary optimization (QUBO) formulation of the cost optimization problem, the QUBO formulation comprising multiple variables with values determined by states of respective qubits, wherein each qubit corresponds to a respective edge of the graph of nodes and edges; obtaining data representing a solution to the cost optimization problem from a quantum computing resource; and initiating an action based on the obtained data representing a solution to the cost optimization problem.

Abstract: A first device may provide a request to establish a secure communication with a second device, and may hide public keys based on a commutative legacy compatible encryption process sharing a modulus and based on quasi-Carmichael numbers larger than the modulus with quadratic residuals. The first device may utilize variable extendable-output function hashing, based on the modulus, with bloom filtering to generate an output that prevents creation of classical rainbow tables, and may utilize a key derivation function to generate a symmetric key based on the output. The first device may establish the secure communication with the second device based on the symmetric key.

Abstract: Implementations are directed to processing a content object model through a ML model to provide a set of base content feature representations, processing a style object model through the ML model to provide a set of base style feature representations, executing iterations including: generating, by the ML model, a set of stylized feature representations for an initial stylized object model, the initial stylized object model having one or more adjusted parameters relative to a previous iteration, determining a total loss based on the set of stylized feature representations, the set of base content feature representations, and the sets of base style feature representations, and determining that the total loss is non-optimized, and in response, initiating a next iteration, executing an iteration including determining that the total loss is optimized, and in response providing the initial stylized object model as output of the iterative process.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated summarization of documents using machine learning and artificial intelligence. The summaries include document category-specific summaries for different categories of documents. To illustrate, a document processing device may receive input data representing a document and provide first feature data based on the input data to one or more machine learning (ML) models to select a document category of a plurality of predefined document categories. The document processing device may provide second feature data based on the input data to a second set of ML models associated with the selected document category to generate annotation data associated with the document. The document processing device may provide the annotation data to a third set of ML models to generate a summary of the document, and the document processing device may generate an output based on the summary.

Abstract: An artificial intelligence (AI) technique to process and query data pertaining to an enterprise. A user raises a request which is processed to predict a knowledge context area based on a predetermined structure of the enterprise. The knowledge context area is predicted from multiple knowledge context areas, on the basis of the received user request and a conversation history of the user in past. Further, a knowledge database is selected from multiple knowledge databases based on the user request and the predicted knowledge context. The knowledge databases include preprocessed data from multiple data sources. The knowledge database is queried on the basis of the user request related to the knowledge context to obtain a result and the result is then displayed as an output.

Abstract: Systems and methods for providing an optimized customer fallout framework are disclosed. A system receives an input from a user via a digital platform, processes the input and historical data to extract a set of quantifiable features, determines an engagement stage from a plurality of engagement stages for the user based on the set of quantifiable features and an n-helix multi-dimensional model, and determines, via a deep learning model corresponding to a combination of the engagement stage and an advanced engagement stage, a set of positive drivers for the user to move from the engagement stage to the advanced engagement stage. Further, the system generate a multi-nodal network comprising a plurality of grids and aggregates the plurality of grids to generate a global grid for the determined set of positive drivers. Based on the global grid, the system dynamically generates personalized recommendations for the user.

Abstract: A process circularity optimization system improves the circularity of industrial processes via a non-linear analysis of process attributes. A process input provides one or more target attributes to be optimized along with an objective function for improving the circularity of a manufacturing process. The input variables to be set to optimize the target attributes along with any constraints are accessed. Multiple input variable value combinations are generated via non-linear processing of the input variables and a combination of input variable values with the highest probability of success is implemented in the manufacturing process to optimize circularity.

Abstract: A system includes circuitry for rewriting blockchains in a non-tamper-evident or tamper-evident operation using a key secret held in portions by multiple individually untrusted parties. The blockchains may include a series of blocks secured by integrity codes that may prevent non-tamper-evident rewrites by non-trusted parties that are not in possession of the key secret or individually-untrusted parties in possession of only a portion of the key secret. In some cases, multiple individually-untrusted parties may combine their portions into the key secret. As a group, the multiple individually-untrusted parties may perform non-tamper-evident operation with respect to at least one integrity code within the blockchain.

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: A system may receive, from one or more data sources, one or more de-identified data sets that include de-identified personal data. The system may receive a request for a feature set of the one or more de-identified data sets, wherein the feature set includes a set of quasi-identifiers included in the de-identified personal data. The system may calculate a re-identification risk score for the set of quasi-identifiers. The system may selectively output, based on the re-identification risk score, one of: actual data, from the one or more de-identified data sets, of the feature set if the re-identification risk score satisfies a condition, or synthetic data, generated by the device from the one or more de-identified data sets, for the feature set, or a combination of the synthetic data and the actual data for the feature set, if the re-identification risk score does not satisfy the condition.

Abstract: This document describes self-cleaning devices. In one aspect, a self-cleaning device includes a fabric having a surface covered with a photocatalyst, one or more light sources embedded in the fabric, and a triggering mechanism that activates a cleaning cycle by activating the one or more light sources. The triggering mechanism can include a pressure sensor. The triggering mechanism can be configured to activate the cleaning cycle in response to detecting a decrease in pressure being applied to the pressure sensor.

Abstract: In some examples, digital twin-based floor layout generation may include receiving, for a floor plan that is to be generated, an activity map that includes movement of at least one user within a digital twin of a specified area. Based on the activity map, embedding vectors may be generated for each room type of a plurality of room types in the specified area. An input boundary feature map may be received. The floor plan may be generated based on an analysis of the embedding vectors for each room type of the plurality of room types and based on an analysis of the input boundary feature map.

Abstract: A method and system for assisting program code development are disclosed. The method may include obtaining multimodal data of a meeting discussing a program code development, extracting a plurality of topics and a plurality of concepts from the multimodal data, identifying a plurality of meeting segments for the plurality of concepts. The method may further include determining a coding intent from program codes for the program code development, aligning the coding intent to a set of topics, identifying the set of concepts associated with the topic aligned with the coding intent, identifying a set of meeting segments associated with the concept. The method may further include determining an alignment metric of the meeting segment based on an alignment metric between the coding intent and the topic, and outputting one or more meeting segments for the coding intent based on alignment metrics of the one or more meeting segments.

Abstract: A remote location monitoring system identifies a place of interest and a subset of artificial satellites that can capture images of the place of interest within a threshold period. The next available artificial satellite with image sensors that can capture images of the place of interest earliest is selected and the cache heating signal is transmitted to be stored in a cache associated with the next available image sensor. The cache heating signal activates particular image sensors for image capture and enables the transmission of meaningful images that enable monitoring the place of interest.

Abstract: The disclosure below is directed to optical/photonic devices and systems configured to control polarization state of light for selective privacy protection in optical imaging. In particular, the various embodiments disclosed below provide an application of polarization manipulation techniques with multiple optical polarization filters to enable partial or full privacy while maintaining functioning of image-based spatial mapping and localization algorithms used in video devices such as virtual reality (VR), augmented reality (AR), and robot/drone cameras. In the disclosed example approaches, the various polarization filters are configured to allow no or negligibly small amount of light from the privacy protected regions of a scene into the cameras in a privacy mode and to reject 100% of all light and enable full privacy in a block mode. The disclosed approaches also dynamically account for orientation changes during operation of the camera devices such that privacy is dynamically protected in real-time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing speech using a spiking neural network acoustic model implemented on a neuromorphic processor are described. In one aspect, a method includes receiving, a trained acoustic model implemented as a spiking neural network (SNN) on a neuromorphic processor of a client device, a set of feature coefficients that represent acoustic energy of input audio received from a microphone communicably coupled to the client device. The acoustic model is trained to predict speech sounds based on input feature coefficients. The acoustic model generates output data indicating predicted speech sounds corresponding to the set of feature coefficients that represent the input audio received from the microphone. The neuromorphic processor updates one or more parameters of the acoustic model using one or more learning rules and the predicted speech sounds of the output data.

Abstract: Methods, systems, and computer-readable storage media for receiving, by a self-healing platform within the container orchestration system, fault data that is representative of two or more error events occurring within a cluster provisioned within the container orchestration system, determining, by the self-healing platform, a set of actions to be executed in response to the two or more error events, providing, by the self-healing platform, a priority value for each error event of the two or more error events, and transmitting, by the self-healing platform, instructions to execute actions in the set of actions based on respective priority values of the two or more error events.

Abstract: In some examples, source code differential pruning-based dataset creation may include receiving source code that includes at least one vulnerability and at least one remediation that remediates the at least one vulnerability, extracting at least one remediated section, and identifying each sentence of the remediated section. A plurality of clusters may be generated based on an analysis of each identified sentence of the remediated section to determine a score with respect to a specified cluster that includes the identified sentence. Further, a determination may be made as to whether the score is greater than a specified threshold. Each identified sentence for which the score is greater than the specified threshold may be designated as a relevant sentence. An auxiliary dataset may be generated based on a plurality of relevant sentences and include at least one relevant vulnerability and at least one relevant remediation that remediates the relevant vulnerability.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support automated failure mode detection for rotating machinery based on vibration analysis. To illustrate, a computing device may receive vibration data from one or more sensors configured to measure vibrations of a rotating machine, such as an engine. The computing device may generate a frequency spectrum based on the vibration data (or receive the frequency spectrum) and compare the frequency spectrum to one or more predetermined frequency models to determine one or more similarity metrics. The one or more predetermined frequency models may each be associated with a respective failure mode of the rotating machine. The computing device may identify a failure mode associated with a predetermined frequency model that corresponds to a similarity metric that satisfies a threshold, and the computing device may output an indication of the identified failure mode.