Abstract: A system includes a plurality of computing nodes that form a blockchain network, wherein one or more of the computing nodes is a metaverse computing node configured to generate a mixed reality environment. A processor of at least one computing node is configured to receive information relating to a suspicious data interaction associated with a data file of a user, simulate, based on the received information, the suspicious data interaction in a synthetic mixed reality environment that is substantially identical to the mixed reality environment, verify the suspicious data interaction while the simulated data interaction is being performed. When the suspicious data interaction cannot be verified, the processor disables one or more future data interactions processed using the same smart contract used to process the suspicious data interaction, and when the suspicious data interaction is successfully verified, terminates the simulated data interaction and process the suspicious data interaction.

Abstract: Systems and methods for reducing distractions from nearby devices when content susceptible to interruption is being consumed. Content susceptible to interruption is determined, and when this content is consumed on one device, nearby devices are identified and their output characteristics are modified. For example, output volume of nearby devices may be reduced, or the volume of the first device may be increased. This process is repeated with different devices as they each output content determined to be susceptible to interruption. Consumers are thus presented with fewer distractions from surrounding devices while they are consuming content that is susceptible to interruption.

Abstract: Systems and methods to enable green mining for centralized and/or decentralized financial systems are discussed. In some cases, the green mining methodologies are incorporated in distributed quantum ledger-based decentralized finance ecosystems, in decentralized financial systems and/or centralized financial systems. A proof of influence algorithm enables miners to accumulate and use reputation points to reduce computational power requirements on their systems and/or reduce energy consumptionInfluence points is an indicator of trustworthiness of the miner established based utilizing a predictive algorithm that analyzes the past mining behavior. Higher reputational scores result in lesser computationally intensive activities, which reduce energy consumption. The proof of influence algorithm rewards miners to encourage participation as validators of new blocks and/or other activities on the decentralized or centralized ledger-based systems.

Abstract: A system and process for generating synthetic fingerprints for metadata security and document verification using generative artificial intelligence (AI), generating smart contracts for electronic notarization using synthetically generated invisible fingerprint metadata, and/or automatic tagging of smart contracts for electronic notarization.

Abstract: A media asset may include date-related information. When viewed out of context it may be unclear what date is referenced by the date-related information. The media asset may be parsed to identify the date-related information within the media asset. The media asset may be analyzed to determine the context for the date related information. An absolute date may be determined based on this context and the date-related information and provided to the user with the media asset.

Abstract: Vehicles, crankshaft assemblies for vehicles, and methods for connecting vehicle accessory components and crankshafts are provided. A vehicle includes an engine configured to produce a linear motion output; a crankshaft configured to convert the linear motion output to rotational motion and terminating at an end with a tapered exterior surface extending to a central surface; an annular ring having a proximal ring surface, a distal ring surface, a ring opening, and a tapered inner surface, wherein the end of the crankshaft is received in the ring opening; and a fastener fixed to the crankshaft and holding the annular ring against the tapered exterior surface.

Abstract: Systems, computer program products, and methods are described herein for detecting manipulation of training data used for machine learning models. The present disclosure is configured to receive an interaction originating from an end-point device; validate the interaction via a primary model; identify a set of triggers within a backdoor model, where the backdoor model is modeled off the primary model capable of undergoing stress testing associated with a set of triggers; pause the interaction upon identification of the set of triggers within the backdoor model; transmit the identified set of triggers to an autonomous artificial general intelligence (AGI) agent, generate a set of synthetic data via the autonomous AGI agent, where the set of synthetic data removes the set of triggers from the primary model; and distribute the set of synthetic data to the primary model to correct the set of triggers within the primary model.

Abstract: An insertion marker mechanism may be used to generate and evaluate insertion markers in media content. The system may place candidate markers at silent pauses and/or anomalies such as changes in pitch/timbre of an audio signal, and/or at points where a video has a blank screen. In response to a request for the media content, the system may place secondary content at a subset of the candidate markers. The system may select different insertion markers in response to different playback requests of the content. Based on feedback such as user engagement with the secondary content and/or drop rate, the system may determine which insertion markers correlate with better engagement and/or retention. Based on the feedback, the system may prioritize content insertion at better performing markers, while continuing to insert a smaller proportion of secondary content randomly at lower performing markers to monitor for changes in user behavior over time.

Abstract: Various embodiments comprise a wireless communication network configured to anonymize network Key Performance Indicators (KPIs) to train a machine learning model. In some examples, the wireless communication network comprises a network analytics system and a KPI store. The network analytics system retrieves the network KPIs generated by network KPI sources. The analytics system filters the network KPIs based on an intended function of the machine learning model. The analytics system identifies correlated ones of the filtered KPIs and groups the correlated ones of the filtered KPIs into KPI groups based on a network condition. For each KPI group, the analytics system sorts the filtered KPIs into KPI ranges. For each KPI range, the analytics system tokenizes the KPI range by converting the filtered KPIs that compose the KPI range into strings. The KPI store stores the tokenized KPIs in a KPI database accessible by the machine learning model for training.

Abstract: A method includes encrypting a request received from a user device to generate an encrypted request. Encrypted data items are searched based on the encrypted request to identify desired encrypted data items. In response to determining that two or more encrypted data items of the desired encrypted data items have a same interaction identification, the two or more encrypted data items are locked and masked, and the masked two or more encrypted data items are removed. Algebraic operations are determined based on the encrypted request. An encrypted response is determined by performing the algebraic operations on the desired encrypted data items. The encrypted response is decrypted to obtain a decrypted response. An error is determined due to the algebraic operations performed on the desired encrypted data items. In response to determining that the error is less than an error threshold, the decrypted response is sent to the user device.

Abstract: An automated solution is provided through quantum computing, AI/ML algorithms, neural networking, and hybrid computing integration in order to provide end-to-end optimization software programs. The system may be implemented by generating unique grade(s) for software program(s) against disparate behavior using quantum computing. Quantum entanglement and superposition will ensure fast calculation and analysis of every code snippet. The grade can be further utilized by classical computing engines, which can operate in layers to generate an intelligent report highlighting granular deviation and select an optimized version of program through decision making algorithms, which can then be integrated to original version after passing validity/compatibility checks and automatically employed.

Abstract: A system includes a plurality of computing nodes that form a blockchain network, wherein one or more of the computing nodes is a metaverse computing node configured to generate a mixed reality environment. A processor of at least one computing node is configured to receive information relating to a suspicious data interaction associated with a data file of a user, simulate, based on the received information, the suspicious data interaction in a synthetic mixed reality environment that is substantially identical to the mixed reality environment, verify the suspicious data interaction while the simulated data interaction is being performed, when the suspicious data interaction cannot be verified, disable one or more future data interactions processed using the same smart contract used to process the suspicious data interaction, and when the suspicious data interaction is successfully verified, terminate the simulated data interaction and process the suspicious data interaction.

Abstract: A computing platform may train, using historical information classification information, an information classification model, which may configure the information classification model to classify information and identify, based on the classification, a storage location. The computing platform may receive, from a user device, a request to store information. The computing platform may identify, using the information classification model, a cloud based storage location for the information. The computing platform may generate an NFT representative of the first cloud based storage location. The computing platform may direct a cloud based storage system to store the information at the cloud based storage location. The computing platform may send, to the user device, the NFT. The computing platform may receive, from the user device, the NFT and a request to access the information. Based on validating the NFT, the computing platform may grant the user device access to the information.

Abstract: Aspects of the disclosure relate to using machine learning models to performing real-time cache updates. A computing system may receive a request to update an unreferenced object of a user interface of an application. A block may be generated in a blockchain stored in a second cache tier. The block may comprise a non-fungible token (NFT) based on the unreferenced object and may indicate that the unreferenced object is not authorized to indicate a system of record update. Based on the request to update the unreferenced object meeting one or more criteria, a block comprising an NFT that is referenced by the unreferenced object may be generated in a first cache tier. A block indicating that the NFT is referenced by the unreferenced object may be generated in a first cache tier and the unreferenced object may be updated in the user interface of the application.

Abstract: Embodiments of the present invention provide a system for generating data models secure from malfeasant manipulation for use in predictive modeling. The system is configured for retrieving training data associated with predictive modeling from a data source, processing the training data retrieved from the data source, transmitting the training data to a local linear model to generate a predictive output, retrieving historical data from the data source, transmitting the predictive output from the local linear model and the historical data retrieved from the data source to a Huber loss estimator module, validating, via the Huber loss estimator module, the predictive output received from the local linear model based on the historical data retrieved from the data source, and determining, via the Huber loss estimator module, if the training data has been manipulated by a malfeasant actor based on validating the one or more data points associated with the predictive output.

Abstract: A system receives an image from a camera, where the image shows a physical device. The system, via deep learning object detection algorithm, determines the identity of the physical device. The system generates a virtual device in a virtual environment, where the virtual device is a virtual representation of the physical device. The system receives a user request that indicates the virtual device is requested to perform an operation. In response, the system establishes a communication path to the physical device and communicates a control signal to the physical device that causes the physical device to perform the operation.

Abstract: In response to receiving a tagging request to map a first non-fungible token (NFT) to a first user device, a processor transmits an authorization request to a second user device to map the first NFT to the first user device. In response to receiving an approval of the request, the processor transmits a request to a minting server of an NFT blockchain network to generate the first NFT for the first user device. The processor stores a token ID of the generated first NFT in a memory. In response to receiving a second authorization from the first user device to perform a data interaction, the processor requests the NFT blockchain network based on the token ID, verification of the first user device. In response to receiving an indication that the first user device is verified, the processor processes the data interaction.

Abstract: Liquid pharmaceutical compositions of apixaban or pharmaceutically acceptable salts thereof are described. More specifically, stable oral liquid pharmaceutical compositions of apixaban for oral administration are provided, wherein the composition is stable for extended periods of time. More specifically, stable liquid pharmaceutical compositions of apixaban at concentrations of 0.4 mg/mL or more are provided. Stable oral liquid compositions of apixaban, methods for their administration, processes for their production, and use of these compositions for treatment of diseases treatable by apixaban are disclosed.

Abstract: Virtual assistant devices and related methods are described that allow users having special needs to communicate with the virtual assistant device. The virtual assistant devices are augmented with additional intelligent sensors, including, but not limited to, three-dimensional optical sensors that are used to capture optical signals of user's non-verbal communication (e.g., sign language, lip movements, gestures or the like). Additionally, the virtual assistant device includes an Artificial Intelligence (AI)-based engine that includes one or more Machine Learning (ML) models trained on user-specific data and used to determine the non-verbal communication of the user (i.e., sign language, lip movements or other gestures) based on inputs derived from the sensors. Moreover, the virtual assistant devices may be configured to generate and display visual information, such as three-dimensional floating avatars of the user and/or the virtual assistant.

Abstract: Arrangements for an intelligent monitoring platform using a cybersecurity mesh and graph neural networks (GNNs) are provided. A platform may train multiple machine learning models (e.g., a GNN model, a cybersecurity engine, and a monitoring model). The platform may generate, using a GNN model, a suspicion score for a received event processing request. Based on determining the suspicion score satisfies a threshold, the platform may generate a threat score using a cybersecurity engine. The platform may generate an anomaly record for the event processing request based on the threat score and using a monitoring model. The platform may determine a preferred node of a cybersecurity mesh for routing the event processing request based on the anomaly record. The platform may determine a threat prevention response based on the preferred node. The platform may initiate one or more security actions based on the threat prevention response.