Abstract: An outlier IC detection method includes acquiring first measured data of a first IC set, training the first measured data for establishing a training model, acquiring second measured data of a second IC set, generating predicted data of the second IC set by using the training model according to the second measured data, generating a bivariate dataset distribution of the second IC set according to the predicted data and the second measured data, acquiring a predetermined Mahalanobis distance on the bivariate dataset distribution of the second IC set, and identifying at least one outlier IC from the second IC set when at least one position of the at least one outlier IC on the bivariate dataset distribution is outside a range of the predetermined Mahalanobis distance.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing secure access to a locker of a locker system based on a token that includes token data and a message authentication code (MAC) to authenticate the token data. The token data includes at least the unique locker identifier for the locker. The MAC can be generated using a secret key. The secret key can be obtained based on a unique derivation key associated with a master key. The locker system can determine whether the token is valid based on the token data, the MAC, and the secret key. In response to a determination that the token is valid, the locker system can send a signal to unlock the locker.

Abstract: The disclosed embodiments include a system and method for generating a predictive model and allocating, based on the model's findings, an ideal amount into a user's account. The system includes a user device and a server. The server can retrieve the user's information, analyze the information, generate the predictive model, train the model, calculate a savings amount and allocate the amount to the appropriate account.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: A submicron-sized Pickering miniemulsion system stabilized by carbon quantum dots solid nanoparticles for biphasic catalysis is disclosed, which breaks the existing limits for homogenization of the immiscible biphasic system and overcomes the issues for big size of solid particles-stabilized emulsion droplets. A method for producing the carbon quantum dot-based catalysts and a process of establishing the Pickering miniemulsion system for biphasic reaction with enhanced catalytic efficiency are also disclosed. The carbon quantum dot-stabilized Pickering miniemulsion features a pH-responsive behavior, with a reversible transition between the emulsification and demulsification, triggering the easy & facile product separation and emulsifier/catalyst recycling in one reaction vessel.

Abstract: The present disclosure provides a multi-factor quantitative analysis method for deformation of a neighborhood tunnel. The method includes the following steps: analyzing monitoring data generated at a tunnel site; simulating collapse occurring at a shallow buried section of a tunnel; determining the degree of influence of each factor on the tunnel and a stratum; and determining quantitative influence of each factor on tunnel deformation. The present disclosure can not only provide an accurate theoretical basis for the construction of the shallow buried section of the small-distance tunnel, but also guarantee safety and cost saving during tunnel construction.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from the user to monitor a plurality of activities executed within the computing device; continually monitoring the plurality of activities executed within the computing device for a predetermined period of time; identifying an indication of an incoming interaction session within the predetermined period of time; automatically verifying a session interaction parameter associated with the incoming interaction session; determining a plurality of inquires associated with the research interaction session based on the at least one session interaction parameter; identifying the plurality of demographic informational items associated with the user; and automatically updating the programmable GUI element to display the plurality of inquires associated with the interaction session and the plurality of demographic informational items associated with the user.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a trained spam upsurge detection machine learning model that determines when a current frequency associated with spam communications received by a current user exceeds a baseline frequency associated with the current user; receiving a permission indicator identifying a permission by the user to detect communications being received by the computing device; receiving an indication of at least one communication being received; determining the at least one communication as a particular spam communication; updating a frequency at which spam communications have been received by the user based at least in part on the particular spam communication; utilizing the trained spam upsurge detection machine learning model to determine that the frequency exceeds a baseline frequency associated with the user; and initiating a scan of one or more dark web resources.

Abstract: A computer-implemented method comprising: instructing a computing device to obtain a permission from the user to monitor a plurality of activities executed within the computing device; receiving a call session request from a computing device associated with a second user; utilizing a trained machine learning algorithm to confirm an identity of the user based on the call session interaction parameter to confirm the call interaction session as a verified call interaction session; generating a fictitious call session interaction parameter that differs from the at least one call session interaction parameter; modifying the call session request into a verified call session request by replacing the call session interaction parameter with the fictitious call session interaction parameter; utilizing a telecommunication network to transmit the verified call session request with the at least one fictitious session interaction parameter to the first calling-enabled computing device; automatically instructing the first c

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

Abstract: The present disclosure provides an exemplary method, system, and computing device that may include the steps of receiving a first indication that information of a user has been detected at one or more dark web resources; classifying the item of the compromised information into an information type category; receiving a permission indicator to detect communications by the computing device; receiving a second indication of a communication; receiving a third indication that the user engages an interaction with the communication; instructing the computing device to execute a technique to obtain data for the communication; receiving the data for the communication; determining the communication is a spam communication; determining a current information type category being discussed during the spam communication; making a determination that the current information type category corresponds to the information type category; and instructing a graphical user interface to display an alert to the user.

Abstract: Disclosed herein are systems and methods for account status monitoring. The method can include receiving user registration information and account information corresponding to a first user account. The account information can include first user credit information. The method can include analyzing, by a machine learning rules engine, the account information and the user registration information to create a user behavior pattern associated with the first user account. The method can further include determining an anomaly in the user behavior pattern indicative of a life event. Based on the life event and the user registration information, the method can determine a second user account and corresponding account information comprising second user credit information. In response, the method can include predicting, by a convolutional neural network, an effect on the second user credit information resulting from the transfer of the first user credit information to the second user account.

Abstract: A system, processes, and a computer-readable storage medium are provided method includes receiving a search query including one or more of a textual phrase, a document, or data including metadata from a user. For example, a processor may execute a theme-generating machine learning algorithm to generate a theme of the search query. Profiles of other users are identified as similar to a profile of the user may be located. A search history of each of the located other users may be located in the collected search history. Content in the searchable content that corresponds to the generated theme of the search query and according to a result of the evaluation of the located search history of each of the other users may be obtained. The obtained content may be scored based on a scoring algorithm and output a set of scored search results to the input/output device.

Abstract: Systems and methods disclosed herein provide automatic expense categorization of transactions or expenditures based on a machine learning (ML) model trained using anonymized transactional data for expenditures that are stored in a public blockchain. The anonymized transactional data for the expenditure and the expense category, may be distributed throughout the blockchain network and recorded in the blockchain. In some implementations, an expenditure may be submitted to the blockchain network for expense categorization. The transactional data for the expenditure may be anonymized to remove confidential and personal identifying information (PII) before it is distributed throughout the blockchain network. Each participating node of the blockchain network may utilize the ML model to identify an expense category for the expenditure. The participating nodes may provide a consensus mechanism in order to arrive at a shared understanding of how to categorize the expenditure.

Abstract: Systems and methods of the present disclosure enable split communication routing. A communication request associated with an electronic activity is received, the communication request specifying a virtual token and a data content, where the virtual token is a proxy for multiple actual data source profiles. A split rule defining a proportion of the data content to be attributed to each actual data source profile is determined. A security score is determined based on each actual data source profile and each proportion of the data content. The communication request is authorized based on the security score exceeding a threshold security score and access to the proportion of the data content in each actual data source profile is enabled. Each proportion of the data content is consolidated into a single response, and the electronic activity is executed with the single response.

Abstract: Systems and methods of location-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: utilizing a trained call annotation machine learning model to determine one or both of an annotating condition and annotating location granularity, and associate one location of a user with one phone number of the user based at least on one or both of the annotating condition and the annotating location granularity; receiving second transactional information of one transaction associated with a first user; extracting second location information from the second transactional information of the one transaction; utilizing the trained call annotation machine learning model to automatically annotate one phone number record of one phone number of the first user, with the second location information at the annotating location granularity to form at least one user-specific location-specific annotated phone number record.

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.