Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for dynamically providing a activity dashboard for display within a graphical user interface. In particular, the disclosed systems can intelligently display, within the activity dashboard, insights of digital activity within an application of a child account. For example, the disclosed systems can monitor digital activity associated with recent contacts and groups, contact updates, and digital media items exchanged in messaging chat threads. In turn, the disclosed system can selectively surface one or more portions (and/or indications) of the monitored digital activity in the activity dashboard along with corresponding actionable options available for user selection.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Abstract: A computing system that is configured to receive requests to send computer executable programs to a data owner system associated with a data source for execution of the computer executable program by the data owner system. The data owner system may store to a blockchain a permitted list of programming functions, function libraries, function syntax definitions, and execution environment requirements. The computing system may be further configured to retrieve the permitted lists. The computing system may be further configured to evaluate the computer executable program using the permitted lists to determine if the computer executable program may be executed by the data owner system. The evaluation may be performed by generating an abstract syntax tree of the computer executable program. The computing system may be further configured to send the computer executable program to the data owner system if the computer executable program satisfies the conditions of the permitted lists.

Abstract: A first system creates and sends encryption key data to multiple data sources. A second system receives data encrypted using the encryption key data from the multiple data sources; the data may include noise data such that, even if decrypted, the original data cannot be discovered. Because the encryption is additively homomorphic, the second system may create encrypted summation data using the encrypted data. The first system separately receives the noise data encrypted using the same technique as the encrypted data. The second system may send the encrypted summation data to the first system, which may then remove the noise data from the encrypted summation data to create unencrypted summation data.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Abstract: A computing system that facilitates execution of protocol for requesting data from one or more data owners. The protocol may specify an execution graph for performing operations collect the requested data. The execution graph may be comprised of execution graph nodes specifying operations such as accessing a database, querying the database, and scrubbing the resulting data of sensitive information. An execution graph may be agreed upon and stored on a blockchain. Storing the execution graph on the blockchain ensures a validated consensus among parties, such as the data owners, for the operations defined within the execution graph. The protocol may include requests for data from multiple data owners. The data received from each data owner may be aggregated. Rules may require the aggregated data meet certain requirements before release such as removing sensitive information or a minimum number of data records to prevent exposure of private data.

Abstract: A thermo-electrochemical reactive capture apparatus includes an anode and a cathode, wherein the anode includes a first catalyst, wherein the cathode includes a second catalyst, a porous ceramic support positioned between the anode and the cathode, an electrolyte mixture in pores of the ceramic support, and a steam flow system on an outer side of the cathode. The outer side of the cathode is opposite an inner side of the cathode and the inner side of the cathode is adjacent to the ceramic support. In addition, the electrolyte mixture is configured to be molten at a temperature below about 600° C.

Abstract: A computing system that facilitates approval and validation of executable code between parties. A template including executable code and specifying certain operations and functions to be performed on protected data, as well as constraints thereto, may be verified and agreed upon by parties. The verified template and/or a hash of the verified template may be stored on a blockchain. Prior to execution of the code certain parameters within the template may be filled and validated by a system that will execute the code. A contract, which too may be agreed upon and stored on the blockchain, may also include other terms governing the parties. The filled template may also be validated, and compared against a blockchain version of the template, by the parties prior to execution of the code and prior to access being granted to protected data. Such verifications and validations ensure that data is only operated on, using a secure system, within the parameters as agreed upon by the parties.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for dynamically providing a activity dashboard for display within a graphical user interface. In particular, the disclosed systems can intelligently display, within the activity dashboard, insights of digital activity within an application of a child account. For example, the disclosed systems can monitor digital activity associated with recent contacts and groups, contact updates, and digital media items exchanged in messaging chat threads. In turn, the disclosed system can selectively surface one or more portions (and/or indications) of the monitored digital activity in the activity dashboard along with corresponding actionable options available for user selection.

Abstract: Computer instructions corresponding to a neural-network model are received and encrypted using an encryption technique. Training data encrypted using the encryption technique is received from a data source. The model is trained using the training data using, for example, a gradient descent technique. If the model performs in accordance with a quality metric, it is sent to a device of a model user.

Abstract: Computer instructions corresponding to a neural-network model are received and encrypted using an encryption technique. Training data encrypted using the encryption technique is received from a data source. The model is trained using the training data using, for example, a gradient descent technique. If the model performs in accordance with a quality metric, it is sent to a device of a model user.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Abstract: System and method relates to an advanced manufactured vapor-fed electrochemical reactor (AM-VFR) system comprising a cathode gas compartment comprising a first inlet, and a first outlet, a catholyte compartment having a centrally located window for a cathode and a membrane, a second inlet, a second outlet, and a reference electrode, an anolyte compartment having a centrally located window for the membrane and an anode, a third inlet and a third outlet and an anode gas compartment having a fourth inlet and a fourth outlet, wherein the cathode, wherein the cathode is disposed between the cathode gas compartment and the catholyte compartment, wherein the membrane is disposed between the catholyte compartment and the anolyte compartment, wherein the anode is disposed between the anolyte compartment and the anode gas compartment, and wherein one or more of the cathode gas compartment, the catholyte compartment, the anolyte compartment and the anode gas compartment are made of a 3D printing plastic.

Abstract: A first component determines encrypted data representing an event and encrypted threshold data corresponding to an outlier of the event. The first system may process the data using, for example, one or more composite integers, and may send the result to a second system. This second system may subtract the data to determine of the encrypted data is greater than, less than, or equal to the encrypted threshold. If so, the second system may determine that the encrypted data corresponds to an outlier of the data. The second system may send an indication of this determination to a third system.

Abstract: A computing system that facilitates approval and validation of executable code between parties. A template including executable code and specifying certain operations and functions to be performed on protected data, as well as constraints thereto, may be verified and agreed upon by parties. The verified template and/or a hash of the verified template may be stored on a blockchain. Prior to execution of the code certain parameters within the template may be filled and validated by a system that will execute the code. A contract, which too may be agreed upon and stored on the blockchain, may also include other terms governing the parties. The filled template may also be validated, and compared against a blockchain version of the template, by the parties prior to execution of the code and prior to access being granted to protected data. Such verifications and validations ensure that data is only operated on, using a secure system, within the parameters as agreed upon by the parties.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for dynamically providing a activity dashboard for display within a graphical user interface. In particular, the disclosed systems can intelligently display, within the activity dashboard, insights of digital activity within an application of a child account. For example, the disclosed systems can monitor digital activity associated with recent contacts and groups, contact updates, and digital media items exchanged in messaging chat threads. In turn, the disclosed system can selectively surface one or more portions (and/or indications) of the monitored digital activity in the activity dashboard along with corresponding actionable options available for user selection.

Abstract: Computer instructions corresponding to a neural-network model are received and encrypted using an encryption technique. Training data encrypted using the encryption technique is received from a data source. The model is trained using the training data using, for example, a gradient descent technique. If the model performs in accordance with a quality metric, it is sent to a device of a model user.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A secure processing component may process data using a transformation layer and may send and receive data to and from a first system. Multiple data-provider systems may send vertically partitioned data to the secure-processing component, which may determine output data corresponding to the multiple data-provider systems.

Abstract: Multiple systems may determine neural-network output data and neural-network parameter data and may transmit the data therebetween to train and run the neural-network model to predict an event given input data. A data-provider system may perform a dot-product operation using encrypted data, and a secure-processing component may decrypt and process that data using an activation function to predict an event. Multiple secure-processing components may be used to perform a multiplication operation using homomorphic encrypted data.

Abstract: A first component determines encrypted data representing an event and encrypted threshold data corresponding to an outlier of the event. The first system may process the data using, for example, one or more composite integers, and may send the result to a second system. This second system may subtract the data to determine of the encrypted data is greater than, less than, or equal to the encrypted threshold. If so, the second system may determine that the encrypted data corresponds to an outlier of the data. The second system may send an indication of this determination to a third system.