Abstract: Systems and methods are provided for implementing an adaptive machine learning platform for security penetration and risk assessment. For example, the system can receive publicly-available information associated with a client computer system, process the information to identify an input feature, and implement a machine learning model to identify the corresponding risk associated with the input feature. The system can recommend a penetration test for discovered weaknesses associated with the input feature and help make changes to the client computer system to improve security and reduce risk overall.

Abstract: Systems and methods are provided for detecting input via a user interface on a computing device, determining that the input triggers a recommended action related to the input and analyzing historical data to extract relevant data for the recommended action. The systems and methods further provide for generating the recommended action based on the extracted relevant data and causing display of the recommended action on the user interface of the computing device.

Abstract: Embodiments of the present disclosure relate generally to microfluidic devices and methods of making microfluidic devices. An exemplary method of making a microfluidic device comprises: providing a substrate; depositing, onto the substrate, a hydrophobic material; and etching, into the substrate, at least one hydrophilic channel into the hydrophobic substrate.

Abstract: Systems and methods are provided for implementing an adaptive machine learning platform for security penetration and risk assessment. For example, the system can receive publicly-available information associated with a client computer system, process the information to identify an input feature, and implement a machine learning model to identify the corresponding risk associated with the input feature. The system can recommend a penetration test for discovered weaknesses associated with the input feature and help make changes to the client computer system to improve security and reduce riskoverall.

Abstract: Systems and methods are provided for implementing an adaptive machine learning platform for security penetration and risk assessment. For example, the system can receive publicly-available information associated with a client computer system, process the information to identify an input feature, and implement a machine learning model to identify the corresponding risk associated with the input feature. The system can recommend a penetration test for discovered weaknesses associated with the input feature and help make changes to the client computer system to improve security and reduce risk overall.

Abstract: Systems and methods are provided for implementing an adaptive machine learning platform for security penetration and risk assessment. For example, the system can receive publicly-available information associated with a client computer system, process the information to identify an input feature, and implement a machine learning model to identify the corresponding risk associated with the input feature . The system can recommend a penetration test for discovered weaknesses associated with the input feature and help make changes to the client computer system to improve security and reduce risk overall.

Abstract: Embodiments of the present disclosure relate generally to microfluidic devices and methods of making microfluidic devices. An exemplary method of making a microfluidic device comprises: providing a substrate; depositing, onto the substrate, a hydrophobic material; and etching, into the substrate, at least one hydrophilic channel into the hydrophobic substrate.

Abstract: A system for building a targeted audience for a present online advertising campaign is disclosed. The system comprises a database for storing data related to each of the plurality of products with each product associated with a previous online advertising campaign and a processor in communication with the database and configured to execute computer-readable instructions causing the processor to utilize the data to identify at least one previous online advertising campaign as being similar to the present online advertising campaign, learn from the identified previous online advertising campaign(s) to predict a probability of conversion of each of a plurality of customers when exposed to an impression of the present online advertising campaign, and build the targeted audience for the present online advertising campaign based on the predicted probability of conversion. A method for building a targeted audience for a present online advertising campaign is also disclosed.

Abstract: Example systems and methods for optimization and attribution of marketing resources are described. In one implementation, a method retrieves transaction data associated with one or more offline stores and user data of users that are exposed to a marketing campaign. The method also correlates the transaction data to the user data to measure marketing attribution and modify the marketing campaign.

Abstract: A system for building a targeted audience for a present online advertising campaign is disclosed. The system comprises a database for storing data related to each of the plurality of products with each product associated with a previous online advertising campaign and a processor in communication with the database and configured to execute computer-readable instructions causing the processor to utilize the data to identify at least one previous online advertising campaign as being similar to the present online advertising campaign, learn from the identified previous online advertising campaign(s) to predict a probability of conversion of each of a plurality of customers when exposed to an impression of the present online advertising campaign, and build the targeted audience for the present online advertising campaign based on the predicted probability of conversion. A method for building a targeted audience for a present online advertising campaign is also disclosed.

Abstract: An approach is provided that receives a change request from a requestor. The change request includes metadata regarding the change, one or more changes, and one or more change assumptions corresponding to at least one of the changes. The change request is stored in a data store of pending requests. One or more systems are identified that correspond to each of the change assumptions. The identified systems are automatically queried with queries that correspond to the change assumptions. Query responses in response to the querying are received from the identified systems. The validity of each of the change assumptions is determined based on the received query responses. If the change assumptions are valid, then the changes included in the change request are processed. On the other hand, if at least one of the change assumptions is invalid, then the change request is rejected.

Abstract: Example systems and methods for optimization and attribution of marketing resources are described. In one implementation, a method retrieves transaction data associated with one or more offline stores and user data of users that are exposed to a marketing campaign. The method also correlates the transaction data to the user data to measure marketing attribution and modify the marketing campaign.

Abstract: An approach is provided that receives a change request from a requestor. The change request includes metadata regarding the change, one or more changes, and one or more change assumptions corresponding to at least one of the changes. The change request is stored in a data store of pending requests. One or more systems are identified that correspond to each of the change assumptions. The identified systems are automatically queried with queries that correspond to the change assumptions. Query responses in response to the querying are received from the identified systems. The validity of each of the change assumptions is determined based on the received query responses. If the change assumptions are valid, then the changes included in the change request are processed. On the other hand, if at least one of the change assumptions is invalid, then the change request is rejected.

Abstract: An approach is provided that receives a change request from a requestor. The change request includes metadata regarding the change, one or more changes, and one or more change assumptions corresponding to at least one of the changes. The change request is stored in a data store of pending requests. One or more systems are identified that correspond to each of the change assumptions. The identified systems are automatically queried with queries that correspond to the change assumptions. Query responses in response to the querying are received from the identified systems. The validity of each of the change assumptions is determined based on the received query responses. If the change assumptions are valid, then the changes included in the change request are processed. On the other hand, if at least one of the change assumptions is invalid, then the change request is rejected.