Systems and methods are provided for the classification of identified security vulnerabilities in software applications, and their automated triage based on machine learning. The disclosed system may generate a report listing detected potential vulnerability issues, and extract features from the report for each potential vulnerability issue. The system may receive policy data and business rules, and compare the extracted features relative to such data and rules. The system may determine a token based on the source code of a potential vulnerability issue, and a vector based on the extracted features of a potential vulnerability issue and based on the token. The system may select a machine learning modelling method and/or an automated triaging method based on the vector, and determine a vulnerability accuracy score based on the vector using the selected method.

Systems and methods are described for leveraging machine learning to deliver time-sensitive media content offers to a consumer over an optimal device and channel. In an implementation, a server system transmits media content offers over an optimal device and channel to a user. The server system captures metadata of at least one of: the user's use of multiple devices, the user's media content preferences, the user's preferred transmission channels for receiving media content offers over the multiple devices, and the user's preferred times for receiving media content offers. The server system prepares a media content offer for transmission to the user. Based on the captured metadata, the server system selects a user device of the multiple user devices and a transmission channel of multiple transmission channels to transmit the media content offer; and transmits the media content offer to the selected user device over the selected transmission channel.

A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

A method for automatically training a machine learning system to detect and identify a sensor triggering event associated with an internet of things (IoT) device is provided. The method may include capturing sensor data and capturing sound clips associated with the IoT device. The method may further include identifying the sensor triggering event associated with the IoT device. The method may further include sending an alert of the identified sensor triggering event. The method may also include correlating the captured sensor data, the captured sound clips, and the identified sensor triggering event. The method may further include identifying a second sensor triggering event by determining similarities between the correlated data associated with the identified sensor triggering event and additional sensor and sound data that is captured based on the second sensor triggering event.

Content delivery optimization and recommendation is disclosed. A manner of delivering a content object to a mobile device may be determined at least in part by applying a behavior model associated with a user of the mobile device to attributes associated with the content object. The behavior model may be generated based at least in part on observed activities of the user. The content object is provided to the mobile device in the determined manner.

A cybersecurity incident is registered at a security incident response platform. At a playbook generation system, details are received of the cybersecurity incident from the security incident response platform. At least some of the details correspond to a set of features of the cybersecurity incident. A set or subset of nearest neighbors of the cybersecurity incident is localized in a feature space. The nearest neighbors of the cybersecurity incident are other cybersecurity incidents having a distance from the cybersecurity incident within the feature space that is defined by differences in features of the nearest neighbors with respect to the set of features of the cybersecurity incident. A playbook is created for responding to the cybersecurity incident having prescriptive procedures based on occurrences of prescriptive procedures previously employed in response to the nearest neighbor cybersecurity incidents. The differences in features of the nearest neighbors with respect to the set of features of the cybersecurity incident are calculated, for at least one feature, using a present-or-equal metric, and for at least one other feature, using a symmetric difference metric. The playbook generation system is also a parent recommendation system, which identifies a parent for the cybersecurity incident, based on distances of the nearest neighbors of the cybersecurity incident in the feature space. The parent recommendation system adjusts, based on the recommended parent or the parent other than the recommended parent being selected, weights of features upon which distances in the feature space are based.

A cybersecurity incident is registered at a security incident response platform. At a playbook generation system, details are received of the cybersecurity incident from the security incident response platform. At least some of the details correspond to a set of features of the cybersecurity incident. A set or subset of nearest neighbors of the cybersecurity incident is localized in a feature space. The nearest neighbors of the cybersecurity incident are other cybersecurity incidents having a distance from the cybersecurity incident within the feature space that is defined by differences in features of the nearest neighbors with respect to the set of features of the cybersecurity incident. A playbook is created for responding to the cybersecurity incident having prescriptive procedures based on occurrences of prescriptive procedures previously employed in response to the nearest neighbor cybersecurity incidents. The differences in features of the nearest neighbors with respect to the set of features of the cybersecurity incident are calculated, for at least one feature, using a present-or-equal metric, and for at least one other feature, using a symmetric difference metric. The playbook generation system is also a parent recommendation system, which identifies a parent for the cybersecurity incident, based on distances of the nearest neighbors of the cybersecurity incident in the feature space. The parent recommendation system adjusts, based on the recommended parent or the parent other than the recommended parent being selected, weights of features upon which distances in the feature space are based.

A network of interconnected digital mirrors within a mall. A plurality of digital mirrors are installed in a participating stores, each comprising a digital screen, a video camera positioned to generate video stream of a user in front of the digital mirror, and a microprocessor. A centralized database stores merchandize data of participating stores. A controller is coupled to the mirrors and the centralized database and preprogrammed to perform the operations comprising: receiving the video stream from the video camera of one of the plurality of digital mirrors; identifying a merchandize item within the video stream; activating a recommendation engine to identify a complementary item from the centralized database that is complementary to the identified merchandize item; sending data regarding the complementary item and a store offering the complementary item to the digital mirror for display.

In some implementations, a system may receive inventory data associated with a data storage system. The inventory data identifies file paths for objects stored in the data storage system. The system may detect patterns in prefixes of the file paths using one or more trained machine learning models. The system may normalize the prefixes of the file paths based on the patterns detected in the prefixes. The system may detect datasets of the objects stored in the data storage system based on the normalized prefixes. The system may compare prefixes associated with the detected datasets with prefixes associated with a set of registered datasets that are registered with a metadata repository. The system may determine, based on comparing the prefixes associated with the detected datasets and the prefixes associated with the set of registered datasets, a respective registration classification for each detected dataset.

The disclosure provides a calculation practicing method, a system, an electronic device and a computer readable storage medium, the calculation practicing method includes: providing a calculation question; identifying the type and content of the calculation question; generating an answer area according to the type and content of the calculation question; receiving an answering operation in which the user inputs the answer string for the calculation question in the answer area; identifying the answer string inputted by the user; and determining whether each of the answer characters in the answer string is correct, if there is an incorrect answer character, it will be marked, so that the calculation practice can be realized through the electronic device, which is convenient for students to carry out training.