Abstract: The disclosed technology relates to intent driven network management. A system is configured to maintain an inventory store comprising records for a set of network entities in a network, wherein each network entity in the set of network entities is associated with a record in the inventory store. The system receives a user intent statement comprising an action and a flow filter representing network data flows on which the action is to be applied and queries, based on the flow filter, the inventory store to identify a plurality of network entities in the set of network entities to which the user intent statement applies. The system generates a plurality of network policies that implement the user intent statement based on the plurality of network entities and the action and enforces the plurality network policies.

Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

Abstract: The embodiments herein relate to operational data analysis (ODA) and, more particularly to automate operational data analysis and generate the analysis report for various products using a web-based multi-tenant product intelligence framework. The system allows the user to configure a data collection process, define schema structure, select a data storage for storing the collected data, select or create a data formatting algorithm, and generate a data report to perform the ODA process. Based on the ODA report, appropriate decisions can be taken by an organization.

Abstract: A system and method for recommending demand-supply agent pairs for transactions uses a deep neural network on data of demand agents to produce a demand agent vector, which is used to select supply agents based on their likelihood of future transaction and to find k nearest neighbor demand agents for each of the demand agents. The candidate supply agents and the k nearest neighbor demand agents are then combined to produce candidate demand-supply agent pairs, which are used to find recommended demand-supply agent pairs by applying modeling using machine learning.

Abstract: Techniques are described for processing anomalies detected using user-specified rules with anomalies detected using machine-learning based behavioral analysis models to identify threat indicators and security threats to a computer network. In an embodiment, anomalies are detected based on processing event data at a network security system that used rules-based anomaly detection. These rules-based detected anomalies are acquired by a network security system that uses machine-learning based anomaly detection. The rules-based detected anomalies are processed along with machine learning detected anomalies to detect threat indicators or security threats to the computer network. The threat indicators and security threats are output as alerts to the network security system that used rules-based anomaly detection.

Abstract: A three-dimensional printing system may include a three-dimensional printer. The three-dimensional printer may include a station to receive and rotate a build material container having an axial port, an auger and at least one at least one actuator to move at least one of the build material container and the auger relative to one another to position the auger through the axial port into the build material container.

Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

Abstract: A security platform employs a variety techniques and mechanisms to detect security related anomalies and threats in a computer network environment. The security platform is “big data” driven and employs machine learning to perform security analytics. The security platform performs user/entity behavioral analytics (UEBA) to detect the security related anomalies and threats, regardless of whether such anomalies/threats were previously known. The security platform can include both real-time and batch paths/modes for detecting anomalies and threats. By visually presenting analytical results scored with risk ratings and supporting evidence, the security platform enables network security administrators to respond to a detected anomaly or threat, and to take action promptly.

Abstract: A method includes determining information indicative of at least one facial characteristic associated with at least one face in the source image, processing the source image using a filter based on the determined information, performing wavelet decomposition on each of the filtered image and the source image, determining weightage factors associated with the wavelet decomposition of each of the filtered image and the source image, based on the determined information, obtaining a wavelet image to generate a texture restored image from the wavelet decomposition of each of the filtered image and the source image based on the weightage factors.

Abstract: Techniques are described for processing anomalies detected using user-specified rules with anomalies detected using machine-learning based behavioral analysis models to identify threat indicators and security threats to a computer network. In an embodiment, anomalies are detected based on processing event data at a network security system that used rules-based anomaly detection. These rules-based detected anomalies are acquired by a network security system that uses machine-learning based anomaly detection. The rules-based detected anomalies are processed along with machine learning detected anomalies to detect threat indicators or security threats to the computer network. The threat indicators and security threats are output as alerts to the network security system that used rules-based anomaly detection.

Abstract: The disclosed technology relates to intent driven network management. A system is configured to maintain an inventory store comprising records for a set of network entities in a network, wherein each network entity in the set of network entities is associated with a record in the inventory store. The system receives a user intent statement comprising an action and a flow filter representing network data flows on which the action is to be applied and queries, based on the flow filter, the inventory store to identify a plurality of network entities in the set of network entities to which the user intent statement applies. The system generates a plurality of network policies that implement the user intent statement based on the plurality of network entities and the action and enforces the plurality network policies.

Abstract: The embodiments herein relate to operational data analysis (ODA) and, more particularly to automate operational data analysis and generate the analysis report for various products using a web-based multi-tenant product intelligence framework. The system allows the user to configure a data collection process, define schema structure, select a data storage for storing the collected data, select or create a data formatting algorithm, and generate a data report to perform the ODA process. Based on the ODA report, appropriate decisions can be taken by an organization.

Abstract: Techniques are described for processing anomalies detected using user-specified rules with anomalies detected using machine-learning based behavioral analysis models to identify threat indicators and security threats to a computer network. In an embodiment, anomalies are detected based on processing event data at a network security system that used rules-based anomaly detection. These rules-based detected anomalies are acquired by a network security system that uses machine-learning based anomaly detection. The rules-based detected anomalies are processed along with machine learning detected anomalies to detect threat indicators or security threats to the computer network. The threat indicators and security threats are output as alerts to the network security system that used rules-based anomaly detection.

Abstract: An image display method is provided. The image display method includes displaying a camera preview screen that provides an image of a subject in real time, capturing one or more images by capturing the camera preview screen, and displaying a photo reel on the camera preview screen, the photo reel being generated by arranging thumbnails of the captured one or more images in a row.

Abstract: The disclosed technology relates to a network agent for generating platform specific network policies. A network agent is configured to receive a platform independent network policy from a network policy system, determine implementation characteristics of the network entity, generate platform specific policies from the platform independent network policy based on the implementation characteristics of the network entity, and implement the platform specific policies on the network entity.

Abstract: A method for switching between a first lens and a second lens in an electronic device includes displaying, by the electronic device, a first frame showing a field of view (FOV) of the first lens; detecting, by the electronic device, an event that causes the electronic device to transition from displaying the first frame to displaying a second frame showing a FOV of the second lens; generating, by the electronic device and based on the detecting the event, at least one intermediate frame for transitioning from the first frame to the second frame; and switching, by the electronic device and based on the detecting the event, from the first lens to the second lens and displaying the second frame, wherein the at least one intermediate frame is displayed after the displaying the first frame and before the displaying the second frame while the switching is performed.

Abstract: The disclosed technology relates to intent driven network management. A system is configured to maintain an inventory store comprising records for a set of network entities in a network, wherein each network entity in the set of network entities is associated with a record in the inventory store. The system receives a user intent statement comprising an action and a flow filter representing network data flows on which the action is to be applied and queries, based on the flow filter, the inventory store to identify a plurality of network entities in the set of network entities to which the user intent statement applies. The system generates a plurality of network policies that implement the user intent statement based on the plurality of network entities and the action and enforces the plurality network policies.

Abstract: A method of transmitting a data flow over a link aggregation group (LAG), including: comparing the bandwidth demand of the data flow with the capacity of a first link assigned to the flow in the LAG; identifying an underutilized second link in the LAG when the bandwidth demand of the flow is greater than the capacity of the link assigned to the flow resulting in excess packets; determining if the second link is available; determining the traffic type of the traffic flow; calculating a time delay and delaying excess packets of the traffic flow by the calculated time delay when the determined traffic type does not include a packet sequence number; and sending excess packets of the traffic flow on the second link.

Abstract: The disclosed technology relates to a network agent for generating platform specific network policies. A network agent is configured to receive a platform independent network policy from a network policy system, determine implementation characteristics of the network entity, generate platform specific policies from the platform independent network policy based on the implementation characteristics of the network entity, and implement the platform specific policies on the network entity.

Abstract: A multilayer stack is described. The multilayer stack includes: (i) one or more inorganic barrier layers for reducing transport of gas or vapor molecules therethrough; (ii) an inorganic reactive layer disposed adjacent to one or more of the inorganic barrier layers, and the reactive layer capable of reacting with the gas or the vapor molecules; and (iii) wherein, in an operational state of the multilayer stack, the vapor or the gas molecules that diffuse through one or more of the inorganic barrier layers react with the inorganic reactive layer, and thereby allow said multilayer stack to be substantially impervious to the gas or the vapor molecules.