Abstract: A computerized method is disclosed that includes operations of obtaining network traffic data between a source device and a destination device, performing a regularity assessment of a first metric of the network traffic data across communication sessions of the source device and the destination device over a given time period by: determining an average of the first metric for each of the communication sessions; establishing an upper bound and a lower bound for the averages of the first metric over the given time period; determining a difference between the upper bound and the lower bound; comparing the difference between the upper bound and the lower bound to a mean of the first metric for each of the communication sessions over the given time period, and determining whether beaconing transmissions are present within the network traffic data based on the regularity assessment of the first metric.

Abstract: The present disclosure relates a method for preventing agglomeration in a mixture of xylitol and calcium aspartate. The method includes the following steps: first adjusting and measuring water activities of xylitol particles and calcium aspartate particles; then mixing the calcium aspartate particles with a water activity in a range of 0.50-0.52 with the xylitol particles with a water activity in a range of 0.48-0.56 to obtain a mixture of xylitol and calcium aspartate; or mixing the calcium aspartate particles with a water activity in a range of 0.52-0.58 with the xylitol particles with a water activity in a range of 0.50-0.60 to obtain a mixture of xylitol and calcium aspartate; or adding maltitol particles in a weight ratio of 5%-20% to a total mass of the xylitol particles and the maltitol particles to the mixture of xylitol and calcium aspartate.

Abstract: Large amounts of data from user interactions with web resources is available as data logs. Analysis may be performed to process the data log in order to determine the characteristics of the user interactions. Data log analysis may include identifying page states, which may be sets of frequent attributes and values that occur together in a session. The data log analysis may also include generating semantic labels of page states, which may describe the function of pages corresponding to different page states. Text mining models may be used to determine the semantic labels. Analysis may also include aggregating sets of page paths to create page journeys. These page journeys may be aggregated over all users, all user sessions, or other subsets of the clickstream. In some embodiments, comparing page journeys may provide recommendations for potential methods to improve the site and enhance user experiences.

Abstract: Large amounts of data from user interactions with web resources is available as data logs. Analysis may be performed to process the data log in order to determine the characteristics of the user interactions. Data log analysis may include identifying page states, which may be sets of frequent attributes and values that occur together in a session. The data log analysis may also include generating semantic labels of page states, which may describe the function of pages corresponding to different page states. Text mining models may be used to determine the semantic labels. Analysis may also include aggregating sets of page paths to create page journeys. These page journeys may be aggregated over all users, all user sessions, or other subsets of the clickstream. In some embodiments, comparing page journeys may provide recommendations for potential methods to improve the site and enhance user experiences.

Abstract: In one aspect, a method includes determining an allocation of nodes in a cluster to application containers using a node capacity and a policy, publishing the allocation to a coordinator, the coordinator configured to manage the nodes comprising a first node, reading the allocation from the coordinator and instructing a container manager in the first node to generate an application container in the first node conforming to the allocation.

Abstract: In various embodiments, a reinforcement learning system is disclosed that identifies recommended associations for users. The reinforcement learning system may store a dataset including preference information that indicates associations between items and the users. A recommended association between a particular user and one or more items may be requested. The reinforcement learning system may select a predicted preference identification algorithm for the particular user based on preference information of the user, information about the items, a recommendation goal, or a combination thereof. The reinforcement learning system may determine a recommended association using the predicted preference identification algorithm and may send the recommended association to the particular user. In some cases, feedback may be used to select a different predicted preference identification algorithm for the user.

Abstract: In various embodiments, a reinforcement learning system is disclosed that identifies recommended associations for users. The reinforcement learning system may store a dataset including preference information that indicates associations between items and the users. A recommended association between a particular user and one or more items may be requested. The reinforcement learning system may select a predicted preference identification algorithm for the particular user and may use the predicted preference identification algorithm to generate virtual preference information for the particular user. The virtual preference information may be included in a graph that indicates similarity values between various users of the dataset and the particular user. The reinforcement learning system may determine a recommended association based on the graph and may send the recommended association to the particular user.

Abstract: Large amounts of data from user interactions with web resources is available as data logs. Analysis may be performed to process the data log in order to determine the characteristics of the user interactions. Data log analysis may include identifying page states, which may be sets of frequent attributes and values that occur together in a session. The data log analysis may also include generating semantic labels of page states, which may describe the function of pages corresponding to different page states. Text mining models may be used to determine the semantic labels. Analysis may also include aggregating sets of page paths to create page journeys. These page journeys may be aggregated over all users, all user sessions, or other subsets of the clickstream. In some embodiments, comparing page journeys may provide recommendations for potential methods to improve the site and enhance user experiences.

Abstract: Large amounts of data from user interactions with web resources is available as data logs. Analysis may be performed to process the data log in order to determine the characteristics of the user interactions. Data log analysis may include identifying page states, which may be sets of frequent attributes and values that occur together in a session. The data log analysis may also include generating semantic labels of page states, which may describe the function of pages corresponding to different page states. Text mining models may be used to determine the semantic labels. Analysis may also include aggregating sets of page paths to create page journeys. These page journeys may be aggregated over all users, all user sessions, or other subsets of the clickstream. In some embodiments, comparing page journeys may provide recommendations for potential methods to improve the site and enhance user experiences.

Abstract: Techniques are disclosed relating to building a graph data structure based on information associated with a plurality of services available over a network. The information may include datasets associated with a plurality of software services available to the set of users. The datasets may be analyzed, wherein the analyzing comprises determining, using one or more machine learning algorithms, a plurality of objects, including objects representing ones of the set of users and a plurality of computing activities. A graph data structure may be formed, comprising the plurality of objects, that indicates relationships between the plurality of objects. The graph data structure may be updated in response to detecting additional computing activities of one or more of the set of users. A plot of a subset of the plurality of objects in the graph data structure may be generated in response to a request. The plot may be caused to be displayed on a display.

Abstract: Techniques are disclosed relating to training a model based a graph data structure. A graph data structure comprising a plurality of objects may be accessed, wherein plurality of objects include objects that represent ones of a set of users and a plurality of computing activities of the set of users within a computing domain. A subset of the plurality of objects that are associated with one or more particular criteria may be identified. A model may be trained using data associated with the subset, wherein the model generates predictive assessments of respective objects within the subset with respect to the one or more particular criteria. A request may be received for a first predictive assessment of a first object in the graph data structure. The first predictive assessment of the first object may be generated using the model.