Abstract: In one embodiment, a method includes accessing a first set of objects associated with an online social network, each object being associated with one or more comments. The method also includes generating a second set of objects from the first set of objects by applying a first filtering criteria to the first set of objects and scoring each object in the second set of objects based on the comments associated with each object. The method further includes generating a training set of objects from the second set of objects by selecting each object from the second set of objects having a score greater than a first threshold score, each object in the training set being associated with a first object-classification. The method further includes determining an object-classifier algorithm for the first object-classification, the object-classifier algorithm being determined through an iterative training process performed one or more times.

Abstract: In an approach to create a rule hierarchy model, a computer receives from a user a set of rules and an association between each rule in the set of rules and a concept of the plurality of concepts in a concept hierarchy. The computer receives set of rules is stored separately from the concept hierarchy. The computer determines a rule hierarchy where a rule of the set of rules is associated with one or more concepts. Furthermore, the computer creates a rule hierarchy model based on the concept hierarchy and the rule hierarchy wherein grouping related rules in a common structure provides efficient management that facilitates rule authoring, browsing, and extraction.

Abstract: Technologies are generally described for methods and systems in a machine learning environment. In some examples, a method may include retrieving training data from a memory. The training data may include training inputs and training labels. The methods may further include determining a set of datasets based on the training inputs. The methods may further include determining a set of out of sample errors based on the training inputs and based on test data. Each out of sample error may correspond to a respective dataset in the set of datasets. The methods may further include generating alternative distribution data based on the set of out of sample errors. The alternative distribution data may be used to determine weights to be applied to the training data.

Abstract: The method includes identifying, by one or more computer processors, a grouping of elements in a storage system. The method further includes identifying, by one or more computer processors, a first element from the identified grouping of elements. The method further includes identifying, by one or more computer processors, a root unit of the first element from the identified grouping of elements. The method further includes determining, by one or more computer processors, a past usage history of computer resources for the identified root unit of the first element. The method further includes calculating, by one or more computer processors, a future usage of computer resources for the identified root unit based upon the determined past usage history.

Abstract: A technology is described for determining a launch workflow for launching multiple computing instances on a physical host in a computing service environment using a predicted launch time. An example method may include receiving a launch plan to launch a plurality of computing instances on a physical host within a computing service environment. A first launch workflow and a second launch workflow may then be determined for the launch plan where the first launch workflow and the second launch workflow may specify different sequences of operations performed to launch the computing instance on the physical host. Predicted launch times for the first launch workflow and the second launch workflow may be obtained and the predicted launch times may be compared to determine a launch workflow used in executing the launch plan.

Abstract: A system is provided for event-based monitoring of a subject's well-being within an unattended setting. A plurality of sensors are disposed within the setting for sensing disparate events, and an analytics processing portion is coupled to the sensors to collectively acquire sensing data therefrom, and map a plurality of sensed data points for a selected combination of disparate events to a conduct adaptively characterized for the subject. The mapping occurs according to a set of pre-established reference event patterns, relative to which each characterized conduct is screened for excessive aberration. The analytics processing portion actuates generation of a graphic user interface displaying at least one reporting page. The reporting page contains for each characterized conduct certain graphic indicia determined responsive to the screening thereof.

Abstract: A content engagement system includes game logic configured to help users and their social contacts to find interesting content. The outputs can be used for optimizing ads, content, search results, etc., on mobile devices, social networking sites and similar domains.

Abstract: Techniques herein train a multilayer perceptron, sparsify edges of a graph such as the perceptron, and store edges and vertices of the graph. Each edge has weight. A computer sparsifies perceptron edges. The computer performs a forward-backward pass on the perceptron to calculate a sparse Hessian matrix. Based on that Hessian, the computer performs quasi-Newton perceptron optimization. The computer repeats this until convergence. The computer stores edges in an array and vertices in another array. Each edge has weight and input and output indices. Each vertex has input and output indices. The computer inserts each edge into an input linked list based on its weight. Each link of the input linked list has the next input index of an edge. The computer inserts each edge into an output linked list based on its weight. Each link of the output linked list comprises the next output index of an edge.

Abstract: Techniques and systems are provided for predictive modeling based on interactions with a network device. For example, a method may include generating a prediction including a correlation between an interaction with a network device and a context, wherein the interaction is associated with a function performed by the network device. Confidence parameters associated with the prediction can be determined. The prediction can be tested by analyzing received interaction data and contextual data, and the analysis can include determining whether the interaction with the network device occurred in the correlated context. A confidence value can be calculated based on the testing outcome, and can be compared to the confidence parameters. A message relating to modification of the confidence parameters can be transmitted.

Abstract: An automatic scaling system and method for reducing state space in reinforced learning for automatic scaling of a multi-tier application uses a state decision tree that is updated with new states of the multi-tier application. When a new state of the multi-tier application is received, the new state is placed in an existing node of the state decision tree only if a first attribute of the new state is same as a first attribute of any state contained in the existing node and a second attribute of the new state is sufficiently similar to a second attribute of each existing state contained in the existing node based on a similarity measurement of the second attribute of each state contained in the existing node with the second attribute of the new state.

Abstract: A system includes a centralized repository for tracking rule content and managing subscriptions to rule content by organizations and providers utilizing the system; a rule-evaluation server for receiving requests for rule-evaluations for specific patients, wherein the server determines content needing to be evaluated and retrieves the content to be used; a rule engine for performing the evaluations, wherein content, patient data, and rule evaluation parameters are provided to the engine, and the engine returns recommendations triggered by the evaluation, if any; an aggregator for aggregating recommendations from multiple sources, detecting and coordinating related recommendations, and applying configuration settings based on the patient and/or provider in context; and a client component for coordinating communication between an electronic health records system, the server, and the aggregator.

Abstract: A general framework for cross-validation of any supervised learning algorithm on a distributed database comprises a multi-layer software architecture that implements training, prediction and metric functions in a C++ layer and iterates processing of different subsets of a data set with a plurality of different models in a Python layer. The best model is determined to be the one with the smallest average prediction error across all database segments.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating reinforcement learning policies. One of the methods includes receiving a plurality of training histories for a reinforcement learning agent; determining a total reward for each training observation in the training histories; partitioning the training observations into a plurality of partitions; determining, for each partition and from the partitioned training observations, a probability that the reinforcement learning agent will receive the total reward for the partition if the reinforcement learning agent performs the action for the partition in response to receiving the current observation; determining, from the probabilities and for each total reward, a respective estimated value of performing each action in response to receiving the current observation; and selecting an action from the pre-determined set of actions from the estimated values in accordance with an action selection policy.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for content recommendation using neural networks. One of the methods includes receiving context information for an action recommendation; processing the context information using a neural network that comprises one or more Bayesian neural network layers to generate, for each of the actions, one or more parameters of a distribution over possible action scores for the action and selecting an action from plurality of possible actions using the parameters of the distributions over the possible action scores for the action.

Abstract: A method comprising using at least one hardware processor for receiving a topic under consideration (TUC); providing the TUC as input to a claim function, wherein the claim function is configured to mine at least one content resource, and applying the claim function to the at least one content resource, to extract claims with respect to the TUC; and providing the TUC as input to a classification function, and applying the classification function to one or more claims of the extracted claims, to output corresponding one or more classification tags, wherein each classification tag is associated with its corresponding claim.

Abstract: Disclosed herein is a computer implemented method and system for analyzing load responsive behavior of infrastructure components in an electronic environment for proactive management of the infrastructure components. Transaction data on multiple application transactions is collected. Load patterns are identified from the collected transaction data for generating load profiles. Data on infrastructure behavior in response to the application transactions is collected. Infrastructure behavior patterns are identified from the infrastructure behavior data for generating behavior profiles. The generated load profiles and the generated behavior profiles are correlated to create a load responsive behavior model. The created load responsive behavior model predicts behavior of the infrastructure components for different load patterns. A live data stream from current application transactions is analyzed using the load responsive behavior model to determine current load responsive behavior.

Abstract: A system, method, and apparatus are provided for automatically establishing an inferred (‘follow’) relationship between a first member and a second member of a user community. Based on passive and/or active signals indicating affinity of the first member for the second member, the system determines whether an inferred connection from the first member to the second member would improve the first member's network within the user community. A potential improvement may be observed if the potential connection would improve some aspect or characteristic of a target or ideal network identified for the first member, and/or if there is more than a threshold level of affinity between the members. The first member's network may be pruned, if necessary, to accommodate a connection to the second member if, for example, the connection would violate a constraint associated with the target or ideal network.

Abstract: A mechanism is provided in a data processing system for predicting and enhancing ingestion time for a set of input documents. The mechanism receives a set of documents to be added to a corpus of the data processing system. The mechanism records document features of each document within the set of documents using an annotation engine within the data processing system. The mechanism predicts an ingestion time for each document within the set of documents based on the document characteristics and a machine learning model. The mechanism assigns the set of documents to data processing system resources to be processed based on the predicted ingestion time for each document.

Abstract: Systems and methods for probabilistic semantic sensing in a sensory network are disclosed. The system receives raw sensor data from a plurality of sensors and generates semantic data including sensed events. The system correlates the semantic data based on classifiers to generate aggregations of semantic data. Further, the system analyzes the aggregations of semantic data with a probabilistic engine to produce a corresponding plurality of derived events each of which includes a derived probability. The system generates a first derived event, including a first derived probability, that is generated based on a plurality of probabilities that respectively represent a confidence of an associated semantic datum to enable at least one application to perform a service based on the plurality of derived events.

Abstract: The invention proposes that a software agent for assisting a user in a virtual environment defined by a first concept map, maintains a record of its experiences in the form of a second concept map. The concept maps are compared to obtain a measure of stimulation. The measure of stimulation is used to derive a comparison value indicative an emotional state of the user, and the comparison value is used in a reward function to guide the behavior of the software agent.