Abstract: Methods performed by a physical computing system include automatically identifying, using at least one trained classifier, an action for responding to an anomaly in the execution of the application in a cloud computing system. The at least one trained classifier relates a metrics set to a result of performing an action for addressing an anomaly. Systems and computer readable media are also described herein.

Abstract: There is provided a method for determining an FL value to be used for optimizing hardware applicable to mobile devices, compact networks, and the like with high precision. The method includes steps of: a computing device (a) applying quantization operations to original values included in an original vector by referring to a BW value and each of FL candidate values, to thereby generate each of quantized vectors, including the quantized values, corresponding to each of the FL candidate values; (b) generating each of weighted quantization loss values, corresponding to each of the FL candidate values, by applying weighted quantization loss operations to information on each of differences between the original values and the quantized values included in each of the quantized vectors; and (c) determining the FL value among the FL candidate values by referring to the weighted quantization loss values and a device using the same.

Abstract: A mechanism is provided in a data processing system for question answering using time weighted evidence. The mechanism receives an input question. The mechanism determines a time focus for the input question and defines a weighting function. The weighting function is a bell curve having a peak at the time focus on a time axis. The mechanism decomposes the input question into one or more queries and applies the one or more queries to a corpus of information to obtain a set of hypothesis evidence. Each item of information within the hypothesis evidence has an associated time value. The mechanism weights the set of hypothesis evidence based on the associated time values according to the weighting function to form time weighted evidence and generates hypotheses for answering the input question based on the time weighted evidence.

Abstract: Each user is represented by a mixture of topics, e.g., one or more topics, and a probability of interest in each topic in the mixture, and given the target user, one or more other users can be recommended, each user that is recommended to the target user is determined to have a topical interest similarity with the target user, e.g., the target user's interest in one or more topics of the mixtures of topics is determined to be similar to a recommended interest in the one or more topics of the mixture of topics. The target user and the one or more recommended users can be said to have similar topical interests. The target user can use the user recommendation to establish an interactive dialogue, for example, with one or more users identified in the user recommendation.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for configuring an artificial neural network to a particular surveillance situation. A number of object classes characteristic for the surveillance situation are selected. The object classes form a subset of the total number of object classes for which the artificial neural network is trained. A database is accessed that includes activation frequency values for the neurons within the artificial neural network. The activation frequency values are a function of the object class. Those neurons having activation frequency values lower than a threshold value for the subset of selected object classes are removed from the artificial neural network.

Abstract: A method and system to identify a time lagged indicator of an event to be predicted are described. The method includes receiving information including an indication of a factor, the factor being a different event than the event to be predicted, and identifying a window period within which the event is statistically correlated with the factor. The method also includes collecting data for a duration of the window period, the data indicating occurrences of the factor and the event, and identifying a time lagged dependency of the event on the factor based on analyzing the data.

Abstract: In one embodiment, techniques are shown and described relating to traffic-based inference of influence domains in a network by using learning machines. In particular, in one embodiment, a management device computes a time-based traffic matrix indicating traffic between pairs of transmitter and receiver nodes in a computer network, and also determines a time-based quality parameter for a particular node in the computer network. By correlating the time-based traffic matrix and time-based quality parameter for the particular node, the device may then determine an influence of particular traffic of the traffic matrix on the particular node.

Abstract: Described is a system for online characterization of biomechanical and cognitive factors relevant to physical rehabilitation and training efforts. A biosensing subsystem senses biomechanical states of a user based on the output of sensors and generates a set of biomechanical data. The set of biomechanical data is transmitted in real-time to an analytics subsystem. The set of biomechanical data is analyzed by the analytics subsystem, and control guidance is sent through a real-time control interface to adjust the user's motions. In one aspect control guidance is sent to a robotic exoskeleton worn by the user to adjust the user's motions.

Abstract: A network of apparatuses that characterizes items is presented. A self-updating apparatus includes a processing unit that has a memory storing parameters that are useful for characterizing different items, and a processing module configured to automatically select sources from which to receive data, modify the parameters based on the data that is received, and to select recipients of modified parameters. Selection of sources and recipients is based on comparison of parameters between the processing module and the sources, and between the processing module and the recipients, respectively. The processing unit may include an artificial intelligence program (e.g., a neural network such as a machine learning program). When used in a network, the processing units may “train” other processing units in the network such that the characterization accuracy and range of each processing unit improves over time.

Abstract: A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a travel-related cognitive persona within the cognitive graph, the travel-related cognitive persona corresponding to an archetype user model, the travel-related cognitive persona comprising a set of nodes in the cognitive graph; associating a user with the travel-related cognitive persona; defining a travel-related cognitive profile within the cognitive graph, the travel-related cognitive profile comprising an instance of the travel-related cognitive persona that references personal data associated with the user; associating the user with the travel-related cognitive profile; and, performing a cognitive computing operation based upon the tra

Abstract: The method includes identifying a log stream from a first device, wherein the first device is of an unknown device type. The method further includes identifying features in the log stream from the first device. The method further includes determining a probability that the log stream from the first device was created by a specific device type based on matching of features of the identified log stream from the first device and features in log streams from a plurality of known device types.

Abstract: The method includes identifying a log stream from a first device, wherein the first device is of an unknown device type. The method further includes identifying features in the log stream from the first device. The method further includes determining a probability that the log stream from the first device was created by a specific device type based on matching of features of the identified log stream from the first device and features in log streams from a plurality of known device types.

Abstract: Software that compares vector representations of question terms and passage terms in question answering systems by performing the following steps: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text; (iii) generating a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and (iv) determining whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

Abstract: A method, system, and computer program product for selecting forecasting model complexity using eigenvalues are provided in the illustrative embodiments A process is represented in a model. The model comprises a mathematical representation of the process in a certain degree. A first portion of historical data generated by the process during a first period is selected and includes an actual value of an outcome of the process and a value of a feature influencing the process during the first period. A prediction is made of a predicted value of the outcome. A difference between the prediction and the actual value of the outcome is determined. The difference is represented as a change in a distribution of eigenvalues. According to the change, a second model is to represent the process. The second model comprises a second mathematical representation of the process in a different degree.

Abstract: Software that compares vector representations of question terms and passage terms in question answering systems by performing the following steps: (i) receiving a question; (ii) generating a plurality of vectors including a first vector representation of a term in the question and a second vector representation of a term in a set of natural language text; (iii) generating a similarity score representing an amount of similarity between the first vector representation and the second vector representation; and (iv) determining whether the set of natural language text is relevant to the question based, at least in part, on the generated similarity score.

Abstract: A computer-implemented method, computer program product, and computing system is provided for providing a framework for logically representing the discretization of logic for a backtracking algorithm. In an implementation, a method may include defining a validation class representing a validation logic to be tested. A processable class may be defined representing a backtracking logic flow to be implemented. The processable class may be associated with the validation class. One or more candidate options may be evaluated based upon, at least in part, the validation logic and the backtracking logic flow.

Abstract: A method, system, and/or computer program product manages the lifecycle of trained models used to deliver cognitive services. One or more processors obtain and deploy a cognitive engine that utilizes artificial intelligence (AI), machine learning, and/or similar algorithms. One or more processors obtain and deploy a version of a trained model that includes data that supports cognitive operations of the cognitive engine within a cognitive service. In response to changes to the input used to produce the trained model, one or more processors obtain and deploy a subsequent version of the trained model in support of the cognitive service.

Abstract: A method, system and computer program product for generating inference graphs over content to answer input inquiries. First, independent factors are produced from the inquiry, and these factors are converted to questions. The questions are then input to a probabilistic question answering system (PQA) that discovers relations which are used to iteratively expand an inference graph starting from the factors and ending with possible answers. A probabilistic reasoning system is used to infer the confidence in each answer by, for example, propagating confidences across relations and nodes in the inference graph as it is expanded. The inference graph generator system can be used to simultaneously bi-directionally generate forward and backward inference graphs that uses a depth controller component to limit the generation of both paths if they do not meet. Otherwise, a joiner process forces the discovery of relations that may join the answers to factors in the inquiry.

Abstract: System and techniques for multifactor intelligent agent control are described herein. A history of intelligent agent activity may be received. The history may include a record of intelligent agent activities and corresponding factors used as intelligent agent input to generate the activities. Detail-level-feedback input from a user may also be received. The detail-level-feedback input is the result of a user manipulating a detail-level-feedback user interface that is generic to any factor. An entry-time corresponding to the user manipulation of the detail-level-feedback user interface may be recorded. A set of factors may be identified from the corresponding factors based on a combination of the history of intelligent agent activity and the entry-time. The intelligent agent may be modified by changing an internally represented influence of members of the set of factors on the intelligent agent. The internally represented influence may be changed in accordance with the detail-level-feedback input.

Abstract: A process for recommending choices to a user uses a first model representing the user's subconscious and a second model representing the user's consciousness. The process comprises receiving data and performing, using the second model, a simulation by modeling the user and a third party to derive a simulation result that determines how the user would react to a situation based at least in part on the received data. Using the first model and the simulation, a first set of recommendation choices based at least in part on the received data is derived, and a set of recommendation choices for presentation based on the first set of recommendation choices is determined and presented to the user. As the user is making a selection, the user is monitored for conscious decisions and subconscious reactions, which are used to update the first model and the second model.