Abstract: Mechanisms are provided for validating a candidate answer to an input question. A candidate answer to an input question is identified using a natural language processing of the input question and a corpus of information from which the candidate answer is identified. A validator is selected to apply to the candidate answer based on a characteristic of a correct answer for the input question. The validator is applied to the candidate answer to evaluate whether or not criteria of the validator are met by the candidate answer. Validation information is generated based the evaluation of whether or not criteria of the validator are met by the candidate answer. The validation information is stored in a validation status object associated with the input question.

Abstract: Certain embodiments involve a model for predicting user behavior. For example, a system accesses user behavior data indicating various users' behaviors during intervals over various periods of time and target behavior data indicating a particular user behavior. The system associates each user with a label that indicates whether a user performed a particular action during or after a time period based on the target behavior data. The system uses the user behavior data to train various deep Restricted Boltzmann Machines (“RBM”) to generate representations of each user over each period of time that indicate the user behavior over the time period. The system generates a predictive model by connecting the RBMs into a deep recurrent neural network and uses the target behavior data associated with each user, along with the representations of each user, as input data to train the deep recurrent neural network to predict user behavior.

Abstract: In some examples, structured and unstructured data is evaluated using one or more predictive models to determine whether a dependent user is at risk for a certain condition. In other examples, structured and unstructured data is evaluated using one or more predictive models to determine a contact plan for contacting dependent users regarding follow-up appointments related to release of the dependent user.

Abstract: A method, system, and computer program product for social collaboration in probabilistic prediction are provided in the illustrative embodiments. A set of predictions is sent to a user device. A prediction in the set of predictions is a probability of an outcome of an event. The probability is computed using a prediction model trained with training data corresponding to the event. An input is received from the user device. The input comprises a new prediction made at the user device using a new prediction model executing on the user device. A difference is determined between the prediction and the new prediction. The prediction model is revised to produce a revised prediction. A revised difference between the revised prediction and the new prediction is smaller than the difference.

Abstract: An output time-series of a cell of a neural network is captured. A subset of a set of data points of the output time-series is consolidated into a singular data point. The singular data point is fitted in a data representation to form a quantified aggregated data point. The quantified aggregated data point is included in an intermediate time-series. Using the intermediate time-series as an input at an intermediate layer of the neural network, an anonymized output time-series is produced from the neural network.

Abstract: The present disclosure relates to a system, method, and computer program for predicting migraines using machine learning. More specifically, the system predicts the likelihood that a user will have a migraine within a certain period of time (e.g., a month) based on migraine causes/triggers X1 . . . n identified from a migraine pattern built using deep neural networks. The system collects data from the user with respect to his/her profile, his/her migraine incidents (having experienced a migraine or not) and his/her daily habits. Based on these data, it creates the user's migraine pattern and combines this pattern with the migraine patterns from similar users (when necessary). As a result, it is able to predict the probability of getting a migraine within a period of time.

Abstract: Embodiments of the invention build models to predict the likelihood of entities that operate in a given identifier space also operating in a disjoined identifier space based on a source panel of entities that operate in one or both of the identifier spaces. In operation, a model building engine builds a model based on features associated with the source panel and features associated with standard populations in the given identifier space. The model is used to determine whether the target entity is more similar to those entities in the source panel that operate only in the given identifier space or those entities in the source panel that operate in both identifier spaces.

Abstract: A commercial process with a dependent variable can be associated with a set of independent variables. The commercial process can continuously provide data collection opportunities. An intervention is designed using a model to predict the dependent outcome. The actual outcome of the intervention can be determined within the window of utility for these data. One objective is to improve intervention outcomes with prediction. Purely random outcomes (no model prediction) and outcomes resulting from the intervention (model operations) are aggregated into separate files—a sequence of control model data files and a sequence of model data files of operational data. These model data files and control model data files are used to analyze model performance and to react automatically when identified conditions warrant.

Abstract: In an approach, a computer determines based, at least in part, on a video of an attendee of a video conference, a first sentiment of the attendee wherein the first sentiment includes at least a sentiment from a sentiment analysis of one or more facial expressions of the attendee and a sentiment from a sentiment analysis of a plurality of the attendee's spoken words. The approach includes a computer receiving an indication of an attendee activity in at least a first application in computing devices accessed by the attendee and determining whether the first sentiment of the attendee is related to the video conference based, in part, on the attendee activity in at least the first application. Responsive to determining that the first sentiment of the attendee is not related to the video conference, the computer discards the first sentiment that is unrelated to the video conference.

Abstract: A method for adapting the operation of an apparatus connected to a network deployed in an environment, including the steps of: collecting data relating to the environment from a plurality of sources; identifying usual environmental situations from an analysis of the collected data; detecting a deviation from at least one identified usual situation; and sending a control to the apparatus for adapting the operation thereof to the detected deviation.

Abstract: Methods and arrangements for managing data collection relating to equipment in an asset network. A model is trained based on historical data relative to equipment in the asset network, wherein the model is employed for recommending at least one action for further data collection from the equipment. The model is adapted based on inputs comprising two or more of: historical sensor data, from one or more sensors obtaining data relative to the equipment; equipment maintenance and/or replacement data; a current system state; and a geographical position of one or more individuals who report data. The adapted model is employed to recommend at least one subsequent best action for collecting data relative to the equipment. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods and apparatus for extending a neural network, reducing its dimension and processing input data are provided. The method of extending a neural network involves selecting, with a processor, a node of a neural network, adding a new node in a layer that includes the selected node, and setting connection weights of the new node based on connection weights of the selected node.

Abstract: A mechanism is provided in a data processing system for article summarization. The mechanism analyzes an article to identify entities and relationships within the article. The article is an item of unstructured content. The mechanism performs information augmentation based on the identified entities and relationships using one or more cognitive services to collect augmented information from a corpus of information. The mechanism generates one or more visualization components based on the identified entities and relationships and the augmented information. The mechanism presents a summarization comprising the one or more visualization components to a user.

Abstract: In some examples, unstructured data is evaluated using a natural language processing model to output a set of subjective indicators. These subjective indicators are scored using a predictive model to determine whether a dependent user has or is likely to develop a particular condition such as a cellular abnormality.

Abstract: The present invention provides a system comprising a neurosynaptic processing device including multiple neurosynaptic core circuits for parallel processing, and a serial processing device including at least one processor core for serial processing. Each neurosynaptic core circuit comprises multiple electronic neurons interconnected with multiple electronic axons via a plurality of synapse devices. The system further comprises an interconnect circuit for coupling the neurosynaptic processing device with the serial processing device. The interconnect circuit enables the exchange of data packets between the neurosynaptic processing device and the serial processing device.

Abstract: A method for predicting a future situation based on an analysis of at least one predictive pattern. The method comprises monitoring a plurality of events carried out by an event processing component, detecting a predictive pattern predictive of a future situation, selecting one of a plurality of proactive actions and an execution time according to its effect on at least one of a probability of occurrence and a cost of occurrence of the future situation, and outputting the selected proactive action.

Abstract: A method includes determining, by a processor of a computing device, a subset of models included in a plurality of models generated based on a genetic algorithm and corresponds to a first epoch of the genetic algorithm. Each of the plurality of models includes data representative of a neural network. The method includes aggregating the subset of models to generate an ensembler. The ensembler, when executed on an input, provides at least a portion of the input to each model of the subset of models to generate a plurality of intermediate outputs. An ensembler output of the ensembler is based on the plurality of intermediate outputs. The method further includes executing the ensembler on input data to determine the ensembler output.

Abstract: A semantic engine including a group of functions or sub-engines configured to operate together to provide a complete analysis and understanding of digital information in order to provide results or subsequent actions. The semantic engine provide a set of algorithms, including a Naïve Bayes (Bayesian) algorithm, intelligent, self-learning semantic understanding algorithm, lexical analysis algorithm, word trimming algorithm, part-of-speech (POS) tagging algorithm, and textual inference algorithm.

Abstract: Methods and a system are provided. A method includes optimizing, by a natural language processing based response optimizer having a processor, responses in an online question and answer session using natural language processing. The optimizing step includes deriving a candidate answer to a question posed in natural language and gathering support for the candidate answer by accessing a social network. The support is weighted by at least one of degree centrality, betweenness centrality, closeness centrality, Eigenvalue, hub, and authority of nodes in the social network that are associated with the support.

Abstract: Methods and a system are provided. A method includes optimizing, by a natural language processing based response optimizer having a processor, responses in an online question and answer session using natural language processing. The optimizing step includes deriving a candidate answer to a question posed in natural language and gathering support for the candidate answer by accessing a social network. The support is weighted by at least one of degree centrality, betweenness centrality, closeness centrality, Eigenvalue, hub, and authority of nodes in the social network that are associated with the support.