Abstract: A computer-implemented system and method for producing and distributing at least one product from at least one plant to at least one customer where discretized plant production data, filtered customer sourcing data, forecasted customer demand data, and forecasted plant electricity pricing data are input into a modified genetic algorithm and an electronic processor solves the modified genetic algorithm and outputs the solution to an interface. The system and method is flexible and can incorporate data as it becomes available to yield intermediate solutions for quick decision making.

Abstract: A method for recommending choices to a user comprises receiving data. A first set of recommendation choices based on the data is derived using a first model that models a user's subconscious reactions (i.e., intuition) to the data, and a second set of recommendation choices based on the data is derived using a second model that models the user's consciousness. The first and second sets of recommendation choices are blended using a blending function based on characteristics of a domain of the data to create a third set of recommendation choices that are arranged in an order, which is presented to the user. A conscious decision of the user and subconscious reactions of the user are monitored, and data representing the subconscious reactions of the user and data representing the conscious decisions of the user are appended to a first and second memory space, respectively.

Abstract: Assessment of selectivity of categorization rules. One or more categorization rules are applied to a set of un-categorized objects to produce a categorization result set representing assignment of objects the set into at least two categories. A selectivity score for the at least one categorization rule is obtained based on statistical information. The numerical selectivity score represents an estimation of accuracy of the at least one categorization rule, and is produced as a result of application of at least one trained selectivity determination algorithm, which is based on application of a plurality of specially-selected categorization rules to a set of pre-categorized training data, with the application of each one producing a uniform grouping of objects.

Abstract: The invention includes methods and systems for analyzing data to determine trends in the data and to identify outliers. The methods and systems include a learning algorithm whereby a data space is co-populated with artificial, evenly distributed data, and then the data space is carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. Additionally, a final quality control measurement is used to merge clusters that are too similar to be meaningful. The invention is widely applicable to data analytics, generally, including financial transactions, retail sales, elections, and sports.

Abstract: Methods, products, apparatus, and systems may identify one or more relevant social communities for one or more questions. Additionally, a user-question affinity value between a user and a question may be determined. In addition, a user-community affinity value between the user and each of a plurality of candidate social communities may be determined. Moreover, a question-community affinity value between the question and each of the plurality of candidate social communities may be determined based on the user-question affinity value and the user-community affinity value. The question-community affinity value determination may involve calculating a running average using the user-question affinity value and the user-community affinity value. The question-community affinity value may identify the one or more relevant social communities from the plurality of candidate social communities.

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, products, apparatus, and systems may identify one or more relevant social communities for one or more questions. Additionally, a user-question affinity value between a user and a question may be determined. In addition, a user-community affinity value between the user and each of a plurality of candidate social communities may be determined. Moreover, a question-community affinity value between the question and each of the plurality of candidate social communities may be determined based on the user-question affinity value and the user-community affinity value. The question-community affinity value determination may involve calculating a running average using the user-question affinity value and the user-community affinity value. The question-community affinity value may identify the one or more relevant social communities from the plurality of candidate social communities.

Abstract: A system for training a neural network. A switch is linked to feature detectors in at least some of the layers of the neural network. For each training case, the switch randomly selectively disables each of the feature detectors in accordance with a preconfigured probability. The weights from each training case are then normalized for applying the neural network to test data.

Abstract: A method of operation of a URL spam detection system includes: identifying a feature dimension of a user action on a social networking system to detect anomalies; extracting URL chunks from a content associated with the user action; aggregating a non-content feature of the user action along the feature dimension into a URL distribution store to produce a feature distribution for each of the URL chunks; determining whether the feature distribution of a particular URL chunk within the URL chunks exceeds an expectation threshold for the feature dimension; and classifying the particular URL chunk as an illegitimate URL when the feature distribution exceeds the expectation threshold to restrict access to a particular URL chunk on a social networking system.

Abstract: A truth maintenance method and system. The method includes receiving by a computer processor from RFID tags embedded in sensors, event data associated with events detected by said sensors. The computer processor associates portions of the event data with associated RFID tags and derives assumption data associated with each portion of the portions. The computer processor retrieves previous assumption data derived from and associated with previous portions of previous event data retrieved from the RFID tags and executes non monotonic logic with respect to the assumption data and the previous assumption data. In response, the computer processor generates and stores updated assumption data associated with the assumption data and the previous assumption data.

Abstract: A hierarchical latent structure setting unit 81 sets a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure. A variational probability computation unit 82 computes a variational probability of a path latent variable that is a latent variable included in a path linking a root node to a target node in the hierarchical latent structure. A component optimization unit 83 optimizes each of the components for the computed variational probability. A gating function optimization unit 84 optimizes a gating function model that is a model for determining a branch direction according to the multivariate data in a node of the hierarchical latent structure, based on the variational probability of the latent variable in the node.

Abstract: The invention provides a system and method for describing polymorphisms or genetic variants based on information about mutations and relationships among them. The invention uses object-oriented concepts to describe variants as variant objects and relations among those variants as variant relation object, each object being an instance of an abstract class of genomic feature and able to contain any number of other objects. Information about genetic disorders is stored in association with the object that represents the pathogenic variant. Genetic test results are used to access corresponding objects to provide a report based on variants or polymorphisms in a patient's genetic material.

Abstract: A system including a global topic model building module that builds a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, a user model building module that builds a plurality of user models, each user model being built based on an activity of a respective user, an expertise model building module that builds an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, a processor to compare a user model of the first user with the expertise model for the topic; and an expertise assessment and learning recommendation module that recommends an activity associated with the set of data to the first user based on the comparison.

Abstract: A method including generating a global topic model based on a set of data that is updated according to an activity of each user of a plurality of users, the global topic model including a topic representation for a topic, generating a plurality of user models, each user model being generated based on the activity of a respective user, generating an expertise model for the topic based on the activity of at least one user of the plurality of users, the expertise model for the topic setting a target level of knowledge for a first user of the plurality of users, comparing a user model of the first user with the expertise model for the topic, the comparing being performed by a processor of a computer system, and recommending an activity associated with the set of data to the first user based on the comparison.

Abstract: An example method for reviewing documents includes scoring documents using an artificial intelligence model, and selecting a subset of highest scoring documents. The method further includes inserting a number of randomly-selected documents into the subset of highest scoring documents to form a set of documents for review, wherein a reviewer cannot differentiate between the randomly-selected documents and the subset of highest scoring documents included in the set of documents for review, and presenting the set of documents for review by the reviewer.

Abstract: A computer-implemented system includes an edge module and at least one input device coupled to the edge module. The at least one input device is configured to generate data input signals. The system also includes a cognitive module coupled to the edge module. The cognitive module includes a perception sub-module coupled to the edge module. The perception sub-module is configured to receive the data input signals. The cognitive module also includes a learning sub-module coupled to the perception sub-module. The learning sub-module is configured to adaptively learn at least in part utilizing the data input signals.

Abstract: The model selection device comprises a model optimization unit which optimizes a model for a mixed distribution, wherein related to an information criterion of complete data, with respect to a hidden variable post-event distribution of the complete data, the model optimization unit optimizes an expected information criterion of the complete data for a pair of a model and a parameter of a component which satisfies a predetermined condition.

Abstract: Spiking neuron network conditionally independent subset classifier apparatus and methods. In some implementations, the network may comprise one or more subset neuron layers configured to determine presence of one or more features in the subset of plurality of conditionally independent features. The output of the subset layer may be coupled to an aggregation layer. State of the subset layer may be configured during training based on training input and a reference signal. During operation, spiking output of the subset layer may be combined by the aggregation layer to produce classifier output. Subset layer output and/or classifier output may be encoded using spike rate, latency, and/or base-n encoding.

Abstract: A recommender system uses an activity decision tree to model the changes in a user's behavior according to a plurality of time-related features. The system determines historical activities for the user, and generates a decision tree for the user's historical activities. Each leaf node of the decision tree is associated with an activity-prediction model that computes a probability for a corresponding activity. The system selects a path of the decision tree from a root node to a leaf node of the decision tree based on a target time. The selected path traverses two or more non-leaf nodes that are each associated with a temporal decision model that compares the target time against a temporal classifier. The system then determines a probability for a user activity based on an activity-prediction model of the selected path.

Abstract: An information classification device (1) is provided with an union of sets determination unit (10) which performs correct/incorrect determination regarding a content to be classified using a union of sets rule, and an individual determination unit (11) which applies a plurality of individual determination rules to the content to be classified which has been determined as correct, determines whether the content matches the condition, and performs correct/incorrect determination again regarding the content to be classified which has been determined as correct on the basis of the determination result of each individual determination rule. The union of sets determination rule is created using a result of correct/incorrect determination previously performed by two or more people with respect to a plurality of contents which are different from the contents to be classified, and also using feature amounts of respective different contents.