Abstract: A method of determining ranges for algorithmic variables for a processor that uses fixed point arithmetic is provided. The method comprises expressing overflow requirements of processor instructions as inequalities. The method also expresses precision requirements and expressiveness requirements as inequalities and merit functions. A global constraint and optimizer tool is used to find ranges for algorithmic variables based on the inequalities and the merit functions. The use of constraint equation solving and optimization finds optimal algorithmic ranges that provide overflow-free arithmetic as well as optimal expressiveness and precision.

Abstract: An efficient, effective and at times superior object detection and/or recognition (ODR) function may be built from a set of Bayesian stumps. Bayesian stumps may be constructed for each feature and object class, and the ODR function may be constructed from the subset of Bayesian stumps that minimize Bayesian error for a particular object class. That is, Bayesian error may be utilized as a feature selection measure for the ODR function. Furthermore, Bayesian stumps may be efficiently implemented as lookup tables with entries corresponding to unequal intervals of feature histograms. Interval widths and entry values may be determined so as to minimize Bayesian error, yielding Bayesian stumps that are optimal in this respect.

Abstract: A method of selecting and presenting content based on learned user preferences is provided. The method includes receiving search input from the user for identifying desired content items and receiving content selection actions from the user. The method further includes analyzing the date, day, and time of content selection actions by the user and analyzing descriptive terms associated with the selected content items to learn a periodicity of user selections of similar content items. In response to subsequent searches by the user, the method calls for selecting and ordering a collection of content items for presentation to the user based on comparing the user's search input to descriptive terms associated with content items and based on the learned periodicities of the user.

Abstract: A computer implemented method and systems used to create and interpret a set of formal glossaries which refer one to the other and are intended to define precisely the terminology of a field of endeavor. Such glossaries are known as intelligent, in the sense that they allow machines to make deductions, with interaction of human actors. Once a word is defined in an intelligent glossary, all the logical consequences of the use of that word in a formal and well-formed sentence are computable. The process includes a question and answer mechanism, which applies the definitions contained in the intelligent glossaries to a given formal sentence. The methods may be applied in the development of knowledge management methods and tools that are based on semantics; for example: modeling of essential knowledge in the field based on the relevant semantics; and computer-aided human-reasoning.

Abstract: A system and method for generating a robot genome. Internal state attribute preferences of internal states and behavior preferences are input by a user, and a set of one or more robot genomes containing initial values of genes is generated. A masking process of each robot genome of the generated robot genome group is performed so attributes are manifested. Robot genomes that underwent the masking process are loaded into a personality engine. A reliability value is calculated from a manifestation result of each robot genome, and reliability values of extracted robot genomes that satisfy a particular user reliability value are determined. Extracted robot genomes are modified when the user reliability value is not satisfied, and a new robot genome group including the modified robot genomes is generated. A robot genome satisfying the user reliability is extracted by iteratively performing the above procedures for the newly generated robot genome group.

Abstract: A method (which can be computer implemented) for inferring component associations among a plurality of components in a distributed computing system includes the steps of obtaining status information for each pertinent component of the plurality of components, forming an N by D matrix, X, based on the status information, and factorizing the matrix X to obtain a first matrix indicative of the component associations to be inferred and a second matrix indicative of failure explanations for corresponding ones of the probe instances. N is a number of probe instances associated with a given time frame. D is a number of the plurality of components for which the associations are to be inferred. Techniques are also presented for forming a database with the status information.

Abstract: A method for adapting to a shift in document content according to one embodiment of the present invention includes receiving at least one labeled seed document; receiving unlabeled documents; receiving at least one predetermined cost factor; training a transductive classifier using the at least one predetermined cost factor, the at least one seed document, and the unlabeled documents; classifying the unlabeled documents having a confidence level above a predefined threshold into a plurality of categories using the classifier; reclassifying at least some of the categorized documents into the categories using the classifier; and outputting identifiers of the categorized documents to at least one of a user, another system, and another process. Methods for separating documents are also presented. Methods for document searching are also presented.

Abstract: A method for training a system to specifically react on a specific input. The method can include defining a set of binary data structures, each representing a real-world component, item, or virtual object; storing each data structure as a binary pattern; creating uniquely identifiable copies of the data structures to represent individual instances of the components, items, or virtual objects; creating a virtual state space of the components, items, or virtual objects by grouping them as relevant for a specific situation; receiving an input to change a status or an attribute value of at least one of the components, items, or virtual objects; storing the received changes in a new version of the applicable data structure instance; analyzing similarities of the stored binary patterns related to a particular action performed; and if a matched binary pattern is identified, proposing at least one possible action related to the matched binary pattern.

Abstract: A system is disclosed for correlating user profiles to software applications. The system may be adapted as part of a software system that interrogates a user profile and reading a learning objective as a first input, interrogates a software application and reading a learning objective as a second input, calculates a relevance of the first input learning objective to the second input learning objective, and adapts the software application in accordance with the determined relevance and/or updating the user profile in accordance with the determined relevance. A method is disclosed that correlates user profiles to software applications by reading a first learning objective from a user profile, reading a second learning objective from a software application, determining a relevance of the first to the second learning objective, and adapting the software application in accordance with the determined relevance and/or updating the user profile in accordance with the determined relevance.

Abstract: A calculation processing apparatus for executing network calculations defined by hierarchically connecting a plurality of logical processing nodes that apply calculation processing to input data, sequentially designates a processing node which is to execute calculation processing based on sequence information that specifies an execution order of calculations of predetermined processing units to be executed by the plurality of processing nodes, so as to implement the network calculations, and executes the calculation processing of the designated processing node in the processing unit to obtain a calculation result. The calculation apparatus allocates partial areas of a memory to the plurality of processing nodes as ring buffers, and writes the calculation result in the memory while circulating a write destination of data to have a memory area corresponding to the amount of the calculation result of the processing unit as a unit.

Abstract: Context-based user behavior data is collected from a search mechanism. This data includes, for a given query, user feedback (implicit and explicit) on the query and context information on the query. A predictive pattern is applied to the context-based user behavior data in order to produce predicted user satisfaction data. Data mining techniques may be used to create and improve one or more predictive patterns. Predicted user satisfaction data can be used to monitor or improve search mechanism performance, via a display reporting the performance or identification of any queries with a shared characteristic and sub-par user satisfaction. A dynamically-improving search mechanism uses the predicted user satisfaction data to improve the performance of the search mechanism.

Abstract: A method of operating a Linear Complementarity Problem (LCP) solver is disclosed, where the LCP solver is characterized by multiple execution units operating in parallel to implement a competent computational method adapted to resolve physics-based LCPs in real-time.

Abstract: Systems and methods for analyzing documents are provided herein. A plurality of documents and user input are received via a computing device. The user input includes hard coding of a subset of the plurality of documents, based on an identified subject or category. Instructions stored in memory are executed by a processor to generate an initial control set, analyze the initial control set to determine at least one seed set parameter, automatically code a first portion of the plurality of documents based on the initial control set and the seed set parameter associated with the identified subject or category, analyze the first portion of the plurality of documents by applying an adaptive identification cycle, and retrieve a second portion of the plurality of documents based on a result of the application of the adaptive identification cycle test on the first portion of the plurality of documents.

Abstract: A system is described that treats a solver as an ordered sequence of steps involving the different objects that have to be simulated and relationships between them. Tasks and the order of the tasks are obtained from each object and relationship. The tasks are merged into a sorted list. The solver traverses the list and passing each task to a corresponding object and relationship where the objects are interleaved during processing. The object or relationship then executes the task.

Abstract: The invention provides techniques for building multiple predictive models of individuals' affinities for attributes of objects and/or services. The accuracies of multiple predictive models are measured and the models are combined based on the measurements, resulting in a more accurate predictive model of individual-specific affinities for attributes of the objects and/or services.

Abstract: A method and system for enforcing rule selection on user email inboxes include an inbox monitor and administrative rules at an email server. The inbox monitor includes a first agent for identifying inactive user inboxes from email messages stored at the email database, and a second agent for identifying, from the inactive user inboxes identified by the first agent, user inboxes without appropriate user rules activated. For each user inbox identified by the second agent, administrative rules are applied to the user inbox if the user inbox has been inactive for at least a predetermined period of time. If the user inbox has not been inactive for at least the predetermined period of time, a notification message is sent to a user associated with the user inbox. The notification message informs the user that administrative rules will be applied to the user inbox if appropriate user rules are not activated.

Abstract: A semantic network includes a number of nodes are interconnected to one another through links (e.g., in a subject/verb/target form) representing relationships between the nodes and one or more of the links have one or more variants representing qualifications of the relationships between the nodes. For each link having one or more variants, the variants may be ordered in configurations. Such ordering of the variants in the configurations may be self-described within the semantic network and may determine precedence of those links belonging to the variants. Some of the links of the network may be nodes of others of the links. The interconnection of at least some of the nodes may define a meta-meta model that defines terms in which particular meta models can be defined, each meta model comprising meta facts regarding the nodes of the semantic network.

Abstract: A method for detection is presented. The method includes extracting data based upon one or more predefined rules. Further, the method includes preprocessing the extracted data based on the predefined rules to generate one or more rule groups. The method also includes instantiating one or more inference engines based on the generated rule groups. Additionally, the method includes processing the extracted data by a corresponding inference engine based on the predefined rules to generate processed data. Systems and computer-readable medium that afford functionality of the type defined by this method is also contemplated in conjunction with the present technique.

Abstract: A method for controlling a decisional process when pursuing an aim in a specific application domain, such as economic, technical, and organizational fields. In the process, a system of agents is created, including creator cognitive agents of portable knowledge objects and constructor agents of knowledge objects by adding supplementary knowledge elements to the knowledge objects created. The system launches the decisional process by tracking the observation of an abnormality in the actual data of the application domain by the creation of an initial knowledge object representative of an alarm index and causes several decisional paths to be established linked to this object, under different points of view of different agents, each in its domain, and the decision is made following merging of decisional paths.

Abstract: A method and system for rank aggregation of entities based on supervised learning is provided. A rank aggregation system provides an order-based aggregation of rankings of entities by learning weights within an optimization framework for combining the rankings of the entities using labeled training data and the ordering of the individual rankings. The rank aggregation system is provided with multiple rankings of entities. The rank aggregation system is also provided with training data that indicates the relative ranking of pairs of entities. The rank aggregation system then learns weights for each of the ranking sources by attempting to optimize the difference between the relative rankings of pairs of entities using the weights and the relative rankings of pairs of entities of the training data.