Abstract: A system for selecting electronic advertisements from an advertisement pool to match the surrounding content is disclosed. To select advertisements, the system takes an approach to content match that focuses on capturing subtler linguistic associations between the surrounding content and the content of the advertisement. The system of the present invention implements this goal by means of simple and efficient semantic association measures dealing with lexical collocations such as conventional multi-word expressions like “big brother” or “strong tea”. The semantic association measures are used as features for training a machine learning model. In one embodiment, a ranking SVM (Support Vector Machines) trained to identify advertisements relevant to a particular context. The trained machine learning model can then be used to rank advertisements for a particular context by supplying the machine learning model with the semantic association measures for the advertisements and the surrounding context.

Abstract: System and methods are provided that facilitate integrated and seamless utilization of location data generated by multiple disparate positioning technologies adequate for outdoor or indoor positioning. Location data from multiple sources is selectively processed based on rules that account for availability, accuracy, cost, reliability of location data. At least one of the multiple sources is based on radio frequency identification (RFID) technology. Rules can be operator specific or can be automatically generated based on historic location data or cost-benefit analysis. Processing of selected data includes formatting data according to a specific standard or protocol, or augmenting the data with metadata. Standardized data is conveyed through a communication link for utilization in various applications such as vendor applications for navigation or web-based services, as well as custom and development applications.

Abstract: Methods and systems for semiconductor testing. In one embodiment, a semiconductor testing method includes one or more of the following stages: defining a rule relating to semiconductor testing, validating the rule, bundling the rule with other rules, correlating the rule with other rules, publishing the rule, actualizing the rule, and follow up relating to the rule. In one embodiment, a semiconductor testing system includes one or more of the following modules: rule creation module(s), analysis module(s), simulation module(s), real time production module(s), and offline production module(s). In one embodiment, user friendly graphical user interface(s) can be used for defining the building blocks of a rule and/or for viewing an optional hierarchy of categories to which the rule belongs.

Abstract: A general purpose set theoretic processor is enhanced 1) by providing multi-function counters in stead of down-counters, 2) by internalizing the composite Boolean Logic function by introducing a two stage (two matrix) programmable composite Boolean Logic functionality wherein the first stage yields logical products of selected aggregation logic responses (or their complements) and the second stage yields logical sums of selected sets of those logical products, and 3) by providing internal selective re-initialization by means of a re-initialization routing matrix functionality that directs logical sums of Composite Boolean Logic sums of products to selected GPSTP cells to be re-initialized.

Abstract: An approach is provided for determining a validity of a command line. A validity of the command line is determined based upon log information of a database, wherein the log information comprises network address information associated with the command line and time point information of the command line.

Abstract: Subject matter disclosed herein may relate to predicting hot terms, and may also relate to creating contextual shortcuts based, at least in part, on the predicted hot terms.

Abstract: A computerized method of representing a dataset includes obtaining a dataset, the dataset defining an attribute space; decomposing the attribute space into a plurality of attribute subspaces; generating a parent taxonomy of the obtained dataset with respect to one of the plurality of attribute subspaces, the parent taxonomy organizing the obtained dataset into a plurality of data subsets; generating a child taxonomy with respect to another one of the plurality of attribute subspaces, the child taxonomy organizing each of the plurality of data subsets within the parent taxonomy into at least one data subset; iteratively repeating generating the child taxonomy until a predetermined termination condition is satisfied, wherein the child taxonomy of a preceding iteration is the parent taxonomy of the current iteration; and assigning category labels to the data subsets.

Abstract: A system for clustering data objects includes a module for calculating an importance value of at least one member in a first data object represented as a variable length vector of 0 to N members and a clustering module for dynamically forming a plurality of clusters containing one or more data objects. The clustering module is configured to associate the first data object with at least one of the plurality of clusters in dependence upon the at least one member's similarity value in comparison to members in other data objects. The clustering module may be configured to cluster the first data object into a plurality of clusters if it has at least two members and each member belongs to a different cluster.

Abstract: A technique is provided for developing a propensity model for customer behavior. Multiple biased samples of customer characteristics and results from past activities are established. Initial propensity models are created for each biased sample. The propensity models established for each biased sample are processed separately from the propensity models established for the other biased samples. A genetic algorithm is used to evolve the propensity models. A select number of propensity models that best fit their respective biased samples are compared to a validation sample that is unbiased. A select number of these propensity models that best fit the validation sample are cross-bred into the propensity models established for each biased sample. The propensity models for each biased sample are then processed again using the genetic algorithms. However, a number of elite propensity models are maintained in their original form and not evolved using the genetic algorithm.

Abstract: A system and method for intelligent data management enables the transport of items within a network by creating a first database defining a transport path of an item from an origin facility to a destination facility, and operations that affect the transport of items. In order to update the transport path and operations, a data management application receives changes. The application, and associated data storage device, includes user profile, table, network model and development model databases. Modules within the application manage users and data, implement ad hoc and holiday schedules, report errors, verify changes and create new databases to reflect the changes.

Abstract: An adaptive interface for a programmable system, for predicting a desired user function, based on user history, as well as machine internal status and context. The apparatus receives an input from the user and other data. A predicted input is presented for confirmation by the user, and the predictive mechanism is updated based on this feedback. Also provided is a pattern recognition system for a multimedia device, wherein a user input is matched to a video stream on a conceptual basis, allowing inexact programming of a multimedia device. The system analyzes a data stream for correspondence with a data pattern for processing and storage. The data stream is subjected to adaptive pattern recognition to extract features of interest to provide a highly compressed representation which may be efficiently processed to determine correspondence.

Abstract: A method and apparatus for automatically generating a target sequence of a plurality of elements selected in accordance with a plurality of user-defined constraints such as a play list of songs. The apparatus comprises a user interface (102) for inputting a plurality of user-defined constraints, a storage device (106) for storing a plurality of elements or music files; and a processor (104) for automatically generating the target sequence or play list by an iterative process in which a solution sequence is found at each iteration to best fit the user-defined constraints. A small, random or biased change is made to the sequence found and if this improves the solution, the new solution is used for the next iteration. The biased change is used to help predict a better result for user-defined constraints which are, ordinarily, difficult to satisfy.

Abstract: Disclosed are methods, systems, and computer-readable media for determining stability of a neural system. The method includes tracking a function world line of an N element neural system within at least one behavioral space, determining whether the tracking function world line is approaching a psychological stability surface, and implementing a quantitative solution that corrects instability if the tracked function world line is approaching the psychological stability surface.

Abstract: A sound classification system for adding and correcting a sound type is disclosed. When the add/correct command processor receives a command to add or correct a sound type, the data in the first database is stored in the second database, and the type adding/correcting device adds the feature of the sound to the first database, and re-calculates the statistical values. Besides, the classifier re-classifies the sample sounds, and the precision calculator calculates a ratio of accurate classification. When the ratio is high, the type adding/correcting device stores, in the feature database, the feature of the sound for which a type is to be added or corrected. When the ratio is low, the second database restores the data back to the first database.

Abstract: In accordance with one aspect of the present exemplary embodiment, a system determines a solution based on received data. An intake component receives an incomplete data set from one or more sources. A recommendation system transforms the incomplete data set into a semantic data set via latent semantic indexing, classifies the semantic data set into an existing cluster and provides one or more solutions of the existing cluster as one or more recommendations.

Abstract: A classification algorithm is separated into one or more input-invariant parts and one or more input-dependent classification parts. The input-invariant parts of the classification algorithm capture the underlying and unchanging relationships between the plurality of data elements being operated upon by the classification algorithm, whereas the one or more classification parts embody the probabilistic labeling of the data elements according to the various classifications. For any given iteration, a user's input is used to modify at least one classification part of the algorithm. Recalculated classification parts (i.e., updated classification results) are determined based on computationally simple combinations of the one or more modified classification parts and the one or more input-invariant parts. Preferably, a graphical user interface is used to solicit user input.

Abstract: The present invention enables identification of events such as target. From training target event data the present a very large number of clusters are formed for each class based on Euclidean distance using a repetitive k-means clustering process. Features from each cluster are identified by extracting out their dominant eigenvectors. Once all of the dominant eigenvectors have been identified, they define the relevant space of the cluster. New target event data is compared to each cluster by projecting it onto the relevant and noise spaces. The more the data lies within the relevant space and the less it lies within the noise space the more similar the data is to a cluster. The new target event data is then classified based on the training target event data.

Abstract: A networked computer system, architecture, and method are provided for optimizing human and intellectual capital in the digital workplace environment.

Abstract: A user's preference structure in respect of alternative “objects” with which the user is presented is captured in a multi-attribute utility function. The user ranks these competing objects in order of the user's relative preference for such objects. A utility function that defines the user's preference structure is provided as output on the basis of this relative ranking. This technique can be used to assist a buyer in selecting between multi-attribute quotes or bids submitted by prospective suppliers to the buyer.

Abstract: Disclosed is a nervous system-based emotion synthesizing apparatus which enables an emotion of a humanoid to be synthesized in a manner similar to that of a human using a nervous system-adjusting mechanism of a human body according to emotions.