Abstract: Systems, methods and software products identify emerging issues from textual customer feedback. A message stream of customer feedback is received. The message stream includes a plurality of unstructured text messages from at least one homogeneous source. A time interval is established. The volume of text messages for the time interval is determined to establish a reference volume. The volume of text messages in subsequent time intervals is measured to establish a trend volume. The trend volume is compared to the reference volume to determine a volumetric change. At least one action is initiated in response to a volumetric change above a pre-determined threshold. At least one action is initiated in response to a volumetric change below a pre-determined threshold.

Abstract: A visualization system that generates visual mash-ups for industrial automation includes a mash-up component that combines output from a subset of disparate sources into a common interface. The disparate sources include at least one of equipment, computers, or devices within an industrial automation environment. A visualization component generates and displays a mash-up visualization that includes information associated with the common interface.

Abstract: The invention relates to a method of multiple criteria decision making. In particular, a method of maintaining a complex article is described that includes the steps of; (a) taking a set of options for replacing each of a plurality of components of the complex article, (b) identifying an optimum solution set from the set of possible replacement options of step (a) that best satisfies a plurality of criteria and (c) implementing an optimum solution of the optimum solution set. The step of identifying an optimum solution set comprises the step of evaluating a plurality of potential solutions from the set of possible options defined in step (a) against the plurality of criteria.

Abstract: Methods, systems, and apparatus are provided to generate a most probable explanation (MPE) for a Bayesian Network (BN). A first data structure is populated by iterating from the leaves of a junction tree (derived from the BN) to the root and retaining maximum potentials along the way. A second data structure records selective index locations that map into the first data structure. These selective locations correspond to selective maximum potentials housed within the first data structure. All the selective maximum potentials are resolved once a root maximum potential is known for a given problem. The selective maximum potentials form a MPE through the junction tree to reach the result.

Abstract: The invention relates to a system and method for providing efficient policy rule updates in policy-based data management. More particularly, the invention relates to a system and method for restraining the size of the set of data objects to be examined after a policy rule evaluation.

Abstract: A system is disclosed for reconciling opinions generated by agents with respect to one or more predicates. The disclosed system may use observed variables and a probabilistic model including latent parameters to estimate a truth score associated with each of the predicates. The truth score, as well as one or more of the latent parameters of the probabilistic model, may be estimated based on the observed variables. The truth score generated by the disclosed system may enable publishers to reliably represent the truth of a predicate to interested users.

Abstract: A method for controlling policy information required by a requested service includes: obtaining a second decision result of a second network on policy information required by a requested service; making decision on the policy information required by the requested service according to a first policy information in the first network and the second decision result; and generating a final decision result on the policy information required by the requested service. A device and a system for controlling policy information required by a requested service are further disclosed. If different networks have different related policies, solutions provided by the present invention may provide a process for controlling policy information required by a requested service.

Abstract: A classification system trains a classifier to classify blocks of the web page into various classifications of the function of the block. The classification system trains a classifier using training web pages. To train a classifier, the classification system identifies the blocks of the training web pages, generates feature vectors for the blocks that include a linguistic feature, and inputs classification labels for each block. The classification system learns the coefficients of the classifier using any of a variety of machine learning techniques. The classification system can then use the classifier to classify blocks of web pages.

Abstract: Data spiders, provide an automated system that can take a file or file store of historic transaction data and create the best set of variables from that data, where “best” means highly predictive. Genetic algorithms are used to parameterized transactions to form groups, which are subjected naïve Bayes score ranking. Variable groups are generated and ranked accord to the score. Data spiders span the full information available, are uncorrelated with previous methods, and are easily interpretable.

Abstract: It is possible to acquire existing techniques in a neural network model currently studied and developed so as to generalize them as an element technique, and provide modeling of a basic unit of bottom-up approach using the neural network by adding new values to the existing techniques. A network learning device builds up a network of basic units in a network section, acquires an input from a sensor input section for evaluating it, changes a coupling weight coefficient by using a correlation operation so that the evaluation value satisfies a predetermined evaluation value, and inserts a new neural network according to need.

Abstract: A software robot apparatus (sobot apparatus) with a genetic code is disclosed. The sobot apparatus includes an application module, an artificial animated object engine, a sobot protocol module, an update agent, an update protocol module, a network interface, a sobot player, a script engine, a player database, a repository interface, a sobot graphic/audio interface, a 3D engine, a system software, a memory device, a display part, and an audio output part.

Abstract: Embodiments of the invention provide methods and apparatuses for classifying electronic documents (e.g., electronic communications) as either spam electronic documents or legitimate electronic documents. In accordance with one embodiment of the invention, each of a plurality of electronic communications is reduced to a corresponding multidimensional vector based on a multi-dimensional vector space. The multi-dimensional vectors represent corresponding electronic documents that have been classified as at least one type of electronic documents. Subsequent electronic documents to be classified are reduced to a corresponding multi-dimensional vector inserted into the multi-dimensional vector space. The electronic documents corresponding to an inserted multi-dimensional vector are classified based upon the proximity of the inserted multi-dimensional vector to at least one previously classified multi-dimensional vectors of the multi-dimensional vector space.

Abstract: A “Classifier Trainer” trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment “multiple instance pruning” (MIP) is introduced for training weak classifiers or “features” of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system.

Abstract: A method for performance bottleneck diagnosis and dependency discovery in distributed systems and computer networks includes receiving a real-valued end-to-end measurement matrix, a number of end-to-end measurements, a number of time points, a number of network components, a loss function, and a plurality of constraints on output matrices. The method further includes learning basic output matrices by best approximation of a transpose of the real-valued end-to-end measurement matrix, selecting a first threshold based on a real-valued mixing-weights matrix and a second threshold based on a real-valued delay matrix, converting the real-valued mixing-weights matrix and the real-valued delay matrix into respective binary matrices using the first threshold and the second threshold, and returning the real-valued mixing-weights matrix, the real-valued delay matrix, and the respective binary matrices to represent bottlenecks dependencies of the distributed system or computer network.

Abstract: Provided is a problem solving system. More specifically, the problem solving system provides an interface operable to receive user supplied input data, the input data having at least one user defined characteristic. The system provides an algorithm pool having a plurality of pre-defined algorithms, each algorithm having at least one associated algorithm characteristic. The system also provides a data source pool having at least one data source, each data source having at least one data characteristic. An algorithm selector is in communication with the interface, algorithm pool and data source pool.

Abstract: According to an aspect of the present invention, there is provided with a data division apparatus which divides multi-dimensional data, including: a data input unit which inputs multi-dimensional data; a division plane candidate creator which creates a plurality of division plane candidates for dividing the multi-dimensional data; a data provisional division unit which provisionally divides the multi-dimensional data by using the division plane candidate to generate clusters; a model generator which generates models from the clusters; an evaluation value calculator which calculates an evaluation value on the basis of the generated models and the multi-dimensional data; a division candidate selector which compares evaluation values respectively corresponding to the division plane candidates and selects a division plane candidate having a highest evaluation value; and a data division unit which divides the multi-dimensional data by using the selected division plane candidate.

Abstract: One embodiment of the present invention provides a method for recommending activities to a user. During operation, the system determines an activity-type distribution based on the user's personal profile and/or population prior information, thereby facilitating prediction of future activities for the user. The system further searches for and receives one or more activities based on the activity-type distribution. The system then scores each received activity and recommends a number of activities to be performed by the user in the future and a number of corresponding venues, based on the activity-type distribution and the weight distribution.

Abstract: When a patient enters a medical situation, healthcare professionals can use various amounts of information in evaluating the situation. However, different information can be beneficial dependent on the medical situation. Moreover, personnel can historically use specific information types regardless of the situation. An artificial neuron network is employed to pre-fetch information that personnel likely will want prior to a request from the personnel. In addition, the artificial neuron network can be trained based on results of presented information.

Abstract: The present invention provides a language, method and system to formulate and evaluate relational Bayesian networks in an c-commerce environment. The present invention employs a specific language for constructing synthetic variables used to predict events in the Bayesian networks. The present system and language allow for efficient and accurate representation, inference, and discovery of the synthetic variables used to model web visitor behavior.

Abstract: A knowledge management system allocating expert resources, method of allocating expert resources and program product therefor. Information requests are provided over networked devices, e.g., over voice and data networks. Data on experts may be stored in an expert database and data on requesters stored in a requestor database. A pairing unit identifies an appropriate expert matched to each request and requestor. The pairing unit includes a request/requestor characterization unit collecting request/requestor attributes, a matching unit matching request/requestor attributes with an expert, and a routing unit routing each incoming request to a selected matching expert.