Abstract: A computer implemented method of constructing formal definitions in intelligent glossaries for interpreting text, comprising the steps of: providing at least one Distinction having a Boundary, an Indication, a Counter-indication and a Frame; modeling each Distinction as a diagram to provide a Distinction Model; verifying each distinction model as being an instantiation of a generic Distinction Pattern; providing at least one Arrangement made of nonintersecting Marks of Distinction containing Indications from the verified Distinction Model; writing at least one Formulation for each Indication appearing in verified Distinction model and Arrangement, providing well-founded Indications; calculating precise Formulations in Natural Language from well-founded Indications by substituting Variables symbols and/or replacing Constants symbols to transform imprecise Formulations into precise Formulations; selecting a Definition type and embedding at least one precise Formulation and Definition type as a formal Definition

Abstract: Systems and methods of the present invention allow for the discovery of physical location information about network assets and the delivery of that information to network administrators. In addition, environmental and other information about network asset locations can be provided to an administrator. Intelligent patch panels and power outlet units are installed in network cabinets to facilitate the acquisition and reporting of physical infrastructure information, including information about network resource availability.

Abstract: An apparatus includes a positioning system; a surface indicator system to collect an indication data set from a surface of a component utilizing a fluorescent penetration process; an indication data processing system to create an output data set in response to the indication data set utilizing a fuzzy logic algorithm; and a microprocessor to provide at least one surface variance in response to the indication data set and the output data set. A method including conducting a surface indication technique for a component; utilizing a positioning algorithm to manipulate positioning equipment in response to the component; directing an indication source to a surface of the component; collecting an indication data set in response to directing the indication source; applying a fuzzy logic analysis in response to the indication data set to provide an output data set; and providing at least one surface variance in response to the output data set.

Abstract: A method and system for analyzing a corpus of data artifacts is disclosed. The method comprises obtaining, by a computer, a semantic representation of the data artifacts, where the semantic representation indicates (1) entities identified in the data artifacts, and (2) semantic relationships among the entities as indicated by the data artifacts. The method further comprises clustering the data artifacts into clusters of semantically related data artifacts based on the semantic representation and inferring additional semantic relationships between pairs of the entities. The inferring comprises applying, on a cluster-by-cluster basis, a multi-tiered network of inference engines to a portion of the semantic representation corresponding to the cluster, where the multi-tiered network of inference engines includes a domain-independent inference tier and a domain-specific inference tier.

Abstract: A computing device receives a training data set that comprises a plurality of sensitive documents and a plurality of non-sensitive documents. The computing device determines a quality of the training data set. The quality may be determined using k-fold cross validation and/or latent semantic indexing. In response to determining that the training data set has a satisfactory quality, the computing device then analyzes the training data set using machine learning to train a machine learning-based detection (MLD) profile, the MLD profile to be used by a data loss prevention (DLP) system to classify new documents as sensitive documents or as non-sensitive documents.

Abstract: Generalized learning rules may be implemented. A framework may be used to enable adaptive signal processing system to flexibly combine different learning rules (supervised, unsupervised, reinforcement learning) with different methods (online or batch learning). The generalized learning framework may employ average performance function as the learning measure thereby enabling modular architecture where learning tasks are separated from control tasks, so that changes in one of the modules do not necessitate changes within the other. Separation of learning tasks from the control tasks implementations may allow dynamic reconfiguration of the learning block in response to a task change or learning method change in real time. The generalized learning apparatus may be capable of implementing several learning rules concurrently based on the desired control application and without requiring users to explicitly identify the required learning rule composition for that application.

Abstract: Methods, systems and computer program products are disclosed for detecting patterns in a data stream that match multi-pattern rules. One embodiment of the invention provides a method of recognizing a specified group of patterns in a data stream. The method comprises identifying a rule for said specified group of patterns in the data stream, and using a first array of finite state machines to scan the data stream for at least some of the patterns in the specified group. For patterns in the specified group that are found in the data stream by the first array of finite state machines, pattern identifiers are sent to a second array of finite state machines. The second array of finite state machines determines if the specified group of patterns is in the data stream in accordance with the identified rule by, at least in part, using said pattern identifiers.

Abstract: Methods for solving a computational problem including minimizing an objective including a set of weights and a dictionary by casting the weights as Boolean variables and alternately using a quantum processor and a non-quantum processor to successively optimize the weights and the dictionary, respectively. A first set of values for the dictionary is guessed and the objective is mapped to a QUBO. A quantum processor is used to optimize the objective for the Boolean weights based on the first set of values for the dictionary by minimizing the resulting QUBO. A non-quantum processor is used to optimize the objective for the dictionary based on the Boolean weights by updating at least some of the columns of the dictionary. These processes are successively repeated until a solution criterion is met. Minimization of the objective may be used to generate features in a learning problem and/or in data compression.

Abstract: A method and apparatus for identifying deformation of a structure. Training deformation data is identified for each training case in a plurality of training cases. Training strain data is identified for each training case in the plurality of training cases. The training deformation data and the training strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model. A group of parameters for the heuristic model is adjusted using the training deformation data and the training strain data for the each training case in the plurality of training cases such that the heuristic model is trained to generate estimated deformation data for the structure based on input strain data. The estimated deformation data has a desired level of accuracy.

Abstract: A server computing system selects a machine learning kernel from a plurality of machine learning kernels using a plurality of training documents. The server computing system identifies a plurality of testing documents from a plurality of electronic discovery documents based on the plurality of training documents. For each of the plurality of machine learning kernels and for each testing document in the plurality of testing documents, the server computing system determines a class of the testing document using a default value for each of a plurality of parameters for the machine learning kernel and evaluates a goodness of fit of the machine learning kernel for the testing document. The server computing system selects a machine learning kernel from the plurality of machine learning kernels and determines a value for at least one of the plurality of parameters for the selected machine learning kernel using a goodness of fit test.

Abstract: A system for generating personalized channels of multimedia content. The system comprises an interface to one or more multimedia sources, wherein the multimedia sources provide multimedia content to the personalized channels of multimedia content; and a server for receiving multimedia content from the one or more multimedia sources through the interface and for serving selected multimedia content to users of the system over one or more of the personalized channels; wherein a user of the system receives personalized multimedia content gathered by the server into the one or more of the personalized channels responsive of preferences of the user as observed by the system for the user, wherein the server is further configured to remove multimedia content from the one or more personalized multimedia content channels based on the preferences of the user.

Abstract: A user interface evolves based on learned idiosyncrasies and collected data of a user. Learned idiosyncrasies and collected data of the user can be stored in a knowledge base. Information from the surrounding environment of the user can be obtained during learning of idiosyncrasies or collection of data. Thought-based statements can be generated based at least in part on the knowledge base and the information from the environment surrounding the user during learning of idiosyncrasies or collection of data. The thought-based statements serve to invoke or respond to subsequent actions of the user. The user interface can be presented so as to allow for interaction with the user based at least in part on the thought-based statements. Furthermore, personality nuances of the user interface can be developed that affect the interaction between the user and the user interface.

Abstract: Computer-implemented systems and methods for synthesis of concept definitions and concept relationships from a domain of data, utilizing different semantic processing protocols such as formal concept analysis and faceted classification synthesis from existing domain concepts that have a confidence gradient built into them. A cognitive of an input agent provides an input of an active concept which is matched against existing domain concepts. The resultant pool of relevant domain concepts is then used to derive virtual concept definitions using a semantic processing protocol. The derivation is then overlaid with a concept of relative proximity of an attribute from another within an attribute set. An additional layer of coherence is given by the relative proximity measure. The end result is a pool of related virtual concept definitions in a tree structure.

Abstract: In one embodiment, a wind power ramp event nowcasting system includes a wind condition analyzer for detecting a wind power ramp signal; a sensor array, situated in an area relative to a wind farm, the sensor array providing data to the wind condition analyzer; a mesoscale numerical model; a neural network pattern recognizer; and a statistical forecast model, wherein the statistical model receives input from the wind condition analyzer, the mesoscale numerical model, and the neural network pattern recognizer; and the statistical forecast model outputs a time and duration for the wind power ramp event (WPRE) for the wind farm.

Abstract: A method of using unlabeled data to train a classifier is disclosed. In one embodiment related to record linkage, the method entails retrieving a set of candidate data records from a master database based on a least one update record. Next, a surrogate learning technique is used to identify one of the candidate data records as a match for the one update record. Lastly, the exemplary method links or merges the update record and the identified one of the candidate data records.

Abstract: A system configured to predict characteristics of an artificial heart is described. The system includes a processor and memory in electronic communication with the processor, and an artificial neural network configured to receive an input vector of a predetermined length to train the artificial neural network, produce an output vector based on the input vector, and compare the output vector with a target vector of the predetermined length. When the output vector does not match the target vector within a predetermined error rate, the network is configured to adjust at least one weight, and when the output vector matches the target vector within the predetermined error rate, the network is configured to execute the input vector to produce an estimate at least one characteristic of the artificial heart.

Abstract: A method and apparatus store media data on a portable electronic device. The method can include monitoring media data storage activity regarding user media data storage activity usage patterns that store media data in a memory in a portable electronic device. The method can include storing the media data storage activity data in the portable electronic device. The method can include triggering a full memory prediction algorithm based on a user action event that affects media data storage in the memory and can include running the full memory prediction algorithm in response to the trigger. The full memory prediction algorithm can predict when the memory will be substantially full based on past user media data storage behavior according to the media data storage activity data. The method can include outputting a potential upcoming full memory indication.

Abstract: Methods, systems and media are taught utilizing ranking techniques in machine learning to learn a ranking function. Specifically, ranking algorithms are applied to learn a ranking function that advantageously minimizes ranking error as a function of targeted ranking order discrepancies between a predetermined first ranking of a training plurality of data elements and a second ranking of the training plurality of data elements by the ranking function. The ranking algorithms taught may be applied to ranking representations of chemical structures and may be particularly advantageous in the field of drug discovery, e.g., for prioritizing chemical structures for drug screenings.

Abstract: Techniques for creating a distributed application flow from a set of rules are provided. The techniques include creating a control-flow graph for each rule, creating one or more dependency links between two or more rules, partitioning a resulting graph, wherein the resulting graph comprises one or more control-flow graphs and one or more dependency links, into one or more operators by determining an optimal set of one or more cuts through the resulting graph such that a cost function is minimized, and generating stream processing flow code from the partitioned graph.

Abstract: A computing device receives a rule that includes information describing conditions associated with a consequence, and identifies rule components corresponding to the rule. The computing device creates a rule formula, based on the rule components, by creating a first-order logic version of the rule and creating a rule formula table based on the first-order logic version of the rule. The computing device stores the rule formula table in a relational database.