Abstract: Mechanisms are provided for performing load balancing of question processing in a Question and Answer (QA) system, implemented by the data processing system, having a plurality of QA system pipelines. The mechanisms receive an input question for processing by the QA system and determine a predicted question difficulty of the input question. The mechanisms select a QA system pipeline from the plurality of QA system pipelines based on the predicted question difficulty and route the input question to the selected QA system pipeline for processing. In addition, the mechanisms process the input question with the selected QA system pipeline to generate an answer for the input question.

Abstract: A “Contact Discriminator” provides various techniques for differentiating between valid and invalid contacts received from any input methodology by one or more touch-sensitive surfaces of a touch-sensitive computing device. Examples of contacts include single, sequential, concurrent, or simultaneous user finger touches (including gesture type touches), pen or stylus touches or inputs, hover-type inputs, or any combination thereof. The Contact Discriminator then acts on valid contacts (i.e., contacts intended as inputs) while rejecting or ignoring invalid contacts or inputs. Advantageously, the Contact Discriminator is further capable of disabling or ignoring regions of input surfaces, such tablet touch screens, that are expected to receive unintentional contacts, or intentional contacts not intended as inputs, for device or application control purposes.

Abstract: A machine generated service cache that utilizes one or more machine learning classifiers is trained using service requests directed to a human-generated service and service responses generated by the human-generated service in response to the service requests. Once the machine generated service cache has been trained to a predetermined level of performance, the machine generated service cache can be utilized to process actual service requests directed to the human-generated service. The machine generated service cache might be utilized to process service requests for which it is not essential that the returned service response be identical to a response that would be generated by the human-generated service.

Abstract: An information classification system includes a server including a knowledge base that receives classification information to be classified, conducts language analysis of the classification information to acquire and classify a plurality of keywords into elements made up of a classification target word and a related word that modifies the classification target word, and conducts a search with the related word, so as to assign a classification identification value to the information; a classification candidate extraction section that extracts the classification identification value that the knowledge base assigns to generate an automatic classification result; and a classification update section that receives the automatic classification result and corrects registered items in the knowledge base with a correction value received through a GUI for classification confirmation while referring to log data that is a processing history about automatic classification for the language analysis and the element classific

Abstract: Embodiments of the invention provide a neural network comprising multiple functional neural core circuits, and a dynamically reconfigurable switch interconnect between the functional neural core circuits. The interconnect comprises multiple connectivity neural core circuits. Each functional neural core circuit comprises a first and a second core module. Each core module comprises a plurality of electronic neurons, a plurality of incoming electronic axons, and multiple electronic synapses interconnecting the incoming axons to the neurons. Each neuron has a corresponding outgoing electronic axon. In one embodiment, zero or more sets of connectivity neural core circuits interconnect outgoing axons in a functional neural core circuit to incoming axons in the same functional neural core circuit.

Abstract: A mobile handset device collects sensor data about the physiological state of the user of the handset. The mobile handset device receives multimedia content, which is consumed on the mobile handset. In a deployment phase, the sensor data is used to classify the user's emotional response to individual pieces of media content consumed on the mobile device. A classification model built in a training phase may be used to map sensor data to classification labels indicative of the user's emotional response to multimedia.

Abstract: A plurality of questions may be obtained for an AMA session. A priority for each of the plurality of questions may be calculated and a question to be answered from the plurality of questions may be determined based on question priority associated with each of the plurality of questions. An expert associated with the AMA session may be presented with the question. An answer to the question may be received from the expert associated with the AMA session and showing the answer to AMA participants may be facilitated.

Abstract: A computerized method for detecting a change point in a data stream by testing whether two sets of samples from the data stream were derived from the same distribution, wherein the test uses the unique convergence properties of the two sample tests to probabilistically find the point which maximizes their value, said point closely approximating the change point.

Abstract: A recognition dictionary generating device includes a unit that acquires plural reference vectors each containing an offset value indicating a degree of importance; a unit that selects a first reference vector belonging to the class same as an input vector and having the minimum distance from the input vector, and a second reference vector belonging to a class different from the input vector and having the minimum distance from the input vector; a unit that acquires a first distance value indicating a distance between the input vector and the first reference vector and a second distance value indicating a distance between the input vector and the second reference vector; a unit that corrects the first reference vector and the second reference vector using a coefficient changing in accordance with a relationship between the first distance value and the second distance value, the first distance value, and the second distance value; and a determining unit that determines a reference vector to be excluded from a

Abstract: According to an embodiment of the present invention, a system optimizes an information processing environment, and comprises at least one processor. The system collects information pertaining to operational behavior of the information processing environment and including a plurality of parameters. A neural network structure is established to associate the parameters to a desired operational performance characteristic for the information processing environment. The neural network structure is trained with the collected information from the information processing environment to produce a model for the information processing environment. The model is optimized to determine values for the parameters and the information processing environment is adjusted based on the determined parameter values to attain the desired operational performance of the information processing environment.

Abstract: A system is provided for automatically predicting actions a user is likely to take in response to receiving data. The system may be configured to monitor and observe a user's interactions with incoming data and to identify patterns of actions the user may take in response to the incoming data. The system may enable a trainer component and a classifier component to determine the probability a user may take a particular action and to make predictions of likely user actions based on the observations of the user and the identified pattern of the user's actions. The system may also be configured to continuously observe the user's actions to fine-tune and adjust the identified patterns of user actions and to update the probabilities of likely user actions in order increase the accuracy of the predicted user action in response to incoming data.

Abstract: Embodiments of the invention relate to a time-division multiplexed neurosynaptic module with implicit memory addressing for implementing a neural network. One embodiment comprises maintaining neuron attributes for multiple neurons and maintaining incoming firing events for different time steps. For each time step, incoming firing events for said time step are integrated in a time-division multiplexing manner. Incoming firing events are integrated based on the neuron attributes maintained. For each time step, the neuron attributes maintained are updated in parallel based on the integrated incoming firing events for said time step.

Abstract: The various embodiments herein provide a method and system for building a relationship hierarchy from big data. The method comprises extracting a plurality of relationships defined between entities from a big data, building relationship recognition models adapted to identify different forms of generic relationships, resolving the relationships by grouping the similar relationships together and separating the relationships which are syntactically and semantically dissimilar and reconciling the resolved relationships to build the relationship hierarchy. The relationship hierarchy comprises groups and subgroups of relationships created based on generic relationship similarity based on a contextual aspect and a specialization aspect using a Language and Domain model. The method of extraction of plurality of relationships from the unstructured data is a self-learning process which uses open information extraction techniques for learning new relationships.

Abstract: User interfaces for tools for estimating a probability that a future event will occur based on user input are described. One set of interfaces include rating, trend, cohort record and source controls each of which when selected provide corresponding data from one predictive model that produces predictions of the likelihood of an event occurring in the future based on analysis of data in a database. The system further displays a process of content produced by a model builder that populates the interfaces, and outputs thereof.

Abstract: Disclosed is a method and system for analyzing a social network, the method comprising: segregating, by a processor, the social network into a plurality of groups of users, wherein each group of users has a unique identity in the social network; mining, by the processor, data from a group of users of the plurality of groups of users; identifying a plurality of social variables from the data, wherein the plurality of social variables comprises at least one of a number of users, connections of the users, interactions of the users, affinity of the users, or posts by the users; equating a social variable of the plurality of social variables with a thermodynamic variable of a plurality of thermodynamic variables; determining quantitative values of the plurality of thermodynamic variables based upon the plurality of social variables; generating a virtual thermodynamics system based upon the quantitative values for analyzing the social network.

Abstract: A monitoring system for determining the future behavior of a financial system includes an empirical model to receive reference data that indicates the normal behavior of the system and input pattern arrays. Each input pattern array has a plurality of input vectors, while each input vector represents a time point and has input values representing a plurality of parameters indicating the current condition of the system. The model generates estimate values based on a calculation that uses an input pattern array and the reference data to determine a similarity measure between the input values and reference data. The estimate values, in the form of an estimate matrix, include at least one estimate vector of inferred estimate values, and represents at least one time point that is not represented by the input vectors. The inferred estimate values are used to determine a future behavior of the financial system.

Abstract: The aspects of the disclosed embodiments include clustering a set of discrete user interface states into groups; presenting the groups on a display of a device; and enabling selection of any state within a presented group, wherein selection of a state returns the user interface to the selected state.

Abstract: In one embodiment, prior to similarity measure computation, concept expansion based on disjunctive normal form (DNF) decomposition and non-conventional reasoning is applied to an original ontology to generate an expanded ontology having the original concepts plus one or more pseudo concepts. As a result, the similarity measure computation can produce results that more accurately reflect a human point of view than convention techniques.

Abstract: Generating a situation-dependent library comprising a user's expected response to tokens representing stimuli that influence the user's affective state, including: receiving samples comprising temporal windows of token instances to which the user was exposed, wherein the token instances have overlapping instantiation periods and are spread over a long period of time that spans different situations; wherein at least one token is expected to elicit from the user a noticeably different affective response in the different situations; receiving target values corresponding to the temporal windows of token instances; the target values represent the user's responses to the token instances from the temporal windows of token instances; training a machine learning-based user response model using the samples and the corresponding target values; and analyzing the machine learning-based user response model to generate the situation-dependent library comprising the user's expected response to tokens, which accounts for the

Abstract: A method and a system for providing personal assistance in daily activities. A method and a system for automatically and actively providing personal assistance, using a vehicle or a portable electronic device, based on detected data regarding a user and the user's environment. The method and system may include a processor, at least one sensor, an output device, a communications unit, and a database. The database may further include a memory and cloud-based database and computing. The method and system may actively and automatically provide personal assistance regarding health, exercise, diet, or nutrition. The method and system may assist the user or a health professional in health diagnosis and treatment.