Abstract: A system, method and computer product for predicting a content attribute are provided by collecting labeled media objects labeled with one or more attributes, constructing a feature vector of content-derived values for each of the labeled media objects, generating a model based on the feature vector and the one or more attributes, and applying the model to one or more unlabeled media objects to generate one or more attribute labels.

Abstract: A method, system, and computer program product identifies attribution of small signal stimulus in noisy response channels. Using machine-learning techniques in a computer, a small signal correlation engine correlates time series stimuli data vectors to time series response data vectors, and generates correlation coefficients that identify contributions of event notifications, including small signal attributes, to aggregated response data. Also using machine-learning techniques in a computer, a learning model simulates variations of stimuli data to predict user responses using the correlation coefficients, including computing a contribution of the small signal attributes of an event notification.

Abstract: Embodiments of the present invention disclose a defect prediction method and apparatus, which relate to the data processing field, and implement accurate and quick locating of a defect in a faulty product. A specific solution is as follows: selecting a training attribute set from a pre-stored product fault record according to a target attribute, and combining the target attribute and the training attribute set into a training set, where the target attribute is a defect attribute of a historical faulty product; generating a classifier set according to the training set, where the classifier set includes at least two tree classifiers; and predicting a defect of a faulty product by using the classifier set as a prediction model. The present invention is used in a process of predicting a defect of a faulty product.

Abstract: An apparatus can include a first state machine engine configured to receive a first portion of a data stream from a processor and a second state machine engine configured to receive a second portion of the data stream from the processor. The apparatus includes a buffer interface configured to enable data transfer between the first and second state machine engines. The buffer interface includes an interface data bus coupled to the first and second state machine engines. The buffer interface is configured to provide data between the first and second state machine engines.

Abstract: The present disclosure provides methods and systems for using variable length representations of machine learning statistics. A method may include storing an n-bit representation of a first statistic at a first n-bit storage cell. A first update to the first statistic may be received, and it may be determined that the first update causes a first loss of precision of the first statistic as stored in the first n-bit storage cell. Accordingly, an m-bit representation of the first statistic may be stored at a first m-bit storage cell based on the determination. The first m-bit storage cell may be associated with the first n-bit storage cell. As a result, upon receiving an instruction to use the first statistic in a calculation, a combination of the n-bit representation and the m-bit representation may be used to perform the calculation.

Abstract: Approaches are described for discovering a formation associated with an agent group engaging in an activity over a window of time. A formation analysis system computes first and second results for an objective function based on first and second sets of role assignments for each agent in the agent group at first and second moments in time, respectively. The formation analysis system iterates by: replacing the first set of role assignments with the second set of role assignments, and determining whether completion criteria have been met based at least in part on comparing the first result with the second result. If the completion criteria have not been met, then the formation analysis system replaces the second set of role assignments with a third set of role assignments that associate each agent in the first agent group with a different role assignment in the third set of role assignments at a third moment in time.

Abstract: A question is received in a computerized answering system via an input device. A plurality of classes corresponding to the question are retrieved from a memory device. One or more answers to the question are generated by a processor. Each answer is associated with a confidence value. The confidence values associated with the answers are compared with thresholds associated with the plurality of classes. The answers are classified by the processor into the plurality of classes based on the comparing. The answers are presented via a display device in accordance with the classification of the answers. A user response to at least one of the answers is detected. At least one of the plurality of classes is adjusted based on the user response.

Abstract: Provided is an information processing apparatus including a learning part performing learning of a model of an environment in which an agent performs action, using an observed value observed in the agent when the agent capable of action performs action, an action determining part determining action to be performed by the agent, based on the model, and a user instruction output part outputting instruction information representing an instruction from a user according to the instruction from the user, wherein the action determining part determines the action performed by the agent according to the instruction information when there is an instruction from the user.

Abstract: A method of learning parameters of a CNN is provided. The method includes steps of: (a) allowing an input value to be delivered to individual multiple element bias layers; (b) allowing the scale layer connected to a specific element bias layer to multiply a predetermined scale value by an output value of the specific element bias layer; (c) (i) allowing a specific element activation layer connected to the scale layer to apply activation function, and (ii) allowing the other individual element activation layers to apply activation functions to output values of the individual element bias layers; (d) allowing a concatenation layer to concatenate an output value of the specific element activation layer and output values of the other element activation layers; (e) allowing the convolutional layer to apply the convolution operation to the concatenated output; and (f) allowing a loss layer to acquire a loss during a backpropagation process.

Abstract: A method and an apparatus for carrying out the method, for ascertaining at least one exit probability from a land area identifiable in a digital map as open terrain, wherein the exit probability is ascertained starting from a position within the land area. The method includes a step of subdividing the land area into a plurality of cells, with an initial transition probability being assigned to at least one transition from one cell to an adjacent cell. The method further includes a step of adapting the initial transition probability of the transition in response to a presence of at least one item of information from the digital map, to obtain an adapted transition probability for the transition, the item of information being associated with the cell or with the adjacent cell. The method also includes ascertaining the exit probability at least using the adapted transition probability of the transition.

Abstract: Context-based sentiment analysis includes: determining whether a piece of comment data included in a comment is context-sensitive, the determination being made with reference to a set of comment features; determining a context sentiment type of an associated context related to the piece of comment data, the associated context being distinct from the comment, in the event that the piece of comment data is determined to be context-sensitive, and the determination being made with reference to at least a set of context features; and classifying the comment data in the event that the piece of comment data is determined to be context-sensitive, the classification being based at least in part on the comment data and the context sentiment type.

Abstract: A quantum processor is operable as a universal adiabatic quantum computing system. The quantum processor includes physical qubits, with at least a first and second communicative coupling available between pairs of qubits via an in-situ tunable superconducting capacitive coupler and an in-situ tunable superconducting inductive coupler, respectively. Tunable couplers provide diagonal and off-diagonal coupling. Compound Josephson junctions (CJJs) of the tunable couplers are responsive to a flux bias to tune a sign and magnitude of a sum of a capacitance of a fixed capacitor and a tunable capacitance which is mediated across a pair of coupling capacitors. The qubits may be hybrid qubits, operable in a flux regime or a charge regime. Qubits may include a pair of CJJs that interrupt a loop of material and which are separated by an island of superconducting material which is voltage biased with respect to a qubit body.

Abstract: Techniques for determining levels of interest, activity, or occupancy at a physical location can include receiving data corresponding to physical parameters sensed by a plurality of sensors at the physical location. The physical parameters can include temperature, humidity, pressure, sound, distance to an object, visible light, infra-red light, motion of objects, acceleration, magnetic field, vibration, and radio signals. Synthetic variables can be generated based on the received data and can represent a processed or combined value for its corresponding physical parameters. The physical parameters and synthetic variables can be stored in a memory device. One or more indicators for a level of: (i) interest, (ii) activity, or (iii) occupancy at the physical location can be generated based on the received data and the one or more synthetic variables by utilizing a machine learning model and output to a user computing device for display in a user interface.

Abstract: Provided is a sensory effect representation apparatus including a collection unit configured to collect user status information about a user status, a storage unit configured to store sensory effects; an analysis unit configured to analyze the user status information to generate a user status prediction pattern, a sensory effect recommendation unit configured to recommend a sensory effect corresponding to the user status prediction pattern, from among the stored sensory effects; and a sensory effect provision unit configured to read a sensory effect corresponding to the sensory effect prediction pattern, from the storage unit and provide the read sensory effect. Accordingly, it is possible to provide a sensory effect appropriate for a user characteristic by predicting a user status and reflecting a feedback on a recommended sensory effect.

Abstract: An example system includes a similarity module, a selection module, and a messaging module. The similarity module may receive a first set of application identifiers identifying applications downloaded or installed on at least one digital device, compare the first set of application identifiers to a second set of application identifiers identifying applications of at least one other user, generate a similarity value based on the comparison, and when the similarity value exceeds a similarity threshold, determine that the first and second sets are sufficiently similar. If the first and second sets are sufficiently similar, the selection module may determine at least one application identifier in the second set that is not in the first set. The messaging module may provide a recommendation to the first user, the recommendation including the at least one application identifier in the second set that is not in the first set.

Abstract: Systems and methods for configuring AI algorithms and knowledge sets within an automated messaging system are providing. In some embodiments, a message is received. A subsection of text from the training message is selected. Likewise, a knowledge set is selected. The knowledge set includes probabilistic associations between a term and a category. The terms in the selected subsection of text are compared to the knowledge sets to generate insights and contexts. The insights enable the categorization of the training message. This categorization has an associated confidence value based upon how strongly the terms in the text subsection are associated with the category (per the selected knowledge set). A low confidence value causes the message to be a candidate for training (a training message). Once identified as a training message, it may be displayed to an AI developer for approval or rejection of the categorization.

Abstract: An information processing apparatus may include a processor to acquire information associated with behavior of a user and information associated with satisfaction degree of the user, and to analyze an association between the information associated with behavior and the information associated with satisfaction degree.

Abstract: Methods and apparatus are provided for analytical processing of provenance data for High Performance Computing workflow optimization. Prediction models for a workflow composed of a plurality of activities are created by (i) generating a plurality of prediction functions from input features and output features of the workflow, wherein each of the prediction functions predicts at least one output feature of at least one of activities of the workflow based on the input features of at least one activity; and (ii) combining the plurality of prediction functions to generate the prediction models, wherein each of the prediction models predicts a final output feature of the workflow based on an input of the workflow for a given execution plan of the workflow. A plurality of the prediction models can be evaluated to select, among the possible execution plans, an instantiation of the workflow for a given input that optimizes a given user goal.

Abstract: Various aspects of a method and system for providing information via an intelligent user interface are disclosed herein. In an embodiment, in response to the receipt of a request from an electronic device, the method includes determination of a first information response that may correspond to a first functional service. A set of second information responses that corresponds to a set of second functional services may be determined based on the first information response. Each of the determined set of second information responses is associated with a corresponding weight. One or more of the determined set of second information responses are selected based on the corresponding weight. The second information responses are value-added responses.

Abstract: Non-mechanistic, differential-equation-free approaches for predicting a particular pharmacokinetic and pharmacodynamic responses of a system to a given input are provided in the form of systems, methods, and devices. These approaches are generally directed to a non-compartmental method of predicting a time-dependent pharmacokinetic response, or pharmacodynamics response, of a component of a system to an input into the system.