Abstract: A system and method for defining and calibrating the inputs to a sequential decision problem using historical data, where the user provides historical data and the system and method forms the historical data (along with other inputs) into at least one of the states, actions, rewards or transitions used in composing and solving the sequential decision problem.

Abstract: An approach is provided in which an information handling system trains multiple classifiers using a set of training samples. The information handling system selects a leader classifier from the multiple classifiers that generates the most amount of correct decisions corresponding to the set of training samples. Next, the information handling system identifies an endorser classifier from the multiple classifiers that generates the highest proportion of correct decisions among the endorser classifier's decisions matching the leader classifier's decisions, and combines the leader classifier and the endorser classifier into a combined classifier stage. In turn, the information handling system utilizes the combined classifier stage to process inquiries and generate results.

Abstract: An indication of an application-related objective is received at a first agent associated with a first device. One or more meta-language based descriptions comprising device data stored in a data store are used to identify a second device from a plurality of devices to help the first device to achieve the application-related objective. At least in part by exchanging with a second agent associated with the second device one or more programmatically determined bids and responses to bids, a mutually compatible set of configurations for the first device and the second device to cooperate with each other are determined to help achieve the application-related objective. The mutually compatible set of configurations comprising parameters are agreed upon by the first device and the second device. The first device and the second device are configured based at least in part on the mutually compatible set of configurations.

Abstract: A server room power management apparatus and method, the power management apparatus comprising a first PDE (power distribution equipment), at least one second PDE and at least one DM (digital meter); wherein a first network interface unit of the first PDE, a second network interface unit of the second PDE and the DM can be connected to become a network; the power management method uses a first processor of the first PDE to collect power consumptions of the second PDE and the DM; wherein, the first processor can work out power usage effectiveness; thus, the apparatus and method do not need an independent server device, that will reach power saving and reduce apparatus costs.

Abstract: Computer-assisted analysis of a data record from observations is provided. The data record contains, for each observation, a data vector that includes values of input variables and a value of a target variable. A neuron network structure is learned from differently initialized neuron networks based on the data record. The neuron networks respectively include an input layer, one or more hidden layers, and an output layer. The input layer includes at least a portion of the input variables, and the output layer includes the target variable. The neuron network structure outputs the mean value of the target variables of the output layers of the neuron networks. Sensitivity values are determined by the neuron network structure and stored. Each sensitivity value is assigned an observation and an input variable. The sensitivity value includes the derivative of the target variable of the assigned observation with respect to the assigned input variable.

Abstract: Aspects of a neural network operation device are described herein. The aspects may include a matrix element storage module configured to receive a first matrix that includes one or more first values, each of the first values being represented in a sequence that includes one or more bits. The matrix element storage module may be further configured to respectively store the one or more bits in one or more storage spaces in accordance with positions of the bits in the sequence. The aspects may further include a numeric operation module configured to calculate an intermediate result for each storage space based on one or more second values in a second matrix and an accumulation module configured to sum the intermediate results to generate an output value.

Abstract: A measured value analysis apparatus includes a controller and a memory, in which the controller: selects one examination object item, which is an examination object, and a plurality of related items which are directly or indirectly related to the examination object item; creates a correlation model between measured values from past measured values of the examination object item and the plurality of related items; and calculates a predicted value of the examination object item by applying the measured values of the related items to the correlation model and calculates divergence between an actual measured value of the examination object item and the predicted value.

Abstract: Systems and methods for preventing machine learning models from negatively affecting mobile devices are provided. For example, a mobile device including a camera, memory devices, and one or more processors are provided. In some embodiments, the processors may be configured to provide images captured by the camera to a machine learning model at a first rate. The processors may also be configured to determine whether one or more of the images includes an object. If one or more of the images includes the object, the processors may be further configured to adjust the first rate of providing the images to the machine learning model to a second rate, and in some embodiments, determine whether to adjust the second rate of providing the images to the machine learning model to a third rate based on output received from the machine learning model.

Abstract: Methods, systems, apparatuses, and computer program products are provided for altering the behavior of an electronic personal assistant based on a situation associated with a mobile device. A situation is sensed with a plurality of sensors to generate sensor data. A situation score is calculated based on the sensor data. Behavior of an electronic personal assistant is altered based on the calculated situation score. In one aspect, the situation is a driving situation in which a driver drives a vehicle on a roadway. In such case, a driving situation score is calculated based on the sensor data, and behavior of the electronic personal assistant is altered based on the calculated driving situation score, such as suspending interactions by the electronic personal assistant with the driver to avoid the driver being distracted.

Abstract: Systems and methods may include receiving, by an expert system, performance data for a monitored system. The systems and methods may include generating a prediction for the monitored system in response to determining that the performance data satisfies a condition. The prediction may identify an anomaly that is predicted to occur. The systems and methods may include receiving, by a filter system, the prediction, information identifying the condition, and user information. The user information may include user preference information and user feedback information. The systems and methods may include determining a filter criteria based on the user information. The filter criteria may be based on the preferences for predictions to be provided to the user and on the historical user feedback regarding the historical predictions. The systems and methods may include providing the prediction to the user in response to determining that the particular prediction satisfies the filter criteria.

Abstract: An inspection device includes a first processor, a second processor, and a server. The first processor detects first coordinates of first feature points from first images in a first image set. The second processor detects second coordinates of second feature points from second images in a second image set. The server generates reference coordinates based on the first coordinates and the second coordinates. The reference coordinates are transmitted to the first processor and the second processor. The first and second image sets correspond to scanned swaths on a wafer.

Abstract: Embodiments herein include a NLP application used to coach a participant who violates a social norm during a conversation. For example, the NLP application can evaluate the textual representation of the conversation to determine if the participant is exhibiting a characteristic of autism or other medical disorder which violates a social norm, and if so, inform the participant. Once a characteristic of autism is identified, a coaching application may output text that informs the participant what particular characteristic he is exhibiting—e.g., the participant is ignoring an attempt by another participant to change the topic of the conversation. In addition to providing notice, in one embodiment, the coaching application suggests a corrective action to the participant. For example, if the participant fails to provide an appropriate response to an emotional statement, the coaching action may suggest a sympathetic statement.

Abstract: A system and method for projecting the likely future path of the subject of a sequential decision problem. The subject of the sequential decision problem takes an action beginning with the starting state of affairs and probabilistically transitions into other states according to the structure of the decision problem, the solution to the decision problem, possibly random events, and the decisions of the subject. The likely future path consists of a sequence of actions taken by the subject, the states the subject will likely be in after taking the projected actions, and the rewards the subject is likely to receive along the future path.

Abstract: A single model engine for receiving and processing a 3D surface model representing the surface of a 3D object, the 3D surface model comprising at least two distinct surface regions associated with at least two different materials. The single model engine automatically produce a set of interior sheets inside the 3D surface model, the set of interior sheets defining interior boundaries and interior volumes of the different materials for the 3D object. The single model engine combines the 3D surface model with the set of interior sheets to produce a single unified model that represents the surface and interior volumes of the 3D object that comprise a single solid object having at least two different materials. At print time, the single model engine performs an export technique to produce an exportable form of the single unified model that can be received and printed by a 3D printer.

Abstract: A processing unit can successively operate layers of a multilayer computational graph (MCG) according to a forward computational order to determine a topic value associated with a document based at least in part on content values associated with the document. The processing unit can successively determine, according to a reverse computational order, layer-specific deviation values associated with the layers based at least in part on the topic value, the content values, and a characteristic value associated with the document. The processing unit can determine a model adjustment value based at least in part on the layer-specific deviation values. The processing unit can modify at least one parameter associated with the MCG based at least in part on the model adjustment value. The MCG can be operated to provide a result characteristic value associated with test content values of a test document.

Abstract: Methods and arrangements for identifying burden comprehension in multimedia content. A contemplated method includes: accepting multimedia input; detecting components of the multimedia input; determining a comprehension burden score of each of the detected components; and thereupon calculating a total comprehension burden score for the multimedia input. Other variants and embodiments are broadly contemplated herein.

Abstract: A technique is able to direct a power signal from a port power controller to one of multiple physical ports. The technique involves activating a set of port power controllers. Each port power controller is constructed and arranged to deliver power to a device through at most one physical port at a time. The technique further involves performing a set of discovery operations to discover device presence, the set of discovery operations providing discovery data. The technique further involves providing, based on the discovery data provided by the set of discovery operations, a set of switching signals to switching circuitry which is coupled to a set of physical ports. The switching circuitry is constructed and arranged to steer power signals from the activated set of port power controllers through the set of physical ports to deliver power to a set of devices.

Abstract: Apparatuses, systems, methods, and computer program products are disclosed for machine learning in a data management product. The apparatus includes an input module, a learned function module, and a results module. The input module is configured to receive an analysis request for the data management product. The learned function module is configured to execute one or more machine learning ensembles to predict one or more unknown values for the data management product. The result module is configured to provide native access, within the data management product, to the one or more unknown values.

Abstract: A neural network includes inputs for receiving input signals, and synapses connected to the inputs and having corrective weights organized in an array. Training images are either received by the inputs as an array or codified as such during training of the network. The network also includes neurons, each having an output connected with at least one input via one synapse and generating a neuron sum array by summing corrective weights selected from each synapse connected to the respective neuron. Furthermore, the network includes a controller that receives desired images in an array, determines a deviation of the neuron sum array from the desired output value array, and generates a deviation array. The controller modifies the corrective weight array using the deviation array. Adding up the modified corrective weights to determine the neuron sum array reduces the subject deviation and generates a trained corrective weight array for concurrent network training.

Abstract: A system, method, and computer program product are provided for providing proactive customer care for issues associated with billing or ordering processes. In use, a likelihood that a customer is going to call a call center to address at least one issue associated with at least one of an ordering process or a billing process is predicted. Additionally, it is determined whether the customer is likely to call the call center based on the predicted likelihood that the customer is going to call the call center. Further, the customer is proactively notified before the customer contacts the call center, if it is determined that the customer is likely to call the call center.