Abstract: A method and system for increasing data quality of a dataset for semi-supervised machine learning analysis. The method includes: receiving known class label information for a portion of the data in the dataset; receiving clustering parameters from a user; determining a data cleanliness factor, and where the data cleanliness factor is below a predetermined cleanliness threshold: assigning data without class label information as a data point to a cluster using the clustering parameters, each cluster having a cluster class label associated with such cluster; and determining a measure of assignment, and where the measure of assignment for each data point is below a predetermined assignment threshold, receiving a class label for such data points, otherwise, assigning the respective cluster class label to each data point with the respective measure of assignment below the predetermined assignment threshold; and otherwise, outputting the dataset with associated class labels for machine learning analysis.

Abstract: There is provided an information processing method and an information processing device to provide positional information with higher accuracy in accordance with situations in the real world. The information processing method includes: generating, by a processor, a feature point list in which three-dimensional coordinates of a feature point are associated with a local feature amount of the feature point, the feature point having been detected from observation information collected around a unit area. The information processing device includes: a computation section configured to extract a feature point and a local feature amount related to the feature point from acquired image information; and a communication section configured to acquire a feature point list on a basis of collected observation information.

Abstract: In some embodiments, an exemplary inventive computer-implemented method may include steps, performed by a processor, of: obtaining training real representations of a real subject; obtaining a training synthetic representation having a visual effect applied to a synthetic subject; training a first neural network and a second neural network by: presenting the first neural network with training real representation and candidate meta-parameters of latent variables for the visual effect to generate a training photorealistic-imitating synthetic representation of the real subject with the visual effect; presenting the second neural network with the training photorealistic-imitating synthetic representation and the training synthetic representation to determine actual meta-parameters of the latent variables of the visual effect, where the actual meta-parameters are meta-parameters at which the second neural network has identified that the training photorealistic-imitating synthetic representation is realistic, and pr

Abstract: A method and an apparatus of screening samples, and a method and an apparatus of searching for service object data are provided. The method of screening samples includes setting up a plurality of screening layers; selecting training samples needed by a current screening layer in the plurality of screening layers; extracting target sample features suitable for the current screening layer from the training samples; determining a screening quantity suitable for the current screening layer using the target sample features; and screening target samples based on the target sample features and the screening quantity. The embodiments of the present disclosure can adaptively adjust respective screening quantities of various screening layers, and can maximally optimize resources, thus balancing effects and performance.

Abstract: A system, method and computer program product is provided. An input signal for classification and a set of pre-classified signals are received, each comprising a vector representation of an object having a plurality of vector elements. A sparse vector comprising a plurality of sparse vector coefficients is determined. Each sparse vector coefficient corresponds to a signal in the set of pre-classified signals and represents the likelihood of a match between the object represented in the input signal and the object represented in the corresponding signal. A largest sparse vector coefficient is compared with a predetermined threshold. If the largest sparse vector coefficient is less than the predetermined threshold, the corresponding signal is removed from the set of pre-classified signals. The determining and comparing are repeated using the input signal and the reduced set of pre-classified signals.

Abstract: A device for use in detecting metallic objects includes a processor and an electromagnetic radiation source in communication with the processor. The electromagnetic radiation source is configured to emit radiation to heat a metallic object. The device also includes a temperature sensor in communication with the processor. The temperature sensor is configured to detect heat emitted from the metallic object. The device also includes an alarm configured to notify an operator of the presence of the metallic object responsive to a determination by the processor that a temperature threshold has been exceeded.

Abstract: A method for operating a teleconference includes receiving a first video and audio signal from a first teleconference participant and receiving a second video and audio signal from a second teleconference participant. The method further includes relaying the first video and audio signal to the second teleconference participant and relaying the second video and audio signal to the first teleconference participant. The method additionally includes analyzing the first video and audio signal using a plurality of behavior analytics to determine a level of participant engagement of the first teleconference participant. The method further includes determining that the first teleconference participant is not engaged when the determined level of participant engagement is below a predetermined threshold.

Abstract: Methods, apparatus, and systems are provided for integrated driver expression recognition and vehicle interior environment classification to detect driver condition for safety. A method includes obtaining an image of a driver of a vehicle and an image of an interior environment of the vehicle. Using a machine learning method, the images are processed to classify a condition of the driver and of the interior environment of the vehicle. The machine learning method includes general convolutional neural network (CNN) and CNN with adaptive filters. The adaptive filters are determined based on influence of filters. The classification results are combined and compared with predetermined thresholds to determine if a decision can be made based on existing information. Additional information is requested by self-motivated learning if a decision cannot be made, and safety is determined based on the combined classification results. A warning is provided to the driver based on the safety determination.

Abstract: One embodiment provides a method, including: receiving, at an information handling device, user command input comprising identification of an object; receiving, at an information handling device, image input comprising at least one object; determining, using a processor, whether the object in the user command input is associated with the at least one object in the image input; and responsive to determining that the object in the user command input is associated with the at least one object, performing a function based on the determination.

Abstract: Provided are an image processing apparatus, an image processing method, and a storage medium that can contribute to formation of electric circuits having different resistance values without causing increase in cost. The image processing apparatus generates print data for printing an electrically conductive print image on a print medium by ejecting a metal particle-containing ink from ejection device, the image processing apparatus has: a setting unit configured to set a printing condition based on a resistance value of the electrically conductive print image so as to change at least one of an amount of ink droplets of the metal particle-containing ink contacting each other and a time to be taken for ejected metal particle-containing ink droplets to contact each other; and a generation unit configured to generate the print data based on image data for the electrically conductive print image and the printing condition.

Abstract: A method for manufacturing a carrier tape housing electronic components with seal materials includes preparing a tape-shaped main body including housing holes penetrating from one surface to other surface along a longitudinal direction, preparing a tape-shaped seal material including an adhesive layer on the one surface and including pairs of terminal electrodes on the adhesive layer, affixing the adhesive layer to the other surface such that respective portions of the paired terminal electrodes are located within each of the housing holes in a planar view, forming cuts in the tape-shaped seal material to separate portions defining and functioning as the seal materials including portions overlapping with the respective housing holes in a planar view from the other portions, and providing an electronic component into each housing hole and connecting the respective portions of the paired terminal electrodes located within each of the housing holes to the electronic component.

Abstract: The invention relates to an electronic card comprising a card body (1) comprising an upper surface (11) and a lower surface (12); a fingerprint sensor (2) flush with the upper surface of said card body; a tactile mark (3) placed on the upper surface of the card body near the fingerprint sensor, said tactile mark having an excess thickness from the upper surface of the card to guide, when said mark is touched, a correct positioning of a finger on said fingerprint sensor, the thickness of the tactile mark being such that the card has a thickness compatible with standard ISO 781 ID1, the thickness of the tactile mark preferably being between 30 and 60 ?m, typically 50 ?m.

Abstract: A radio tag reading device performs wireless communication with a plurality of radio tags to acquire predetermined information from the plurality of radio tags. The radio tag reading device includes a controller configured to perform a first acquiring process and a second acquiring process. The first acquiring process includes acquiring predetermined information from a first radio tag among the plurality of radio tags by transmitting a predetermined request one or more times by wireless communication at a first transmission rate. The second acquiring process includes acquiring predetermined information from a second radio tag different from the first radio tag within the plurality of radio tags by transmitting the request one or more times by wireless communication at a second transmission rate slower than the first transmission rate after the acquisition of the predetermined information in the first acquiring process is ended.

Abstract: A wireless (e.g., near field or RF) communication device, and methods of manufacturing the same are disclosed. The method of manufacturing the wireless communication device includes forming an integrated circuit on a first substrate, printing stud bumps on input and/or output terminals of the integrated circuit, forming an antenna on a second substrate, and electrically connecting ends of the antenna to the stud bumps. The antenna is configured to (i) receive and (ii) transmit or broadcast wireless signals.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems, including a method for placing a card into an alert state. An alert detection indication is received during an attempted data exchange using a permanent account number (PAN) associated with a card. The alert detection indication is received by an electronic controller embedded in the card and is received through an interface. The alert detection indication indicates that the PAN has an alert state identified for the PAN by an entity that issued the card. An operational mode of the card is modified into the alert state by the electronic controller and is based on receipt of the alert detection indication. An electronic display indicating that the card is in the alert state is displayed by the electronic controller.

Abstract: In a tracking device, a crescent-shaped printed circuit board partially encircles a battery to minimize thickness of the tracking device. A speaker and a light-emitting diode emit alerts upon command of a control apparatus or in response to a motion or temperature sensed by a sensor. A local network has one hub for tracking a plurality of tracking devices and a wider area network has multiple hubs for more detailed tracking of the plurality of tracking devices. The wide area network tracks the plurality of tracking devices anywhere and stores data of each tracking device including its last known position and its sensor data.

Abstract: The present technology is an automatically configuring virtual assistant. The virtual assistant is assigned to an existing conversation space and, based on analyzing the existing conversation space the virtual assistant has been assigned to, is associated with at least one contextual cue of an existing conversation space. The analysis includes natural language processing of a title, a topic, or a past conversation of the existing conversation space in order to determine the at least one contextual cue.

Abstract: The present disclosure relates to an apparatus and method of allocating a question according to a question type or question feature. A question allocating apparatus for the same may include a question analysis unit generating at least one of question type information and question feature information of a current question; and a question allocating unit determining an answer generating unit suitable for the current questions among a plurality of answer generating units based on at least one of the question type information, and the question feature information, and allocating the current question to at least one answer generating including the determined answer generating unit.

Abstract: The present disclosure relates to method and system for unbiased execution of tasks using neural response analysis of users by neural response analysis system. The neural response analysis system comprises providing one or more predefined neural stimulus, to plurality of users, receive neural responses from plurality of users in view of one or more predefined neural stimulus, correlate one or more neural responses of plurality of users with corresponding data associated with each of plurality of users stored in stimulus response mapping database to create logical matrix indicative of one or more solution parameters for resolving task, create solution model to resolve task based on logical matrix, where solution model eliminates biases and preferences of plurality of users determined based on correlation.

Abstract: A deep approximation neural network architecture which extrapolates data over unseen states for demand response applications in order to control distribution systems like product distribution systems of which energy distribution systems, e.g. heat or electrical power distribution, are one example. The method is a model-free control technique mainly in the form of Reinforcement Learning (RL) where a controller learns from interaction with the system to be controlled to control product distributions of which energy distribution systems, e.g. heat or electrical power distribution, are one example.

Abstract: Neuromorphic devices are described. A neuromorphic device may include a pre-synaptic neuron; row lines extending from the pre-synaptic neuron in a first direction; a post-synapse neuron; a column line extending from the post-synaptic neuron in a second direction perpendicular to the first direction; and synapses disposed in intersection regions between the row lines and the column line. The synapses may include a first synapse having a first memristor; and a second synapse having a first selection device and a second memristor.

Abstract: Cooperative neural networks reinforcement learning may be performed by obtaining an action and observation sequence, inputting each time frame of the action and observation sequence sequentially into a first neural network including a plurality of first parameters and a second neural network including a plurality of second parameters, approximating an action-value function using the first neural network, and updating the plurality of second parameters to approximate a policy of actions by using updated first parameters.

Abstract: Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.

Abstract: Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.

Abstract: Systems and methods are provided for performing predictive assignments pertaining to genetic information. One embodiment is a system that includes a genetic prediction server. The genetic prediction server includes an interface that acquires records that each indicate one or more genetic variants determined to exist within an individual, and a controller. The controller selects one or more machine learning models that utilize the genetic variants as input, and loads the machine learning models. For each individual in the records: the controller predictively assigns at least one characteristic to that individual by operating the machine learning models based on at least one genetic variant indicated in the records for that individual. The controller also generates a report indicating at least one predictively assigned characteristic for at least one individual, and transmits a command via the interface for presenting the report at a display.

Abstract: An optoelectronic code reader (10) having at least one light receiving element (24) for generating image data from reception light and an evaluation unit (26) with a classifier (30) being implemented in the evaluation unit (26) for assigning code information to code regions (20) of the image date, wherein the classifier (30) is configured for machine learning and is trained by means of supervised learning based on codes read by a classic decoder (28) which does not make use of methods of machine learning.

Abstract: There is provided an information processing apparatus to efficiently realize control learning in accordance with an environment in the real world, the information processing apparatus including: a generating unit configured to generate response information relating to a control target in an environmental model generated on a basis of an environmental parameter; and a transmitting unit configured to transmit the response information and the environmental parameter to a learning unit which performs machine learning relating to control of the control target.

Abstract: The present invention provides a knowledge graph construction method and device. The method includes: obtaining structured data, where the structured data includes a first entity name of a first entity and attribute information corresponding to the first entity name, and the attribute information includes a first attribute and a first attribute value; performing, based on measurement of a similarity between the first entity and a second entity in a knowledge graph, entity alignment processing on the first entity, where the measurement of the similarity includes at least one of the following types: measurement of a character similarity, measurement of a structure similarity of a classification tree on which an entity is located, and measurement of an attribute similarity; and importing the structured data into the knowledge graph according to an entity alignment processing result. Embodiments may ensure correctness of data in the knowledge graph.

Abstract: In one embodiment of the present invention, environment information corresponding to a user is received. A target environment is simulated in augmented reality based on the environment information. Physiological information corresponding to the user is received. A cognitive state of the user is determined based on the physiological information. In response to determining that the cognitive state of the user is not a target cognitive state, the target environment is modified in augmented reality to achieve the target cognitive state.

Abstract: Implementations are directed to a field services management (FSM) platform by providing a services layer including a plurality of micro-services, each micro-service executing one or more FSM tasks associated with an asset, providing at least one presentation layer including a plurality of channels, through which a user communicates with the FSP platform, receiving, by a virtual agent, and through a channel of the one or more channels, input data from one of the user and the asset, transmitting, by the virtual agent, at least a portion of the input data to an artificial intelligence (AI) system, receiving, by the virtual agent, response data from the AI system, the response data representing an intent, initiating at least one micro-service to perform at least one FSM task based on the intent, the at least one micro-service being performed based on interactions between the user and the virtual agent to progress through a work-flow of the at least one micro-service, and storing interaction data and asset data in

Abstract: In one embodiment of the present invention, environment information corresponding to a user is received. A target environment is simulated in augmented reality based on the environment information. Physiological information corresponding to the user is received. A cognitive state of the user is determined based on the physiological information. In response to determining that the cognitive state of the user is not a target cognitive state, the target environment is modified in augmented reality to achieve the target cognitive state.

Abstract: In one embodiment of the present invention, environment information corresponding to a user is received. A target environment is simulated in augmented reality based on the environment information. Physiological information corresponding to the user is received. A cognitive state of the user is determined based on the physiological information. In response to determining that the cognitive state of the user is not a target cognitive state, the target environment is modified in augmented reality to achieve the target cognitive state.

Abstract: An acquisition portion obtains the daily gas consumption amount of a tank from a gas meter. A consumption amount predicting portion predicts future daily gas consumption amounts for the set number of days using the latest gas consumption amount having the same day of the week among the gas consumption amounts obtained by the acquisition portion. A replacement day predicting portion, as a remaining amount predicting portion, predicts the remaining gas amount in the tank using the gas consumption amounts obtained by the acquisition portion and the future gas consumption amounts for the set number of days predicted by the consumption amount predicting portion.

Abstract: Methods and apparatus are described herein for selecting, from one or more electronic data sources, a candidate textual statement associated with an entity, identifying one or more attributes of the candidate textual statement; and determining, based on the identified one or more attributes of the candidate textual statement, a measure of suitability of the candidate textual statement for presentation as a testimonial about the entity.

Abstract: Disclosed herein are system, method, and computer program product embodiments for detecting erroneous data. In an embodiment, a data monitoring system may store an initial dataset. The data monitoring system may analyze the initial dataset to generate rules associated with the initial dataset. The data monitoring system may receive a new data entry from a client device intended to be associated with the initial dataset. The data monitoring system may compare the new data entry to the previously determined rules to determine if the new data entry complies. If so, the data monitoring system may store the new data entry. If not, the data monitoring system may generate an alert, requesting a confirmation that the noncompliant data entry is correct. If the noncompliant data is confirmed as correct, the data monitoring system may store the new data entry and update the rules associated with the updated dataset.

Abstract: A model parameter value estimator includes a plant model which is a preset physical model simulating an operation of a target product, to which a model parameter value is input, and which computes a process value. A measurement value of the target product and a plurality of process values computed by the plant model are input to a model parameter value estimation section, which performs Bayesian updating on a probability density function accumulated in an accumulation section while regarding a function generated on the basis of accuracy evaluation of each of the plurality of process values with respect to the measurement value as a likelihood function. It is thereby possible to provide an estimator and an estimation method for model parameter value estimation capable of estimating a model parameter value even if a distribution profile and statistics for the probability density function are unknown or difficult to estimate.

Abstract: Systems and methods for automated node status determination are disclosed herein. The system can include a memory. The memory can include a Q-matrix that can include data identifying a relationship between at least a portion of a standard and at least one item of an assessment. The system can include at least one server. The at least one server can provide a first interim assessment including a first plurality of items. Each of the items can correspond to a child evidence node in a Bayesian network. The at least one server can generate first evidence by evaluating responses received to the first plurality of items from the first interim assessment and can calculate a mastery probability according to the Bayesian network of at least one parent node in the Bayesian network based on the generated first evidence.

Abstract: Systems and methods are provided for readout of a qubit. A readout resonator is coupled to a transmission line and a compound Josephson junction coupler couples the qubit to the readout resonator. A coupling controller controls the coupling strength of the compound Josephson junction coupler such that a coupling between the qubit and the readout resonator is a first value when a state of the qubit is being read and a second value during operation of the qubit.

Abstract: Passive and actives approaches to mitigating the effects of spin-bath polarization are described and illustrated. Such may, for example, include at least partially depolarizing the spin-bath polarization, for instance by: performing an annealing cycle by the quantum processor to generate a final state of a qubit of the quantum processor; flipping the final state of the qubit of the quantum processor to an opposite state; and latching the qubit in the opposite state for a predetermined duration.

Abstract: A photonic parallel network can be used to sample combinatorially hard distributions of Ising problems. The photonic parallel network, also called a photonic processor, finds the ground state of a general Ising problem and can probe critical behaviors of universality classes and their critical exponents. In addition to the attractive features of photonic networks—passivity, parallelization, high-speed and low-power—the photonic processor exploits dynamic noise that occurs during the detection process to find ground states more efficiently.

Abstract: Methods and apparatus for enhancing simulated annealing with quantum fluctuations. In one aspect, a method includes obtaining an input state; performing simulated annealing on the input state with a temperature reduction schedule until a decrease in energy is below a first minimum value; terminating the simulated annealing in response to determining that the decrease in energy is below the first minimum level; outputting a first evolved state and first temperature value; reducing the temperature to a minimum temperature value; performing quantum annealing on the first evolved state with a transversal field increase schedule until a completion of a second event occurs; terminating the quantum annealing in response to determining that a completion of the second event has occurred; outputting a second evolved state as a subsequent input state for the simulated annealing, and determining that the completion of the first event has occurred.

Abstract: Methods, systems, and apparatus for quantum phase estimation. In one aspect, an apparatus includes a quantum circuit comprising: a first quantum register comprising at least one ancilla qubit, a second quantum register comprising one or more qubits, wherein the second quantum register is prepared in a quantum state that is not an eigenstate of a unitary operator operating on the first and second quantum register; and a phase learning system, configured to learn phases of the eigenvalues of the unitary operator.

Abstract: Described herein are methods, systems, and media for generating a quantum-ready or quantum-enabled software development kit (SDK) for a quantum computing system. Such methods may comprise accepting user input from an application at an application interface, which application is executed on a digital computer, and implementing one or more algorithms, at an algorithms layer, that may be solved heuristically or exactly depending on the requirements of the user input. The one or more algorithms may abstract away a complexity of the application; transforming the one or more algorithms from the application space into the one or more instructions in polynomial unconstrained binary optimization (PUBO) form. The one or more instructions may be executed in PUBO form at the common interface of the solver layer.

Abstract: A distributed machine learning framework implemented with heterogeneous data platforms reduces data copying and exploits memory/computation resources of the different data platforms. A configuration component includes information to set up the system. A persistency component manages storage of data and a model trained by machine learning. A proxy repository includes predefined proxies for communication between heterogeneous data platform nodes and execution of the machine learning procedure. A machine learning execution component comprises three layers. A bottom work node layer within the data platform performs computations of the machine learning procedure. A middle server node layer comprising one server node per data platform, communicates with the work nodes to coordinate jobs on that data platform. An upper layer comprises a central server node communicating with server nodes and coordinating jobs of the different platforms.

Abstract: Systems, methods, and non-transitory computer readable media are configured to train a machine learning model. The training can be based on a training set of embeddings of a first type and a training set of embeddings of a second type. The machine learning model can be trained to receive an embedding of a second type and to output a corresponding embedding of the first type. A given embedding of the second type can be provided as input to the machine learning model. An embedding of the first type can be obtained from the machine learning model. The embedding of the first type can correspond to the given embedding of the second type.

Abstract: A system includes a repository storing trained machine learning models and metadata corresponding to the trained machine learning models. The system provides an interface for performing operations on the trained machine learning models and corresponding metadata stored in the repository, and evaluates trained machine learning models that are linked to the data sources and stored in the repository to obtain resulting predictions for a payload of records of data contained in the data sources. A method includes configuring one or more data sources, ingesting training data from the configured data sources, identifying a scenario for prediction, training one or more machine learning models on the ingested training data for the identified scenario, reviewing performance of the trained machine learning models and storing the trained machine learning models and corresponding metadata in A model repository, and dynamically creating a user interface for interacting with the stored trained machine learning models.

Abstract: A data storage system includes: a host including a processor and a memory; and a remote storage device separate from the host and configured to communicate with the host via an external network. The remote storage device includes: a non-volatile memory device; and a controller configured to control the non-volatile memory device. The controller is configured to create K-metadata objects corresponding to each file stored on the memory device independently of the host, and the K-metadata objects store data describing attributes of the corresponding file stored on the memory device.

Abstract: Various embodiments provide a validation decision tree, which is a type of decision tree that assists in tuning hyperparameters used to build or train another decision tree, which may be a traditional decision tree. In particular, some embodiments use a validation decision tree or a validation decision tree ensemble to evaluate a space of hyperparameters for a decision tree. Additionally, some embodiments use a validation decision tree or a validation decision tree ensemble to evaluate the space of hyperparameters without need for retraining the validation decision tree or validation decision tree ensemble.

Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.

Abstract: Systems and methods are provided for a content-based security for computing devices. An example method includes identifying content rendered by a mobile application, the content being rendered during a session, generating feature vectors from the content and determining that the feature vectors do not match a classification model. The method also includes providing, in response to the determination that the feature vectors do not match the classification model, a challenge configured to authenticate a user of the mobile device. Another example method includes determining a computing device is located at a trusted location, capturing information from a session, the information coming from content rendered by a mobile application during the session, generating feature vectors for the session, and repeating this until a training criteria is met. The method also includes training a classification model using the feature vectors and authenticating a user of the device using the trained classification model.