Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.

Abstract: A system and method is disclosed for classifying time-series data provided to a machine-learning model from a continuous sensor signal. The data may be “windowed” or “divided” into a smaller data segment using a first stage classifier where an “event of interest” may be identified. The first stage classifier may employ an algorithm that prohibits false negative identifications. The data segment detected as including an event of interest may then be transmitted to a second stage classifier operable to performs a full classification on the data segment. The multi-stage network may require less power and a less complex structure.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining one or more neural network architectures of a neural network for performing a video processing neural network task. In one aspect, a method comprises: at each of a plurality of iterations: selecting a parent neural network architecture from a set of neural network architectures; training a neural network having the parent neural network architecture to perform the video processing neural network task, comprising determining trained values of connection weight parameters of the parent neural network architecture; generating a new neural network architecture based at least in part on the trained values of the connection weight parameters of the parent neural network architecture; and adding the new neural network architecture to the set of neural network architectures.

Abstract: An electronic device comprises: a transmission antenna configured to transmit transmission waves; a reception antenna configured to receive reflected waves resulting from reflection of the transmission waves; and a controller. The controller is configured to detect an object reflecting the transmission waves, based on a transmission signal transmitted as the transmission waves and a reception signal received as the reflected waves. The controller is configured to classify a detection result of the object reflecting the transmission waves depending on a degree of certainty, and output the classified detection result.

Abstract: An imaging device having a function of processing an image is provided. The imaging device has an additional function such as image processing, can hold analog data obtained by an image capturing operation in a pixel, and can extract data obtained by multiplying the analog data by a predetermined weight coefficient. Difference data between adjacent light-receiving devices can be obtained in a pixel, and data on luminance gradient can be obtained. When the data is taken in a neural network or the like, inference of distance data or the like can be performed. Since enormous volume of image data in the state of analog data can be held in pixels, processing can be performed efficiently.

Abstract: To identify a target engagement sequence with a highest likelihood of realizing an opportunity, a target engagement sequence generator uses models (artificial recurrent neural network (RNN) and a hidden Markov model (HMM)) trained with historical time series data for a particular combination of values for opportunity characteristics. The trained RNN identifies a sequence of personas for realizing the opportunity described by the opportunity characteristics values. Data from regression analysis indicates key individuals for realizing an opportunity within each organizational classification that occurred within the historical data. The HMM identifies the importance of each persona in the sequence of personas with communicates to the key individuals. The resulting sequence of individuals indicates an optimal sequence of individuals and order for contacting those individuals in order to realize an opportunity.

Abstract: Disclosed is a training data generating apparatus and method to change output data inappropriate for training a student model to an ignore value such that the inappropriate output data is not used to train the student model, change output data appropriate for training the student model such that the student model outputs an improved result in comparison to output data of a teacher model, and change a label value in a form of probability to an identifier corresponding to intervals divided based on threshold label values using the teacher model based on input data.

Abstract: A processor-implemented method of performing a convolution operation is provided. The method includes obtaining input feature map data and kernel data, determine the kernel data based on a number of input channels of the input feature map, a number of output channels of an output feature map, and a number of groups of the input feature map data and a number of groups of the kernel data related to the convolution operation, and performing the convolution operation based on the input feature map data and the determined kernel data.

Abstract: Embodiments disclosed are directed to a computing system that performs steps to automatically generate fine-grained call reasons from customer service call transcripts. The computing system extracts, using a natural language processing (NLP) technique, a set of events from a set of text strings of speaker turns. The computing system then identifies a set of clusters of events based on the set of events and labels each cluster of events in the set of clusters of events to generate a set of labeled clusters of events. Subsequently, the computing system assigns each event in the set of events to a respective labeled cluster of events in the set of labeled clusters of events.

Abstract: A method and apparatus for subsurface data processing includes determining a set of clusters based at least in part on measurement vectors associated with different depths or times in subsurface data, defining clusters in a subsurface data by classes associated with a state mode, reducing a quantity of the subsurface data based at least in part on the classes, and storing the reduced quantity of the subsurface data and classes with the state model in a training database for a machine learning process.

Abstract: A mechanism and method for deciding whether a bearing is faulty or not. The method is performed by a controller. The method includes obtaining a signal representing vibrations of the bearing. The vibrations are obtained during operation of the bearing. The method includes applying signal segmentation to the signal in at least two frequency bands in order to identify any shock pulse in the signal. The method includes deciding whether the bearing is faulty or not depending on whether or not the signal has any shock pulse.

Abstract: Tracking a current and/or previous position, velocity, acceleration, and/or heading of an object using sensor data may comprise determining whether to associate a current object detection generated from recently received (e.g., current) sensor data with a previous object detection generated from formerly received sensor data. In other words, a track may identify that an object detected in former sensor data is the same object detected in current sensor data. However, multiple types of sensor data may be used to detect objects and some objects may not be detected by different sensor types or may be detected differently, which may confound attempts to track an object. An ML model may be trained to receive outputs associated with different sensor types and/or a track associated with an object, and determine a data structure comprising a region of interest, object classification, and/or a pose associated with the object.

Abstract: A classification system according to an embodiment includes a score calculation unit, a determination unit, and a classification unit. The score calculation unit calculates respective scores of predetermined classes from input data. The determination unit determines whether the input data belongs to anyone of the classes based on the respective scores of the classes, which are calculated by the score calculation unit. The classification unit determines which one of the classes the input data belongs to, based on the calculated scores when the determination unit determines that the input data belongs to anyone of the classes and determines that the input data belongs to an unknown class that is other than the classes when the determination unit determines that the input data does not belong the classes.

Abstract: A method, computer program, and computer system is provided for streaming immersive media. The method includes ingesting content in a two-dimensional format; converting the ingested content to a three-dimensional format based on a neural network; and streaming the converted content to a client end-point.

Abstract: A visual search recommendation engine utilizes an image quality indication model for a visual search recommendation. Specifically, the visual search recommendation engine receives a search image as a search query at a search engine. The visual search recommendation engine provides the search image as an input into the image quality indication model, which is trained to output an image quality indication based on image aspects of the search image. A plurality of images are identified from an image corpus. The visual search recommendation engine determines an image similarity based on a comparison between the plurality of images from the image corpus and the search image. The image quality indication and the image similarity indicate a search query performance for the search image. A first image exceeding the search query performance is identified. The first image is provided for display at the search engine.

Abstract: The invention is machine learning based method of, or system configured for, identifying candidate, small, drug-like molecules, in which a tensor network representation of molecular quantum states of a dataset of small, drug-like molecules is provided as an input to a machine learning system, such as a neural network system. The machine learning method or system may is itself configured as a tensor network. A training dataset may be used to train the machine learning system, and the training dataset is a tensor network representation of the molecular quantum states of small drug-like molecules.

Abstract: Systems and methods for aggregating data. The system is configured to receive metadata from an interactive graphical user interface (GUI) of a user device, aggregate field values from the data stored on one or more databases based on the received metadata and generate filter instructions based on the received metadata. The system is further configured to transmit the aggregated field values and the filter instructions to the user device, receive a user-customized filter set and subscription request for a synthetic symbol associated with the user-customized filter set from the user device, and create the synthetic symbol responsive to the subscription request. Moreover, the system aggregates one or more data values from the data stored on the databases associated with the created synthetic symbol and generates instructions to display the data values on the interactive GUI in accordance with the user-customized filter set associated with the created synthetic symbol.

Abstract: The principles described herein relate to the training and implementation of a model designed to estimate the probability of new security incidents being true incidents. This occurs in an environment where a service such as a SIEM monitors a network of computing systems and other resources and detects a variety of incidents that could be security threats. These incidents are reported to the SOC for investigation and the SOC will take appropriate action to mitigate potential threats of true security breaches. As part of the investigation process, the SOC can label whether a security incident is true, false or benign. After labeling enough security incidents a model can be produced to estimate the probability that new security incidents are true incidents. This would help the SOC filter through security incidents more efficiently and allow for quicker response of the most likely security breaches.

Abstract: A technique for intruder detection is described. Communications for a data source in an organization are intercepted and analyzed to identify an intruder detection signature. An intrusion is determined based on the intruder detection signature and an alarm generated based on the intrusion.

Abstract: An error determination device includes a class estimation process observation unit configured to acquire data in a process of being estimated, from a class estimation unit that estimates a class of data to be classified and generate an estimation process feature vector based on the acquired data; and an error determination unit configured to accept input of the estimation process feature vector generated by the class estimation process observation unit and a classification result output from the class estimation unit and determine whether the classification result is correct or incorrect based on the estimation process feature vector and the classification result, wherein the error determination unit is a functional part generated by machine learning based on an estimation process feature vector list created by adding a pseudo feature vector to an estimation process feature vector list generated by the class estimation process observation unit and on a learning error-correction list indicating that a class co

Abstract: Various embodiments a method and apparatus for unmanned driving behavior decision-making and model training, and an electronic device. The method includes: acquiring sample data, wherein the sample data includes a sample image; extracting a sample feature vector corresponding to the sample data, wherein a feature vector of the sample image is extracted by manifold dimension reduction; and based on the sample feature vector, training by semi-supervised learning to obtain a target decision-making model, wherein the target decision-making model is used for decision-making classification.

Abstract: A method and system for diagnosis of error coding faults from multiple instruments are provided. The method includes acquiring sampling data series of a combination of instruments in a petrochemical process, determining a type of the sampling data series, and performing error diagnosis according to the type. The present disclosure can solve the error coding problem in a multi-instrument cooperation mode and provide safe and reliable data guarantee for safe and efficient petrochemical production.

Abstract: Technology for inspection for detecting a defect of a printed matter using machine logic informed by machine learning. Some embodiments of the present invention may include one, or more, of the following features: (i) generates defect datasets; (ii) generates defect libraries; (iii) uses the generated defect libraries for deep learning training; and (iv) uses machine learning to detect defects using computer code (for example, a *.jpg format file) corresponding to an image of a piece of printed matter instead of using a visual image (that is, an image of the type that is created when a person takes a picture using a traditional film camera).

Abstract: Disclosed are systems and methods that automatically classify, segment, and parse content data using artificial intelligence and natural language processing technology, and generate graphical user interfaces that allow end users to dynamically filter content data for display. The systems processes volumes of content data to identify interrogative data, content sources that generated the interrogative data, and subject identifiers relating to the content data. The system generates graphical user interfaces that allow end users to effectively filter the data by choosing between layouts that display one or more of the various categories of data, including the interrogative data, content source identifiers, and/or subject identifiers.

Abstract: A decoder includes a feature extraction circuit for calculating one or more feature vectors. An acoustic model circuit is coupled to receive one or more feature vectors from and assign one or more likelihood values to the one or more feature vectors. A memory architecture that utilizes on-chip state lattices and an off-chip memory for storing states of transition of the decoder is used to reduce reading and writing to the off-chip memory. The on-chip state lattice is populated with at least one of the states of transition stored in the off-chip memory. An on-chip word is generated from a snapshot from the on-chip state lattice. The on-chip state lattice and the on-chip word lattice act as an on-chip cache to reduce reading and writing to the off-chip memory.

Abstract: A method for accelerating machine learning on a computing device is described. The method includes hosting a neural network in a first inference accelerator and a second inference accelerator. The neural network split between the first inference accelerator and the second inference accelerator. The method also includes routing intermediate inference request results directly between the first inference accelerator and the second inference accelerator. The method further includes generating a final inference request result from the intermediate inference request results.

Abstract: The disclosed computer-implemented method may include receiving input voice data synchronous with a visual state of a user interface of the third-party application, generating multiple sentence alternatives for the received input voice data, identifying a best sentence of the multiple sentence alternatives, executing a dialog script for the third-party application using the best sentence, the dialog script generating a response to the received voice data comprising output voice data and a corresponding visual response, and providing the visual response and the output voice data to the third-party application, the third-party application playing the output voice data synchronous with updating the user interface based on the visual response. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Embodiments provide an odor identification system including an operation array unit including at least two or more sensors which interact with odor causative substances included in an odor factor of a gas sample, a sensor data processing unit processing data obtained by interaction with the odor factor in the operation array unit, an odor factor information storing unit storing information of the odor factor and the interaction pattern information of the odor factor in advance, and a pattern identification unit identifying the odor factor on the basis of an interaction pattern while referring to the pattern processed by the sensor data processing unit and the information of the odor factor information storing unit, and collating the interaction pattern with the known odor information, wherein the odor of the object to be measured is contained.

Abstract: There is provided with an image identification apparatus. An extraction unit extracts a feature value of an image from image data using a Neural Network (NN). A processing unit identifies the image based on the feature value extracted by the extraction unit. The NN comprises a plurality of calculation layers connected hierarchically. The NN includes a plurality of sub-neural networks for performing processing of calculation layers after a specific calculation layer. Mutually different data from an output of the specific calculation layer are respectively inputted to the plurality of sub-neural networks.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images using recurrent attention. One of the methods includes determining a location in the first image; extracting a glimpse from the first image using the location; generating a glimpse representation of the extracted glimpse; processing the glimpse representation using a recurrent neural network to update a current internal state of the recurrent neural network to generate a new internal state; processing the new internal state to select a location in a next image in the image sequence after the first image; and processing the new internal state to select an action from a predetermined set of possible actions.

Abstract: A model learning apparatus of the present invention has a question/answer-pair expansion unit and a translation-model learning unit. The question/answer-pair expansion unit generates expansion question/answer pairs by increasing the number of question/answer pairs associated with an index indicating that it sounds more like the character. The translation-model learning unit learns a translation model and a reverse translation model by using the expansion question/answer pairs. A response selecting apparatus of the present invention has a record unit, a document search unit, a score calculation unit, and a ranking unit. The record unit records question/answer pairs and the above described learned translation model. The score calculation unit obtains a translation likelihood which is a numerical value based on the probability of obtaining the answer from the input question and calculates a score of each of a search-result question/answer pair with respect to the input question.

Abstract: Systems and methods for classifying product feedback by an electronic device are described. According to certain aspects, an electronic device may receive consumer feedback entries associated with various products, where each entry may include an initial classification. The electronic device may analyze each entry using a machine learning model to determine a subsequent classification for the entry. When there is a mismatch between classifications, the electronic device may present information associated with the entry for review by a user, where the user may specify a final classification for the entry, and the electronic device may update the machine learning model for use in subsequent analyses.

Abstract: An entity learning recognition method and computer program product include learning training a model based on a combination of an original entity and an augmented entity in an augmented database, where the entity includes an image that is used for a training of the model and where the training is based on a visual element portion of the image with added noise.

Abstract: A system and method for automated software testing that uses machine learning algorithms to automatically generate and implement software testing based on an automated analysis of the software. In an embodiment, a mobile software application comprising one or more screens is processed through a trained machine learning algorithm to identify screens and objects, understand the operational flow of the application, define priorities and dependencies within the application, define validation tests, and automatically generate one or more testing scenarios for the application. The testing scenarios may then be fed to an automated execution module which installs the application on one or more physical or virtual devices and performs testing on the application installed on those devices according to the testing scenario.

Abstract: An apparatus and method are provided for controlling autonomous driving of a vehicle which may derive predicted paths of a pedestrian and a two-wheel vehicle during autonomous driving of the vehicle so as to minimize accidents. The method includes calculating first height information allocating a first gradient that descends in a proceeding direction of objects, including a vehicle and a pedestrian, from respective positions of the objects based on dynamic information of the objects, calculating second height information allocating a second gradient based on a probability that the pedestrian will occupy infrastructure, calculating final height information by fusing the first height information and the second height information, generating a predicted path of the pedestrian, determining a driving strategy of a host vehicle based on a predicted path of the host vehicle and the predicted path of the pedestrian.

Abstract: According to some embodiments, a method performed by a classification scanner comprises receiving an electronic message and determining a classification that applies to the electronic message. The classification is determined based on an express indication from a user. The method further comprises providing a machine learning trainer with the electronic message and an identification of the classification that applies to the electronic message. The machine learning trainer is adapted to determine a machine learning policy that associates attributes of the electronic message with the classification.

Abstract: A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.

Abstract: A model-based engineering system (MBSE) tool includes functionality for sharing an MBSE model with multiple outside vendors using multiple versions of the MBSE model. A restricted version of the MBSE model is managed by a model maker (or other user with access rights) inside of an organization includes all of the model's engineering elements. The engineering elements are tagged with metadata that is analyzed to determine which elements are shareable and which are unshareable outside of the organization. An unrestricted version of the MBSE model is then created, either directly from the restricted version or through an intermediary version (referred to as the “stripped version”), to include only shareable engineering elements. This unrestricted version may then be shared with the outside vendors, and changes made by either in the restricted or unrestricted versions may be incorporated in the other versions, providing a truly collaborative MBSE experience.

Abstract: A method and system that efficiently selects sensors without requiring advanced expertise or extensive experience even in a case of new machines and unknown failures. An abnormality detection system includes a storage unit for storing a latent variable model and a joint probability model, an acquisition unit for acquiring sensor data that is output by a sensor, a measurement unit for measuring the probability of the sensor data acquired by the acquisition unit based on the latent variable model and the joint probability model stored by the storage unit, a determination unit for determining whether the sensor data is normal or abnormal based on the probability of the sensor data measured by the measurement unit, and a learning unit for learning the latent variable model and the joint probability model based on the sensor data output by the sensor.

Abstract: The present invention relates to a breast image analysis method with four mammogram images which are input to a convolutional neural network as one input and a system therefor and the system includes an image receiving unit which receives four mammogram images; an image size adjusting unit which adjusts a size of a mammogram image received from the image receiving unit; a preprocessing unit which performs preprocessing on the mammogram image adjusted by the image size adjusting unit; a convolutional neural network (CNN)-based CNN learning unit which generates learning information by learning the mammogram image preprocessed by the preprocessing unit; and a CNN inference unit which receives the learning information learned from the CNN learning unit and a mammogram image to be classified from the image receiving unit to diagnose a breast abnormality.

Abstract: Techniques for classifying and processing physical objects are disclosed. In an example, a computer system may receive first data indicating that a first machine learning model of a robotic system is incapable to classify a physical object according to at least one of a set of predetermined classifications. The computer system may also receive second data corresponding to one or more attributes associated with the physical object. A second machine learning model of the computer system may determine a cluster of physical objects that includes an identifier of the physical object, whereby the identifier is included in the cluster based at least in part on the first data and a common attribute with other physical objects of the cluster. The computer system may then determine data for processing subsequent physical objects that are determined to have the common attribute.

Abstract: An image determination device includes: a training model which outputs, on the basis of an image to be examined, output data indicating a determination result about the image; a training part which trains the training model to output, by using training data including a training image and label data, output data indicating the label data associated with the training image, when the training image is input to the training model; a dividing part which divides the training data into a plurality of pieces of sub-training data; a measurement part which measures accuracy of determination when the training part trains the training model by using each of the plurality of pieces of sub-training data; and selection part which selects at least any one among the plurality of pieces of sub-training data on the basis of the accuracy of determination.

Abstract: A plurality of images are received from one or more social media platforms associated with a user. For a selected image of the plurality of images, a plurality of text descriptions are generated. The plurality of text descriptions are computer-generated captions that describe features of the selected image of the plurality of images. The plurality of text descriptions are processed through a natural language processing model. Based on processing, a plurality of interest contexts are derived from the plurality of text descriptions. A mapping of each of the plurality of interest contexts to one or more predefined categories associated with an online marketplace is generated. Based the mapping of each of the plurality of interest contexts to the one or more predefined categories, a user device associated with the user is caused to display an app page or web page associated with the one or more predefined categories.

Abstract: A respective set of features, including emotion-related features, are extracted from segments of a video for which a preview is to be generated. A subset of the segments is chosen using the features and filtering criteria including at least one emotion-based filtering criterion. Respective weighted preview-suitability scores are assigned to the segments of the subset using at least a metric of similarity between individual segments and a plot summary of the video. The scores are used to select and combine segments to form a preview for the video.

Abstract: Methods, devices and processor-readable media for an integrated teacher-student machine learning system. One or more teacher-student modules are trained as part of the teacher neural network training. Each student sub-network uses a portion of the teacher neural network to generate an intermediate feature map, then provides the intermediate feature map to a student sub-network to generate inferences. The student sub-network may use a feature enhancement block to map the intermediate feature map to a subsequent feature map. A compression block may be used to compress intermediate feature map data for transmission in some embodiments.

Abstract: Systems, devices, and methods are disclosed for decision making based on plasticity rules of a neural network. A method may include obtaining a multilayered model. The multilayered model may include an input layer including one or more input units. The multilayered model may include one or more hidden layers including one or more hidden units. Each input unit may have a first connection with at least one hidden unit. The multilayered model may include an output layer including one or more output units. The method may also include receiving an input at a first input unit. The method may include sending a first signal from the first input unit to at least one hidden unit via a first connection comprising a first strength. The method may also include making a decision based on the model receiving the input.

Abstract: A computer-implemented method is presented for discovering new material candidates from a chemical database. The method includes extracting a feature vector from a chemical formula, learning a prediction model for predicting property values from the feature vector with a sparse kernel model employing the chemical database, selecting an existing material from a list of existing materials sorted in descending order based on the predicted property values by the prediction model learned in the learning step, selecting a basis material from a list of basis materials sorted in descending order of absolute reaction magnitudes to the selected existing material, and generating the new material candidates as variants of the selected existing material with consideration of the selected basis material.

Abstract: An information processing device is configured to output work information related to work performed by a serving person, the information processing device including an image acquisition unit configured to acquire an original image including a served person and a plurality of served objects that the serving person serves, an image division unit configured to divide the original image into a served-person image, in which the served person is captured, and a plurality of served-object images, in which each served object is captured, a scene estimation unit configured to estimate a scene, which is the situation the serving person is in, by using a first trained model, a chunk estimation unit configured to estimate a chunk, which is information dividing or suggesting the work information, by using one of a plurality of second trained models, and an output unit configured to output the chunk.

Abstract: Techniques for securing displayed data on computing devices are disclosed. One example technique includes upon determining that the computing device is unlocked, capturing and analyzing an image in a field of view of the camera of the computing device to determine whether the image includes a human face. In response to determining that the image includes a human face, the technique includes determining facial attributes of the human face in the image via facial recognition and whether the human face is that of an authorized user of the computing device. In response to determining that the human face is not one of an authorized user of the computing device, the technique includes converting user data on the computing device from an original language to a new language to output on a display of the computing device, thereby securing the displayed user data even when the computing device is unlocked.

Abstract: Software-based solutions may mitigate physical damage to multi-layer networks, such as neural networks having shortcut (residual) connections. An example includes: providing a multi-layer network comprising a plurality of nodes; for each of a plurality of training cases: determining a set of dropout nodes, based at least on a damage model having a probability of a node being selected for dropout that is based at least on a target operating environment of the multi-layer network, wherein the probability of a node being selected is spatially correlated; and training the multi-layer network with the determined set of dropout nodes disabled (with a different set of dropout nodes for different training cases). In some examples the damage model involves expected physical radiation damage to a computing device hosting the multi-layer network, such as on board an aircraft or an earth-orbiting satellite. Thus, multiple degrees of expected damage may be addressed.