Abstract: A method and system for generating best performing data models for datasets is disclosed. The method includes obtaining input dataset from external data sources. The method includes dividing dataset into three parts based on type of data variables and data samples. The method includes retrieving data modelling methods from a database and generating first set of data models for the first part of the dataset. The method includes determining first fitness function for the first part of the dataset based on the first set of data model. The method includes generating second set of data models for the second part of the dataset and determining second fitness function based on the second set of data model. Further, the method includes determining best performing data model among the first set of data models and the second set of data model. Additionally, the method includes outputting the best performing data model.

Abstract: The present disclosure provides systems for encoding transforms based on intent to be useable by developers. The system receives input datasets, generates output datasets, and identifies characteristics of the input or output datasets. Using these characteristics, the system can encode transforms associated with the datasets based on intent. The system stores and/or associates the intent-encoded transforms in data profiles, so that the system enables the transforms to be searched, recommended, and/or combined.

Abstract: A system comprising a causal convolutional neural network to autoregressively generate a succession of values of a data item conditioned upon previously generated values of the data item. The system includes support memory for a set of support data patches each of which comprises an encoding of an example data item. A soft attention mechanism attends to one or more patches when generating the current item value. The soft attention mechanism determines a set of scores for the support data patches, for example in the form of a soft attention query vector dependent upon the previously generated values of the data item. The soft attention query vector is used to query the memory. When generating the value of the data item at a current iteration layers of the causal convolutional neural network are conditioned upon the support data patches weighted by the scores.

Abstract: A virtual machine's (VM's) usage of a resource over a first time period may be monitored to determine a load pattern for the VM. A time series analysis of the load pattern may be performed to generate a predictive resource usage model, the predictive resource usage model indicating one or more predicted variations in the usage of the resource by the VM over a second time period. A predicted resource usage of the VM at a future time that is within the second time period may be determined based, at least in part, on the predictive resource usage model. An amount of the resource to allocate to the VM at a current time may be determined based, at least in part, on the predicted resource usage of the VM at the future time and the actual resource usage of the VM at the current time.

Abstract: A task appropriateness determination apparatus includes a first learning unit causing artificial intelligence (AI) to learn image information of an index indicating a target object in a task appropriately completed state, an appropriate image provider providing an image possibly including the target object in the appropriately completed state and the index, a second learning unit causing the AI to detect an image where the index is present from the provided image after learning of the index, and learn image information of the target object in the image where the index is present, an image capturing unit capturing an image of a region including the target object and the index at least after the task, and a task appropriateness determiner determining that the task has been appropriately performed in response to detection by the AI of the target object roughly identical to the learned target object in the appropriately completed state.

Abstract: A system and method for inferring operator intent by detecting operator focus incorporates cameras positioned within a cockpit or control space of a vehicle and oriented at an operator of the vehicle. The cameras capture images of the operator in a control seat; the images are analyzed (either individually or sequentially) to determine a gaze and/or body pose of the operator (including, e.g., a position and orientation of the torso and limbs). By comparing the determined gaze and/or body pose to the positions and orientations of potential focus targets within the control space (e.g., windows, display units, and/or control panels that the operator may engage with visually and/or physically), the system predicts the most likely future focus target or targets: what the operator is most likely to visually and/or physically engage with next. Operator intent may be further analyzed to identify potentially abnormal or anomalous behavior.

Abstract: Systems and methods are provided for transmitting functional safety statistics within a system. A video source produces a video data stream. A functional safety system driver accumulates functional safety statistics from at least one system and writes the functional safety statistics onto an associated system memory. A display sub-system driver writes a frame of the video data stream to the system memory. The display sub-system driver formats the functional safety statistics as video data and appends the functional safety statistics to a portion of the frame of video that is reserved for the functional safety statistics. A display sub-system transmits the frame of the video data stream to a host processor, which extracts the functional safety statistics from the video frame.

Abstract: Data analysis plays a crucial role to get significant information out of the data. A real time system and method for analyzing data streams have been provided. The system can utilize many different types of data formats such as numeric, text, video, audio, image, or combination thereof. The analysis takes place as per the requirement using an analytical engine and an intermediate output is generated. The intermediate output is further processed using a distributed real time business rule processing engine to determine required conditions in the data. The business rules comprise one or more set of meta data. On match of the business rule, the system triggers an alert or propagates the required information to integrating solution for required actions. The system and method are technology and communication protocol agnostic, and designed with highly efficient load balanced technique, thereby facilitating highly concurrent data processing with minimal latency.

Abstract: Automatic extractions of situations includes creating a situation image includes accessing a conversation between a first user and a second user, and generating an abstract knowledge graph at one or more textual levels. The method also includes generating one or more manifests by pruning the abstract knowledge graph and segmenting the pruned abstract knowledge graph. The method further includes converting the one or more manifests into the situation image.

Abstract: In one aspect a computerized method for detecting anomalies in expense reports of an enterprise includes the step of implementing a semantic analysis algorithm on an expense report data submitted by an employee, wherein the expense report data is provided in a computer-readable format. The method includes the step of, with one or more machine learning algorithms, detecting an anomaly in expense report data. The method includes the step of obtaining an augmentation of the expense report data with a set of web scale data. The method includes the step of verifying receipts associated with an expense report. The method includes the step of determining that the employee or any employee has previously claimed an expense in the expense report data. The method includes the step of identifying an inappropriate expense in the expense report data.

Abstract: Embodiments for identifying stochastic models representing the individual decision makers in a computing environment by a processor. One or more non-deterministic (stochastic, probabilistic) models may be identified according to a sequence of outcomes from decisions of each of a plurality of decision makers.

Abstract: Embodiments of the present invention relate to providing intelligent alerting and automation for marketing analytics software. In implementation, intelligent alerting is initiated by a user, which enables deep learning models to analyze various data patterns. Intelligent alerting learns about preferences and data consumption patterns of the user with marketing analytics software. Intelligent alerting also accounts for and learns from any manually created alerts set up by the user and/or alerts created automatically by anomaly detection. Intelligent alerting analyzes all other users within the organization of the user to find similar users based on their consumption patterns. An on-demand game may be provided to the user to determine the criticality of one metric change over another. This enables intelligent alerting to automatically provide alerts which pass a critical threshold of importance to the user and context to help the user understand why a metric changes in a significant way.

Abstract: A system which, with data provided by one or more sensors, detects a user's alteration of the geometries of parts of his face, head, neck, and/or shoulders. It determines the extent of each alteration and normalizes it with respect to the maximum possible range of each alteration so as to assign to each part-specific alteration a numeric score indicative of its extent. The normalized part-specific scores are combined so as to produce a composite numeric code representative of the complete set of simultaneously-executed geometric alterations. Each composite code is translated, or interpreted, relative to an appropriate context defined by an embodiment, an application executing on an embodiment, or by the user. For example, each composite code might be interpreted as, or assigned to, a specific alphanumeric letter, a color, a musical note, etc.

Abstract: A plurality of training iterations is conducted for a machine learning task. A given iteration includes generating a version of a stacking model using a portion of a labeled data set. Proposed labels are then obtained in the iteration using the generated version of the stacking model for a set of unlabeled records. The unlabeled records and their proposed labels are then used to generate versions of base models for the iteration. After the training iterations are completed, a trained ensemble of models including the stacking model and the base models is stored.

Abstract: A system, method, and computer program product for identifying consumer items more likely to be bought by an individual user. In some embodiments, a collaborative filter may be used to rank items based on the degree to which they match user preferences. The collaborative filter may be hierarchical and may take various factors into consideration. Example factors may include the similarity among items based on observable features, a summary of aggregate online search behavior across multiple users, the item features determined to be most important to the individual user, and a baseline item against which a conditional probability of another item being selected is measured.

Abstract: An embodiment includes building a hierarchical data structure using a hybrid hierarchical clustering process. The hybrid hierarchical clustering process comprises one or more iterations of a level-building process. An embodiment of the level-building process comprises building, by a classical processor, a first parent level of a current uppermost level of the hierarchical data structure by clustering classically-encoded clusters of the current uppermost level. The embodiment of the level-building process also comprises identifying, by a quantum processor, a set of candidate clustering options for clustering quantum-encoded clusters of the current uppermost level for a second parent level, including forming each of the set of candidate clustering options in parallel using respective different quantum encoding spaces. The embodiment of the level-building process also comprises building, by the classical processor, the second parent level based on a subset of the candidate clustering options.

Abstract: A classification-based diagnosis for detecting and predicting faults in physical system (e.g. an electronic circuit or rail switch) is disclosed. Some embodiments make use of partial system model information (e.g., system topology, components behavior) to simplify the classifier complexity (e.g., reduce the number of parameters). Some embodiments of the method use a Bayesian approach to derive a classifier structure.

Abstract: Methods, systems, and apparatus related to dynamic distribution of an artificial neural network among multiple processing nodes based on real-time monitoring of a processing load on each node. In one approach, a server acts as an intelligent artificial intelligence (AI) gateway. The server receives data regarding a respective operating status for each of monitored processing devices. The monitored processing devices perform processing for an artificial neural network (ANN). The monitored processing devices each perform processing for a portion of the neurons in the ANN. The portions are distributed in response to monitoring the processing load on each processing device (e.g., to better utilize processing power across all of the processing devices).

Abstract: Methods are provided for nucleic acid analysis via a platform which incorporates a digital sample partitioning platform such as a microfluidic chip or digital droplet platform and instrumentation to accomplish universal amplification, High Resolution Melting (HRM), and machine learning within reactions simultaneously.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing a machine learning model that is trained to perform a machine learning task. In one aspect, a method comprises receiving a request to train a machine learning model on a set of training examples; determining a set of one or more meta-data values characterizing the set of training examples; using a mapping function to map the set of meta-data values characterizing the set of training examples to data identifying a particular machine learning model architecture; selecting, using the particular machine learning model architecture, a final machine learning model architecture for performing the machine learning task; and training a machine learning model having the final machine learning model architecture on the set of training examples.

Abstract: A system can be utilized to determine media clips for media content based on a comparison between subtitles of the media content and information associated with a third-party system. The comparisons can be utilized to identify subtitles based on the comparison and determine the media clips based on portions of the media content associated with the identified subtitles. A smoothing algorithm can be applied to the media clips to modify start times and end times of the media clips. The system can cause the media clips or the corresponding media content to be output by an external device.

Abstract: According to an embodiment, an inference system includes a recurrent neural network circuit, an inference neural network, and a control circuit. The recurrent neural network circuit receives M input signals and outputs N intermediate signals, where M is an integer of 2 or more and N is an integer of 2 or more. The inference neural network circuit receives the N intermediate signals and outputs L output signals, where L is an integer of 2 or more. The control circuit adjusts a plurality of coefficients that are set to the recurrent neural network circuit and adjusts a plurality of coefficients that are set to the inference neural network circuit. The control circuit adjusts the coefficients set to the recurrent neural network circuit according to a total delay time period from timing for applying the M input signals until timing for firing the L output signals.

Abstract: Provided are continuously learning and optimizing artificial intelligence (AI) adaptive neural network (ANN) computer modeling methods and systems, designated “human affect computer modeling” (HACM) or “affective neuron” (AN) and, more particularly, to AI methods, systems and devices, that can recognize, interpret, process and simulate human reactions and affects such as emotional responses to internal and external sensory stimuli, that provides real-time reinforcement learning modeling that reproduces human affects and/or reactions, wherein the human affect computer modeling (HACM) can be used singularly or collectively for modeling and predicting complex human reactions and affects.

Abstract: The present disclosure describes a device, computer-readable medium, and method for providing logistical support for robots. In one example, the method includes receiving, at a centralized support center that is in communication with a plurality of robots, a query from a first robot of the plurality of robots that has been deployed to perform a task, wherein the query indicates an error encountered by the first robot and evidence of the error collected by the first robot, formulating, at the centralized support center, a proposed solution to resolve the error, wherein the formulating comprises soliciting an analysis of the evidence by a party other than the first robot, and delivering, by the centralized support center, the proposed solution to the first robot.

Abstract: A computing device (200) for authenticating a user (110) is provided. The computing device is operative to display a first text (131) to the user, acquire a representation of the user subvocalizing a part of the first text, derive a user phrasing signature from the acquired representation, and authenticate the user in response to determining that the user phrasing signature and a reference phrasing signature fulfil a similarity condition. Optionally, the computing device may be further operative to determine if the user is authorized to read the first text. Further optionally, the computing device may be operative to reveal obfuscated parts of the first text in response to determining that the user is authorized to read the first text, or to discontinue displaying the first text, or to obfuscate at least part of the first text, in response to determining that the user is not authorized to read the first text.

Abstract: The present disclosure relates to a system and a method for identifying possible problematic behaviour in gambling, in particular online gambling involving monetary transactions. One embodiment relates to a computer implemented method for detection of possible problem gaming behaviour of a subject engaged in one or more 5 games involving monetary transactions, the method comprising the steps of obtaining a dataset comprising the subjects gaining transactions over a time period, analysing the gaining behaviour of said subject by modelling against a trained model employing artificial intelligence, and predicting and/or detecting Possible Problem Gambling Behavior (PPGB) of said subject based on the analysis wherein the trained model is 10 trained based on one or more behavioral targets at outcome.

Abstract: Disclosed are techniques for generating features to train a predictive model to predict a customer lifetime value or churn rate. In one embodiment, a method is disclosed comprising receiving a record that includes a plurality of fields and selecting a value associated with a selected field in the plurality of fields. The method then queries a lookup table comprising a mapping of values to aggregated statistics using the value and receives an aggregated statistic based on the querying. Next, the method generates a feature vector by annotating the record with the aggregated statistic. The method uses this feature vector as an input to a predictive model.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to facilitate end-user defined policy management. An example apparatus includes an edge node interface to detect addition of a networked user device to a service gateway, and to extract publish information from the networked user device. The example apparatus also includes a device context manager to identify tag parameters based on the publish information from the networked user device, and a tag manager to prohibit unauthorized disclosure of the networked user device by setting values of the tag parameters based on a user profile associated with a type of the networked user device.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for a scheme evaluation authentication system to evaluate user action and machine learned pattern recognition for misappropriation activity prevention. In this way, bot and token deployment is utilized for identification of user actions to generate a user idiosyncrasy database. Using the database, the system creates a real-time scheme evaluation authentication for user authentication upon user attempted authentications. As such, a user may by authenticate irrespective of user inputs, but instead based on a match of user actions at the authentication location in real-time compared to the idiosyncrasy database.

Abstract: A multi device coordination control device includes: a degree-of-service-achievement calculator configured to acquire sensor information from one or more sensor devices disposed on the same physical space, and convert the acquired sensor information to a degree of service achievement; a target control amount calculation part configured to make a learning device allow an input of the degree of service achievement and compute therefrom an actuator control amount for each of the actuator devices, to thereby obtain the actuator control amount as an output value; and an actuator controller configured to convert the actuator control amount obtained as the output value to a control instruction in accordance with each of the actuator devices, and transmit the control instruction to each of the actuator devices, to thereby make the each of the actuator devices execute the control instruction.

Abstract: Enhanced techniques and circuitry are presented herein for providing responses to user questions from among digital documentation sources spanning various documentation formats, versions, and types. One example includes a method comprising receiving a user question directed to subject having a documentation corpus, determining a set of passages of the documentation corpus related to the user question, ranking the set of passages according to relevance to the user question, forming semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity, and providing a response to the user question based at least on a selected semantic cluster.

Abstract: A system and method for a dynamic-link communication process between an electronic device and a target product or service by serving content or facilitating communication between two electronic devices. The first step is engagement by an electronic device with an initiator that is linked with a specific product or service that triggers a text message to auto-populate on the electronic device. The message comprising metadata about the specific product or service. The second step is for the electronic device to send the auto-populated message to a message gateway thus initiating the communication between the electronic device and a product or service. The message gateway works with a management service and one or more databases to send content back to the electronic device or to facilitate and convert messages between the electronic device and a second electronic device, the second electronic device associated with a product or service.

Abstract: A computer-implemented method of training a model for making time-series predictions of a computer-controlled system. The model uses a stochastic differential equation (SDE) comprising a drift component and a diffusion component. The drift component has a predefined part representing domain knowledge, that is received as an input to the training; and a trainable part. When training the model, values of the set of SDE variables at a current time point are predicted based on their values at a previous time point, and based on this, the model is refined. In order to predict the values of the set of SDE variables, the predefined part of the drift component is evaluated to get a first drift, and the first drift is combined with a second drift obtained by evaluating the trainable part of the drift component.

Abstract: An inquiry system is provided in which an inquiry unit of a user of a machine and/or a device makes inquiries related to the machine and/or the device to inquiry a plurality of center systems of suppliers, and includes: a relay device which connects the inquiry unit and the inquiry center systems; and a storage unit which stores first and second identification information related to the machine and/or the device, the inquiry unit includes: an acquisition unit which acquires the first identification information; and a first communication unit which transmits the first identification information acquired to the relay device, the relay device includes: a second communication unit which communicates between the inquiry unit and the inquiry center systems; and a selection unit which selects, based on the first and second identification information, the inquiry center system and the second communication unit connects the inquiry unit and the inquiry center system.

Abstract: A computer-implemented method comprises receiving training data corresponding to a plurality of trips, the training data including at least a value for a set of attributes for each of the plurality of trips, the set of attributes including an indication of when in a week a trip is taken or in which of a plurality of meal-based time zones in a day the trip is taken, the training data including an actual travel time for each of the plurality of trips. The method further comprises creating and storing, in computer memory, a digital model that is configured to predict a travel time for a future trip based on the training data, the digital model including a set of parameters corresponding to the set of attributes, the digital model including a plurality of sets of values for the set of parameters.

Abstract: An apparatus in one embodiment comprises a distributed data processing system in which multiple processing devices communicate with one another over at least one network. The distributed data processing system is configured to obtain reads of biological samples of respective sample sources, with each of the biological samples containing genomic material from a plurality of distinct microorganisms within an environment of a corresponding one of the sample sources, and to perform distributed data analytics to provide surveillance functionality characterizing at least one of a disease, an infection and a contamination as involving genomic material from multiple ones of the sample source. Performing distributed data analytics illustratively comprises performing local analytics in respective ones of a plurality of data zones, and performing global analytics utilizing results of the local analytics performed in the respective data zones.

Abstract: A computer device includes at least one processor in communication with at least one memory device. The at least one processor is programmed to store, in the at least one memory device, a model for simulating a portion of an assembly line and receive scan data of a first inspection of a product being assembled, execute the model using the scan data as inputs to generate a final profile of the product, compare the final profile to one or more thresholds, determine if the final profile exceeds at least one of the one or more thresholds, and adjust the first device if the determination is that the final profile exceeds at least one of the one or more thresholds.

Abstract: Classification of an input task data set by meta level continual learning includes analyzing first and second training data sets in a task space to generate first and second meta weights and a slow weight value, and comparing an input task data set to the slow weight to generate a fast weight. The first and second meta weights are parameterized with the fast weight value to update the slow weight value, whereby a value is associated with the input task data set, thereby classifying the input task data set by meta level continual learning.

Abstract: Embodiments of the invention are directed to a system, method, and computer program product for processing data analytics within memory. The system first receives a request for data associated with a database. The system utilizes a feature catalog and a deep learning neural network to determine the data that comprises the one or more desired features. The system can also provide the requested data to a computing device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for improving operational efficiency within a data center by modeling data center performance and predicting power usage efficiency. An example method receives a state input characterizing a current state of a data center. For each data center setting slate, the state input and the data center setting slate are processed through an ensemble of machine learning models. Each machine learning model is configured to receive and process the state input and the data center setting slate to generate an efficiency score that characterizes a predicted resource efficiency of the data center if the data center settings defined by the data center setting slate are adopted t. The method selects, based on the efficiency scores for the data center setting slates, new values for the data center settings.

Abstract: Disclosed is a computer-implemented method for generating a prediction model. The model can be for use in processing machine event data to predict behavior of a plurality of industrial machines under supervision. The prediction model can be configured to determine current and future states of the industrial machines. The method can include: extracting event features from event codes and structuring the event features into feature vectors; and generating the prediction model by clustering the feature vectors into a plurality of vector clusters, the vector clusters being assigned to respective machine states. The prediction model can be constructed based on event data from a first industrial machine and be applied to control an operating state of a second industrial machine.

Abstract: Systems, computer-readable media, and methods are provided. Relevant documents related to a specific entity are identified based on document metadata. Text and associated spatial coordinates are extracted based on relevant document pages. Significant document entities and associated spatial locations are identified. Page ranking is based on the extracted text and the spatial coordinates, the significant document entities, and image vector representations of the pages. A deep learning language model that utilizes the text and the spatial coordinates, layout information of the document entities, and the image vector representations of the pages is used to extract the user-defined attributes from the relevant document pages. First attribute values associated with the user-defined attributes are aggregated from the pages of one of the relevant documents into a single record. Second attribute values associated with the user-defined attributes are aggregated across the relevant documents.

Abstract: Systems and methods are provided for automatic speech recognition. In the method, the system obtains a padded sequence by processing a plurality of acoustic signals. The system compresses the padded sequence by reducing the size of the padded sequence to obtain a compressed sequence. The system inputs the compressed sequence into a pre-trained encoder neural network to obtain an encoded sequence and then decompresses the encoded sequence by recovering the encoded sequence to an original sequential ordering. The system inputs the encoded sequence to a decoding module to obtain recognition texts.

Abstract: A set of log entries associated with a memory sub-system is caused to be stored in a first log subject to a wrapping process. A summarized log entry representing data of a portion of the set of log entries matching a pattern is generated and caused to be stored in a second log not subject to the wrapping process, where the summarized log entry is preserved from deletion via the wrapping process of the first log. The set of log entries is deleted from the first log by executing the wrapping process.

Abstract: A piezoelectric adaptive mesh includes multiple piezoelectric fibers that include piezoelectric structures that can act as sensor and/or actuators to enhance a person's comfort. The piezoelectric structures communicate with a controller and/or a software processing system and may identify the position of a user and make adjustments through the actuators to increase user comfort by providing support, assistance, treatment, and/or temperature adjustment.

Abstract: The present invention provides a gas detector for detecting a volatile organic compound (VOC) gas. The gas detector comprises at least one transducer comprising at least one nanowire comprising an arene compound to capture a VOC gas. An electronic characteristic of the transducer changes when a VOC gas is captured by the arene compound. The present invention also provides a mobile device; a nanowire; a nanowire matrix; a transducer; a use of a gas detector; a method of detecting a VOC gas; and a method of manufacturing a gas detector.

Abstract: A method for evaluating and/or scoring pharmaceutical/life science technology is provided. The method includes importing data of a publication; transforming the data into a structured schema; ingesting the structured schema to determine a context of the data and draw associations between the data and a plurality of profiles; and generating a score based on the associations between the raw data and the profiles. The method may also include generating meta-data based on the determined context of the data and/or one or more quantitative metrics having a temporal component based on the ingested data. Related apparatus, systems, techniques and articles are also described.

Abstract: According to one embodiment of the invention, a method for detecting driving anomalies comprises steps of: with at least one algorithm, processing raw data from On-Board Diagnostics of a car to generate time-series data, with the time series data as input, using an automatic driving behavior separating technology to identify a plurality of driving behaviors; with the driving behaviors as input, using an artificial intelligence technology to build up a driving anomaly detection model without labeling the driving behaviors; and issuing an alarm for driving anomaly identified according to an analyzing result of alarm signature of the driving behaviors.

Abstract: Systems and computer-implemented methods facilitating automatic report generation, population and information prompting employing artificial intelligence technology are provided.

Abstract: Methods and apparatus disclosed herein autonomously evaluate replacement algorithms. An example system associated with a live environment executing a current algorithm includes at least one memory, instructions, and processor circuitry to execute the instructions to manage execution of the current algorithm in the live environment, the live environment associated with a deployment platform, and manage, based on receipt of at least one potential replacement algorithm, a shadow environment causes execution of the at least one potential replacement algorithm in the shadow environment, the shadow environment instantiated separately from the live environment, the shadow environment utilizes data from the live environment during execution of the at least one potential replacement algorithm in the shadow environment.