Abstract: The present invention discloses a collaborative design method using an event-triggered scheme (ETS) and a Takagi-Sugeno (T-S) fuzzy H? controller in a network environment. For the problem about the unmanned surface vehicle control based on a switching T-S fuzzy system under an aperiodic DoS attack, the present invention provides an H? controller design method based on the event-triggered scheme. The characteristics of the unmanned surface vehicle system under the DoS attack are analyzed, and external disturbance in the navigation process is added into an unmanned surface vehicle motion model to establish an unmanned surface vehicle switching system model. The stability of the system is analyzed by piecewise Lyapunov functionals, such that controller gain and event-triggered scheme weight matrix parameters are obtained, thus ensuring that a networked unmanned surface vehicle navigation system has the ability to resist the DoS attack and the external disturbance.

Abstract: A control device includes an exterior panel comprising multiple regions, including a groove region and a surrounding region that surrounds the groove region. The control device further includes a sensor layer comprising one or more sensors to detect touch inputs performed on the groove region and the surrounding region of the exterior panel. The control device further includes a control module configured to operate a plurality of devices. The control module is configured to detect a first touch input performed by a user on the groove region and a second touch input performed on the surrounding region. Based at least in part on the location of the touch inputs the control module operates respective devices of the plurality of devices.

Abstract: Disclosed herein is a method of a communication device operating in a wireless communication network for managing power consumption of the device. The device is configured to operate according to first, second and third operational states for communication with a network node associated with the communication network.

Abstract: Techniques are described for providing an IT and security operations mobile application for managing IT and security operations instances of an IT and security operations application via a mobile device. The IT and security operations mobile application can be linked to the IT and security operations application to enable the IT and security operations application to send messages (e.g., notifications, alerts, action requests, etc.) related the occurrences of incidents/events in an IT environment, such as security-related incident, that can impact the operation of the IT environment. The IT and security operations mobile application enables a user to respond to the messages by initiating actions that are sent to the IT and security operations application for executing within the IT environment.

Abstract: A search method using an artificial intelligence based information retrieval model and a method for training the artificial intelligence based information retrieval model used for the method are provided. In the method, even if there is no labeled data and only a corpus exists, the artificial intelligence based information retrieval model can be trained using the weak-supervision methodology. Search can be performed by dividing documents into passages having short lengths. Compared to an information retrieval model based on unsupervised learning, improved search results are provided.

Abstract: A computer-implemented method includes: receiving an inquiry request message identifying a first payment transaction having a plurality of transaction parameters and a risk score, where the risk score is generated by a machine-learning model based on the plurality of transaction parameters; for each transaction parameter of the plurality of transaction parameters, perturbing a value of the transaction parameter and re-analyzing the first payment transaction with the machine-learning model to generate a perturbed risk score based on the perturbed transaction parameter; determining at least one impact parameter from the plurality of transaction parameters by comparing the perturbed risk scores generated for each of the plurality of transaction parameters; and generating an inquiry response message based on the at least one impact parameter.

Abstract: A method for monitoring performance of a ML system includes receiving a data stream via a processor and generating a first plurality of metrics based on the data stream. The processor also generates input data based on the data stream, and sends the input data to a machine learning (ML) model for generation of intermediate output and model output based on the input data. The processor also generates a second plurality of metrics based on the intermediate output, and a third plurality of metrics based on the model output. An alert is generated based on at least one of the first plurality of metrics, the second plurality of metrics, or the third plurality of metrics, and a signal representing the alert is sent for display to a user via an interface.

Abstract: Described herein are embodiments of a reinforcement learning based large-scale multi-objective ranking system. Embodiments of the system may be used for optimizing short-video recommendation on a video sharing platform. Multiple competing ranking objective and implicit selection bias in user feedback are the main challenges in real-world platform. In order to address those challenges, multi-gate mixture of experts (MMoE) and soft actor critic (SAC) are integrated together into a MMoE_SAC system. Experiment results demonstrate that embodiments of the MMoE_SAC system may greatly reduce a loss function compared to systems only based on single strategies.

Abstract: A computer-implemented system may include a treatment device configured to be manipulated by a user while the user is performing a treatment plan and a patient interface comprising an output device configured to present telemedicine information associated with a telemedicine session. The computer-implemented system may also include a first computing device configured to: receive treatment data pertaining to the user while the user uses the treatment device to perform the treatment plan; write to an associated memory, for access by an artificial intelligence engine, the treatment data; receive, from the artificial intelligence engine, at least one prediction; identify a threshold corresponding to the at least one prediction; and, in response to a determination that the at least one prediction is outside of the range of the threshold, update the treatment data pertaining to the user to indicate the at least one prediction.

Abstract: A computer-based method, system, and computer program product for automatically identifying significant events for food traceability. The method may comprise receiving a series of events from an agriculture supply chain entity, automatically determining, at a machine learning model of an event analysis module, one or more events in the series having a significance for food traceability greater than a threshold, and automatically reporting the one or more events to a ledger.

Abstract: A medical information processing apparatus comprises an obtaining unit that obtains medical information, a learning unit that performs learning on a function of the medical information processing apparatus using the medical information, an evaluation data holding unit that holds evaluation data in which a correct answer to be obtained by executing the function is known, the evaluation data being for evaluating a learning result of the learning unit, an evaluating unit that evaluates a learning result obtained through learning, based on the evaluation data, and an accepting unit that accepts an instruction to apply a learning result of the learning unit to the function.

Abstract: A file commenting method includes displaying file content in an instant messaging client, displaying comment content entered via a commenting operation performed on a portion of the file content, and displaying a session page when the commenting operation is completed, the session page including the portion of the file content, the comment content, and a file identifier corresponding to the file content.

Abstract: A calibrated lithographic model may be used to generate a lithographic model output based on an integrated circuit (IC) design layout. Next, at least a chemical parameter may be extracted from the lithographic model output. A calibrated defect rate model may then be used to predict a defect rate for the IC design layout based on the chemical parameter.

Abstract: Aspects of the disclosure relate to predicting the spread of malicious software. The computing platform may identify malicious software at a computing device and may input characteristics of the malicious software into a machine learning model to produce time horizons for the malicious software. The computing platform may identify, using a knowledge graph and based on the time horizons, subsets of computing devices, each corresponding to a particular time horizon. The computing platform may perform, at a time within a first time horizon, a first security action for a first subset of computing devices within the first time horizon and a second security action for a second subset of computing devices located within a second time horizon, where the first time horizon and the second time horizon indicate that the first subset will be affected by the malicious software prior to the second subset.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. A training sample is first received from a source. A prediction is generated according to the training sample and based on one or more parameters associated with a model. A metric characterizing the prediction is also determined. The prediction and the metric are transmitted to the source to facilitate a determination on whether a ground truth label for the training sample is to be provided. When the ground truth label is received from the source, the one or more parameters of the model are updated based on the prediction and the ground truth label.

Abstract: Methods, systems, and computer-readable mediums for generating, by an artificial intelligence engine, treatment plans for optimizing a user outcome. The method comprises receiving attribute data associated with a user. The attribute data comprises one or more symptoms associated with the user. The method also comprises, while the user uses a treatment apparatus to perform a first treatment plan for the user, receiving measurement data associated with the user. The method further comprises generating, by the artificial intelligence engine configured to use one or more machine learning models, a second treatment plan for the user. The generating is based on at least the attribute data associated with the user and the measurement data associated with the user. The second treatment plan comprises a description of one or more predicted disease states of the user. The method also comprises transmitting, to a computing device, the second treatment plan for the user.

Abstract: In some embodiments, a method includes generating a trained decision tree with a set of nodes based on input data and a partitioning objective, and generating a modified decision tree by recursively passing the input data through the trained decision tree, recursively calculating, for each of the nodes, an associated set of metrics, and recursively defining an association between each of the nodes and the associated set of metrics. A node from a set of nodes of the modified decision tree is identified that violates a user-specified threshold value, associated with a user, for at least one of the metrics. The method also includes causing transmission of a signal to a compute device of the user, the signal including a representation of the identified node.

Abstract: An artificial intelligence (AI) sequencer is provided. The Al sequencer includes a queue manager configured to manage a plurality of queues for maintaining data of AI jobs, wherein an AI job includes processing of one or more AI functions; a scheduler for scheduling execution of data maintained by the plurality of queues; a plurality of job processing units (JPUs), wherein each of the plurality JPUs is configured to at least generate an execution sequence for an AI job; and a plurality of dispatchers connected to a plurality of AI accelerators, wherein each of the plurality of dispatchers is configured to dispatch at least a function of the AI job to an AI accelerator, wherein a function is dispatched to an AI accelerator at an order determined by an execution sequence created for a respective AI job.

Abstract: A method includes obtaining pairs of in-phase (I) and quadrature (Q) samples associated with a signal to be demodulated. The method also includes providing a set of the I/Q pairs to a trained AI/ML model. The set of the I/Q pairs includes an I/Q pair associated with a symbol being demodulated and at least one I/Q pair associated with at least one prior symbol that has been demodulated. In addition, the method includes using the trained AI/ML model to generate a symbol estimate for the symbol based on the set of the I/Q pairs, where the symbol estimate represents a portion of data that is encoded in the signal.

Abstract: An electronic device may determine whether a machine-learning model is operating within predefined limits. In particular, the electronic device may receive, from another electronic device, instructions for the machine-learning model, a reference input and a predetermined output of the machine-learning model for the reference input. Note that the instructions may include an architecture of the machine-learning model, weights associated with the machine-learning model and/or a set of pre-processing transformations for use when executing the machine-learning model on images. In response, the electronic device may configure the machine-learning model based on the instructions. Then, the electronic device may calculate an output of the machine-learning model for the reference input. Next, the electronic device may determine whether the machine-learning model is operating within predefined limits based on the output and the predetermined output.

Abstract: A method is performed at a server system having one or more processors and memory storing instructions for execution by the one or more processors. The server system provides a content service. The method includes providing a first media item for playback based on a request from an application executing on an electronic device. The method includes receiving data associated with a behavior of a first user of the content service. The data associated with the behavior of the first user includes an indication of at least a first user input for controlling the playback of the first media item. The method includes using the received data to provide a media recommendation to the electronic device.

Abstract: An apparatus for controlling a vehicle includes a memory, a network interface, and a processor, where the processor may check whether a fingerprint recognition module in the vehicle supports fast identity online (FIDO), may request for an encryption key to register the FIDO, may encrypt an input pin code to transmit the encrypted pin code to a financial information processing server, may transmit vehicle and account information for the FIDO registration to a FIDO authentication server when receiving an authentication token from the financial information processing server, may generate information on the FIDO registration when receiving policy information from the FIDO authentication server, and may transmit the information on the FIDO registration to the FIDO authentication server.

Abstract: A computer-implemented method, a computer system and a computer program product create rules for a rule-based natural language interface for databases (NLIDB). The method may include receiving a natural language query from a user. The method may also include generating a first explanation for the natural language query using a deep learning model and a second explanation for the natural language query using the rule-based NLIDB and validating whether the first and second explanations correctly represent the natural language query. The method may further include identifying the database value in the first explanation in response to the first explanation correctly representing the natural language query and the second explanation not correctly representing the natural language query. Lastly, the method may include creating a rule in a table for the rule-based natural language interface for databases that associates the database value with the original word of the natural language query.

Abstract: Systems, methods, and apparatuses are described herein for performing sentiment analysis on electronic communications relating to one or more image-based communications methods, such as emoji. Message data may be received. The message data may correspond to a message that is intended to be sent but has not yet been sent to an application. Using a first machine learning model, one or more subsets of the plurality of emoji may be determined. The one or more subsets of the plurality of emoji may comprise one or more different types and quantities of emoji, and may each correspond to the same or a different sentiment. Using a second machine learning model, one or more emojis may be selected from the one or more subsets. The one or more emojis selected may correspond to responses to the message.

Abstract: Certain aspects of the present disclosure provide techniques for concurrently performing inferences using a machine learning model and optimizing parameters used in executing the machine learning model. An example method generally includes receiving a request to perform inferences on a data set using the machine learning model and performance metric targets for performance of the inferences. At least a first inference is performed on the data set using the machine learning model to meet a latency specified for generation of the first inference from receipt of the request. While performing the at least the first inference, operational parameters resulting in inference performance approaching the performance metric targets are identified based on the machine learning model and operational properties of the computing device. The identified operational parameters are applied to performance of subsequent inferences using the machine learning model.

Abstract: Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Abstract: A first classification is received from a neural network regarding a training dataset sent to the neural network. A modified training dataset with a perturbation of the training dataset is identified, where this modified training dataset causes the neural network to return a second classification. The perturbation is analyzed to identify a negative rule of the neural network.

Abstract: A system includes: a natural language processing (NLP) model trained in a training domain and configured to perform natural language processing on an input dataset; an accuracy module configured to: calculate a domain shift metric based on the input dataset; and calculate a predicted decrease in accuracy of the NLP model attributable to domain shift relative to the training domain based on the domain shift metric; and a retraining module configured to selectively trigger a retraining of the NLP model based on the predicted decrease in accuracy of the NLP model.

Abstract: A method and system for mapping labels of documents is described. A training set including a plurality of documents and at least one map can be retrieved. Each document can include a plurality of labels, and the at least one map can represent associations between the labels of one document and another document in the set. Each document (or group of documents) in the set can include certain features. These features can relate to the labels in the documents. Each label can correspond to one or more data points (or datasets) in each documents. In one example embodiment, the map can be generated based on the features extracted from each document.

Abstract: A computer-implemented method, a computer program product, and a computer system for managing permissions in cloud computing. A computer detects n times of attempts of an action in cloud computing, where the n times of attempts are initiated by a user who has no permission to perform the action, where n is a predetermined number triggering generation of a request for a permission to perform the action. A computer generates the request for the permission for the user. A computer determines whether the request has been pre-approved. In response to determining that the request has been pre-approved, a computer automatically approves the request. In response to determining that the request has not been pre-approved, a computer adds metadata about the user to the request and sends the request with the metadata to a cloud administrator, where the cloud administrator approves or denies the request based on the metadata.

Abstract: The present disclosure describes a patent management system and method for remediating insufficiency of input data for a machine learning system. A plurality of data vectors using data are extracted from a plurality of data sources. A user input with respect to an input data context is received, the input data context correspond to a subset of the plurality of data elements. An input vector based on the user input is generated and a set of matching data vectors are determined from the plurality of data vectors based on the input vector. An insufficiency of the input data is determined based on a comparison of a number of matching data vectors with a first pre-determined threshold, and/or a variance with a second pre-determined threshold. Further, the set of matching data vectors are expanded by modifying the input vector when the input data is determined to be insufficient.

Abstract: We present the architecture of a high-performance constraint solver R-Solve that extends the gains made in SAT performance over the past fifteen years on static decision problems to problems that require on-the-fly adaptation, solution space exploration and optimization. R-Solve facilitates collaborative parallel solving and provides an efficient system for unrestricted incremental solving via Smart Repair. R-Solve can address problems in dynamic planning and constrained optimization involving complex logical and arithmetic constraints.

Abstract: Normalizing external application data is disclosed, including: receiving external application data associated with an external application; determining normalized metadata based at least in part on inferring from the external application data; and using the normalized metadata to monitor activities at the external application.

Abstract: A method includes identifying a plurality of images corresponding to sky conditions and isolating cloud pixels from sky pixels in each of the plurality of images. Responsive to determining percentage of cloud pixels in one or more of the plurality of images meets a threshold value, the method further includes determining predicted cloud movement relative to sun position. The method further includes causing a tint level of an electrochromic device to be controlled based on the predicted cloud movement relative to the sun position.

Abstract: Systems and methods for generating user notifications to a set of users of a social networking service is presented. For each user of a set of users of the social networking service, one or more machine learning models selects an optimal notification channel, an optimal notification template, and optimal personalization content for configurable elements of a selected notification template. Each of these determinations/selections is made according to and based on a likelihood of increased user engagement with the social networking service. Upon determining the notification channel, notification template, and personalizations to the template, the notification is generated and sent to the corresponding user.

Abstract: Systems, methods, computing platforms, and storage media for decomposing data are disclosed. Exemplary implementations may receive an input data set, identify a first set of criteria for analyzing the input data set, identify a first and a second data subset, wherein the first and second data subset are defined by a first and a second set of filters, respectively, identify a first set of parent sets, each parent set being a subset of the input data set and a superset of the second data subset, and each parent set being defined by a subset of the second set of filters that define the second data subset, generate a directed acyclic graph based at least in part on identifying the first set of parent sets, the first data subset, and/or the second data subset, and display, via a user interface, the directed acyclic graph to a user.

Abstract: Systems, methods, and software can be used for anomaly detection. In some aspect, a number of training events are obtained. A data structure represented by a decision tree is generated based on the number of training events. A to-be-scored event is obtained and a traversed path is determined for the to-be-scored event. An anomaly score is computed based on the traversed path and the to-be-scored event is determined to be an anomalous or normal event based on the anomaly score.

Abstract: Techniques for generating a personalization identifier that is usable by a skill to customize output of supplemental content to a user, without the skill being able to determine an identity of the user based on the personalization identifier, are described. A personalization identifier may be generated to be specific to a skill, such that different skills receive different personalization identifiers with respect to the same user. The personalization identifier may be generated by performing a one-way hash of a skill identifier, and a user profile identifier and/or a device identifier. User-perceived latency may be reduced by generating the personalization identifier at least partially in parallel to performing ASR processing and/or NLU processing.

Abstract: Disclosed are systems, methods, and non-transitory computer-readable media for sharing, on a distributed database, a database application to a first user of the distributed database, the database application generated by a second user of the distributed database. The training dataset includes a first database training dataset from the first user of the distributed database and a second database training dataset from the second user of the distributed database, the first database training dataset and the second database training dataset including non-overlapping dataset features. The database application further identifies a query from the second user to train the machine learning model on the training dataset and generates a trained machine learning model by training the machine learning model on a joined dataset according to the query. The database application generates outputs from the trained machine learning model by applying the trained machine learning model on new data.

Abstract: A facility for optimizing machine learning models is described. The facility obtains a description of a machine learning model and a hardware target for the machine learning model. The facility obtains optimization result data from a repository of optimization result data. The facility optimizes the machine learning model for the hardware target based on the optimization result data.

Abstract: The present disclosure relates to deep learning image classification oriented to heterogeneous computing devices. According to embodiments of the present disclosure, the deep learning model can be modeled as an original directed acyclic graph, with nodes representing operators of the deep learning model and directed edges representing data transmission between the operators. Then, a new directed acyclic graph is generated by replacing the directed edges in the original directed acyclic graph with new nodes and adding two directed edges to maintain a topological structure.

Abstract: Disclosed herein are arrangements that facilitate the transfer of knowledge from models for a source data-processing domain to models for a target data-processing domain. A convolutional neural network space for a source domain is factored into a first classification space and a first reconstruction space. The first classification space stores class information and the first reconstruction space stores domain-specific information. A convolutional neural network space for a target domain is factored into a second classification space and a second reconstruction space. The second classification space stores class information and the second reconstruction space stores domain-specific information. Distribution of the first classification space and the second classification space is aligned.

Abstract: In implementations of systems for identifying templates based on fonts, a computing device implements an identification system to receive input data describing a selection of a font included in a collection of fonts. The identification system generates an embedding that represents the font in a latent space using a machine learning model trained on training data to generate embeddings for digital templates in the latent space based on intent phrases associated with the digital templates and embeddings for fonts in the latent space based on intent phrases associated with the fonts. A digital template included in a collection of digital templates is identified based on the embedding that represents the font and an embedding that represents the digital template in the latent space. The identification system generates an indication of the digital template for display in a user interface.

Abstract: The present disclosure describes a method, system, and computer readable medium for facilitating predictive maintenance of testing machine using a combination of deep learning. The method comprises performing receiving plurality of tested data of a plurality of products being tested by the testing machine. The method further comprises applying a predictive model, having predictive model parameters, upon the plurality of tested data to predict a plurality of future test data corresponding to the plurality of products. The method further comprises determine a deviation between the plurality of tested data and the plurality of future test data, wherein the deviation indicates fault in the testing machine. The method further comprises determine a fault level of the testing machine by comparing the deviation with a predefined threshold and determining, during run-time, the maintenance required for the testing machine based on the fault level.

Abstract: The present disclosure provides platforms, systems, media, and methods for capturing clinical cases and expert-derived treatment rationales to facilitate biomedical decision making, which can include virtual clinical trials that continuously learn from the experiences of all patients, on all treatments, and all the time. Algorithms such as Bayesian machine learning methods can be applied to coordinate such virtual trials.

Abstract: Detection systems, methods and computer program products comprising a non-transitory tangible storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method for anomaly detection, a detected anomaly being indicative of an undesirable event. A detection system comprises a computer and an anomaly detection engine executable by the computer, the anomaly detection engine configured to perform a method comprising receiving data comprising a plurality m of multidimensional data points (MDDPs), each data point having n features, constructing a dictionary D based on the received data, embedding dictionary D into a lower dimension embedded space and classifying, based in the lower dimension embedded space, a MDDP as an anomaly or as normal.

Abstract: An approach is described with respect to dynamically constructing and configuring a conversational agent learning model. Various aspects of the conversational agent learning model may be constructed and updated without continuous intervention of a domain administrator. A method pertaining to such approach may include retrieving a corpus of information. The corpus of information may include records from a set of repositories and external data, including data from social networks or applications. The method further may include configuring the conversational agent learning model based upon the retrieved corpus of information. The method further may include deploying the conversational agent learning model by facilitating interaction between the conversational agent learning model and a plurality of clients. The method further may include updating the conversational agent learning model to address any modification to the corpus of information.

Abstract: A method and system are provided for training a machine-learning (ML) system/module and to provide an ML model. In one embodiment, a method includes using a labeled entities set to train a machine learning (ML) system, to obtain an ML model, and using the trained ML model to predict labels for entities in an unlabeled entities set, yielding a machine-labeled entities set. One or more individual ML models may be trained and used in this way, where each individual ML model corresponds to a respective document source. The document sources can be identified via classification of a corpus of documents. The prediction of labels provides a respective confidence score for each machine-labeled entity. The method also includes selecting from the machine-labeled entities set, a subset of machine-labeled entities having a respective confidence score at least equal to a threshold confidence score; and updating the labeled entities set by adding thereto the selected subset of machine-labeled entities.

Abstract: In a data processing system, at least one processing node is configured to perform computations for a multi-stage process whilst at least one other processor performs the load/unload operations required to calculate a subsequent stage of the multi stage process. An exchange of data then occurs between the processing nodes. At a later time, at least one processing node performs calculations using the data loaded from storage, whilst at least one other processor performs the load/unload operations required to calculate a subsequent stage of the multi stage process.

Abstract: The present invention provides an artificial intelligence-assisted printed electronics self-guided optimization method, which integrates machine learning technology with printed electronics. According to variables that impact printing quality of a microelectronic printer, a user sets up experimental groups, prints samples with the microelectronic printer according to parameters in the experiment groups, characterizes printing effects, and evaluates the printing quality. The characterization result is analyzed by machine learning, and printing parameters that correspond to a best printing effect are obtained; then, the parameters are fed back to the user, and the user configures the printer according to the fed-back parameters, thereby improving printing quality.