Abstract: One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

Abstract: Aspects of the disclosure relate to intelligent bot performance tracking and analysis. A computing platform may receive a work queue of items to be processed using a bot. The computing platform may receive, in real-time with processing of the work queue using the bot, metadata associated with the work queue. Based on the metadata, the computing platform may assign, in real-time, a value metric associated with completion of each item in the work queue. Based on the assigned value metric, the computing platform may identify a robotic process automation cost associated with processing the work queue via the bot. The computing platform may compare, the robotic process automation cost to a cost to process the work queue via another operation, and determine a performance metric for the bot based on the comparison. The computing platform may dynamically generate and transmit, in real-time, an indication of the determined bot performance metric.

Abstract: Systems and methods of computing classifications for and migrating digital content that includes accessing a digital content corpus within a source data storage system; in response to accessing the digital content corpus, for each distinct item of digital content of the plurality of distinct items of digital content: computing, via one or more digital content machine learning classification models, a content classification inference; identifying automated digital content handling tasks of a plurality of distinct digital content handling tasks based on the content classification inference; executing the automated content handling tasks identified for each distinct item of digital content, wherein executing the automated content handling tasks includes: designating a storage location within a target data storage system based on the in-migration content classification inference; and migrating a respective item of digital content from the source data storage system to the designated storage location within the ta

Abstract: Disclosed examples include an automated online experimentation mechanism that can perform model selection from a large pool of models with a relatively small number of online experiments. The probability distribution of the metric of interest that contains the model uncertainty is derived from a Bayesian surrogate model trained using historical logs. Disclosed techniques can be applied to identify a superior model by sequentially selecting and deploying a list of models from the candidate set that balance exploration-exploitation.

Abstract: A computing platform may be configured to (i) obtain an input dataset, (ii) construct a graph from the input dataset, (iii) for a given node within the constructed graph, generate a first type of embedding vector using a first embedding technique (e.g., a shallow embedding technique) and a second type of embedding vector using a second embedding technique that differs from the first embedding technique (e.g., a deep embedding technique), and (iv) use the first and second types of embedding vectors for the given node and a data science model to render a given prediction for the given node.

Abstract: An AI-based platform for enabling intelligent orchestration and management of power and energy is disclosed. The platform includes a digital twin system having a digital twin of a mining environment. The digital twin includes at least one parameter that is detected by a sensor of the mining environment. In some disclosed embodiments, the at least one parameter is associated with one or more of an unmined portion of the mining environment a mining of materials from the mining environment, a smart container event involving a smart container associated with the mining environment, a physiological status of a miner associated with the mining environment, a transaction-related event associated with the mining environment, and a compliance of the mining environment with one or more contractual, regulatory, and/or legal policies.

Abstract: There is provided a system and method of runtime examination of a semiconductor specimen. The method includes obtaining a runtime image representative of an inspection area of the specimen, the runtime image having a relatively low signal-to-noise ratio (SNR); and processing the runtime image using a machine learning (ML) model to obtain examination data specific for a given examination application, wherein the ML model is previously trained for the given examination application using one or more training samples, each training sample representative of a respective reference area sharing the same design pattern as the inspection area and comprising: a first training image of the respective reference area having a relatively low SNR; and label data indicative of ground truth in the respective reference area pertaining to the given examination application, the label data obtained by annotating a second training image of the respective reference area having a relatively high SNR.

Abstract: A computer-implemented method for performing Learning from Demonstrations, particularly Imitation Learning, based on data associated with a first domain, particularly a source domain. The method includes: determining first data characterizing a demonstrator of the first domain, wherein particularly the first data characterizes sensor data of the demonstrator and/or sensor data of at least one spectator observing the demonstrator, determining first knowledge from the first domain based on the first data, transferring at least a part of the first knowledge to a second domain, particularly a target domain.

Abstract: A data processing service facilitates the creation and processing of data processing pipelines that process data processing jobs defined with respect to a set of tasks in a sequence and with data dependencies associated with each separate task such that the output from one task is used as input for a subsequent task. In various embodiments, the set of tasks include at least one cleanroom task that is executed in a cleanroom station and at least one non-cleanroom task executed in an execution environment of a user where each task is configured to read one or more input datasets and transform the one or more input datasets into one or more output datasets.

Abstract: A method of retrieving a document according to an embodiment of the present application includes: acquiring a user retrieval query; calculating a user inquiry vector in a unit of sentence from the user retrieval query and acquiring a first document candidate group based on similarity between the calculated user inquiry vector and an embedding vector of a document stored in a retrieval database; acquiring a second document candidate group based on similarity between a text included in the user retrieval query and a text of the document stored in the retrieval database; and determining a summarization target document based on the first document candidate group and the second document candidate group.

Abstract: An electronic device and a method of training a classification model for age-related macular degeneration (AMD) are provided. The method includes the following steps. Training data is obtained. A loss function vector corresponding to the training data is calculated based on a machine learning algorithm, in which the loss function vector includes a first loss function value corresponding to a first classification of AMD and a second loss function value corresponding to a second classification of AMD, the first classification corresponds to a first group, and the second classification corresponds to one of the first group and a second group. The first loss function value is updated according to the second loss function value and a group penalty weight in response to the second classification corresponding to the second group to generate an updated loss function vector. The classification model is trained according to the updated loss function vector.

Abstract: In an approach, a processor receives device identification information corresponding to at least one device local to a location of a transaction. A processor receives notification of an infected user. A processor determines that the infected user is associated with the transaction. A processor identifies a second user from the device identification information. A processor sends a notification to the second user.

Abstract: Systems and methods for intelligently clustering alerts and applying a multi-stage scoring approach to prioritize and effectively address cybersecurity alerts are disclosed. The multi-stage scoring approach may involve applying Time Series Frequency-Inverse Document Frequency (TSF-IDF) Scores that represent a novelty of a cluster and Confidence Scores that represent a measure of accuracy based on prior performance including true positives and other indicia of accuracy.

Abstract: Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

Abstract: A method for predicting a property associated with a product unit. The method may include: obtaining a plurality of data sets, wherein each of the plurality of data sets includes data associated with a spatial distribution of a parameter across the product unit; representing each of the plurality of data sets as a multidimensional object; obtaining a convolutional neural network model trained with previously obtained multidimensional objects and properties of previous product units; and applying the convolutional neural network model to the plurality of multidimensional objects representing the plurality of data sets, to predict the property associated with the product unit.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support secure training of machine learning (ML) models that preserves privacy in untrusted environments using distributed executable file packages. The executable file packages may include files, libraries, scripts, and the like that enable a cloud service provider configured to provide ML model training based on non-encrypted data to also support homomorphic encryption of data and ML model training with one or more clients, particularly for a diagnosis prediction model trained using medical data. Because the training is based on encrypted client data, private client data such as patient medical data may be used to train the diagnosis prediction model without exposing the client data to the cloud service provider or others. Using homomorphic encryption enables training of the diagnosis prediction model using encrypted data without requiring decryption prior to training.

Abstract: A method includes identifying, from online clustering data, an internet protocol (IP) pair. The method further includes determining, by a processing device during an offline process, that the IP pair is part of a botnet. The method further includes, in response to the determining, appending data associated with the botnet to the online clustering data.

Abstract: A method and system of determining whether a stationary vehicle is a blocking vehicle to improve control of an autonomous vehicle. A perception engine may detect a stationary vehicle in an environment of the autonomous vehicle from sensor data received by the autonomous vehicle. Responsive to this detection, the perception engine may determine feature values of the environment of the vehicle from sensor data (e.g., features of the stationary vehicle, other object(s), the environment itself). The autonomous vehicle may input these feature values into a machine-learning model to determine a probability that the stationary vehicle is a blocking vehicle and use the probability to generate a trajectory to control motion of the autonomous vehicle.

Abstract: A method for training a machine learning algorithm including uncertainties. The method includes: pre-training the algorithm based on initially collected data by a control unit in order to obtain an initial model, determining a set of channels, the data originating from channels contained in the set of channels being intended to be used for retraining the initial model, based on an established data level and on the respective influence, which the data originating from one of the channels have on uncertainties instantaneously contained in the initial model, transferring detected data originating from the individual channels of the set of channels to the control unit, and retraining of the initial model by the control unit based on the data transferred to the control unit.

Abstract: Systems and methods are directed to controlling components of a utility grid. The system can receive data samples including signals detected at one or more portions of a utility grid. The system can construct a matrix having a first dimension and a second dimension. The system can train a machine learning model based on the matrix to predict values for signals of the utility grid not provided in the matrix. The system can receive bounds for one or more input variables, constraints on one or more output variables, and a performance objective for the utility grid. The system can determine, based on the machine learning model and via an optimization technique, an adjustment to a component of the utility grid that satisfies the performance objective. The system can provide the adjustment to the component of the utility grid to satisfy the performance objective.

Abstract: Systems and methods provide for deriving values for a set of features, for each session of a plurality of sessions that each comprise one or more actions performed by a given user on one or more webpages of a website. The systems and method further provide for generating an initial frustration score for each session of the plurality of sessions by analyzing the set of features for each session of the plurality of sessions using a first machine learning model trained to generate an initial frustration score based on values derived for a set of features for each session of a given set of sessions.

Abstract: Systems and methods for searching subsets of a search space. The system includes a memory with programmable instructions for searching a search space stored thereon, and processor for executing the programmable instructions. A user enters a search query, for example, via the user interface. The system receives a search command including the search query. The system performs a first search of a first subset of the of the search space using the search query. The results from the first search are presented to the user. The system receives a second, supplemental search command from the user, and responsive to the second, supplemental search command, performs a second search, using the same search query, of a second subset of the search space.

Abstract: Processing within a computing environment is facilitated using a corpus processing system to assess and enhance quality of a corpus of unstructured documents for a specified task. The processing includes referencing, by a corpus processing engine, the corpus of unstructured documents to obtain unstructured document data, and applying, by a corpus quality metrics engine, a set of quality metrics to the document data to obtain a set of quality metric scores. Further, the process includes automatically selecting, by a quality metric selection engine, a subset of task-relevant quality metrics using the quality metric scores and the specified task, and automatically transforming, at least in part, multiple documents of the corpus to remediate one or more identified issues with the documents. The automatically transforming results in remediated documents tuned for the specified task, which are provided for the specified task to be performed.

Abstract: A vehicle system includes one or more sensors configured to capture aspects of an environment and a computing device. The computing device is configured to receive information about the environment captured by the one or more sensors, determine one or more structures within the environment based on the received information, select a kernel that is parameterized for predicting a vehicle trajectory based on the one or more structures determined within the environment, and perform a convolution of the selected kernel and an array defining the environment, wherein the convolution predicts a future trajectory of a vehicle within the environment.

Abstract: Multiple distinct control descriptors, each specifying an algorithm and values of one or more parameters of the algorithm, are created. A plurality of tuples, each indicating a respective record of a data set and a respective descriptor, are generated. The tuples are distributed among a plurality of compute resources such that the number of distinct descriptors indicated in the tuples received at a given resource is below a threshold. The algorithm is executed in accordance with the descriptors' parameters at individual compute resources.

Abstract: Techniques are disclosed relating to the execution of machine learning models on client devices, particularly in the context of transaction risk evaluation. This reduces computational burden on server systems. In various embodiments, a server system may receive, from a client device, a request to perform a first operation and select a first machine learning model, from a set of machine learning models, to send to the client device. In some embodiments the first machine learning model is executable, by the client device, to generate model output data for the first operation based on one or more encrypted input data values that are encrypted with a cryptographic key inaccessible to the client device. The server system may send the first machine learning model to the client device and then receive, from the client device, a response message that indicates whether the first operation is authorized based on the model output data.

Abstract: The technology relates to machine responses to anomalies detected using machine learning based anomaly detection. In particular, to receiving evaluations of production events, prepared using activity models constructed on per-tenant and per-user basis using an online streaming machine learner that transforms an unsupervised learning problem into a supervised learning problem by fixing a target label and learning a regressor without a constant or intercept. Further, to responding to detected anomalies in near real-time streams of security-related events of tenants, the anomalies detected by transforming the events in categorized features and requiring a loss function analyzer to correlate, essentially through an origin, the categorized features with a target feature artificially labeled as a constant.

Abstract: Virtual platform system for use in a cloud-based system, comprising: a virtual platform simulator configured to represent in software a physical remote client device and to have this representation interact with a virtual platform application; a process virtual machine configured to execute program instructions of the virtual platform application and comprising a code morpher component for transforming the program instructions of the virtual platform application into native program instructions for execution on a physical host machine of the cloud-based system; and interception components for capturing transactions from the virtual platform simulator and the process virtual machine. The transactions are related to the execution of the program instructions of the virtual platform application.

Abstract: Custom source code generation models are generated by tuning a pre-trained deep learning model by freezing the model parameters and optimizing a prefix. The tuning process is distributed across a user space and a model space where the embedding and output layers are performed in the user space and the execution of the model is performed in a model space that is isolated from the user space. The tuning process updates the embeddings of the prefix across the separate execution spaces in a manner that preserves the privacy of the data used in the tuning process.

Abstract: A big data analysis system may include a big data repository communicatively coupled to a data accumulation server and a predictive graph processing system. The data accumulation server may be configured to receive information from a plurality of data sources, the information corresponding to user interaction with one or more computing devices associated with an organization via a networked computing system, store the information received from the plurality of sources in the big data repository; and monitor the plurality of data sources to update the data stored in the big data repository. The predictive graph processing system is configured to receive information stored in the big data repository, transform the information received from the big data repository into a predictive graph data set based on a predictive model, and store the predictive graph data set to a visualization data repository.

Abstract: Implementing artificial intelligence, specifically, machine learning techniques to identify malicious emails and, in response, identifying and conducting actions, including reporting the malicious emails to identified internal and/or external entities and preventing the malicious emails from being delivered to email client mailboxes. The machine learning techniques rely on malicious email patterns identified, at least, from previously identified malicious emails and data resulting from continuously crawling the Web and threat intelligence sources. Further, the email clients may be configured to include an add-on feature in which the user can provide a single input to report the email as being suspicious, which results in further analysis to determine whether the email is, in fact, a malicious email.

Abstract: The exemplary embodiments disclose a method, a computer program product, and a computer system for determining that one or more model pipelines satisfy one or more constraints. The exemplary embodiments may include detecting a user uploading data, one or more constraints, and one or more model pipelines, collecting the data, the one or more constraints, and the one or more model pipelines, and determining that one or more of the model pipelines satisfies all of the one or more constraints based on applying one or more algorithms to the collected data, constraints, and model pipelines.

Abstract: A method and system for intelligently organizing one or more groups of relevant files may include retrieving a user data signal including user-specific data, analyzing the user data signal to identify a parameter relating to file relevance, identifying one or more relevant files in a storage medium based on the parameter at least one of a user category property, a lifecycle stage property, a relevant activity property, or an activity level property of one or more files in the storage medium, organizing the one or more relevant file into the one or more groups of relevant files, and providing for display data relating to the one or more groups of relevant files.

Abstract: Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for a wearable multimedia device and cloud computing platform with an application ecosystem for processing multimedia data captured by the wearable multimedia device. In an embodiment, a method comprises: receiving, by one or more processors of a cloud computing platform, context data from a wearable multimedia device, the wearable multimedia device including at least one data capture device for capturing the context data; creating a data processing pipeline with one or more applications based on one or more characteristics of the context data and a user request; processing the context data through the data processing pipeline; and sending output of the data processing pipeline to the wearable multimedia device or other device for presentation of the output.

Abstract: According to one embodiment, in nth (n is a natural number) processing, a first node calculates a first gradient to update a first weight and a second node calculates a second gradient to update the first weight. In mth (m is a natural number) processing, a third node calculates a third gradient to update a third weight and a fourth node calculates a fourth gradient to update the third weight. If the calculation by the first and second nodes is faster than the calculation by the third and fourth nodes, in n+1th processing, a second weight updated from the first weight is further updated using the first and second gradients, and, in m+1th processing, a fourth weight updated from the third weight is further updated using the first to fourth gradients.

Abstract: A centralized skills management server, a computer-readable storage medium, and a computer-implemented method for skills inference are described herein. The method includes executing a web-based application on a remote computing system operated by a user associated with a tenant and extracting skills-related terms associated with the execution of the web-based application. The method includes interfacing with the global skills graph via an API and importing standardized skill tags relating to the extracted skills-related terms.

Abstract: This disclosure relates to systems and methods for identifying fraudulent email addresses associated with an electronic payment service. In some implementations, a computing device receives an email with a prefix having a number of characters and characterized by a prefix length indicative of the number of characters in the prefix. The computing device identifies each of a number of bigrams is identified within the prefix, and determines a row and column distance for each bigram between two consecutive characters of the bigram as positioned on a keyboard. The computing device calculates a Euclidean distance between the two consecutive characters of the bigram based on the row and column distances, and determines a normalized distance based on the prefix length and an average of the Euclidean distances calculated for the number of bigrams in the prefix. The normalized distance is compared with a value to classify the email as suspicious or as not suspicious.

Abstract: The technology disclosed proposes using a combination of computationally cheap, less-accurate bag of words (BoW) model and computationally expensive, more-accurate long short-term memory (LSTM) model to perform natural processing tasks such as sentiment analysis. The use of cheap, less-accurate BoW model is referred to herein as “skimming”. The use of expensive, more-accurate LSTM model is referred to herein as “reading”. The technology disclosed presents a probability-based guider (PBG). PBG combines the use of BoW model and the LSTM model. PBG uses a probability thresholding strategy to determine, based on the results of the BoW model, whether to invoke the LSTM model for reliably classifying a sentence as positive or negative. The technology disclosed also presents a deep neural network-based decision network (DDN) that is trained to learn the relationship between the BoW model and the LSTM model and to invoke only one of the two models.

Abstract: Methods and devices are provided for performing federated learning. A global model is distributed from a server to a plurality of client devices. At each of the plurality of client devices: model inversion is performed on the global model to generate synthetic data; the global model is on an augmented dataset of collected data and the synthetic data to generate a respective client model; and the respective client model is transmitted to the server. At the server: client models are received from the plurality of client devices, where each client model is received from a respective client device of the plurality of client devices; model inversion is performed on each client model to generate a synthetic dataset; the client models are averaged to generate an averaged model; and the averaged model is trained using the synthetic dataset to generate an updated model.

Abstract: Methods, apparatus, and processor-readable storage media for automated subscription management for remote infrastructure are provided herein.

Abstract: A method of generating a circuit model used to simulate an integrated circuit may include generating first feature element data and second feature element data by classifying feature data of a target semiconductor device according to measurement conditions, generating first target data and second target data by preprocessing the first feature element data and the second feature element data, respectively, generating a first machine learning model using the first target data and extracting a second machine learning model using the second target data, and generating the circuit model used to simulate the integrated circuit using the first machine learning model and the second machine learning model.

Abstract: Methods, computer program products, and systems are presented. The methods include, for instance: obtaining communication data streams, extracting data relevant to a point of view of a user, and generating a point of view record in a knowledge base that may be utilized by another user communicating with the user.

Abstract: Methods and systems for managing artificial intelligence (AI) models are disclosed. To manage AI models, poisoned training data introduced into an instance of the AI models may be identified and the impact of the poisoned training data on the AI models may be efficiently mitigated. To do so, a first poisoned AI model instance may be obtained. Rather than re-training an un-poisoned AI model instance to remove the impact of poisoned training data, the first poisoned AI model instance may be selectively un-trained whenever poisoned training data is found in the training dataset. Subsequently, weights of the first poisoned AI model instance may be adjusted to account for future training data. As poisoned training data may occur infrequently, selectively un-training the AI model may conserve computing resources and minimize AI model downtime when compared to a full or partial re-training process of an un-poisoned AI model instance.

Abstract: A processor may initiate metadata discovery. The processor may identify an asset category and asset count. The processor may determine whether one or more assets can be imported. The processor may determine whether an import was successful. The processor may terminate the import.

Abstract: Systems, methods, and computer readable media for vehicular search recommendations using machine learning. A computing model may receive a query for a vehicular recommendation. The model may be trained based on historical queries, webpages visited, and attributes of vehicles. The model may generate a decision tree comprising a plurality of paths for processing the query, the plurality of paths comprising a subset of a plurality of available paths for processing the query, each path associated with a plurality of search phases. The model may select, based on the decision tree, a first path of the plurality of paths for processing the query. The model may select, based on the first path, a first search phase of the plurality of search phases as corresponding to the query. The model may then return a search result corresponding to the first search phase as responsive to the query.

Abstract: A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.

Abstract: Various embodiments may be generally directed to the use of an adversarial learning framework for persona-based dialogue modeling. In some embodiments, automated multi-turn dialogue response generation may be performed using a persona-based hierarchical recurrent encoder-decoder-based generative adversarial network (phredGAN). Such a phredGAN may feature a persona-based hierarchical recurrent encoder-decoder (PHRED) generator and a conditional discriminator. In some embodiments, the conditional discriminator may include an adversarial discriminator that is provided with attribute representations as inputs. In some other embodiments, the conditional discriminator may include an attribute discriminator, and attribute representations may be handled as targets of the attribute discriminator. The embodiments are not limited in this context.

Abstract: The present disclosure generally relates to systems and methods for searching media content. In some implementation examples, a search system receives an input query, generates a query embedding of the input query, and generates a bias mitigation transformation associated with a sensitive attribute. Based on the query embedding and the bias mitigation transformation, the search system generates a transformed query embedding that suppresses at least a portion of the query embedding related to the sensitive attribute. Using the transformed query embedding, the search system executes a similarity search in a media embedding model to identify one or more media embeddings that are similar to the transformed query embedding and transmits the one or more media embeddings.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support cooperative training of machine learning (ML) models that preserves privacy in untrusted environments. For example, a server (or cloud-based computing device(s)) may be configured to “split” an initial ML model into various partial ML models, some of which are provided to client devices for training based on client-specific data. Output data generated during the training at the client devices may be provided to the server for use in training corresponding server-side partial ML models. After training of the partial ML models is complete, the server may aggregate the trained partial ML models to construct an aggregate ML model for deployment to the client devices. Because the client data is not shared with other entities, privacy is maintained, and the splitting of the ML models enables offloading of computing resource-intensive training from client devices to the server.

Abstract: A system and method for generating a decision tree having a plurality of nodes, arranged hierarchically as parent nodes and child nodes, comprising: generating a node including: receiving i) training data including data instances, each data instance having a plurality of attributes and a corresponding label, ii) instance weightings, iii) a valid domain for each attribute generated, and iv) an accumulated weighted sum of predictions for a branch of the decision tree; and associating one of a plurality of binary prediction of an attribute with each node including selecting the one of the plurality of binary predictions having a least amount of error; in accordance with a determination that the node includes child nodes, repeat the generating the node step for the child nodes; and in accordance with a determination that the node is a terminal node, associating the terminal node with an outcome classifier; and displaying the decision tree including the plurality of nodes arranged hierarchically.