Abstract: A system and method for connected vehicle risk detection are presented. The includes computing risk scores for a plurality of behaviors detected with respect to a connected vehicle, wherein each of the detected plurality of behaviors is associated with the connected vehicle based on a contextual vehicle state of the connected vehicle; aggregating the computed risk scores to determine an aggregated risk score; determining a risk level for the connected vehicle based on the aggregated risk score; and causing execution of at least one mitigation action based on the determined risk level.

Abstract: Disclosed is an operation method of a storage device supporting a multi-stream, which includes receiving an input/output request from an external host, generating a plurality of stream identifier candidates by performing machine learning on the input/output request based on a plurality of machine learning models that are based on different machine learning algorithms, generating a model ratio based on a characteristic of the input/output request, applying the model ratio to the plurality of stream identifier candidates to allocate a final stream identifier for the input/output request, and storing write data corresponding to the input/output request in a nonvolatile memory device of the storage device based on the final stream identifier.

Abstract: A visualization tool for machine learning models obtains metadata from a first training node at which a multi-layer machine learning model is being trained. The metadata includes a parameter of an internal layer of the model. The tool determines a plurality of metrics from the metadata, including respective loss function values corresponding to several training iterations of the model. The tool indicates the loss function values and the internal layer parameter values via a graphical interface.

Abstract: A natural language description of a desired function to be achieved using an automated communication flow is received. A prompt template specifically for a communication channel is selected based on an analysis of the natural language description of the desired function to be achieved. A prompt for a large language model is automatically generated based on the natural language description, including by inserting at least a portion of the selected prompt template in the automatically generated prompt. The automatically generated prompt is provided to the pre-trained large language model. Based on an output of the large language model, an automated communication flow to be implemented for the communication channel is automatically generated.

Abstract: Systems and methods of generating an interface including elements related to a next best state prediction are disclosed. A request for an interface including a user identifier is received. A next state prediction engine receives a sequence unit set including at least one sequence unit associated with the user identifier and a set of features associated with the at least one sequence unit and generates at least one next state prediction using a trained sequential prediction model. The trained sequential prediction model is configured to receive the sequence unit set and the set of features for the at least one sequence unit and output at least one predicted next state for the sequence unit set. An interface generation engine generates an interface including at least one element related to the at least one predicted next state and transmits the interface to a user device associated with the user identifier.

Abstract: A data processing service receives a request from a first collaborator to create a clean room for data sharing collaboration with at least a second collaborator. In response, the data processing service creates an execution environment separate from the data environment of the first collaborator and the second collaborator. The first and second collaborators can then add content into the clean room in the form of data tables and executable notebooks. Approval from each collaborator is required before a notebook can be executed using any data table shared into the clean room. Upon receiving notebook approval from each collaborator, the data processing service creates a notebook job to execute the notebook on one or more cluster computing resources of the data processing service to generate an output.

Abstract: A method may include: retrieving a plurality of code snippets from code repositories; generating a syntax representation, a property representation for each of the code snippets; receiving a query comprising a query code snippet, natural language keywords, and/or a string pattern; performing string-based matching and parser/syntax tree matching on the query and the tree representations, syntax matching on the query and the syntax representations, and property matching on the query and the property representations, wherein each of the matchings results in a score; combining the scores of the string-based matching, the parser/syntax tree matching, the syntax matching, and/or the property matching; identifying a plurality of code snippets of interest based on the combined scores; classifying the code snippets of interest using a machine learning classifier; outputting a list of the code snippets of interest with their classifications; and training the machine learning classifier based on user feedback.

Abstract: An apparatus for training a reinforcement learning model according to an embodiment includes a starting point determinator configured to determine starting points from an input value of a combinatorial optimization problem, a multi-explorer configured to generate exploration trajectories by performing exploration from each of the starting points using a reinforcement learning model, a trajectory evaluator configured to calculate an evaluation value of each of the exploration trajectories using an evaluation function of the combinatorial optimization problem, a baseline calculator configured to calculate a baseline for the input value from the evaluation value of each exploration trajectory, an advantage calculator configured to calculate an advantage of each of the exploration trajectories using the evaluation value of each exploration trajectory and the baseline, and a parameter updater configured to update parameters of the reinforcement learning model by using the exploration trajectories and the advantage

Abstract: There is provided a providing device including a processing unit that enables acquisition of one or both of control information for causing artificial intelligence to function in a device and information for specifying the control information from a distributed network.

Abstract: A conflict-free parallel radix sorting algorithm, and devices and systems implementing this algorithm, schedules memory copies of data elements of a large dataset so that there is always a single copy to each target memory each cycle of operation for the system implementing the algorithm. The conflict-free parallel radix sorting algorithm eliminates memory copying conflicts in copying data elements from different source memories to the same target memory and in this way maintains maximum throughput for the copying of data elements from source memories to target memories, reducing the time required to sort the data elements of the large dataset.

Abstract: A method for controlling a physical system. The method includes training a neural network to output, for a plurality of tasks, a result of the task carried out, in each case in response to the input of a control configuration of the physical system and the input of a value of a task input parameter; ascertaining a control configuration for a further task with the aid of Bayesian optimization, the neural network, parameterized by the task input parameter, being used as a model for the relationship between control configuration and result; and controlling the physical system according to the control configuration to carry out the further task.

Abstract: A lifelong learning intrusion detection system and methods are provided. The system may capture network data directed to a host node. The host node may include a honeypot. The honeypot may emulate operation of a physical or virtual device to attract malicious activity. The system may classify, based on a supervised machine learning model, the network data as being not malicious or not malicious. The system may classify, based on an unsupervised machine learning model, the network data as being anomalous or not anomalous. The system may alter operation of the honeypot to induce malicious activity. The system may determine, after operation of the honeypot is altered, the honeypot is accessed. The system may retrain the supervised machine learning model and/or unsupervised machine learning model based the network data.

Abstract: Disclosed herein is a computer-implemented tool that facilitates data analysis by use of machine learning (ML) techniques. The tool cooperates with a data intake and query system and provides a graphical user interface (GUI) that enables a user to train and apply a variety of different ML models on user-selected datasets of stored machine data. The tool can provide active guidance to the user, to help the user choose data analysis paths that are likely to produce useful results and to avoid data analysis paths that are less likely to produce useful results.

Abstract: Described in detail herein is a forecasting system. In one embodiment, the system can generate forecast data for the amount of labor and physical objects needed at various facilities.

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 from a plurality of data sources are extracted. A user input with respect to an input data context is received. 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. Data vectors in the set of matching data vectors are determined to be thick data or thin data 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 based on a selection of a recommendation.

Abstract: The present disclosure relates to a computing system. The computing system may include a memory system including a plurality of memory devices configured to store raw data and a near data processor (NDP) configured to receive the raw data by a first bandwidth from the plurality of memory devices and generate intermediate data by performing a first operation on the raw data, and a host device coupled to the memory system by a second bandwidth and determining a resource to perform a second operation on the intermediate data based on a bandwidth ratio and a data size ratio.

Abstract: Automatic partitioning of a machine learning model may be performed for training across multiple processing devices. A training job for a machine learning model may specify a number of partitions for a machine learning model. An optimization parameter may be determined for the machine learning model. Different partitions of the machine learning model to be trained across multiple processing devices may be determined based on the specified number of partitions and the optimization parameter. A schedule for executing the training job may be generated according to the respective partitions of the machine learning model. The training job may be executed according to the schedule.

Abstract: Systems and methods for distributed training of deep learning models are disclosed. An example local device to train deep learning models includes a reference generator to label input data received at the local device to generate training data, a trainer to train a local deep learning model and to transmit the local deep learning model to a server that is to receive a plurality of local deep learning models from a plurality of local devices, the server to determine a set of weights for a global deep learning model, and an updater to update the local deep learning model based on the set of weights received from the server.

Abstract: Techniques for anomaly and causality detection are described. An example includes receiving time series data; performing anomaly detection on the received time series data to detect at least one anomaly using an anomaly detection model; detecting a causal relationship between measures, wherein a set of measures are related when a first of the set of measures has a causal influence on a second of the set of measures, wherein a single time series is a metric and a measure is a numerical or categorical quantity a metric describes; and outputting a result of the anomaly and causality relationship detections.

Abstract: Implementations are described herein for training and applying machine learning models to digital images capturing plants, and to other data indicative of attributes of individual plants captured in the digital images, to recognize individual plants in distinction from other individual plants. In various implementations, a digital image that captures a first plant of a plurality of plants may be applied, along with additional data indicative of an additional attribute of the first plant observed when the digital image was taken, as input across a machine learning model to generate output. Based on the output, an association may be stored in memory, e.g., of a database, between the digital image that captures the first plant and one or more previously-captured digital images of the first plant.

Abstract: Data verification in federate learning is faster and simpler. As artificial intelligence grows in usage, data verification is needed to prove custody and/or control. Electronic data representing an original version of training data may be hashed to generate one or more digital signatures. The digital signatures may then be incorporated into one or more blockchains for historical documentation. Any auditor may then quickly verify and/or reproduce the training data using the digital signatures. For example, a current version of the training data may be hashed and compared to the digital signatures generated from the current version of the training data. If the digital signatures match, then the training data has not changed since its creation. However, if the digital signatures do not match, then the training data has changed since its creation. The auditor may thus flag the training data for additional investigation and scrutiny.

Abstract: A user experience theme description is obtained, along with a new user experience feature image set. The theme description and new user experience feature image set are input into a generative adversarial network (GAN). The GAN is trained to output multiple user experience designs based on the new feature image set. The multiple designs are displayed on an electronic display device that includes an eye gaze tracking system. User interface elements and their corresponding positions within a user interface are identified based on eye gaze of a user towards the electronic display device. The position and type of user interface elements are compared between a desired user interface design and a generated user interface design. Errors between the desired user interface design and the generated user interface design are input as feedback into the GAN to further enhance the results.

Abstract: Automatically selecting data for machine learning datasets is provided. The method comprises receiving an input dataset and user-specified data quality metrics. The input dataset is matched to a subset of candidate datasets in a repository according to schema characteristics. A second subset of candidate datasets having a distance from the input dataset above a specified threshold is selected from the first subset of candidate datasets. The second subset of candidate datasets are merged into a merged dataset. Top ranked samples above a specified second threshold are identified from the merged dataset based on the user-specified data quality metrics. The input dataset, augmented with the top ranked samples, is returned to the user.

Abstract: Embodiments of the present disclosure include a system for optimizing an artificial neural network by configuring a model, based on a plurality of training parameters, to execute a training process, monitoring a plurality of statistics produced upon execution of the training process, and adjusting one or more of the training parameters, based on one or more of the statistics, to maintain at least one of the statistics within a predetermined range. In some embodiments, artificial intelligence (AI) processors may execute a training process on a model, the training process having an associated set of training parameters. Execution of the training process may produce a plurality of statistics. Control processor(s) coupled to the AI processor(s) may receive the statistics, and in accordance therewith, adjust one or more of the training parameters to maintain at least one of the statistics within a predetermined range during execution of the training process.

Abstract: According to implementations of the subject matter described herein, there is provided a solution of providing a health index of a service. In this solution, a plurality of incident information sets associated with a plurality of services are obtained. The plurality of services are provisioned in a computing environment. An incident information set indicates at least one incident reported during operation of a service. Respective health indices are determined for the plurality of services based on respective ones of the plurality of incident information sets and a health classification policy. The respective health indices indicate respective health statuses of the plurality of services and being determined from a same health index range. Through unified use of incident information, the determined health indices can indicate universal and consistent health statuses for different services.

Abstract: Provided is a label inference system including a data generator configured to generate a training set and a test set, each including a plurality of images labeled with experts' annotations, a data trainer configured to perform training for a base model based on the generated training set and test set, a determiner configured to identify whether an evaluation metric f1 of the training model satisfies a base evaluation metric f1base, and a data inference unit configured to perform inference using the training set, the test set, and an unlabeled data set with the training model satisfying the base evaluation metric f1base.

Abstract: A system may provide for the design and/or modification of network slices associated with a wireless network. The wireless network may include different slices that are associated different sets of service parameters. Slices may include radio access networks (“RANs”), core networks, or other types of networks, which may include respective sets of network functions (“NFs”), which may perform specific functions with respect to a given RAN and/or core network. Different slices, RANs, core networks, and/or NFs may be associated with particular policies and/or tags which may be specified by one or more users associated with a first access level. One or more users associated with a second access level may configure portions of the wireless network, and the policies and/or tags associated with particular slices, RANs, core networks, or NFs may be automatically implemented by an orchestration system that configures the wireless network based on the provided configuration information.

Abstract: A possible region of encoding results of anomalous samples is limited. An encoder storage unit 14 stores an encoder for projecting an input feature value into a latent space in which the latent space is a closed manifold, a normal distribution obtained by learning normal data and an anomalous distribution obtained by learning anomalous data are held on the manifold, and a decoder for reconstructing the output of the encoder. An encoding unit 15 obtains a reconstruction result output by the decoder when a feature value of target data is input to the encoder. An anomaly score calculation unit 16 calculates an anomaly score of the target data based on distances between the reconstruction result and the normal distribution and distances between the reconstruction result and the anomalous distribution.

Abstract: A computerized method is disclosed for grouping alerts through machine learning while implementing certain time constraints. The method includes receiving an alert to be assigned to any of a plurality of existing issues or to a newly created issue, the alert including a temporal field that includes a timestamp of an arrival time of the alert, wherein an issue is a grouping of one or more alerts, determining a subset of existing issues from the plurality of existing issues that each satisfy time constraints, wherein the time constraints correspond to (i) a time elapsed between a most recent alert of a first existing issue and a timestamp of the alert, or (ii) a maximum issue time length of the first existing issue, and deploying a trained machine learning model to assign the alert to either an existing issue of the subset of existing issues or a newly created issue.

Abstract: Methods and systems are described herein for generating parameterized records using communications data. A generation system may be used to facilitate generating the parameterized records. The system may encode a plurality of electronic messages into bag-of-words embeddings and input a plurality of bag-of-words embeddings into a machine learning model to identify electronic messages containing itemized records, each including corresponding item identifiers. The system may transmit a request to identify server records associated with the itemized records, wherein the request comprises the corresponding item identifiers, and wherein the record identification server uses the corresponding item identifiers to identify the itemized records. The system may receive the server records each of which includes corresponding parameters.

Abstract: A method for detecting anomalies has the following steps: Obtaining a long-term recording having a plurality of first audio segments associated to respective first time windows; analyzing the plurality of the first audio segments to obtain, for each of the plurality of the first audio segments, a first characteristic vector describing the respective first audio segment; obtaining a further recording having one or more second audio segments associated to respective second time windows; analyzing the one or more second audio segments to obtain one or more characteristic vectors describing the one or more second audio segments ABCD; matching the one or more second characteristic vectors with the plurality of the first characteristic vectors to recognize at least one anomaly, like a temporal, sound or spatial anomaly.

Abstract: Using active learning to detect Protected Health Information (“PHI”) in documents stored as unannotated natural language data by selecting an initial chunk of text from the documents; forming a gold standard data via annotating the text by a human, the annotating identifies and tags PHI required to de-identify the text; training, using machine learning and the text before and after the annotating, a model having rules for PHI detection; querying, using a strategy, the documents to select a next chunk of text; machine annotating the text using the trained model; updating the gold standard data via correcting the machine annotation of the text by the human, wherein an amount of corrections in the updated gold standard data indicates a quality of the machine annotation; and iterating the steps starting at training, until the quality of the machine annotation is higher than a predetermined quality threshold.

Abstract: The techniques disclosed herein enable systems to train machine learning models using benign augmentation to enabled resistance various data poisoning attacks. This is achieved by first training a machine learning model using an initial dataset that is trustworthy and originates from a known source. The initial dataset is then modified to include known attack triggers such as syntactic paraphrasing to generate an augmented dataset. The augmented dataset is then used to train a robust machine learning model based using the initially trained machine learning model. The resultant robust machine learning model is then enabled to detect and resist attacks captured by the augmented dataset. The robust machine learning model can be retrained using an untrusted dataset that includes various compromised inputs in conjunction with the augmented dataset. Retraining results in an updated robust machine learning model that can learn and resist various data poisoning attacks on the fly.

Abstract: Methods and systems are provided for verifying an input provided at a controller including detecting a finger gesture on a surface of the controller. Responsive to detecting the finger gesture, multi-modal data is collected from a plurality of sensors and components tracking the finger gesture. The multi-modal data is used to generate an ensemble model using machine learning algorithm. The ensemble model is trained in accordance to training rules defined for different finger gestures. An output is identified from the ensemble model for the finger gesture. The output is interpreted to define an input for an interactive application selected for interaction.

Abstract: Apparatus, media, methods, and systems for data storage systems and methods for autonomously adapting data storage system performance, lifetime, capacity and/or operational requirements. A data storage system may comprise a controller and one or more non-volatile memory devices. The controller is configured to determine a category for a workload of one or more operations being processed by the data storage system using a machine-learned model. The controller is configured to determine an expected degradation of the one or more non-volatile memory devices. The controller is configured to adjust, based on the expected degradation and an actual usage of physical storage of the data storage system by a host system, an amount of physical storage of the data storage system available to the host system.

Abstract: A method for encoding text includes grouping text as a sequence of bytes, the text comprising a string of characters, each byte corresponding to a character in the text. For each byte of the sequence of bytes: (a) each bit is processed from most significant bit to least significant bit to generate a context; and (b) a subsequent bit is predicted, using a prediction model, based on the context generated based on previously processed bits, prediction of the prediction model being a combination of predictions of a plurality of sub-models. An encoded bitstream is output based on the predicted bits. The encoded bitstream includes encoded data corresponding to the text.

Abstract: A request to perform a set of map reduce jobs that generate a plurality of cross products from a dataset based on a join field is received. The join field indicates that each of the plurality of cross products is to be generated from a corresponding subset of the dataset. Responsive to receiving the request to perform the set of map reduce jobs, the set of map reduce jobs on the dataset to generate the plurality of cross products is performed. The final output data of the set of map reduce jobs is stored. The final output data comprises the plurality of cross products.

Abstract: A system for varying optimization solutions using constraints based on an endpoint, the system including at least a processor, a memory communicatively connected to the at least a processor, the memory containing instructions configuring the processor to receive process data including a plurality of impediments, generate an endpoint using a module configured to, analyze the plurality of impediments by extracting a feature from each impediment of the plurality of impediments, classify a plurality of impediments using the extracted features to a plurality of identifiers, rank the plurality of identifiers based on severity score, output the endpoint based on an identifier severity score, identify a plurality of nodes, receive at least a constraint, locate in the plurality of nodes an outlier cluster based on the endpoint, determine an outlier process as a function of the outlier cluster, and determine a visual element data structure as a function of the outlier process.

Abstract: Methods and apparatus, including computer program products, implementing and using techniques for generating a map of physical features in a physical environment. Feature datasets are received from several mobile agents. A feature dataset includes data describing a detected physical feature, a geolocation of the feature, and a timestamp representing when the physical feature was detected. The geolocation data for the different feature datasets are integrated into a map of the physical environment and displayed to a user. One or more regions of interest are identified on the map. The regions of interest include geographical areas for which no feature datasets or an insufficient number of feature datasets have been collected. In response to identifying the regions of interest, one or more mobile agents are instructed collect supplemental feature datasets for the regions of interest. The map is updated with the supplemental feature datasets for the identified regions of interest.

Abstract: The present disclosure describes a method, system, and apparatus for using a machine learning system to configure and optimize complex, distributed computer networks. The machine learning system receives an input related to a computer network and classifies the input using either a supervised learning approach or an unsupervised learning approach. From the classification of the input, the machine learning system builds a first training domain and determines a steady state network configuration for the computer network. After determining a steady state network configuration for the computer network, the machine learning system receives a plurality of inputs from one or more sensors or agents distributed throughout the computer network. The machine learning system compares the plurality of inputs to the steady state network configuration to detect a deviation from the first steady state network configuration.

Abstract: A computing system is provided, which is configured to instantiate a testing environment, define an extract, transform, load (ETL) pipeline within the testing environment, generate a test data set to be inputted into the ETL pipeline, generate assert data predicting an output of the ETL pipeline based on the generated test data set, input the test data set into the ETL pipeline to generate output data, compare the assert data to the output data, and validate the output data using the assert data to generate validation data.

Abstract: Various methods, apparatuses/systems, and media for integration of user defined metrics of cloud hosted relational databases with cloud monitoring services are disclosed. A processor receives via a serverless function implemented within the processor a configuration file containing configuration of collection of database connection string and respective metrics query. The serverless function connects to the secrets provider to fetch a password or authorization token to connect with a plurality of different types of cloud hosted relational databases. The processor returns, in response to receiving the respective metrics query, a table with two columns where one column includes a user defined metric name and the other column includes the metric query to fetch a user defined metric value for the given user defined metric name from the databases; and automatically integrates the fetched user defined metric value of the databases with the cloud monitoring service.

Abstract: A method of controlling an industrial system including at least one agent, the method including providing a representation of the industrial system as a finite state machine, the state machine including a plurality of nodes and a plurality of edges, where each node represents a discrete system state of the industrial system, each edge represents an action for a state transition between system states represented by two of the nodes, and at least one execution value is associated with at least one of the edges; executing at least one action by the at least one agent, the at least one action being represented by at least one of the edges; and modifying at least one execution value associated with at least one of the edges representing the at least one executed action, based on an outcome of the at least one executed action.

Abstract: A method for mitigating an adversarial attack includes receiving input data. The input data includes sensor data from a plurality of sensors and map data. The method further includes monitoring, in real time, an environment around an autonomous vehicle to identify a region that is possibly subject to an adversarial attack and determining a probability of the adversarial attack in the region. The method further includes determining whether the probability of the adversarial attack in the region that is possibly subject to the adversarial attack is greater than a predetermined threshold and, in response, planning a motion of the autonomous vehicle by taking into account the adversarial attack to generate a planned motion. The method further includes controlling a host vehicle to move in accordance with the planned motion.

Abstract: A computing node includes a NIC and processing circuitry configured to select a subset of computing resources from a set of available computing resources to initiate a parameter sweep associated with a parameter sweep request received. A plurality of settings is applied to each computing resource of the subset to generate a plurality of resource mappings during the parameter sweep. Each resource mapping of the plurality of resource mappings indicates at least one computing resource of the subset and a corresponding at least one setting of the plurality of settings. Telemetry information for the subset of computing resources is retrieved, the telemetry information is generated during the parameter sweep. A resource mapping of the plurality of resource mappings is selected based on a comparison of the telemetry information with an SLO. A reconfiguration of the available computing resources is performed based on the selected resource mapping.

Abstract: A system and method for providing machine learning algorithms with multi-source, real-time, and context-aware real-world data use in artificial intelligence applications include providing a server and a plurality of elements connected to the server and to each other via a network, each connected element including one or more sensory mechanisms. The server includes a memory and a processor. The memory stores a persistent virtual world system including virtual replicas of real world entities created and edited via a replica editor and updated via multi-source sensory data captured by the sensory mechanisms. Each virtual replica includes data and instructions including multi-source sensory data. The processor is configured to perform data preparation thereby generating machine learning data sets, and to perform machine learning algorithms on the data sets, generating trained machine learning models for holistically inferring new data and optimizing a system composed of real world entities.

Abstract: Techniques for responding to a healthcare inquiry from a user are disclosed. In one particular embodiment, the techniques may be realized as a method for responding to a healthcare inquiry from a user, according to a set of instructions stored on a memory of a computing device and executed by a processor of the computing device, the method comprising the steps of: classifying an intent of the user based on the healthcare inquiry; instantiating a conversational engine based on the intent; eliciting, by the conversational engine, information from the user; and presenting one or more medical recommendations to the user based at least in part on the information.

Abstract: Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

Abstract: One or more embodiments of the present specification provide privacy protection-based multicollinearity detection methods, apparatuses, and systems. Data alignment is performed by a member device on respective local feature data with other member devices to construct a joint feature matrix. Privacy protection-based multi-party matrix multiplication computation is performed to compute a product matrix of a transposed matrix of the joint feature matrix and the joint feature matrix. An inverse matrix of the product matrix is determined based on respective submatrices of the product matrix. A variance inflation factor of each attribute feature is determined by the member device with the other member devices using respective submatrices of the inverse matrix and the respective local feature data. Multicollinearity is determined by the member device with the other member devices based on fragment data of the variance inflation factor of each attribute feature.

Abstract: Embodiments include techniques for developing a model framework for remote unit monitoring condition-based maintenance. The techniques include collecting data associated with unplanned service requests, and generating one or more models from the collected data. The techniques also include predicting unplanned service requests based at least in part on the one or more models, and transmitting an output of the prediction of the unplanned service request.