Abstract: This application related to Artificial Intelligence technical field and discloses a robot and a method for predicting an emotion status by a robot. The method includes: determining a first emotion status of a first user, where the first emotion status is an emotion status of the first user at a first moment; predicting a second emotion status based on the first emotion status and a first emotion prediction model, where the second emotion status is an emotion status of the first user at a second moment, and the second moment is later than the first moment; and outputting a response to the first user based on the second emotion status.

Abstract: Methods, apparatus, systems and articles of manufacture to classify a first file are disclosed herein. Example apparatus include a feature hash generator to generate respective sets of one or more feature hashes for respective features of the first file. The number of the one or more feature hashes to be generated is based on an ability of the feature to distinguish the first file from a second file. The apparatus also includes a bit setter to set respective bits of a first fuzzy hash value based on respective ones of the one or more feature hashes, a classifier to assign the first file to a class associated with a second file based on a similarity between the first fuzzy hash value and a second fuzzy hash value for a second file.

Abstract: An industrial monitoring system comprises a monitoring device attached to an industrial device by a bond. Sensor data collected by the monitoring device during a commissioning period is received and used to train a machine learning model. Subsequent to the commissioning period, additional sensor data is collected by the monitoring device. An abnormal state of the bond between the monitoring device and industrial device is determined based on the additional sensor data and a characteristic inferred by the trained machine learning model. A notification of the abnormal state is generated.

Abstract: The technology disclosed provides systems and methods that display database objects representing entities, placement channels and workflow status in an insurance or other risk transfer program. Specially coded distribution trees and graphs of nodes represent the overall flow and, optionally, status of placement workflows. Node and edge data generated for display depicts multiple risk placements on behalf of at least one requesting party through intermediaries to responding parties. Line width of edges represents calculated relative size of placements, and pattern, style, and color of edges can depict statuses of the risk placements. Another geo-coded map display also depicts as nodes a requesting party, intermediaries and responding parties, connected by edges that represent communication channels.

Abstract: A method for determining a solution to a constrained optimization problem includes programming a weights matrix of a Hopfield network with a first encoded matrix representation of an initial constrained optimization problem. The method also includes employing the Hopfield network to determine a solution to the initial constrained optimization problem. Additionally, the method includes encoding a plurality of constrained optimization problems associated with a target constrained optimization problem into a plurality of encoded matrix representations each of which are a combination of the first and the second encoded matrix representations. The plurality of encoded matrix representations increases in convergence to the second encoded matrix representation of the target constrained optimization problem sequentially. The method further includes re-programming the weights matrix of the Hopfield network in an iterative manner with the plurality of encoded matrix representations.

Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

Abstract: Aspects of the disclosure relate to methods for controlling a vehicle having an autonomous driving mode. For instance, sensor data may be received from one or more sensors of the perception system of the vehicle, the sensor data identifying characteristics of an object perceived by the perception system. When it is determined that the object is no longer being perceived by the one or more sensors of the perception system, predicted characteristics for the object may be generated based on one or more of the identified characteristics. The predicted characteristics of the object may be used to control the vehicle in the autonomous driving mode such that the vehicle is able to respond to the object when it is determined that the object is no longer being perceived by the one or more sensors of the perception system.

Abstract: An event graph schema for a technology landscape may be determined, where the technology landscape is characterized using scores assigned to performance metrics. The event graph schema may include a plurality of nodes corresponding to the performance metrics and the scores, and directional edges connecting node pairs of the plurality of nodes, with each directional edge having a score-dependent validity criteria defined by scores of a corresponding node pair. Anomalous scores associated with an event within the technology landscape may be used to find anomalous nodes. Valid edges connecting two of the anomalous nodes and satisfying the score-dependent validity criteria thereof may be used to determine at least one path that includes the valid edges and connected anomalous nodes. In this way, it is possible to traverse the at least one path to identify at least one of the connected anomalous nodes as a root cause node of the event.

Abstract: In at least one embodiment, the present description is directed to a computer system, having at least components of a server, including a processor and a non-transient storage subsystem, storing a computer program including instructions that, when executed by the processor, cause the processor to at least: electronically receive a model for one or more operating conditions, one or more threshold criteria, and facility operating data for each respective facility of a plurality of facilities; validate the one or more threshold criteria to be one or more acceptable bias criteria; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the non-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

Abstract: Technologies for a neural belief reasoner model generative models that specifies belief functions are described. Aspects include receiving, by a device operatively coupled to a processor, a request for a belief function, and processing, by the device, the request for the belief function in the generative model based on trained probability parameters and a minimization function to determine a generalized belief function defined by fuzzy sets. Data corresponding to the generalized belief function is output, e.g., as a belief value and plausibility value.

Abstract: Systems and methods are provided for authorizing an electronic transaction. In one implementation at least one processor is programmed to receive electronic transaction data and historical transaction data, the electronic transaction data including an entity identifier component and an amount component of an electronic transaction; determine, based on the entity identifier component and the amount component, a location of the electronic transaction in a space of a distributed representation space, the distributed representation space comprising a mapping of electronic transaction components in a high-order space; determine locations of the historical transaction data in the distributed representation space; determine a decision boundary in the distributed representation space based on the locations of the historical transaction data; and authorize the electronic transaction based on the location of the electronic transaction being within the decision boundary.

Abstract: A hybrid quantum-classical (HQC) computer takes advantage of the available quantum coherence to maximally enhance the power of sampling on noisy quantum devices, reducing measurement number and runtime compared to VQE. The HQC computer derives inspiration from quantum metrology, phase estimation, and the more recent “alpha-VQE” proposal, arriving at a general formulation that is robust to error and does not require ancilla qubits. The HQC computer uses the “engineered likelihood function” (ELF) to carry out Bayesian inference. The ELF formalism enhances the quantum advantage in sampling as the physical hardware transitions from the regime of noisy intermediate-scale quantum computers into that of quantum error corrected ones. This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.

Abstract: A method and system of machine learning-augmented geophysical inversion includes obtaining measured data; obtaining prior subsurface data; (a) partially training a data autoencoder with the measured data to learn a fraction of data space representations and generate a data space encoder; (b) partially training a model autoencoder with the prior subsurface data to learn a fraction of model space representations and generate a model space decoder; (c) forming an augmented forward model with the model space decoder, the data space encoder, and a physics-based forward model; (d) solving an inversion problem with the augmented forward model to generate an inversion solution; and iteratively repeating (a)-(d) until convergence of the inversion solution, wherein, for each iteration: partially training the data and model autoencoders starts with learned weights from an immediately-previous iteration; and solving the inversion problem starts with super parameters from the previous iteration.

Abstract: A learning device is configured to perform learning of a decision tree by gradient boosting. The learning device includes a plurality of learning units and a plurality of model memories. The plurality of learning units are configured to perform learning of the decision tree using learning data divided to be stored in a plurality of data memories. The plurality of model memories are each configured to store data of the decision tree learned by corresponding one of the plurality of learning units.

Abstract: An explainable transducer transformer (XTT) may be a finite state transducer, together with an Explainable Transformer. Variants of the XTT may include an explainable Transformer-Encoder and an explainable Transformer-Decoder. An exemplary Explainable Transducer may be used as a partial replacement in trained Explainable Neural Network (XNN) architectures or logically equivalent architectures. An Explainable Transformer may replace black-box model components of a Transformer with white-box model equivalents, in both the sub-layers of the encoder and decoder layers of the Transformer. XTTs may utilize an Explanation and Interpretation Generation System (EIGS), to generate explanations and filter such explanations to produce an interpretation of the answer, explanation, and its justification.

Abstract: A data analysis device 10 comprises: a frequency analysis unit 11 that performs frequency analysis, under a predetermined condition, on each piece of a plurality of training data pieces including a plurality of class training data pieces some of which have been assigned a label indicating the data class; a cluster analysis unit 12 that clusters the frequency analyzed training data pieces into a number of classes of frequency analyzed training data; a computation unit 13 that computes, on the basis of the clusters, the degree to which frequency analyzed training data pieces assigned the same label are not included in the same cluster; and a selection unit 14 that selects, as a clustering model for assigning a label to a training data piece, clustering results according to the cluster analysis unit 12 when the smallest degree was computed, from among the plurality of computed degrees.

Abstract: Embodiments relate to an intelligent computer platform to use ground truth data to rate source reliability, and application of the source reliability. A computer system, computer program product, and computer implemented method apply the ground truth data to evaluate a source, assess source reliability, and dynamically apply the assessed reliability. The assessed reliability is dynamically applied to response data, with the application yielding selective presentation of the response data based on the applied reliability assessment.

Abstract: Artifacts, including parameters are data sets, associated with experiment tasks are stored at an experiment management service. A query specifying a particular value of a parameter and a particular data set is received, and an indication of an experiment result associated with the particular data set and the particular parameter value is provided.

Abstract: A method for clustering based on unsupervised learning according to an embodiment of the invention enables clustering for newly generated patterns and is robust against noise, and does not require tagging for training data. According to one or more embodiments of the invention, noise is accurately removed using three-dimensional stacked spatial auto-correlation, and multivariate spatial probability distribution values and polar coordinate system spatial probability distribution values are used as learning features for clustering model generation, making them robust to noise, rotation, and fine unusual shapes. In addition, clusters resulting from clustering are classified into multi-level clusters, and stochastic automatic evaluation of normal/defect clusters is possible only with measurement data without a label.

Abstract: System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.

Abstract: An approach is provided for evaluating a performance of a query. A risk of selecting a low performance access path for a query is determined. The risk is determined to exceed a risk threshold. Based on the risk exceeding the risk threshold and using a machine learning optimizer, first costs of access paths for the query are determined. Using a cost-based database optimizer, second costs of the access paths are determined. Using a strong classifier operating on the first costs and the second costs, a final access path for the query is selected from the access paths.

Abstract: The present disclosure provides a UAV video aesthetic quality evaluation method based on multi-modal deep learning, which establishes a UAV video aesthetic evaluation data set, analyzes the UAV video through a multi-modal neural network, extracts high-dimensional features, and concatenates the extracted features, thereby achieving aesthetic quality evaluation of the UAV video. There are four steps, step one to: establish a UAV video aesthetic evaluation data set, which is divided into positive samples and negative samples according to the video shooting quality; step two to: use SLAM technology to restore the UAV's flight trajectory and to reconstruct a sparse 3D structure of the scene; step three to: through a multi-modal neural network, extract features of the input UAV video on the image branch, motion branch, and structure branch respectively; and step four to: concatenate the features on multiple branches to obtain the final video aesthetic label and video scene type.

Abstract: A system and method for detecting changes in webpages are provided. The method includes creating a snapshot for a webpage addressed by an identifier, wherein the identifier is designated in a received request for detecting changes in the webpage, wherein the created snapshot is a current snapshot of a most recent version of the webpage; retrieving at least one recent snapshot of the webpage, wherein the recent snapshot was created immediately prior to the current snapshot and represents an immediate previous version of the webpage; and comparing the current snapshot to the at least one recent snapshot to detect any changes, wherein the comparison is based on changes in visible elements in the webpage.

Abstract: A method, computing platform, and computer program product are provided. A computing platform receives, for a clinical trial, study design information including a set of parameters related to a success of a clinical trial and factors related to a relevance of the parameters. The computing platform provides multiple sets of varying parameter values. At least one trained machine learning model is applied to the study design information including the sets of parameter values to predict multiple success scores including a predicted overall success score. The computing platform outputs the provided set of values for the set of parameters that are associated with the at least one trained machine learning model predicting a best overall success score for the clinical trial.

Abstract: A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Abstract: A computing device determines a recommendation. A confidence matrix is computed using a predefined weight value. (A) A first parameter matrix is updated using the confidence matrix, a predefined response matrix, a first step-size parameter value, and a first direction matrix. The predefined response matrix includes a predefined response value by each user to each item and at least one matrix value for which a user has not provided a response to an item. (B) A second parameter matrix is updated using the confidence matrix, the predefined response matrix, a second step-size parameter value, and a second direction matrix. (C) An objective function value is updated based on the first and second parameter matrices. (D) The first and second parameter matrices are trained by repeating (A) through (C). The first and second parameter matrices output for use in predicting a recommended item for a requesting user.

Abstract: A personalized emoji dictionary, such as for use with emoji-first messaging. Text messaging is automatically converted to emojis by an emoji-first application so that only emojis are communicated from one client device to another client device. Each client device has a personalized emoji library of emojis that are mapped to words, which libraries are customizable and unique to the users of the client devices, such that the users can communicate secretly in code. Upon receipt of a string of emojis, a user can select the emoji string to convert to text if desired, for a predetermined period of time.

Abstract: Aspects described herein may relate to methods, systems, and apparatuses that determine one or more categories associated with a dataset, or a portion thereof. The determination may be performed based on one or more tags associated with the dataset and/or a description associated with the dataset. Further, the determination may be performed by searching an ontology based on the one or more tags and/or the description. The determination may be performed by using a machine-learning model based on the one or more tags and/or the description. Once the one or more categories associated with the dataset are determined, the one or more categories may be used as a basis for modifying the dataset and/or validating the dataset.

Abstract: Embodiments for creating planning tasks are provided. A plurality of atoms are generated. The plurality of atoms are partitioned into a plurality of variables. A casual graph is generated based on the plurality of variables. A layered graph including interchanging variable value layers and action layers is created based on the casual graph. A planning task is generated based on the layered graph.

Abstract: A method for managing data includes obtaining a workload generation request, wherein the workload generation request specifies a security compliant rule, in response to the workload generation request: selecting a first set of resource devices using a resource allocation master list, initiating a security compliance test on the first set of resource devices to obtain a security compliance result, making a first determination, based on the security compliance result, that the first set of resource devices meet a security compliance criterion, and in response to the first determination: storing a virtual certificate in a security compliance database based on the security compliance result, and allocating the first set of resource devices to a workload based on the workload generation request.

Abstract: Systems and methods for identifying a condition of a user. A treatment apparatus is configured to be manipulated by the user for performing an exercise, and an interface is communicably coupled to the treatment apparatus. One or more sensors are configured to sense one or more characteristics of an anatomical structure of the user. A processing device and a memory is communicatively coupled to the processing device. The memory includes computer readable instructions, that when executed by the processing device, cause the processing device to: receive, from the sensors, one or more sensor inputs representative of the one or more of characteristics of the anatomical structures; calculate an infection probability of a disease based on the one or more characteristics of the anatomical structures; and output, to the interface, a representation of the infection probability.

Abstract: An automatic dock servicing system can monitor status of dock stations and/or dock station components, analyze the status to determine whether it satisfies a service condition for one or more service actions and, when service conditions are satisfied, cause the corresponding service actions to be performed. In some embodiments, a control panel can track a number of trailer loading/unloading cycles at the dock station. When the number of cycles reaches a threshold for performing a service on the dock station, the control panel can automatically initiate the service action. Some service actions that can be initiated by placing a maintenance or part request, sending control signals to a control panel or to dock station components, and/or sending messages or providing remote controls to other personnel.

Abstract: A graph database for outbreak tracking and management is disclosed. In an example embodiment, an outbreak management system includes a memory device storing instructions that define a graph database for disease outbreak tracking. The instructions specify for a given host that a host node is created and an episode node is connected to the host node via a ‘case’ link. The episode node is associated with episode parameters that are related to a disease classification of the host. In addition, the instructions specify that an outbreak node is connected to the episode node via a ‘part of’ link to indicate that the host has become part of an outbreak of the disease. The outbreak node is connected to a definition node via a ‘defined as’ link. The definition node specifies disease parameters of the disease that is related to the outbreak node.

Abstract: A text stream source is accessed that includes a plurality of text content items. Unique word groupings are determined for the plurality of text content items. A burst detection algorithm is executed to determine word groupings that are currently bursting and that started within a specified time period. Based on the word groupings, an issue is determined based on identifying a set of texts forming at least one clique.

Abstract: A computer processor generates a topic-based dataset based on parsing content received from a plurality of information sources, which includes historical data and scientific data, associated with a location of a natural resource. The processor generates a plurality of clusters, respectively corresponding to like-topic data of the topic-based dataset. The processor determines a plurality of hypotheses, respectively corresponding to the plurality of clusters of the like-topic data, wherein the plurality of hypotheses are based on features associated with each of the plurality of clusters of the like-topic data. The processor combines pairs of clusters, based on a similarity heuristic applied to the one or more pairs of clusters, and the processor determines a plurality of probabilities respectively corresponding to a validity of each hypothesis of the plurality of hypotheses, associated with the location of a natural resource.

Abstract: A method, system, and computer program product for adaptive network provisioning. The method may include storing a plurality of use case records in a use case repository, where each use case record provides a diagnostic definition of a security threat to a SIEM environment. The method may also include storing metadata for a plurality of attributes of subscribers to the SIEM environment. The method may also include storing use cases that the subscribers have deployed from the use case repository. The method may also include setting up a new subscriber, where setting up the new subscriber includes: receiving a set of attributes of the new subscriber; searching a metadata store to identify subscribers with attributes that are similar to the set of attributes; and selecting an initial set of use cases for the new subscriber based on use cases deployed by the identified subscribers.

Abstract: Provided are embodiments of a multi-task learning system with hardware acceleration that includes a resistive random access memory crossbar array. Aspects of the invention includes an input layer that has one or more input layer nodes for performing one or more tasks of the multi-task learning system, a hidden layer that has one or more hidden layer nodes, and a shared hidden layer that has one or more shared hidden layer nodes which represent a parameter, wherein the shared hidden layer nodes are coupled to each of the one or more hidden layer nodes of the hidden layer.

Abstract: Apparatus, systems, and/or methods may provide travel assistance. For example, a determination may be made that a user is to begin driving in the near future. In addition, a determination may be made of one or more predicted destinations when the user is to begin driving in the near future. Thus, travel assistance may be provided to the user based on the one or more predicted destinations when the user is to begin driving in the near future.

Abstract: A computer-implemented method, system and computer program product for analyzing test result failures using artificial intelligence models. A first machine learning model is trained to differentiate between a bug failure and a test failure within the test failures based on the failure attributes and historical failures. The failure type for each failed test in test failure groups is then determined using the first machine learning model. The failed tests in the test failure groups are then clustered into a set of clusters according to the failure attributes and the determined failure type for each failed test. A root cause failure for each cluster is identified based on the set of clusters and the failure attributes. The root cause of an unclassified failure is predicted using a second machine learning model trained to predict a root cause of the unclassified failure based on identifying the root cause failure for each cluster.

Abstract: In at least one embodiment, the present description is directed to a computer system, having at least components of a server, including a processor and a non-transient storage subsystem, storing a computer program including instructions that, when executed by the processor, cause the processor to at least: electronically receive a model for one or more operating conditions, one or more threshold criteria, and facility operating data for each respective facility of a plurality of facilities; validate the one or more threshold criteria to be one or more acceptable bias criteria; iteratively perform one or more iterations of outlier bias reduction in the facility operating data based on the model; determine, based on non-biased facility operating data, a non-biased performance standard for the one or more operating conditions; and track, based on the non-biased performance standard and the facility operating data, operating performance of each respective facility of the plurality of facilities.

Abstract: An artificial intelligence engine that has two or more modules cooperating with each other in order to create one or more machine learning models that use an adaptive semantic learning for knowledge representations and reasoning. The modules cause encoding the representations and reasoning from one or more sources in a particular field with terminology used by one or more human sources in that field into a set of rules that act as constraints and that are graphed into a neural network understandable by a first machine learning model, and then ii) adapting an interpretation of that set of encoded rules. The understanding of that set of encoded rules is adapted by i) allowing for semantically similar terms and ii) by conclusions derived from training data, to create an understanding of that set of encoded rules utilized by the machine learning model and the AI engine.

Abstract: Some embodiments provide a non-transitory machine-readable medium that stores a program. The program receives a set of words. The program further retrieves an entry from a knowledge base comprising a plurality of entries. Each entry includes a text description of a concept. The program also determines an embedding for the entry based on the text description of the concept. The program further iteratively determines an embedding for a word in the set of words, increasing a size of a window of words in the set of words, and calculating a confidence score for the entry with respect to the word based on the embedding for the entry and the embedding for the word until a successive calculated confidence score decreases below a previous calculated confidence score. The program also determines that a window of words in the set of words having a previous size represents an entity.

Abstract: A risk evaluation method is disclosed. A machine learning is conducted by using a neural network by inputting training data. A data distance corresponding to a permission level is calculated based on restoration data and the training data, the permission level being among a plurality of layers of the neural network, the restoration data being generated by using at least one weight of a plurality of permission level weights at the permission level, the plurality of permission level weights being among a plurality of weights of synapses at the plurality of layers, the plurality of weights being generated by the machine learning.

Abstract: A data receiver receives at least one signal from a data transmitter and determines therefrom at least one environment parameter of the data transmitter or of an environment of the data transmitter. The data receiver is configured to identify an operational state of the data transmitter or of a component of the data transmitter by comparing the at least one environment parameter so received or an environment parameter profile determined on the basis of the at least one environment parameter, with an environment parameter reference profile which describes an expected environment parameter or an expected environment parameter profile in the environment of the data transmitter.

Abstract: A search engine is modified to perform increasingly precise image searching using iterative Natural Language (NL) interactions. From an NL search input, the modification extracts a set of input features, which includes a set of response features corresponding to an NL statement in the NL search input and a set of image features from a seed image in the NL search input. The modification performs image analysis on an image result in a result set of a query including at least some of the input features. In a next iteration of NL interactions, at least some of the result set is provided. An NL response in the iteration is added to a cumulative NL basis, and a revised result set is provided, which includes a new image result corresponding to a new response feature extracted from the cumulative NL basis.

Abstract: An autonomous vehicle is described herein. The autonomous vehicle generates segmentation scenes based upon lidar data generated by a lidar sensor system of the autonomous vehicle. The lidar data includes points indicative of positions of objects in a driving environment of the autonomous vehicle. The segmentation scenes comprise regions that are indicative of the objects in the driving environment. The autonomous vehicle generates scores for each segmentation scene based upon characteristics of each segmentation scenes and selects a segmentation scene based upon the scores. The autonomous vehicle then operates based upon the segmentation scene.

Abstract: A system and method for updating and correcting facts that receives proposed values for facts from users and determines a correctness score which is used to automatically accept or reject the proposed values.

Abstract: Measurements of energy usage including details of power consumption can be stored for power usage devices. The measurements of energy usage can be used to predict future consumption for each of the power usage devices. A power consumption can be modified using the prediction. The energy cost can be optimized based using the prediction, such as, for example, by modifying a power consumption of one of the power usage devices.

Abstract: Systems and methods for determining elevation based on structural modeling and light detection and ranging (LIDAR) data are disclosure. LIDAR bare earth data corresponding to an area within a parcel boundary is obtained as preliminary elevation data. A basis of structure boundary is determined for a structure within the parcel boundary based on an absence of the LIDAR bare earth data within a region in the area. Three-dimensional models are generated based on photographic data, to represent portions of the structure that affect LIDAR signals. A structure boundary for the structure is determined based on the basis of structure boundary in combination with supplemental elevation data generated using the three-dimensional models. Adjacent grade values are determined based on a portion of the preliminary elevation data and supplemental elevation data corresponding to an area between the structure boundary and a buffer boundary.

Abstract: An online system receives member-created content. The system identifies member interactions with the member-created content. The system then calculates a creator scores for members, and content scores for content. The system identifies members of the online system as creators when the creator scores transgress a threshold, and the system provides to members of the online system follow recommendations for member based on the creator scores for the members.