Abstract: A system and method for predicting a remaining oil life of oil in a gearbox of an air turbine starter of a vehicle. The method includes generating a temperature data, generating an environmental data set by an environmental sensor, predicting a remaining oil life based on the temperature data set and the environmental data set and scheduling a maintenance event in response to the prediction of the remaining oil life.

Abstract: A system configured for implementing continuous authentication for a software application is disclosed. The system receives a request from a user to login to an account of the user on the software application. The software application uses open authentication to allow the user to login to the account of the user. Once the user is logged in, the system activates continuous authentication based on monitored user behavior information associated with the user received from one or more organizations. The system monitors accessing the account of the user by monitoring behaviors of a person accessing the account of the user. The system determines whether the behaviors of that person correspond to the monitored user behavior information of the user. If the behaviors of that person correspond to the monitored user behavior information of the user, the system grants the first person to the account of the first user.

Abstract: A system and a method are disclosed for recommending a set of actions to be performed to improve a target performance metric of a client application. An action recommendation system receives the target performance metric from a user associated with the client application. The action recommendation system determines features of the client application describing characteristics and performance history of the client application. The features of the client application and the target performance metric is provided as input to a machine learning model that outputs sets of target features that are likely to result in improvement for the target performance metric. The action recommendation system ranks the sets of target features and selects one of the sets based on the ranking. The action recommendation system determines a set of recommended actions based on the selected set of target features and presents the set of recommended actions to the user.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning. A reinforcement learning neural network selects actions to be performed by an agent interacting with an environment to perform a task in an attempt to achieve a specified result. The reinforcement learning neural network has at least one input to receive an input observation characterizing a state of the environment and at least one output for determining an action to be performed by the agent in response to the input observation. The system includes a reward function network coupled to the reinforcement learning neural network. The reward function network has an input to receive reward data characterizing a reward provided by one or more states of the environment and is configured to determine a reward function to provide one or more target values for training the reinforcement learning neural network.

Abstract: Aspects of the technology described herein relate to detecting degrade ultrasound imaging frame rate. Some embodiments include receiving ultrasound data from the ultrasound device, generating ultrasound images from the ultrasound data, taking one or more measurements of ultrasound imaging frame rate based on the ultrasound images, comparing the one or more measurements of ultrasound imaging frame rate to an reference ultrasound imaging frame rate value, and based on a result of comparing the one or more measurements of ultrasound imaging frame rate to the reference ultrasound imaging frame rate value, providing a notification and/or disabling an ultrasound imaging preset and/or an ultrasound imaging mode with which the ultrasound device was configured.

Abstract: According to one embodiment, a monitoring system includes a processor. The processor accepts first data output from a first detector. The first detector detects a signal caused by equipment. The processor performs a first determination when a first value is in a first state. The first value indicates a state of the first detector or an environment where the equipment is provided. The first determination determines a condition of the equipment by using a first model and the first data. The processor performs a second determination when the first value is in a second state different from the first state. The second determination determines the condition of the equipment by using a second model and the first data. The second model is different from the first model.

Abstract: Systems and/or techniques for facilitating online-monitoring of machine learning models are provided. In various embodiments, a system can receive monitoring settings associated with a machine learning model to be monitored. In various cases, the monitoring settings can identify a first set of data features that are generated as output by the machine learning model. In various cases, the monitoring settings can identify a second set of data features that are received as input by the machine learning model. In various aspects, the system can compute a first set of statistical metrics based on the first set of data features. In various cases, the first set of statistical metrics can characterize a performance quality of the machine learning model. In various instances, the system can compute a second set of statistical metrics based on the second set of data features.

Abstract: A variety of signal extraction algorithms are employed to construct indicators or synthetic data proxy variables or factors from one or multiple data sources with specialized data handling options to resolve problems associated with economic and financial data: revisions, missing observations, publication lags, spontaneous arrival. A forecast evaluation module aids in the selection of the signal extraction procedure that has produced the most accurate factor.

Abstract: Systems and methods for creating indicators to quantify and index correlation risk that is market-wide among a broad set of asset classes or portfolio specific relative to an investor's portfolio holdings. The present disclosure relates to risk management in financial markets, and in particular to systems and methods for quantifying and indexing correlation risk such that these indices can serve as underlying assets for futures and options or other financial instruments that investors would use to hedge against the risk.

Abstract: Implementations set forth herein relate to a camera calibration system for generating various types of calibration data by maneuvering a camera through a variety of different calibration test systems. The calibration data generated by the camera calibration system can be transmitted to the camera, which can locally store the calibration data. The calibration data can include spatial frequency response value data, which can be generated according to a spatial frequency response test that is performed by the camera calibration system. The calibration data can also include field of view values and distortion values that are generated according to a distortion test that is also performed by the camera calibration system. The camera calibration system can maneuver the camera through a variety of different calibration tests, as well as transmit any resulting calibration data to the camera for storage.

Abstract: Methods and devices for determining movement associated with an antenna of a receiver are disclosed herein. An example method includes generating a first acceleration signal associated with the antenna, wherein the first acceleration signal includes one or more substantially non-zero axial components. The method may further include establishing an acceleration signature corresponding to the antenna based on the first acceleration signal, and generating a second acceleration signal associated with the antenna, wherein the second acceleration signal includes one or more substantially non-zero axial components. The method may further include determining a signal difference between the acceleration signature and the second acceleration signal, wherein the signal difference is attributable to a movement of the antenna. The method may further include generating an alert signal indicating the movement.

Abstract: An energy supply system comprises a plurality of grids that include an energy supply source and demanding equipment that demands energy supplied from the energy supply source, and a first management unit that manages the plurality of grids. Each of the plurality of grids includes a second management unit configured to control the energy supply source and the demanding equipment and adjust demand and supply of energy inside the grid. The first and second management unit include: a first obtainment unit that obtains states of the grids; a judgment unit that judges whether a supply of energy from an outside of the grids is necessary based on a result obtained by the first obtainment unit; and a supply unit that supplies energy to a grid that has been judged to be in need of energy supply from an outside.

Abstract: Techniques for providing an efficient real-time hierarchy based on change events are disclosed. In some embodiments, a computer-implemented method comprises: storing a hierarchy table comprising hierarchy data that represents a snapshot state of a hierarchy tree of entities at a first point in time and having been last updated at the first point in time; receiving one or more user requests to change entity data representing entities of the hierarchy tree; storing one or more change events in a queue based on the user request(s); receiving a query request for the hierarchy tree; in response to the receiving of the query request, generating a query result based on the hierarchy table stored in the database and the change event(s) stored in the queue; and performing a function of an enterprise application platform using the query result.

Abstract: Systems and techniques for active asset monitoring are presented. A system can collect a set of voltage measurements from one or more assets. The system can also perform learning associated with the set of voltage measurements and generate a set of digital signatures that includes a set of patterns regarding the set of voltage measurements. Furthermore, the system can determine monitor performance of an asset based on the set of digital signatures that includes the set of patterns regarding the set of voltage measurements.

Abstract: A spurious outlier detection-system is provided. The system includes a memory having computer-readable instructions stored therein and a processor configured to execute the computer-readable instructions to receive time-series data from one or more sensors and/or applications, process the time-series data to detect one or more change points based on a pre-defined cost function. The processor is configured to identify data chunks between the change points using pre-determined window sizes and to estimate smooth reconstructed values (SRVs) for each of the change point data chunks between two consecutive change points to identify one or more global outliers from the SRVs. The processor is configured to determine distribution of the global outliers using kernel density for each change point data chunk and identify one or more true outliers from the distribution of the global outliers based upon a skewness of the distribution.

Abstract: A voice assistant is provided whereby, in response to receipt of a voice instruction for requesting a recommended piece of content from a user, the voice assistant receives, from a system, a response including information on a recommended piece of content determined using another playback history having a feature similar to a feature of playback histories stored in association with a user ID of the user, and outputs the piece of content.

Abstract: A performance diagnosis device performs data collection of operation data of an air conditioner, and a model database indicates performance corresponding to each operating condition of the air conditioner. The device uses the operation data and the model database to obtain reference data that is a combination of a plurality of operating conditions and a reference value. A performance evaluation unit compares the reference data and evaluation data that is to be evaluated, and evaluates the performance of the air conditioner. A reference data update unit compares the reference data and the evaluation data when the operating conditions match with each other, and updates the reference data. As a result, the deterioration diagnosis technology can update unique reference values that reflect the dimensional errors of each device, differences in installation conditions, and differences in operating conditions to appropriate values with respect to the deterioration of the performance of air conditioners.

Abstract: Methods and systems for training a machine learning model include training a machine learning model using training data. A status of the machine learning model's training is determined based on an accuracy curve of the machine learning model over the course of the training. Parameters of the training are adjusted based on the status. Training of the machine learning model is completed using the adjusted parameters.

Abstract: The present invention relates to the field of environment monitoring. The present invention solves the problem in the prior art of large errors arising when using spectrophotometry to monitor a water quality COD index, and provides a method for constructing a water quality index prediction model and a method for monitoring a water quality index.

Abstract: Identifying input feature significance for a machine learning model is provided. A set of scoring payload data corresponding to a set of input features of the machine learning model is sliced into a predefined number of batches. Using the sliced set of scoring payload data, a correlation coefficient matrix for each respective input feature of the machine learning model is generated based on input of each particular input feature into the machine learning model and a corresponding output from the machine learning model for each particular input feature. A correlation coefficient value is extracted from the correlation coefficient matrix for each particular input feature and the corresponding output from the machine learning model. A set of significant input features having a corresponding correlation coefficient value greater than a predefined correlation coefficient threshold level is identified. A set of action steps is performed regarding the set of significant input features.

Abstract: Example implementations described herein involve a new data-driven analytical redundancy relationship (ARR) generation for fault detection and isolation. The proposed solution uses historical data during normal operation to extract the data-driven ARRs among sensor measurements, and then uses them for fault detection and isolation. The proposed solution thereby does not need to rely on the system model, can detect and isolate more faults than traditional data-driven methods, can work when the system is not fully observable, and does not rely on a vast amount of historical fault data, which can save on memory storage or database storage. The proposed solution can thereby be practical in many real cases where there are data limitations.

Abstract: Techniques for forecasting using deep factor models with random effects are described. A forecasting framework combines the strengths of both classical and neural forecasting methods in a global-local framework for forecasting multiple time series. A global model captures the common latent patterns shared by all time series, while a local model explains the variations at the individual level.

Abstract: Systems and methods including one or more processing modules and one or more non-transitory storage modules storing computing instructions configured to run on the one or more processing modules and perform acts of calculating a halo weight for at least one item of a set of items, where the halo weight can comprise an effect sales of the at least one item has on sales of the set of items; calculating an item-wise elasticity for the at least one item; calculating a line-wise elasticity for the at least one item of the set of items; calculating an aggregate elasticity for the at least one item using the item-wise elasticity and the line-wise elasticity; calculating a demand forecast for the at least one item of the set of items; optimizing an objective function comprising the halo weight, the aggregate elasticity, the demand forecast, and at least one external constraint; adjusting at least one price of the at least one item based on the objective function, as optimized; and displaying the at least one price, a

Abstract: A failure prediction device is provided with a machine learning device configured to learn the state of a brake of a motor with respect to data on the brake. The machine learning device observes brake operating state data indicative of an operating state of the brake when the brake is in a normal state, as state variables representative of a current environmental state, and uses the observed state variables to learn a distribution of the state variables with the brake in the normal state.

Abstract: A building system for building data point prediction, the building system comprising one or more memory devices configured to store instructions, that, when executed by one or more processors, cause the one or more processors to receive first building data for a building data point of a building and generate training data, the training data comprising a probability distribution sequence comprising a first probability distribution for the building data point. The instructions cause the one or more processors to train a prediction model based on the training data, receive second building data for the building data point, and predict, for one or more time-steps into the future, one or more second probability distributions with the second building data based on the prediction model, each of the one or more second probability distributions being a probability distribution for the building data point at one of the one or more time-steps.

Abstract: Application-initiated network traffic is intercepted and analyzed by an application firewall in order to identify streams of traffic for a target application. An application signature generator preprocesses the raw data packets from the intercepted network traffic by tokenizing the data packets and then weighting each token according to its importance for application identification. The weighted features for each data packet are clustered using an unsupervised learning model, and the resulting clusters are iteratively refined and re-clustered using a proximity score between the clusters and feature vectors for key tokens for the target application. The application signature generator generates a signature for the clusters corresponding to the target application which the application firewall implements for filtering network traffic.

Abstract: A system and computer-implemented method for use in generating an investment strategy for retirement income growth utilizing a crowdsourcing recommendation as an input is provided. Information about the user may be gathered. A goal or range of goals may be provided by the user or may be proposed by a computing device based on the user information. Crowdsourcing members who satisfy threshold criteria may be automatically selected to provide a recommendation on the user goal and/or the proposed investment strategy. A user may select a specific crowd member for a recommendation. An impact of the crowdsourcing recommendation on the user's retirement income growth may be estimated. Steps to implement the crowdsourcing recommendation may be provided to the user. As a result, the user may remain anonymous and may receive recommendations on one or more goals and/or investment strategies that they may not have received otherwise.

Abstract: There is proposed a method for obtaining a data segment by a client device capable of communicating with a plurality of content delivery networks (CDN1, CDN2), the method comprising the following steps: for each network of the plurality of networks, calculating (100) a score associated with the network indicative of a quality of transmission of data from the network to the client device; randomly drawing (102) a network in the plurality of networks, the random draw being adapted so that a probability of drawing a network of the plurality associated with a score indicative of a first quality of transmission is higher than a probability of drawing a network of the plurality associated with a score indicative of a second quality of transmission lower than the first quality of transmission; and requiring (104) the drawn network for the client device to obtain the data segment from the drawn network.

Abstract: A system model creating and simulation device for a process control system obtains description files of process modules including inputs, outputs and process control devices therebetween, where each description file specifies, for the corresponding module, connection terminals in the form of inputs and outputs, the process control devices and the interconnection of process control devices between the connection terminals, analyses of the description files, having determining a connection terminal of a first process module matching a connection terminal of a second process module, determines connections between the process modules based on the analysis, having determining a connection between the matching connection terminals, selecting simulation objects corresponding to the process control devices of the process modules, interconnecting the simulation objects according to data in the description files as well as through the connections determined between the process modules for forming a simulation model, an

Abstract: The disclosed embodiments include methods and systems for providing predictive quality analysis. Consistent with disclosed embodiments, a system may receive input data associated with a software program and compare the input data with one or more predetermined analysis parameters. The system may further determine at least one risk rating based on the comparison, wherein each risk rating corresponds to a distinct software category. The system may perform additional operations, including determining at least one adjustment to the software program based on the determined at least one risk rating, and prioritizing the at least one adjustment based on a predetermined adjustment priority standard. Furthermore, the system may provide a report including at least an indication of the at least one prioritized adjustment, a timeline for implementing the at least one prioritized adjustment, and plan implementing the at least one prioritized adjustment.

Abstract: A processing system of including at least one processor may obtain a query to retrieve a set of information from a graph database, the query providing a criterion for identifying graph database vertices that are relevant to the query, identify the graph database vertices that are relevant to the query in accordance with the criterion, obtain vertex identifiers of the relevant vertices, sort the vertex identifiers into a list in a sequential order, identify a first subset of the vertex identifiers in the list that corresponds to a first result index and a result size, access a first subset of the vertices that is identified as being relevant to the query and that is identified by the first subset of the vertex identifiers, retrieve a first subset of the set of information from the first subset of the vertices, and provide the first subset in a first results page.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating customized recommendations for environmentally-conscious cloud computing frameworks for replacing computing resources of existing datacenters. One of the methods involves receiving, through a user interface presented on a display of a computing device, data regarding a user's existing datacenter deployment and the user's preferences for the new cloud computing framework, generating one or more recommendations for environmentally-conscious cloud computing frameworks based on the received data, and presenting such recommendations through the user interface for the user's review and consideration.

Abstract: A safety system for a vehicle may include one or more processors configured to determine, based on a friction prediction model, one or more predictive friction coefficients between the ground and one or more tires of the ground vehicle using first ground condition data and second ground condition data. The first ground condition data represent conditions of the ground at or near the position of the ground vehicle, and the second ground condition data represent conditions of the ground in front of the ground vehicle with respect to a driving direction of the ground vehicle. The one or more processors are further configured to determine driving conditions of the ground vehicle using the determined one or more predictive friction coefficients.

Abstract: Apparatuses, methods and storage medium associated with compensating for a sensor deficiency in a heterogeneous sensor array are disclosed herein. In embodiments, an apparatus may include a compute device to aggregate perception data from individual perception pipelines, each of which is associated with respective one of different types of sensors of a heterogeneous sensor set, to identify a characteristic associated with a space to be monitored by the heterogeneous sensor set; detect a sensor deficiency associated with a first sensor of the sensors; and in response to a detection of the sensor deficiency, derive next perception data for more than one of the individual perception pipelines from sensor data originating from at least one second sensor of the sensors. Other embodiments may be disclosed or claimed.

Abstract: A bio-signal quality assessment apparatus may include: a bio-signal obtainer configured to obtain a bio-signal; and a processor configured to extract periodic signals from the obtained bio-signal, and determine a signal quality index based on at least one of similarity between the extracted periodic signals and signal variability of the obtained bio-signal.

Abstract: Provided is an information processing device that acquires appearance information associated with an appearance of a target object to be a target of drawing, and determines a parameter associated with a resolution of the target object and used in the drawing of a condition of the target object as viewed from a given viewpoint, on the basis of a color distribution on the surface of the target object and information associated with a distance from the viewpoint to the target object, the color distribution being specified from the acquired appearance information.

Abstract: A parameter estimation apparatus 10 is an apparatus for calculating a threshold for separating a plurality of data points into outliers and inliers and estimating a parameter fitting the inliers. The parameter estimation apparatus 10 includes: a parameter estimation unit 13 configured to estimate the parameter by using the plurality of data points as input; a threshold setting unit 14 configured to calculate the threshold based on statistical information of residuals of the data points; and a convergence determination unit 15 configured to determine whether or not convergence of the estimation of the parameter is reached based on the estimated parameter and the calculated threshold, and to cause the parameter estimation unit 13 and the threshold setting unit 14 to each execute processing again if the convergence determination unit 15 determines that convergence is not reached.

Abstract: Techniques discussed herein are directed to identifying health assessment data of a set of computing instances of a distributed computing system. The health assessment data may be collected from the computing instances and stored in a first distributed cache. When a request for health assessment data for one or more computing instances is received, the health assessment data may be retrieved from the first distributed cache, provided to the requesting entity, and stored in a second distributed cache. A subsequent request may cause new health assessment data to be retrieved from the first distributed cache and compared to the stored data of the second distributed cache. Changes in the health assessment data may be identified and data indicating those changes may be provided in response to the subsequent request. One or more remedial actions may be performed in response to the health assessment data obtained.

Abstract: In accordance with some embodiments, an apparatus for privacy protection is provided. The apparatus includes a housing arranged to hold a second device; one or more sensors, at least partially supported by the housing, operable to continuously collect biometric data of a user; an authentication neural network, operable to extract from the biometric data a plurality feature vectors associated with a plurality of identifiable scores; and a decision unit, coupled to the authentication neural network, operable to generate an authentication score as a function of the plurality of identifiability scores and the plurality of feature vectors, determine whether or not the authentication score satisfies an authentication threshold, and gate electronic access to the second device base on whether or not the authentication score satisfies the authentication threshold.

Abstract: A measurement device has: a slider which holds a substrate and is movable parallel to the XY plane; a drive system that drives the slider; a position measurement system which emits beams from a head section to a measurement surface in which grating section are provided on the slider, which receives respective return beams of the beams from the measurement surface, and which is capable of measuring position information in at least directions of three degrees of freedom including the absolute position coordinates of the slider; a mark detection system that detects a mark on the substrate; and a controller which detects the marks on the substrate using the mark detection system while controlling the drive of the slider, and which obtains the absolute position coordinates of each mark based on the detection result of each mark and measurement information by the position measurement system at the time of detection.

Abstract: A gas sensing device includes one or more chemo-resistive gas sensors; one or more heating elements for heating each of the gas sensors; a preprocessing block for filtering signal samples in order to generate filtered signal samples for each of the gas sensors; an information extraction block for generating representations for the filtered signal samples for each of the gas sensors based on dynamic characteristics of the received filtered signal samples of the respective gas sensor; and a decision making block for receiving the representations, wherein the decision making block includes a trained model based algorithm stage having an input layer and an output layer, wherein the decision making block includes trained models, wherein the decision making block creates sensing results based on output values of the output layer of the algorithm stage, and wherein the output values are created by using the trained models.

Abstract: Methods, systems, and computer readable media to control a cyber physical system using an observer-based controller are described.

Abstract: A reference time point which is equivalent to a fault-prediction-execution time point is sequentially changed by setting, as a symptom sensing period, a period from a time point which is a predetermined first period-length before the reference time point until the reference time point, and by setting, as a prediction target period, a period from the reference time point until a time point which is a predetermined second period-length after the reference time point if a fault does not occur in the period from the reference time point until the time point which is the second period-length after the reference time point, and a period from the reference time point until a fault-occurrence time point if a fault occurs in the period from the reference time point until the time point which is the second period-length after the reference time point, and machine learning is performed by using, as an explanatory variable, a feature quantity based on the operation log information of the symptom sensing period, and using

Abstract: The present application discloses a method for detecting an abnormity in an optical path or measuring and adjusting of a dynamic balance of a gantry in a CT system, comprising performing, by a gantry controlled by a controller, a test scan along an optical path of the CT system, the optical path being a path along which rays pass from a ray source to a detector. The method further comprises obtaining, by a processor, data relating to the test scan, and based on the data relating to the test scan. The method further comprises determining, by the processor, a status characteristic index of the optical path or an amount of dynamic imbalance of the gantry. The method further comprises analyzing, by the processor, a result of the status characteristic index; determining, by the processor, whether the optical path is abnormal, or determining whether a dynamic balance of the gantry satisfies a requirement based on a result of the analysis of the amount of dynamic imbalance.

Abstract: A computer-implemented method for performing machine condition monitoring for fault diagnosis includes collecting multivariate time series data from a plurality of sensors in a machine and partitioning the multivariate time series data into a plurality of segment clusters. Each segment cluster corresponds to one of a plurality of class labels related to machine condition monitoring. Next, the segment clusters are clustered into segment cluster prototypes. The segment clusters and the segment cluster prototypes are used to learn a discriminative model that predicts a class label. Then, as new multivariate time series data is collected from the sensors in the machine, the discriminative model may be used to predict a new class label corresponding to segments included in the new multivariate time series data. If the new class label indicates a potential fault in operation of the machine, a notification may be provided to one or more users.

Abstract: In one embodiment, a device makes a first determination as to whether a time series of a performance metric for a first network path exhibits seasonality. The device makes a second determination as to whether the time series exhibits a trend. The device predicts, based on the first determination and on the second determination, the performance metric for the first network path during a future time period. The device assesses, based on the performance metric predicted for the first network path, whether a measure of application experience will degrade for application traffic sent via the first network path. The device causes the application traffic to be proactively switched to a second network path, when the measure of application experience is expected to degrade.

Abstract: A method performed by a device may include identifying a plurality of samples of textual content; performing tokenization of the plurality of samples to generate a respective plurality of tokenized samples; performing embedding of the plurality of tokenized samples to generate a sample matrix; determining groupings of attributes of the sample matrix using a convolutional neural network; determining context relationships between the groupings of attributes using a bidirectional long short term memory (LSTM) technique; selecting predicted labels for the plurality of samples using a model, wherein the model selects, for a particular sample of the plurality of samples, a predicted label of the predicted labels from a plurality of labels based on respective scores of the particular sample with regard to the plurality of labels and based on a nonparametric paired comparison of the respective scores; and providing information identifying the predicted labels.

Abstract: A method for determining a position of a geological feature in a formation includes acquiring a seismic dataset, wherein the seismic dataset is based on signals of one or more seismic sensors and determining a set of indicators of candidate discontinuities in the formation based on the seismic dataset. The method also includes labeling a subset of the set of indicators of candidate discontinuities using a neural network with a label based on the set of indicators of candidate discontinuities, wherein the label distinguishes an indicator of a candidate discontinuity between being an indicator of a target discontinuity or being an indicator of a non-target discontinuity and determining the position of the geological feature in the formation, wherein the geological feature in the formation is associated with at least one target discontinuity based on the subset of the set of indicators of candidate discontinuities.

Abstract: Assistance is provided to policy exception approvers that make decisions on whether to grant or deny policy exception requests. A determination can be made regarding an amount of impact associated with granting a policy exception request based on a comparison of conditions before and after policy enactment. The probability of an impact occurring can be determined as a result of granting the policy exception based on past impact occurrences. A risk rating can be computed as a function of the amount of impact and the probability of the impact, and a visual representation of the risk rating can be generated and displayed to the policy exception approvers.

Abstract: Tags may be used in decisions by an access management service regarding access of computing resources (“resources”) by principals (e.g., users, roles, etc.). The tags may also be used to determine cost information, for grouping resources and/or principals, and for other reasons. The tags may be assigned to principals, to resources, or both. The resource may be a virtual or physical type of computing resource. Tags may be metadata, which may include a key-value pair. Tags may include email addresses, cost centers, project identifiers, location, team name, etc. The value may be a number, letters, or a combination of both. In some embodiments, the values may be limited to certain numbers or bytes, and some numbers and/or letter combinations may be excluded for special use.