Abstract: An example method includes receiving georeferenced satellite images of a geographic region that includes electrical assets. Segmentation maps are generated by providing the georeferenced satellite images to fully convolutional networks to classify pixels of the georeferenced satellite images as either trees or non-trees. Rasters of the geographic region are generated based on the segmentation maps and vectors are generated based on the rasters. A vector includes one or more polygons, a polygon representing a tree and having a set of coordinates defining the polygon. Canopy height models are generated based on received digital surface models. The canopy height models include heights of trees in the geographic region. Heights of trees are associated with polygons. A height of a polygon is compared to a distance between the polygon and an electrical asset to identify a tree as a potential hazard. Notifications thereof are provided.

Abstract: A method of altering an operating state of a transportation system may include: receiving social data from a plurality of social data sources about at least one occupant of a vehicle of the transportation system; analyzing, at a first neural network, the social data and associating the social data with a route plan for the vehicle occupied by the at least one occupant; predicting, by the first neural network, an effect on a satisfaction of the at least one occupant based on the route plan of the vehicle through an analysis of the social data; and altering, by the first neural network, a route plan of the transportation system responsive to the predicted effect.

Abstract: A computing system obtains text that includes words and provides the text as input to an emotional classifier model that has been trained based upon emotional classification. The computing system obtains a textual embedding of the computer-readable text as output of the emotional classifier model. The computing system generates a phoneme sequence based upon the words of the text. The computing system, generates, by way of an encoder of a text to speech (TTS) model, a phoneme encoding based upon the phoneme sequence. The computing system provides the textual embedding and the phoneme encoding as input to a decoder of the TTS model. The computing system causes speech that includes the words to be played over a speaker based upon output of the decoder of the TTS model, where the speech reflects an emotion underlying the text due to the textual embedding provided to the encoder.

Abstract: Aspects of the present invention relate to computer processes adapted to identify influencers and other users interested in certain topics and uses thereof.

Abstract: A system and methods are provided for an immersive group lighting interaction. A lighting infrastructure includes a plurality of independently controllable lighting devices. According to an embodiment, users may be granted access to control at least a portion of the lighting infrastructure. Rather than requiring the user to manually control the lighting infrastructure using, for example, the touch screen, a sensor associated with the user's mobile device may track the user. The sensor data may be classified and then mapped to a corresponding control instruction to control the lighting infrastructure. In another embodiment, a computing system receives sensor classifications from multiple users. If a threshold amount of a type of classification is received, the computing system controls a lighting infrastructure according to that type of classification.

Abstract: Aspects of the disclosure relate to computing hardware and software for dynamic control limit configuration. A computing platform may receive and format historical data. The computing platform may input the formatted data into a control limit prediction model, which may output predicted control limits by weighting, using an ensemble model, outputs from a plurality of other models to produce predicted control limits. The computing platform may adjust existing control limits based on predicted control limits to create actual control limits. The computing platform may receive real time data, and may identify, using the actual control limits and the real time data, a deviation score for the real time data. The computing platform may compare the deviation score to the actual control limits. Based on detecting that the deviation score breaches the actual control limits, the computing platform may send an indication of the breach.

Abstract: A method including determining, in real-time, a diversity preference score for a user based at least in part on an anchor item chosen by the user via a user interface executed on a user device of the user. The method also can include determining, in real-time, a comparison result between the diversity preference score and a diversity preference threshold. The method further can include generating, in real-time, a personalized recommendation pool based on (a) the comparison result, (b) a complementary recommendation pool generated based at least in part on the anchor item, and (c) a diversity objective function. In many embodiments, when the comparison result indicates that the diversity preference score is greater than the diversity preference threshold, the diversity objective function can be associated with cross-domain diversity.

Abstract: Techniques and apparatus for training a neural network to classify audio into one of a plurality of categories and using such a trained neural network. An example method generally includes receiving a data set including a plurality of audio samples. A relaxed feature-normalized data set is generated by normalizing each audio sample of the plurality of audio samples. A neural network is trained to classify audio into one of a plurality of categories based on the relaxed feature-normalized data set, and the trained neural network is deployed.

Abstract: Systems and methods for creating maps used in navigating autonomous vehicles are disclosed. In one implementation at least one processor is programmed to receive drive information from each of a plurality of vehicles that traverse different entrance-exit combinations of a road junction; for each of the entrance-exit combinations, align three-dimensional feature points in the drive information to generate a plurality of aligned three-dimensional feature point groups, one for each entrance-exit combination of the road junction; correlate one or more three-dimensional feature points in each of the plurality of aligned three-dimensional feature point groups with one or more three-dimensional feature points included in every other aligned three-dimensional feature point group from among the plurality of aligned three-dimensional feature point groups; and generate a sparse map based on the correlation, the sparse map including a target trajectory associated with each of the entrance-exit combinations.

Abstract: Aspects of the present invention relate to computer processes adapted to identify influencers and other users interested in certain topics and uses thereof.

Abstract: Provided is a system for analyzing features associated with entities using an embedding tree, the system including at least one processor programmed or configured to receive a dataset associated with a plurality of entities, wherein the dataset comprises a plurality of data instances for a plurality of entities. The processor may be programmed or configured to generate at least two embeddings based on the dataset and determine split criteria for partitioning an embedding space of at least one embedding tree associated with the dataset based on feature data associated with an entity and embedding data associated with the at least two embeddings. The processor may be programmed or configured to generate at least one embedding tree having a plurality of nodes based on the split criteria. Methods and computer program products are also provided.

Abstract: A method for calculating traffic flow changes includes detecting a traffic event. The method further includes determining an affected area of the traffic event and determining an investigation area based on the affected area. The method further includes selecting at least one vehicle located within the investigation area and calculating a change in traffic flow due to the traffic event based on a comparison of a predicted traffic flow with a current traffic flow, wherein the current traffic flow is based on information received from the at least one vehicle. The method further includes updating the affected area based on the change in traffic flow and calculating an updated change in traffic flow based on the updated affected area when the updated affected area is larger than a predetermined threshold area.

Abstract: The disclosure provides a system, a method and a computer program product for dynamic scoring of a plurality of incidents. The system is configured to retrieve an incident of the plurality of incidents. The incident comprises at least one alert and is associated with a security breach. The system further generates an enriched alert based on enrichment of the at least one alert. The enrichment is based on security related data of the security breach. The system further identifies one or more entities and one or more observables associated with the generated enriched alert. The system determines a score for the retrieved incident based on at least the generated enriched alert and the generated behavioural entity model. The system is further configured to dynamically update the determined score for the retrieved incident based on an analysis of at least the retrieved incident and the generated behavioural entity model.

Abstract: A data processing system for generating predictive maintenance models is disclosed, including one or more processors, a memory, and a plurality of instructions stored in the memory. The instructions are executable to receive a historical dataset relating to each of a plurality of systems, including maintenance data and operational data. The instructions are further executable to display one or more algorithm templates and one or more data features calculated from the operational data, in a graphical user interface. The instructions are further executable to receive a selection of an algorithm template, a data feature, and a value of a parameter associated with the algorithm template, and to train and evaluate the selected algorithm template on the selected data feature according to the received value. The instructions are further executable to display a result of a metric of the evaluation, and generate a predictive maintenance model using the selected algorithm template.

Abstract: Devices and techniques are generally described for detection of abusive computational nodes. In various examples, first input data describing a first plurality of computational nodes and first data identifying a dimension along which to parse the first plurality of computational nodes may be received. A first computing device may generate input graph data representing the first plurality of computational nodes. The computational nodes of the first plurality of computational nodes may share a same value for the dimension are connected to one another in the input graph data. In various examples, a first graph machine learning model and at least one known abusive computational node may be used to determine a first set of candidate computational nodes for further evaluation. In some cases, network access of a first computational node of the first set of candidate computational nodes may be terminated.

Abstract: A non-transitory computer-readable medium comprising computer-executable instructions that, when executed, are configured to cause a processor to perform operations that include receiving process input data associated with one or more automation devices, determining a plurality of operating conditions corresponding to the one or more automation devices; clustering the process input data based on the plurality of operating conditions, receiving a decision tree representative of the plurality of operating conditions and corresponding process input data, determining a splitting criterion for one or more nodes of the decision tree based on the clustered process input data, wherein the splitting criterion is configured to link the process input data to at least two operating conditions based on the decision tree, generating control logic for the one or more automation devices based on the decision tree and splitting criterion, and sending the control logic to the one or more automation devices.

Abstract: A nuclide identification method, a system and an electronic device are provided, which relates to the field of nuclide identification. According to statistical characteristics, a background probability density function and a Compton probability density function in a ROI are determined. An energy Bayesian factor and a time Bayesian factor are determined based on energy and time interval information in a sequence of the nuclear detection events obtained by measurement. By combining the two factors, Compton plateau can be effectively identified and distinguished.

Abstract: In a search and tracking method for full time-domain laser detection of space debris, a set of latest precision orbital parameters of a debris object and start and end moments of a current transit of the object are first obtained. Search-specific guidance data is generated based on the above information and in combination with estimation of a maximum along-track error of the orbital parameters of the object during the current transit. A DLR system performs multi-elevation search on the object based on the search-specific guidance data, obtains a plurality of pieces of detection data of the object after detecting the object during the search, determines an along-track error of the orbital parameters of the object based on the detection data, and corrects the orbital parameters of the object in real time based on the along-track error, so as to guide the DLR system to subsequently track and detect the object.

Abstract: Systems and methods ingest extensive data regarding real-time transactions to determine correlation between a variety of parameters and possible fraud events. Possible fraud can be predicted, and notifications delivered to a fraud team. Correlations can be discovered using unsupervised machine learning, and particular systems and methods can utilize natural language processing to both receive and provide information concerning fraud risk and countermeasures.

Abstract: In a first device, a local classification and a local classification confidence score corresponding to an event input are computed. At the first device in response to a broadcast request, a remote classification and a remote classification confidence score corresponding to the event input are received, the remote classification and the remote classification confidence score being computed at a second device. At the first device, a consensus classification including the most frequent classification from a set of all received remote classifications and the local classification is formed, provided the number of classifications including the most frequent classification exceeds a threshold. In response to a consensus classification confidence score corresponding to the consensus classification exceeding a confidence threshold, a local classification model is updated. Based on the local classification and the consensus classification, the event input is assigned to a classification.

Abstract: A computer implemented method and a system for facilitating modifying environments based on user preferences is provided. Accordingly, the method may include receiving, using a communication device, interaction data of interactions of a user in relation to experiential environments of two or more experiential environments from user devices. Further, the computer implemented method may include analyzing, using a processing device, the interaction data. Further, the computer implemented method may include determining, using the processing device, two or more user preferences associated with the user based on the analyzing of the interaction data. Further, the computer implemented method may include provisioning, using the processing device, two or more content corresponding to the two or more experiential environments based on the two or more user preferences. Further, the computer implemented method may include storing, using a storage device, the interaction data and the two or more user preferences.

Abstract: A system creates a mapping of a storage intent, defined for a digital asset, to one of the primary storage tiers, in a primary protection storage system, which matches the storage intent. The system receives a request to store a copy of the digital asset and stores the copy with the storage intent in the primary storage tier, based on the mapping of the storage intent to the primary storage tier. Upon receiving a request to replicate the copy of the digital asset, the system creates a mapping of the storage intent to one of the secondary storage tiers, in a secondary protection storage system, which matches the storage intent. The system replicates each copy of the digital asset, which is stored in the primary storage tier, and the storage intent to the secondary storage tier, based on the mapping of the storage intent to the secondary storage tier.

Abstract: A method of providing a video stream based on machine learning in an electronic device according to various example embodiments may include receiving a source video stream which is streamed from a first device to at least one other device, confirming whether an event is detected on the source video stream using a learning model trained through machine learning on the basis of at least one frame of the source video stream, and determining whether to restrict streaming of the source video stream from the first device on the basis of the event detection. In addition to the method, other example embodiments are possible.

Abstract: An interface and improved data intake and query system is described herein that allows users to define metrics and that aggregates metric values regardless of the level at which a metric is defined and/or the level at which metric values are available. The improved data intake and query system can initialize a sketch in response to a user providing one or more metric definitions. The initialized sketch includes one or more instances, where each instance produces an output and collects metric value(s), appends the metric value(s) to the output, and forwards the appended data to a process function downstream in a data processing pipeline. The process function separates the output and the metric value(s), sending the output further downstream in the data processing pipeline and sending the metric value(s) to a parallel process function that sits outside the data processing pipeline. The parallel process function can persist the metric value(s).

Abstract: The present disclosure provides systems and methods that leverage machine-learned models in conjunction with user-associated data and disease prevalence mapping to predict disease infections with improved user privacy. In one example, a computer-implemented method can include obtaining, by a user computing device associated with a user, a machine-learned prediction model configured to predict a probability that the user may be infected with a disease based at least in part on user-associated data associated with the user. The method can further include receiving, by the user computing device, the user-associated data associated with the user. The method can further include providing, by the user computing device, the user-associated data as input to the machine-learned prediction model, the machine-learned prediction model being implemented on the user computing device.

Abstract: Provided are a data processing method and apparatus, an electronic device, and a medium, and relates to the technical field of computers, and in particular to the field of autonomous driving and intelligent transportation. An implementation is: obtaining risk information in driving scenario data of a driving scenario; determining a risk factor set based on the risk information, where the risk factor set includes a risk factor existing in the driving scenario data; and generating a simulated risk scenario based on the risk factor set, where the simulated risk scenario reflects at least one risk factor in the risk factor set.

Abstract: Systems and methods are described for selecting content item identifiers for display. The system may identify a set of content items that are likely to be requested in the future based on a history of content item requests. The system then selects a first plurality of content categories using a category selection neural net and selects a first set of recommended content items for the first plurality of content categories. The system increases a reward score for the first plurality of content categories based on receiving a request for a content item that is included in the first set of recommended content items. The system also decreases the reward score for the first plurality of content categories based on determining that the requested content item is included in the set of content items that are likely to be requested in the future. The neural net is trained based on the reward score of the first plurality of content categories to reinforce reward score maximization.

Abstract: A clinical diagnostic analyzer for performing a virtual crossover study on quality control (QC) material includes a processor, memory, measurement hardware, and an input panel/display. The analyzer acquires data from a peer group relating to a new lot of quality control material to calculate a predicted mean and standard deviation based on that peer group data and adjusted for bias in the laboratory process. As new analyses are run on the new lot of QC material, the predicted mean and standard deviation are updated to incorporate the actual data on a weighted basis.

Abstract: Embodiments of this disclosure include an image processing method and apparatus. The method may include obtaining feature points corresponding to training images and feature center points of images in a second quantity of categories and obtaining a feature condition probability distribution that the feature points collide with corresponding feature center points. The method may further include performing network training to obtain target feature center points of the images in the second quantity of categories. The method may further include mapping the first quantity of feature points and the second quantity of target feature center points to a Hamming space, to obtain hash codes of the first quantity of training images and hash center points of the images in the second quantity of categories. The method may further include performing network training using the hash condition probability distribution and the ideal condition probability distribution, to obtain an image hash model.

Abstract: A method, apparatus and a product for data generalization for predictive models. The method comprising: obtaining a training dataset that comprises a plurality of training instances and predicted labels thereof, wherein each training instance is a valuation of a set of features, wherein the set of features comprises a feature having a domain, wherein the predicted label of each training instance is a label predicted thereto by a predictive model; training an auxiliary model using the training dataset; based on the auxiliary model, determining an alternative set of features that is a generalization of the set of features, wherein the alternative set of features comprises a generalized feature having a generalized domain, wherein each value in the generalized domain corresponds to one or more values in the domain; obtaining a generalized instance having a valuation of the alternative set of features; and determining a label for the generalized instance.

Abstract: Described systems and techniques determine causal associations between events that occur within an information technology landscape. Individual situations that are likely to represent active occurrences requiring a response may be identified as causal event clusters, without requiring manual tuning to determine cluster boundaries. Consequently, it is possible to identify root causes, analyze effects, predict future events, and prevent undesired outcomes, even in complicated, dispersed, interconnected systems.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate quantum state preparation of a probability distribution to perform amplitude estimation are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise a data loader component that prepares a quantum state of a probability distribution based on structure of a quantum amplitude estimation algorithm. The computer executable components can further comprise an operator component that constructs a quantum operator based on the quantum state to perform quantum amplitude estimation.

Abstract: There is a need for more effective and efficient predictive data analysis based at least in part on categorical input data. This need can be addressed by, for example, solutions for performing predictive data analysis that utilize at least one of categorical level merging, mutual-information-based feature filtering, feature-correlation-based feature filtering to generate training data feature value arrangements, as well as training and using categorical input machine learning models trained using the training data feature value arrangements.

Abstract: A control system includes an obtainer that obtains sensing data output from a sensor that performs sensing of an outside of a mobile body, a detector that detects a position of an object outside the mobile body based on the sensing data, a movement predictor that predicts a movement of the object based on the sensing data, a costmap generator that generates a first costmap based on the position detected of the object and a second costmap based on the movement predicted of the object, a path generator that generates a path for the mobile body based on the first costmap, a determination generator that generates a movement determination of the mobile body based on the second costmap and the path generated, and a controller that controls the movement of the mobile body in accordance with the path generated and the movement determination.

Abstract: A neural network system can select content based on user and item content embeddings in an approach that can be updated in real time on the user device without server support. Requests for content sent to the server can include an anonymous user embedding that includes data describing the user's inputs. The content that is nearest to the user embedding in a joint embedding space can be returned as suggested content.

Abstract: Embodiments of the present disclosure relate to a method, system, and computer program product for efficient execution of a decision tree. According to the method, respective target values of a plurality of attributes of a target entity are obtained. Representations of a plurality of leaf nodes of a decision tree are obtained. Each of the representations indicates respective statistic values of a plurality of attributes of historical entities and a statistic prediction result determined from historical prediction results output at a respective one of the plurality of leaf nodes for the historical entities. Distance measures between the target entity and the plurality of leaf nodes are determined based on the target values and the statistic values indicated by the representations of the plurality of leaf nodes. A target prediction result for the target entity is determined based on the distance measures and the statistic prediction results of the historical entities.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for dialogue systems. A transcription of a user utterance is obtained. The transcription of the utterance is tokenized to identify multiple tokens for the utterance. Token-level utterance encodings corresponding to different tokens of the transcription are generated. A system action encoding from data indicating system actions previously performed by the dialogue system are generated. A dialogue context vector based on the utterance encoding and the system action encoding are generated. The token-level utterance encodings, the system action encoding, and the dialogue context vector are processed using a slot tagger to produce token-level output vectors. A limited set of candidate token classifications for the tokens of the user utterance are determined based on the token-level utterance encodings. A response for output is provided in response to the user utterance.

Abstract: An intention identification model learning method includes receiving positive data that corresponds to a first skill, generating, based on the positive data that corresponds to the first skill, negative data that corresponds to the first skill, determining a second skill similar to the first skill, obtaining data that corresponds to each second skill, generating a second base model based on the data that corresponds to the second skill and a first base model stored on the server, and performing learning based on the second base model, the positive data, and the negative data that correspond to the first skill, and generating an intention identification model.

Abstract: In various examples, the present disclosure relates to using temporal filters for automated real-time classification. The technology described herein improves the performance of a multiclass classifier that may be used to classify a temporal sequence of input signals—such as input signals representative of video frames. A performance improvement may be achieved, at least in part, by applying a temporal filter to an output of the multiclass classifier. For example, the temporal filter may leverage classifications associated with preceding input signals to improve the final classification given to a subsequent signal. In some embodiments, the temporal filter may also use data from a confusion matrix to correct for the probable occurrence of certain types of classification errors. The temporal filter may be a linear filter, a nonlinear filter, an adaptive filter, and/or a statistical filter.

Abstract: The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset by optimizing the baseline model to predict user-item interactions in a first training dataset for the applicable shop. Each of the shop-specific models is then tested using a second training dataset for the shop. A loss is calculated for each shop-specific model based on the model's predicted user-item interactions and the actual user-item interactions in the second training dataset for the shop. A global loss is calculated based on each of the shop-specific losses, and the baseline model is updated to minimize the global loss.

Abstract: An audio processing method by which input data are obtained that includes first sound data representing first components of a first frequency band, included in a first sound corresponding to a first sound source, second sound data representing second components of the first frequency band, included in a second sound corresponding to a second sound source, and mix sound data representing mix components of an input frequency band including a second frequency band, the mix components being included in a mix sound of the first sound and the second sound. The input data are then input to a trained estimation model, to generate at least one of first output data representing first estimated components within an output frequency band including the second frequency band, included in the first sound, or second output data representing second estimated components within the output frequency band, included in the second sound.

Abstract: A course prediction device (1) includes a blind spot calculation unit (11) and a prediction unit (12). The blind spot calculation unit (11) acquires a sensing result sequentially from a sensor which is arranged in a mobile object and which detects whether an object exists, and calculates, based on sensor information, a blind spot area expressing such an area that an object existing therein cannot be detected by the sensor. The blind spot calculation unit (11) also detects an object entering the blind spot area based on the sensor information. The prediction unit (12) predicts a course in the blind spot area of the detected object based on the sensing result.

Abstract: One example method of operation may include identifying a content thread in a content sharing forum, assigning a bot to process messages of the content thread, identifying, via the bot, a question message, parsing the question message content to identify topics of interest, processing the messages of the content thread to identify response information related to the question message, determining the response information to the question message requires third party information, and transmitting a request, with the question message content, to one or more other bots operating in other content sharing forums.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for computing a backward looking surprise metric for autonomously driven vehicles. One of the methods includes obtaining first data representing one or more previously predicted states of an agent along one or more predicted trajectories of the agent at a first time step. Second data representing one or more states of the agent at a subsequent time step is obtained. A surprise score is computed from a measure of a difference between the first data computed for the one or more predicted trajectories for the prior time step and the second data computed for the one or more predicted states for the subsequent time step.

Abstract: A computing device, method, and system are provided of improving data quality to conserve computational resources. The computing device receives a raw dataset. One or more data quality metric goals corresponding to the received raw dataset are received. A schema of the dataset is determined. An initial set of validation nodes is identified based on the schema of the dataset. The initial set of validation nodes are executed. A next set of validation nodes are iteratively expanded and executed based on the schema of the dataset until a termination criterion is reached. A corrected dataset of the raw dataset is provided based on the iterative execution of the initial and next set of validation nodes.

Abstract: Techniques for implementing Bayesian model for refining and petrochemical wall thickness monitoring are presented. The construction of a model may be automated for each piping circuit or piece of major fixed equipment, utilizing, for example, specific component data, historical thickness measurements, inspection practices and related inspection program information. The model contains nodes describing the most significant sources of variability, namely component original thicknesses, wall thickness degradation over time, corrosion rates and thickness measurement error.

Abstract: Various embodiments include systems and methods to implement a security platform providing cyberattack detection. The security platform may, with respect to a cloud compute environment, use audit log data that is associated with a particular domain of operational activity within the cloud compute environment. Based on a domain specific graph representative of factor data and dependencies between factor data, the security platform may determine a behavioral anomaly that is associated with a cyberattack.

Abstract: A computerized method for detection of categorical drift within an incoming data stream. Herein, an error threshold is computed based on a first set of training data samples selected to detect categorical drift occurring for a data stream. Thereafter, probability distributions associated with content of a first and second data samples of the data stream are computed. Analytics are conducted to compute a difference between content of the first probability distribution that is based on a first data point of the first data sample and content of the second probability distribution that is based on a first data point of the second data sample. After computing the difference, that categorical drift is determined whether categorical drift detection has been conducted.

Abstract: A machine learning algorithm is trained on a number of microscopic images and a measure of outcome of each image. Each image is divided into tiles. The measure of outcome is assigned to each tile of the image. The tiles are then used to train the machine learning algorithm. The trained algorithm may then be used to evaluate images.

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.