Abstract: An approach is provided in which the approach constructs a 3-dimensional (3D) matrix based on a plurality of historical transactions performed by a user. The 3D matrix includes a set of features, a set of rows, and a set of channels. The approach trains a convolutional neural network using the 3D matrix, and then uses the trained convolutional neural network to predict a risk level of a new transaction initiated by the user. The approach transmits an alert message based on the predicted risk level.

Abstract: A method and system for modeling fibrous composites. Initially, material properties are obtained for a model of a fibrous composite, where the model includes integration points and unit cells. For each integration point, composite level stresses and strains are determined based on the material properties, the composite level stresses and strains are decomposed into component level stresses and strains for the integration point, the component level stresses and strains are used to calculate failure quotients at the integration point, an appropriate material reduction model is applied at a component level based on the failure quotients to detect a component failure, the component failure is upscaled to determine updated material properties at a composite level, and the updated material properties are incorporated into the model. At this stage, a composite failure is detected based on the updated model.

Abstract: Disclosed herein are systems and methods to efficiently execute predictions models to identify future values associated with various nodes. A server retrieves a set of nodes and generates a primary prediction model using data aggregated based on all nodes. The server then executes various clustering algorithms in order to segment the nodes into different clusters. The server then generates a secondary (corrective) prediction model to calculate a correction needed to improve the results achieved by executing the primary prediction model for each cluster. When a node with unknown/limited data and attributes is identified, the server identifies a cluster most similar the new node and further identifies a corresponding secondary prediction model. The server then executes the primary prediction model in conjunction with the identified secondary prediction model to populate a graphical user interface with an accurate predicted future attribute for the new node.

Abstract: A method for training a Neural-Network (NN), the method includes receiving a plurality of NN training tasks, each training task including (i) a respective preprocessing phase that preprocesses data to be provided as input data to the NN, and (ii) a respective computation phase that trains the NN using the preprocessed data. The plurality of NN training tasks is executed, including: (a) a commonality is identified between the input data required by computation phases of two or more of the training tasks, and (b) in response to identifying the commonality, one or more preprocessing phases are executed that produce the input data jointly for the two or more training tasks.

Abstract: Provided is a method for classifying information technology (IT) service request messages. The method may include receiving data associated with an IT service request message, determining a plurality of number values associated with a plurality of characters included in the IT service request message, generating a vector that includes index values, generating a first bitmap based on generating the vector, generating a second bitmap based on the first bitmap, where the second bitmap has a first dimension and a second dimension, and where the first dimension and the second dimension are equal, and determining a classification of the IT service request message using a neural network algorithm. A system and computer program product are also disclosed.

Abstract: A system for classifying resources to niche models includes a computing device configured to receive a plurality of resource data corresponding to a plurality of resources, generate a plurality of resource models, generating a resource model corresponding to the resource as a function of the plurality of resource data and the merit quantitative field, compute a niche model having a plurality of niche data and an output quantitative field, combine the niche model with at least a selected resource model corresponding to a selected resource of the plurality of resources by classifying the output quantitative field to at least a selected merit quantitative field of the resource model and a niche datum of the plurality of niche data to at least a datum of the plurality of resource data, and provide an indication of the at least a selected resource model to a client device of the niche model.

Abstract: Some embodiments herein describe a radio frequency power semiconductor device that include a first non-linear filter network for compensating for lower frequency noise of a power amplifier. The first non-linear filter network can include a plurality of infinite impulse response filters and corresponding corrective elements to correct for a non-linear portion of the power amplifier. The radio frequency power semiconductor device can further include a second non-linear filter network for compensating for broadband distortion. The second non-linear filter network can be connected in parallel to the first non-linear filter network. The broadband distortion can include digital predistortion and the narrowband distortion can include charge trapping effects. The first non-linear filter network can comprise Laguerre filters. The second non-linear filter network can comprise general memory polynomial filters.

Abstract: A device may determine an association between a second set of parameters and a third set of parameters using a pseudoinversion network and a multiple regression procedure. The device may determine semantic embeddings based on a set of semantic descriptions of the second set of parameters. The device may determine a semantic similarity between parameters of the second set of parameters based on the semantic embeddings. The device may determine a consistency error based on the semantic similarity. The device may generate, using a regression-based learning model technique, a matrix representing an association between the second set of parameters and the third set of parameters based on the association and the consistency error. The device may perform an action based on the matrix.

Abstract: A human flow estimation system comprises: a sensor network comprising a plurality of sensors arranged in a to-be-estimated region for detecting the human flow; a model building module configured to build a human flow state model based on arrangement positions of the sensors, and build a sensor network model based on data of the sensors; and a human flow estimation module configured to estimate the human flow and provide a data weight of the estimated human flow based on the human flow state model and the sensor network model. The human flow estimation system further comprises a failure detection module configured to detect whether each sensor in the sensor network is abnormal, and the model building module is further configured to adjust the human flow state model and the sensor network model when an exception exists on the sensor.

Abstract: There is provided an information processing device which efficiently executes machine learning. The information processing device according to one embodiment includes: an obtaining unit which obtains a source code including a code which defines Forward processing of each layer constituting a neural network; a storage unit which stores an association relationship between each Forward processing and Backward processing associated with each Forward processing; and an executing unit which successively executes each code included in the source code, and which calculates an output value of the Forward processing defined by the code based on an input value at a time of execution of each code, and generates a reference structure for Backward processing in a layer associated with the code based on the association relationship stored in the storage unit.

Abstract: An embodiment of the present disclosure provides an arc detection method, in which an apparatus detects arcs, comprising the steps of: obtaining time series data for measured values of an electric current flowing in a wire; calculating first statistical values indicating dispersion degrees with time of the measured values or dispersion degrees with time of variances of the measured values from the time series data; and determining that an arc occurs in the wire or that the possibility of arc occurrence in the wire is high in a case when at least one of the first statistical values is out of a predefined range.

Abstract: A method for defending a network of electronic devices from cyberattacks includes obtaining information about a plurality of devices and information about communication links between the plurality of devices and surrounding environment and determining types of the communication links using heuristic rules. The types of communication links are compared using corresponding link profiles. One or more similar communication links are identified based on the comparison. A cluster of devices is generated by combining a subset of the plurality of devices. The cluster includes one or more devices having one or more similar communication links. A surrounding environment profile is generated for the generated cluster of devices. When a cyberattack is detected on one of the devices in the cluster, the surrounding environment profile is modified for the cluster of devices in order to defend all devices in the cluster from the cyberattack.

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for using reject inference to generate synthetic data for modifying lead scoring models. For example, the disclosed system identifies an original dataset corresponding to an output of a lead scoring model that generates scores for a plurality of prospects to indicate a likelihood of success of prospects of the plurality of prospects. In one or more embodiments, the disclosed system selects a reject inference model by performing simulations on historical prospect data associated with the original dataset. Additionally, the disclosed system uses the selected reject inference model to generate an imputed dataset by generating synthetic outcome data representing simulated outcomes of rejected prospects in the original dataset. The disclosed system then uses the imputed dataset to modify the lead scoring model by modifying at least one parameter of the lead scoring model using the synthetic outcome data.

Abstract: A method and apparatus for building an image model, where the apparatus generates a target image model that includes layers duplicated from a layers of a reference image model and an additional layer, and trains the additional layer.

Abstract: This invention predicts results for a media clip posted to a social media influencer channel by maintaining a database of results data for media clips where an influencer channel includes media clips that include unstructured data, and structured data, and then provide to a first machine learning model a first set of channel data, extracting a first set of features, predicting a value for the first target variable, providing to a second machine learning model a second set of channel data including a second selection of structured data, and the predicted value of the first target variable, extracting a second set of features, and predicting a value for the second target variable.

Abstract: The described technology is generally directed towards policy based navigation control. Map inputs including, e.g., information about blocked routes or other map information, can be collected from mobile devices. Policies can be applied to the map inputs in order to generate navigation advisories that synthesize information from multiple map inputs. For example, a size and shape of a route blockage zone can be determined from multiple discrete map inputs. In some embodiments, the techniques disclosed herein can be applied in connection with shared overlay maps to support automated, real-time, cross-platform sharing of map information, including navigation advisories, among digital navigational map users, including but not limited to unmanned ground vehicles.

Abstract: The present disclosure provides a novel advanced ensemble learning strategy for soft sensor development with semi-supervised model. The main target of the soft sensor is to improve the prediction performance with a limited number of labeled data samples, under the ensemble learning framework. Firstly, in order to improve the prediction accuracy of sub-models for ensemble modeling, a novel sample selection mechanism is established to select the most significantly estimated data samples. Secondly, the Bagging method is employed to both of the labeled and selected data-set, and the two different kinds of datasets are matched based on the Dissimilarity (DISSIM) algorithm. As a result, the proposed method guarantees the diversity and accuracy of the sub-models which are two significant issues of the ensemble learning. In this work, the soft sensor is constructed upon the Gaussian Process Regression (GPR) model.

Abstract: Neural Networks such as Deep Neural Networks (DNNs) output calibrated probabilities that substantially represent frequencies of occurrences of events. A DNN propagates uncertainty information of a unit of the DNN from an input to an output of the DNN. The uncertain information measures a degree of consistency of the test data with training data used to train a DNN. The uncertainty information of all units of the DNN can be propagated. Based on the uncertainty information, the DNN outputs probability scores that reflect received input data that is substantially different from the training data.

Abstract: A computer-implemented system is provided that includes a learning network component that determines respective weights assigned to respective node inputs of the learning network in accordance with a learning phase of the learning network and trains a variable separator component to differentially change learning rates of the learning network component. A differential rate component applies at least one update learning rate to adjust at least one weight assigned to at least one of the respective node inputs and applies at least one other update learning rate to adjust the respective weight assigned to at least one other of the respective node inputs in accordance with the variable separator component during the learning phase of the learning network.

Abstract: In one embodiment, a device identifies a sudden change in a time series of a quality of service metric for a first path in a network that violates a service level agreement threshold associated with application traffic conveyed via the first path. The device predicts a length of time that the sudden change in the time series will last. The device determines, based in part on the length of time that the sudden change in the time series is predicted to last, that the application traffic should be rerouted onto a second path in the network. The device causes the application traffic to be rerouted onto the second path in the network.

Abstract: A method of predicting a central fishing ground of flying squid family Ommastrephidae, includes three steps of setting spatial and temporal dimension, setting environmental factor, and establishing a central fishing ground prediction model. The spatial and temporal dimension includes three levels of spatial dimensions, and two levels of temporal dimensions of week and month. An SST is selected as a main environmental factor, and two environmental factors, i.e., SSH and Chl-a, are selected as a supplement. The environmental factors include four situations. According to the setting situations of the spatial and temporal dimension and the environmental factor, a set of sample schemes of 24 situations is established using permutation and combination method. An error backward propagation neural network model is established, wherein an input layer inputs data of the sample scheme set, and an output layer outputs a CPUE or a fishing ground grading index converted from the CPUE.

Abstract: Methods, systems, and computer program products for automatically generating datasets by processing collaboration forums using artificial intelligence techniques are provided herein.

Abstract: Aspects of the disclosure provide for controlling an autonomous vehicle. For instance, a first probability distribution may be generated for the vehicle at a first future point in time using a generative model for predicting expected behaviors of objects and a set of characteristics for the vehicle at an initial time expected to be perceived by an observer. Planning system software of the vehicle may be used to generate a trajectory for the vehicle to follow. A second probability distribution may be generated for a second future point in time using the generative model based on the trajectory and a set of characteristics for the vehicle at the first future point expected to be perceived by the observer. A surprise assessment may be generated by comparing the first probability distribution to the second probability distribution. The vehicle may be controlled based on the surprise assessment.

Abstract: A computer-implemented method, system and computer program product for improving prediction accuracy in machine learning techniques. A teacher model is constructed, where the teacher model generates a weight for each data case. The current student model is then trained using training data and the weights generated by the teacher model. After training the current student model, the current student model generates state features, which are used by the teacher model to generate new weights. A candidate student model is then trained using training data and these new weights. A reward is generated by comparing the current student model with the candidate student model using training and testing data, which is used to update the teacher model if a stopping rule has not been satisfied. Upon a stopping rule being satisfied, the weights generated by the teacher model are deemed to be the “optimal” weights which are returned to the user.

Abstract: A system may forecast a plurality of workload measurements for a database management system (DBMS) at respective times based on a workload model. The system may determine, based on the forecasted workload measurements, configuration parameter sets optimized for the DBMS at the respective times. The system may generate a reconfiguration plan. The system may determine a performance gain that would result from reconfiguring nodes of the DBMS with the configurations parameter sets. In addition, the system may determine a performance loss that would result from the respective databases of the nodes being inaccessible during reconfiguration with the configuration parameter sets. The system may select a reconfiguration plan in response to the performance gain and the performance loss satisfying a fitness criterion. The system may cause, at the reconfiguration times, the nodes to begin reconfiguration with the configuration parameter sets included in the selected reconfiguration plan.

Abstract: A disease development risk prediction system 10 includes: a data generation means 11 which generates combination data by combining at least two different types of receipt data using a combination key, wherein the receipt data includes an insured person number for an insured person which was converted using a predetermined method, a birth date or birth year and month which are both age-identifiable items, and gender, and the combination key combines the converted insured person number, age-identifiable items, and gender; and a model generation means 12 which uses the generated combination data to generate a prediction model predicting a risk of the insured person of developing a predetermined disease.

Abstract: A technique capable of providing a new function usable as an activation function is provided. An inference apparatus includes a receiving unit that receives input of target data; and an inference unit that executes a predetermined inference process with respect to the target data using a neural network model. The neural network model includes a plurality of processing layers, and, as the processing layers, one or more activation function layers that convert an input value by a predetermined activation function. The activation function of at least one of the activation function layers is configured as a function of a waveform the output value of which changes, in a first range, to approach a maximum value as an input value increases and, in a second range, away from a minimum value as the input value increases, such that the output values in the first and second ranges are not the same.

Abstract: An architecture for predicting and modeling geological characteristics of a reservoir includes one or more neural networks, a static modeling module, a dynamic modeling module, and a fuzzy inference engine to provide recommendations for drilling a wellbore. The neural networks receive log data for coordinates along a well trajectory, and determine a geophysical relationship for a property of a subterranean formation as a function of distance vectors between the coordinates along the well trajectory and one or more sets of randomly generated coordinates. The static modeling module generates three-dimensional static models of a volume of interest based on predicted properties of formations residing therein from the neural networks.

Abstract: A self-adaptive illuminating device includes an illumination module and a driver module. The illumination module includes multiple electrically coupled illuminating units and a sampler. The sampler is electrically coupled to the illuminating units. The sampler tests at least one electrical property of the illuminating units by using a test signal for generating a feedback parameter. The driver module is electrically coupled to the illumination module. The driver module includes a signal processing unit and a power transformer. The signal processing unit is coupled to the sampler. The signal processing unit generates an operating parameter based on the feedback signal. The power transformer is electrically coupled to the signal processing unit and the illuminating units. The power transformer generates a resulting driving power based on the operating parameter and the initial driving power, and drives the illuminating units using the resulting driving power.

Abstract: Metrology methods, modules and systems are provided, for using machine learning algorithms to improve the metrology accuracy and the overall process throughput. Methods comprise calculating training data concerning metrology metric(s) from initial metrology measurements, applying machine learning algorithm(s) to the calculated training data to derive an estimation model of the metrology metric(s), deriving measurement data from images of sites on received wafers, and using the estimation model to provide estimations of the metrology metric(s) with respect to the measurement data. While the training data may use two images per site, in operation a single image per site may suffice—reducing the measurement time to less than half the current measurement time. Moreover, confidence score(s) may be derived as an additional metrology and process control, and deep learning may be used to enhance the accuracy and/or speed of the metrology module.

Abstract: The system described herein can include neural networks with noise-injection layers. The noise-injection layers can enable the neural networks to be trained such that the neural networks are able to maintain their classification and prediction performance in the presence of noisy data signals. Once trained, the parameters from the neural networks with noise-injection layers can be used in the neural networks of systems that include resistive random-access memory (ReRAM), memristors, or phase change memory (PCM), which use analog signals that can introduce noise into the system. The use of ReRAM, memristors, or PCM can enable large-scale parallelism that improves the speed and computational efficiency of neural network training and classification. Using the parameters from the neural networks trained with noise-injection layers, enables the neural networks to make robust predictions and calculations in the presence of noisy data.

Abstract: First FEA mesh model representing 3-D geometry of a carbon fiber reinforced composite (CFRC) product/part, pre-forming fiber orientation and desired reference fiber direction at a particular location on the product/part are received. First FEA mesh model contains finite elements associated with respective material properties for carbon fibers and binding matrix. Pre-forming fiber orientation includes number of fibers and relative angles amongst the fibers. Pre-forming 2-D shape of a workpiece used for manufacturing the product/part is obtained by conducting a one-step inverse numerical simulation that numerically expands the first to a second FEA mesh model based on numerically-calculated structural behaviors according to respective material properties. Pre-forming fiber orientation is superimposed on the second FEA mesh model with the desired reference fiber direction being preserved.

Abstract: A method and apparatus for determining when actual wear of a flash memory device differs from a reliability state. Configuration files of a reliability-state classification neural network model are stored. The operation of a flash memory device is monitored to identify current physical characteristic values. A read of the flash memory device is performed to determine a number of errors. A neural network operation is performed using as input a set of threshold voltage shift offset values currently being used to perform reads of the flash memory device and the calculated number of errors, to identify a predicted reliability state. The identified current physical characteristic values are compared to corresponding tags associated with the predicted reliability state and a flag or other indication is stored when the comparison indicates that the identified current physical characteristic values do not correspond to the respective tags associated with the predicted reliability state.

Abstract: Systems, methods, and other embodiments associated with detecting an electric vehicle charging event. In one embodiment, from electricity consumption data from a known set of electric vehicle owners, the method encodes usage values from time intervals with a symbol from a series of symbols representing a level of electricity consumption during the time interval. The encoding generates an encoded consumption pattern of symbols for each electrical vehicle owner. An EV charge motif is identified that represents an EV charging event. One or more machine learning classifiers is trained to identify the EV charge motifs from the known set of electric vehicle owners and to distinguish from non-charge motifs to identify EV charges from unknown data sets.

Abstract: Machine learning techniques are used to predict values of fixed parameters when given reference values of critical parameters. For example, a neural network can be trained based on one or more critical parameters and a low-dimensional real-valued vector associated with a spectrum, such as a spectroscopic ellipsometry spectrum or a specular reflectance spectrum. Another neural network can map the low-dimensional real-valued vector. When using two neural networks, one neural network can be trained to map the spectra to the low-dimensional real-valued vector. Another neural network can be trained to predict the fixed parameter based on the critical parameters and the low-dimensional real-valued vector from the other neural network.

Abstract: Methods are provided for training weights of an artificial neural network to be implemented by inference computing apparatus in which the trained weights are stored as programmed conductance states of respective predetermined memristive devices. Such a method includes deriving for the memristive devices a probability distribution indicating distribution of conductance errors for the devices in the programmed conductance states. The method further comprises, in a digital computing apparatus: training the weights via an iterative training process in which the weights are repeatedly updated in response to processing by the network of training data which is propagated over the network via the weights; and applying noise dependent on said probability distribution to weights used in the iterative training process.

Abstract: Computer systems, data processing methods, and computer-readable media are provided to run original networks. An exemplary computer system includes first and second processors a memory storing offline models and corresponding input data of a plurality of original networks, and a runtime system configured to run on the first processor. The runtime system, when runs on the first processor, causes the first processor to implement a plurality of virtual devices comprising a data processing device configured to obtain an offline model and corresponding input data of an original network from the memory, an equipment management device configured to control turning on or off of the second processor, and a task execution device configured to control the second processor to run the offline model of the original network.

Abstract: Artificial intelligence-based watershed hydrology analysis and management having of a network of weather stations and artificial drainage systems with artificial and natural reservoir management through locks and pumping stations. Methods and systems evaluate hydrologic risk in each area and analyse the consequences of future precipitations using neural network assisted simulation. Hydrographs calculated for each sub-basin, streams and rivers in the basin simulates the behavior of the basin under different scenarios corresponding to different types of management of the operation of locks and/or pumps and compares its results in terms of loss of flooded area, economic loss in each area, loss for flooding of urban areas, etc. Optimization of the simulation by artificial intelligence meta-heuristic algorithms, multi-layered neural network acts as a search engine to find mitigation solutions and best configurations of resource management controls to minimize socio-economic impacts on each basin.

Abstract: Methods, systems, and apparatus, including computer programs encoded on non-transitory computer storage medium(s), for efficiently executing autonomous driving (AD) simulations. The AD simulation can include segmented time events that include variations. The simulation including its variations can be implemented as a graph structure in which the simulation may be executed in a manner similar to traversing a graph.

Abstract: Optimization of a neural network, for example in a video codec at the decoder side, may be guided to limit overfitting. The encoder may encode video(s) with different qualities for different frames in the video. Low-quality frames may be used as both input and ground-truth during optimization. High-quality frames may be used to optimize the neural network so that higher-quality versions of lower-quality inputs may be predicted. The neural network may be trained to make such predictions by making a prediction based on a constructed low-quality input for which the corresponding high-quality version is known, comparing the prediction to the high-quality version, and fine-tuning the neural network to improve its ability to predict a high-quality version of a low-quality input. To limit overfitting, the neural network may be concurrently or in an alternating fashion trained with low-quality input for which a higher-quality version of the low-quality input is known.

Abstract: A vehicle allocation system is provided, which operates to move a vehicle to a predetermined point in response to a request from a user. The vehicle allocation system includes a boarding point determination apparatus, a control apparatus, and a user terminal apparatus. The boarding point determination apparatus includes a processor that operates to specify a second user who is expected to board in a predetermined range including a point associated with a first user, and calculate as the predetermined point a common point at which users including at least the first user and the second user board.

Abstract: Systems and methods of task implementation are extended as provided herein and target the web crawling process through a step of submitting a request by a customer to a web crawler. The systems and methods allow a more complex request for a web crawler to be defined in order to receive more specific data. In one aspect, a method for data extraction and gathering from a Network by a Service provider infrastructure include the following steps: checking the parameters of a request received from a User's Device, adjusting the request parameters according to pre-established Scraping logic, selecting a Proxy according to the criteria of the pre-established Scraping logic, sending the adjusted request to the Target through the selected Proxy, checking metadata received from the Target, and forwarding the data to the User's device.

Abstract: A text-based egotism level detection system and a process for detecting egotism level in alpha-numeric textual information via artificial intelligence (AI), deep learning, and natural language processing (NLP) are disclosed. The text-based egotism level detection system and process for detecting egotism level in alpha-numeric textual information via AI, deep learning, and NLP detects egotism in text using a convolution neural network (CNN) for deep learning. The text-based egotism level detection system and process for detecting egotism level in alpha-numeric textual information via AI, deep learning CNN, and NLP builds, maintains, utilizes, and updates an egotism detection text language processing model that is generated from a huge amount of text data including sentences that are designated as egotistic sentences or not egotistic sentences based on a deep understanding of egotism and data science.

Abstract: A text error correction method and a text error correction apparatus based on a recurrent neural network of artificial intelligence are provided. The method includes: acquiring text data to be error-corrected; performing error correction on the text data to be error-corrected by using a trained recurrent neural network model so as to generate error-corrected text data.

Abstract: Systems and methods are disclosed for processing an electronic image corresponding to a specimen. One method for processing the electronic image includes: receiving a target electronic image of a slide corresponding to a target specimen, the target specimen including a tissue sample from a patient, applying a machine learning system to the target electronic image to determine deficiencies associated with the target specimen, the machine learning system having been generated by processing a plurality of training images to predict stain deficiencies and/or predict a needed recut, the training images including images of human tissue and/or images that are algorithmically generated; and based on the deficiencies associated with the target specimen, determining to automatically order an additional slide to be prepared.

Abstract: A printer system connects a plurality of printers and a terminal via a network. First, the system inputs data for a new print from the terminal. Next, based on the inputted data, the system uses a learned model that has learned to select one printer among the plurality of printers based on data that was used in previous printing by the plurality of printers, to infer a printer suited to the new print from the plurality of printers. As a result of the inference, the system conveys the obtained printer to the terminal.

Abstract: Described is a system for performing probabilistic computations on mobile platform sensor data. The system translates a Bayesian model representing input mobile platform sensor data to a spiking neuronal network unit that implements the Bayesian model. Using the spiking neuronal network unit, conditional probabilities are computed for the input mobile platform sensor data, where the input mobile platform sensor data is a time series of mobile platform error codes encoded as neuronal spikes. The neuronal spikes are decoded and represent a mobile platform failure mode. The system causes the mobile platform to initiate a mitigation action based on the mobile platform failure mode.

Abstract: Some embodiments are associated with a system and method for deep learning unsupervised anomaly prediction in Internet of Things (IoT) sensor networks or manufacturing execution systems. The system and method use an unsupervised predictive GAN model with multi-layer perceptrons (MLP) as generator and discriminator.

Abstract: The disclosed computer-implemented method may include (i) receiving a first transport request and a second transport request, (ii) evaluating a fitness of matching the first and second transport requests to be fulfilled by a transport provider, based at least partly on a transportation overlap between the first and second transport requests, (iii) generating a simulated future transport request, (iv) evaluating a fitness of matching the first transport request with the simulated future transport request, based at least in part on a transportation overlap between the first transport request and the simulated future transport request, and (v) matching the first and second transport requests based at least in part on the fitness of matching the first and second transport requests and based at least in part on the fitness of matching the first transport request with the simulated future transport request. Various other methods, systems, and computer-readable media are disclosed.

Abstract: A method for automatically generating documentation for an artificial intelligence model includes receiving, by a computing device, an artificial intelligence model. The computing device accesses a model facts policy that indicates data to be collected for artificial intelligence models. The computing device collects artificial intelligence model facts regarding the artificial intelligence model according to the model facts policy. The computing device accesses a factsheet template. The factsheet template provides a schema for an artificial intelligence model factsheet for the artificial intelligence model. The computing device populates the artificial intelligence model factsheet using the factsheet template with the artificial intelligence model facts related to the artificial intelligence model.