Abstract: Novel tools and techniques for the machine discovery and rapid agglomeration of similar states are provided. A system includes a plurality of network devices, and a decision system. The plurality of network devices may be configured to generate a respective data stream. The decision system may include a processor and a non-transitory computer readable medium including instructions executable by the processor. The system may obtain, via the plurality of network devices, one or more data streams, determine an anomaly has occurred on a data stream of the one or more data streams, determine a set of data streams of the one or more streams sharing at least one of a set of centroids associated with the data stream, and determine, based on the set of data streams, the occurrence of an incident.

Abstract: An artificial intelligence agent is machine trained and used to provide physically-based rendering settings. By using deep learning and/or other machine training, settings of multiple rendering parameters may be provided for consistent imaging even in physically-based rendering.

Abstract: A neural network is implemented as a memristive neuromorphic circuit that includes a neuron circuit and a memristive device connected to the neuron circuit. An input voltage is sensed at a first terminal of a memristive device during a feedforward operation of the neural network. An error voltage is sensed at a second terminal of the memristive device during an error backpropagation operation of the neural network. In accordance with a training rule, a desired conductance change for the memristive device is computed based on the sensed input voltage and the sensed error voltage. Then a training voltage is applied to the memristive device. Here, the training voltage is proportional to a logarithmic value of the desired conductance change.

Abstract: Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.

Abstract: A method of biasing a deep neural network includes determining whether an element has an increased probability of being present in an input to the network. The method also includes adjusting a bias of activation functions of neurons in the network to increase sensitivity to the element. In one configuration, the bias is adjusted without adjusting weights of the network. The method further includes adjusting an output of the network based on the biasing.

Abstract: The invention relates to predictive browsing. A set of words for use with an experience matrix are formed, wherein the words are descriptive of a context of a system such as a current web page, and wherein said experience matrix comprises sparse vectors associated with words. At least a part of at least one sparse vector of said experience matrix is accessed to form a prediction output, and suggestions of web pages are provided to a user in response to said prediction output.

Abstract: Systems and methods that quantify document relevance for a document relative to a training corpus and select a best match or best matches are provided herein. Methods may include generating an example-based explanation for relevancy of a document to a training corpus by executing a support vector machine classifier, the support vector machine classifier performing a centroid classification of a relevant document in a term frequency-inverse document frequency features space relative to training examples in a training corpus, and generating an example-based explanation by selecting a best match for the relevant document from the training examples based upon the centroid classification.

Abstract: A self-adjusting resource-provisioning system that infers the existence of extrinsic events by monitoring external information sources. When an external source satisfies a threshold condition, the system, as a function of historical records, correlates the inferred event with a likelihood that a Web site or other computerized entity's resource-utilization will reach a certain level at a future time. The system adjusts the available amount of resources to handle the predicted utilization level. If the system fails to accurately predict the actual utilization level, the system adjusts the condition to more accurately predict utilization in the future. If no threshold condition predicts an unexpected change in resource utilization, the system adjusts parameters of an existing condition or creates a new condition to better correlate utilization with future extrinsic events.

Abstract: A method is provided of initializing a chip having synaptic NVRAM cells connected row-wise by word lines and column-wise by bit lines. The method includes selecting each word line through a row decoder connected to all word lines to switch all synaptic NVRAM cells of the selected lines. The method includes driving, on the selected lines, a wave generated by a PLL circuit connected to the row decoder. The method includes generating standing waves from the wave on the selected lines by implementing a resonance detection point at an input end of each word line. The method includes applying a write voltage on all bit lines through a column decoder connected to all bit lines. The method includes simultaneously driving each of the synaptic NVRAM cells of the selected lines by different writing currents for different durations in order to set different analog values to the synaptic NVRAM cells.

Abstract: Systems and methods are disclosed for early access to captured images including receiving a request for at least one image of a geographic area from a client application of an operator user device; querying records within a geospatial database to locate one or more records of images accessible by the geospatial database and depicting at least a portion of the geographic area; reading information within the one or more records depicting at least a portion of the geographic area to determine a status of an image within the one or more records, the status of the image indicating that the image is an in process captured image in which the image has not been fully processed; and presenting at least a portion of the image to the client application of the operator user device with a status indicator indicating the stage in processing of the geographic area.

Abstract: A convolution processing apparatus and method are disclosed. The convolution processing apparatus may include a controller configured to load a pixel of an input image and skip a process associated with the pixel in response to a value of the loaded pixel being 0, a filter bank including at least one filter and configured to extract at least one kernel element corresponding to the pixel from the at least one filter based on an index of the pixel and an input channel of the pixel, and a multiplier-accumulator (MAC) configured to perform a convolution operation based on the value of the pixel and a value of the at least one kernel element and accumulatively store an operation result of the convolution operation, the operation result corresponding to an output image.

Abstract: Associative memory systems, methods and computer program products are provided. An associative memory system includes a distributed associative memory base including a network of networks of associative memory networks. A respective associative memory network includes associations among a respective observer memories and a plurality of observed memories that are observed by the respective observer memory. Ones of the associative memory networks are physically and/or logically independent from other ones of the associative memory networks. A processing system is configured to observe associations into and imagine associations from, the distributed associative memory base using multiple streaming queues that correspond to respective ones of multiple rows in the associative memory networks. The processing system is further configured to determine a cognitive distance between a term and a class of terms, the cognitive distance being returned responsive to a query of the distributed associative memory base.

Abstract: The dimensionality of user data can be reduced in order to allow cross-category data to be used to determine recommendations, advertising, or other supplemental content within a specific category. A first reduction in dimensionality results from rolling up category nodes to higher-level nodes. User data for the higher-level nodes can be used to train a neural network, with a user signature being generated using node values from a hidden layer of the trained model. The user signature can then be used to train a category specific model in order to obtain category-specific recommendations, determine category-specific advertising, or select other supplemental content based at least in part upon cross-category data.

Abstract: A computer-implemented method for verifying authentication requests using IP addresses may include (i) collecting, by a computing system, data on IP address changes from a set of endpoint devices, (ii) creating, by the computing system using the data on IP address changes, a virtual IP address distance map based on a likelihood of change from at least one origin IP address to at least one destination IP address, (iii) automatically detecting, by the computing system, a change in an IP address of a client device, (iv) determining, by the computing system and based on the virtual IP address distance map, that the change in the IP address of the client device indicates that an authentication request from the client device is suspicious, and (v) performing, by the computing system, a security action to secure the client device. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A contact center includes: a microprocessor; a computer readable medium, coupled to the microprocessor, to store, for one or more contact center objects, contact center information; and a microprocessor executable report generator, The report generator, when executed, receives, from a user, a selected contact center object, retrieves, from the computer readable medium, a set of data elements associated with the selected contact center object, the set of data elements defining a first space having a first dimension, projects the set of data elements onto a subspace of the first space to form a transformed set of data elements, the subspace having a second dimension less than the first dimension, and provides, for display to a user, the transformed set of data elements.

Abstract: A system and method for monitoring and performing project development for a project within a company is disclosed. These include a communication interface that receives information regarding the project development. These include a processor and memory that identify an initial analysis of the project, produce a storyboard of the project and general projection for completing the project, input the storyboard into an initial monitoring gate, perform a series of sprints to complete ones of the plurality of storyboard stories, monitor the burndown in the storyboarding, review the backlog of the plurality of stories, and interactively perform a second series of sprints until project is complete. The system and method include a display device for displaying the status of the project development and identifies infrastructure of the company in use in the project.

Abstract: One or more embodiments may include techniques to computer generate one or more plots based on computational clustering performed by a system. Embodiments include performing clustering on a dataset to generate a number of clusters of data for the dataset. The clusters may be processed and used to generate the one or more plots. In some embodiments, the plots may include one or more variables plotted against a weighted average score associated with a cluster, the plot may visually indicate the effect that the one or more variables has on the predicted outcome. The one or more plots may be presented in a display on a display device. In some embodiments, the plots may be segmented and each segment may correspond with a number of individual curves. The segmented curves may be plotted and displayed on the display device.

Abstract: A clustering machine can cluster descriptive vectors in a balanced manner. The clustering machine calculates distances between pairs of descriptive vectors and generates clusters of vectors arranged in a hierarchy. The clustering machine determines centroid vectors of the clusters, such that each cluster is represented by its corresponding centroid vector. The clustering machine calculates a sum of inter-cluster vector distances between pairs of centroid vectors, as well as a sum of intra-cluster vector distances between pairs of vectors in the clusters. The clustering machine calculates multiple scores of the hierarchy by varying a scalar and calculating a separate score for each scalar. The calculation of each score is based on the two sums previously calculated for the hierarchy. The clustering machine may select or otherwise identify a balanced subset of the hierarchy by finding an extremum in the calculated scores.

Abstract: Compressing a machine learning network, such as a neural network, includes replacing one layer in the neural network with compressed layers to produce the compressed network. The compressed network may be fine-tuned by updating weight values in the compressed layer(s).

Abstract: Fault tolerant querying of data distributed across multiple nodes is accomplished by each node determining and reporting its own health status and indexing status to the other nodes in the cluster via a gossip protocol. A coordinator node then prioritizes replica nodes based on the received status of the other nodes and sends query requests to those nodes based on the prioritization. Should a node fail to provide an response to a query request, further query requests are sent to a next highest priority replica node containing the relevant data. This results in improved query performance by avoiding busy nodes and further provides a fault tolerant approach to data queries.

Abstract: The present disclosure provides enhanced group recommendation for selection of a service item with respect to a service class, and communicates the enhanced group recommendation to a sub-group of service users. Initial service scores for service items in the service class are generated by aggregating individual service preferences of sub-group members with respect to the service class. Group preferences for service items in the service class are then predicted using statistic data on previous service selections of sub-group members with respect to the service class. The initial service scores are aggregated with the predicted group preferences to generate of weighted service scores with respect to service items in the service class. One of the service items in the service class that has the highest weighted score is the group recommendation is communicated to the sub-group members.

Abstract: An information presentation device includes: a situation estimation unit configured to identify one or more tasks based on input information that has been input and task information indicating one or more tasks indicating a situation; a load estimation unit configured to identify one or more sub-tasks based on each of the identified tasks and sub-task information indicating, for each situation of a task, one or more sub-tasks, which are work elements, having a possibility of being executed by a worker, and to acquire a load demand amount based on the identified sub-tasks and sub-task demand amount information indicating a load demand amount, which is a mental load for each sub-task; a capacity level estimation unit configured to acquire a capacity level based on the acquired load demand amount; and a presentation information selection unit configured to select information to be presented based on the capacity level.

Abstract: Computer implemented system and method for classifying users is provided. The method includes obtaining a data set from one or more sources corresponding to one or more users; segmenting the data set into segments, each segment comprising one or more data points, wherein each data point is specific and unique to a user; identifying difficult regions from one or more segments; computing a standard deviation of distances between the data points within each of the identified difficult regions for each of the identified difficult regions; selecting a radial basis function (RBF) kernel based on the standard deviation computed for each of the identified difficult regions; generating a support vector machine model on each of the identified difficult regions based on the selected RBF kernel to obtain a hyper plane comprising support vectors and weight assigned to the vectors; and classifying the users into a category based on the hyper plane.

Abstract: Embodiments are directed to managing documents where clauses in a document may be identified. Evaluations of the clauses may be provided based on evaluators and machine learning (ML) models that assign each of the clauses to a category and a confidence score. Actions associated with the clauses may be monitored including updates to content of the clauses. Inconsistent evaluations associated with the clauses be identified. The ML models may be retrained based on the content of the clauses associated with the inconsistent evaluations.

Abstract: Architecture that enables the identification of entities such as people and content in live broadcasts (e.g., streaming content (e.g., video) of live events) and non-live presentations (e.g., movies), in realtime, using recognition processes. This can be accomplished by extracting live data related to a live event. With respect to people entities, filtering can be performed to identify the named (people) entities from the extracted live data, and trending topics discovered as relate to the named entities, as associated with the live event. Multiple images of the named entities that capture the named entities under different conditions are captured for the named entities. The images are then processed to extract and learn facial features (train one or more models), and facial recognition is then performed on faces in the video using the trained model(s).

Abstract: A method includes: identifying, by a processor, one or more physical arrangements of resources in a customer contact center; predicting, by the processor, for each of the one or more physical arrangements of resources, a predicted performance metric of the physical arrangement of resources in accordance with one or more working environment parameters of the customer contact center; identifying, by the processor, a particular physical arrangement of the one or more physical arrangements having a corresponding predicted performance metric satisfying a threshold performance among the one or more physical arrangements; and outputting, by the processor, the particular physical arrangement of resources in the customer contact center.

Abstract: Embodiments of the present invention are directed to a system and method of calculating a match score and mapping the match score to a level. Interested contractors in a list are ranked based on a rank score that is calculated for each of the interested contractor using a set of factors. The rank score is a combination of factor scores associated with the factors. A mapping engine, implementing a machine learning model, maps each of the interested contractors to one of at least two levels based on the set of factor scores, by comparing the set of factor scores to historical data collected to determine the likelihood of the contractor having that set being chosen by the client. Any interested contractor who has been mapped to the highest level is distinguished from others in the list. The mapping engine continuously learns from the historical data to improve future mappings.

Abstract: A method may include selecting a document from a user's corpus, analyzing content of the selected document, and determining a target content group based on the content of the selected document. The method may further include locating a relevant document based on the determined target content group, and presenting the relevant document to the user. In particular, the target content group may be selected form a plurality of predetermined content groups, wherein the predetermined content groups are automatically generated based on clustering of documents of a content group into subgroups that are similar to each other.

Abstract: System and method for estimating package implementation effort of software development life cycle (SDLC) activities are disclosed. In an embodiment, information associated with a plurality of configuration parameters of a SDLC activity is obtained. The configuration parameters include an organization, an industry vertical, business criticality of functionalities within the industry vertical, influencing factors and a utilization factor. Further, one or more of configuration factors associated with the SDLC activity are determined based on the information associated with the configuration parameters. Furthermore, functional effort for configuration of the SDLC activity is estimated based on the configuration factors. In addition, information associated with a plurality of customization parameters of the SDLC activity is obtained. Moreover, customization effort for customization of the SDLC activity is estimated based on the information associated with the customization parameters.

Abstract: This disclosure relates to digital image segmentation and region of interest identification. A computer implemented image segmentation method and system are particularly disclosed, including a predictive model trained based on a deep fully convolutional neural network. The model is trained using a loss function in at least one intermediate layer in addition to a loss function at the final stage of the full convolutional neural network. The predictive segmentation model trained in such a manner requires less training parameters and facilitates quicker and more accurate identification of relevant local and global features in the input image. In one implementation, the fully convolutional neural network is further supplemented with a conditional adversarial neural networks iteratively trained with the fully convolutional neural network as a discriminator measuring the quality of the predictive model generated by the fully convolutional neural network.

Abstract: The invention relates to a computer-implemented method for classifying events present in a sample, wherein each event is characterized by a multidimensional set of parameters obtained by means of hardware and/or software, wherein the values of the parameters associated with each event define the position coordinates of said event in a multidimensional space. Said method comprises the following stages: a) clustering the events in groups, b) checking if within each formed group there is a connection between events exceeding a maximum distance threshold, c) calculating the affinity between each pair of previously generated sample groups, d) comparing each sample group with at least one reference group stored in at least one database, e) classifying the sample groups based on the comparisons with the reference groups.

Abstract: The present invention relates to a method, system, and computer program product for determining storage device weight values to use to select one of the storage devices to use as a target storage to which data from a source storage is migrated. A determination is made, for each of the storage devices, of static parameter values for static parameters comprising attributes of the storage device and dynamic parameter values for dynamic parameters providing device health information determined by accessing the storage device to determine operational conditions at the storage device. Storage device weight values are determined as a function of the static parameter values and the dynamic parameter values of the device. The determined storage device weight values are used to select one of the storage devices as the target storage to which data from the source storage is migrated.

Abstract: Methods and systems for creating a measurement model based on measured training data are presented. The trained measurement model is used to calculate process parameter values, structure parameter values, or both, directly from measured data collected from other wafers. The measurement models receive measurement data directly as input and provide process parameter values, structure parameter values, or both, as output. The measurement model enables the direct measurement of process parameters. Measurement data from multiple targets is collected for model building, training, and measurement. In some examples, the use of measurement data associated with multiple targets eliminates, or significantly reduces, the effect of under layers in the measurement result, and enables more accurate measurements. Measurement data collected for model building, training, and measurement, may be derived from measurements performed by a combination of multiple, different measurement techniques.

Abstract: Disclosed herein are systems and methods for displaying images and contextually relevant content on a digital content platform, such as a web page. In one embodiment, for example, the systems and methods include receiving a request for content call from an end-user device, identifying content that is contextually relevant to the image, delivering the content to the end-user device, and delivering a computer-readable instruction that causes the end-user device to perform an animation that displays the content in a spatial relationship with respect to the image as originally published. The spatial relationship is defined such that the content appears to be displayed on a backside of the image.

Abstract: A computer-implemented method of adaptively controlling an autonomous operation of a vehicle is provided. The method includes steps of (a) in a critic network in a computing system configured to autonomously control the vehicle, determining, using samples of passively collected data and a state cost, an estimated average cost, and an approximated cost-to-go function that produces a minimum value for a cost-to-go of the vehicle when applied by an actor network; and (b) in an actor network in the computing system and operatively coupled to the critic network, determining a control input to apply to the vehicle that produces the minimum value for the cost-to-go, wherein the actor network is configured to determine the control input by estimating a noise level using the average cost, a cost-to-go determined from the approximated cost-to-go function, a control dynamics for a current state of the vehicle, and the passively collected data.

Abstract: Access to a secure resource is controlled. Data relating to social media history information, demographic information, and psychometric information regarding a user having access to a secure resource is mined by a computer system. Mined data that indicates a potential security risk that meets a predefined security criteria with respect to the secure resource is identified by the computer system. A security risk level based on the identified data is determined. A level of access to the secure resource by the user computing device is set, based on one or more predetermined threshold values that are met, in response to a determination regarding such. Access is granted to the secure resource in accordance with the set level of access, in response to receiving a request to access the secure resource by the computer system.

Abstract: A computer-implemented method is provided for autonomously controlling a vehicle to perform a vehicle operation. The method includes steps of applying a passive actor-critic reinforcement learning method to passively-collected data relating to the vehicle operation, to learn a control policy configured for controlling the vehicle so as to perform the vehicle operation with a minimum expected cumulative cost; and controlling the vehicle in accordance with the control policy to perform the vehicle operation.

Abstract: A method and a system for generating a target character sequence from a semantic representation including a sequence of characters are provided. The method includes adapting a target background model, built from a vocabulary of words, to form an adapted background model. The adapted background model accepts subsequences of an input semantic representation as well as words from the vocabulary. The input semantic representation is represented as a sequence of character embeddings, which are input to an encoder. The encoder encodes each of the character embeddings to generate a respective character representation. A decoder then generates a target sequence of characters, based on the set of character representations. At a plurality of time steps, a next character in the target sequence is selected as a function of a previously generated character(s) of the target sequence and the adapted background model.

Abstract: Techniques are disclosed for extracting micro-features at a pixel-level based on characteristics of one or more images. Importantly, the extraction is unsupervised, i.e., performed independent of any training data that defines particular objects, allowing a behavior-recognition system to forgo a training phase and for object classification to proceed without being constrained by specific object definitions. A micro-feature extractor that does not require training data is adaptive and self-trains while performing the extraction. The extracted micro-features are represented as a micro-feature vector that may be input to a micro-classifier which groups objects into object type clusters based on the micro-feature vectors.

Abstract: A method of learning parameters of a CNN is provided. The method includes steps of: (a) allowing an input value to be delivered to individual multiple element bias layers; (b) allowing the scale layer connected to a specific element bias layer to multiply a predetermined scale value by an output value of the specific element bias layer; (c) (i) allowing a specific element activation layer connected to the scale layer to apply activation function, and (ii) allowing the other individual element activation layers to apply activation functions to output values of the individual element bias layers; (d) allowing a concatenation layer to concatenate an output value of the specific element activation layer and output values of the other element activation layers; (e) allowing the convolutional layer to apply the convolution operation to the concatenated output; and (f) allowing a loss layer to acquire a loss during a backpropagation process.

Abstract: One or more embodiments provide a system and circuit for image distortion correction. The system includes neurosynaptic core circuits that: receive a set of inputs comprising image dimensions and pixel distortion coefficients for at least one image frame via at least one input core circuit, map each distorted pixel to zero or more undistorted pixels by processing the set of inputs corresponding to each pixel of the at least one image frame by the at least one input core circuit, and route corresponding pixel intensity values of each distorted pixel to output undistorted pixels for each image frame via at least one output core circuit.

Abstract: A clustering coefficient-based adaptive clustering method, according to the categories of extracted data point pairs and the magnitude of association relations between data points extracted each time, determining to which category the data points belong, and the number of the categories, and establishing association relations between the data points and association relations between the categories; pre-segmenting each category and calculating the intra-category similarities of two sub-categories and inter-category similarities of the two sub-categories, judging whether the two pre-segmented sub-categories satisfy a segmentation condition, if so, then accepting the pre-segmentation; if not, then canceling the pre-segmentation; calculating the intra-category similarities and inter-category similarities of two categories having an association relation, judging whether the two categories satisfy a merging condition, if so, then merging the two categories to generate a new category; if not, then abandoning the mer

Abstract: Methods and systems for receiving a request to implement a neural network comprising an average pooling layer on a hardware circuit, and in response, generating instructions that when executed by the hardware circuit, cause the hardware circuit to, during processing of a network input by the neural network, generate a layer output tensor that is equivalent to an output of the average pooling neural network layer by performing a convolution of an input tensor to the average pooling neural network layer and a kernel with a size equal to a window of the average pooling neural network layer and composed of elements that are each an identity matrix to generate a first tensor, and performing operations to cause each element of the first tensor to be divided by a number of elements in the window of the average pooling neural network layer to generate an initial output tensor.

Abstract: Methods and systems for receiving a request to implement a neural network comprising an average pooling layer on a hardware circuit, and in response, generating instructions that when executed by the hardware circuit, cause the hardware circuit to, during processing of a network input by the neural network, generate a layer output tensor that is equivalent to an output of the average pooling neural network layer by performing a convolution of an input tensor to the average pooling neural network layer and a kernel with a size equal to a window of the average pooling neural network layer and composed of elements that are each an identity matrix to generate a first tensor, and performing operations to cause each element of the first tensor to be divided by a number of elements in the window of the average pooling neural network layer to generate an initial output tensor.

Abstract: In particular embodiments, a computer server may receive information regarding an event proposal from an event organizer, where the information includes an identification of multiple invitees, and where the information is missing one or more significant details of the event proposal. The computer server may send the information regarding the event proposal to one or more client devices, where each of the client devices is associated with one of the invitees. The computer server may receive feedback regarding the event proposal from at least one of the invitees, and based on the received feedback, the computer server may determine at least one of the missing significant details of the event proposal. The computer server may send information regarding final details of an event based on the event proposal to one or more of the client devices associated with the invitees.

Abstract: A processor-implemented method, system, and/or computer program product mitigate subjectively disturbing content. A context-based data gravity wells membrane supports one or more gravity wells, which hold subjectively disturbing synthetic context-based objects made up of a non-contextual data object, a first context object, and a second context object. The first context object defines the non-contextual data object, and the second context object describes how subjectively disturbing content generated by combining the non-contextual data object and the first context object is according to predefined parameters described by the second context object. New content is passed across the context-based data gravity wells membrane.

Abstract: A neuron circuit for use in a neural network is disclosed. The neural network includes a plurality of field effect transistors having confined channels. The sources and drains of the field effect transistors are connected in series. A plurality of input terminals for receiving a plurality of input voltages may be connected to a drain terminal of a corresponding field effect transistor. The threshold voltages of the field effect transistors can be programmed by increasing or decreasing a number of excess minority carriers in the confined channels, thereby programming the resistance presented by the field effect transistor.

Abstract: Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.

Abstract: An approach for distinguishing a business rule from a non-business rule in a computer program is provided. A rule in the program is identified based on a conditional statement within the rule. Whether the rule performs underlying operation of the program independent of a business function of the program is determined. The rule and metadata of the rule are searched for a key word which indicates part of a business transaction. Whether a sequence of program steps in the rule matches a predetermined sequence of program steps indicative of a business rule or whether the sequence of program steps in the rule matches a predetermined sequence of program steps indicative of underlying operation of the program independent of the business function of the program is determined. If the rule is a business rule is determined based on both determining steps and both searching steps.

Abstract: A hydraulic fracturing system with pump failure detection includes an engine, transmission, hydraulic fracturing pump and a driveshaft coupled between the transmission and the hydraulic fracturing pump to transfer torque from the engine to the hydraulic fracturing pump. A torque sensor is positioned and configured to measure torque acting on the driveshaft, with the torque sensor generating torque measurement data. A controller is programmed to analyze the torque measurement data and identify a pump failure mode based on the torque measurement data.